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installation.html create mode 100644 model_zoo.html create mode 100644 multigpu.html create mode 100644 performance_hardware.html create mode 100644 stylesheets/pygment_trac.css create mode 100644 stylesheets/reset.css create mode 100644 stylesheets/styles.css create mode 100644 tutorial/convolution.html create mode 100644 tutorial/data.html create mode 100644 tutorial/fig/backward.jpg create mode 100644 tutorial/fig/forward.jpg create mode 100644 tutorial/fig/forward_backward.png create mode 100644 tutorial/fig/layer.jpg create mode 100644 tutorial/fig/logreg.jpg create mode 100644 tutorial/forward_backward.html create mode 100644 tutorial/index.html create mode 100644 tutorial/interfaces.html create mode 100644 tutorial/layers.html create mode 100644 tutorial/loss.html create mode 100644 tutorial/net_layer_blob.html create mode 100644 tutorial/solver.html diff --git a/CMakeLists.txt b/CMakeLists.txt new file mode 100644 index 00000000000..ae47e461736 --- /dev/null +++ b/CMakeLists.txt @@ -0,0 +1,106 @@ +# Building docs script +# Requirements: +# sudo apt-get install doxygen texlive ruby-dev +# sudo gem install jekyll execjs therubyracer + +if(NOT BUILD_docs OR NOT DOXYGEN_FOUND) + return() +endif() + +################################################################################################# +# Gather docs from /examples/**/readme.md +function(gather_readmes_as_prebuild_cmd target gathered_dir root) + set(full_gathered_dir ${root}/${gathered_dir}) + + file(GLOB_RECURSE readmes ${root}/examples/readme.md ${root}/examples/README.md) + foreach(file ${readmes}) + # Only use file if it is to be included in docs. + file(STRINGS ${file} file_lines REGEX "include_in_docs: true") + + if(file_lines) + # Since everything is called readme.md, rename it by its dirname. + file(RELATIVE_PATH file ${root} ${file}) + get_filename_component(folder ${file} PATH) + set(new_filename ${full_gathered_dir}/${folder}.md) + + # folder value might be like /readme.md. That's why make directory. + get_filename_component(new_folder ${new_filename} PATH) + add_custom_command(TARGET ${target} PRE_BUILD + COMMAND ${CMAKE_COMMAND} -E make_directory ${new_folder} + COMMAND ln -sf ${root}/${file} ${new_filename} + COMMENT "Creating symlink ${new_filename} -> ${root}/${file}" + WORKING_DIRECTORY ${root} VERBATIM) + endif() + endforeach() +endfunction() + +################################################################################################ +# Gather docs from examples/*.ipynb and add YAML front-matter. +function(gather_notebooks_as_prebuild_cmd target gathered_dir root) + set(full_gathered_dir ${root}/${gathered_dir}) + + if(NOT PYTHON_EXECUTABLE) + message(STATUS "Python interpeter is not found. Can't include *.ipynb files in docs. Skipping...") + return() + endif() + + file(GLOB_RECURSE notebooks ${root}/examples/*.ipynb) + foreach(file ${notebooks}) + file(RELATIVE_PATH file ${root} ${file}) + set(new_filename ${full_gathered_dir}/${file}) + + get_filename_component(new_folder ${new_filename} PATH) + add_custom_command(TARGET ${target} PRE_BUILD + COMMAND ${CMAKE_COMMAND} -E make_directory ${new_folder} + COMMAND ${PYTHON_EXECUTABLE} scripts/copy_notebook.py ${file} ${new_filename} + COMMENT "Copying notebook ${file} to ${new_filename}" + WORKING_DIRECTORY ${root} VERBATIM) + endforeach() + + set(${outputs_var} ${outputs} PARENT_SCOPE) +endfunction() + +################################################################################################ +########################## [ Non macro part ] ################################################## + +# Gathering is done at each 'make doc' +file(REMOVE_RECURSE ${PROJECT_SOURCE_DIR}/docs/gathered) + +# Doxygen config file path +set(DOXYGEN_config_file ${PROJECT_SOURCE_DIR}/.Doxyfile CACHE FILEPATH "Doxygen config file") + +# Adding docs target +add_custom_target(docs COMMAND ${DOXYGEN_EXECUTABLE} ${DOXYGEN_config_file} + WORKING_DIRECTORY ${PROJECT_SOURCE_DIR} + COMMENT "Launching doxygen..." VERBATIM) + +# Gathering examples into docs subfolder +gather_notebooks_as_prebuild_cmd(docs docs/gathered ${PROJECT_SOURCE_DIR}) +gather_readmes_as_prebuild_cmd(docs docs/gathered ${PROJECT_SOURCE_DIR}) + +# Auto detect output directory +file(STRINGS ${DOXYGEN_config_file} config_line REGEX "OUTPUT_DIRECTORY[ \t]+=[^=].*") +if(config_line) + string(REGEX MATCH "OUTPUT_DIRECTORY[ \t]+=([^=].*)" __ver_check "${config_line}") + string(STRIP ${CMAKE_MATCH_1} output_dir) + message(STATUS "Detected Doxygen OUTPUT_DIRECTORY: ${output_dir}") +else() + set(output_dir ./doxygen/) + message(STATUS "Can't find OUTPUT_DIRECTORY in doxygen config file. Try to use default: ${output_dir}") +endif() + +if(NOT IS_ABSOLUTE ${output_dir}) + set(output_dir ${PROJECT_SOURCE_DIR}/${output_dir}) + get_filename_component(output_dir ${output_dir} ABSOLUTE) +endif() + +# creates symlink in docs subfolder to code documentation built by doxygen +add_custom_command(TARGET docs POST_BUILD VERBATIM + COMMAND ln -sfn "${output_dir}/html" doxygen + WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/docs + COMMENT "Creating symlink ${PROJECT_SOURCE_DIR}/docs/doxygen -> ${output_dir}/html") + +# for quick launch of jekyll +add_custom_target(jekyll COMMAND jekyll serve -w -s . -d _site --port=4000 + WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/docs + COMMENT "Launching jekyll..." VERBATIM) diff --git a/CNAME b/CNAME new file mode 100644 index 00000000000..eee1ae26d98 --- /dev/null +++ b/CNAME @@ -0,0 +1 @@ +caffe.berkeleyvision.org diff --git a/README.md b/README.md new file mode 100644 index 00000000000..8f1781e3634 --- /dev/null +++ b/README.md @@ -0,0 +1,5 @@ +# Caffe Documentation + +To generate the documentation, run `$CAFFE_ROOT/scripts/build_docs.sh`. + +To push your changes to the documentation to the gh-pages branch of your or the BVLC repo, run `$CAFFE_ROOT/scripts/deploy_docs.sh `. diff --git a/development.html b/development.html new file mode 100644 index 00000000000..89eb2249b35 --- /dev/null +++ b/development.html @@ -0,0 +1,189 @@ + + + + + + + + + Caffe | Developing and Contributing + + + + + + + + + + + + + +
+
+

Caffe

+

+ Deep learning framework by the BVLC +

+

+ Created by +
+ Yangqing Jia +
+ Lead Developer +
+ Evan Shelhamer +

+
+
+ +

Development and Contributing

+ +

Caffe is developed with active participation of the community.
+The BVLC brewers welcome all contributions!

+ +

The exact details of contributions are recorded by versioning and cited in our acknowledgements. +This method is impartial and always up-to-date.

+ +

License

+ +

Caffe is licensed under the terms in LICENSE. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.

+ + + +

Caffe uses a shared copyright model: each contributor holds copyright over their contributions to Caffe. The project versioning records all such contribution and copyright details.

+ +

If a contributor wants to further mark their specific copyright on a particular contribution, they should indicate their copyright solely in the commit message of the change when it is committed. Do not include copyright notices in files for this purpose.

+ +

Documentation

+ +

This website, written with Jekyll, acts as the official Caffe documentation – simply run scripts/build_docs.sh and view the website at http://0.0.0.0:4000.

+ +

We prefer tutorials and examples to be documented close to where they live, in readme.md files. +The build_docs.sh script gathers all examples/**/readme.md and examples/*.ipynb files, and makes a table of contents. +To be included in the docs, the readme files must be annotated with YAML front-matter, including the flag include_in_docs: true. +Similarly for IPython notebooks: simply include "include_in_docs": true in the "metadata" JSON field.

+ +

Other docs, such as installation guides, are written in the docs directory and manually linked to from the index.md page.

+ +

We strive to provide lots of usage examples, and to document all code in docstrings. +We absolutely appreciate any contribution to this effort!

+ +

Versioning

+ +

The master branch receives all new development including community contributions. +We try to keep it in a reliable state, but it is the bleeding edge, and things do get broken every now and then. +BVLC maintainers will periodically make releases by marking stable checkpoints as tags and maintenance branches. Past releases are catalogued online.

+ +

Issues & Pull Request Protocol

+ +

Post Issues to propose features, report bugs, and discuss framework code. +Large-scale development work is guided by milestones, which are sets of Issues selected for bundling as releases.

+ +

Please note that since the core developers are largely researchers, we may work on a feature in isolation for some time before releasing it to the community, so as to claim honest academic contribution. +We do release things as soon as a reasonable technical report may be written, and we still aim to inform the community of ongoing development through Github Issues.

+ +

When you are ready to develop a feature or fixing a bug, follow this protocol:

+ +
    +
  • Develop in feature branches with descriptive names. Branch off of the latest master.
    • +
  • Bring your work up-to-date by rebasing onto the latest master when done. +(Groom your changes by interactive rebase, if you’d like.)
    • +
  • Pull request your contribution to BVLC/caffe’s master branch for discussion and review. +
      +
    • Make PRs as soon as development begins, to let discussion guide development.
      • +
    • A PR is only ready for merge review when it is a fast-forward merge, and all code is documented, linted, and tested – that means your PR must include tests!
      • +
    +
    • +
  • When the PR satisfies the above properties, use comments to request maintainer review.
    • +
+ +

The following is a poetic presentation of the protocol in code form.

+ +

Shelhamer’s “life of a branch in four acts”

+ +

Make the feature branch off of the latest bvlc/master

+ +
git checkout master
+git pull upstream master
+git checkout -b feature
+# do your work, make commits
+
+
+ +

Prepare to merge by rebasing your branch on the latest bvlc/master

+ +
# make sure master is fresh
+git checkout master
+git pull upstream master
+# rebase your branch on the tip of master
+git checkout feature
+git rebase master
+
+
+ +

Push your branch to pull request it into BVLC/caffe:master

+ +
git push origin feature
+# ...make pull request to master...
+
+
+ +

Now make a pull request! You can do this from the command line (git pull-request -b master) if you install hub. Hub has many other magical uses.

+ +

The pull request of feature into master will be a clean merge. Applause.

+ +

Historical note: Caffe once relied on a two branch master and dev workflow. +PRs from this time are still open but these will be merged into master or closed.

+ +

Testing

+ +

Run make runtest to check the project tests. New code requires new tests. Pull requests that fail tests will not be accepted.

+ +

The gtest framework we use provides many additional options, which you can access by running the test binaries directly. One of the more useful options is --gtest_filter, which allows you to filter tests by name:

+ +
# run all tests with CPU in the name
+build/test/test_all.testbin --gtest_filter='*CPU*'
+
+# run all tests without GPU in the name (note the leading minus sign)
+build/test/test_all.testbin --gtest_filter=-'*GPU*'
+
+
+ +

To get a list of all options googletest provides, simply pass the --help flag:

+ +
build/test/test_all.testbin --help
+
+
+ +

Style

+ +
    +
  • Run make lint to check C++ code.
    • +
  • Wrap lines at 80 chars.
    • +
  • Follow Google C++ style and Google python style + PEP 8.
    • +
  • Remember that “a foolish consistency is the hobgoblin of little minds,” so use your best judgement to write the clearest code for your particular case.
    • +
+ + +
+
+ + diff --git a/doxygen/absval__layer_8hpp_source.html b/doxygen/absval__layer_8hpp_source.html new file mode 100644 index 00000000000..a6647e2a4e6 --- /dev/null +++ b/doxygen/absval__layer_8hpp_source.html @@ -0,0 +1,89 @@ + + + + + + + +Caffe: include/caffe/layers/absval_layer.hpp Source File + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
absval_layer.hpp
+
+
+
1 #ifndef CAFFE_ABSVAL_LAYER_HPP_
2 #define CAFFE_ABSVAL_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/neuron_layer.hpp"
11 
12 namespace caffe {
13 
24 template <typename Dtype>
25 class AbsValLayer : public NeuronLayer<Dtype> {
26  public:
27  explicit AbsValLayer(const LayerParameter& param)
28  : NeuronLayer<Dtype>(param) {}
29  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
30  const vector<Blob<Dtype>*>& top);
31 
32  virtual inline const char* type() const { return "AbsVal"; }
33  virtual inline int ExactNumBottomBlobs() const { return 1; }
34  virtual inline int ExactNumTopBlobs() const { return 1; }
35 
36  protected:
38  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
39  const vector<Blob<Dtype>*>& top);
40  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
41  const vector<Blob<Dtype>*>& top);
42 
60  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
61  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
62  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
63  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
64 };
65 
66 } // namespace caffe
67 
68 #endif // CAFFE_ABSVAL_LAYER_HPP_
caffe::AbsValLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: absval_layer.hpp:33
+
caffe::AbsValLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Computes .
Definition: absval_layer.cpp:17
+
caffe::AbsValLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: absval_layer.cpp:9
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::AbsValLayer
Computes .
Definition: absval_layer.hpp:25
+
caffe::AbsValLayer::type
virtual const char * type() const
Returns the layer type.
Definition: absval_layer.hpp:32
+
caffe::AbsValLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Computes the error gradient w.r.t. the absolute value inputs.
Definition: absval_layer.cpp:25
+
caffe::AbsValLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::NeuronLayer
An interface for layers that take one blob as input ( ) and produce one equally-sized blob as output ...
Definition: neuron_layer.hpp:19
+
caffe::AbsValLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::AbsValLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: absval_layer.hpp:34
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/accuracy__layer_8hpp_source.html b/doxygen/accuracy__layer_8hpp_source.html new file mode 100644 index 00000000000..f888cca62bc --- /dev/null +++ b/doxygen/accuracy__layer_8hpp_source.html @@ -0,0 +1,93 @@ + + + + + + + +Caffe: include/caffe/layers/accuracy_layer.hpp Source File + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
accuracy_layer.hpp
+
+
+
1 #ifndef CAFFE_ACCURACY_LAYER_HPP_
2 #define CAFFE_ACCURACY_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/loss_layer.hpp"
11 
12 namespace caffe {
13 
18 template <typename Dtype>
19 class AccuracyLayer : public Layer<Dtype> {
20  public:
29  explicit AccuracyLayer(const LayerParameter& param)
30  : Layer<Dtype>(param) {}
31  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
32  const vector<Blob<Dtype>*>& top);
33  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
34  const vector<Blob<Dtype>*>& top);
35 
36  virtual inline const char* type() const { return "Accuracy"; }
37  virtual inline int ExactNumBottomBlobs() const { return 2; }
38 
39  // If there are two top blobs, then the second blob will contain
40  // accuracies per class.
41  virtual inline int MinTopBlobs() const { return 1; }
42  virtual inline int MaxTopBlobs() const { return 2; }
43 
44  protected:
69  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
70  const vector<Blob<Dtype>*>& top);
71 
72 
74  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
75  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
76  for (int i = 0; i < propagate_down.size(); ++i) {
77  if (propagate_down[i]) { NOT_IMPLEMENTED; }
78  }
79  }
80 
81  int label_axis_, outer_num_, inner_num_;
82 
83  int top_k_;
84 
86  bool has_ignore_label_;
88  int ignore_label_;
90  Blob<Dtype> nums_buffer_;
91 };
92 
93 } // namespace caffe
94 
95 #endif // CAFFE_ACCURACY_LAYER_HPP_
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::AccuracyLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: accuracy_layer.hpp:37
+
caffe::AccuracyLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: accuracy_layer.cpp:11
+
caffe::AccuracyLayer::ignore_label_
int ignore_label_
The label indicating that an instance should be ignored.
Definition: accuracy_layer.hpp:88
+
caffe::AccuracyLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Definition: accuracy_layer.cpp:48
+
caffe::AccuracyLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: accuracy_layer.cpp:23
+
caffe::AccuracyLayer
Computes the classification accuracy for a one-of-many classification task.
Definition: accuracy_layer.hpp:19
+
caffe::AccuracyLayer::MaxTopBlobs
virtual int MaxTopBlobs() const
Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required...
Definition: accuracy_layer.hpp:42
+
caffe::AccuracyLayer::has_ignore_label_
bool has_ignore_label_
Whether to ignore instances with a certain label.
Definition: accuracy_layer.hpp:86
+
caffe::AccuracyLayer::MinTopBlobs
virtual int MinTopBlobs() const
Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
Definition: accuracy_layer.hpp:41
+
caffe::AccuracyLayer::AccuracyLayer
AccuracyLayer(const LayerParameter &param)
Definition: accuracy_layer.hpp:29
+
caffe::AccuracyLayer::type
virtual const char * type() const
Returns the layer type.
Definition: accuracy_layer.hpp:36
+
caffe::AccuracyLayer::nums_buffer_
Blob< Dtype > nums_buffer_
Keeps counts of the number of samples per class.
Definition: accuracy_layer.hpp:90
+
caffe::AccuracyLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Not implemented – AccuracyLayer cannot be used as a loss.
Definition: accuracy_layer.hpp:74
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/annotated.html b/doxygen/annotated.html new file mode 100644 index 00000000000..dd193689b3b --- /dev/null +++ b/doxygen/annotated.html @@ -0,0 +1,190 @@ + + + + + + + +Caffe: Class List + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + +
+ +
+
+ + +
+ +
+ +
+
+
Class List
+
+
+
Here are the classes, structs, unions and interfaces with brief descriptions:
+
[detail level 1234]
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 ►NcaffeA layer factory that allows one to register layers. During runtime, registered layers can be called by passing a LayerParameter protobuffer to the CreateLayer function:
+
+
+ +
+Generated on Mon Nov 21 2016 11:23:49 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/argmax__layer_8hpp_source.html b/doxygen/argmax__layer_8hpp_source.html new file mode 100644 index 00000000000..1d916f0f8b0 --- /dev/null +++ b/doxygen/argmax__layer_8hpp_source.html @@ -0,0 +1,89 @@ + + + + + + + +Caffe: include/caffe/layers/argmax_layer.hpp Source File + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
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+ + +
+ +
+ + +
+
+
+
argmax_layer.hpp
+
+
+
1 #ifndef CAFFE_ARGMAX_LAYER_HPP_
2 #define CAFFE_ARGMAX_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 namespace caffe {
11 
23 template <typename Dtype>
24 class ArgMaxLayer : public Layer<Dtype> {
25  public:
38  explicit ArgMaxLayer(const LayerParameter& param)
39  : Layer<Dtype>(param) {}
40  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
41  const vector<Blob<Dtype>*>& top);
42  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
43  const vector<Blob<Dtype>*>& top);
44 
45  virtual inline const char* type() const { return "ArgMax"; }
46  virtual inline int ExactNumBottomBlobs() const { return 1; }
47  virtual inline int ExactNumTopBlobs() const { return 1; }
48 
49  protected:
62  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
63  const vector<Blob<Dtype>*>& top);
65  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
66  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
67  NOT_IMPLEMENTED;
68  }
69  bool out_max_val_;
70  size_t top_k_;
71  bool has_axis_;
72  int axis_;
73 };
74 
75 } // namespace caffe
76 
77 #endif // CAFFE_ARGMAX_LAYER_HPP_
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::ArgMaxLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: argmax_layer.cpp:11
+
caffe::ArgMaxLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: argmax_layer.hpp:46
+
caffe::ArgMaxLayer
Compute the index of the max values for each datum across all dimensions .
Definition: argmax_layer.hpp:24
+
caffe::ArgMaxLayer::type
virtual const char * type() const
Returns the layer type.
Definition: argmax_layer.hpp:45
+
caffe::ArgMaxLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Definition: argmax_layer.cpp:55
+
caffe::ArgMaxLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: argmax_layer.hpp:47
+
caffe::ArgMaxLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: argmax_layer.cpp:33
+
caffe::ArgMaxLayer::ArgMaxLayer
ArgMaxLayer(const LayerParameter &param)
Definition: argmax_layer.hpp:38
+
caffe::ArgMaxLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Not implemented (non-differentiable function)
Definition: argmax_layer.hpp:65
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/arrowdown.png b/doxygen/arrowdown.png new file mode 100644 index 0000000000000000000000000000000000000000..0b63f6d38c4b9ec907b820192ebe9724ed6eca22 GIT binary patch literal 246 zcmVkw!R34#Lv2LOS^S2tZA31X++9RY}n zChwn@Z)Wz*WWHH{)HDtJnq&A2hk$b-y(>?@z0iHr41EKCGp#T5?07*qoM6N<$f(V3Pvj6}9 literal 0 HcmV?d00001 diff --git a/doxygen/arrowright.png b/doxygen/arrowright.png new file mode 100644 index 0000000000000000000000000000000000000000..c6ee22f937a07d1dbfc27c669d11f8ed13e2f152 GIT binary patch literal 229 zcmV^P)R?RzRoKvklcaQ%HF6%rK2&ZgO(-ihJ_C zzrKgp4jgO( fd_(yg|3PpEQb#9`a?Pz_00000NkvXXu0mjftR`5K literal 0 HcmV?d00001 diff --git a/doxygen/base__conv__layer_8hpp_source.html b/doxygen/base__conv__layer_8hpp_source.html new file mode 100644 index 00000000000..5c5102cbcdc --- /dev/null +++ b/doxygen/base__conv__layer_8hpp_source.html @@ -0,0 +1,94 @@ + + + + + + + +Caffe: include/caffe/layers/base_conv_layer.hpp Source File + + + + + + + + + +
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Caffe +
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base_conv_layer.hpp
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1 #ifndef CAFFE_BASE_CONVOLUTION_LAYER_HPP_
2 #define CAFFE_BASE_CONVOLUTION_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 #include "caffe/util/im2col.hpp"
10 
11 namespace caffe {
12 
17 template <typename Dtype>
18 class BaseConvolutionLayer : public Layer<Dtype> {
19  public:
20  explicit BaseConvolutionLayer(const LayerParameter& param)
21  : Layer<Dtype>(param) {}
22  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
23  const vector<Blob<Dtype>*>& top);
24  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
25  const vector<Blob<Dtype>*>& top);
26 
27  virtual inline int MinBottomBlobs() const { return 1; }
28  virtual inline int MinTopBlobs() const { return 1; }
29  virtual inline bool EqualNumBottomTopBlobs() const { return true; }
30 
31  protected:
32  // Helper functions that abstract away the column buffer and gemm arguments.
33  // The last argument in forward_cpu_gemm is so that we can skip the im2col if
34  // we just called weight_cpu_gemm with the same input.
35  void forward_cpu_gemm(const Dtype* input, const Dtype* weights,
36  Dtype* output, bool skip_im2col = false);
37  void forward_cpu_bias(Dtype* output, const Dtype* bias);
38  void backward_cpu_gemm(const Dtype* input, const Dtype* weights,
39  Dtype* output);
40  void weight_cpu_gemm(const Dtype* input, const Dtype* output, Dtype*
41  weights);
42  void backward_cpu_bias(Dtype* bias, const Dtype* input);
43 
44 #ifndef CPU_ONLY
45  void forward_gpu_gemm(const Dtype* col_input, const Dtype* weights,
46  Dtype* output, bool skip_im2col = false);
47  void forward_gpu_bias(Dtype* output, const Dtype* bias);
48  void backward_gpu_gemm(const Dtype* input, const Dtype* weights,
49  Dtype* col_output);
50  void weight_gpu_gemm(const Dtype* col_input, const Dtype* output, Dtype*
51  weights);
52  void backward_gpu_bias(Dtype* bias, const Dtype* input);
53 #endif
54 
56  inline int input_shape(int i) {
57  return (*bottom_shape_)[channel_axis_ + i];
58  }
59  // reverse_dimensions should return true iff we are implementing deconv, so
60  // that conv helpers know which dimensions are which.
61  virtual bool reverse_dimensions() = 0;
62  // Compute height_out_ and width_out_ from other parameters.
63  virtual void compute_output_shape() = 0;
64 
66  Blob<int> kernel_shape_;
68  Blob<int> stride_;
70  Blob<int> pad_;
72  Blob<int> dilation_;
74  Blob<int> conv_input_shape_;
76  vector<int> col_buffer_shape_;
78  vector<int> output_shape_;
79  const vector<int>* bottom_shape_;
80 
81  int num_spatial_axes_;
82  int bottom_dim_;
83  int top_dim_;
84 
85  int channel_axis_;
86  int num_;
87  int channels_;
88  int group_;
89  int out_spatial_dim_;
90  int weight_offset_;
91  int num_output_;
92  bool bias_term_;
93  bool is_1x1_;
94  bool force_nd_im2col_;
95 
96  private:
97  // wrap im2col/col2im so we don't have to remember the (long) argument lists
98  inline void conv_im2col_cpu(const Dtype* data, Dtype* col_buff) {
99  if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
100  im2col_cpu(data, conv_in_channels_,
101  conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
102  kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
103  pad_.cpu_data()[0], pad_.cpu_data()[1],
104  stride_.cpu_data()[0], stride_.cpu_data()[1],
105  dilation_.cpu_data()[0], dilation_.cpu_data()[1], col_buff);
106  } else {
107  im2col_nd_cpu(data, num_spatial_axes_, conv_input_shape_.cpu_data(),
108  col_buffer_shape_.data(), kernel_shape_.cpu_data(),
109  pad_.cpu_data(), stride_.cpu_data(), dilation_.cpu_data(), col_buff);
110  }
111  }
112  inline void conv_col2im_cpu(const Dtype* col_buff, Dtype* data) {
113  if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
114  col2im_cpu(col_buff, conv_in_channels_,
115  conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
116  kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
117  pad_.cpu_data()[0], pad_.cpu_data()[1],
118  stride_.cpu_data()[0], stride_.cpu_data()[1],
119  dilation_.cpu_data()[0], dilation_.cpu_data()[1], data);
120  } else {
121  col2im_nd_cpu(col_buff, num_spatial_axes_, conv_input_shape_.cpu_data(),
122  col_buffer_shape_.data(), kernel_shape_.cpu_data(),
123  pad_.cpu_data(), stride_.cpu_data(), dilation_.cpu_data(), data);
124  }
125  }
126 #ifndef CPU_ONLY
127  inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) {
128  if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
129  im2col_gpu(data, conv_in_channels_,
130  conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
131  kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
132  pad_.cpu_data()[0], pad_.cpu_data()[1],
133  stride_.cpu_data()[0], stride_.cpu_data()[1],
134  dilation_.cpu_data()[0], dilation_.cpu_data()[1], col_buff);
135  } else {
136  im2col_nd_gpu(data, num_spatial_axes_, num_kernels_im2col_,
137  conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
138  kernel_shape_.gpu_data(), pad_.gpu_data(),
139  stride_.gpu_data(), dilation_.gpu_data(), col_buff);
140  }
141  }
142  inline void conv_col2im_gpu(const Dtype* col_buff, Dtype* data) {
143  if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
144  col2im_gpu(col_buff, conv_in_channels_,
145  conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
146  kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
147  pad_.cpu_data()[0], pad_.cpu_data()[1],
148  stride_.cpu_data()[0], stride_.cpu_data()[1],
149  dilation_.cpu_data()[0], dilation_.cpu_data()[1], data);
150  } else {
151  col2im_nd_gpu(col_buff, num_spatial_axes_, num_kernels_col2im_,
152  conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
153  kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(),
154  dilation_.gpu_data(), data);
155  }
156  }
157 #endif
158 
159  int num_kernels_im2col_;
160  int num_kernels_col2im_;
161  int conv_out_channels_;
162  int conv_in_channels_;
163  int conv_out_spatial_dim_;
164  int kernel_dim_;
165  int col_offset_;
166  int output_offset_;
167 
168  Blob<Dtype> col_buffer_;
169  Blob<Dtype> bias_multiplier_;
170 };
171 
172 } // namespace caffe
173 
174 #endif // CAFFE_BASE_CONVOLUTION_LAYER_HPP_
caffe::BaseConvolutionLayer::kernel_shape_
Blob< int > kernel_shape_
The spatial dimensions of a filter kernel.
Definition: base_conv_layer.hpp:66
+
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::BaseConvolutionLayer::input_shape
int input_shape(int i)
The spatial dimensions of the input.
Definition: base_conv_layer.hpp:56
+
caffe::BaseConvolutionLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: base_conv_layer.cpp:12
+
caffe::BaseConvolutionLayer
Abstract base class that factors out the BLAS code common to ConvolutionLayer and DeconvolutionLayer...
Definition: base_conv_layer.hpp:18
+
caffe::BaseConvolutionLayer::EqualNumBottomTopBlobs
virtual bool EqualNumBottomTopBlobs() const
Returns true if the layer requires an equal number of bottom and top blobs.
Definition: base_conv_layer.hpp:29
+
caffe::BaseConvolutionLayer::MinBottomBlobs
virtual int MinBottomBlobs() const
Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
Definition: base_conv_layer.hpp:27
+
caffe::BaseConvolutionLayer::pad_
Blob< int > pad_
The spatial dimensions of the padding.
Definition: base_conv_layer.hpp:70
+
caffe::BaseConvolutionLayer::MinTopBlobs
virtual int MinTopBlobs() const
Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
Definition: base_conv_layer.hpp:28
+
caffe::BaseConvolutionLayer::col_buffer_shape_
vector< int > col_buffer_shape_
The spatial dimensions of the col_buffer.
Definition: base_conv_layer.hpp:76
+
caffe::BaseConvolutionLayer::output_shape_
vector< int > output_shape_
The spatial dimensions of the output.
Definition: base_conv_layer.hpp:78
+
caffe::BaseConvolutionLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: base_conv_layer.cpp:186
+
caffe::BaseConvolutionLayer::conv_input_shape_
Blob< int > conv_input_shape_
The spatial dimensions of the convolution input.
Definition: base_conv_layer.hpp:74
+
caffe::BaseConvolutionLayer::stride_
Blob< int > stride_
The spatial dimensions of the stride.
Definition: base_conv_layer.hpp:68
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
caffe::BaseConvolutionLayer::dilation_
Blob< int > dilation_
The spatial dimensions of the dilation.
Definition: base_conv_layer.hpp:72
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/base__data__layer_8hpp_source.html b/doxygen/base__data__layer_8hpp_source.html new file mode 100644 index 00000000000..0d28d18fcf1 --- /dev/null +++ b/doxygen/base__data__layer_8hpp_source.html @@ -0,0 +1,92 @@ + + + + + + + +Caffe: include/caffe/layers/base_data_layer.hpp Source File + + + + + + + + + +
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Caffe +
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base_data_layer.hpp
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1 #ifndef CAFFE_DATA_LAYERS_HPP_
2 #define CAFFE_DATA_LAYERS_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/data_transformer.hpp"
8 #include "caffe/internal_thread.hpp"
9 #include "caffe/layer.hpp"
10 #include "caffe/proto/caffe.pb.h"
11 #include "caffe/util/blocking_queue.hpp"
12 
13 namespace caffe {
14 
20 template <typename Dtype>
21 class BaseDataLayer : public Layer<Dtype> {
22  public:
23  explicit BaseDataLayer(const LayerParameter& param);
24  // LayerSetUp: implements common data layer setup functionality, and calls
25  // DataLayerSetUp to do special data layer setup for individual layer types.
26  // This method may not be overridden except by the BasePrefetchingDataLayer.
27  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
28  const vector<Blob<Dtype>*>& top);
29  // Data layers should be shared by multiple solvers in parallel
30  virtual inline bool ShareInParallel() const { return true; }
31  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
32  const vector<Blob<Dtype>*>& top) {}
33  // Data layers have no bottoms, so reshaping is trivial.
34  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
35  const vector<Blob<Dtype>*>& top) {}
36 
37  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
38  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
39  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
40  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
41 
42  protected:
43  TransformationParameter transform_param_;
44  shared_ptr<DataTransformer<Dtype> > data_transformer_;
45  bool output_labels_;
46 };
47 
48 template <typename Dtype>
49 class Batch {
50  public:
51  Blob<Dtype> data_, label_;
52 };
53 
54 template <typename Dtype>
55 class BasePrefetchingDataLayer :
56  public BaseDataLayer<Dtype>, public InternalThread {
57  public:
58  explicit BasePrefetchingDataLayer(const LayerParameter& param);
59  // LayerSetUp: implements common data layer setup functionality, and calls
60  // DataLayerSetUp to do special data layer setup for individual layer types.
61  // This method may not be overridden.
62  void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
63  const vector<Blob<Dtype>*>& top);
64 
65  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
66  const vector<Blob<Dtype>*>& top);
67  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
68  const vector<Blob<Dtype>*>& top);
69 
70  // Prefetches batches (asynchronously if to GPU memory)
71  static const int PREFETCH_COUNT = 3;
72 
73  protected:
74  virtual void InternalThreadEntry();
75  virtual void load_batch(Batch<Dtype>* batch) = 0;
76 
77  Batch<Dtype> prefetch_[PREFETCH_COUNT];
78  BlockingQueue<Batch<Dtype>*> prefetch_free_;
79  BlockingQueue<Batch<Dtype>*> prefetch_full_;
80 
81  Blob<Dtype> transformed_data_;
82 };
83 
84 } // namespace caffe
85 
86 #endif // CAFFE_DATA_LAYERS_HPP_
caffe::Batch
Definition: base_data_layer.hpp:49
+
caffe::BasePrefetchingDataLayer
Definition: base_data_layer.hpp:55
+
caffe::Layer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
Definition: layer.hpp:341
+
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::BaseDataLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: base_data_layer.hpp:34
+
caffe::BaseDataLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: base_data_layer.hpp:37
+
caffe::BaseDataLayer::ShareInParallel
virtual bool ShareInParallel() const
Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
Definition: base_data_layer.hpp:30
+
caffe::BaseDataLayer
Provides base for data layers that feed blobs to the Net.
Definition: base_data_layer.hpp:21
+
caffe::Layer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)=0
Using the CPU device, compute the layer output.
+
caffe::InternalThread
Definition: internal_thread.hpp:19
+
caffe::BaseDataLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: base_data_layer.hpp:39
+
caffe::BlockingQueue
Definition: blocking_queue.hpp:10
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
caffe::BaseDataLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: base_data_layer.cpp:21
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/batch__norm__layer_8hpp_source.html b/doxygen/batch__norm__layer_8hpp_source.html new file mode 100644 index 00000000000..2c4dd90ffac --- /dev/null +++ b/doxygen/batch__norm__layer_8hpp_source.html @@ -0,0 +1,90 @@ + + + + + + + +Caffe: include/caffe/layers/batch_norm_layer.hpp Source File + + + + + + + + + +
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Caffe +
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batch_norm_layer.hpp
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1 #ifndef CAFFE_BATCHNORM_LAYER_HPP_
2 #define CAFFE_BATCHNORM_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 namespace caffe {
11 
39 template <typename Dtype>
40 class BatchNormLayer : public Layer<Dtype> {
41  public:
42  explicit BatchNormLayer(const LayerParameter& param)
43  : Layer<Dtype>(param) {}
44  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
45  const vector<Blob<Dtype>*>& top);
46  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
47  const vector<Blob<Dtype>*>& top);
48 
49  virtual inline const char* type() const { return "BatchNorm"; }
50  virtual inline int ExactNumBottomBlobs() const { return 1; }
51  virtual inline int ExactNumTopBlobs() const { return 1; }
52 
53  protected:
54  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
55  const vector<Blob<Dtype>*>& top);
56  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
57  const vector<Blob<Dtype>*>& top);
58  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
59  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
60  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
61  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
62 
63  Blob<Dtype> mean_, variance_, temp_, x_norm_;
64  bool use_global_stats_;
65  Dtype moving_average_fraction_;
66  int channels_;
67  Dtype eps_;
68 
69  // extra temporarary variables is used to carry out sums/broadcasting
70  // using BLAS
71  Blob<Dtype> batch_sum_multiplier_;
72  Blob<Dtype> num_by_chans_;
73  Blob<Dtype> spatial_sum_multiplier_;
74 };
75 
76 } // namespace caffe
77 
78 #endif // CAFFE_BATCHNORM_LAYER_HPP_
caffe::BatchNormLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: batch_norm_layer.cpp:169
+
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::BatchNormLayer
Normalizes the input to have 0-mean and/or unit (1) variance across the batch.
Definition: batch_norm_layer.hpp:40
+
caffe::BatchNormLayer::type
virtual const char * type() const
Returns the layer type.
Definition: batch_norm_layer.hpp:49
+
caffe::BatchNormLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: batch_norm_layer.cpp:52
+
caffe::BatchNormLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::BatchNormLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: batch_norm_layer.hpp:51
+
caffe::BatchNormLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the CPU device, compute the layer output.
Definition: batch_norm_layer.cpp:87
+
caffe::BatchNormLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: batch_norm_layer.cpp:10
+
caffe::BatchNormLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: batch_norm_layer.hpp:50
+
caffe::BatchNormLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/batch__reindex__layer_8hpp_source.html b/doxygen/batch__reindex__layer_8hpp_source.html new file mode 100644 index 00000000000..4d28d9eab10 --- /dev/null +++ b/doxygen/batch__reindex__layer_8hpp_source.html @@ -0,0 +1,89 @@ + + + + + + + +Caffe: include/caffe/layers/batch_reindex_layer.hpp Source File + + + + + + + + + +
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Caffe +
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batch_reindex_layer.hpp
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1 #ifndef CAFFE_BATCHREINDEX_LAYER_HPP_
2 #define CAFFE_BATCHREINDEX_LAYER_HPP_
3 
4 #include <utility>
5 #include <vector>
6 
7 #include "caffe/blob.hpp"
8 #include "caffe/layer.hpp"
9 #include "caffe/proto/caffe.pb.h"
10 
11 namespace caffe {
12 
20 template <typename Dtype>
21 class BatchReindexLayer : public Layer<Dtype> {
22  public:
23  explicit BatchReindexLayer(const LayerParameter& param)
24  : Layer<Dtype>(param) {}
25  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
26  const vector<Blob<Dtype>*>& top);
27 
28  virtual inline const char* type() const { return "BatchReindex"; }
29  virtual inline int ExactNumBottomBlobs() const { return 2; }
30  virtual inline int ExactNumTopBlobs() const { return 1; }
31 
32  protected:
45  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
46  const vector<Blob<Dtype>*>& top);
47  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
48  const vector<Blob<Dtype>*>& top);
49 
65  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
66  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
67  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
68  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
69 
70  private:
71  struct pair_sort_first {
72  bool operator()(const std::pair<int, int> &left,
73  const std::pair<int, int> &right) {
74  return left.first < right.first;
75  }
76  };
77  void check_batch_reindex(int initial_num, int final_num,
78  const Dtype* ridx_data);
79 };
80 
81 } // namespace caffe
82 
83 #endif // CAFFE_BATCHREINDEX_LAYER_HPP_
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::BatchReindexLayer::type
virtual const char * type() const
Returns the layer type.
Definition: batch_reindex_layer.hpp:28
+
caffe::BatchReindexLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: batch_reindex_layer.hpp:29
+
caffe::BatchReindexLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Definition: batch_reindex_layer.cpp:33
+
caffe::BatchReindexLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: batch_reindex_layer.cpp:9
+
caffe::BatchReindexLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::BatchReindexLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Computes the error gradient w.r.t. the reordered input.
Definition: batch_reindex_layer.cpp:52
+
caffe::BatchReindexLayer
Index into the input blob along its first axis.
Definition: batch_reindex_layer.hpp:21
+
caffe::BatchReindexLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: batch_reindex_layer.hpp:30
+
caffe::BatchReindexLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/bc_s.png b/doxygen/bc_s.png new file mode 100644 index 0000000000000000000000000000000000000000..224b29aa9847d5a4b3902efd602b7ddf7d33e6c2 GIT binary patch literal 676 zcmV;V0$crwP)y__>=_9%My z{n931IS})GlGUF8K#6VIbs%684A^L3@%PlP2>_sk`UWPq@f;rU*V%rPy_ekbhXT&s z(GN{DxFv}*vZp`F>S!r||M`I*nOwwKX+BC~3P5N3-)Y{65c;ywYiAh-1*hZcToLHK ztpl1xomJ+Yb}K(cfbJr2=GNOnT!UFA7Vy~fBz8?J>XHsbZoDad^8PxfSa0GDgENZS zuLCEqzb*xWX2CG*b&5IiO#NzrW*;`VC9455M`o1NBh+(k8~`XCEEoC1Ybwf;vr4K3 zg|EB<07?SOqHp9DhLpS&bzgo70I+ghB_#)K7H%AMU3v}xuyQq9&Bm~++VYhF09a+U zl7>n7Jjm$K#b*FONz~fj;I->Bf;ule1prFN9FovcDGBkpg>)O*-}eLnC{6oZHZ$o% zXKW$;0_{8hxHQ>l;_*HATI(`7t#^{$(zLe}h*mqwOc*nRY9=?Sx4OOeVIfI|0V(V2 zBrW#G7Ss9wvzr@>H*`r>zE z+e8bOBgqIgldUJlG(YUDviMB`9+DH8n-s9SXRLyJHO1!=wY^79WYZMTa(wiZ!zP66 zA~!21vmF3H2{ngD;+`6j#~6j;$*f*G_2ZD1E;9(yaw7d-QnSCpK(cR1zU3qU0000< KMNUMnLSTYoA~SLT literal 0 HcmV?d00001 diff --git a/doxygen/bdwn.png b/doxygen/bdwn.png new file mode 100644 index 0000000000000000000000000000000000000000..940a0b950443a0bb1b216ac03c45b8a16c955452 GIT binary patch literal 147 zcmeAS@N?(olHy`uVBq!ia0vp^>_E)H!3HEvS)PKZC{Gv1kP61Pb5HX&C2wk~_T + + + + + + +Caffe: include/caffe/util/benchmark.hpp Source File + + + + + + + + + +
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Caffe +
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benchmark.hpp
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1 #ifndef CAFFE_UTIL_BENCHMARK_H_
2 #define CAFFE_UTIL_BENCHMARK_H_
3 
4 #include <boost/date_time/posix_time/posix_time.hpp>
5 
6 #include "caffe/util/device_alternate.hpp"
7 
8 namespace caffe {
9 
10 class Timer {
11  public:
12  Timer();
13  virtual ~Timer();
14  virtual void Start();
15  virtual void Stop();
16  virtual float MilliSeconds();
17  virtual float MicroSeconds();
18  virtual float Seconds();
19 
20  inline bool initted() { return initted_; }
21  inline bool running() { return running_; }
22  inline bool has_run_at_least_once() { return has_run_at_least_once_; }
23 
24  protected:
25  void Init();
26 
27  bool initted_;
28  bool running_;
29  bool has_run_at_least_once_;
30 #ifndef CPU_ONLY
31  cudaEvent_t start_gpu_;
32  cudaEvent_t stop_gpu_;
33 #endif
34  boost::posix_time::ptime start_cpu_;
35  boost::posix_time::ptime stop_cpu_;
36  float elapsed_milliseconds_;
37  float elapsed_microseconds_;
38 };
39 
40 class CPUTimer : public Timer {
41  public:
42  explicit CPUTimer();
43  virtual ~CPUTimer() {}
44  virtual void Start();
45  virtual void Stop();
46  virtual float MilliSeconds();
47  virtual float MicroSeconds();
48 };
49 
50 } // namespace caffe
51 
52 #endif // CAFFE_UTIL_BENCHMARK_H_
caffe::Timer
Definition: benchmark.hpp:10
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::CPUTimer
Definition: benchmark.hpp:40
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/bias__layer_8hpp_source.html b/doxygen/bias__layer_8hpp_source.html new file mode 100644 index 00000000000..f2ad1438360 --- /dev/null +++ b/doxygen/bias__layer_8hpp_source.html @@ -0,0 +1,91 @@ + + + + + + + +Caffe: include/caffe/layers/bias_layer.hpp Source File + + + + + + + + + +
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bias_layer.hpp
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1 #ifndef CAFFE_BIAS_LAYER_HPP_
2 #define CAFFE_BIAS_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 namespace caffe {
11 
21 template <typename Dtype>
22 class BiasLayer : public Layer<Dtype> {
23  public:
24  explicit BiasLayer(const LayerParameter& param)
25  : Layer<Dtype>(param) {}
26  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
27  const vector<Blob<Dtype>*>& top);
28  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
29  const vector<Blob<Dtype>*>& top);
30 
31  virtual inline const char* type() const { return "Bias"; }
32  virtual inline int MinBottomBlobs() const { return 1; }
33  virtual inline int MaxBottomBlobs() const { return 2; }
34  virtual inline int ExactNumTopBlobs() const { return 1; }
35 
36  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
37  const vector<Blob<Dtype>*>& top);
38  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
39  const vector<Blob<Dtype>*>& top);
40  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
41  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
42  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
43  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
44 
45  private:
46  Blob<Dtype> bias_multiplier_;
47  int outer_dim_, bias_dim_, inner_dim_, dim_;
48 };
49 
50 
51 
52 } // namespace caffe
53 
54 #endif // CAFFE_BIAS_LAYER_HPP_
caffe::BiasLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: bias_layer.hpp:34
+
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::BiasLayer
Computes a sum of two input Blobs, with the shape of the latter Blob "broadcast" to match the shape o...
Definition: bias_layer.hpp:22
+
caffe::BiasLayer::MaxBottomBlobs
virtual int MaxBottomBlobs() const
Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is requi...
Definition: bias_layer.hpp:33
+
caffe::BiasLayer::type
virtual const char * type() const
Returns the layer type.
Definition: bias_layer.hpp:31
+
caffe::BiasLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the CPU device, compute the layer output.
Definition: bias_layer.cpp:72
+
caffe::BiasLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::BiasLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: bias_layer.cpp:90
+
caffe::BiasLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: bias_layer.cpp:40
+
caffe::BiasLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::BiasLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: bias_layer.cpp:10
+
caffe::BiasLayer::MinBottomBlobs
virtual int MinBottomBlobs() const
Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
Definition: bias_layer.hpp:32
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/blob_8hpp_source.html b/doxygen/blob_8hpp_source.html new file mode 100644 index 00000000000..ef092c79b22 --- /dev/null +++ b/doxygen/blob_8hpp_source.html @@ -0,0 +1,97 @@ + + + + + + + +Caffe: include/caffe/blob.hpp Source File + + + + + + + + + +
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Caffe +
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blob.hpp
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1 #ifndef CAFFE_BLOB_HPP_
2 #define CAFFE_BLOB_HPP_
3 
4 #include <algorithm>
5 #include <string>
6 #include <vector>
7 
8 #include "caffe/common.hpp"
9 #include "caffe/proto/caffe.pb.h"
10 #include "caffe/syncedmem.hpp"
11 
12 const int kMaxBlobAxes = 32;
13 
14 namespace caffe {
15 
23 template <typename Dtype>
24 class Blob {
25  public:
26  Blob()
27  : data_(), diff_(), count_(0), capacity_(0) {}
28 
30  explicit Blob(const int num, const int channels, const int height,
31  const int width);
32  explicit Blob(const vector<int>& shape);
33 
35  void Reshape(const int num, const int channels, const int height,
36  const int width);
51  void Reshape(const vector<int>& shape);
52  void Reshape(const BlobShape& shape);
53  void ReshapeLike(const Blob& other);
54  inline string shape_string() const {
55  ostringstream stream;
56  for (int i = 0; i < shape_.size(); ++i) {
57  stream << shape_[i] << " ";
58  }
59  stream << "(" << count_ << ")";
60  return stream.str();
61  }
62  inline const vector<int>& shape() const { return shape_; }
71  inline int shape(int index) const {
72  return shape_[CanonicalAxisIndex(index)];
73  }
74  inline int num_axes() const { return shape_.size(); }
75  inline int count() const { return count_; }
76 
85  inline int count(int start_axis, int end_axis) const {
86  CHECK_LE(start_axis, end_axis);
87  CHECK_GE(start_axis, 0);
88  CHECK_GE(end_axis, 0);
89  CHECK_LE(start_axis, num_axes());
90  CHECK_LE(end_axis, num_axes());
91  int count = 1;
92  for (int i = start_axis; i < end_axis; ++i) {
93  count *= shape(i);
94  }
95  return count;
96  }
103  inline int count(int start_axis) const {
104  return count(start_axis, num_axes());
105  }
106 
118  inline int CanonicalAxisIndex(int axis_index) const {
119  CHECK_GE(axis_index, -num_axes())
120  << "axis " << axis_index << " out of range for " << num_axes()
121  << "-D Blob with shape " << shape_string();
122  CHECK_LT(axis_index, num_axes())
123  << "axis " << axis_index << " out of range for " << num_axes()
124  << "-D Blob with shape " << shape_string();
125  if (axis_index < 0) {
126  return axis_index + num_axes();
127  }
128  return axis_index;
129  }
130 
132  inline int num() const { return LegacyShape(0); }
134  inline int channels() const { return LegacyShape(1); }
136  inline int height() const { return LegacyShape(2); }
138  inline int width() const { return LegacyShape(3); }
139  inline int LegacyShape(int index) const {
140  CHECK_LE(num_axes(), 4)
141  << "Cannot use legacy accessors on Blobs with > 4 axes.";
142  CHECK_LT(index, 4);
143  CHECK_GE(index, -4);
144  if (index >= num_axes() || index < -num_axes()) {
145  // Axis is out of range, but still in [0, 3] (or [-4, -1] for reverse
146  // indexing) -- this special case simulates the one-padding used to fill
147  // extraneous axes of legacy blobs.
148  return 1;
149  }
150  return shape(index);
151  }
152 
153  inline int offset(const int n, const int c = 0, const int h = 0,
154  const int w = 0) const {
155  CHECK_GE(n, 0);
156  CHECK_LE(n, num());
157  CHECK_GE(channels(), 0);
158  CHECK_LE(c, channels());
159  CHECK_GE(height(), 0);
160  CHECK_LE(h, height());
161  CHECK_GE(width(), 0);
162  CHECK_LE(w, width());
163  return ((n * channels() + c) * height() + h) * width() + w;
164  }
165 
166  inline int offset(const vector<int>& indices) const {
167  CHECK_LE(indices.size(), num_axes());
168  int offset = 0;
169  for (int i = 0; i < num_axes(); ++i) {
170  offset *= shape(i);
171  if (indices.size() > i) {
172  CHECK_GE(indices[i], 0);
173  CHECK_LT(indices[i], shape(i));
174  offset += indices[i];
175  }
176  }
177  return offset;
178  }
188  void CopyFrom(const Blob<Dtype>& source, bool copy_diff = false,
189  bool reshape = false);
190 
191  inline Dtype data_at(const int n, const int c, const int h,
192  const int w) const {
193  return cpu_data()[offset(n, c, h, w)];
194  }
195 
196  inline Dtype diff_at(const int n, const int c, const int h,
197  const int w) const {
198  return cpu_diff()[offset(n, c, h, w)];
199  }
200 
201  inline Dtype data_at(const vector<int>& index) const {
202  return cpu_data()[offset(index)];
203  }
204 
205  inline Dtype diff_at(const vector<int>& index) const {
206  return cpu_diff()[offset(index)];
207  }
208 
209  inline const shared_ptr<SyncedMemory>& data() const {
210  CHECK(data_);
211  return data_;
212  }
213 
214  inline const shared_ptr<SyncedMemory>& diff() const {
215  CHECK(diff_);
216  return diff_;
217  }
218 
219  const Dtype* cpu_data() const;
220  void set_cpu_data(Dtype* data);
221  const int* gpu_shape() const;
222  const Dtype* gpu_data() const;
223  const Dtype* cpu_diff() const;
224  const Dtype* gpu_diff() const;
225  Dtype* mutable_cpu_data();
226  Dtype* mutable_gpu_data();
227  Dtype* mutable_cpu_diff();
228  Dtype* mutable_gpu_diff();
229  void Update();
230  void FromProto(const BlobProto& proto, bool reshape = true);
231  void ToProto(BlobProto* proto, bool write_diff = false) const;
232 
234  Dtype asum_data() const;
236  Dtype asum_diff() const;
238  Dtype sumsq_data() const;
240  Dtype sumsq_diff() const;
241 
243  void scale_data(Dtype scale_factor);
245  void scale_diff(Dtype scale_factor);
246 
255  void ShareData(const Blob& other);
264  void ShareDiff(const Blob& other);
265 
266  bool ShapeEquals(const BlobProto& other);
267 
268  protected:
269  shared_ptr<SyncedMemory> data_;
270  shared_ptr<SyncedMemory> diff_;
271  shared_ptr<SyncedMemory> shape_data_;
272  vector<int> shape_;
273  int count_;
274  int capacity_;
275 
276  DISABLE_COPY_AND_ASSIGN(Blob);
277 }; // class Blob
278 
279 } // namespace caffe
280 
281 #endif // CAFFE_BLOB_HPP_
caffe::Blob::channels
int channels() const
Deprecated legacy shape accessor channels: use shape(1) instead.
Definition: blob.hpp:134
+
caffe::Blob::shape
int shape(int index) const
Returns the dimension of the index-th axis (or the negative index-th axis from the end...
Definition: blob.hpp:71
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::Blob::asum_diff
Dtype asum_diff() const
Compute the sum of absolute values (L1 norm) of the diff.
Definition: blob.cpp:227
+
caffe::Blob::ShareDiff
void ShareDiff(const Blob &other)
Set the diff_ shared_ptr to point to the SyncedMemory holding the diff_ of Blob other – useful in La...
Definition: blob.cpp:144
+
caffe::Blob::ShareData
void ShareData(const Blob &other)
Set the data_ shared_ptr to point to the SyncedMemory holding the data_ of Blob other – useful in La...
Definition: blob.cpp:138
+
caffe::Blob::asum_data
Dtype asum_data() const
Compute the sum of absolute values (L1 norm) of the data.
Definition: blob.cpp:192
+
caffe::Blob::height
int height() const
Deprecated legacy shape accessor height: use shape(2) instead.
Definition: blob.hpp:136
+
caffe::Blob::Reshape
void Reshape(const int num, const int channels, const int height, const int width)
Deprecated; use Reshape(const vector<int>& shape).
Definition: blob.cpp:12
+
caffe::Blob::sumsq_diff
Dtype sumsq_diff() const
Compute the sum of squares (L2 norm squared) of the diff.
Definition: blob.cpp:299
+
caffe::Blob::scale_diff
void scale_diff(Dtype scale_factor)
Scale the blob diff by a constant factor.
Definition: blob.cpp:367
+
caffe::Blob::CanonicalAxisIndex
int CanonicalAxisIndex(int axis_index) const
Returns the &#39;canonical&#39; version of a (usually) user-specified axis, allowing for negative indexing (e...
Definition: blob.hpp:118
+
caffe::Blob::CopyFrom
void CopyFrom(const Blob< Dtype > &source, bool copy_diff=false, bool reshape=false)
Copy from a source Blob.
Definition: blob.cpp:415
+
caffe::Blob::sumsq_data
Dtype sumsq_data() const
Compute the sum of squares (L2 norm squared) of the data.
Definition: blob.cpp:262
+
caffe::Blob::scale_data
void scale_data(Dtype scale_factor)
Scale the blob data by a constant factor.
Definition: blob.cpp:334
+
caffe::Blob::num
int num() const
Deprecated legacy shape accessor num: use shape(0) instead.
Definition: blob.hpp:132
+
caffe::Blob::width
int width() const
Deprecated legacy shape accessor width: use shape(3) instead.
Definition: blob.hpp:138
+
caffe::Blob::count
int count(int start_axis) const
Compute the volume of a slice spanning from a particular first axis to the final axis.
Definition: blob.hpp:103
+
caffe::Blob::count
int count(int start_axis, int end_axis) const
Compute the volume of a slice; i.e., the product of dimensions among a range of axes.
Definition: blob.hpp:85
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/blocking__queue_8hpp_source.html b/doxygen/blocking__queue_8hpp_source.html new file mode 100644 index 00000000000..93e7832ca7f --- /dev/null +++ b/doxygen/blocking__queue_8hpp_source.html @@ -0,0 +1,80 @@ + + + + + + + +Caffe: include/caffe/util/blocking_queue.hpp Source File + + + + + + + + + +
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blocking_queue.hpp
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1 #ifndef CAFFE_UTIL_BLOCKING_QUEUE_HPP_
2 #define CAFFE_UTIL_BLOCKING_QUEUE_HPP_
3 
4 #include <queue>
5 #include <string>
6 
7 namespace caffe {
8 
9 template<typename T>
10 class BlockingQueue {
11  public:
12  explicit BlockingQueue();
13 
14  void push(const T& t);
15 
16  bool try_pop(T* t);
17 
18  // This logs a message if the threads needs to be blocked
19  // useful for detecting e.g. when data feeding is too slow
20  T pop(const string& log_on_wait = "");
21 
22  bool try_peek(T* t);
23 
24  // Return element without removing it
25  T peek();
26 
27  size_t size() const;
28 
29  protected:
35  class sync;
36 
37  std::queue<T> queue_;
38  shared_ptr<sync> sync_;
39 
40 DISABLE_COPY_AND_ASSIGN(BlockingQueue);
41 };
42 
43 } // namespace caffe
44 
45 #endif
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::BlockingQueue::sync
Definition: blocking_queue.cpp:12
+
caffe::BlockingQueue
Definition: blocking_queue.hpp:10
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/bnll__layer_8hpp_source.html b/doxygen/bnll__layer_8hpp_source.html new file mode 100644 index 00000000000..6d93ce07278 --- /dev/null +++ b/doxygen/bnll__layer_8hpp_source.html @@ -0,0 +1,86 @@ + + + + + + + +Caffe: include/caffe/layers/bnll_layer.hpp Source File + + + + + + + + + +
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bnll_layer.hpp
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1 #ifndef CAFFE_BNLL_LAYER_HPP_
2 #define CAFFE_BNLL_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/neuron_layer.hpp"
11 
12 namespace caffe {
13 
31 template <typename Dtype>
32 class BNLLLayer : public NeuronLayer<Dtype> {
33  public:
34  explicit BNLLLayer(const LayerParameter& param)
35  : NeuronLayer<Dtype>(param) {}
36 
37  virtual inline const char* type() const { return "BNLL"; }
38 
39  protected:
41  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
42  const vector<Blob<Dtype>*>& top);
43  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
44  const vector<Blob<Dtype>*>& top);
45 
62  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
63  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
64  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
65  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
66 };
67 
68 } // namespace caffe
69 
70 #endif // CAFFE_BNLL_LAYER_HPP_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::BNLLLayer::type
virtual const char * type() const
Returns the layer type.
Definition: bnll_layer.hpp:37
+
caffe::BNLLLayer
Computes if ; otherwise.
Definition: bnll_layer.hpp:32
+
caffe::NeuronLayer
An interface for layers that take one blob as input ( ) and produce one equally-sized blob as output ...
Definition: neuron_layer.hpp:19
+
caffe::BNLLLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Computes the error gradient w.r.t. the BNLL inputs.
Definition: bnll_layer.cpp:24
+
caffe::BNLLLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::BNLLLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::BNLLLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Computes if ; otherwise.
Definition: bnll_layer.cpp:11
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/caffe_8hpp_source.html b/doxygen/caffe_8hpp_source.html new file mode 100644 index 00000000000..32806abee55 --- /dev/null +++ b/doxygen/caffe_8hpp_source.html @@ -0,0 +1,77 @@ + + + + + + + +Caffe: include/caffe/caffe.hpp Source File + + + + + + + + + +
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Caffe +
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caffe.hpp
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1 // caffe.hpp is the header file that you need to include in your code. It wraps
2 // all the internal caffe header files into one for simpler inclusion.
3 
4 #ifndef CAFFE_CAFFE_HPP_
5 #define CAFFE_CAFFE_HPP_
6 
7 #include "caffe/blob.hpp"
8 #include "caffe/common.hpp"
9 #include "caffe/filler.hpp"
10 #include "caffe/layer.hpp"
11 #include "caffe/layer_factory.hpp"
12 #include "caffe/net.hpp"
13 #include "caffe/parallel.hpp"
14 #include "caffe/proto/caffe.pb.h"
15 #include "caffe/solver.hpp"
16 #include "caffe/solver_factory.hpp"
17 #include "caffe/util/benchmark.hpp"
18 #include "caffe/util/io.hpp"
19 #include "caffe/util/upgrade_proto.hpp"
20 
21 #endif // CAFFE_CAFFE_HPP_
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1AbsValLayer-members.html b/doxygen/classcaffe_1_1AbsValLayer-members.html new file mode 100644 index 00000000000..d76e78d6e5a --- /dev/null +++ b/doxygen/classcaffe_1_1AbsValLayer-members.html @@ -0,0 +1,120 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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Caffe +
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caffe::AbsValLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::AbsValLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AbsValLayer(const LayerParameter &param) (defined in caffe::AbsValLayer< Dtype >)caffe::AbsValLayer< Dtype >inlineexplicit
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::AbsValLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::AbsValLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::AbsValLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::AbsValLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::AbsValLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::AbsValLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::AbsValLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
NeuronLayer(const LayerParameter &param) (defined in caffe::NeuronLayer< Dtype >)caffe::NeuronLayer< Dtype >inlineexplicit
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::NeuronLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::AbsValLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1AbsValLayer.html b/doxygen/classcaffe_1_1AbsValLayer.html new file mode 100644 index 00000000000..2d013f0ce2d --- /dev/null +++ b/doxygen/classcaffe_1_1AbsValLayer.html @@ -0,0 +1,485 @@ + + + + + + + +Caffe: caffe::AbsValLayer< Dtype > Class Template Reference + + + + + + + + + +
+
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+
Caffe +
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+ + +
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+
+Public Member Functions | +Protected Member Functions | +List of all members
+
+
caffe::AbsValLayer< Dtype > Class Template Reference
+
+
+ +

Computes $ y = |x| $. + More...

+ +

#include <absval_layer.hpp>

+
+Inheritance diagram for caffe::AbsValLayer< Dtype >:
+
+
+ + +caffe::NeuronLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ AbsValLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::NeuronLayer< Dtype >
+ NeuronLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Computes $ y = |x| $. More...
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the error gradient w.r.t. the absolute value inputs. More...
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::AbsValLayer< Dtype >

+ +

Computes $ y = |x| $.

+
Parameters
+ + + +
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $
    2. +
+
topoutput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the computed outputs $ y = |x| $
    2. +
+
+
+
+

Member Function Documentation

+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::AbsValLayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the error gradient w.r.t. the absolute value inputs.

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
    +
  1. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
    2. +
+
propagate_downsee Layer::Backward.
bottominput Blob vector (length 2)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \mathrm{sign}(x) \frac{\partial E}{\partial y} $ if propagate_down[0]
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::AbsValLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::NeuronLayer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::AbsValLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::NeuronLayer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::AbsValLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+ +

Computes $ y = |x| $.

+
Parameters
+ + + +
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $
    2. +
+
topoutput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the computed outputs $ y = |x| $
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::AbsValLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
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caffe::AccuracyLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::AccuracyLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AccuracyLayer(const LayerParameter &param)caffe::AccuracyLayer< Dtype >inlineexplicit
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::AccuracyLayer< Dtype >inlineprotectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::AccuracyLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::AccuracyLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
has_ignore_label_caffe::AccuracyLayer< Dtype >protected
ignore_label_caffe::AccuracyLayer< Dtype >protected
inner_num_ (defined in caffe::AccuracyLayer< Dtype >)caffe::AccuracyLayer< Dtype >protected
IsShared() constcaffe::Layer< Dtype >inline
label_axis_ (defined in caffe::AccuracyLayer< Dtype >)caffe::AccuracyLayer< Dtype >protected
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::AccuracyLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::AccuracyLayer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::AccuracyLayer< Dtype >inlinevirtual
nums_buffer_caffe::AccuracyLayer< Dtype >protected
outer_num_ (defined in caffe::AccuracyLayer< Dtype >)caffe::AccuracyLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::AccuracyLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
top_k_ (defined in caffe::AccuracyLayer< Dtype >)caffe::AccuracyLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::AccuracyLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1AccuracyLayer.html b/doxygen/classcaffe_1_1AccuracyLayer.html new file mode 100644 index 00000000000..12e701dbc9d --- /dev/null +++ b/doxygen/classcaffe_1_1AccuracyLayer.html @@ -0,0 +1,548 @@ + + + + + + + +Caffe: caffe::AccuracyLayer< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
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caffe::AccuracyLayer< Dtype > Class Template Reference
+
+
+ +

Computes the classification accuracy for a one-of-many classification task. + More...

+ +

#include <accuracy_layer.hpp>

+
+Inheritance diagram for caffe::AccuracyLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

 AccuracyLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Not implemented – AccuracyLayer cannot be used as a loss.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+int label_axis_
 
+int outer_num_
 
+int inner_num_
 
+int top_k_
 
+bool has_ignore_label_
 Whether to ignore instances with a certain label.
 
+int ignore_label_
 The label indicating that an instance should be ignored.
 
+Blob< Dtype > nums_buffer_
 Keeps counts of the number of samples per class.
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::AccuracyLayer< Dtype >

+ +

Computes the classification accuracy for a one-of-many classification task.

+

Constructor & Destructor Documentation

+ +

§ AccuracyLayer()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
caffe::AccuracyLayer< Dtype >::AccuracyLayer (const LayerParameter & param)
+ 		+inlineexplicit
+
+
Parameters
+ + +
paramprovides AccuracyParameter accuracy_param, with AccuracyLayer options:
    +
  • top_k (optional, default 1). Sets the maximum rank $ k $ at which a prediction is considered correct. For example, if $ k = 5 $, a prediction is counted correct if the correct label is among the top 5 predicted labels.
    • +
+
+
+
+ +
+
+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::AccuracyLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::AccuracyLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+
Parameters
+ + + +
bottominput Blob vector (length 2)
    +
  1. $ (N \times C \times H \times W) $ the predictions $ x $, a Blob with values in $ [-\infty, +\infty] $ indicating the predicted score for each of the $ K = CHW $ classes. Each $ x_n $ is mapped to a predicted label $ \hat{l}_n $ given by its maximal index: $ \hat{l}_n = \arg\max\limits_k x_{nk} $
    2. +
  2. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
    3. +
+
topoutput Blob vector (length 1)
    +
  1. $ (1 \times 1 \times 1 \times 1) $ the computed accuracy: $ \frac{1}{N} \sum\limits_{n=1}^N \delta\{ \hat{l}_n = l_n \} $, where $ \delta\{\mathrm{condition}\} = \left\{ \begin{array}{lr} 1 & \mbox{if condition} \\ 0 & \mbox{otherwise} \end{array} \right. $
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::AccuracyLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MaxTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::AccuracyLayer< Dtype >::MaxTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some maximum number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::AccuracyLayer< Dtype >::MinTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::AccuracyLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1AccuracyLayer.png b/doxygen/classcaffe_1_1AccuracyLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..00f3579e0987106c5223491ae86143764c6c4dfc GIT binary patch literal 744 zcmeAS@N?(olHy`uVBq!ia0vp^2Z1<%gBeIpU-r-gNJ#|vgt-3y{~ySF@#br3|Dg#$ z78oBmaDcV*jy#adQ4-`A%m7pb0#{Fk7%?y~m3z84hEy=Vo%^=$wE_=oyYR~Q|4(es zwAkO5Z$-GRST~`$*EQzRfns7m!9yG`K$+}h0{wtW~hHqmXesoF^xH6o5#FI z2}!!ACdUihXo;GCT_TF(T-dieDR+P7T@NWr@cEp!N0t3*@dxI{jh~(`FxdND=0;RX zn0db7=ES1Pjaxk?Req|SP2{{3Vcoz#f~N zFl*V$bjCZY*d4CFn)l%23)zL$P3jKMIsOXwM6n30bz~4&O+5Y(r@8dXRu2Z*YypNN zSwOtX?SsuDea3G;IksnB2#S+(3*~rrZZiM;)HS^i&VFHFe>%hLOX}D8f7gGz6&(?M zyLQ+0!9i3j>MB=}fPI~uLrcJKN%=_Z>$7H|KZtpfXxtFLcNChTK22WQ%mvv4FO#mPbR%8GG literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1AdaDeltaSolver-members.html b/doxygen/classcaffe_1_1AdaDeltaSolver-members.html new file mode 100644 index 00000000000..e9ada0d6f63 --- /dev/null +++ b/doxygen/classcaffe_1_1AdaDeltaSolver-members.html @@ -0,0 +1,143 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::AdaDeltaSolver< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::AdaDeltaSolver< Dtype >, including all inherited members.

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action_request_function_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
AdaDeltaPreSolve() (defined in caffe::AdaDeltaSolver< Dtype >)caffe::AdaDeltaSolver< Dtype >protected
AdaDeltaSolver(const SolverParameter &param) (defined in caffe::AdaDeltaSolver< Dtype >)caffe::AdaDeltaSolver< Dtype >inlineexplicit
AdaDeltaSolver(const string &param_file) (defined in caffe::AdaDeltaSolver< Dtype >)caffe::AdaDeltaSolver< Dtype >inlineexplicit
add_callback(Callback *value) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
ApplyUpdate() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
callbacks() const (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
callbacks_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
CheckSnapshotWritePermissions() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
ClipGradients() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
ComputeUpdateValue(int param_id, Dtype rate) (defined in caffe::AdaDeltaSolver< Dtype >)caffe::AdaDeltaSolver< Dtype >protectedvirtual
current_step_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
DISABLE_COPY_AND_ASSIGN(AdaDeltaSolver) (defined in caffe::AdaDeltaSolver< Dtype >)caffe::AdaDeltaSolver< Dtype >protected
DISABLE_COPY_AND_ASSIGN(SGDSolver) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
DISABLE_COPY_AND_ASSIGN(Solver) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
DisplayOutputBlobs(const int net_id) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
GetLearningRate() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
GetRequestedAction() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
history() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >inline
history_ (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
Init(const SolverParameter &param) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
InitTestNets() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
InitTrainNet() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
iter() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
iter_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
losses_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
net() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
net_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Normalize(int param_id) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
param() const (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
param_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
PreSolve() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
Regularize(int param_id) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
requested_early_exit_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Restore(const char *resume_file) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
RestoreSolverStateFromBinaryProto(const string &state_file) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
RestoreSolverStateFromHDF5(const string &state_file) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
root_solver_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SetActionFunction(ActionCallback func) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
SGDSolver(const SolverParameter &param) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >inlineexplicit
SGDSolver(const string &param_file) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >inlineexplicit
smoothed_loss_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Snapshot() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
SnapshotFilename(const string extension) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SnapshotSolverState(const string &model_filename) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
SnapshotSolverStateToBinaryProto(const string &model_filename) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
SnapshotSolverStateToHDF5(const string &model_filename) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
SnapshotToBinaryProto() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SnapshotToHDF5() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Solve(const char *resume_file=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >virtual
Solve(const string resume_file) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
Solver(const SolverParameter &param, const Solver *root_solver=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >explicit
Solver(const string &param_file, const Solver *root_solver=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >explicit
Step(int iters) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
temp_ (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
Test(const int test_net_id=0) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
test_nets() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
test_nets_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
TestAll() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
type() constcaffe::AdaDeltaSolver< Dtype >inlinevirtual
update_ (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
UpdateSmoothedLoss(Dtype loss, int start_iter, int average_loss) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
~Solver() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inlinevirtual
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+ + diff --git a/doxygen/classcaffe_1_1AdaDeltaSolver.html b/doxygen/classcaffe_1_1AdaDeltaSolver.html new file mode 100644 index 00000000000..b109f6dc2a3 --- /dev/null +++ b/doxygen/classcaffe_1_1AdaDeltaSolver.html @@ -0,0 +1,298 @@ + + + + + + + +Caffe: caffe::AdaDeltaSolver< Dtype > Class Template Reference + + + + + + + + + +
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caffe::AdaDeltaSolver< Dtype > Class Template Reference
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+Inheritance diagram for caffe::AdaDeltaSolver< Dtype >:
+
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+ + +caffe::SGDSolver< Dtype > +caffe::Solver< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ AdaDeltaSolver (const SolverParameter &param)
 
+ AdaDeltaSolver (const string &param_file)
 
+virtual const char * type () const
 Returns the solver type.
 
- Public Member Functions inherited from caffe::SGDSolver< Dtype >
+ SGDSolver (const SolverParameter &param)
 
+ SGDSolver (const string &param_file)
 
+const vector< shared_ptr< Blob< Dtype > > > & history ()
 
- Public Member Functions inherited from caffe::Solver< Dtype >
+ Solver (const SolverParameter &param, const Solver *root_solver=NULL)
 
+ Solver (const string &param_file, const Solver *root_solver=NULL)
 
+void Init (const SolverParameter &param)
 
+void InitTrainNet ()
 
+void InitTestNets ()
 
+void SetActionFunction (ActionCallback func)
 
+SolverAction::Enum GetRequestedAction ()
 
+virtual void Solve (const char *resume_file=NULL)
 
+void Solve (const string resume_file)
 
+void Step (int iters)
 
+void Restore (const char *resume_file)
 
+void Snapshot ()
 
+const SolverParameter & param () const
 
+shared_ptr< Net< Dtype > > net ()
 
+const vector< shared_ptr< Net< Dtype > > > & test_nets ()
 
+int iter ()
 
+const vector< Callback * > & callbacks () const
 
+void add_callback (Callback *value)
 
+void CheckSnapshotWritePermissions ()
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+void AdaDeltaPreSolve ()
 
+virtual void ComputeUpdateValue (int param_id, Dtype rate)
 
+ DISABLE_COPY_AND_ASSIGN (AdaDeltaSolver)
 
- Protected Member Functions inherited from caffe::SGDSolver< Dtype >
+void PreSolve ()
 
+Dtype GetLearningRate ()
 
+virtual void ApplyUpdate ()
 
+virtual void Normalize (int param_id)
 
+virtual void Regularize (int param_id)
 
+virtual void ClipGradients ()
 
+virtual void SnapshotSolverState (const string &model_filename)
 
+virtual void SnapshotSolverStateToBinaryProto (const string &model_filename)
 
+virtual void SnapshotSolverStateToHDF5 (const string &model_filename)
 
+virtual void RestoreSolverStateFromHDF5 (const string &state_file)
 
+virtual void RestoreSolverStateFromBinaryProto (const string &state_file)
 
+ DISABLE_COPY_AND_ASSIGN (SGDSolver)
 
- Protected Member Functions inherited from caffe::Solver< Dtype >
+string SnapshotFilename (const string extension)
 
+string SnapshotToBinaryProto ()
 
+string SnapshotToHDF5 ()
 
+void TestAll ()
 
+void Test (const int test_net_id=0)
 
+void DisplayOutputBlobs (const int net_id)
 
+void UpdateSmoothedLoss (Dtype loss, int start_iter, int average_loss)
 
+ DISABLE_COPY_AND_ASSIGN (Solver)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::SGDSolver< Dtype >
+vector< shared_ptr< Blob< Dtype > > > history_
 
+vector< shared_ptr< Blob< Dtype > > > update_
 
+vector< shared_ptr< Blob< Dtype > > > temp_
 
- Protected Attributes inherited from caffe::Solver< Dtype >
+SolverParameter param_
 
+int iter_
 
+int current_step_
 
+shared_ptr< Net< Dtype > > net_
 
+vector< shared_ptr< Net< Dtype > > > test_nets_
 
+vector< Callback * > callbacks_
 
+vector< Dtype > losses_
 
+Dtype smoothed_loss_
 
+const Solver *const root_solver_
 
+ActionCallback action_request_function_
 
+bool requested_early_exit_
 
+
The documentation for this class was generated from the following files: +
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+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
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caffe::AdaGradSolver< Dtype > Member List
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This is the complete list of members for caffe::AdaGradSolver< Dtype >, including all inherited members.

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action_request_function_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
AdaGradSolver(const SolverParameter &param) (defined in caffe::AdaGradSolver< Dtype >)caffe::AdaGradSolver< Dtype >inlineexplicit
AdaGradSolver(const string &param_file) (defined in caffe::AdaGradSolver< Dtype >)caffe::AdaGradSolver< Dtype >inlineexplicit
add_callback(Callback *value) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
ApplyUpdate() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
callbacks() const (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
callbacks_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
CheckSnapshotWritePermissions() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
ClipGradients() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
ComputeUpdateValue(int param_id, Dtype rate) (defined in caffe::AdaGradSolver< Dtype >)caffe::AdaGradSolver< Dtype >protectedvirtual
constructor_sanity_check() (defined in caffe::AdaGradSolver< Dtype >)caffe::AdaGradSolver< Dtype >inlineprotected
current_step_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
DISABLE_COPY_AND_ASSIGN(AdaGradSolver) (defined in caffe::AdaGradSolver< Dtype >)caffe::AdaGradSolver< Dtype >protected
DISABLE_COPY_AND_ASSIGN(SGDSolver) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
DISABLE_COPY_AND_ASSIGN(Solver) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
DisplayOutputBlobs(const int net_id) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
GetLearningRate() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
GetRequestedAction() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
history() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >inline
history_ (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
Init(const SolverParameter &param) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
InitTestNets() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
InitTrainNet() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
iter() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
iter_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
losses_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
net() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
net_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Normalize(int param_id) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
param() const (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
param_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
PreSolve() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
Regularize(int param_id) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
requested_early_exit_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Restore(const char *resume_file) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
RestoreSolverStateFromBinaryProto(const string &state_file) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
RestoreSolverStateFromHDF5(const string &state_file) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
root_solver_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SetActionFunction(ActionCallback func) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
SGDSolver(const SolverParameter &param) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >inlineexplicit
SGDSolver(const string &param_file) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >inlineexplicit
smoothed_loss_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Snapshot() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
SnapshotFilename(const string extension) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SnapshotSolverState(const string &model_filename) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
SnapshotSolverStateToBinaryProto(const string &model_filename) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
SnapshotSolverStateToHDF5(const string &model_filename) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
SnapshotToBinaryProto() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SnapshotToHDF5() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Solve(const char *resume_file=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >virtual
Solve(const string resume_file) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
Solver(const SolverParameter &param, const Solver *root_solver=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >explicit
Solver(const string &param_file, const Solver *root_solver=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >explicit
Step(int iters) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
temp_ (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
Test(const int test_net_id=0) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
test_nets() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
test_nets_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
TestAll() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
type() constcaffe::AdaGradSolver< Dtype >inlinevirtual
update_ (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
UpdateSmoothedLoss(Dtype loss, int start_iter, int average_loss) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
~Solver() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1AdaGradSolver.html b/doxygen/classcaffe_1_1AdaGradSolver.html new file mode 100644 index 00000000000..43887fba3c1 --- /dev/null +++ b/doxygen/classcaffe_1_1AdaGradSolver.html @@ -0,0 +1,298 @@ + + + + + + + +Caffe: caffe::AdaGradSolver< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +List of all members
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caffe::AdaGradSolver< Dtype > Class Template Reference
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+Inheritance diagram for caffe::AdaGradSolver< Dtype >:
+
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+ + +caffe::SGDSolver< Dtype > +caffe::Solver< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ AdaGradSolver (const SolverParameter &param)
 
+ AdaGradSolver (const string &param_file)
 
+virtual const char * type () const
 Returns the solver type.
 
- Public Member Functions inherited from caffe::SGDSolver< Dtype >
+ SGDSolver (const SolverParameter &param)
 
+ SGDSolver (const string &param_file)
 
+const vector< shared_ptr< Blob< Dtype > > > & history ()
 
- Public Member Functions inherited from caffe::Solver< Dtype >
+ Solver (const SolverParameter &param, const Solver *root_solver=NULL)
 
+ Solver (const string &param_file, const Solver *root_solver=NULL)
 
+void Init (const SolverParameter &param)
 
+void InitTrainNet ()
 
+void InitTestNets ()
 
+void SetActionFunction (ActionCallback func)
 
+SolverAction::Enum GetRequestedAction ()
 
+virtual void Solve (const char *resume_file=NULL)
 
+void Solve (const string resume_file)
 
+void Step (int iters)
 
+void Restore (const char *resume_file)
 
+void Snapshot ()
 
+const SolverParameter & param () const
 
+shared_ptr< Net< Dtype > > net ()
 
+const vector< shared_ptr< Net< Dtype > > > & test_nets ()
 
+int iter ()
 
+const vector< Callback * > & callbacks () const
 
+void add_callback (Callback *value)
 
+void CheckSnapshotWritePermissions ()
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void ComputeUpdateValue (int param_id, Dtype rate)
 
+void constructor_sanity_check ()
 
+ DISABLE_COPY_AND_ASSIGN (AdaGradSolver)
 
- Protected Member Functions inherited from caffe::SGDSolver< Dtype >
+void PreSolve ()
 
+Dtype GetLearningRate ()
 
+virtual void ApplyUpdate ()
 
+virtual void Normalize (int param_id)
 
+virtual void Regularize (int param_id)
 
+virtual void ClipGradients ()
 
+virtual void SnapshotSolverState (const string &model_filename)
 
+virtual void SnapshotSolverStateToBinaryProto (const string &model_filename)
 
+virtual void SnapshotSolverStateToHDF5 (const string &model_filename)
 
+virtual void RestoreSolverStateFromHDF5 (const string &state_file)
 
+virtual void RestoreSolverStateFromBinaryProto (const string &state_file)
 
+ DISABLE_COPY_AND_ASSIGN (SGDSolver)
 
- Protected Member Functions inherited from caffe::Solver< Dtype >
+string SnapshotFilename (const string extension)
 
+string SnapshotToBinaryProto ()
 
+string SnapshotToHDF5 ()
 
+void TestAll ()
 
+void Test (const int test_net_id=0)
 
+void DisplayOutputBlobs (const int net_id)
 
+void UpdateSmoothedLoss (Dtype loss, int start_iter, int average_loss)
 
+ DISABLE_COPY_AND_ASSIGN (Solver)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::SGDSolver< Dtype >
+vector< shared_ptr< Blob< Dtype > > > history_
 
+vector< shared_ptr< Blob< Dtype > > > update_
 
+vector< shared_ptr< Blob< Dtype > > > temp_
 
- Protected Attributes inherited from caffe::Solver< Dtype >
+SolverParameter param_
 
+int iter_
 
+int current_step_
 
+shared_ptr< Net< Dtype > > net_
 
+vector< shared_ptr< Net< Dtype > > > test_nets_
 
+vector< Callback * > callbacks_
 
+vector< Dtype > losses_
 
+Dtype smoothed_loss_
 
+const Solver *const root_solver_
 
+ActionCallback action_request_function_
 
+bool requested_early_exit_
 
+
The documentation for this class was generated from the following files: +
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+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1AdaGradSolver.png b/doxygen/classcaffe_1_1AdaGradSolver.png new file mode 100644 index 0000000000000000000000000000000000000000..6222d1a0ef778f32ff1f2c73f9b5bf0d4711e4ef GIT binary patch literal 1143 zcmeAS@N?(olHy`uVBq!ia0vp^2Y|SPgBeJ+af+A#DTx4|5ZC|z{{xvX-h3_XKQsZz z0^RR^V~%zn=2{zxw{$ zAsK>mtMBPdUD9nXtZ^YY;qX$Gm)fxmH=4E{sf)U8_Uvh{?5cB99jg7OU)vNi zGjZF*CEdSx9>i+RoBHfgdh)Kv8rRP=Mg(8~^lVbel5B>rTNw*h7BXGC`TmoJwN7g5_C2`amAd+s8I%97)>84@f3?2$QXWHq_Xd^P)fEiEDk?9d z?>^-6yfh!~LtDmYmGjue4)}j%kceE&Y$Mk!+{5?bOMOn$L&i;9j7PK>8I-0nFiiAf zXz);Fa9AS5pb*5#Ah42!p(BKe!39L;GxxOX^=4w@uL5IRDzV-Z$UE zs;<4bJufw=bREmB>Ro4R^0%Bd;Pbe&aTi0~<4yIMdiP`)ZU`plS)Mw2XJP)bbx+T# z-h3{wV4oR-o@rK!uD`4$SIgJs><`lCD9&NhxtaPW=h}w4-^`hFH+;T5=>kKhDt)zTySJR;NU>PEUH9fBl1H0Q=dd`g4rYtv+9B zA`MgT%a?1uRu%QiDmh*ImrvsN|J6>DM3&~;XO=T(bZu6d`#OzbS&hdf*1PY30d4NZ z;MoZsw^UT~2mRB+KthvZ2*ME@HjlJTRz1hJJ43Vu2Nkj)SC=MR@TsHAl z?&eI0fyIeC!}&LL+#CGgZcKik#PHzjtIy8!o41Ng^Io=`;r(95Jw2tb!>;d=-NjI_ z$@+lb)up{Poqsp~zx!?D-8I)=?Vk1H;U+)E6|2|Vrad%kc>SxJ{Wi~I$%f#_1MB0r zX`bD7{PwKs@N}Izv(@h{PWiAW`0iysw}PAv_LPTw!t?WT3t~4a z<}plDF=EdRkB=_9@@8jcviJv^)AM6@tQF;Lu>SnaYWnSG3{{M;bnpE+*fLr6HPenI zZ-ME$)corYP{<32W7iHcTUD#NEOXYetM&(9`Z-;W T{H6*lB^W$i{an^LB{Ts53qJ=k literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1AdamSolver-members.html b/doxygen/classcaffe_1_1AdamSolver-members.html new file mode 100644 index 00000000000..878bd4033e8 --- /dev/null +++ b/doxygen/classcaffe_1_1AdamSolver-members.html @@ -0,0 +1,143 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::AdamSolver< Dtype > Member List
+
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+ +

This is the complete list of members for caffe::AdamSolver< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
action_request_function_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
AdamPreSolve() (defined in caffe::AdamSolver< Dtype >)caffe::AdamSolver< Dtype >protected
AdamSolver(const SolverParameter &param) (defined in caffe::AdamSolver< Dtype >)caffe::AdamSolver< Dtype >inlineexplicit
AdamSolver(const string &param_file) (defined in caffe::AdamSolver< Dtype >)caffe::AdamSolver< Dtype >inlineexplicit
add_callback(Callback *value) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
ApplyUpdate() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
callbacks() const (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
callbacks_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
CheckSnapshotWritePermissions() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
ClipGradients() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
ComputeUpdateValue(int param_id, Dtype rate) (defined in caffe::AdamSolver< Dtype >)caffe::AdamSolver< Dtype >protectedvirtual
current_step_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
DISABLE_COPY_AND_ASSIGN(AdamSolver) (defined in caffe::AdamSolver< Dtype >)caffe::AdamSolver< Dtype >protected
DISABLE_COPY_AND_ASSIGN(SGDSolver) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
DISABLE_COPY_AND_ASSIGN(Solver) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
DisplayOutputBlobs(const int net_id) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
GetLearningRate() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
GetRequestedAction() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
history() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >inline
history_ (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
Init(const SolverParameter &param) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
InitTestNets() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
InitTrainNet() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
iter() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
iter_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
losses_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
net() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
net_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Normalize(int param_id) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
param() const (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
param_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
PreSolve() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
Regularize(int param_id) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
requested_early_exit_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Restore(const char *resume_file) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
RestoreSolverStateFromBinaryProto(const string &state_file) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
RestoreSolverStateFromHDF5(const string &state_file) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
root_solver_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SetActionFunction(ActionCallback func) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
SGDSolver(const SolverParameter &param) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >inlineexplicit
SGDSolver(const string &param_file) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >inlineexplicit
smoothed_loss_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Snapshot() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
SnapshotFilename(const string extension) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SnapshotSolverState(const string &model_filename) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
SnapshotSolverStateToBinaryProto(const string &model_filename) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
SnapshotSolverStateToHDF5(const string &model_filename) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
SnapshotToBinaryProto() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SnapshotToHDF5() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Solve(const char *resume_file=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >virtual
Solve(const string resume_file) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
Solver(const SolverParameter &param, const Solver *root_solver=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >explicit
Solver(const string &param_file, const Solver *root_solver=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >explicit
Step(int iters) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
temp_ (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
Test(const int test_net_id=0) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
test_nets() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
test_nets_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
TestAll() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
type() constcaffe::AdamSolver< Dtype >inlinevirtual
update_ (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
UpdateSmoothedLoss(Dtype loss, int start_iter, int average_loss) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
~Solver() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inlinevirtual
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+Generated on Mon Nov 21 2016 11:23:37 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1AdamSolver.html b/doxygen/classcaffe_1_1AdamSolver.html new file mode 100644 index 00000000000..1c11ba5a805 --- /dev/null +++ b/doxygen/classcaffe_1_1AdamSolver.html @@ -0,0 +1,309 @@ + + + + + + + +Caffe: caffe::AdamSolver< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +List of all members
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caffe::AdamSolver< Dtype > Class Template Reference
+
+
+ +

AdamSolver, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. Described in [1]. + More...

+ +

#include <sgd_solvers.hpp>

+
+Inheritance diagram for caffe::AdamSolver< Dtype >:
+
+
+ + +caffe::SGDSolver< Dtype > +caffe::Solver< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ AdamSolver (const SolverParameter &param)
 
+ AdamSolver (const string &param_file)
 
+virtual const char * type () const
 Returns the solver type.
 
- Public Member Functions inherited from caffe::SGDSolver< Dtype >
+ SGDSolver (const SolverParameter &param)
 
+ SGDSolver (const string &param_file)
 
+const vector< shared_ptr< Blob< Dtype > > > & history ()
 
- Public Member Functions inherited from caffe::Solver< Dtype >
+ Solver (const SolverParameter &param, const Solver *root_solver=NULL)
 
+ Solver (const string &param_file, const Solver *root_solver=NULL)
 
+void Init (const SolverParameter &param)
 
+void InitTrainNet ()
 
+void InitTestNets ()
 
+void SetActionFunction (ActionCallback func)
 
+SolverAction::Enum GetRequestedAction ()
 
+virtual void Solve (const char *resume_file=NULL)
 
+void Solve (const string resume_file)
 
+void Step (int iters)
 
+void Restore (const char *resume_file)
 
+void Snapshot ()
 
+const SolverParameter & param () const
 
+shared_ptr< Net< Dtype > > net ()
 
+const vector< shared_ptr< Net< Dtype > > > & test_nets ()
 
+int iter ()
 
+const vector< Callback * > & callbacks () const
 
+void add_callback (Callback *value)
 
+void CheckSnapshotWritePermissions ()
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+void AdamPreSolve ()
 
+virtual void ComputeUpdateValue (int param_id, Dtype rate)
 
+ DISABLE_COPY_AND_ASSIGN (AdamSolver)
 
- Protected Member Functions inherited from caffe::SGDSolver< Dtype >
+void PreSolve ()
 
+Dtype GetLearningRate ()
 
+virtual void ApplyUpdate ()
 
+virtual void Normalize (int param_id)
 
+virtual void Regularize (int param_id)
 
+virtual void ClipGradients ()
 
+virtual void SnapshotSolverState (const string &model_filename)
 
+virtual void SnapshotSolverStateToBinaryProto (const string &model_filename)
 
+virtual void SnapshotSolverStateToHDF5 (const string &model_filename)
 
+virtual void RestoreSolverStateFromHDF5 (const string &state_file)
 
+virtual void RestoreSolverStateFromBinaryProto (const string &state_file)
 
+ DISABLE_COPY_AND_ASSIGN (SGDSolver)
 
- Protected Member Functions inherited from caffe::Solver< Dtype >
+string SnapshotFilename (const string extension)
 
+string SnapshotToBinaryProto ()
 
+string SnapshotToHDF5 ()
 
+void TestAll ()
 
+void Test (const int test_net_id=0)
 
+void DisplayOutputBlobs (const int net_id)
 
+void UpdateSmoothedLoss (Dtype loss, int start_iter, int average_loss)
 
+ DISABLE_COPY_AND_ASSIGN (Solver)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::SGDSolver< Dtype >
+vector< shared_ptr< Blob< Dtype > > > history_
 
+vector< shared_ptr< Blob< Dtype > > > update_
 
+vector< shared_ptr< Blob< Dtype > > > temp_
 
- Protected Attributes inherited from caffe::Solver< Dtype >
+SolverParameter param_
 
+int iter_
 
+int current_step_
 
+shared_ptr< Net< Dtype > > net_
 
+vector< shared_ptr< Net< Dtype > > > test_nets_
 
+vector< Callback * > callbacks_
 
+vector< Dtype > losses_
 
+Dtype smoothed_loss_
 
+const Solver *const root_solver_
 
+ActionCallback action_request_function_
 
+bool requested_early_exit_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::AdamSolver< Dtype >

+ +

AdamSolver, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. Described in [1].

+

[1] D. P. Kingma and J. L. Ba, "ADAM: A Method for Stochastic Optimization." arXiv preprint arXiv:1412.6980v8 (2014).

+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:37 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1AdamSolver.png b/doxygen/classcaffe_1_1AdamSolver.png new file mode 100644 index 0000000000000000000000000000000000000000..5cc81e00c194a670e6b5c36bd8db5857b28a4a48 GIT binary patch literal 1097 zcmeAS@N?(olHy`uVBq!ia0vp^Yk|0fgBeIJ5}5r3NJ#|vgt-3y{~ySF@#br3|Dg#$ z78oBmaDcV*jy#adQ4-`A%m7pb0#{Fk7%?y~-|%#C45?szJNIJWY6Bh@e&Ln>|DW8h zJK=_+NLlIHToq+E)q|b>J#Y5~d1+qmQ1YCtk*TWb=Be#zxXXLD&T5sd)s=B+DxUIR zH`PbS&i1+#Xr38=TRx_wFaN^4JXKHg62IN;iM_8j_2!zqzWi;~#47?-Yf87|g@#L* zPx=zLe9OK1qbm-Jt}?cDxUN&Q<=U0rJv&t@YnSBq%dYi)&c9{uXTi1cg$JuWcZ;?C zJaRW|uY2~@@HF>q|66aX4^_KbcZJ29xJRWM^sP0#tELxOKYRP~E&JYl7q0%b%6992 z=XiYI^24}g}E%i4pJBlx+`7?9`U1B<-k;#}e zWebC$S5$+9s;)!NBrydyPi}$5OISMsk)>~44@D2VUk2D zm=!$t>a6`9H23PmSHD`W?K6BPw(HsThjVtXl&<`&m|OiOBi3sBjL09~p1rwp?Ptfe z^%kG{GOm9y54hXDaf02Iz3#jBt-slKZQhp8H`thO1^WEDWO3=1`Mhhovd6E@xFxis z=WXUaKCYcSsSDpX){f6u6sQli1<4RlW`M+!Cu2uXJ zaXW4G?%LLa3r@5N{Tjx+FQ z#)kGLXZxJJX@B&aNk03+9qzAB*Ofngx%%6ceG|8DQ=idsVo}d4K$C_;Y_3F(lXWzTG&HspQupABG(P&=mLO)v?o?bSA%$_j&8q VczAN=EnvQ9@O1TaS?83{1OTP8A1VL< literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1ArgMaxLayer-members.html b/doxygen/classcaffe_1_1ArgMaxLayer-members.html new file mode 100644 index 00000000000..0c3996ee914 --- /dev/null +++ b/doxygen/classcaffe_1_1ArgMaxLayer-members.html @@ -0,0 +1,123 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::ArgMaxLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::ArgMaxLayer< Dtype >, including all inherited members.

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AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
ArgMaxLayer(const LayerParameter &param)caffe::ArgMaxLayer< Dtype >inlineexplicit
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
axis_ (defined in caffe::ArgMaxLayer< Dtype >)caffe::ArgMaxLayer< Dtype >protected
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ArgMaxLayer< Dtype >inlineprotectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::ArgMaxLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::ArgMaxLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ArgMaxLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
has_axis_ (defined in caffe::ArgMaxLayer< Dtype >)caffe::ArgMaxLayer< Dtype >protected
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ArgMaxLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
out_max_val_ (defined in caffe::ArgMaxLayer< Dtype >)caffe::ArgMaxLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ArgMaxLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
top_k_ (defined in caffe::ArgMaxLayer< Dtype >)caffe::ArgMaxLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::ArgMaxLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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+ + diff --git a/doxygen/classcaffe_1_1ArgMaxLayer.html b/doxygen/classcaffe_1_1ArgMaxLayer.html new file mode 100644 index 00000000000..104f40d9cca --- /dev/null +++ b/doxygen/classcaffe_1_1ArgMaxLayer.html @@ -0,0 +1,507 @@ + + + + + + + +Caffe: caffe::ArgMaxLayer< Dtype > Class Template Reference + + + + + + + + + +
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caffe::ArgMaxLayer< Dtype > Class Template Reference
+
+
+ +

Compute the index of the $ K $ max values for each datum across all dimensions $ (C \times H \times W) $. + More...

+ +

#include <argmax_layer.hpp>

+
+Inheritance diagram for caffe::ArgMaxLayer< Dtype >:
+
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+ + +caffe::Layer< Dtype > + +
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+Public Member Functions

 ArgMaxLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Not implemented (non-differentiable function)
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+bool out_max_val_
 
+size_t top_k_
 
+bool has_axis_
 
+int axis_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::ArgMaxLayer< Dtype >

+ +

Compute the index of the $ K $ max values for each datum across all dimensions $ (C \times H \times W) $.

+

Intended for use after a classification layer to produce a prediction. If parameter out_max_val is set to true, output is a vector of pairs (max_ind, max_val) for each image. The axis parameter specifies an axis along which to maximise.

+

NOTE: does not implement Backwards operation.

+

Constructor & Destructor Documentation

+ +

§ ArgMaxLayer()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
caffe::ArgMaxLayer< Dtype >::ArgMaxLayer (const LayerParameter & param)
+ 		+inlineexplicit
+
+
Parameters
+ + +
paramprovides ArgMaxParameter argmax_param, with ArgMaxLayer options:
    +
  • top_k (optional uint, default 1). the number $ K $ of maximal items to output.
    • +
  • out_max_val (optional bool, default false). if set, output a vector of pairs (max_ind, max_val) unless axis is set then output max_val along the specified axis.
    • +
  • axis (optional int). if set, maximise along the specified axis else maximise the flattened trailing dimensions for each index of the first / num dimension.
    • +
+
+
+
+ +
+
+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::ArgMaxLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::ArgMaxLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ArgMaxLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+
Parameters
+ + + +
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $
    2. +
+
topoutput Blob vector (length 1)
    +
  1. $ (N \times 1 \times K) $ or, if out_max_val $ (N \times 2 \times K) $ unless axis set than e.g. $ (N \times K \times H \times W) $ if axis == 1 the computed outputs $ y_n = \arg\max\limits_i x_{ni} $ (for $ K = 1 $).
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ArgMaxLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ArgMaxLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:37 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ArgMaxLayer.png b/doxygen/classcaffe_1_1ArgMaxLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..304e91559809add779f227db459b7ac92ab32073 GIT binary patch literal 753 zcmeAS@N?(olHy`uVBq!ia0vp^+kiNLgBeIR8`rM{QW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;gO?QLh(%{*uMaz{^c%S`mXr13SeUEt|6m6xjyeO_NZ zRi&~t(f+&Do9pd&OO!pYf914hRX=9==5g({#P|7=f&y&gWn9g7zbsBDo3!NouZFp? zx7#J(P0l?%*Lc3}U-M;oPtK~o3=!XO`a0L|-pZ_Bk}yt7ok;irlExAW(x+$m17Uy=S~o=A16P(xwV4Ti;AHZTIc&)jqA5Szr( zR$c=yZs~+6VrB<4baNU4(G(PLDYpD{VVL0yQ?qr0eEC<^19i8Izs_Elapc*BjjI~} z&atU1OxipDZNU+{H|Et$G9eC^DwF5Pe_sF5E;@4myp5GnZ+3h;lN-jh>iOxo?`?mI zk}DYIY--NQl~2n^PvTtaKBqKWxb|}H)I}}1zJIrcy2+Lm1|?o!`q*#E#?Q00?moZx zLBu_0{~{*)gyW|ZCuQxuJv%vhcG!|bR-e=FUj4rJ(6)E74kv?KYsIEDe`fnOTj`|d z#_$g|z<}LRDg8Dr*L*{4+?#pq+vln7|8o4pvg=v5%Y=kyO}ZjwT{>x%|C6wZ+Q-;- zU7Y9rb>{2hm6I7tRww7Ke_8$e{l@A#*?krdVsG?*VU)Q#f7 + + + + + + +Caffe: Member List + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
caffe::BNLLLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::BNLLLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BNLLLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BNLLLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
BNLLLayer(const LayerParameter &param) (defined in caffe::BNLLLayer< Dtype >)caffe::BNLLLayer< Dtype >inlineexplicit
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BNLLLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BNLLLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlinevirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
NeuronLayer(const LayerParameter &param) (defined in caffe::NeuronLayer< Dtype >)caffe::NeuronLayer< Dtype >inlineexplicit
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::NeuronLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::BNLLLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:38 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1BNLLLayer.html b/doxygen/classcaffe_1_1BNLLLayer.html new file mode 100644 index 00000000000..55d59576e6a --- /dev/null +++ b/doxygen/classcaffe_1_1BNLLLayer.html @@ -0,0 +1,371 @@ + + + + + + + +Caffe: caffe::BNLLLayer< Dtype > Class Template Reference + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
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+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+Public Member Functions | +Protected Member Functions | +List of all members
+
+
caffe::BNLLLayer< Dtype > Class Template Reference
+
+
+ +

Computes $ y = x + \log(1 + \exp(-x)) $ if $ x > 0 $; $ y = \log(1 + \exp(x)) $ otherwise. + More...

+ +

#include <bnll_layer.hpp>

+
+Inheritance diagram for caffe::BNLLLayer< Dtype >:
+
+
+ + +caffe::NeuronLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ BNLLLayer (const LayerParameter &param)
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::NeuronLayer< Dtype >
+ NeuronLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Computes $ y = x + \log(1 + \exp(-x)) $ if $ x > 0 $; $ y = \log(1 + \exp(x)) $ otherwise. More...
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the error gradient w.r.t. the BNLL inputs. More...
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::BNLLLayer< Dtype >

+ +

Computes $ y = x + \log(1 + \exp(-x)) $ if $ x > 0 $; $ y = \log(1 + \exp(x)) $ otherwise.

+
Parameters
+ + + +
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $
    2. +
+
topoutput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the computed outputs $ y = \left\{ \begin{array}{ll} x + \log(1 + \exp(-x)) & \mbox{if } x > 0 \\ \log(1 + \exp(x)) & \mbox{otherwise} \end{array} \right. $
    2. +
+
+
+
+

Member Function Documentation

+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::BNLLLayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the error gradient w.r.t. the BNLL inputs.

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
    +
  1. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
    2. +
+
propagate_downsee Layer::Backward.
bottominput Blob vector (length 2)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} $ if propagate_down[0]
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::BNLLLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+ +

Computes $ y = x + \log(1 + \exp(-x)) $ if $ x > 0 $; $ y = \log(1 + \exp(x)) $ otherwise.

+
Parameters
+ + + +
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $
    2. +
+
topoutput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the computed outputs $ y = \left\{ \begin{array}{ll} x + \log(1 + \exp(-x)) & \mbox{if } x > 0 \\ \log(1 + \exp(x)) & \mbox{otherwise} \end{array} \right. $
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/layers/bnll_layer.hpp
    • +
  • src/caffe/layers/bnll_layer.cpp
    • +
+
+ +
+Generated on Mon Nov 21 2016 11:23:38 for Caffe by   +doxygen + 1.8.12 +
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caffe::BaseConvolutionLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::BaseConvolutionLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)=0caffe::Layer< Dtype >protectedpure virtual
backward_cpu_bias(Dtype *bias, const Dtype *input) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
backward_cpu_gemm(const Dtype *input, const Dtype *weights, Dtype *output) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inlineprotectedvirtual
backward_gpu_bias(Dtype *bias, const Dtype *input) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
backward_gpu_gemm(const Dtype *input, const Dtype *weights, Dtype *col_output) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
BaseConvolutionLayer(const LayerParameter &param) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >inlineexplicit
bias_term_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
bottom_dim_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
bottom_shape_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
channel_axis_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
channels_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
col_buffer_shape_caffe::BaseConvolutionLayer< Dtype >protected
compute_output_shape()=0 (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protectedpure virtual
conv_input_shape_caffe::BaseConvolutionLayer< Dtype >protected
dilation_caffe::BaseConvolutionLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::BaseConvolutionLayer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
force_nd_im2col_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)=0caffe::Layer< Dtype >protectedpure virtual
forward_cpu_bias(Dtype *output, const Dtype *bias) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
forward_cpu_gemm(const Dtype *input, const Dtype *weights, Dtype *output, bool skip_im2col=false) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
forward_gpu_bias(Dtype *output, const Dtype *bias) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
forward_gpu_gemm(const Dtype *col_input, const Dtype *weights, Dtype *output, bool skip_im2col=false) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
group_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
input_shape(int i)caffe::BaseConvolutionLayer< Dtype >inlineprotected
is_1x1_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
IsShared() constcaffe::Layer< Dtype >inline
kernel_shape_caffe::BaseConvolutionLayer< Dtype >protected
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BaseConvolutionLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::BaseConvolutionLayer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::BaseConvolutionLayer< Dtype >inlinevirtual
num_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
num_output_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
num_spatial_axes_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
out_spatial_dim_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
output_shape_caffe::BaseConvolutionLayer< Dtype >protected
pad_caffe::BaseConvolutionLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BaseConvolutionLayer< Dtype >virtual
reverse_dimensions()=0 (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protectedpure virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
stride_caffe::BaseConvolutionLayer< Dtype >protected
top_dim_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::Layer< Dtype >inlinevirtual
weight_cpu_gemm(const Dtype *input, const Dtype *output, Dtype *weights) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
weight_gpu_gemm(const Dtype *col_input, const Dtype *output, Dtype *weights) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
weight_offset_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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+Generated on Mon Nov 21 2016 11:23:37 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1BaseConvolutionLayer.html b/doxygen/classcaffe_1_1BaseConvolutionLayer.html new file mode 100644 index 00000000000..fdf601fecc1 --- /dev/null +++ b/doxygen/classcaffe_1_1BaseConvolutionLayer.html @@ -0,0 +1,547 @@ + + + + + + + +Caffe: caffe::BaseConvolutionLayer< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
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caffe::BaseConvolutionLayer< Dtype > Class Template Referenceabstract
+
+
+ +

Abstract base class that factors out the BLAS code common to ConvolutionLayer and DeconvolutionLayer. + More...

+ +

#include <base_conv_layer.hpp>

+
+Inheritance diagram for caffe::BaseConvolutionLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > +caffe::ConvolutionLayer< Dtype > +caffe::DeconvolutionLayer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ BaseConvolutionLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+void forward_cpu_gemm (const Dtype *input, const Dtype *weights, Dtype *output, bool skip_im2col=false)
 
+void forward_cpu_bias (Dtype *output, const Dtype *bias)
 
+void backward_cpu_gemm (const Dtype *input, const Dtype *weights, Dtype *output)
 
+void weight_cpu_gemm (const Dtype *input, const Dtype *output, Dtype *weights)
 
+void backward_cpu_bias (Dtype *bias, const Dtype *input)
 
+void forward_gpu_gemm (const Dtype *col_input, const Dtype *weights, Dtype *output, bool skip_im2col=false)
 
+void forward_gpu_bias (Dtype *output, const Dtype *bias)
 
+void backward_gpu_gemm (const Dtype *input, const Dtype *weights, Dtype *col_output)
 
+void weight_gpu_gemm (const Dtype *col_input, const Dtype *output, Dtype *weights)
 
+void backward_gpu_bias (Dtype *bias, const Dtype *input)
 
+int input_shape (int i)
 The spatial dimensions of the input.
 
+virtual bool reverse_dimensions ()=0
 
+virtual void compute_output_shape ()=0
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)=0
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)=0
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+Blob< int > kernel_shape_
 The spatial dimensions of a filter kernel.
 
+Blob< int > stride_
 The spatial dimensions of the stride.
 
+Blob< int > pad_
 The spatial dimensions of the padding.
 
+Blob< int > dilation_
 The spatial dimensions of the dilation.
 
+Blob< int > conv_input_shape_
 The spatial dimensions of the convolution input.
 
+vector< int > col_buffer_shape_
 The spatial dimensions of the col_buffer.
 
+vector< int > output_shape_
 The spatial dimensions of the output.
 
+const vector< int > * bottom_shape_
 
+int num_spatial_axes_
 
+int bottom_dim_
 
+int top_dim_
 
+int channel_axis_
 
+int num_
 
+int channels_
 
+int group_
 
+int out_spatial_dim_
 
+int weight_offset_
 
+int num_output_
 
+bool bias_term_
 
+bool is_1x1_
 
+bool force_nd_im2col_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::BaseConvolutionLayer< Dtype >

+ +

Abstract base class that factors out the BLAS code common to ConvolutionLayer and DeconvolutionLayer.

+

Member Function Documentation

+ +

§ EqualNumBottomTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual bool caffe::BaseConvolutionLayer< Dtype >::EqualNumBottomTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns true if the layer requires an equal number of bottom and top blobs.

+

This method should be overridden to return true if your layer expects an equal number of bottom and top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::BaseConvolutionLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::BaseConvolutionLayer< Dtype >::MinBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::BaseConvolutionLayer< Dtype >::MinTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::BaseConvolutionLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:37 for Caffe by   +doxygen + 1.8.12 +
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caffe::BaseDataLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::BaseDataLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BaseDataLayer< Dtype >inlinevirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BaseDataLayer< Dtype >inlinevirtual
BaseDataLayer(const LayerParameter &param) (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >explicit
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
data_transformer_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected
DataLayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >inlinevirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)=0caffe::Layer< Dtype >protectedpure virtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BaseDataLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
output_labels_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BaseDataLayer< Dtype >inlinevirtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::BaseDataLayer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
transform_param_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected
type() constcaffe::Layer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:37 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1BaseDataLayer.html b/doxygen/classcaffe_1_1BaseDataLayer.html new file mode 100644 index 00000000000..8ee134e4dbc --- /dev/null +++ b/doxygen/classcaffe_1_1BaseDataLayer.html @@ -0,0 +1,358 @@ + + + + + + + +Caffe: caffe::BaseDataLayer< Dtype > Class Template Reference + + + + + + + + + +
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Caffe +
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+Public Member Functions | +Protected Attributes | +List of all members
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+
caffe::BaseDataLayer< Dtype > Class Template Reference
+
+
+ +

Provides base for data layers that feed blobs to the Net. + More...

+ +

#include <base_data_layer.hpp>

+
+Inheritance diagram for caffe::BaseDataLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > +caffe::BasePrefetchingDataLayer< Dtype > +caffe::MemoryDataLayer< Dtype > +caffe::DataLayer< Dtype > +caffe::ImageDataLayer< Dtype > +caffe::WindowDataLayer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ BaseDataLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+virtual void DataLayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Attributes

+TransformationParameter transform_param_
 
+shared_ptr< DataTransformer< Dtype > > data_transformer_
 
+bool output_labels_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+ + + + + + + + + + + + +

+Additional Inherited Members

- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)=0
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::BaseDataLayer< Dtype >

+ +

Provides base for data layers that feed blobs to the Net.

+

TODO(dox): thorough documentation for Forward and proto params.

+

Member Function Documentation

+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::BaseDataLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +

Reimplemented in caffe::BasePrefetchingDataLayer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
virtual void caffe::BaseDataLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+inlinevirtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:37 for Caffe by   +doxygen + 1.8.12 +
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caffe::BasePrefetchingDataLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::BasePrefetchingDataLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BaseDataLayer< Dtype >inlinevirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BaseDataLayer< Dtype >inlinevirtual
BaseDataLayer(const LayerParameter &param) (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >explicit
BasePrefetchingDataLayer(const LayerParameter &param) (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >explicit
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
data_transformer_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected
DataLayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >inlinevirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BasePrefetchingDataLayer< Dtype >virtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BasePrefetchingDataLayer< Dtype >virtual
InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadinline
InternalThreadEntry() (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protectedvirtual
is_started() const (defined in caffe::InternalThread)caffe::InternalThread
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BasePrefetchingDataLayer< Dtype >virtual
load_batch(Batch< Dtype > *batch)=0 (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protectedpure virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
must_stop() (defined in caffe::InternalThread)caffe::InternalThreadprotected
output_labels_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
prefetch_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected
PREFETCH_COUNT (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >static
prefetch_free_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected
prefetch_full_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BaseDataLayer< Dtype >inlinevirtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::BaseDataLayer< Dtype >inlinevirtual
StartInternalThread()caffe::InternalThread
StopInternalThread()caffe::InternalThread
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
transform_param_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected
transformed_data_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected
type() constcaffe::Layer< Dtype >inlinevirtual
~InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadvirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:37 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1BasePrefetchingDataLayer.html b/doxygen/classcaffe_1_1BasePrefetchingDataLayer.html new file mode 100644 index 00000000000..c97f4f99395 --- /dev/null +++ b/doxygen/classcaffe_1_1BasePrefetchingDataLayer.html @@ -0,0 +1,338 @@ + + + + + + + +Caffe: caffe::BasePrefetchingDataLayer< Dtype > Class Template Reference + + + + + + + + + +
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caffe::BasePrefetchingDataLayer< Dtype > Class Template Referenceabstract
+
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+
+Inheritance diagram for caffe::BasePrefetchingDataLayer< Dtype >:
+
+
+ + +caffe::BaseDataLayer< Dtype > +caffe::InternalThread +caffe::Layer< Dtype > +caffe::DataLayer< Dtype > +caffe::ImageDataLayer< Dtype > +caffe::WindowDataLayer< Dtype > + +
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+Public Member Functions

+ BasePrefetchingDataLayer (const LayerParameter &param)
 
void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
- Public Member Functions inherited from caffe::BaseDataLayer< Dtype >
+ BaseDataLayer (const LayerParameter &param)
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+virtual void DataLayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
- Public Member Functions inherited from caffe::InternalThread
void StartInternalThread ()
 
void StopInternalThread ()
 
+bool is_started () const
 
+ + + +

+Static Public Attributes

+static const int PREFETCH_COUNT = 3
 
+ + + + + + + + + + + + + +

+Protected Member Functions

+virtual void InternalThreadEntry ()
 
+virtual void load_batch (Batch< Dtype > *batch)=0
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
- Protected Member Functions inherited from caffe::InternalThread
+bool must_stop ()
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+Batch< Dtype > prefetch_ [PREFETCH_COUNT]
 
+BlockingQueue< Batch< Dtype > * > prefetch_free_
 
+BlockingQueue< Batch< Dtype > * > prefetch_full_
 
+Blob< Dtype > transformed_data_
 
- Protected Attributes inherited from caffe::BaseDataLayer< Dtype >
+TransformationParameter transform_param_
 
+shared_ptr< DataTransformer< Dtype > > data_transformer_
 
+bool output_labels_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Member Function Documentation

+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::BasePrefetchingDataLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::BaseDataLayer< Dtype >.

+ +
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The documentation for this class was generated from the following files: +
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caffe::Batch< Dtype > Member List
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data_ (defined in caffe::Batch< Dtype >)caffe::Batch< Dtype >
label_ (defined in caffe::Batch< Dtype >)caffe::Batch< Dtype >
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+Public Attributes

+Blob< Dtype > data_
 
+Blob< Dtype > label_
 
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The documentation for this class was generated from the following file: +
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caffe::BatchNormLayer< Dtype > Member List
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This is the complete list of members for caffe::BatchNormLayer< Dtype >, including all inherited members.

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AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BatchNormLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BatchNormLayer< Dtype >protectedvirtual
batch_sum_multiplier_ (defined in caffe::BatchNormLayer< Dtype >)caffe::BatchNormLayer< Dtype >protected
BatchNormLayer(const LayerParameter &param) (defined in caffe::BatchNormLayer< Dtype >)caffe::BatchNormLayer< Dtype >inlineexplicit
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
channels_ (defined in caffe::BatchNormLayer< Dtype >)caffe::BatchNormLayer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
eps_ (defined in caffe::BatchNormLayer< Dtype >)caffe::BatchNormLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::BatchNormLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::BatchNormLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BatchNormLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BatchNormLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BatchNormLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
mean_ (defined in caffe::BatchNormLayer< Dtype >)caffe::BatchNormLayer< Dtype >protected
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
moving_average_fraction_ (defined in caffe::BatchNormLayer< Dtype >)caffe::BatchNormLayer< Dtype >protected
num_by_chans_ (defined in caffe::BatchNormLayer< Dtype >)caffe::BatchNormLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BatchNormLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
spatial_sum_multiplier_ (defined in caffe::BatchNormLayer< Dtype >)caffe::BatchNormLayer< Dtype >protected
temp_ (defined in caffe::BatchNormLayer< Dtype >)caffe::BatchNormLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::BatchNormLayer< Dtype >inlinevirtual
use_global_stats_ (defined in caffe::BatchNormLayer< Dtype >)caffe::BatchNormLayer< Dtype >protected
variance_ (defined in caffe::BatchNormLayer< Dtype >)caffe::BatchNormLayer< Dtype >protected
x_norm_ (defined in caffe::BatchNormLayer< Dtype >)caffe::BatchNormLayer< Dtype >protected
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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caffe::BatchNormLayer< Dtype > Class Template Reference
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Normalizes the input to have 0-mean and/or unit (1) variance across the batch. + More...

+ +

#include <batch_norm_layer.hpp>

+
+Inheritance diagram for caffe::BatchNormLayer< Dtype >:
+
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+Public Member Functions

+ BatchNormLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+Blob< Dtype > mean_
 
+Blob< Dtype > variance_
 
+Blob< Dtype > temp_
 
+Blob< Dtype > x_norm_
 
+bool use_global_stats_
 
+Dtype moving_average_fraction_
 
+int channels_
 
+Dtype eps_
 
+Blob< Dtype > batch_sum_multiplier_
 
+Blob< Dtype > num_by_chans_
 
+Blob< Dtype > spatial_sum_multiplier_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::BatchNormLayer< Dtype >

+ +

Normalizes the input to have 0-mean and/or unit (1) variance across the batch.

+

This layer computes Batch Normalization as described in [1]. For each channel in the data (i.e. axis 1), it subtracts the mean and divides by the variance, where both statistics are computed across both spatial dimensions and across the different examples in the batch.

+

By default, during training time, the network is computing global mean/variance statistics via a running average, which is then used at test time to allow deterministic outputs for each input. You can manually toggle whether the network is accumulating or using the statistics via the use_global_stats option. For reference, these statistics are kept in the layer's three blobs: (0) mean, (1) variance, and (2) moving average factor.

+

Note that the original paper also included a per-channel learned bias and scaling factor. To implement this in Caffe, define a ScaleLayer configured with bias_term: true after each BatchNormLayer to handle both the bias and scaling factor.

+

[1] S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network + Training by Reducing Internal Covariate Shift." arXiv preprint arXiv:1502.03167 (2015).

+

TODO(dox): thorough documentation for Forward, Backward, and proto params.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::BatchNormLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::BatchNormLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::BatchNormLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::BatchNormLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:37 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1BatchNormLayer.png b/doxygen/classcaffe_1_1BatchNormLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..76e626b3257138c1a4bb0f24d018c72dd20e665f GIT binary patch literal 762 zcmeAS@N?(olHy`uVBq!ia0vp^$ALJ2gBeK9+A)zINJ#|vgt-3y{~ySF@#br3|Dg#$ z78oBmaDcV*jy#adQ4-`A%m7pb0#{Fk7%?y~b$YruhEy=Vo%^=$u>y}Pzj@I8{}Z=s zD&BaKeE7|l*KR$}%o-)$G#~tvI&0ZWwxmf*)?a@xVTpy5%p?(a`zIcnr@wk$3SIhh zef1QTx3AM>Hh=bIsFi8I>=CqIuXxJS(=$E1t6!GNoSwdD(;K@7cTQc_Syp_wV$zb= zyH0KVwTb_7j8(tx^aJ5HK4tsFo}TS}X=RngtL(#tNsrc;o%}3Q=OXW^?`}6Q?Q)2_ zv~c#tYprdE)vlkboVjFja{koszSD#se!J*s*OPg7UjCWm4}bpPZr}Ln`C`MrM}BVj zdTLF{U6VBKt3oNSr>cC8f8TF)UH#QmmC{wpv+DN?-ame^TX4!^Y2_?Cm6yd}?=(A4 zdi3I?q3`7{f0-(_%Q2W=@U@WZuWq>0^0>2*d&2I5o+?%W&t_(YJ~nm-8D72yU??yY znpiMA$|ztsa`6E}$Kr#G0`6!E#HR*%nG0|}cx?((D0qA`!=5cGbILM4Pm_0leQ9gl z>eZ_k^I1GQceXw4k?!%8&+YmSD=?IWSe{~f;$!nWK6iGbRpmxaH%Z0wscvUw*ZWBa zN^aVC$LYC(*yW?0-5Kr4TepFNa<6Zt_S&+Nf76mJzi(#QcWByuS@kl{ZbdOnLkX1AUeG@t&H8=7hHCS~7^H8XQ8 zzFk}S^uzB}lNW#6En75+zxv&+=Kb^g4}UH^_E0`my1YO_{$GvBvg_yC*M_I9&AOEv zc;0MT+)R&H(M>X`z+iu(`~7{t)XA+?tRDhPf9x$#{%777*Rw$8u0-Hx#t+@_u-g|K e + + + + + + +Caffe: Member List + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
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+ +
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+
+
+
caffe::BatchReindexLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::BatchReindexLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BatchReindexLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BatchReindexLayer< Dtype >protectedvirtual
BatchReindexLayer(const LayerParameter &param) (defined in caffe::BatchReindexLayer< Dtype >)caffe::BatchReindexLayer< Dtype >inlineexplicit
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::BatchReindexLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::BatchReindexLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BatchReindexLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BatchReindexLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlinevirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BatchReindexLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::BatchReindexLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:38 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1BatchReindexLayer.html b/doxygen/classcaffe_1_1BatchReindexLayer.html new file mode 100644 index 00000000000..8624440334a --- /dev/null +++ b/doxygen/classcaffe_1_1BatchReindexLayer.html @@ -0,0 +1,468 @@ + + + + + + + +Caffe: caffe::BatchReindexLayer< Dtype > Class Template Reference + + + + + + + + + +
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+
+Classes | +Public Member Functions | +Protected Member Functions | +List of all members
+
+
caffe::BatchReindexLayer< Dtype > Class Template Reference
+
+
+ +

Index into the input blob along its first axis. + More...

+ +

#include <batch_reindex_layer.hpp>

+
+Inheritance diagram for caffe::BatchReindexLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ BatchReindexLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the error gradient w.r.t. the reordered input. More...
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::BatchReindexLayer< Dtype >

+ +

Index into the input blob along its first axis.

+

This layer can be used to select, reorder, and even replicate examples in a batch. The second blob is cast to int and treated as an index into the first axis of the first blob.

+

Member Function Documentation

+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::BatchReindexLayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the error gradient w.r.t. the reordered input.

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
    +
  1. $ (M \times ...) $: containing error gradients $ \frac{\partial E}{\partial y} $ with respect to concatenated outputs $ y $
    2. +
+
propagate_downsee Layer::Backward.
bottominput Blob vector (length 2):
    +
  • $ \frac{\partial E}{\partial y} $ is de-indexed (summing where required) back to the input x_1
    • +
  • This layer cannot backprop to x_2, i.e. propagate_down[1] must be false.
    • +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::BatchReindexLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::BatchReindexLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::BatchReindexLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+
Parameters
+ + + +
bottominput Blob vector (length 2+)
    +
  1. $ (N \times ...) $ the inputs $ x_1 $
    2. +
  2. $ (M) $ the inputs $ x_2 $
    3. +
+
topoutput Blob vector (length 1)
    +
  1. $ (M \times ...) $: the reindexed array $ y = x_1[x_2] $
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::BatchReindexLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:38 for Caffe by   +doxygen + 1.8.12 +
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caffe::BiasLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::BiasLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BiasLayer< Dtype >virtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BiasLayer< Dtype >virtual
BiasLayer(const LayerParameter &param) (defined in caffe::BiasLayer< Dtype >)caffe::BiasLayer< Dtype >inlineexplicit
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::BiasLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BiasLayer< Dtype >virtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BiasLayer< Dtype >virtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BiasLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::BiasLayer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::BiasLayer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BiasLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::BiasLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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+ + diff --git a/doxygen/classcaffe_1_1BiasLayer.html b/doxygen/classcaffe_1_1BiasLayer.html new file mode 100644 index 00000000000..6c5791a8a36 --- /dev/null +++ b/doxygen/classcaffe_1_1BiasLayer.html @@ -0,0 +1,431 @@ + + + + + + + +Caffe: caffe::BiasLayer< Dtype > Class Template Reference + + + + + + + + + +
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caffe::BiasLayer< Dtype > Class Template Reference
+
+
+ +

Computes a sum of two input Blobs, with the shape of the latter Blob "broadcast" to match the shape of the former. Equivalent to tiling the latter Blob, then computing the elementwise sum. + More...

+ +

#include <bias_layer.hpp>

+
+Inheritance diagram for caffe::BiasLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ BiasLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::BiasLayer< Dtype >

+ +

Computes a sum of two input Blobs, with the shape of the latter Blob "broadcast" to match the shape of the former. Equivalent to tiling the latter Blob, then computing the elementwise sum.

+

The second input may be omitted, in which case it's learned as a parameter of the layer. Note: in case bias and scaling are desired, both operations can be handled by ScaleLayer configured with bias_term: true.

+

Member Function Documentation

+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::BiasLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::BiasLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MaxBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::BiasLayer< Dtype >::MaxBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some maximum number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::BiasLayer< Dtype >::MinBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::BiasLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/layers/bias_layer.hpp
    • +
  • src/caffe/layers/bias_layer.cpp
    • +
+
+ +
+Generated on Mon Nov 21 2016 11:23:38 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1BiasLayer.png b/doxygen/classcaffe_1_1BiasLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..a72d32a19682cd07773ad4ff83f5d2a4b2e7d0d0 GIT binary patch literal 706 zcmeAS@N?(olHy`uVBq!ia0vp^3xPO*gBeJ=IX<-jQW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;->tV zwTHrQdA~9|lqco+%Q5#*^6A~xn*E>GD4*FcW3}d!#_tUO$mbdHR?%&QWiyPwAW!V1gYoGMRJE~=p$YrG|OJ>Y#xxFpdnES!v zDh8jmFFED=Zwf9~uVM1*v#@{oDz#ySn4*JEw}8SjR}O*8N-Q0jf=oxYa4;rCwJ;d! zIyKBdmj3TCBgiYbo5f%wOjPdS=GK3&E`6=P&hSj~*5ip!zOCX*W!FUh^&H-lx;9ku`EPp`w0)P}k<|I73vUOx?g*X{H1$pBRR4B8@3*IRJ=SuH z*7&XWxX$%P(E`Vay4@j`%S*t4=RIq?T-o`={tcykf3wfa+iv9FTh5oOFD>r1@{i@2 zwH04}-~4{;&bO`M?+$gTe~DWM4fA)G + + + + + + +Caffe: Member List + + + + + + + + + +
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This is the complete list of members for caffe::BilinearFiller< Dtype >, including all inherited members.

+ + + + + + +
BilinearFiller(const FillerParameter &param) (defined in caffe::BilinearFiller< Dtype >)caffe::BilinearFiller< Dtype >inlineexplicit
Fill(Blob< Dtype > *blob) (defined in caffe::BilinearFiller< Dtype >)caffe::BilinearFiller< Dtype >inlinevirtual
Filler(const FillerParameter &param) (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >inlineexplicit
filler_param_ (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >protected
~Filler() (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:38 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1BilinearFiller.html b/doxygen/classcaffe_1_1BilinearFiller.html new file mode 100644 index 00000000000..2a81ae83b92 --- /dev/null +++ b/doxygen/classcaffe_1_1BilinearFiller.html @@ -0,0 +1,123 @@ + + + + + + + +Caffe: caffe::BilinearFiller< Dtype > Class Template Reference + + + + + + + + + +
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caffe::BilinearFiller< Dtype > Class Template Reference
+
+
+ +

Fills a Blob with coefficients for bilinear interpolation. + More...

+ +

#include <filler.hpp>

+
+Inheritance diagram for caffe::BilinearFiller< Dtype >:
+
+
+ + +caffe::Filler< Dtype > + +
+ + + + + + + + + +

+Public Member Functions

+ BilinearFiller (const FillerParameter &param)
 
+virtual void Fill (Blob< Dtype > *blob)
 
- Public Member Functions inherited from caffe::Filler< Dtype >
+ Filler (const FillerParameter &param)
 
+ + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Filler< Dtype >
+FillerParameter filler_param_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::BilinearFiller< Dtype >

+ +

Fills a Blob with coefficients for bilinear interpolation.

+

A common use case is with the DeconvolutionLayer acting as upsampling. You can upsample a feature map with shape of (B, C, H, W) by any integer factor using the following proto.

layer {
name: "upsample", type: "Deconvolution"
bottom: "{{bottom_name}}" top: "{{top_name}}"
convolution_param {
kernel_size: {{2 * factor - factor % 2}} stride: {{factor}}
num_output: {{C}} group: {{C}}
pad: {{ceil((factor - 1) / 2.)}}
weight_filler: { type: "bilinear" } bias_term: false
}
param { lr_mult: 0 decay_mult: 0 }
}

Please use this by replacing {{}} with your values. By specifying num_output: {{C}} group: {{C}}, it behaves as channel-wise convolution. The filter shape of this deconvolution layer will be (C, 1, K, K) where K is kernel_size, and this filler will set a (K, K) interpolation kernel for every channel of the filter identically. The resulting shape of the top feature map will be (B, C, factor * H, factor * W). Note that the learning rate and the weight decay are set to 0 in order to keep coefficient values of bilinear interpolation unchanged during training. If you apply this to an image, this operation is equivalent to the following call in Python with Scikit.Image.

out = skimage.transform.rescale(img, factor, mode='constant', cval=0)
The documentation for this class was generated from the following file: +
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+Generated on Mon Nov 21 2016 11:23:38 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1BilinearFiller.png b/doxygen/classcaffe_1_1BilinearFiller.png new file mode 100644 index 0000000000000000000000000000000000000000..f6d934dcbbdd338e1097d774cf4c5305a5e3130c GIT binary patch literal 742 zcmeAS@N?(olHy`uVBq!ia0vp^8-O@~gBeKn>GHn=QW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;b4?bLx27nl`GgTz-#6k`}12%WUW@)}?w@iW>eeNl z4Zf1x_LjTmJ<|>EryrwEY;Vw>X7Rlst#L9>KHK&k=7s-H9~M7gru@C2N;3D~cDZkQ z6DJ8(yZ*hfR8R4w+pEKx=jNAldcIm5|G%|{qr!ZP%HE~t8Ez*|TT=c-)7#_HdJ~YR z)HbMmy&_x@IW1LzvE(9if=Vyjp9&r~8>tVI%XJ<-Y)GHZ@M1M5&xK%Ni3Q8m3>^G@ z6B_2tI=}!762_`5Cgv|!SlC{yL{aeI2e)F&L>C4LcbJ~JjQ0MI`}zJpNk41G@ah&{ zNp)6FrFFjg0cr7s!%W)`-Tuw?;bk6!sOp0sOSALlpTC|HH7mJ5dYf9T%?rL9{r>oE z@3w{ObF=@r!Mr5u_R+lLMNCT)ZOrZ$&05>5fB7g^THzw( z3jg-|}p+dE;(doB2kxeUq5_$!XUbgQqj(EU}xj zZO?U82D^3A^@{PVHP8KD&Ti=E6 + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::Blob< Dtype > Member List
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This is the complete list of members for caffe::Blob< Dtype >, including all inherited members.

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asum_data() constcaffe::Blob< Dtype >
asum_data() const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
asum_data() const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
asum_diff() constcaffe::Blob< Dtype >
asum_diff() const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
asum_diff() const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
Blob() (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >inline
Blob(const int num, const int channels, const int height, const int width)caffe::Blob< Dtype >explicit
Blob(const vector< int > &shape) (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >explicit
CanonicalAxisIndex(int axis_index) constcaffe::Blob< Dtype >inline
capacity_ (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >protected
channels() constcaffe::Blob< Dtype >inline
CopyFrom(const Blob< Dtype > &source, bool copy_diff=false, bool reshape=false)caffe::Blob< Dtype >
count() const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >inline
count(int start_axis, int end_axis) constcaffe::Blob< Dtype >inline
count(int start_axis) constcaffe::Blob< Dtype >inline
count_ (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >protected
cpu_data() const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
cpu_diff() const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
data() const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >inline
data_ (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >protected
data_at(const int n, const int c, const int h, const int w) const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >inline
data_at(const vector< int > &index) const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >inline
diff() const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >inline
diff_ (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >protected
diff_at(const int n, const int c, const int h, const int w) const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >inline
diff_at(const vector< int > &index) const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >inline
DISABLE_COPY_AND_ASSIGN(Blob) (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >protected
FromProto(const BlobProto &proto, bool reshape=true) (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
gpu_data() const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
gpu_diff() const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
gpu_shape() const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
height() constcaffe::Blob< Dtype >inline
LegacyShape(int index) const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >inline
mutable_cpu_data() (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
mutable_cpu_diff() (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
mutable_gpu_data() (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
mutable_gpu_diff() (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
num() constcaffe::Blob< Dtype >inline
num_axes() const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >inline
offset(const int n, const int c=0, const int h=0, const int w=0) const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >inline
offset(const vector< int > &indices) const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >inline
Reshape(const int num, const int channels, const int height, const int width)caffe::Blob< Dtype >
Reshape(const vector< int > &shape)caffe::Blob< Dtype >
Reshape(const BlobShape &shape) (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
ReshapeLike(const Blob &other) (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
scale_data(Dtype scale_factor)caffe::Blob< Dtype >
scale_data(unsigned int scale_factor) (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
scale_data(int scale_factor) (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
scale_diff(Dtype scale_factor)caffe::Blob< Dtype >
scale_diff(unsigned int scale_factor) (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
scale_diff(int scale_factor) (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
set_cpu_data(Dtype *data) (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
shape() const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >inline
shape(int index) constcaffe::Blob< Dtype >inline
shape_ (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >protected
shape_data_ (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >protected
shape_string() const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >inline
ShapeEquals(const BlobProto &other) (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
ShareData(const Blob &other)caffe::Blob< Dtype >
ShareDiff(const Blob &other)caffe::Blob< Dtype >
sumsq_data() constcaffe::Blob< Dtype >
sumsq_data() const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
sumsq_data() const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
sumsq_diff() constcaffe::Blob< Dtype >
sumsq_diff() const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
sumsq_diff() const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
ToProto(BlobProto *proto, bool write_diff=false) const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
ToProto(BlobProto *proto, bool write_diff) const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
ToProto(BlobProto *proto, bool write_diff) const (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
Update() (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
Update() (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
Update() (defined in caffe::Blob< Dtype >)caffe::Blob< Dtype >
width() constcaffe::Blob< Dtype >inline
+ +
+Generated on Mon Nov 21 2016 11:23:38 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1Blob.html b/doxygen/classcaffe_1_1Blob.html new file mode 100644 index 00000000000..67ed1a2de22 --- /dev/null +++ b/doxygen/classcaffe_1_1Blob.html @@ -0,0 +1,626 @@ + + + + + + + +Caffe: caffe::Blob< Dtype > Class Template Reference + + + + + + + + + +
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caffe::Blob< Dtype > Class Template Reference
+
+
+ +

A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers, Nets, and Solvers interact. + More...

+ +

#include <blob.hpp>

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ Blob (const int num, const int channels, const int height, const int width)
 Deprecated; use Blob(const vector<int>& shape).
 
+ Blob (const vector< int > &shape)
 
+void Reshape (const int num, const int channels, const int height, const int width)
 Deprecated; use Reshape(const vector<int>& shape).
 
void Reshape (const vector< int > &shape)
 Change the dimensions of the blob, allocating new memory if necessary. More...
 
+void Reshape (const BlobShape &shape)
 
+void ReshapeLike (const Blob &other)
 
+string shape_string () const
 
+const vector< int > & shape () const
 
int shape (int index) const
 Returns the dimension of the index-th axis (or the negative index-th axis from the end, if index is negative). More...
 
+int num_axes () const
 
+int count () const
 
int count (int start_axis, int end_axis) const
 Compute the volume of a slice; i.e., the product of dimensions among a range of axes. More...
 
int count (int start_axis) const
 Compute the volume of a slice spanning from a particular first axis to the final axis. More...
 
int CanonicalAxisIndex (int axis_index) const
 Returns the 'canonical' version of a (usually) user-specified axis, allowing for negative indexing (e.g., -1 for the last axis). More...
 
+int num () const
 Deprecated legacy shape accessor num: use shape(0) instead.
 
+int channels () const
 Deprecated legacy shape accessor channels: use shape(1) instead.
 
+int height () const
 Deprecated legacy shape accessor height: use shape(2) instead.
 
+int width () const
 Deprecated legacy shape accessor width: use shape(3) instead.
 
+int LegacyShape (int index) const
 
+int offset (const int n, const int c=0, const int h=0, const int w=0) const
 
+int offset (const vector< int > &indices) const
 
void CopyFrom (const Blob< Dtype > &source, bool copy_diff=false, bool reshape=false)
 Copy from a source Blob. More...
 
+Dtype data_at (const int n, const int c, const int h, const int w) const
 
+Dtype diff_at (const int n, const int c, const int h, const int w) const
 
+Dtype data_at (const vector< int > &index) const
 
+Dtype diff_at (const vector< int > &index) const
 
+const shared_ptr< SyncedMemory > & data () const
 
+const shared_ptr< SyncedMemory > & diff () const
 
+const Dtype * cpu_data () const
 
+void set_cpu_data (Dtype *data)
 
+const int * gpu_shape () const
 
+const Dtype * gpu_data () const
 
+const Dtype * cpu_diff () const
 
+const Dtype * gpu_diff () const
 
+Dtype * mutable_cpu_data ()
 
+Dtype * mutable_gpu_data ()
 
+Dtype * mutable_cpu_diff ()
 
+Dtype * mutable_gpu_diff ()
 
+void Update ()
 
+void FromProto (const BlobProto &proto, bool reshape=true)
 
+void ToProto (BlobProto *proto, bool write_diff=false) const
 
+Dtype asum_data () const
 Compute the sum of absolute values (L1 norm) of the data.
 
+Dtype asum_diff () const
 Compute the sum of absolute values (L1 norm) of the diff.
 
+Dtype sumsq_data () const
 Compute the sum of squares (L2 norm squared) of the data.
 
+Dtype sumsq_diff () const
 Compute the sum of squares (L2 norm squared) of the diff.
 
+void scale_data (Dtype scale_factor)
 Scale the blob data by a constant factor.
 
+void scale_diff (Dtype scale_factor)
 Scale the blob diff by a constant factor.
 
void ShareData (const Blob &other)
 Set the data_ shared_ptr to point to the SyncedMemory holding the data_ of Blob other – useful in Layers which simply perform a copy in their Forward pass. More...
 
void ShareDiff (const Blob &other)
 Set the diff_ shared_ptr to point to the SyncedMemory holding the diff_ of Blob other – useful in Layers which simply perform a copy in their Forward pass. More...
 
+bool ShapeEquals (const BlobProto &other)
 
+template<>
void Update ()
 
+template<>
void Update ()
 
+template<>
unsigned int asum_data () const
 
+template<>
int asum_data () const
 
+template<>
unsigned int asum_diff () const
 
+template<>
int asum_diff () const
 
+template<>
unsigned int sumsq_data () const
 
+template<>
int sumsq_data () const
 
+template<>
unsigned int sumsq_diff () const
 
+template<>
int sumsq_diff () const
 
+template<>
void scale_data (unsigned int scale_factor)
 
+template<>
void scale_data (int scale_factor)
 
+template<>
void scale_diff (unsigned int scale_factor)
 
+template<>
void scale_diff (int scale_factor)
 
+template<>
void ToProto (BlobProto *proto, bool write_diff) const
 
+template<>
void ToProto (BlobProto *proto, bool write_diff) const
 
+ + + +

+Protected Member Functions

+ DISABLE_COPY_AND_ASSIGN (Blob)
 
+ + + + + + + + + + + + + +

+Protected Attributes

+shared_ptr< SyncedMemory > data_
 
+shared_ptr< SyncedMemory > diff_
 
+shared_ptr< SyncedMemory > shape_data_
 
+vector< int > shape_
 
+int count_
 
+int capacity_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::Blob< Dtype >

+ +

A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers, Nets, and Solvers interact.

+

TODO(dox): more thorough description.

+

Member Function Documentation

+ +

§ CanonicalAxisIndex()

+ +
+
+
+template<typename Dtype>
+ + + + + +
+ + + + + + + + +
int caffe::Blob< Dtype >::CanonicalAxisIndex (int axis_index) const
+ 		+inline
+
+ +

Returns the 'canonical' version of a (usually) user-specified axis, allowing for negative indexing (e.g., -1 for the last axis).

+
Parameters
+ + +
axis_indexthe axis index. If 0 <= index < num_axes(), return index. If -num_axes <= index <= -1, return (num_axes() - (-index)), e.g., the last axis index (num_axes() - 1) if index == -1, the second to last if index == -2, etc. Dies on out of range index.
+
+
+ +
+
+ +

§ CopyFrom()

+ +
+
+
+template<typename Dtype>
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::Blob< Dtype >::CopyFrom (const Blob< Dtype > & source,
bool copy_diff = false,
bool reshape = false 
)
+
+ +

Copy from a source Blob.

+
Parameters
+ + + + +
sourcethe Blob to copy from
copy_diffif false, copy the data; if true, copy the diff
reshapeif false, require this Blob to be pre-shaped to the shape of other (and die otherwise); if true, Reshape this Blob to other's shape if necessary
+
+
+ +
+
+ +

§ count() [1/2]

+ +
+
+
+template<typename Dtype>
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
int caffe::Blob< Dtype >::count (int start_axis,
int end_axis 
) const
+ 		+inline
+
+ +

Compute the volume of a slice; i.e., the product of dimensions among a range of axes.

+
Parameters
+ + + +
start_axisThe first axis to include in the slice.
end_axisThe first axis to exclude from the slice.
+
+
+ +
+
+ +

§ count() [2/2]

+ +
+
+
+template<typename Dtype>
+ + + + + +
+ + + + + + + + +
int caffe::Blob< Dtype >::count (int start_axis) const
+ 		+inline
+
+ +

Compute the volume of a slice spanning from a particular first axis to the final axis.

+
Parameters
+ + +
start_axisThe first axis to include in the slice.
+
+
+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + + + + +
void caffe::Blob< Dtype >::Reshape (const vector< int > & shape)
+
+ +

Change the dimensions of the blob, allocating new memory if necessary.

+

This function can be called both to create an initial allocation of memory, and to adjust the dimensions of a top blob during Layer::Reshape or Layer::Forward. When changing the size of blob, memory will only be reallocated if sufficient memory does not already exist, and excess memory will never be freed.

+

Note that reshaping an input blob and immediately calling Net::Backward is an error; either Net::Forward or Net::Reshape need to be called to propagate the new input shape to higher layers.

+ +
+
+ +

§ shape()

+ +
+
+
+template<typename Dtype>
+ + + + + +
+ + + + + + + + +
int caffe::Blob< Dtype >::shape (int index) const
+ 		+inline
+
+ +

Returns the dimension of the index-th axis (or the negative index-th axis from the end, if index is negative).

+
Parameters
+ + +
indexthe axis index, which may be negative as it will be "canonicalized" using CanonicalAxisIndex. Dies on out of range index.
+
+
+ +
+
+ +

§ ShareData()

+ +
+
+
+template<typename Dtype >
+ + + + + + + + +
void caffe::Blob< Dtype >::ShareData (const Blob< Dtype > & other)
+
+ +

Set the data_ shared_ptr to point to the SyncedMemory holding the data_ of Blob other – useful in Layers which simply perform a copy in their Forward pass.

+

This deallocates the SyncedMemory holding this Blob's data_, as shared_ptr calls its destructor when reset with the "=" operator.

+ +
+
+ +

§ ShareDiff()

+ +
+
+
+template<typename Dtype >
+ + + + + + + + +
void caffe::Blob< Dtype >::ShareDiff (const Blob< Dtype > & other)
+
+ +

Set the diff_ shared_ptr to point to the SyncedMemory holding the diff_ of Blob other – useful in Layers which simply perform a copy in their Forward pass.

+

This deallocates the SyncedMemory holding this Blob's diff_, as shared_ptr calls its destructor when reset with the "=" operator.

+ +
+
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/blob.hpp
    • +
  • src/caffe/blob.cpp
    • +
+
+ +
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BlockingQueue() (defined in caffe::BlockingQueue< T >)caffe::BlockingQueue< T >explicit
DISABLE_COPY_AND_ASSIGN(BlockingQueue) (defined in caffe::BlockingQueue< T >)caffe::BlockingQueue< T >protected
peek() (defined in caffe::BlockingQueue< T >)caffe::BlockingQueue< T >
pop(const string &log_on_wait="") (defined in caffe::BlockingQueue< T >)caffe::BlockingQueue< T >
push(const T &t) (defined in caffe::BlockingQueue< T >)caffe::BlockingQueue< T >
queue_ (defined in caffe::BlockingQueue< T >)caffe::BlockingQueue< T >protected
size() const (defined in caffe::BlockingQueue< T >)caffe::BlockingQueue< T >
sync_ (defined in caffe::BlockingQueue< T >)caffe::BlockingQueue< T >protected
try_peek(T *t) (defined in caffe::BlockingQueue< T >)caffe::BlockingQueue< T >
try_pop(T *t) (defined in caffe::BlockingQueue< T >)caffe::BlockingQueue< T >
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caffe::BlockingQueue< T > Class Template Reference
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+Classes

class  sync
 
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+void push (const T &t)
 
+bool try_pop (T *t)
 
+T pop (const string &log_on_wait="")
 
+bool try_peek (T *t)
 
+T peek ()
 
+size_t size () const
 
+ + + +

+Protected Member Functions

+ DISABLE_COPY_AND_ASSIGN (BlockingQueue)
 
+ + + + + +

+Protected Attributes

+std::queue< T > queue_
 
+shared_ptr< sync > sync_
 
+
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+boost::mutex mutex_
 
+boost::condition_variable condition_
 
+
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    +
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CPUTimer() (defined in caffe::CPUTimer)caffe::CPUTimerexplicit
elapsed_microseconds_ (defined in caffe::Timer)caffe::Timerprotected
elapsed_milliseconds_ (defined in caffe::Timer)caffe::Timerprotected
has_run_at_least_once() (defined in caffe::Timer)caffe::Timerinline
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+virtual void Start ()
 
+virtual void Stop ()
 
+virtual float MilliSeconds ()
 
+virtual float MicroSeconds ()
 
- Public Member Functions inherited from caffe::Timer
+virtual float Seconds ()
 
+bool initted ()
 
+bool running ()
 
+bool has_run_at_least_once ()
 
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+Additional Inherited Members

- Protected Member Functions inherited from caffe::Timer
+void Init ()
 
- Protected Attributes inherited from caffe::Timer
+bool initted_
 
+bool running_
 
+bool has_run_at_least_once_
 
+cudaEvent_t start_gpu_
 
+cudaEvent_t stop_gpu_
 
+boost::posix_time::ptime start_cpu_
 
+boost::posix_time::ptime stop_cpu_
 
+float elapsed_milliseconds_
 
+float elapsed_microseconds_
 
+
The documentation for this class was generated from the following files:
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Brew enum name (defined in caffe::Caffe)caffe::Caffe
CheckDevice(const int device_id) (defined in caffe::Caffe)caffe::Caffestatic
CPU enum value (defined in caffe::Caffe)caffe::Caffe
cublas_handle() (defined in caffe::Caffe)caffe::Caffeinlinestatic
cublas_handle_ (defined in caffe::Caffe)caffe::Caffeprotected
curand_generator() (defined in caffe::Caffe)caffe::Caffeinlinestatic
curand_generator_ (defined in caffe::Caffe)caffe::Caffeprotected
DeviceQuery() (defined in caffe::Caffe)caffe::Caffestatic
FindDevice(const int start_id=0) (defined in caffe::Caffe)caffe::Caffestatic
Get() (defined in caffe::Caffe)caffe::Caffestatic
GPU enum value (defined in caffe::Caffe)caffe::Caffe
mode() (defined in caffe::Caffe)caffe::Caffeinlinestatic
mode_ (defined in caffe::Caffe)caffe::Caffeprotected
random_generator_ (defined in caffe::Caffe)caffe::Caffeprotected
rng_stream() (defined in caffe::Caffe)caffe::Caffeinlinestatic
root_solver() (defined in caffe::Caffe)caffe::Caffeinlinestatic
root_solver_ (defined in caffe::Caffe)caffe::Caffeprotected
set_mode(Brew mode) (defined in caffe::Caffe)caffe::Caffeinlinestatic
set_random_seed(const unsigned int seed) (defined in caffe::Caffe)caffe::Caffestatic
set_root_solver(bool val) (defined in caffe::Caffe)caffe::Caffeinlinestatic
set_solver_count(int val) (defined in caffe::Caffe)caffe::Caffeinlinestatic
SetDevice(const int device_id) (defined in caffe::Caffe)caffe::Caffestatic
solver_count() (defined in caffe::Caffe)caffe::Caffeinlinestatic
solver_count_ (defined in caffe::Caffe)caffe::Caffeprotected
~Caffe() (defined in caffe::Caffe)caffe::Caffe
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class  RNG
 
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+Public Types

enum  Brew { CPU, +GPU + }
 
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+Static Public Member Functions

+static Caffe & Get ()
 
+static RNG & rng_stream ()
 
+static cublasHandle_t cublas_handle ()
 
+static curandGenerator_t curand_generator ()
 
+static Brew mode ()
 
+static void set_mode (Brew mode)
 
+static void set_random_seed (const unsigned int seed)
 
+static void SetDevice (const int device_id)
 
+static void DeviceQuery ()
 
+static bool CheckDevice (const int device_id)
 
+static int FindDevice (const int start_id=0)
 
+static int solver_count ()
 
+static void set_solver_count (int val)
 
+static bool root_solver ()
 
+static void set_root_solver (bool val)
 
+ + + + + + + + + + + + + +

+Protected Attributes

+cublasHandle_t cublas_handle_
 
+curandGenerator_t curand_generator_
 
+shared_ptr< RNG > random_generator_
 
+Brew mode_
 
+int solver_count_
 
+bool root_solver_
 
+
The documentation for this class was generated from the following files:
    +
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This is the complete list of members for caffe::Caffe::RNG, including all inherited members.

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generator() (defined in caffe::Caffe::RNG)caffe::Caffe::RNG
operator=(const RNG &) (defined in caffe::Caffe::RNG)caffe::Caffe::RNG
RNG() (defined in caffe::Caffe::RNG)caffe::Caffe::RNG
RNG(unsigned int seed) (defined in caffe::Caffe::RNG)caffe::Caffe::RNGexplicit
RNG(const RNG &) (defined in caffe::Caffe::RNG)caffe::Caffe::RNGexplicit
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+Classes

class  Generator
 
+ + + + + + + + + +

+Public Member Functions

+ RNG (unsigned int seed)
 
+ RNG (const RNG &)
 
+RNG & operator= (const RNG &)
 
+void * generator ()
 
+
The documentation for this class was generated from the following files:
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  • src/caffe/common.cpp
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Generator() (defined in caffe::Caffe::RNG::Generator)caffe::Caffe::RNG::Generatorinline
Generator(unsigned int seed) (defined in caffe::Caffe::RNG::Generator)caffe::Caffe::RNG::Generatorinlineexplicit
rng() (defined in caffe::Caffe::RNG::Generator)caffe::Caffe::RNG::Generatorinline
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+Public Member Functions

+ Generator (unsigned int seed)
 
+caffe::rng_t * rng ()
 
+
The documentation for this class was generated from the following file:
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caffe::ConcatLayer< Dtype > Member List
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This is the complete list of members for caffe::ConcatLayer< Dtype >, including all inherited members.

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AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ConcatLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ConcatLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
concat_axis_ (defined in caffe::ConcatLayer< Dtype >)caffe::ConcatLayer< Dtype >protected
concat_input_size_ (defined in caffe::ConcatLayer< Dtype >)caffe::ConcatLayer< Dtype >protected
ConcatLayer(const LayerParameter &param) (defined in caffe::ConcatLayer< Dtype >)caffe::ConcatLayer< Dtype >inlineexplicit
count_ (defined in caffe::ConcatLayer< Dtype >)caffe::ConcatLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::ConcatLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ConcatLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ConcatLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ConcatLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::ConcatLayer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
num_concats_ (defined in caffe::ConcatLayer< Dtype >)caffe::ConcatLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ConcatLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::ConcatLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
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caffe::ConcatLayer< Dtype > Class Template Reference
+
+
+ +

Takes at least two Blobs and concatenates them along either the num or channel dimension, outputting the result. + More...

+ +

#include <concat_layer.hpp>

+
+Inheritance diagram for caffe::ConcatLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ ConcatLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the error gradient w.r.t. the concatenate inputs. More...
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+int count_
 
+int num_concats_
 
+int concat_input_size_
 
+int concat_axis_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::ConcatLayer< Dtype >

+ +

Takes at least two Blobs and concatenates them along either the num or channel dimension, outputting the result.

+

Member Function Documentation

+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::ConcatLayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the error gradient w.r.t. the concatenate inputs.

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
    +
  1. $ (KN \times C \times H \times W) $ if axis == 0, or $ (N \times KC \times H \times W) $ if axis == 1: containing error gradients $ \frac{\partial E}{\partial y} $ with respect to concatenated outputs $ y $
    2. +
+
propagate_downsee Layer::Backward.
bottominput Blob vector (length K), into which the top gradient $ \frac{\partial E}{\partial y} $ is deconcatenated back to the inputs $ \left[ \begin{array}{cccc} \frac{\partial E}{\partial x_1} & \frac{\partial E}{\partial x_2} & ... & \frac{\partial E}{\partial x_K} \end{array} \right] = \frac{\partial E}{\partial y} $
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::ConcatLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ConcatLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+
Parameters
+ + + +
bottominput Blob vector (length 2+)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x_1 $
    2. +
  2. $ (N \times C \times H \times W) $ the inputs $ x_2 $
    3. +
  3. ...
    4. +
+
    +
  • K $ (N \times C \times H \times W) $ the inputs $ x_K $
    • +
+
topoutput Blob vector (length 1)
    +
  1. $ (KN \times C \times H \times W) $ if axis == 0, or $ (N \times KC \times H \times W) $ if axis == 1: the concatenated output $ y = [\begin{array}{cccc} x_1 & x_2 & ... & x_K \end{array}] $
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ConcatLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::ConcatLayer< Dtype >::MinBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ConcatLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:38 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ConcatLayer.png b/doxygen/classcaffe_1_1ConcatLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..46ff253b6799b49d013871454baeeb7cf472fd1f GIT binary patch literal 723 zcmeAS@N?(olHy`uVBq!ia0vp^8-X~0gBeIBzE8OTq$C1-LR|m<{|{uoc=NTi|Ih>= z3ycpOIKbL@M;^%KC<*clW&kPzfvcxNj2IZ0l001;Ln;{G&P_c3NrA_ezr6GRfA#%Z zIU;6iHXGL~9(-e!xOC3eEp@54x8-VW^t|K}b!5_$)vh|8`=k{46j#ldydaP2KL4D0|M`rGC#@XtDGqi?d=!UY=ZMJ4H20#?}1f7gGak&r9rgh2-Pj zp5?ima@$BzZvEDJ>B~tWv%D`w%6EP~-}U3zk_}d>OH}m2OAN{dk2e`=p4anNyS8ZE zm986VXMXKTf2X*w>&{kBtDQ$RGmhMt@$B%|sS@VrKe8t!ht7L^>dWruH@s341xaZMl*MQiJldH zBsFQR*JNYwjh`-sK72c6$FbewnYn_;oeWle#C5J=-l@=S9Yvd zy5xSxN;I?kk%&oBj*mZUjqKxRmg`>qy0oqNMuqnI!qi*keYr!VZU zH|1;9@^5GC-YNgw_BSw4CbN~jc=6+ZV};0%=SL17*xzq>KzwTlV+{yT{Z-4McxF+3 Y*Jag>;$93wz!b>f>FVdQ&MBb@07f-bR{#J2 literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1ConstantFiller-members.html b/doxygen/classcaffe_1_1ConstantFiller-members.html new file mode 100644 index 00000000000..77a58980d78 --- /dev/null +++ b/doxygen/classcaffe_1_1ConstantFiller-members.html @@ -0,0 +1,85 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::ConstantFiller< Dtype > Member List
+
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This is the complete list of members for caffe::ConstantFiller< Dtype >, including all inherited members.

+ + + + + + +
ConstantFiller(const FillerParameter &param) (defined in caffe::ConstantFiller< Dtype >)caffe::ConstantFiller< Dtype >inlineexplicit
Fill(Blob< Dtype > *blob) (defined in caffe::ConstantFiller< Dtype >)caffe::ConstantFiller< Dtype >inlinevirtual
Filler(const FillerParameter &param) (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >inlineexplicit
filler_param_ (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >protected
~Filler() (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:38 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ConstantFiller.html b/doxygen/classcaffe_1_1ConstantFiller.html new file mode 100644 index 00000000000..a1b86123e4e --- /dev/null +++ b/doxygen/classcaffe_1_1ConstantFiller.html @@ -0,0 +1,123 @@ + + + + + + + +Caffe: caffe::ConstantFiller< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +List of all members
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caffe::ConstantFiller< Dtype > Class Template Reference
+
+
+ +

Fills a Blob with constant values $ x = 0 $. + More...

+ +

#include <filler.hpp>

+
+Inheritance diagram for caffe::ConstantFiller< Dtype >:
+
+
+ + +caffe::Filler< Dtype > + +
+ + + + + + + + + +

+Public Member Functions

+ ConstantFiller (const FillerParameter &param)
 
+virtual void Fill (Blob< Dtype > *blob)
 
- Public Member Functions inherited from caffe::Filler< Dtype >
+ Filler (const FillerParameter &param)
 
+ + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Filler< Dtype >
+FillerParameter filler_param_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::ConstantFiller< Dtype >

+ +

Fills a Blob with constant values $ x = 0 $.

+
The documentation for this class was generated from the following file: +
+ +
+Generated on Mon Nov 21 2016 11:23:38 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ConstantFiller.png b/doxygen/classcaffe_1_1ConstantFiller.png new file mode 100644 index 0000000000000000000000000000000000000000..e13961f4ab1e51ce98b20bf270285942a715f68a GIT binary patch literal 757 zcmeAS@N?(olHy`uVBq!ia0vp^+kiNLgBeIR8`rM{QW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;Da*+YHvtN+}!f2``M z+gGZ8-Q!ZVUY7m5aNEm1doJ4XDtd-jtd|wu^4wD9aq_jy=Q+!lRPC?b(r)oap82zg z=c|Wn%eeCG&s=zN-S^G6l^f=4<9+e}ut7N%8H(W&hrXAP)BX6hw%g)_@!A!d*_xbFNf9wNq zZ=3vtkVQqPtXLpJv~Bw!-_8>%It;NniHs zdMZv#w zSaxW$?pr5&zx@2?+JBbYUYg$jR(-tg$p3=N+qOT02BrPy4d)L=ub;%UJh{6b zRfTMsK}thT4U#DvB1sG|J%V>gTe~ HDWM4fydiWQ literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1ContrastiveLossLayer-members.html b/doxygen/classcaffe_1_1ContrastiveLossLayer-members.html new file mode 100644 index 00000000000..ed6b233cc5c --- /dev/null +++ b/doxygen/classcaffe_1_1ContrastiveLossLayer-members.html @@ -0,0 +1,124 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::ContrastiveLossLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::ContrastiveLossLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::ContrastiveLossLayer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ContrastiveLossLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ContrastiveLossLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
ContrastiveLossLayer(const LayerParameter &param) (defined in caffe::ContrastiveLossLayer< Dtype >)caffe::ContrastiveLossLayer< Dtype >inlineexplicit
diff_ (defined in caffe::ContrastiveLossLayer< Dtype >)caffe::ContrastiveLossLayer< Dtype >protected
diff_sq_ (defined in caffe::ContrastiveLossLayer< Dtype >)caffe::ContrastiveLossLayer< Dtype >protected
dist_sq_ (defined in caffe::ContrastiveLossLayer< Dtype >)caffe::ContrastiveLossLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::ContrastiveLossLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ContrastiveLossLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ContrastiveLossLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ContrastiveLossLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
LossLayer(const LayerParameter &param) (defined in caffe::LossLayer< Dtype >)caffe::LossLayer< Dtype >inlineexplicit
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::LossLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
summer_vec_ (defined in caffe::ContrastiveLossLayer< Dtype >)caffe::ContrastiveLossLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::ContrastiveLossLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:38 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ContrastiveLossLayer.html b/doxygen/classcaffe_1_1ContrastiveLossLayer.html new file mode 100644 index 00000000000..ab2b0a21f11 --- /dev/null +++ b/doxygen/classcaffe_1_1ContrastiveLossLayer.html @@ -0,0 +1,503 @@ + + + + + + + +Caffe: caffe::ContrastiveLossLayer< Dtype > Class Template Reference + + + + + + + + + +
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caffe::ContrastiveLossLayer< Dtype > Class Template Reference
+
+
+ +

Computes the contrastive loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks. + More...

+ +

#include <contrastive_loss_layer.hpp>

+
+Inheritance diagram for caffe::ContrastiveLossLayer< Dtype >:
+
+
+ + +caffe::LossLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ ContrastiveLossLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual bool AllowForceBackward (const int bottom_index) const
 
- Public Member Functions inherited from caffe::LossLayer< Dtype >
+ LossLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual bool AutoTopBlobs () const
 For convenience and backwards compatibility, instruct the Net to automatically allocate a single top Blob for LossLayers, into which they output their singleton loss, (even if the user didn't specify one in the prototxt, etc.).
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Computes the contrastive loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks. More...
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the Contrastive error gradient w.r.t. the inputs. More...
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+Blob< Dtype > diff_
 
+Blob< Dtype > dist_sq_
 
+Blob< Dtype > diff_sq_
 
+Blob< Dtype > summer_vec_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::ContrastiveLossLayer< Dtype >

+ +

Computes the contrastive loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks.

+
Parameters
+ + + +
bottominput Blob vector (length 3)
    +
  1. $ (N \times C \times 1 \times 1) $ the features $ a \in [-\infty, +\infty]$
    2. +
  2. $ (N \times C \times 1 \times 1) $ the features $ b \in [-\infty, +\infty]$
    3. +
  3. $ (N \times 1 \times 1 \times 1) $ the binary similarity $ s \in [0, 1]$
    4. +
+
topoutput Blob vector (length 1)
    +
  1. $ (1 \times 1 \times 1 \times 1) $ the computed contrastive loss: $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks.
    2. +
+
+
+
+

Member Function Documentation

+ +

§ AllowForceBackward()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
virtual bool caffe::ContrastiveLossLayer< Dtype >::AllowForceBackward (const int bottom_index) const
+ 		+inlinevirtual
+
+

Unlike most loss layers, in the ContrastiveLossLayer we can backpropagate to the first two inputs.

+ +

Reimplemented from caffe::LossLayer< Dtype >.

+ +
+
+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::ContrastiveLossLayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the Contrastive error gradient w.r.t. the inputs.

+

Computes the gradients with respect to the two input vectors (bottom[0] and bottom[1]), but not the similarity label (bottom[2]).

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
    +
  1. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
    2. +
+
propagate_downsee Layer::Backward.
bottominput Blob vector (length 2)
    +
  1. $ (N \times C \times 1 \times 1) $ the features $a$; Backward fills their diff with gradients if propagate_down[0]
    2. +
  2. $ (N \times C \times 1 \times 1) $ the features $b$; Backward fills their diff with gradients if propagate_down[1]
    3. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::ContrastiveLossLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::LossLayer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ContrastiveLossLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+ +

Computes the contrastive loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks.

+
Parameters
+ + + +
bottominput Blob vector (length 3)
    +
  1. $ (N \times C \times 1 \times 1) $ the features $ a \in [-\infty, +\infty]$
    2. +
  2. $ (N \times C \times 1 \times 1) $ the features $ b \in [-\infty, +\infty]$
    3. +
  3. $ (N \times 1 \times 1 \times 1) $ the binary similarity $ s \in [0, 1]$
    4. +
+
topoutput Blob vector (length 1)
    +
  1. $ (1 \times 1 \times 1 \times 1) $ the computed contrastive loss: $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks.
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ContrastiveLossLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::LossLayer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:38 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ContrastiveLossLayer.png b/doxygen/classcaffe_1_1ContrastiveLossLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..d6b674d3d1ff37a80d741a36d15f8e941d89b09f GIT binary patch literal 1151 zcmeAS@N?(olHy`uVBq!ia0vp^kAb*@gBeIFC9io7q$C1-LR|m<{|{uoc=NTi|Ih>= z3ycpOIKbL@M;^%KC<*clW&kPzfvcxNj2IYL_&i-4Ln;{G&V4v-u>z0V`uWel{FSe~ zEvOneW2)cw%^P2S+{`3c{_2PWyy~!&c`OYMYWSk!Y)5H z|1(Wx?nP}0`99e>(#dc9ls)aQPVRZVO*LEA0>gMZtGFQ#pX!lNHzhPlm`+{n>|Hu3_>W}T1!Zo?$ zYG-rj)*IJX$S(HfvVJ~!ihWX!>+;+nzXhky`pNWW`u>dXO8##1uk~WY&(AxI|Gxd4 z@p@HffUnEsXGdu?r54MsIZTC{NU-VWtqEn%wW)4o5Iqu9)#r{@0F%k-|hdgoi8yWs=N7U$;Ttrw-fHHc{@Mz)|2S! z?DEQ#r24o2q>8t>{b9>pe%vrB`1+@3)^7FF*6i`TEnd#!Zy43y-@W@Q|H~`U2h^hb z3oS3G#lCt|pRBVjd*9TOb(<@W`K`Nk=SI-=r4RZpF1x)>ZJp$XNyKfk!VM*7Q{dtdH=BI8Z6ulVoUlCSyR zhqum{7J7Di+Ukqj!fwaFBPUG%{MFEg|8C57Tle2|*!$1>Sm2R{p2Ho2k2yX#!P2np hzIm5Te5YTNkDeYgebIv5zk#I%gQu&X%Q~loCICvNJK+ET literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1ConvolutionLayer-members.html b/doxygen/classcaffe_1_1ConvolutionLayer-members.html new file mode 100644 index 00000000000..c289b36c501 --- /dev/null +++ b/doxygen/classcaffe_1_1ConvolutionLayer-members.html @@ -0,0 +1,154 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
caffe::ConvolutionLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::ConvolutionLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ConvolutionLayer< Dtype >protectedvirtual
backward_cpu_bias(Dtype *bias, const Dtype *input) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
backward_cpu_gemm(const Dtype *input, const Dtype *weights, Dtype *output) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ConvolutionLayer< Dtype >protectedvirtual
backward_gpu_bias(Dtype *bias, const Dtype *input) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
backward_gpu_gemm(const Dtype *input, const Dtype *weights, Dtype *col_output) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
BaseConvolutionLayer(const LayerParameter &param) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >inlineexplicit
bias_term_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
bottom_dim_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
bottom_shape_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
channel_axis_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
channels_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
col_buffer_shape_caffe::BaseConvolutionLayer< Dtype >protected
compute_output_shape() (defined in caffe::ConvolutionLayer< Dtype >)caffe::ConvolutionLayer< Dtype >protectedvirtual
conv_input_shape_caffe::BaseConvolutionLayer< Dtype >protected
ConvolutionLayer(const LayerParameter &param)caffe::ConvolutionLayer< Dtype >inlineexplicit
dilation_caffe::BaseConvolutionLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::BaseConvolutionLayer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
force_nd_im2col_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ConvolutionLayer< Dtype >protectedvirtual
forward_cpu_bias(Dtype *output, const Dtype *bias) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
forward_cpu_gemm(const Dtype *input, const Dtype *weights, Dtype *output, bool skip_im2col=false) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ConvolutionLayer< Dtype >protectedvirtual
forward_gpu_bias(Dtype *output, const Dtype *bias) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
forward_gpu_gemm(const Dtype *col_input, const Dtype *weights, Dtype *output, bool skip_im2col=false) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
group_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
input_shape(int i)caffe::BaseConvolutionLayer< Dtype >inlineprotected
is_1x1_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
IsShared() constcaffe::Layer< Dtype >inline
kernel_shape_caffe::BaseConvolutionLayer< Dtype >protected
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BaseConvolutionLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::BaseConvolutionLayer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::BaseConvolutionLayer< Dtype >inlinevirtual
num_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
num_output_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
num_spatial_axes_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
out_spatial_dim_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
output_shape_caffe::BaseConvolutionLayer< Dtype >protected
pad_caffe::BaseConvolutionLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BaseConvolutionLayer< Dtype >virtual
reverse_dimensions() (defined in caffe::ConvolutionLayer< Dtype >)caffe::ConvolutionLayer< Dtype >inlineprotectedvirtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
stride_caffe::BaseConvolutionLayer< Dtype >protected
top_dim_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::ConvolutionLayer< Dtype >inlinevirtual
weight_cpu_gemm(const Dtype *input, const Dtype *output, Dtype *weights) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
weight_gpu_gemm(const Dtype *col_input, const Dtype *output, Dtype *weights) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
weight_offset_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:39 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ConvolutionLayer.html b/doxygen/classcaffe_1_1ConvolutionLayer.html new file mode 100644 index 00000000000..773f07480bd --- /dev/null +++ b/doxygen/classcaffe_1_1ConvolutionLayer.html @@ -0,0 +1,400 @@ + + + + + + + +Caffe: caffe::ConvolutionLayer< Dtype > Class Template Reference + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+Public Member Functions | +Protected Member Functions | +List of all members
+
+
caffe::ConvolutionLayer< Dtype > Class Template Reference
+
+
+ +

Convolves the input image with a bank of learned filters, and (optionally) adds biases. + More...

+ +

#include <conv_layer.hpp>

+
+Inheritance diagram for caffe::ConvolutionLayer< Dtype >:
+
+
+ + +caffe::BaseConvolutionLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

 ConvolutionLayer (const LayerParameter &param)
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::BaseConvolutionLayer< Dtype >
+ BaseConvolutionLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
+virtual bool reverse_dimensions ()
 
+virtual void compute_output_shape ()
 
- Protected Member Functions inherited from caffe::BaseConvolutionLayer< Dtype >
+void forward_cpu_gemm (const Dtype *input, const Dtype *weights, Dtype *output, bool skip_im2col=false)
 
+void forward_cpu_bias (Dtype *output, const Dtype *bias)
 
+void backward_cpu_gemm (const Dtype *input, const Dtype *weights, Dtype *output)
 
+void weight_cpu_gemm (const Dtype *input, const Dtype *output, Dtype *weights)
 
+void backward_cpu_bias (Dtype *bias, const Dtype *input)
 
+void forward_gpu_gemm (const Dtype *col_input, const Dtype *weights, Dtype *output, bool skip_im2col=false)
 
+void forward_gpu_bias (Dtype *output, const Dtype *bias)
 
+void backward_gpu_gemm (const Dtype *input, const Dtype *weights, Dtype *col_output)
 
+void weight_gpu_gemm (const Dtype *col_input, const Dtype *output, Dtype *weights)
 
+void backward_gpu_bias (Dtype *bias, const Dtype *input)
 
+int input_shape (int i)
 The spatial dimensions of the input.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::BaseConvolutionLayer< Dtype >
+Blob< int > kernel_shape_
 The spatial dimensions of a filter kernel.
 
+Blob< int > stride_
 The spatial dimensions of the stride.
 
+Blob< int > pad_
 The spatial dimensions of the padding.
 
+Blob< int > dilation_
 The spatial dimensions of the dilation.
 
+Blob< int > conv_input_shape_
 The spatial dimensions of the convolution input.
 
+vector< int > col_buffer_shape_
 The spatial dimensions of the col_buffer.
 
+vector< int > output_shape_
 The spatial dimensions of the output.
 
+const vector< int > * bottom_shape_
 
+int num_spatial_axes_
 
+int bottom_dim_
 
+int top_dim_
 
+int channel_axis_
 
+int num_
 
+int channels_
 
+int group_
 
+int out_spatial_dim_
 
+int weight_offset_
 
+int num_output_
 
+bool bias_term_
 
+bool is_1x1_
 
+bool force_nd_im2col_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::ConvolutionLayer< Dtype >

+ +

Convolves the input image with a bank of learned filters, and (optionally) adds biases.

+

Caffe convolves by reduction to matrix multiplication. This achieves high-throughput and generality of input and filter dimensions but comes at the cost of memory for matrices. This makes use of efficiency in BLAS.

+

The input is "im2col" transformed to a channel K' x H x W data matrix for multiplication with the N x K' x H x W filter matrix to yield a N' x H x W output matrix that is then "col2im" restored. K' is the input channel * kernel height * kernel width dimension of the unrolled inputs so that the im2col matrix has a column for each input region to be filtered. col2im restores the output spatial structure by rolling up the output channel N' columns of the output matrix.

+

Constructor & Destructor Documentation

+ +

§ ConvolutionLayer()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
caffe::ConvolutionLayer< Dtype >::ConvolutionLayer (const LayerParameter & param)
+ 		+inlineexplicit
+
+
Parameters
+ + +
paramprovides ConvolutionParameter convolution_param, with ConvolutionLayer options:
    +
  • num_output. The number of filters.
    • +
  • kernel_size / kernel_h / kernel_w. The filter dimensions, given by kernel_size for square filters or kernel_h and kernel_w for rectangular filters.
    • +
  • stride / stride_h / stride_w (optional, default 1). The filter stride, given by stride_size for equal dimensions or stride_h and stride_w for different strides. By default the convolution is dense with stride 1.
    • +
  • pad / pad_h / pad_w (optional, default 0). The zero-padding for convolution, given by pad for equal dimensions or pad_h and pad_w for different padding. Input padding is computed implicitly instead of actually padding.
    • +
  • dilation (optional, default 1). The filter dilation, given by dilation_size for equal dimensions for different dilation. By default the convolution has dilation 1.
    • +
  • group (optional, default 1). The number of filter groups. Group convolution is a method for reducing parameterization by selectively connecting input and output channels. The input and output channel dimensions must be divisible by the number of groups. For group $ \geq 1 $, the convolutional filters' input and output channels are separated s.t. each group takes 1 / group of the input channels and makes 1 / group of the output channels. Concretely 4 input channels, 8 output channels, and 2 groups separate input channels 1-2 and output channels 1-4 into the first group and input channels 3-4 and output channels 5-8 into the second group.
    • +
  • bias_term (optional, default true). Whether to have a bias.
    • +
  • engine: convolution has CAFFE (matrix multiplication) and CUDNN (library kernels + stream parallelism) engines.
    • +
+
+
+
+ +
+
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/layers/conv_layer.hpp
    • +
  • src/caffe/layers/conv_layer.cpp
    • +
+
+ +
+Generated on Mon Nov 21 2016 11:23:39 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ConvolutionLayer.png b/doxygen/classcaffe_1_1ConvolutionLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..d808aebfd8128daf56ac867d8fd1b6736f3cc669 GIT binary patch literal 1179 zcmeAS@N?(olHy`uVBq!ia0vp^FMzm%gBeKPY7Q3wQW60^A+G=b{|Cvv`C8h4XabN0 z#s>}@VC}pk59D%`1o;Is02P72)l(rx3=AwhgX&-@FQ7rbWEskU5NCsn%O<=IuGk8LhqioTazBNpBowWCO=wJcKf~K-yZS6c-Gjg4$JA5 zljnAB)#wgq%<6Cd`qSoB_kyjpt7b2We%Y6m?B1KXkMV!8`|R>Bj4L*ND&J!8x5GMX z^}3s@YxK@)o^8Dn7VEj}>HCatUj;+MCs|#cm{+Q+^0M-;Y=-{x?M-FHb#ME;F4bRQ zn7YJ6YQZEC&+rSC^PUTHJUCy69OrnGGqV?Rr~&B7eFeCb(!w%2)-I&um z|G}R(_ogu~OTJ?+>7YM-@`QP;Urtr>UAo-Rw~AZ98&B|Kji3EXGiNO`?%-6|2a7Z- zwyL#}_Dnv%SEe%LKK*Xa-;f=fy=Tg~dy%yZ%qQ=?IdKJ_$N^w%%;h|wyw&0Q)#z`Z zvc6v49-T37TKVaD`BBxccdK4Hcg<>parsPL+sB#3k1sK;SjqTm(izR1*lL067j53u zl}+B0YaO9S1L zm)WH94b$C4HxyF;YCGgkSfmve_`dm?*w4QUe$U+b<4Spn_!Gfr#pPQxnx8UmdTO{e zEpZQ%jIeOiGLu~ApZwEpIfh7rpr>mdKI;Vst E03;nMUH||9 literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1CropLayer-members.html b/doxygen/classcaffe_1_1CropLayer-members.html new file mode 100644 index 00000000000..51e2588015b --- /dev/null +++ b/doxygen/classcaffe_1_1CropLayer-members.html @@ -0,0 +1,120 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::CropLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::CropLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::CropLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::CropLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
CropLayer(const LayerParameter &param) (defined in caffe::CropLayer< Dtype >)caffe::CropLayer< Dtype >inlineexplicit
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::CropLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::CropLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::CropLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::CropLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::CropLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
offsets (defined in caffe::CropLayer< Dtype >)caffe::CropLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::CropLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::CropLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:39 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1CropLayer.html b/doxygen/classcaffe_1_1CropLayer.html new file mode 100644 index 00000000000..9cb6689e354 --- /dev/null +++ b/doxygen/classcaffe_1_1CropLayer.html @@ -0,0 +1,407 @@ + + + + + + + +Caffe: caffe::CropLayer< Dtype > Class Template Reference + + + + + + + + + +
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caffe::CropLayer< Dtype > Class Template Reference
+
+
+ +

Takes a Blob and crop it, to the shape specified by the second input Blob, across all dimensions after the specified axis. + More...

+ +

#include <crop_layer.hpp>

+
+Inheritance diagram for caffe::CropLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ CropLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + +

+Protected Attributes

+vector< int > offsets
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::CropLayer< Dtype >

+ +

Takes a Blob and crop it, to the shape specified by the second input Blob, across all dimensions after the specified axis.

+

TODO(dox): thorough documentation for Forward, Backward, and proto params.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::CropLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::CropLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::CropLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::CropLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/layers/crop_layer.hpp
    • +
  • src/caffe/layers/crop_layer.cpp
    • +
+
+ +
+Generated on Mon Nov 21 2016 11:23:39 for Caffe by   +doxygen + 1.8.12 +
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caffe::DataLayer< Dtype > Member List
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This is the complete list of members for caffe::DataLayer< Dtype >, including all inherited members.

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AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BaseDataLayer< Dtype >inlinevirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BaseDataLayer< Dtype >inlinevirtual
BaseDataLayer(const LayerParameter &param) (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >explicit
BasePrefetchingDataLayer(const LayerParameter &param) (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >explicit
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
data_transformer_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected
DataLayer(const LayerParameter &param) (defined in caffe::DataLayer< Dtype >)caffe::DataLayer< Dtype >explicit
DataLayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) (defined in caffe::DataLayer< Dtype >)caffe::DataLayer< Dtype >virtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::DataLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BasePrefetchingDataLayer< Dtype >virtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BasePrefetchingDataLayer< Dtype >virtual
InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadinline
InternalThreadEntry() (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protectedvirtual
is_started() const (defined in caffe::InternalThread)caffe::InternalThread
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BasePrefetchingDataLayer< Dtype >virtual
load_batch(Batch< Dtype > *batch) (defined in caffe::DataLayer< Dtype >)caffe::DataLayer< Dtype >protectedvirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::DataLayer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::DataLayer< Dtype >inlinevirtual
must_stop() (defined in caffe::InternalThread)caffe::InternalThreadprotected
output_labels_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
prefetch_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected
PREFETCH_COUNT (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >static
prefetch_free_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected
prefetch_full_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected
reader_ (defined in caffe::DataLayer< Dtype >)caffe::DataLayer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BaseDataLayer< Dtype >inlinevirtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::DataLayer< Dtype >inlinevirtual
StartInternalThread()caffe::InternalThread
StopInternalThread()caffe::InternalThread
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
transform_param_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected
transformed_data_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected
type() constcaffe::DataLayer< Dtype >inlinevirtual
~DataLayer() (defined in caffe::DataLayer< Dtype >)caffe::DataLayer< Dtype >virtual
~InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadvirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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+
+
+
+Inheritance diagram for caffe::DataLayer< Dtype >:
+
+
+ + +caffe::BasePrefetchingDataLayer< Dtype > +caffe::BaseDataLayer< Dtype > +caffe::InternalThread +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ DataLayer (const LayerParameter &param)
 
+virtual void DataLayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
- Public Member Functions inherited from caffe::BasePrefetchingDataLayer< Dtype >
+ BasePrefetchingDataLayer (const LayerParameter &param)
 
void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
- Public Member Functions inherited from caffe::BaseDataLayer< Dtype >
+ BaseDataLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
- Public Member Functions inherited from caffe::InternalThread
void StartInternalThread ()
 
void StopInternalThread ()
 
+bool is_started () const
 
+ + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void load_batch (Batch< Dtype > *batch)
 
- Protected Member Functions inherited from caffe::BasePrefetchingDataLayer< Dtype >
+virtual void InternalThreadEntry ()
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
- Protected Member Functions inherited from caffe::InternalThread
+bool must_stop ()
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+DataReader reader_
 
- Protected Attributes inherited from caffe::BasePrefetchingDataLayer< Dtype >
+Batch< Dtype > prefetch_ [PREFETCH_COUNT]
 
+BlockingQueue< Batch< Dtype > * > prefetch_free_
 
+BlockingQueue< Batch< Dtype > * > prefetch_full_
 
+Blob< Dtype > transformed_data_
 
- Protected Attributes inherited from caffe::BaseDataLayer< Dtype >
+TransformationParameter transform_param_
 
+shared_ptr< DataTransformer< Dtype > > data_transformer_
 
+bool output_labels_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+ + + + +

+Additional Inherited Members

- Static Public Attributes inherited from caffe::BasePrefetchingDataLayer< Dtype >
+static const int PREFETCH_COUNT = 3
 
+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::DataLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MaxTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::DataLayer< Dtype >::MaxTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some maximum number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::DataLayer< Dtype >::MinTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/layers/data_layer.hpp
    • +
  • src/caffe/layers/data_layer.cpp
    • +
+
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bodies_ (defined in caffe::DataReader)caffe::DataReaderprotectedstatic
body_ (defined in caffe::DataReader)caffe::DataReaderprotected
DataReader(const LayerParameter &param) (defined in caffe::DataReader)caffe::DataReaderexplicit
DISABLE_COPY_AND_ASSIGN(DataReader) (defined in caffe::DataReader)caffe::DataReaderprotected
free() const (defined in caffe::DataReader)caffe::DataReaderinline
full() const (defined in caffe::DataReader)caffe::DataReaderinline
queue_pair_ (defined in caffe::DataReader)caffe::DataReaderprotected
source_key(const LayerParameter &param) (defined in caffe::DataReader)caffe::DataReaderinlineprotectedstatic
~DataReader() (defined in caffe::DataReader)caffe::DataReader
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caffe::DataReader Class Reference
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Reads data from a source to queues available to data layers. A single reading thread is created per source, even if multiple solvers are running in parallel, e.g. for multi-GPU training. This makes sure databases are read sequentially, and that each solver accesses a different subset of the database. Data is distributed to solvers in a round-robin way to keep parallel training deterministic. + More...

+ +

#include <data_reader.hpp>

+ + + + + + +

+Classes

class  Body
 
class  QueuePair
 
+ + + + + + + +

+Public Member Functions

+ DataReader (const LayerParameter &param)
 
+BlockingQueue< Datum * > & free () const
 
+BlockingQueue< Datum * > & full () const
 
+ + + +

+Protected Member Functions

+ DISABLE_COPY_AND_ASSIGN (DataReader)
 
+ + + +

+Static Protected Member Functions

+static string source_key (const LayerParameter &param)
 
+ + + + + +

+Protected Attributes

+const shared_ptr< QueuePair > queue_pair_
 
+shared_ptr< Body > body_
 
+ + + +

+Static Protected Attributes

+static map< const string, boost::weak_ptr< DataReader::Body > > bodies_
 
+

Detailed Description

+

Reads data from a source to queues available to data layers. A single reading thread is created per source, even if multiple solvers are running in parallel, e.g. for multi-GPU training. This makes sure databases are read sequentially, and that each solver accesses a different subset of the database. Data is distributed to solvers in a round-robin way to keep parallel training deterministic.

+
The documentation for this class was generated from the following files: +
+ +
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Body(const LayerParameter &param) (defined in caffe::DataReader::Body)caffe::DataReader::Bodyexplicit
DataReader (defined in caffe::DataReader::Body)caffe::DataReader::Bodyfriend
DISABLE_COPY_AND_ASSIGN(Body) (defined in caffe::DataReader::Body)caffe::DataReader::Bodyprotected
InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadinline
InternalThreadEntry() (defined in caffe::DataReader::Body)caffe::DataReader::Bodyprotectedvirtual
is_started() const (defined in caffe::InternalThread)caffe::InternalThread
must_stop() (defined in caffe::InternalThread)caffe::InternalThreadprotected
new_queue_pairs_ (defined in caffe::DataReader::Body)caffe::DataReader::Bodyprotected
param_ (defined in caffe::DataReader::Body)caffe::DataReader::Bodyprotected
read_one(db::Cursor *cursor, QueuePair *qp) (defined in caffe::DataReader::Body)caffe::DataReader::Bodyprotected
StartInternalThread()caffe::InternalThread
StopInternalThread()caffe::InternalThread
~Body() (defined in caffe::DataReader::Body)caffe::DataReader::Bodyvirtual
~InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadvirtual
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+Public Member Functions

+ Body (const LayerParameter &param)
 
- Public Member Functions inherited from caffe::InternalThread
void StartInternalThread ()
 
void StopInternalThread ()
 
+bool is_started () const
 
+ + + + + + + + + + +

+Protected Member Functions

+void InternalThreadEntry ()
 
+void read_one (db::Cursor *cursor, QueuePair *qp)
 
+ DISABLE_COPY_AND_ASSIGN (Body)
 
- Protected Member Functions inherited from caffe::InternalThread
+bool must_stop ()
 
+ + + + + +

+Protected Attributes

+const LayerParameter param_
 
+BlockingQueue< shared_ptr< QueuePair > > new_queue_pairs_
 
+ + + +

+Friends

+class DataReader
 
+
The documentation for this class was generated from the following files: +
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+ + diff --git a/doxygen/classcaffe_1_1DataReader_1_1Body.png b/doxygen/classcaffe_1_1DataReader_1_1Body.png new file mode 100644 index 0000000000000000000000000000000000000000..018b4a8b2b6bb5093500afbb2750358c6905b369 GIT binary patch literal 664 zcmeAS@N?(olHy`uVBq!ia0vp^Q-L^ugBeJwM;@;NQW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;Ygr+Ar*{o=f3TGtia>SZyt32|HSR8 z0XYgQ)3*fO6kU90Nnua%2lr?C#|vdncREiJyS!}Dk{M=GRgQj{skJotoY_xR&$*Y1 z6aVclm!4m@q-w21SnJ9=o|Bew1@1Kq*RNdsb6-UEjOjOj%$ccqEo)QA+2cpgmv#h9 zt@xLecRDfO&oX=Olwec7M)kjio~gx;uHICaJ9;|U(`#-`jpy4P3s2={86|q%$lZGN zbX9y%XuWuRkoCHiYNB(VY}i-5;&bh~V?QR{`gnO?SLg@jtLJ~8I(NcSzHUv8^KHqJ zP_1ltEqQyZ=1E^tbG1CXGJ`#Xd}e!1N{X8H%AzmXl<|+MEyJ|SlS2BBcSU#y*)9}2 z5;ZyNyn;i>Y%YamzN`Y5moar@1~VSnvXUX`RtSS(u2#bgGf@X0BetA~(%N4(3B&P{& zd3whFebUc;v(l5?O7${+8>^OxuJH>lPv6S3uPHg}_r6(Tm&_B_G+mZC)1F*$bIPkt z>wl^Jjg7w>Fg1lQq^#!D3zO{FsMC>iUTyJ9|4=i1_Cdu7^Fw$4Z{7FSJLsm6zVp+4 pzW*=Pr#HL@`&}p0OLOfq`=iS8_w0h*a)HT+!PC{xWt~$(69A + + + + + + +Caffe: Member List + + + + + + + + + +
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DISABLE_COPY_AND_ASSIGN(QueuePair) (defined in caffe::DataReader::QueuePair)caffe::DataReader::QueuePair
free_ (defined in caffe::DataReader::QueuePair)caffe::DataReader::QueuePair
full_ (defined in caffe::DataReader::QueuePair)caffe::DataReader::QueuePair
QueuePair(int size) (defined in caffe::DataReader::QueuePair)caffe::DataReader::QueuePairexplicit
~QueuePair() (defined in caffe::DataReader::QueuePair)caffe::DataReader::QueuePair
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caffe::DataReader::QueuePair Class Reference
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+Public Member Functions

+ QueuePair (int size)
 
+ DISABLE_COPY_AND_ASSIGN (QueuePair)
 
+ + + + + +

+Public Attributes

+BlockingQueue< Datum * > free_
 
+BlockingQueue< Datum * > full_
 
+
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data_mean_ (defined in caffe::DataTransformer< Dtype >)caffe::DataTransformer< Dtype >protected
DataTransformer(const TransformationParameter &param, Phase phase) (defined in caffe::DataTransformer< Dtype >)caffe::DataTransformer< Dtype >explicit
InferBlobShape(const Datum &datum)caffe::DataTransformer< Dtype >
InferBlobShape(const vector< Datum > &datum_vector)caffe::DataTransformer< Dtype >
InitRand()caffe::DataTransformer< Dtype >
mean_values_ (defined in caffe::DataTransformer< Dtype >)caffe::DataTransformer< Dtype >protected
param_ (defined in caffe::DataTransformer< Dtype >)caffe::DataTransformer< Dtype >protected
phase_ (defined in caffe::DataTransformer< Dtype >)caffe::DataTransformer< Dtype >protected
Rand(int n)caffe::DataTransformer< Dtype >protectedvirtual
rng_ (defined in caffe::DataTransformer< Dtype >)caffe::DataTransformer< Dtype >protected
Transform(const Datum &datum, Blob< Dtype > *transformed_blob)caffe::DataTransformer< Dtype >
Transform(const vector< Datum > &datum_vector, Blob< Dtype > *transformed_blob)caffe::DataTransformer< Dtype >
Transform(Blob< Dtype > *input_blob, Blob< Dtype > *transformed_blob)caffe::DataTransformer< Dtype >
Transform(const Datum &datum, Dtype *transformed_data) (defined in caffe::DataTransformer< Dtype >)caffe::DataTransformer< Dtype >protected
~DataTransformer() (defined in caffe::DataTransformer< Dtype >)caffe::DataTransformer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:39 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1DataTransformer.html b/doxygen/classcaffe_1_1DataTransformer.html new file mode 100644 index 00000000000..abb31b65279 --- /dev/null +++ b/doxygen/classcaffe_1_1DataTransformer.html @@ -0,0 +1,361 @@ + + + + + + + +Caffe: caffe::DataTransformer< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
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caffe::DataTransformer< Dtype > Class Template Reference
+
+
+ +

Applies common transformations to the input data, such as scaling, mirroring, substracting the image mean... + More...

+ +

#include <data_transformer.hpp>

+ + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ DataTransformer (const TransformationParameter &param, Phase phase)
 
+void InitRand ()
 Initialize the Random number generations if needed by the transformation.
 
void Transform (const Datum &datum, Blob< Dtype > *transformed_blob)
 Applies the transformation defined in the data layer's transform_param block to the data. More...
 
void Transform (const vector< Datum > &datum_vector, Blob< Dtype > *transformed_blob)
 Applies the transformation defined in the data layer's transform_param block to a vector of Datum. More...
 
void Transform (Blob< Dtype > *input_blob, Blob< Dtype > *transformed_blob)
 Applies the same transformation defined in the data layer's transform_param block to all the num images in a input_blob. More...
 
vector< int > InferBlobShape (const Datum &datum)
 Infers the shape of transformed_blob will have when the transformation is applied to the data. More...
 
vector< int > InferBlobShape (const vector< Datum > &datum_vector)
 Infers the shape of transformed_blob will have when the transformation is applied to the data. It uses the first element to infer the shape of the blob. More...
 
+ + + + + + +

+Protected Member Functions

virtual int Rand (int n)
 Infers the shape of transformed_blob will have when the transformation is applied to the data. It uses the first element to infer the shape of the blob. More...
 
+void Transform (const Datum &datum, Dtype *transformed_data)
 
+ + + + + + + + + + + +

+Protected Attributes

+TransformationParameter param_
 
+shared_ptr< Caffe::RNG > rng_
 
+Phase phase_
 
+Blob< Dtype > data_mean_
 
+vector< Dtype > mean_values_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::DataTransformer< Dtype >

+ +

Applies common transformations to the input data, such as scaling, mirroring, substracting the image mean...

+

Member Function Documentation

+ +

§ InferBlobShape() [1/2]

+ +
+
+
+template<typename Dtype >
+ + + + + + + + +
vector< int > caffe::DataTransformer< Dtype >::InferBlobShape (const Datum & datum)
+
+ +

Infers the shape of transformed_blob will have when the transformation is applied to the data.

+
Parameters
+ + +
datumDatum containing the data to be transformed.
+
+
+ +
+
+ +

§ InferBlobShape() [2/2]

+ +
+
+
+template<typename Dtype >
+ + + + + + + + +
vector< int > caffe::DataTransformer< Dtype >::InferBlobShape (const vector< Datum > & datum_vector)
+
+ +

Infers the shape of transformed_blob will have when the transformation is applied to the data. It uses the first element to infer the shape of the blob.

+
Parameters
+ + +
datum_vectorA vector of Datum containing the data to be transformed.
+
+
+ +
+
+ +

§ Rand()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
int caffe::DataTransformer< Dtype >::Rand (int n)
+ 		+protectedvirtual
+
+ +

Infers the shape of transformed_blob will have when the transformation is applied to the data. It uses the first element to infer the shape of the blob.

+
Parameters
+ + + +
mat_vectorA vector of Mat containing the data to be transformed. Generates a random integer from Uniform({0, 1, ..., n-1}).
nThe upperbound (exclusive) value of the random number.
+
+
+
Returns
A uniformly random integer value from ({0, 1, ..., n-1}).
+ +
+
+ +

§ Transform() [1/3]

+ +
+
+
+template<typename Dtype >
+ + + + + + + + + + + + + + + + + + +
void caffe::DataTransformer< Dtype >::Transform (const Datum & datum,
Blob< Dtype > * transformed_blob 
)
+
+ +

Applies the transformation defined in the data layer's transform_param block to the data.

+
Parameters
+ + + +
datumDatum containing the data to be transformed.
transformed_blobThis is destination blob. It can be part of top blob's data if set_cpu_data() is used. See data_layer.cpp for an example.
+
+
+ +
+
+ +

§ Transform() [2/3]

+ +
+
+
+template<typename Dtype >
+ + + + + + + + + + + + + + + + + + +
void caffe::DataTransformer< Dtype >::Transform (const vector< Datum > & datum_vector,
Blob< Dtype > * transformed_blob 
)
+
+ +

Applies the transformation defined in the data layer's transform_param block to a vector of Datum.

+
Parameters
+ + + +
datum_vectorA vector of Datum containing the data to be transformed.
transformed_blobThis is destination blob. It can be part of top blob's data if set_cpu_data() is used. See memory_layer.cpp for an example.
+
+
+ +
+
+ +

§ Transform() [3/3]

+ +
+
+
+template<typename Dtype >
+ + + + + + + + + + + + + + + + + + +
void caffe::DataTransformer< Dtype >::Transform (Blob< Dtype > * input_blob,
Blob< Dtype > * transformed_blob 
)
+
+ +

Applies the same transformation defined in the data layer's transform_param block to all the num images in a input_blob.

+
Parameters
+ + + +
input_blobA Blob containing the data to be transformed. It applies the same transformation to all the num images in the blob.
transformed_blobThis is destination blob, it will contain as many images as the input blob. It can be part of top blob's data.
+
+
+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:39 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1DeconvolutionLayer-members.html b/doxygen/classcaffe_1_1DeconvolutionLayer-members.html new file mode 100644 index 00000000000..545f4400268 --- /dev/null +++ b/doxygen/classcaffe_1_1DeconvolutionLayer-members.html @@ -0,0 +1,154 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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+
caffe::DeconvolutionLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::DeconvolutionLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::DeconvolutionLayer< Dtype >protectedvirtual
backward_cpu_bias(Dtype *bias, const Dtype *input) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
backward_cpu_gemm(const Dtype *input, const Dtype *weights, Dtype *output) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::DeconvolutionLayer< Dtype >protectedvirtual
backward_gpu_bias(Dtype *bias, const Dtype *input) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
backward_gpu_gemm(const Dtype *input, const Dtype *weights, Dtype *col_output) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
BaseConvolutionLayer(const LayerParameter &param) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >inlineexplicit
bias_term_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
bottom_dim_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
bottom_shape_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
channel_axis_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
channels_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
col_buffer_shape_caffe::BaseConvolutionLayer< Dtype >protected
compute_output_shape() (defined in caffe::DeconvolutionLayer< Dtype >)caffe::DeconvolutionLayer< Dtype >protectedvirtual
conv_input_shape_caffe::BaseConvolutionLayer< Dtype >protected
DeconvolutionLayer(const LayerParameter &param) (defined in caffe::DeconvolutionLayer< Dtype >)caffe::DeconvolutionLayer< Dtype >inlineexplicit
dilation_caffe::BaseConvolutionLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::BaseConvolutionLayer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
force_nd_im2col_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::DeconvolutionLayer< Dtype >protectedvirtual
forward_cpu_bias(Dtype *output, const Dtype *bias) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
forward_cpu_gemm(const Dtype *input, const Dtype *weights, Dtype *output, bool skip_im2col=false) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::DeconvolutionLayer< Dtype >protectedvirtual
forward_gpu_bias(Dtype *output, const Dtype *bias) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
forward_gpu_gemm(const Dtype *col_input, const Dtype *weights, Dtype *output, bool skip_im2col=false) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
group_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
input_shape(int i)caffe::BaseConvolutionLayer< Dtype >inlineprotected
is_1x1_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
IsShared() constcaffe::Layer< Dtype >inline
kernel_shape_caffe::BaseConvolutionLayer< Dtype >protected
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BaseConvolutionLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::BaseConvolutionLayer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::BaseConvolutionLayer< Dtype >inlinevirtual
num_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
num_output_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
num_spatial_axes_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
out_spatial_dim_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
output_shape_caffe::BaseConvolutionLayer< Dtype >protected
pad_caffe::BaseConvolutionLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BaseConvolutionLayer< Dtype >virtual
reverse_dimensions() (defined in caffe::DeconvolutionLayer< Dtype >)caffe::DeconvolutionLayer< Dtype >inlineprotectedvirtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
stride_caffe::BaseConvolutionLayer< Dtype >protected
top_dim_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::DeconvolutionLayer< Dtype >inlinevirtual
weight_cpu_gemm(const Dtype *input, const Dtype *output, Dtype *weights) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
weight_gpu_gemm(const Dtype *col_input, const Dtype *output, Dtype *weights) (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
weight_offset_ (defined in caffe::BaseConvolutionLayer< Dtype >)caffe::BaseConvolutionLayer< Dtype >protected
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:39 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1DeconvolutionLayer.html b/doxygen/classcaffe_1_1DeconvolutionLayer.html new file mode 100644 index 00000000000..852dfadf54d --- /dev/null +++ b/doxygen/classcaffe_1_1DeconvolutionLayer.html @@ -0,0 +1,355 @@ + + + + + + + +Caffe: caffe::DeconvolutionLayer< Dtype > Class Template Reference + + + + + + + + + +
+
+ + + + + + +
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+Public Member Functions | +Protected Member Functions | +List of all members
+
+
caffe::DeconvolutionLayer< Dtype > Class Template Reference
+
+
+ +

Convolve the input with a bank of learned filters, and (optionally) add biases, treating filters and convolution parameters in the opposite sense as ConvolutionLayer. + More...

+ +

#include <deconv_layer.hpp>

+
+Inheritance diagram for caffe::DeconvolutionLayer< Dtype >:
+
+
+ + +caffe::BaseConvolutionLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ DeconvolutionLayer (const LayerParameter &param)
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::BaseConvolutionLayer< Dtype >
+ BaseConvolutionLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
+virtual bool reverse_dimensions ()
 
+virtual void compute_output_shape ()
 
- Protected Member Functions inherited from caffe::BaseConvolutionLayer< Dtype >
+void forward_cpu_gemm (const Dtype *input, const Dtype *weights, Dtype *output, bool skip_im2col=false)
 
+void forward_cpu_bias (Dtype *output, const Dtype *bias)
 
+void backward_cpu_gemm (const Dtype *input, const Dtype *weights, Dtype *output)
 
+void weight_cpu_gemm (const Dtype *input, const Dtype *output, Dtype *weights)
 
+void backward_cpu_bias (Dtype *bias, const Dtype *input)
 
+void forward_gpu_gemm (const Dtype *col_input, const Dtype *weights, Dtype *output, bool skip_im2col=false)
 
+void forward_gpu_bias (Dtype *output, const Dtype *bias)
 
+void backward_gpu_gemm (const Dtype *input, const Dtype *weights, Dtype *col_output)
 
+void weight_gpu_gemm (const Dtype *col_input, const Dtype *output, Dtype *weights)
 
+void backward_gpu_bias (Dtype *bias, const Dtype *input)
 
+int input_shape (int i)
 The spatial dimensions of the input.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::BaseConvolutionLayer< Dtype >
+Blob< int > kernel_shape_
 The spatial dimensions of a filter kernel.
 
+Blob< int > stride_
 The spatial dimensions of the stride.
 
+Blob< int > pad_
 The spatial dimensions of the padding.
 
+Blob< int > dilation_
 The spatial dimensions of the dilation.
 
+Blob< int > conv_input_shape_
 The spatial dimensions of the convolution input.
 
+vector< int > col_buffer_shape_
 The spatial dimensions of the col_buffer.
 
+vector< int > output_shape_
 The spatial dimensions of the output.
 
+const vector< int > * bottom_shape_
 
+int num_spatial_axes_
 
+int bottom_dim_
 
+int top_dim_
 
+int channel_axis_
 
+int num_
 
+int channels_
 
+int group_
 
+int out_spatial_dim_
 
+int weight_offset_
 
+int num_output_
 
+bool bias_term_
 
+bool is_1x1_
 
+bool force_nd_im2col_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::DeconvolutionLayer< Dtype >

+ +

Convolve the input with a bank of learned filters, and (optionally) add biases, treating filters and convolution parameters in the opposite sense as ConvolutionLayer.

+

ConvolutionLayer computes each output value by dotting an input window with a filter; DeconvolutionLayer multiplies each input value by a filter elementwise, and sums over the resulting output windows. In other words, DeconvolutionLayer is ConvolutionLayer with the forward and backward passes reversed. DeconvolutionLayer reuses ConvolutionParameter for its parameters, but they take the opposite sense as in ConvolutionLayer (so padding is removed from the output rather than added to the input, and stride results in upsampling rather than downsampling).

+
The documentation for this class was generated from the following files: +
+ +
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caffe::DevicePair Member List
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This is the complete list of members for caffe::DevicePair, including all inherited members.

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compute(const vector< int > devices, vector< DevicePair > *pairs) (defined in caffe::DevicePair)caffe::DevicePairstatic
device() (defined in caffe::DevicePair)caffe::DevicePairinline
device_ (defined in caffe::DevicePair)caffe::DevicePairprotected
DevicePair(int parent, int device) (defined in caffe::DevicePair)caffe::DevicePairinline
parent() (defined in caffe::DevicePair)caffe::DevicePairinline
parent_ (defined in caffe::DevicePair)caffe::DevicePairprotected
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+ + diff --git a/doxygen/classcaffe_1_1DevicePair.html b/doxygen/classcaffe_1_1DevicePair.html new file mode 100644 index 00000000000..713c58a0b68 --- /dev/null +++ b/doxygen/classcaffe_1_1DevicePair.html @@ -0,0 +1,114 @@ + + + + + + + +Caffe: caffe::DevicePair Class Reference + + + + + + + + + +
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caffe::DevicePair Class Reference
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+Public Member Functions

+ DevicePair (int parent, int device)
 
+int parent ()
 
+int device ()
 
+ + + +

+Static Public Member Functions

+static void compute (const vector< int > devices, vector< DevicePair > *pairs)
 
+ + + + + +

+Protected Attributes

+int parent_
 
+int device_
 
+
The documentation for this class was generated from the following files: +
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+ + diff --git a/doxygen/classcaffe_1_1DropoutLayer-members.html b/doxygen/classcaffe_1_1DropoutLayer-members.html new file mode 100644 index 00000000000..f8c0e9150a6 --- /dev/null +++ b/doxygen/classcaffe_1_1DropoutLayer-members.html @@ -0,0 +1,124 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::DropoutLayer< Dtype > Member List
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This is the complete list of members for caffe::DropoutLayer< Dtype >, including all inherited members.

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AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::DropoutLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::DropoutLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
DropoutLayer(const LayerParameter &param)caffe::DropoutLayer< Dtype >inlineexplicit
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::DropoutLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::DropoutLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::DropoutLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
NeuronLayer(const LayerParameter &param) (defined in caffe::NeuronLayer< Dtype >)caffe::NeuronLayer< Dtype >inlineexplicit
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
rand_vec_caffe::DropoutLayer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::DropoutLayer< Dtype >virtual
scale_caffe::DropoutLayer< Dtype >protected
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
threshold_caffe::DropoutLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::DropoutLayer< Dtype >inlinevirtual
uint_thres_ (defined in caffe::DropoutLayer< Dtype >)caffe::DropoutLayer< Dtype >protected
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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+ + diff --git a/doxygen/classcaffe_1_1DropoutLayer.html b/doxygen/classcaffe_1_1DropoutLayer.html new file mode 100644 index 00000000000..2589945e16b --- /dev/null +++ b/doxygen/classcaffe_1_1DropoutLayer.html @@ -0,0 +1,460 @@ + + + + + + + +Caffe: caffe::DropoutLayer< Dtype > Class Template Reference + + + + + + + + + +
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caffe::DropoutLayer< Dtype > Class Template Reference
+
+
+ +

During training only, sets a random portion of $x$ to 0, adjusting the rest of the vector magnitude accordingly. + More...

+ +

#include <dropout_layer.hpp>

+
+Inheritance diagram for caffe::DropoutLayer< Dtype >:
+
+
+ + +caffe::NeuronLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

 DropoutLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::NeuronLayer< Dtype >
+ NeuronLayer (const LayerParameter &param)
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+Blob< unsigned int > rand_vec_
 when divided by UINT_MAX, the randomly generated values $u\sim U(0,1)$
 
+Dtype threshold_
 the probability $ p $ of dropping any input
 
+Dtype scale_
 the scale for undropped inputs at train time $ 1 / (1 - p) $
 
+unsigned int uint_thres_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::DropoutLayer< Dtype >

+ +

During training only, sets a random portion of $x$ to 0, adjusting the rest of the vector magnitude accordingly.

+
Parameters
+ + + +
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $
    2. +
+
topoutput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the computed outputs $ y = |x| $
    2. +
+
+
+
+

Constructor & Destructor Documentation

+ +

§ DropoutLayer()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
caffe::DropoutLayer< Dtype >::DropoutLayer (const LayerParameter & param)
+ 		+inlineexplicit
+
+
Parameters
+ + +
paramprovides DropoutParameter dropout_param, with DropoutLayer options:
    +
  • dropout_ratio (optional, default 0.5). Sets the probability $ p $ that any given unit is dropped.
    • +
+
+
+
+ +
+
+

Member Function Documentation

+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::DropoutLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+
Parameters
+ + + +
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $
    2. +
+
topoutput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the computed outputs. At training time, we have $ y_{\mbox{train}} = \left\{ \begin{array}{ll} \frac{x}{1 - p} & \mbox{if } u > p \\ 0 & \mbox{otherwise} \end{array} \right. $, where $ u \sim U(0, 1)$ is generated independently for each input at each iteration. At test time, we simply have $ y_{\mbox{test}} = \mathbb{E}[y_{\mbox{train}}] = x $.
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::DropoutLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::DropoutLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Reimplemented from caffe::NeuronLayer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:40 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1DropoutLayer.png b/doxygen/classcaffe_1_1DropoutLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..79dc5cd42e31746573f29fa637bc0e3916a8cb2a GIT binary patch literal 1073 zcmeAS@N?(olHy`uVBq!ia0vp^+km)(gBeKH#yxxuq$C1-LR|m<{|{uoc=NTi|Ih>= z3ycpOIKbL@M;^%KC<*clW&kPzfvcxNj2IZ04|uvbhEy=Vo%`_8D+L}G|LKzV|4&@+ z70smUwRgVc%+ANFE^LrtIAo|kgCay&l;C3sJozAjPUJM5~u=i(^2^`{rB zth}cB=6Fo|&vl9Axhk14?>B6J^2A0zyZY_g4cFCYZ(DM$X6Mx0YZu%PpHcCA|COzJ z`?mOHJIlT)ho7s>y}g`sZB*$FmAP3AyZ_{Dh;IoAY+t)_<7>g>2hBHrs?8J*sNq*M zTeSBO@3y;EuYZcw@chbGGh3UG_a^|3S0BuQ&6Y_7`l+RW+>C zQ9m%JJxC@;bz;iBrFr7(^+SX_cdg)_T>tO*=jS)VSJpHCHu~D15-zTopGY&FY5gc$5u%^IdNNgwcTvd>&+`=j;wsh#U{f7@);lzfpieLnBDx9``jtt>KF{b!Z0P11E` zz6b7aYV6PKo0d_nuxf$cH@Wn^w@%+nh&H$0&1#=8KlNEh?ETw%Wu-q(N5!SwzBm6{ zU)kon_y1Sf6+hBXlHUI-$9&=8^xI)Vljm+)zb)_mM)k(Ba^J!YQ(Y!iPTv)KrcJw3 zrPf#c)sGAH&HUd&U)?{j&*!i5&1p;QLqVy8DSFbLlH-1wOJA~l(7ns>?DCu&+iy>~ zQ=D{vW%`qOBGy`*4fBZ(QBZ(~L`_Sb>94~83{^FS{#9`MEanek{RbHq-U+wD;Y`gCzt#;mK?ZE!#*7{90 zw~xDhVz#`+243};P#z8C$@3g-g)ym zr~G_bwCAdlEqn4e^rqHsn|)#9=3MPgPv4s%yA!W2es%15&;;&_ab2hG%>5Mes%&P( znn?^bi#~dB8!T!3&#wPw^_BgNwKIR+zQOPhmd>vIZ%vJyw(<+VuDq8b--O9}!2HeN M>FVdQ&MBb@0Nu6}z5oCK literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1DummyDataLayer-members.html b/doxygen/classcaffe_1_1DummyDataLayer-members.html new file mode 100644 index 00000000000..86fa527087a --- /dev/null +++ b/doxygen/classcaffe_1_1DummyDataLayer-members.html @@ -0,0 +1,121 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::DummyDataLayer< Dtype >inlineprotectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::DummyDataLayer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
DummyDataLayer(const LayerParameter &param) (defined in caffe::DummyDataLayer< Dtype >)caffe::DummyDataLayer< Dtype >inlineexplicit
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::DummyDataLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
fillers_ (defined in caffe::DummyDataLayer< Dtype >)caffe::DummyDataLayer< Dtype >protected
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::DummyDataLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::DummyDataLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::DummyDataLayer< Dtype >inlinevirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
refill_ (defined in caffe::DummyDataLayer< Dtype >)caffe::DummyDataLayer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::DummyDataLayer< Dtype >inlinevirtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::DummyDataLayer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::DummyDataLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:40 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1DummyDataLayer.html b/doxygen/classcaffe_1_1DummyDataLayer.html new file mode 100644 index 00000000000..a6bc7ae123f --- /dev/null +++ b/doxygen/classcaffe_1_1DummyDataLayer.html @@ -0,0 +1,410 @@ + + + + + + + +Caffe: caffe::DummyDataLayer< Dtype > Class Template Reference + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
+
caffe::DummyDataLayer< Dtype > Class Template Reference
+
+
+ +

Provides data to the Net generated by a Filler. + More...

+ +

#include <dummy_data_layer.hpp>

+
+Inheritance diagram for caffe::DummyDataLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ DummyDataLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + +

+Protected Attributes

+vector< shared_ptr< Filler< Dtype > > > fillers_
 
+vector< bool > refill_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::DummyDataLayer< Dtype >

+ +

Provides data to the Net generated by a Filler.

+

TODO(dox): thorough documentation for Forward and proto params.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::DummyDataLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::DummyDataLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::DummyDataLayer< Dtype >::MinTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
virtual void caffe::DummyDataLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+inlinevirtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:40 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1DummyDataLayer.png b/doxygen/classcaffe_1_1DummyDataLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..b02d852328abc165aeebdac5ccb63b37ad4a9486 GIT binary patch literal 768 zcmeAS@N?(olHy`uVBq!ia0vp^XMi|>gBeIBMP&T|QW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;SZyxmj|K#gt z0y#pr0=7yz`RvqIIrc2UV7<=iO+89to>I=jZj-96v`kzg_tnscWAzrs{$7Rd3hkEqb{)S;;eK^0~t~ zjBop29-Cfz+TwSw#b5Jf>z@hsM`|I_YE9cELH#l}`{>(#PHW??co*DUh zPy3m^Q>>@Lj6I*7{OnBDtxOaAQ%3`GI zxPs?$9%wZ3iE1@f|Ni~_zSu`wzy6q0x2>#h=B$mA_o$ZZ-B>n-!T$9VwOEDh6E@xq z%g;8>Y@aXL_@`p=^6cvx33opHSt_Q}ce|r^w#=ozf@iEgWkCn77&$MfsY&x{Ijo@< zkg6{8Slq&T#@7ooc_-|h-*mF@(vM9~7u`@uJ*%1U#!plG%r<4&GZ&NQws*cemuH;* z&&v4N74;pnX1JF)I;QEnQ;KYv&NioV|{ d;3VNtpYc$;UO;n}4KTSfc)I$ztaD0e0sx@gTZRAt literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1ELULayer-members.html b/doxygen/classcaffe_1_1ELULayer-members.html new file mode 100644 index 00000000000..6d7a451638f --- /dev/null +++ b/doxygen/classcaffe_1_1ELULayer-members.html @@ -0,0 +1,120 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::ELULayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::ELULayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ELULayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ELULayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
ELULayer(const LayerParameter &param)caffe::ELULayer< Dtype >inlineexplicit
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ELULayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ELULayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlinevirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
NeuronLayer(const LayerParameter &param) (defined in caffe::NeuronLayer< Dtype >)caffe::NeuronLayer< Dtype >inlineexplicit
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::NeuronLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::ELULayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:40 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ELULayer.html b/doxygen/classcaffe_1_1ELULayer.html new file mode 100644 index 00000000000..78cf6e844a5 --- /dev/null +++ b/doxygen/classcaffe_1_1ELULayer.html @@ -0,0 +1,392 @@ + + + + + + + +Caffe: caffe::ELULayer< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +List of all members
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+
caffe::ELULayer< Dtype > Class Template Reference
+
+
+ +

Exponential Linear Unit non-linearity $ y = \left\{ \begin{array}{lr} x & \mathrm{if} \; x > 0 \\ \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 \end{array} \right. $. + More...

+ +

#include <elu_layer.hpp>

+
+Inheritance diagram for caffe::ELULayer< Dtype >:
+
+
+ + +caffe::NeuronLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

 ELULayer (const LayerParameter &param)
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::NeuronLayer< Dtype >
+ NeuronLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the error gradient w.r.t. the ELU inputs. More...
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::ELULayer< Dtype >

+ +

Exponential Linear Unit non-linearity $ y = \left\{ \begin{array}{lr} x & \mathrm{if} \; x > 0 \\ \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 \end{array} \right. $.

+

Constructor & Destructor Documentation

+ +

§ ELULayer()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
caffe::ELULayer< Dtype >::ELULayer (const LayerParameter & param)
+ 		+inlineexplicit
+
+
Parameters
+ + +
paramprovides ELUParameter elu_param, with ELULayer options:
    +
  • alpha (optional, default 1). the value $ \alpha $ by which controls saturation for negative inputs.
    • +
+
+
+
+ +
+
+

Member Function Documentation

+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::ELULayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the error gradient w.r.t. the ELU inputs.

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
    +
  1. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
    2. +
+
propagate_downsee Layer::Backward.
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} 1 & \mathrm{if} \; x > 0 \\ y + \alpha & \mathrm{if} \; x \le 0 \end{array} \right. $ if propagate_down[0].
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ELULayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+
Parameters
+ + + +
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $
    2. +
+
topoutput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the computed outputs $ y = \left\{ \begin{array}{lr} x & \mathrm{if} \; x > 0 \\ \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 \end{array} \right. $.
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/layers/elu_layer.hpp
    • +
  • src/caffe/layers/elu_layer.cpp
    • +
+
+ +
+Generated on Mon Nov 21 2016 11:23:40 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ELULayer.png b/doxygen/classcaffe_1_1ELULayer.png new file mode 100644 index 0000000000000000000000000000000000000000..7700467437a3710ea99ee229deb9f6200066a2aa GIT binary patch literal 1044 zcmeAS@N?(olHy`uVBq!ia0vp^n}N84gBeK9F4tNQq$C1-LR|m<{|{uoc=NTi|Ih>= z3ycpOIKbL@M;^%KC<*clW&kPzfvcxNj2IZ07kj!mhEy=VoqKW9D+M0caC75-|JCDd zmkMcQSYDqqai+yxtr;z@93Q7n35xPj@r?31sN?yME6wxMb&=28-mZG4@^aO-}we6?J>3I)z`#cCt*PuaccdhycF#MP!=eHoDT?ABwhucpubzM9dj@93{~ zZPDJ%yqj%Jq(Ae1Recus?U&llrqxjrPj1ebCiFXCUi;TSZ8syXe!VlJZr8lr`v#Ia z?D?k_Y+<@>t}=7^m*+ySFV{LxQZv0+S^xX}r_5)OPxKS!x&3Ri@t(B3DRR;u(KC~t zthX-q(p;;`Ab*!Z>~hnY?YFBga2ws9IK4S;qUA5P1MwO&7=h{-fZk`QXEkov-z0e; z{vgkT^$Bbr!VQ>zXro96%)_9*p~cT)Cmq|Blss+M{PQGkac5m*wm~AJb%VrmSwq#%I%mH)zF`)}%kXwRoYO;d}8zGU=UpuNN3e z8QH(hS@W&T{EXSG*u4SU&+MHuXXfi}tCV$I`*NPR&U-sIQ$N-I{Nv>H;>MwNCvzTw z0yJ)Q`fYQcxKo9<{{7kVR^pbk>DmL|cs-wK{@uy4`o2Po6FaPwCso_(7bg z;Dm+tl=R!%at${!>MVtZ`fJ_Kf2aL>aVJaeoaM29W?O&DBwczf^!f4i$wv)8#-BO- zfdc>#2kK@lY<+FS&y;fF z*7hT(RWod~K7T*+H2ZMP)_K2YZ{x6ib}QM#Hcc<$XG{0@-?t2OZf@Lr3YZ$bcZc*> zow(zD+4R|?pt+yAZKN+>4&68Bd7|zAzMB@;fr0lAoEleZspq`bP0 Hl+XkK<8%z8 literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1EltwiseLayer-members.html b/doxygen/classcaffe_1_1EltwiseLayer-members.html new file mode 100644 index 00000000000..c7049d09379 --- /dev/null +++ b/doxygen/classcaffe_1_1EltwiseLayer-members.html @@ -0,0 +1,123 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
caffe::EltwiseLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::EltwiseLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::EltwiseLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::EltwiseLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
coeffs_ (defined in caffe::EltwiseLayer< Dtype >)caffe::EltwiseLayer< Dtype >protected
EltwiseLayer(const LayerParameter &param) (defined in caffe::EltwiseLayer< Dtype >)caffe::EltwiseLayer< Dtype >inlineexplicit
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::EltwiseLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::EltwiseLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::EltwiseLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::EltwiseLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
max_idx_ (defined in caffe::EltwiseLayer< Dtype >)caffe::EltwiseLayer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::EltwiseLayer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
op_ (defined in caffe::EltwiseLayer< Dtype >)caffe::EltwiseLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::EltwiseLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
stable_prod_grad_ (defined in caffe::EltwiseLayer< Dtype >)caffe::EltwiseLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::EltwiseLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:40 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1EltwiseLayer.html b/doxygen/classcaffe_1_1EltwiseLayer.html new file mode 100644 index 00000000000..d48810d2232 --- /dev/null +++ b/doxygen/classcaffe_1_1EltwiseLayer.html @@ -0,0 +1,416 @@ + + + + + + + +Caffe: caffe::EltwiseLayer< Dtype > Class Template Reference + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
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+
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+
+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
+
caffe::EltwiseLayer< Dtype > Class Template Reference
+
+
+ +

Compute elementwise operations, such as product and sum, along multiple input Blobs. + More...

+ +

#include <eltwise_layer.hpp>

+
+Inheritance diagram for caffe::EltwiseLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ EltwiseLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+EltwiseParameter_EltwiseOp op_
 
+vector< Dtype > coeffs_
 
+Blob< int > max_idx_
 
+bool stable_prod_grad_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::EltwiseLayer< Dtype >

+ +

Compute elementwise operations, such as product and sum, along multiple input Blobs.

+

TODO(dox): thorough documentation for Forward, Backward, and proto params.

+

Member Function Documentation

+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::EltwiseLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::EltwiseLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::EltwiseLayer< Dtype >::MinBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::EltwiseLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:40 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1EltwiseLayer.png b/doxygen/classcaffe_1_1EltwiseLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..274b9c3244ff67d1979c90c57e631a76e7c3e9d3 GIT binary patch literal 738 zcmeAS@N?(olHy`uVBq!ia0vp^8-O@~gBeKn>GHn=QW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;v<`7>0jeN zQ&g_Ir%; z{oWq+IsIsFs-n&PS3mN7?w*!Q@Q*h5Fw zJ-eN;m3-vwX4Uer_cY?gYl;r~zJPmvD-@8vV|f3CAVG)ZI)$S+_|iGSFZ zd)tMH<0Ah7pIN*=o~Ii_kZ4W zi(&ePl+?|y4q4ljvRs<{H1gis8SjLdS#A_sS*!&HpKrwGVD6ar;hUw~TQB_B6#Xae z&Y9Wf2kf4PiJQ(jz}J7Hvgq8dHKqD@7+QV&))++lofzdil^$q;|rl + + + + + + +Caffe: Member List + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
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+
+
caffe::EmbedLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::EmbedLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::EmbedLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::EmbedLayer< Dtype >protectedvirtual
bias_multiplier_ (defined in caffe::EmbedLayer< Dtype >)caffe::EmbedLayer< Dtype >protected
bias_term_ (defined in caffe::EmbedLayer< Dtype >)caffe::EmbedLayer< Dtype >protected
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EmbedLayer(const LayerParameter &param) (defined in caffe::EmbedLayer< Dtype >)caffe::EmbedLayer< Dtype >inlineexplicit
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::EmbedLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::EmbedLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::EmbedLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::EmbedLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
K_ (defined in caffe::EmbedLayer< Dtype >)caffe::EmbedLayer< Dtype >protected
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::EmbedLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
M_ (defined in caffe::EmbedLayer< Dtype >)caffe::EmbedLayer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
N_ (defined in caffe::EmbedLayer< Dtype >)caffe::EmbedLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::EmbedLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::EmbedLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:40 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1EmbedLayer.html b/doxygen/classcaffe_1_1EmbedLayer.html new file mode 100644 index 00000000000..22e7c7ee6b1 --- /dev/null +++ b/doxygen/classcaffe_1_1EmbedLayer.html @@ -0,0 +1,419 @@ + + + + + + + +Caffe: caffe::EmbedLayer< Dtype > Class Template Reference + + + + + + + + + +
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+
Caffe +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
+
caffe::EmbedLayer< Dtype > Class Template Reference
+
+
+ +

A layer for learning "embeddings" of one-hot vector input. Equivalent to an InnerProductLayer with one-hot vectors as input, but for efficiency the input is the "hot" index of each column itself. + More...

+ +

#include <embed_layer.hpp>

+
+Inheritance diagram for caffe::EmbedLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ EmbedLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+int M_
 
+int K_
 
+int N_
 
+bool bias_term_
 
+Blob< Dtype > bias_multiplier_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::EmbedLayer< Dtype >

+ +

A layer for learning "embeddings" of one-hot vector input. Equivalent to an InnerProductLayer with one-hot vectors as input, but for efficiency the input is the "hot" index of each column itself.

+

TODO(dox): thorough documentation for Forward, Backward, and proto params.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::EmbedLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::EmbedLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::EmbedLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::EmbedLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:40 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1EmbedLayer.png b/doxygen/classcaffe_1_1EmbedLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..e1f7813dfb761fde936ee6bec96b517c64a5b786 GIT binary patch literal 722 zcmeAS@N?(olHy`uVBq!ia0vp^>w!3cgBeIhbX@HQQW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;Se?8^@fA#&J z1(&dv{z^ORbm-AsW^c=FOMjo*q;uLKY2uPZlS)-hzscc~9(`#rU+SCZH|d?$+WE&< ztIT|D_kNe>_KU{%g3TwD_)p)XDifU&EcZO}`0V*2Qw^#%d^|YW*YMx768xkkXFd-}}=1_4RM;!aHZ> zO#W5%B)5J-%DP+itJmj$WB06D!9Mrv|9_VIPX8A9etb`VVZCg;XV=vyQpG?#tM5L(8>+tNb;|b#pN|yZyL^^`FQoExcizO>^Z$Eu z9m-EPZI#-&bej2keXYbV3AZzD>&!OuW2}D5SC(CE?Cc(N;Z@DQ(6xSlQV;xkWvYGj zO7EV^!>=c2J$-e3?}3MFi=Wh#YVQ7UO{jd2b&zNB*Sw}nE=ShAl!>%|tC?4k>venc z-syeKmR-l)@8zvL-FNzpD=;L>bNU}wN9|AFGynB<=QsSe`_1=;@84!WW5yK|xohtO z>or!ccfa?I{Xopt|JP*Se`RkFu>DZ~c)s$S@0#!WE9D>mv)acHf8_~l$7K+{H8 + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::EuclideanLossLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::EuclideanLossLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::EuclideanLossLayer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::EuclideanLossLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::EuclideanLossLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
diff_ (defined in caffe::EuclideanLossLayer< Dtype >)caffe::EuclideanLossLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
EuclideanLossLayer(const LayerParameter &param) (defined in caffe::EuclideanLossLayer< Dtype >)caffe::EuclideanLossLayer< Dtype >inlineexplicit
ExactNumBottomBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::EuclideanLossLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::EuclideanLossLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::LossLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
LossLayer(const LayerParameter &param) (defined in caffe::LossLayer< Dtype >)caffe::LossLayer< Dtype >inlineexplicit
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::EuclideanLossLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::EuclideanLossLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:40 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1EuclideanLossLayer.html b/doxygen/classcaffe_1_1EuclideanLossLayer.html new file mode 100644 index 00000000000..d8aab6be767 --- /dev/null +++ b/doxygen/classcaffe_1_1EuclideanLossLayer.html @@ -0,0 +1,464 @@ + + + + + + + +Caffe: caffe::EuclideanLossLayer< Dtype > Class Template Reference + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
+
caffe::EuclideanLossLayer< Dtype > Class Template Reference
+
+
+ +

Computes the Euclidean (L2) loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ for real-valued regression tasks. + More...

+ +

#include <euclidean_loss_layer.hpp>

+
+Inheritance diagram for caffe::EuclideanLossLayer< Dtype >:
+
+
+ + +caffe::LossLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ EuclideanLossLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual bool AllowForceBackward (const int bottom_index) const
 
- Public Member Functions inherited from caffe::LossLayer< Dtype >
+ LossLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
+virtual bool AutoTopBlobs () const
 For convenience and backwards compatibility, instruct the Net to automatically allocate a single top Blob for LossLayers, into which they output their singleton loss, (even if the user didn't specify one in the prototxt, etc.).
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Computes the Euclidean (L2) loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ for real-valued regression tasks. More...
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the Euclidean error gradient w.r.t. the inputs. More...
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + +

+Protected Attributes

+Blob< Dtype > diff_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::EuclideanLossLayer< Dtype >

+ +

Computes the Euclidean (L2) loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ for real-valued regression tasks.

+
Parameters
+ + + +
bottominput Blob vector (length 2)
    +
  1. $ (N \times C \times H \times W) $ the predictions $ \hat{y} \in [-\infty, +\infty]$
    2. +
  2. $ (N \times C \times H \times W) $ the targets $ y \in [-\infty, +\infty]$
    3. +
+
topoutput Blob vector (length 1)
    +
  1. $ (1 \times 1 \times 1 \times 1) $ the computed Euclidean loss: $ E = \frac{1}{2n} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $
    2. +
+
+
+
+

This can be used for least-squares regression tasks. An InnerProductLayer input to a EuclideanLossLayer exactly formulates a linear least squares regression problem. With non-zero weight decay the problem becomes one of ridge regression – see src/caffe/test/test_sgd_solver.cpp for a concrete example wherein we check that the gradients computed for a Net with exactly this structure match hand-computed gradient formulas for ridge regression.

+

(Note: Caffe, and SGD in general, is certainly not the best way to solve linear least squares problems! We use it only as an instructive example.)

+

Member Function Documentation

+ +

§ AllowForceBackward()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
virtual bool caffe::EuclideanLossLayer< Dtype >::AllowForceBackward (const int bottom_index) const
+ 		+inlinevirtual
+
+

Unlike most loss layers, in the EuclideanLossLayer we can backpropagate to both inputs – override to return true and always allow force_backward.

+ +

Reimplemented from caffe::LossLayer< Dtype >.

+ +
+
+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::EuclideanLossLayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the Euclidean error gradient w.r.t. the inputs.

+

Unlike other children of LossLayer, EuclideanLossLayer can compute gradients with respect to the label inputs bottom[1] (but still only will if propagate_down[1] is set, due to being produced by learnable parameters or if force_backward is set). In fact, this layer is "commutative" – the result is the same regardless of the order of the two bottoms.

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
    +
  1. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
    2. +
+
propagate_downsee Layer::Backward.
bottominput Blob vector (length 2)
    +
  1. $ (N \times C \times H \times W) $ the predictions $\hat{y}$; Backward fills their diff with gradients $ \frac{\partial E}{\partial \hat{y}} = \frac{1}{n} \sum\limits_{n=1}^N (\hat{y}_n - y_n) $ if propagate_down[0]
    2. +
  2. $ (N \times C \times H \times W) $ the targets $y$; Backward fills their diff with gradients $ \frac{\partial E}{\partial y} = \frac{1}{n} \sum\limits_{n=1}^N (y_n - \hat{y}_n) $ if propagate_down[1]
    3. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::EuclideanLossLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+ +

Computes the Euclidean (L2) loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ for real-valued regression tasks.

+
Parameters
+ + + +
bottominput Blob vector (length 2)
    +
  1. $ (N \times C \times H \times W) $ the predictions $ \hat{y} \in [-\infty, +\infty]$
    2. +
  2. $ (N \times C \times H \times W) $ the targets $ y \in [-\infty, +\infty]$
    3. +
+
topoutput Blob vector (length 1)
    +
  1. $ (1 \times 1 \times 1 \times 1) $ the computed Euclidean loss: $ E = \frac{1}{2n} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $
    2. +
+
+
+
+

This can be used for least-squares regression tasks. An InnerProductLayer input to a EuclideanLossLayer exactly formulates a linear least squares regression problem. With non-zero weight decay the problem becomes one of ridge regression – see src/caffe/test/test_sgd_solver.cpp for a concrete example wherein we check that the gradients computed for a Net with exactly this structure match hand-computed gradient formulas for ridge regression.

+

(Note: Caffe, and SGD in general, is certainly not the best way to solve linear least squares problems! We use it only as an instructive example.)

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::EuclideanLossLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Reimplemented from caffe::LossLayer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:40 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1EuclideanLossLayer.png b/doxygen/classcaffe_1_1EuclideanLossLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..cd4d7167fbc2d5aff4484139680f75c4df93c169 GIT binary patch literal 1122 zcmeAS@N?(olHy`uVBq!ia0vp^w}H5WgBeIhy60&FDTx4|5ZC|z{{xvX-h3_XKQsZz z0^uvfzbFM0$n0K~+hj;n>Tunx` zhpRp}cW%9LeV*)WUncA4yt{7aq+hf!kMy5$`k>pLo=o4j{KwZ;-urhjZOxy`+cW=N ztK3|A$z_S!9MAnxzjEeINtsu*|LV@V)qY+<%dNtGoQwV=oU=Eh@y4!;N=JPs)j5NF zGzH`}|814?o*S}$u(V}hyLup}|M-pq=FPSj+ghb(>b_QSa8Z?Zupu5Fh&DO@*=EHI z)l3i7*(yhaH+crx+)bRl%ypgrB)_Zwlsv6{*YAH9IH_cjbdLUZ_UrGHvaKd8@rzBq zZC2cQW6QSdaqa;+r*GG5-a7PHaoV*<38uBCXUrZwkXTD9v#PVXF(?6~9aR$uBnyR7|d zS?{{`w>jsfbLyt{-AcbMUTD0mbe4Q)^yfXMAEck3)!$L`WzD@W>E~y!ot$<*J8Gti z^)=7WM_a!Bky`yd`s=67r9qSKE$b7l`z-d~B3eI6VO!o7kHX1I_%&~;XwIADJL%EO zlV-p$SjJ$toc+K8sTprSN{HtrY*W9rp+I?a>RgtN6)DUe%ZbMo-ODm(v55*FnBZmB zPyxbO8*RJK#LH@HJ(xE=@h59RK=t=s-?molw_0`Z+bf&J3^!JO`)3keoXDWQD(Chx z9l`2v|8s9Uh@STCK6=fd_UYxchqhC;|9|w{Hs|-HD6ue^=<~{O8=~W;?!L8&27z_v+C0#qQBK9#XYlJa(1Tm{1s`& zWjT32zy6u`JneE;_~UII?{A#@wyu5c)Ra8C-Cpi* zH^1)QAsJj8v9Ajl?6Nm%LS{W*%Xr5kzwY4E#pz#{T|YN_Z9&@q99^C7vsZVXwTfIF z>3gMiu1UO4#QF6bfeHI+?CQx3``7htjoESZzxKAcEe<(%GbTP_```vks(bVHXU + + + + + + +Caffe: Member List + + + + + + + + + +
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Caffe +
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+
caffe::ExpLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::ExpLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ExpLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ExpLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
ExpLayer(const LayerParameter &param)caffe::ExpLayer< Dtype >inlineexplicit
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ExpLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ExpLayer< Dtype >protectedvirtual
inner_scale_ (defined in caffe::ExpLayer< Dtype >)caffe::ExpLayer< Dtype >protected
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ExpLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
NeuronLayer(const LayerParameter &param) (defined in caffe::NeuronLayer< Dtype >)caffe::NeuronLayer< Dtype >inlineexplicit
outer_scale_ (defined in caffe::ExpLayer< Dtype >)caffe::ExpLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::NeuronLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::ExpLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:40 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ExpLayer.html b/doxygen/classcaffe_1_1ExpLayer.html new file mode 100644 index 00000000000..d2bd7294afd --- /dev/null +++ b/doxygen/classcaffe_1_1ExpLayer.html @@ -0,0 +1,451 @@ + + + + + + + +Caffe: caffe::ExpLayer< Dtype > Class Template Reference + + + + + + + + + +
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Caffe +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
+
caffe::ExpLayer< Dtype > Class Template Reference
+
+
+ +

Computes $ y = \gamma ^ {\alpha x + \beta} $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $. + More...

+ +

#include <exp_layer.hpp>

+
+Inheritance diagram for caffe::ExpLayer< Dtype >:
+
+
+ + +caffe::NeuronLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

 ExpLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::NeuronLayer< Dtype >
+ NeuronLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the error gradient w.r.t. the exp inputs. More...
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + +

+Protected Attributes

+Dtype inner_scale_
 
+Dtype outer_scale_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::ExpLayer< Dtype >

+ +

Computes $ y = \gamma ^ {\alpha x + \beta} $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $.

+

Constructor & Destructor Documentation

+ +

§ ExpLayer()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
caffe::ExpLayer< Dtype >::ExpLayer (const LayerParameter & param)
+ 		+inlineexplicit
+
+
Parameters
+ + +
paramprovides ExpParameter exp_param, with ExpLayer options:
    +
  • scale (optional, default 1) the scale $ \alpha $
    • +
  • shift (optional, default 0) the shift $ \beta $
    • +
  • base (optional, default -1 for a value of $ e \approx 2.718 $) the base $ \gamma $
    • +
+
+
+
+ +
+
+

Member Function Documentation

+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::ExpLayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the error gradient w.r.t. the exp inputs.

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
    +
  1. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
    2. +
+
propagate_downsee Layer::Backward.
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} y \alpha \log_e(gamma) $ if propagate_down[0]
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ExpLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+
Parameters
+ + + +
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $
    2. +
+
topoutput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the computed outputs $ y = \gamma ^ {\alpha x + \beta} $
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ExpLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/layers/exp_layer.hpp
    • +
  • src/caffe/layers/exp_layer.cpp
    • +
+
+ +
+Generated on Mon Nov 21 2016 11:23:40 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ExpLayer.png b/doxygen/classcaffe_1_1ExpLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..3fee55966c4bd03260819d2d18060a7d6d385e25 GIT binary patch literal 1064 zcmeAS@N?(olHy`uVBq!ia0vp^n}N84gBeK9F4tNQq$C1-LR|m<{|{uoc=NTi|Ih>= z3ycpOIKbL@M;^%KC<*clW&kPzfvcxNj2IZ0w|lxchEy=VoqKW9D+M0c@bdIO|NZx6 zWpavaxsxn2^W@CD{tE(Po*nDgyx!%f7Unt2>!6P3Kdv;-PuB%LZ_3enrt)&twBz?b zXn7trKJ@3ti!C=Lim&&o#9oztW~&q3GTp>)Gi!0x=4}3S&h5Qs<|n@x8d!T?vfi6! z_vco9+6=Yd(>Om_-1wL2(;b+p=gGS_{an2EGjX-ntD2gs#XBEyeKmdd_SK9d>C4rH zvjgwn?7FFT_E(K_ZRbwWz1uujJy;}WRcN+s=Mg)R=XtjK6V1X>ZNrz%UzdK{+=r$3 zk+0;Fu!P@jDxaVHn^Rl6E8pYN0yeAn|Nrf(nN=zBlYK_}#CpYZlb)@Dt z>vMb7y|$XpP*cJ@!$;WYe%@zC{}VOt^Z2UVp1qJ}xF2A^{6l*N;~&v848R~@sAn~9 z*xw|1ApRiFgY^k)AHq?j73^Q{S~s~fbkedQ1Mf-un<6Luxtbg|&G+b>pHr6XTX$o| z+IRKJtF6xTKUEjMZrg8IZa!s6o6+aG8@h+QrIz1(QuBSzJy|dBdGT-GO}$(9LMYGw ze(~kpId?>Mo6q3S{(YXu+&@>@xKv{QhnWif&2Q)Xn*aQf`|Qno#U!^ z{dMll)2^;xNA|>WOg{JK?ls56E2(yG)~x?k7Jla1?7OzX*Uzk-F=yuK?pvvPTz2wL zT<5%Z`rO|w(`V1rl20-FcdBZg&-$yI-%6|LS%uHs=GgHG`+CpUXO@geCyZstch2 literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1Filler-members.html b/doxygen/classcaffe_1_1Filler-members.html new file mode 100644 index 00000000000..28f2d1e011f --- /dev/null +++ b/doxygen/classcaffe_1_1Filler-members.html @@ -0,0 +1,84 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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This is the complete list of members for caffe::Filler< Dtype >, including all inherited members.

+ + + + + +
Fill(Blob< Dtype > *blob)=0 (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >pure virtual
Filler(const FillerParameter &param) (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >inlineexplicit
filler_param_ (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >protected
~Filler() (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >inlinevirtual
+ +
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+ + diff --git a/doxygen/classcaffe_1_1Filler.html b/doxygen/classcaffe_1_1Filler.html new file mode 100644 index 00000000000..1544e87c2ec --- /dev/null +++ b/doxygen/classcaffe_1_1Filler.html @@ -0,0 +1,125 @@ + + + + + + + +Caffe: caffe::Filler< Dtype > Class Template Reference + + + + + + + + + +
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caffe::Filler< Dtype > Class Template Referenceabstract
+
+
+ +

Fills a Blob with constant or randomly-generated data. + More...

+ +

#include <filler.hpp>

+
+Inheritance diagram for caffe::Filler< Dtype >:
+
+
+ + +caffe::BilinearFiller< Dtype > +caffe::ConstantFiller< Dtype > +caffe::GaussianFiller< Dtype > +caffe::MSRAFiller< Dtype > +caffe::PositiveUnitballFiller< Dtype > +caffe::UniformFiller< Dtype > +caffe::XavierFiller< Dtype > + +
+ + + + + + +

+Public Member Functions

+ Filler (const FillerParameter &param)
 
+virtual void Fill (Blob< Dtype > *blob)=0
 
+ + + +

+Protected Attributes

+FillerParameter filler_param_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::Filler< Dtype >

+ +

Fills a Blob with constant or randomly-generated data.

+
The documentation for this class was generated from the following file: +
+ +
+Generated on Mon Nov 21 2016 11:23:41 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1Filler.png b/doxygen/classcaffe_1_1Filler.png new file mode 100644 index 0000000000000000000000000000000000000000..249a5acc01c1b0b6aedf56ff191a524a01270fee GIT binary patch literal 2433 zcmaJ@do)}57G}6=v`VK{LdXotZIvOOjc0q2X4GS;7^5Cj!jxMf;#oysqf{yl^(e*k zcBbk?qol+mlD68QZs=``wjL>3uLvR`Lqz5v)%(xAXRWh;zi*#?_TJ~)=X~qr_;?{z zRkT&)rrO5M!x$$ENb+=JslE%EW)A(!6ZkD2s(j~jJ z7bYj5KlLAbFp~M5%LQ5_ZII`*S6TgAmkV;qi*@DJBzgI-6qOg0b9QYxEx&0^T-4M@T9lDJuZsC2(Uc#)h3 z$)a4e{&G9ZFbS9aD_#qFG7RzYyfx$%BW^#$v;M?HtBKX;#^Pd4oEnz-%$?P*BA=|N zd5U5U#&d1p<{^U$1~Oi>lHFJ(8ZnsLRe=OTb#K4m)R z1!vIhm&GW|?aHHR>BCN*HcFj#jzgaM3LwMkANGvgr<&bU8FJgkRa8Cyd>MN?NA6Me zG`EpcBh_e&f4%RvT2cSmee1?=9}~7XaJc~4o?24t@SNqvjX5a4R7l$zr98WEE4i|H z1k*cGHpCmBeV(YeJEy5i86wFKe}j~vmIH%3Bzw1zA^f*fW6Cl_ z!-#*&lp$B81P|yP87evp+h}bA6?9&}ZnUzu(12Us_T0m8MI>o=sss$4KPzFy&B$F3jim-cwF7lXxi_tV3=_p8ZFt|0RZbL zj2}AD1Nzxl(0L!2DY|(Ejg~&E1AxrUo6GdWY4+xAqNRDUAOVdw6<7)#=8ErB9m%Yu z05i68Zy5)kw8sO${wAn6kj>63Q>{&9crDI_Hw94GlhyG^aeGHDH=%Gm7Fi3hxG(? zw6zx;#-xeyazyDVU7C(R+H~PqS1WNe{>^pdqu@1u$68s6TR8t^df)!k>x;) zgXcOWJ?LOYqG~J$kA-%ZkyF#_MzRSf&2@bjxW^@H;OF!c3UA#@fK>_Xs@W4nkCjyc z>$O6`Gyq`hqQ(9yHsKwGR>X2_rT^L?wnm;vtD-0!TR(2lp~3KpZ?^~%<%*e@3i`6=<@i1C;sM+G1 z=!}-pwB_3kMUxa}!eJ>;O}R$s$~VjFOFmW(kFzi_N^5oy?)yXYVpGrxyD3!C@6IxP z!fhSWwh8^HINF3q*eiL;4 z-tjVHu&&@n6?T@@88Cpyy0bQGR_UHSH$|tdqw2uNOr_Gb>dDcyk+nsO_$cRErLjNV z8obxY&q7U_%%`Hmn7;T#%#aP;r}@*$p)NKF*S@q55;G1N5R9qPH6Z^ygukO-wX>x^ zqWA|<>f)e&ctr0zJxJyVzCW;^63zNFD!v@nrVA;4fYBG7!UUy89-KDHb!5ML?^ziz z-e?2IDx2*)!;6l8kWDa-0%@?$>aeXWqLW+bST`2lkmkm^79XSGk7NmEgy%3dN6zhZ ztj7i3)J3wBm#Y_b^uyD8n@!;Bjgx2Xi+IDgM4?uFWhB0JyG0FyV__zloU5KSVp@}< zVZn{|#A;v}!A@1T+YPBA1A$KfUaM(JK+ngy6|vL#X&!aQU~v>BE+{G`Wa{r0r-t4L z67#tR;hxQ2Ne_cxe^25MA=<{8dLj@BftQhY1P;`URm*vyu6JQ*E73${+4o-h!a!%r z10C8a3vn6joJo+qU zo65MUgJ0w=2TX-U<@lqQytj2wpaE$&-vvWa(>E9B)zey_Xo7o*ATw@F&FT-NI*GNm Q@oOcAa`SSf9Q*mwU*pT1cK`qY literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1FilterLayer-members.html b/doxygen/classcaffe_1_1FilterLayer-members.html new file mode 100644 index 00000000000..c79fd100c92 --- /dev/null +++ b/doxygen/classcaffe_1_1FilterLayer-members.html @@ -0,0 +1,121 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::FilterLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::FilterLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::FilterLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::FilterLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
FilterLayer(const LayerParameter &param) (defined in caffe::FilterLayer< Dtype >)caffe::FilterLayer< Dtype >inlineexplicit
first_reshape_ (defined in caffe::FilterLayer< Dtype >)caffe::FilterLayer< Dtype >protected
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::FilterLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::FilterLayer< Dtype >protectedvirtual
indices_to_forward_ (defined in caffe::FilterLayer< Dtype >)caffe::FilterLayer< Dtype >protected
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::FilterLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::FilterLayer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::FilterLayer< Dtype >inlinevirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::FilterLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::FilterLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:41 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1FilterLayer.html b/doxygen/classcaffe_1_1FilterLayer.html new file mode 100644 index 00000000000..7dcb9d11569 --- /dev/null +++ b/doxygen/classcaffe_1_1FilterLayer.html @@ -0,0 +1,518 @@ + + + + + + + +Caffe: caffe::FilterLayer< Dtype > Class Template Reference + + + + + + + + + +
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caffe::FilterLayer< Dtype > Class Template Reference
+
+
+ +

Takes two+ Blobs, interprets last Blob as a selector and filter remaining Blobs accordingly with selector data (0 means that the corresponding item has to be filtered, non-zero means that corresponding item needs to stay). + More...

+ +

#include <filter_layer.hpp>

+
+Inheritance diagram for caffe::FilterLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ FilterLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the error gradient w.r.t. the forwarded inputs. More...
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + +

+Protected Attributes

+bool first_reshape_
 
+vector< int > indices_to_forward_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::FilterLayer< Dtype >

+ +

Takes two+ Blobs, interprets last Blob as a selector and filter remaining Blobs accordingly with selector data (0 means that the corresponding item has to be filtered, non-zero means that corresponding item needs to stay).

+

Member Function Documentation

+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::FilterLayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the error gradient w.r.t. the forwarded inputs.

+
Parameters
+ + + + +
topoutput Blob vector (length 1+), providing the error gradient with respect to the outputs
propagate_downsee Layer::Backward.
bottominput Blob vector (length 2+), into which the top error gradient is copied
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::FilterLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+
Parameters
+ + + +
bottominput Blob vector (length 2+)
    +
  1. $ (N \times C \times H \times W) $ the inputs to be filtered $ x_1 $
    2. +
  2. ...
    3. +
  3. $ (N \times C \times H \times W) $ the inputs to be filtered $ x_K $
    4. +
  4. $ (N \times 1 \times 1 \times 1) $ the selector blob
    5. +
+
topoutput Blob vector (length 1+)
    +
  1. $ (S \times C \times H \times W) $ () the filtered output $ x_1 $ where S is the number of items that haven't been filtered $ (S \times C \times H \times W) $ the filtered output $ x_K $ where S is the number of items that haven't been filtered
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::FilterLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::FilterLayer< Dtype >::MinBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::FilterLayer< Dtype >::MinTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::FilterLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:41 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1FilterLayer.png b/doxygen/classcaffe_1_1FilterLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..3980de2064a64f9deede23e54b2b557359f844f1 GIT binary patch literal 707 zcmeAS@N?(olHy`uVBq!ia0vp^%YZn5gBeIJnfuQXNJ#|vgt-3y{~ySF@#br3|Dg#$ z78oBmaDcV*jy#adQ4-`A%m7pb0#{Fk7%?y~1$nwShEy=Votrp$v4Vh$eD2kM|L4~j zE;{32P-Gr$SekRQ@L*caIfI(C$Z1diB~4tiJ3GlMXo}i071x(iXFGwvEp`Vwb*=I`U}Hp^_zZRRt!zqwWY?B|@tx<1?1F@IL`e6_l{vSpLG z@tMiB>Cde8rT#Y!GyhcPS!8uk=YQA?b+PAJLGKO!b`@VRpK<$~xu|ZTRCnnW-SEU) zmu}36y-<86+_Eb7sCL1zxn3f+4%;&r{AAFCAGmkclm6asa>;vO?$R= zuHMO*r+0KVGFin=VlypJ>XZMHIAigY`3Gf|{RuErefizQP*u}QIC#>cF9%|#rS9Cz zu;?puL47Uzv-9V}3+mXPHGh(}*w0d2X5}zPP0e8nvG`x|nJGcL_IogtUq8aqaS4cn z_&;Ba_TQR*XqQ^NoxlOtrw-S{OAW4{eAYPgNBEg{)(mkg65ie4{`8~af0^fz?w?X@ zYAieWi&se8%D>f=^4iKkqG4|zL;LM}XD&QB@0(+A{IK7QGpR~9EY8p7fB8mq+4>bz zZwKn8x7PdfEkE`%q36zi zT9iGPPhe%^264V$_YcV|{loLj;A#6NPJtDjoB~Th_-lx-=F;7-`Gu}1C!0PIum+|$ N22WQ%mvv4FO#sSWPMrV% literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1FlattenLayer-members.html b/doxygen/classcaffe_1_1FlattenLayer-members.html new file mode 100644 index 00000000000..b25108f95d9 --- /dev/null +++ b/doxygen/classcaffe_1_1FlattenLayer-members.html @@ -0,0 +1,119 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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+
caffe::FlattenLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::FlattenLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::FlattenLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::FlattenLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::FlattenLayer< Dtype >inlinevirtual
FlattenLayer(const LayerParameter &param) (defined in caffe::FlattenLayer< Dtype >)caffe::FlattenLayer< Dtype >inlineexplicit
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::FlattenLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlinevirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::FlattenLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::FlattenLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:41 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1FlattenLayer.html b/doxygen/classcaffe_1_1FlattenLayer.html new file mode 100644 index 00000000000..122c74d827e --- /dev/null +++ b/doxygen/classcaffe_1_1FlattenLayer.html @@ -0,0 +1,459 @@ + + + + + + + +Caffe: caffe::FlattenLayer< Dtype > Class Template Reference + + + + + + + + + +
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caffe::FlattenLayer< Dtype > Class Template Reference
+
+
+ +

Reshapes the input Blob into flat vectors. + More...

+ +

#include <flatten_layer.hpp>

+
+Inheritance diagram for caffe::FlattenLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ FlattenLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the error gradient w.r.t. the concatenate inputs. More...
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::FlattenLayer< Dtype >

+ +

Reshapes the input Blob into flat vectors.

+

Note: because this layer does not change the input values – merely the dimensions – it can simply copy the input. The copy happens "virtually" (thus taking effectively 0 real time) by setting, in Forward, the data pointer of the top Blob to that of the bottom Blob (see Blob::ShareData), and in Backward, the diff pointer of the bottom Blob to that of the top Blob (see Blob::ShareDiff).

+

Member Function Documentation

+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::FlattenLayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the error gradient w.r.t. the concatenate inputs.

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
propagate_downsee Layer::Backward.
bottominput Blob vector (length K), into which the top error gradient is (virtually) copied
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::FlattenLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::FlattenLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::FlattenLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+
Parameters
+ + + +
bottominput Blob vector (length 2+)
    +
  1. $ (N \times C \times H \times W) $ the inputs
    2. +
+
topoutput Blob vector (length 1)
    +
  1. $ (N \times CHW \times 1 \times 1) $ the outputs – i.e., the (virtually) copied, flattened inputs
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::FlattenLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:41 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1FlattenLayer.png b/doxygen/classcaffe_1_1FlattenLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..e0e28aecb4076db9d2ff9b95cdc22aee0bb0eab0 GIT binary patch literal 721 zcmeAS@N?(olHy`uVBq!ia0vp^>w!3cgBeIhbX@HQQW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;0IvizjBXW^>{zL}G6PhPUy>-Fm2 zQ&c|hJHMN4CDYfBW_LX<*~Wf)_wt6(>`zwjt?vDqA6qSDdt=+JJ1Lh-6O}wKZCzJk zZ}+x6^4ZyKsce62uKc?`W7kyk=}WxsSDe1i{Jynw^Qx|gm47?mA1SZco}JVcD-))6 z?NNn2|DJ6T*Pg3>k4M)$*s8j9qHikSp7fs<4{cuy zpO3cLaVicNiecY>*nmRuT=diJ&$h08@^kOYz5lj;KKNwGl#TQF-_Oo@-mJZ3=ch6| zhG((Q@;fW??HF2C9RA-M-&b%i?vDKDvd90d_A~Iq18MF#&7~$i{}?4IRYUBqy?6yo Oe+-_kelF{r5}E+1S9uWt literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1GPUParams-members.html b/doxygen/classcaffe_1_1GPUParams-members.html new file mode 100644 index 00000000000..aad49b1c853 --- /dev/null +++ b/doxygen/classcaffe_1_1GPUParams-members.html @@ -0,0 +1,92 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::GPUParams< Dtype > Member List
+
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+ +

This is the complete list of members for caffe::GPUParams< Dtype >, including all inherited members.

+ + + + + + + + + + + + + +
configure(Solver< Dtype > *solver) const (defined in caffe::GPUParams< Dtype >)caffe::GPUParams< Dtype >
data() const (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inline
data_ (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
diff() const (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inline
diff_ (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
DISABLE_COPY_AND_ASSIGN(Params) (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
GPUParams(shared_ptr< Solver< Dtype > > root_solver, int device) (defined in caffe::GPUParams< Dtype >)caffe::GPUParams< Dtype >
Params(shared_ptr< Solver< Dtype > > root_solver) (defined in caffe::Params< Dtype >)caffe::Params< Dtype >explicit
size() const (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inline
size_ (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
~GPUParams() (defined in caffe::GPUParams< Dtype >)caffe::GPUParams< Dtype >virtual
~Params() (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:41 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1GPUParams.html b/doxygen/classcaffe_1_1GPUParams.html new file mode 100644 index 00000000000..7ce2d11c57c --- /dev/null +++ b/doxygen/classcaffe_1_1GPUParams.html @@ -0,0 +1,134 @@ + + + + + + + +Caffe: caffe::GPUParams< Dtype > Class Template Reference + + + + + + + + + +
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caffe::GPUParams< Dtype > Class Template Reference
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+Inheritance diagram for caffe::GPUParams< Dtype >:
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+Public Member Functions

+ GPUParams (shared_ptr< Solver< Dtype > > root_solver, int device)
 
+void configure (Solver< Dtype > *solver) const
 
- Public Member Functions inherited from caffe::Params< Dtype >
+ Params (shared_ptr< Solver< Dtype > > root_solver)
 
+size_t size () const
 
+Dtype * data () const
 
+Dtype * diff () const
 
+ + + + + + + + + + + +

+Additional Inherited Members

- Protected Member Functions inherited from caffe::Params< Dtype >
+ DISABLE_COPY_AND_ASSIGN (Params)
 
- Protected Attributes inherited from caffe::Params< Dtype >
+const size_t size_
 
+Dtype * data_
 
+Dtype * diff_
 
+
The documentation for this class was generated from the following files: +
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+Generated on Mon Nov 21 2016 11:23:41 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1GPUParams.png b/doxygen/classcaffe_1_1GPUParams.png new file mode 100644 index 0000000000000000000000000000000000000000..3f8a811f79b50c55947bff4f1256dda3653218a1 GIT binary patch literal 1066 zcmeAS@N?(olHy`uVBq!ia0vp^tAV(KgBeJ!=X<>wNJ#|vgt-3y{~ySF@#br3|Dg#$ z78oBmaDcV*jy#adQ4-`A%m7pb0#{Fk7%?y~@APzW45?szJNMzFPYOJ){L>}x|DR~T zRhK1WYw70sr+sAJZOicBQ%w0ksrcOF4js>6jR@6~@dvd$|5+WLb!pMJ-M`%@>8)I| zYu^`{*N5Lf_`SI!b*l2~ZNF6)XU@Itb8SWRqOi+vORWVwOLaaUz2Eg`#*7J*N|wZ4 zdvwh1%$5x^8nh{#~~g?>`Zx9W}}CMfI9z zOx~9`Yd5p0yzFWUo%Ba^&7>#mt+UTro>ye3zssO@(Pho{+cq17H{J_e-Z;0@^cTZ{ ze2oZ3pgM+sTwx6LtlACxn?w)9ALM$lK7sW^_y(pQ+9=YROHDdG7=XbC(PNdgH-6W0 z2IG0t3v*@9A3Z%km#-i$_3F)~>}y(CRxI8iYr}YM^_pGhcI6hGeDU|UZtL35;b)E> zpY`U`uDK!s$Bp8d)}H9^Ok{kcvF7M4yE7%+KlS!=7sdYM$-ge%(|ffoH*Ia@@u;~a zYtpQS&4SwJdeQI!#{nF;Iw?q!6uVMch<=gwY{A<>4hr1~|&z6>zJp23p=){$b z3xHwj`*!{-W`_G$I_3U`M`XJ)zj@uip|4kX!R7fY85_)DAtZO#eCd>B0uB4~7$#>Z zUAvttccU}n{ff&6&K;4eWq4pNLTqRP1AFS6;AVnX?Z}nwx6h+n%+jF5f)4_N`?9)bayS@z&SY-3vPtwfwYT zkLabgd&#+nWiQBW)MHTHw%7cw_v4Lc{S$K!m94q9WA{ASw~Vj-czbT&oT+z=`E_F4 z_g?)cUpK_rr`%nAJ!{j{Utg=X)-GVXyUTI9v2!Jn2ht?5o-0 z|Fr)wy)M17eq&w3uf2P`F0F^>yIq@~&G{_zi~S9w>k*k(%<905&EV + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::GaussianFiller< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::GaussianFiller< Dtype >, including all inherited members.

+ + + + + + + +
Fill(Blob< Dtype > *blob) (defined in caffe::GaussianFiller< Dtype >)caffe::GaussianFiller< Dtype >inlinevirtual
Filler(const FillerParameter &param) (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >inlineexplicit
filler_param_ (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >protected
GaussianFiller(const FillerParameter &param) (defined in caffe::GaussianFiller< Dtype >)caffe::GaussianFiller< Dtype >inlineexplicit
rand_vec_ (defined in caffe::GaussianFiller< Dtype >)caffe::GaussianFiller< Dtype >protected
~Filler() (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >inlinevirtual
+ +
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+ + diff --git a/doxygen/classcaffe_1_1GaussianFiller.html b/doxygen/classcaffe_1_1GaussianFiller.html new file mode 100644 index 00000000000..2b029ee10cc --- /dev/null +++ b/doxygen/classcaffe_1_1GaussianFiller.html @@ -0,0 +1,127 @@ + + + + + + + +Caffe: caffe::GaussianFiller< Dtype > Class Template Reference + + + + + + + + + +
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caffe::GaussianFiller< Dtype > Class Template Reference
+
+
+ +

Fills a Blob with Gaussian-distributed values $ x = a $. + More...

+ +

#include <filler.hpp>

+
+Inheritance diagram for caffe::GaussianFiller< Dtype >:
+
+
+ + +caffe::Filler< Dtype > + +
+ + + + + + + + + +

+Public Member Functions

+ GaussianFiller (const FillerParameter &param)
 
+virtual void Fill (Blob< Dtype > *blob)
 
- Public Member Functions inherited from caffe::Filler< Dtype >
+ Filler (const FillerParameter &param)
 
+ + + + + + +

+Protected Attributes

+shared_ptr< SyncedMemory > rand_vec_
 
- Protected Attributes inherited from caffe::Filler< Dtype >
+FillerParameter filler_param_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::GaussianFiller< Dtype >

+ +

Fills a Blob with Gaussian-distributed values $ x = a $.

+
The documentation for this class was generated from the following file: +
+ +
+Generated on Mon Nov 21 2016 11:23:41 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1GaussianFiller.png b/doxygen/classcaffe_1_1GaussianFiller.png new file mode 100644 index 0000000000000000000000000000000000000000..1ec8d42ef4ada6fd6a52609a49c3014cd929c7d6 GIT binary patch literal 760 zcmeAS@N?(olHy`uVBq!ia0vp^JApWWgBeH`S*WN0DTx4|5ZC|z{{xvX-h3_XKQsZz z0^CwgBx7)QUksIp5B&wdrH#eC7fGNsAjGa+o{rfwe*uf+0T8)pT{j%^0Yl# zssG&L5@)Phea_9CY^!?{my}vN}n8kesil_Vg6gSM~rg3o4Zx! zUgG?;r+KZ<<%*l7Z`e-=K0C42asMIT=}VO7s(fFV@bu7j)%6ZlPwu_T{Koy{r{J;z zjc3vGXD^QE=(-a(Z)+UDdo_b#jtu>8OdAT3q zwYfF7x8=Gq*7%q=XqxK&d~2rtYs-`J51tpFtH$qSP1qu4c_2c!s9|H&6NcoiCm4@q zO=Rx5s>Cj_+Lg~BtXn2w4Vr?eR)IrPlo)(gz~uW*y!ZdEd0@)($!r!udsG`67y}h;X>$k0)e0F`Uj;7w3vwy?iY1}xL^mfzU zt@+a8Zo+*Fo#&a+uKf!BmA}1u z-~6sUjF~5Gy^r3T@SJ_BL-$vvaQ{_{m)cEJHFw|W7%=fR_xevxyViE?5!Q`;d{1({ z>@|g{;y1rnE?MQX=hM%`IlBuA8RZt}zCY0$D*rt7Le5vcbGdn%?s}0&c%E5ppLHkc z)8WKCMjy?ed*0OQZD;t?=l7@hkJ?V}5C+-5DwVTWFlznH*ut>qA}qA}bL5>kgkQg^=sWeU@~R!boFyt=akR{06*AgsQ>@~ literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1HDF5DataLayer-members.html b/doxygen/classcaffe_1_1HDF5DataLayer-members.html new file mode 100644 index 00000000000..2c6b13af16c --- /dev/null +++ b/doxygen/classcaffe_1_1HDF5DataLayer-members.html @@ -0,0 +1,128 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::HDF5DataLayer< Dtype > Member List
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+ +

This is the complete list of members for caffe::HDF5DataLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::HDF5DataLayer< Dtype >inlineprotectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::HDF5DataLayer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
current_file_ (defined in caffe::HDF5DataLayer< Dtype >)caffe::HDF5DataLayer< Dtype >protected
current_row_ (defined in caffe::HDF5DataLayer< Dtype >)caffe::HDF5DataLayer< Dtype >protected
data_permutation_ (defined in caffe::HDF5DataLayer< Dtype >)caffe::HDF5DataLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::HDF5DataLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
file_permutation_ (defined in caffe::HDF5DataLayer< Dtype >)caffe::HDF5DataLayer< Dtype >protected
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::HDF5DataLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::HDF5DataLayer< Dtype >protectedvirtual
HDF5DataLayer(const LayerParameter &param) (defined in caffe::HDF5DataLayer< Dtype >)caffe::HDF5DataLayer< Dtype >inlineexplicit
hdf_blobs_ (defined in caffe::HDF5DataLayer< Dtype >)caffe::HDF5DataLayer< Dtype >protected
hdf_filenames_ (defined in caffe::HDF5DataLayer< Dtype >)caffe::HDF5DataLayer< Dtype >protected
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::HDF5DataLayer< Dtype >virtual
LoadHDF5FileData(const char *filename) (defined in caffe::HDF5DataLayer< Dtype >)caffe::HDF5DataLayer< Dtype >protectedvirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::HDF5DataLayer< Dtype >inlinevirtual
num_files_ (defined in caffe::HDF5DataLayer< Dtype >)caffe::HDF5DataLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::HDF5DataLayer< Dtype >inlinevirtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::HDF5DataLayer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::HDF5DataLayer< Dtype >inlinevirtual
~HDF5DataLayer() (defined in caffe::HDF5DataLayer< Dtype >)caffe::HDF5DataLayer< Dtype >virtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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+ + diff --git a/doxygen/classcaffe_1_1HDF5DataLayer.html b/doxygen/classcaffe_1_1HDF5DataLayer.html new file mode 100644 index 00000000000..da740a21ceb --- /dev/null +++ b/doxygen/classcaffe_1_1HDF5DataLayer.html @@ -0,0 +1,428 @@ + + + + + + + +Caffe: caffe::HDF5DataLayer< Dtype > Class Template Reference + + + + + + + + + +
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caffe::HDF5DataLayer< Dtype > Class Template Reference
+
+
+ +

Provides data to the Net from HDF5 files. + More...

+ +

#include <hdf5_data_layer.hpp>

+
+Inheritance diagram for caffe::HDF5DataLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ HDF5DataLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
+virtual void LoadHDF5FileData (const char *filename)
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+std::vector< std::string > hdf_filenames_
 
+unsigned int num_files_
 
+unsigned int current_file_
 
+hsize_t current_row_
 
+std::vector< shared_ptr< Blob< Dtype > > > hdf_blobs_
 
+std::vector< unsigned int > data_permutation_
 
+std::vector< unsigned int > file_permutation_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::HDF5DataLayer< Dtype >

+ +

Provides data to the Net from HDF5 files.

+

TODO(dox): thorough documentation for Forward and proto params.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::HDF5DataLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::HDF5DataLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::HDF5DataLayer< Dtype >::MinTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
virtual void caffe::HDF5DataLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+inlinevirtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:41 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1HDF5DataLayer.png b/doxygen/classcaffe_1_1HDF5DataLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..864cf98852e0cea03fa125b600ce83801b12fd8a GIT binary patch literal 760 zcmeAS@N?(olHy`uVBq!ia0vp^M}RnhgBeH`_G|F~DTx4|5ZC|z{{xvX-h3_XKQsZz z0^3GAD>?H-#u>c zQq_g72V>taArFbaJ-Sc)vnsGP2t?P?{`$v8@cBn^OU$OG~=N=dA@MH5x^?*tJMQjlcqFaxj_3OFA6y-ga~YB{oETg#v@mom;A9kV6lPLr zQe$xdYHi?{CBOg-6$T|V1rN3cdA)q4#26C|Q@mB-o#cPz{MqxfU2kaLzPIuFZpMG& z2e?JQGstE+*jVw`i*(#l+5dXx{nm_i3=dpb7v;SB6>+;P+t%ghL&a+^&GlzXo1a_< z3QlwL+pHgqCq~LPTz;3ZeVhJen{{`cZY;IZKNep-$>v;sr~LbspRHnc+t}EVwl8J8Ld2 cN&L$wbw%^s-1?oaz+}qc>FVdQ&MBb@0K&pgdjJ3c literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1HDF5OutputLayer-members.html b/doxygen/classcaffe_1_1HDF5OutputLayer-members.html new file mode 100644 index 00000000000..e374759c794 --- /dev/null +++ b/doxygen/classcaffe_1_1HDF5OutputLayer-members.html @@ -0,0 +1,127 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::HDF5OutputLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::HDF5OutputLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::HDF5OutputLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::HDF5OutputLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
data_blob_ (defined in caffe::HDF5OutputLayer< Dtype >)caffe::HDF5OutputLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::HDF5OutputLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::HDF5OutputLayer< Dtype >inlinevirtual
file_id_ (defined in caffe::HDF5OutputLayer< Dtype >)caffe::HDF5OutputLayer< Dtype >protected
file_name() const (defined in caffe::HDF5OutputLayer< Dtype >)caffe::HDF5OutputLayer< Dtype >inline
file_name_ (defined in caffe::HDF5OutputLayer< Dtype >)caffe::HDF5OutputLayer< Dtype >protected
file_opened_ (defined in caffe::HDF5OutputLayer< Dtype >)caffe::HDF5OutputLayer< Dtype >protected
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::HDF5OutputLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::HDF5OutputLayer< Dtype >protectedvirtual
HDF5OutputLayer(const LayerParameter &param) (defined in caffe::HDF5OutputLayer< Dtype >)caffe::HDF5OutputLayer< Dtype >inlineexplicit
IsShared() constcaffe::Layer< Dtype >inline
label_blob_ (defined in caffe::HDF5OutputLayer< Dtype >)caffe::HDF5OutputLayer< Dtype >protected
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::HDF5OutputLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::HDF5OutputLayer< Dtype >inlinevirtual
SaveBlobs() (defined in caffe::HDF5OutputLayer< Dtype >)caffe::HDF5OutputLayer< Dtype >protectedvirtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::HDF5OutputLayer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::HDF5OutputLayer< Dtype >inlinevirtual
~HDF5OutputLayer() (defined in caffe::HDF5OutputLayer< Dtype >)caffe::HDF5OutputLayer< Dtype >virtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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caffe::HDF5OutputLayer< Dtype > Class Template Reference
+
+
+ +

Write blobs to disk as HDF5 files. + More...

+ +

#include <hdf5_output_layer.hpp>

+
+Inheritance diagram for caffe::HDF5OutputLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ HDF5OutputLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
+std::string file_name () const
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
+virtual void SaveBlobs ()
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+bool file_opened_
 
+std::string file_name_
 
+hid_t file_id_
 
+Blob< Dtype > data_blob_
 
+Blob< Dtype > label_blob_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::HDF5OutputLayer< Dtype >

+ +

Write blobs to disk as HDF5 files.

+

TODO(dox): thorough documentation for Forward and proto params.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::HDF5OutputLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::HDF5OutputLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::HDF5OutputLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
virtual void caffe::HDF5OutputLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+inlinevirtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:41 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1HDF5OutputLayer.png b/doxygen/classcaffe_1_1HDF5OutputLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..4e12033def8be6b6f997b66fc24b3ace90d18d12 GIT binary patch literal 774 zcmeAS@N?(olHy`uVBq!ia0vp^=YcqYgBeJ6=3o2`q$C1-LR|m<{|{uoc=NTi|Ih>= z3ycpOIKbL@M;^%KC<*clW&kPzfvcxNj2IZ0rh2+KhEy=VoqMt9u>y}P|8?WP|NZZ& zBzQMu_U!hRY?~Xid&8y+jGlWo<=!@%cta&qN>|QPD!ki$(y^=j6;2(eo~gWCb?9^a z_wY$quGP*<7YJ3TwedBdRN^0N^SkWWWX{YvX~y%u*zf+9e1f@u@e8$?cIv?mvnW3dKEdBqv+Fz8N7jaB)X3oJ= zN1~_vUHsr<``g+w!MhJEZ+|VG9AX$*XL|1V%&1#+4{Bz8{oHmu^6A&$8FicLYU+!v z^G`krIvq9beg337=ikYH{(ADCn&;eC9k2f1S+oE2`>391Wls9|c#)&d}H_lkG?}YlUZ|7y0V*;9=TULto zaG#u=lK=hg`N~-b8Rnd*$Z3D9-1oQcxsBbnFWbU-6yw-0_Rs$CTJ~1IB7aSM=Kl{* zZ?SSscv(;o@q4=THHmr0o3!ul*|xmk%h{@vrTYSRb^`-e+@><9BGO2`+*L8PBJ@YP z&D4c!uX=QTmeuIclIylu+E|%C|GezHWX1h{H(R;n<{PEHwhY>=)mXHD-Bo|}e(hDO z7Mgfn^I5&EsLt-}ubr3f>%@Mt*?zY6@MG-_Su@UkiTeBF-?MLLEBHHb z9SLtU=HCbg2Y2c3T!-l2$*OYJYD@<);T3K0RX97e!l + + + + + + +Caffe: Member List + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
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+ +
+ + +
+
+
+
caffe::HingeLossLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::HingeLossLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::LossLayer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::HingeLossLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::HingeLossLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
HingeLossLayer(const LayerParameter &param) (defined in caffe::HingeLossLayer< Dtype >)caffe::HingeLossLayer< Dtype >inlineexplicit
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::LossLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
LossLayer(const LayerParameter &param) (defined in caffe::LossLayer< Dtype >)caffe::LossLayer< Dtype >inlineexplicit
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::LossLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::HingeLossLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:42 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1HingeLossLayer.html b/doxygen/classcaffe_1_1HingeLossLayer.html new file mode 100644 index 00000000000..3fa9ba71a95 --- /dev/null +++ b/doxygen/classcaffe_1_1HingeLossLayer.html @@ -0,0 +1,377 @@ + + + + + + + +Caffe: caffe::HingeLossLayer< Dtype > Class Template Reference + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
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+ + + + + + + + +
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+ + +
+ +
+ + +
+
+
+Public Member Functions | +Protected Member Functions | +List of all members
+
+
caffe::HingeLossLayer< Dtype > Class Template Reference
+
+
+ +

Computes the hinge loss for a one-of-many classification task. + More...

+ +

#include <hinge_loss_layer.hpp>

+
+Inheritance diagram for caffe::HingeLossLayer< Dtype >:
+
+
+ + +caffe::LossLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ HingeLossLayer (const LayerParameter &param)
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::LossLayer< Dtype >
+ LossLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
+virtual bool AutoTopBlobs () const
 For convenience and backwards compatibility, instruct the Net to automatically allocate a single top Blob for LossLayers, into which they output their singleton loss, (even if the user didn't specify one in the prototxt, etc.).
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Computes the hinge loss for a one-of-many classification task. More...
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the hinge loss error gradient w.r.t. the predictions. More...
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::HingeLossLayer< Dtype >

+ +

Computes the hinge loss for a one-of-many classification task.

+
Parameters
+ + + +
bottominput Blob vector (length 2)
    +
  1. $ (N \times C \times H \times W) $ the predictions $ t $, a Blob with values in $ [-\infty, +\infty] $ indicating the predicted score for each of the $ K = CHW $ classes. In an SVM, $ t $ is the result of taking the inner product $ X^T W $ of the D-dimensional features $ X \in \mathcal{R}^{D \times N} $ and the learned hyperplane parameters $ W \in \mathcal{R}^{D \times K} $, so a Net with just an InnerProductLayer (with num_output = D) providing predictions to a HingeLossLayer and no other learnable parameters or losses is equivalent to an SVM.
    2. +
  2. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
    3. +
+
topoutput Blob vector (length 1)
    +
  1. $ (1 \times 1 \times 1 \times 1) $ the computed hinge loss: $ E = \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p $, for the $ L^p $ norm (defaults to $ p = 1 $, the L1 norm; L2 norm, as in L2-SVM, is also available), and $ \delta\{\mathrm{condition}\} = \left\{ \begin{array}{lr} 1 & \mbox{if condition} \\ -1 & \mbox{otherwise} \end{array} \right. $
    2. +
+
+
+
+

In an SVM, $ t \in \mathcal{R}^{N \times K} $ is the result of taking the inner product $ X^T W $ of the features $ X \in \mathcal{R}^{D \times N} $ and the learned hyperplane parameters $ W \in \mathcal{R}^{D \times K} $. So, a Net with just an InnerProductLayer (with num_output = $k$) providing predictions to a HingeLossLayer is equivalent to an SVM (assuming it has no other learned outside the InnerProductLayer and no other losses outside the HingeLossLayer).

+

Member Function Documentation

+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::HingeLossLayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the hinge loss error gradient w.r.t. the predictions.

+

Gradients cannot be computed with respect to the label inputs (bottom[1]), so this method ignores bottom[1] and requires !propagate_down[1], crashing if propagate_down[1] is set.

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
    +
  1. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
    2. +
+
propagate_downsee Layer::Backward. propagate_down[1] must be false as we can't compute gradients with respect to the labels.
bottominput Blob vector (length 2)
    +
  1. $ (N \times C \times H \times W) $ the predictions $t$; Backward computes diff $ \frac{\partial E}{\partial t} $
    2. +
  2. $ (N \times 1 \times 1 \times 1) $ the labels – ignored as we can't compute their error gradients
    3. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::HingeLossLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+ +

Computes the hinge loss for a one-of-many classification task.

+
Parameters
+ + + +
bottominput Blob vector (length 2)
    +
  1. $ (N \times C \times H \times W) $ the predictions $ t $, a Blob with values in $ [-\infty, +\infty] $ indicating the predicted score for each of the $ K = CHW $ classes. In an SVM, $ t $ is the result of taking the inner product $ X^T W $ of the D-dimensional features $ X \in \mathcal{R}^{D \times N} $ and the learned hyperplane parameters $ W \in \mathcal{R}^{D \times K} $, so a Net with just an InnerProductLayer (with num_output = D) providing predictions to a HingeLossLayer and no other learnable parameters or losses is equivalent to an SVM.
    2. +
  2. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
    3. +
+
topoutput Blob vector (length 1)
    +
  1. $ (1 \times 1 \times 1 \times 1) $ the computed hinge loss: $ E = \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p $, for the $ L^p $ norm (defaults to $ p = 1 $, the L1 norm; L2 norm, as in L2-SVM, is also available), and $ \delta\{\mathrm{condition}\} = \left\{ \begin{array}{lr} 1 & \mbox{if condition} \\ -1 & \mbox{otherwise} \end{array} \right. $
    2. +
+
+
+
+

In an SVM, $ t \in \mathcal{R}^{N \times K} $ is the result of taking the inner product $ X^T W $ of the features $ X \in \mathcal{R}^{D \times N} $ and the learned hyperplane parameters $ W \in \mathcal{R}^{D \times K} $. So, a Net with just an InnerProductLayer (with num_output = $k$) providing predictions to a HingeLossLayer is equivalent to an SVM (assuming it has no other learned outside the InnerProductLayer and no other losses outside the HingeLossLayer).

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:42 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1HingeLossLayer.png b/doxygen/classcaffe_1_1HingeLossLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..8d2c6369a22610b27bc9548d995eba9e0c487dfb GIT binary patch literal 1098 zcmeAS@N?(olHy`uVBq!ia0vp^M}WA4gBeKnKic94q$C1-LR|m<{|{uoc=NTi|Ih>= z3ycpOIKbL@M;^%KC<*clW&kPzfvcxNj2IZ0Z+f~ohEy=VoqKW8V=W%m+s8wH{`cQ^ zQbPJ<+mRHt>Zen-eY0TZSm>q@H}iSrzFjj^U*7WK@a$U9GGPfv6?f7sv7L6FR;yor z%&(ZHV)~WuNb=LeuaX5~%_o_xnJn=7n6Bjvuiam)I*c}D-7S!RdnxLU$YuA#Yg9a! zm+~b=pZGT;?d6;3X)DYxea-fXJw4m|lJVU374`2F=FPBJ?R?9oWxipHA=}0~?!{rR zwAh8SH|n!kbIcBV{aNRjfJ=7$ZMCOjJ04#?*1jxhcRbgZ|KRFa3~CSSAhBH zx3AsJ)=Ixy92<3->FAe>2J=*3&fFz1+xmC%tfiYTUY9Dg^SUDa@|Wqdbx+T#W-ebY z@b$cGi?N~MulISIX4iYHX#Zdwm$;`TB5$sx;MM8vFJ#n(V`lEw7X*f6!R}kaJ>ddt zW8XOZD3FQ-~+T0d02&1O5r{O@Ax#MP5;o~%+j^V1?s-8f0; zY8~Gz>%j1zi(jdD)-P)me=s-w{d|WWBbOG5G>(~^p1+m|IZaa1P*l-udMEJN=DfK0 zf%a179lQ21J}Udo)CCMz{7mV#VW76%p({;KfK z{Ga>1Z8g<*{{EG?wQ}hLo-?l;Y^?a1rCA)j>sc@MGAvlgwega^<&N+Fw|Ku-6z=~x zX=7Gd!PeTpCzh2SuHEw5>NWF&Y1xgrd}i6eV7;xWRewFsezoCB(??>Oeda;iBX>VN zQ@2HN)nwV0>f6$VUlgL!H|!0zTc+GHf8${bhn&Q9oa^t+w^+aVPG+X8y77LC``0Jj zwfg3Aqriu+TwtxQ)plog`QECqyH=ajXBsSP-*n@g%XdGU=8DIDKTd3Oh%K5N y-=Hp)_QQbX!2fkE3>^zV__(%_@8y!*zwGQs0~ms~g + + + + + + +Caffe: Member List + + + + + + + + + +
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Caffe +
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caffe::Im2colLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::Im2colLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Im2colLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Im2colLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
bottom_dim_ (defined in caffe::Im2colLayer< Dtype >)caffe::Im2colLayer< Dtype >protected
channel_axis_ (defined in caffe::Im2colLayer< Dtype >)caffe::Im2colLayer< Dtype >protected
channels_ (defined in caffe::Im2colLayer< Dtype >)caffe::Im2colLayer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
dilation_caffe::Im2colLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::Im2colLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::Im2colLayer< Dtype >inlinevirtual
force_nd_im2col_ (defined in caffe::Im2colLayer< Dtype >)caffe::Im2colLayer< Dtype >protected
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Im2colLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Im2colLayer< Dtype >protectedvirtual
Im2colLayer(const LayerParameter &param) (defined in caffe::Im2colLayer< Dtype >)caffe::Im2colLayer< Dtype >inlineexplicit
IsShared() constcaffe::Layer< Dtype >inline
kernel_shape_caffe::Im2colLayer< Dtype >protected
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Im2colLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
num_ (defined in caffe::Im2colLayer< Dtype >)caffe::Im2colLayer< Dtype >protected
num_spatial_axes_ (defined in caffe::Im2colLayer< Dtype >)caffe::Im2colLayer< Dtype >protected
pad_caffe::Im2colLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Im2colLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
stride_caffe::Im2colLayer< Dtype >protected
top_dim_ (defined in caffe::Im2colLayer< Dtype >)caffe::Im2colLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::Im2colLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:42 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1Im2colLayer.html b/doxygen/classcaffe_1_1Im2colLayer.html new file mode 100644 index 00000000000..8e29a5c32da --- /dev/null +++ b/doxygen/classcaffe_1_1Im2colLayer.html @@ -0,0 +1,441 @@ + + + + + + + +Caffe: caffe::Im2colLayer< Dtype > Class Template Reference + + + + + + + + + +
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Caffe +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
+
caffe::Im2colLayer< Dtype > Class Template Reference
+
+
+ +

A helper for image operations that rearranges image regions into column vectors. Used by ConvolutionLayer to perform convolution by matrix multiplication. + More...

+ +

#include <im2col_layer.hpp>

+
+Inheritance diagram for caffe::Im2colLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ Im2colLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+Blob< int > kernel_shape_
 The spatial dimensions of a filter kernel.
 
+Blob< int > stride_
 The spatial dimensions of the stride.
 
+Blob< int > pad_
 The spatial dimensions of the padding.
 
+Blob< int > dilation_
 The spatial dimensions of the dilation.
 
+int num_spatial_axes_
 
+int bottom_dim_
 
+int top_dim_
 
+int channel_axis_
 
+int num_
 
+int channels_
 
+bool force_nd_im2col_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::Im2colLayer< Dtype >

+ +

A helper for image operations that rearranges image regions into column vectors. Used by ConvolutionLayer to perform convolution by matrix multiplication.

+

TODO(dox): thorough documentation for Forward, Backward, and proto params.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::Im2colLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::Im2colLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::Im2colLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::Im2colLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:42 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1Im2colLayer.png b/doxygen/classcaffe_1_1Im2colLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..c35d4cb50666f635a26d55c35d0fd7d054e99aba GIT binary patch literal 727 zcmeAS@N?(olHy`uVBq!ia0vp^Yk@d`gBeK1)Us{^QW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;+}eI2Id}6}U$a>CXDXh%uCTs+ zc(HC~X55?Tv^|ffe%pWS%Go_TRj&SWi2k=WL*Hl4RZYQp*WUj)QQcg&TSo2d(Yq&) zL|xgwY0=isO?K-m>@ROqx@LBA>MNuBoofwsS-;5@_$`0`QPRDN5V7bANn`to8g=MWNpudv$jkUS?sCp zX;^hp@ARe~r3Qw-j691MX8ix>c1X7C8>_XzyNE5D)frqeFEJg-AR4cmKP5=YO{Kxn zB$F`-gs&{FjorU}&)R%d2EW&G4ZClxt9&cDU@_m@T@_Uc;#F+tGj`8okkZ=nJ3BA1 zcJ2P>W-a*=&MEWKlRxLg7leskGrQeq^)0%+LAsr}IO?a-Wt)5Vz6DF|UbpvY@3+#o z$6{*J+Kty|)H4 zZtEynUG#ch!ZqK6TW)8)-2QY=wJoE91OJ};Kjv~QtL6F~eCzmz{k^&e#7&`I-ct4$ e7zi%^8K0D^++MwoEgqN(89ZJ6T-G@yGywnr{9?BN literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1ImageDataLayer-members.html b/doxygen/classcaffe_1_1ImageDataLayer-members.html new file mode 100644 index 00000000000..71ddd35632f --- /dev/null +++ b/doxygen/classcaffe_1_1ImageDataLayer-members.html @@ -0,0 +1,143 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::ImageDataLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::ImageDataLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BaseDataLayer< Dtype >inlinevirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BaseDataLayer< Dtype >inlinevirtual
BaseDataLayer(const LayerParameter &param) (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >explicit
BasePrefetchingDataLayer(const LayerParameter &param) (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >explicit
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
data_transformer_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected
DataLayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) (defined in caffe::ImageDataLayer< Dtype >)caffe::ImageDataLayer< Dtype >virtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::ImageDataLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::ImageDataLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BasePrefetchingDataLayer< Dtype >virtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BasePrefetchingDataLayer< Dtype >virtual
ImageDataLayer(const LayerParameter &param) (defined in caffe::ImageDataLayer< Dtype >)caffe::ImageDataLayer< Dtype >inlineexplicit
InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadinline
InternalThreadEntry() (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protectedvirtual
is_started() const (defined in caffe::InternalThread)caffe::InternalThread
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BasePrefetchingDataLayer< Dtype >virtual
lines_ (defined in caffe::ImageDataLayer< Dtype >)caffe::ImageDataLayer< Dtype >protected
lines_id_ (defined in caffe::ImageDataLayer< Dtype >)caffe::ImageDataLayer< Dtype >protected
load_batch(Batch< Dtype > *batch) (defined in caffe::ImageDataLayer< Dtype >)caffe::ImageDataLayer< Dtype >protectedvirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
must_stop() (defined in caffe::InternalThread)caffe::InternalThreadprotected
output_labels_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
prefetch_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected
PREFETCH_COUNT (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >static
prefetch_free_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected
prefetch_full_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected
prefetch_rng_ (defined in caffe::ImageDataLayer< Dtype >)caffe::ImageDataLayer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BaseDataLayer< Dtype >inlinevirtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::BaseDataLayer< Dtype >inlinevirtual
ShuffleImages() (defined in caffe::ImageDataLayer< Dtype >)caffe::ImageDataLayer< Dtype >protectedvirtual
StartInternalThread()caffe::InternalThread
StopInternalThread()caffe::InternalThread
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
transform_param_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected
transformed_data_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected
type() constcaffe::ImageDataLayer< Dtype >inlinevirtual
~ImageDataLayer() (defined in caffe::ImageDataLayer< Dtype >)caffe::ImageDataLayer< Dtype >virtual
~InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadvirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:42 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ImageDataLayer.html b/doxygen/classcaffe_1_1ImageDataLayer.html new file mode 100644 index 00000000000..eac018894c5 --- /dev/null +++ b/doxygen/classcaffe_1_1ImageDataLayer.html @@ -0,0 +1,378 @@ + + + + + + + +Caffe: caffe::ImageDataLayer< Dtype > Class Template Reference + + + + + + + + + +
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Caffe +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
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caffe::ImageDataLayer< Dtype > Class Template Reference
+
+
+ +

Provides data to the Net from image files. + More...

+ +

#include <image_data_layer.hpp>

+
+Inheritance diagram for caffe::ImageDataLayer< Dtype >:
+
+
+ + +caffe::BasePrefetchingDataLayer< Dtype > +caffe::BaseDataLayer< Dtype > +caffe::InternalThread +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ ImageDataLayer (const LayerParameter &param)
 
+virtual void DataLayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::BasePrefetchingDataLayer< Dtype >
+ BasePrefetchingDataLayer (const LayerParameter &param)
 
void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
- Public Member Functions inherited from caffe::BaseDataLayer< Dtype >
+ BaseDataLayer (const LayerParameter &param)
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
- Public Member Functions inherited from caffe::InternalThread
void StartInternalThread ()
 
void StopInternalThread ()
 
+bool is_started () const
 
+ + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void ShuffleImages ()
 
+virtual void load_batch (Batch< Dtype > *batch)
 
- Protected Member Functions inherited from caffe::BasePrefetchingDataLayer< Dtype >
+virtual void InternalThreadEntry ()
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
- Protected Member Functions inherited from caffe::InternalThread
+bool must_stop ()
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+shared_ptr< Caffe::RNG > prefetch_rng_
 
+vector< std::pair< std::string, int > > lines_
 
+int lines_id_
 
- Protected Attributes inherited from caffe::BasePrefetchingDataLayer< Dtype >
+Batch< Dtype > prefetch_ [PREFETCH_COUNT]
 
+BlockingQueue< Batch< Dtype > * > prefetch_free_
 
+BlockingQueue< Batch< Dtype > * > prefetch_full_
 
+Blob< Dtype > transformed_data_
 
- Protected Attributes inherited from caffe::BaseDataLayer< Dtype >
+TransformationParameter transform_param_
 
+shared_ptr< DataTransformer< Dtype > > data_transformer_
 
+bool output_labels_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+ + + + +

+Additional Inherited Members

- Static Public Attributes inherited from caffe::BasePrefetchingDataLayer< Dtype >
+static const int PREFETCH_COUNT = 3
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::ImageDataLayer< Dtype >

+ +

Provides data to the Net from image files.

+

TODO(dox): thorough documentation for Forward and proto params.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::ImageDataLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::ImageDataLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following file: +
+ +
+Generated on Mon Nov 21 2016 11:23:42 for Caffe by   +doxygen + 1.8.12 +
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caffe::InfogainLossLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::InfogainLossLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::LossLayer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::InfogainLossLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::InfogainLossLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::InfogainLossLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
infogain_ (defined in caffe::InfogainLossLayer< Dtype >)caffe::InfogainLossLayer< Dtype >protected
InfogainLossLayer(const LayerParameter &param) (defined in caffe::InfogainLossLayer< Dtype >)caffe::InfogainLossLayer< Dtype >inlineexplicit
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::InfogainLossLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
LossLayer(const LayerParameter &param) (defined in caffe::LossLayer< Dtype >)caffe::LossLayer< Dtype >inlineexplicit
MaxBottomBlobs() constcaffe::InfogainLossLayer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::InfogainLossLayer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::InfogainLossLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::InfogainLossLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:42 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1InfogainLossLayer.html b/doxygen/classcaffe_1_1InfogainLossLayer.html new file mode 100644 index 00000000000..c3f47a61d76 --- /dev/null +++ b/doxygen/classcaffe_1_1InfogainLossLayer.html @@ -0,0 +1,580 @@ + + + + + + + +Caffe: caffe::InfogainLossLayer< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
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caffe::InfogainLossLayer< Dtype > Class Template Reference
+
+
+ +

A generalization of MultinomialLogisticLossLayer that takes an "information gain" (infogain) matrix specifying the "value" of all label pairs. + More...

+ +

#include <infogain_loss_layer.hpp>

+
+Inheritance diagram for caffe::InfogainLossLayer< Dtype >:
+
+
+ + +caffe::LossLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ InfogainLossLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::LossLayer< Dtype >
+ LossLayer (const LayerParameter &param)
 
+virtual bool AutoTopBlobs () const
 For convenience and backwards compatibility, instruct the Net to automatically allocate a single top Blob for LossLayers, into which they output their singleton loss, (even if the user didn't specify one in the prototxt, etc.).
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 A generalization of MultinomialLogisticLossLayer that takes an "information gain" (infogain) matrix specifying the "value" of all label pairs. More...
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the infogain loss error gradient w.r.t. the predictions. More...
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + +

+Protected Attributes

+Blob< Dtype > infogain_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::InfogainLossLayer< Dtype >

+ +

A generalization of MultinomialLogisticLossLayer that takes an "information gain" (infogain) matrix specifying the "value" of all label pairs.

+

Equivalent to the MultinomialLogisticLossLayer if the infogain matrix is the identity.

+
Parameters
+ + + +
bottominput Blob vector (length 2-3)
    +
  1. $ (N \times C \times H \times W) $ the predictions $ \hat{p} $, a Blob with values in $ [0, 1] $ indicating the predicted probability of each of the $ K = CHW $ classes. Each prediction vector $ \hat{p}_n $ should sum to 1 as in a probability distribution: $ \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 $.
    2. +
  2. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
    3. +
  3. $ (1 \times 1 \times K \times K) $ (optional) the infogain matrix $ H $. This must be provided as the third bottom blob input if not provided as the infogain_mat in the InfogainLossParameter. If $ H = I $, this layer is equivalent to the MultinomialLogisticLossLayer.
    4. +
+
topoutput Blob vector (length 1)
    +
  1. $ (1 \times 1 \times 1 \times 1) $ the computed infogain multinomial logistic loss: $ E = \frac{-1}{N} \sum\limits_{n=1}^N H_{l_n} \log(\hat{p}_n) = \frac{-1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^{K} H_{l_n,k} \log(\hat{p}_{n,k}) $, where $ H_{l_n} $ denotes row $l_n$ of $H$.
    2. +
+
+
+
+

Member Function Documentation

+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::InfogainLossLayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the infogain loss error gradient w.r.t. the predictions.

+

Gradients cannot be computed with respect to the label inputs (bottom[1]), so this method ignores bottom[1] and requires !propagate_down[1], crashing if propagate_down[1] is set. (The same applies to the infogain matrix, if provided as bottom[2] rather than in the layer_param.)

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
    +
  1. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
    2. +
+
propagate_downsee Layer::Backward. propagate_down[1] must be false as we can't compute gradients with respect to the labels (similarly for propagate_down[2] and the infogain matrix, if provided as bottom[2])
bottominput Blob vector (length 2-3)
    +
  1. $ (N \times C \times H \times W) $ the predictions $ \hat{p} $; Backward computes diff $ \frac{\partial E}{\partial \hat{p}} $
    2. +
  2. $ (N \times 1 \times 1 \times 1) $ the labels – ignored as we can't compute their error gradients
    3. +
  3. $ (1 \times 1 \times K \times K) $ (optional) the information gain matrix – ignored as its error gradient computation is not implemented.
    4. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::InfogainLossLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::LossLayer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::InfogainLossLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+ +

A generalization of MultinomialLogisticLossLayer that takes an "information gain" (infogain) matrix specifying the "value" of all label pairs.

+

Equivalent to the MultinomialLogisticLossLayer if the infogain matrix is the identity.

+
Parameters
+ + + +
bottominput Blob vector (length 2-3)
    +
  1. $ (N \times C \times H \times W) $ the predictions $ \hat{p} $, a Blob with values in $ [0, 1] $ indicating the predicted probability of each of the $ K = CHW $ classes. Each prediction vector $ \hat{p}_n $ should sum to 1 as in a probability distribution: $ \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 $.
    2. +
  2. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
    3. +
  3. $ (1 \times 1 \times K \times K) $ (optional) the infogain matrix $ H $. This must be provided as the third bottom blob input if not provided as the infogain_mat in the InfogainLossParameter. If $ H = I $, this layer is equivalent to the MultinomialLogisticLossLayer.
    4. +
+
topoutput Blob vector (length 1)
    +
  1. $ (1 \times 1 \times 1 \times 1) $ the computed infogain multinomial logistic loss: $ E = \frac{-1}{N} \sum\limits_{n=1}^N H_{l_n} \log(\hat{p}_n) = \frac{-1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^{K} H_{l_n,k} \log(\hat{p}_{n,k}) $, where $ H_{l_n} $ denotes row $l_n$ of $H$.
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::InfogainLossLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::LossLayer< Dtype >.

+ +
+
+ +

§ MaxBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::InfogainLossLayer< Dtype >::MaxBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some maximum number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::InfogainLossLayer< Dtype >::MinBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::InfogainLossLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Reimplemented from caffe::LossLayer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:42 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1InfogainLossLayer.png b/doxygen/classcaffe_1_1InfogainLossLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..b1799ecc9a4ece199f8920944b2937140bb0e867 GIT binary patch literal 1114 zcmeAS@N?(olHy`uVBq!ia0vp^7l62fgBeIJTt4*4M^&FMbRmyAl_a^PO?eNWk|lVg%b&b`#(3 znE5JmSJ^6gPK5+rWhtZWHLEq>H5WW%np6_IzF}K#bdmAew_C5b?u~xuZ@($qyQRM- zQmKsm)JiYgF-NMntuORk&kUA%`}0m!uUPY` zRZHDdKG;3t{r;qS4x|wMO}h{^@xAPk#3bR)$s!O5!nzBpxBk%2f8EBIHTB&$yVAE0 zf_7M?nKsDHZEw`RSSPbWlJ`I-*M)7u-g)o-{=c35{@I_4drRL5Ph*|F@9dKoW%a)% z#zrgEJn_!%Jf{D^$(r%jT2R8g|98G@(dV9%Gn*$1J}*?gvGR1-yJdE5uh=XUW^=Az zb)Q*M{N9o1pXs-gMenbC&UNCg1M8L;r(%Jw@5>#2?+IRTY@)I8wboZrA&fU$b2HSo zi(a_d7Ry-V{+i`P`?G+*4=X1rCP&?|IUDeq&z61FyMV_hOy5oDFOvV#aPP}4*-fG6 za%O49m2Nq%_5FW*sgmESIg*B)HuZO>d=KD!88wx;BD}u%qT%mD+TYGQ*y`IhG*?yb z + + + + + + +Caffe: Member List + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
caffe::InnerProductLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::InnerProductLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::InnerProductLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::InnerProductLayer< Dtype >protectedvirtual
bias_multiplier_ (defined in caffe::InnerProductLayer< Dtype >)caffe::InnerProductLayer< Dtype >protected
bias_term_ (defined in caffe::InnerProductLayer< Dtype >)caffe::InnerProductLayer< Dtype >protected
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::InnerProductLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::InnerProductLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::InnerProductLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::InnerProductLayer< Dtype >protectedvirtual
InnerProductLayer(const LayerParameter &param) (defined in caffe::InnerProductLayer< Dtype >)caffe::InnerProductLayer< Dtype >inlineexplicit
IsShared() constcaffe::Layer< Dtype >inline
K_ (defined in caffe::InnerProductLayer< Dtype >)caffe::InnerProductLayer< Dtype >protected
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::InnerProductLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
M_ (defined in caffe::InnerProductLayer< Dtype >)caffe::InnerProductLayer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
N_ (defined in caffe::InnerProductLayer< Dtype >)caffe::InnerProductLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::InnerProductLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
transpose_caffe::InnerProductLayer< Dtype >protected
type() constcaffe::InnerProductLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:42 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1InnerProductLayer.html b/doxygen/classcaffe_1_1InnerProductLayer.html new file mode 100644 index 00000000000..73a989b88e7 --- /dev/null +++ b/doxygen/classcaffe_1_1InnerProductLayer.html @@ -0,0 +1,423 @@ + + + + + + + +Caffe: caffe::InnerProductLayer< Dtype > Class Template Reference + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
+
caffe::InnerProductLayer< Dtype > Class Template Reference
+
+
+ +

Also known as a "fully-connected" layer, computes an inner product with a set of learned weights, and (optionally) adds biases. + More...

+ +

#include <inner_product_layer.hpp>

+
+Inheritance diagram for caffe::InnerProductLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ InnerProductLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+int M_
 
+int K_
 
+int N_
 
+bool bias_term_
 
+Blob< Dtype > bias_multiplier_
 
+bool transpose_
 if true, assume transposed weights
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::InnerProductLayer< Dtype >

+ +

Also known as a "fully-connected" layer, computes an inner product with a set of learned weights, and (optionally) adds biases.

+

TODO(dox): thorough documentation for Forward, Backward, and proto params.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::InnerProductLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::InnerProductLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::InnerProductLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::InnerProductLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:42 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1InnerProductLayer.png b/doxygen/classcaffe_1_1InnerProductLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..2a8ba8953f0c30986df69e85f135b8424c674162 GIT binary patch literal 774 zcmeAS@N?(olHy`uVBq!ia0vp^mw-5cgBeIh+uvRcq$C1-LR|m<{|{uoc=NTi|Ih>= z3ycpOIKbL@M;^%KC<*clW&kPzfvcxNj2IZ0rh2+KhEy=VoqMtGu>y}Pzj@IA|C6`t zvgA1JS~&NuQAgmrB?8C49el87Q|@iE={Hm|)pS)nrDC}~C%Ii$_fT}%Y3aFZ)rTLi z>%1q$y<}eEF4rFOYl7chk4w8>Io}dDy{5jTa8{|u%aiN&O4xn3=*pY)GC4uXGid#; z2}Kdr#)nNR^JYpIegAiV#;%Fx)0c3+a?`TcULvk`?o~ig|C9R_?5}i}RIEC?PRo#A z?b@O|bJHOuK{K^-U{r0xn%P%{6%YsysqrSZ_UsX~y?O#*Mnm>`NeD>E&%QH~Z92#ys5}Qd7J0v1ib7E1};j``_8Sy;7BuUpf1Qc!=kueQR4)JQrsx zdR}_5OF8%UHec0-_b=ECrayDub$40vEdG~%CeoK%JXSwu>{;K+Vz5h0=)j984+fwP z#-4SA1oo^HaGK%G(lB$&1O~V3PD~Q9=^o$reVnqL)gthXDMMWQ&i3Sn)u;N_?<;Nk zG9yQo`NFlxE9WQreP+y9q3||a@OAP0`o6c9>?e0_UfERnchc-HXDn`WZqomK*F*8m z4135}oyvam*1auFG?v%C+;lUwtFT2`!m`q`%TxZ z`YE>K?kC^PLMtRsw|=gE-08S=l;oQMrSYpKtogl4`R&)W z6QaDoZSrbATrImYVwdihMPA3gTu8kYYGvykZWbqfQhwjF$WN8e+T||Jj!TrT#KL|EIJ4`k~L#z(mX7>FVdQ&MBb@02lyyBme*a literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1InputLayer-members.html b/doxygen/classcaffe_1_1InputLayer-members.html new file mode 100644 index 00000000000..d100666d91b --- /dev/null +++ b/doxygen/classcaffe_1_1InputLayer-members.html @@ -0,0 +1,119 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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Caffe +
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+
caffe::InputLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::InputLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::InputLayer< Dtype >inlineprotectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::InputLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::InputLayer< Dtype >inlineprotectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
InputLayer(const LayerParameter &param) (defined in caffe::InputLayer< Dtype >)caffe::InputLayer< Dtype >inlineexplicit
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::InputLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::InputLayer< Dtype >inlinevirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::InputLayer< Dtype >inlinevirtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::InputLayer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::InputLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:42 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1InputLayer.html b/doxygen/classcaffe_1_1InputLayer.html new file mode 100644 index 00000000000..45659ada915 --- /dev/null +++ b/doxygen/classcaffe_1_1InputLayer.html @@ -0,0 +1,403 @@ + + + + + + + +Caffe: caffe::InputLayer< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +List of all members
+
+
caffe::InputLayer< Dtype > Class Template Reference
+
+
+ +

Provides data to the Net by assigning tops directly. + More...

+ +

#include <input_layer.hpp>

+
+Inheritance diagram for caffe::InputLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ InputLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::InputLayer< Dtype >

+ +

Provides data to the Net by assigning tops directly.

+

This data layer is a container that merely holds the data assigned to it; forward, backward, and reshape are all no-ops.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::InputLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::InputLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::InputLayer< Dtype >::MinTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
virtual void caffe::InputLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+inlinevirtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:42 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1InputLayer.png b/doxygen/classcaffe_1_1InputLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..fce2a19c2926347cba68a1ff44e238cb9ab624c9 GIT binary patch literal 704 zcmeAS@N?(olHy`uVBq!ia0vp^OMp0lgBeIJ$k3|+QW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;4|mHo>eRPo>YBMZPV=kyr%g~eArsX&nBlc3MczMZ@lR_Y1ggxJ2E$? zOFlcfFLl{1zfJ$lm*qV{py#Wxi72_hy;uBQ>r)p_*Gi&eaSU;2cv%c>%a-&lJ<<1Ge0U%oU&wlCdeBg|GeO|^u1hC%*gVZ zv7o$``N`vttW)kY{7l(n|1{j8?;Tsm#Ris+3&i68Tr-2b%EdV!+>@HXAPB;*9PO-M zx!)68a5aakAk6!)oCV9B%Z+o?XY(gcxGT$07NT(dvaVsxx&J+%a~dPlu3!JOxolG= z?~LV$La$A5d>IN4|G+#^ToO-q&Y;~al|i46UGHoJUl@dM*a z4!=Z2)0lUPZ1s%zSjSWpohyFbHp1~%&&{_!d|D^=D$Hp&Y0WxcE8v-Y>&yK*mEEQ{ z9n4&BHJk9&_=YWeo^`2gJEx214Ew`ba~!vynbx-TF3<&cCa2CWzROz>rGGu9pz2Xn zJ^z!?WBv|tX*Jv`2kfsv!z5r|d$8BbW51Z)WtD!nyIX_;lN*DltDnm{r-UW|Cmct7 literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1InternalThread-members.html b/doxygen/classcaffe_1_1InternalThread-members.html new file mode 100644 index 00000000000..003b0f80de5 --- /dev/null +++ b/doxygen/classcaffe_1_1InternalThread-members.html @@ -0,0 +1,87 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::InternalThread Member List
+
+
+ +

This is the complete list of members for caffe::InternalThread, including all inherited members.

+ + + + + + + + +
InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadinline
InternalThreadEntry() (defined in caffe::InternalThread)caffe::InternalThreadinlineprotectedvirtual
is_started() const (defined in caffe::InternalThread)caffe::InternalThread
must_stop() (defined in caffe::InternalThread)caffe::InternalThreadprotected
StartInternalThread()caffe::InternalThread
StopInternalThread()caffe::InternalThread
~InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadvirtual
+ +
+Generated on Mon Nov 21 2016 11:23:42 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1InternalThread.html b/doxygen/classcaffe_1_1InternalThread.html new file mode 100644 index 00000000000..5143248eb73 --- /dev/null +++ b/doxygen/classcaffe_1_1InternalThread.html @@ -0,0 +1,160 @@ + + + + + + + +Caffe: caffe::InternalThread Class Reference + + + + + + + + + +
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caffe::InternalThread Class Reference
+
+
+ +

#include <internal_thread.hpp>

+
+Inheritance diagram for caffe::InternalThread:
+
+
+ + +caffe::BasePrefetchingDataLayer< Dtype > +caffe::DataReader::Body +caffe::P2PSync< Dtype > +caffe::DataLayer< Dtype > +caffe::ImageDataLayer< Dtype > +caffe::WindowDataLayer< Dtype > + +
+ + + + + + + + +

+Public Member Functions

void StartInternalThread ()
 
void StopInternalThread ()
 
+bool is_started () const
 
+ + + + + +

+Protected Member Functions

+virtual void InternalThreadEntry ()
 
+bool must_stop ()
 
+

Detailed Description

+

Virtual class encapsulate boost::thread for use in base class The child class will acquire the ability to run a single thread, by reimplementing the virtual function InternalThreadEntry.

+

Member Function Documentation

+ +

§ StartInternalThread()

+ +
+
+ + + + + + + +
void caffe::InternalThread::StartInternalThread ()
+
+

Caffe's thread local state will be initialized using the current thread values, e.g. device id, solver index etc. The random seed is initialized using caffe_rng_rand.

+ +
+
+ +

§ StopInternalThread()

+ +
+
+ + + + + + + +
void caffe::InternalThread::StopInternalThread ()
+
+

Will not return until the internal thread has exited.

+ +
+
+
The documentation for this class was generated from the following files: +
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+Generated on Mon Nov 21 2016 11:23:42 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1InternalThread.png b/doxygen/classcaffe_1_1InternalThread.png new file mode 100644 index 0000000000000000000000000000000000000000..ac6886f8e0a8dd101f29d28e6bebed17ad2e3160 GIT binary patch literal 2743 zcmb_eeK=HU8z0OvDGj#TN*!XQ)l1t(AIl((5X~fWXu}MWZOBJbXnfVGA^Au)?T9j4 z$x%M?F&K+AG}k6WNsXC7)5=t4P-ey$jQ7m6@4o-O?{)3DuJhdYb3gaFpWl5y=l-3O zbqMEfpub)pfj}5|dSDMD5J&*lGf_J5n1JGX!(#0rZ-U#>(h{t`6U)xzb>_pG=I-d| z_##VS3Qtk-hjG3L*hzg2J|TREK2IxpWAeTAf1Cu!a|FX(Le;c_&S^kw>iL2rcc5 z7w(N{E$s(0TV=KLG+D9it3}0=;a43j(c>q?`bn-164gIrBKwFDg^n;KW%GKKU4WTO zI!Oo~m!A>bYG3KTBveVI3-l@W!CNdAYt*r^j@pdhwKVIm-b=BO=xbWuIoVF*`$op( z#^d`)#kXfHM`{aOuO+WG<*SCX#B#-MwfQu|j^j+Fep!U2@Vq$6FOfNnHzpYvV~ zl+{EqoPBOlrBpfiD}?X6jX1mXuCIw*$E>HOs;1Vdx9q5%7Rrn$=?JZJ8U!9GVe_!{ z`&vDeq1hd$M=2=7;1YFB3=f+QIU!LsI88yLQp9JO!+JV!!igqwxX$T@>KNKBjiYSS zzh^6woA)iK>#vE{ap8tKj%J&5ON~iWS%jCyl!7XmSf6psngFKQM){~mpHR1DiW4vd^8V15}bH=UU?R4nF&LZ;UMwx@# zsl4RgmDT!8W&GXw%{dU{r})A$wNR)S92sfI?lr1{c>Be|{ni__{lTF5Z?;6X+cOy1 zDK}sHP?}nl5VShA8<;GJ;R;*6B!D>&;?wXHh0mpdJc%TT&)pz|pr|h3dPV;^e>(O6+^Srrrxns`60TIu{q&%qsBiK4B+i1IgSEpTP6ZDSOuO%nXFB8{(W{6X{A_&b^wJN71Ju-K8Iv(^5q}U-4Krb5&E* zUlRuwpWv9g7wX#~D5v!aFv+3MWToYH|9lW*@;Un|9&7v2nXC2=u8~r<$4*QOuD5RP zQ#9qBUiE_qLEQJ^rt6MToecrM42Zhrv0iPJ4&t4IPEozL^H{DOjK{leNH*H7pMY$=&9e(*Aac;?c3kvHM}y1OT3FExZ8tcG~=OQ z^uxsmVMBK#)1I!Vn1LVjZM!820dKTD_IopplgH+*2oMX$^w92~V;r{@=Uz@tv)7Y; ze7|QI)2ZAr@LW85Zujq}6i1_ymtNvhiNBM(W(SjU!-I<6`hk3!J*VP@Z9UW?H>YsT zN8_kPS4?G5rtqPdzdoD|K?beZEQhdn7WP&gw~{K=S9|$?y-P&GDiGWj>`T}c-}Y++ z@i-a;mu@7~P$9@_sB}g3_KRGdD+?3!rkh$Im{2pg(djWzYE!Vw=gs+F&E<%8mh-mD z!;}8QLSM1){rV)dB@j#H>MR&9ecVw8eORZ85tl(9zR`5g|Iujo$TX*4{%TpILL}bw zA67xeN44iU|20OtNr=P(Pu!Z-b=5C-s|C`HDL_i@-!+01cHVdK!h&NT_!_JLv zSzH}ytNH4)OpC?3YCNSq4g}Bq)MHn?9|VWl*W7AbW+3QRBAyKRy`kw^DM@}Wm1pC+ z0Ad7M6bMJpxq8m-oj>UiD0A!^V#!KiuKE*mH;Ir{xn8i9!~esGlK2wf>`!srbGCe5 zzG^oysFbs0D=eO()slR{K>=rA12N7imsaEtVp!w!rbrRBp)uRBy3OeI@SUOCT`AAn zA5{F{S2y96$GB!8pT2{6dNro4CHclrBbbu3=ACS^z;XwT%n4#L+bH1R=0+YX#lgAo zY0aEZdcw8wRLjZ!*StsJtNp~nps`F$YX`uqdsLq1H&DSmI@aZwy8Dt}K{!hJr|!p0 zj9?^bB7b6Qnlrg^h-F}~5*)qxW`e}p`^i?Fa2T3djh_%v`Og3vuBb9P)KlyCPMr0O!{r$4jd@H8-R*ta(W!;u zbZlkb5KB^0&E0ppzpdPkFBV?XeI+jQDKnMgV3eix)NMcG$5X!7;t)qRcUJT@4}2UF zet0nzn^3zra0MJ}?6DJ7pvz)L{6HBpKq4$HN$^Y+j3rfya@x!6ObVMG{tjWJ)^_+& zZy0->96ngCVX-F^9aE?ua7;z2r_Q~rgj$N7 ze;!|y2&>h_##^7volAQLL@?%;Cn%O0sm|&nlv}xhgQRKIkKwQ|v + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::LRNLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::LRNLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
alpha_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::LRNLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::LRNLayer< Dtype >protectedvirtual
beta_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
channels_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
CrossChannelBackward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom) (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protectedvirtual
CrossChannelBackward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom) (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protectedvirtual
CrossChannelForward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protectedvirtual
CrossChannelForward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::LRNLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::LRNLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::LRNLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::LRNLayer< Dtype >protectedvirtual
height_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
IsShared() constcaffe::Layer< Dtype >inline
k_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::LRNLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
LRNLayer(const LayerParameter &param) (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >inlineexplicit
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
num_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
pool_layer_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
pool_output_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
pool_top_vec_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
power_layer_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
power_output_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
power_top_vec_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
pre_pad_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
product_bottom_vec_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
product_input_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
product_layer_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::LRNLayer< Dtype >virtual
scale_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
size_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
split_layer_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
split_top_vec_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
square_bottom_vec_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
square_input_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
square_layer_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
square_output_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
square_top_vec_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::LRNLayer< Dtype >inlinevirtual
width_ (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protected
WithinChannelBackward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom) (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protectedvirtual
WithinChannelForward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) (defined in caffe::LRNLayer< Dtype >)caffe::LRNLayer< Dtype >protectedvirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:43 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1LRNLayer.html b/doxygen/classcaffe_1_1LRNLayer.html new file mode 100644 index 00000000000..647e9c798cb --- /dev/null +++ b/doxygen/classcaffe_1_1LRNLayer.html @@ -0,0 +1,500 @@ + + + + + + + +Caffe: caffe::LRNLayer< Dtype > Class Template Reference + + + + + + + + + +
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Caffe +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
+
caffe::LRNLayer< Dtype > Class Template Reference
+
+
+ +

Normalize the input in a local region across or within feature maps. + More...

+ +

#include <lrn_layer.hpp>

+
+Inheritance diagram for caffe::LRNLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ LRNLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
+virtual void CrossChannelForward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void CrossChannelForward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void WithinChannelForward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void CrossChannelBackward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 
+virtual void CrossChannelBackward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 
+virtual void WithinChannelBackward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+int size_
 
+int pre_pad_
 
+Dtype alpha_
 
+Dtype beta_
 
+Dtype k_
 
+int num_
 
+int channels_
 
+int height_
 
+int width_
 
+Blob< Dtype > scale_
 
+shared_ptr< SplitLayer< Dtype > > split_layer_
 
+vector< Blob< Dtype > * > split_top_vec_
 
+shared_ptr< PowerLayer< Dtype > > square_layer_
 
+Blob< Dtype > square_input_
 
+Blob< Dtype > square_output_
 
+vector< Blob< Dtype > * > square_bottom_vec_
 
+vector< Blob< Dtype > * > square_top_vec_
 
+shared_ptr< PoolingLayer< Dtype > > pool_layer_
 
+Blob< Dtype > pool_output_
 
+vector< Blob< Dtype > * > pool_top_vec_
 
+shared_ptr< PowerLayer< Dtype > > power_layer_
 
+Blob< Dtype > power_output_
 
+vector< Blob< Dtype > * > power_top_vec_
 
+shared_ptr< EltwiseLayer< Dtype > > product_layer_
 
+Blob< Dtype > product_input_
 
+vector< Blob< Dtype > * > product_bottom_vec_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::LRNLayer< Dtype >

+ +

Normalize the input in a local region across or within feature maps.

+

TODO(dox): thorough documentation for Forward, Backward, and proto params.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::LRNLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::LRNLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::LRNLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::LRNLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/layers/lrn_layer.hpp
    • +
  • src/caffe/layers/lrn_layer.cpp
    • +
+
+ +
+Generated on Mon Nov 21 2016 11:23:43 for Caffe by   +doxygen + 1.8.12 +
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caffe::LSTMLayer< Dtype > Member List
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This is the complete list of members for caffe::LSTMLayer< Dtype >, including all inherited members.

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AllowForceBackward(const int bottom_index) constcaffe::RecurrentLayer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::RecurrentLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
cont_input_blob_ (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::RecurrentLayer< Dtype >inlinevirtual
expose_hidden_caffe::RecurrentLayer< Dtype >protected
FillUnrolledNet(NetParameter *net_param) constcaffe::LSTMLayer< Dtype >protectedvirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::RecurrentLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::RecurrentLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
last_layer_index_caffe::RecurrentLayer< Dtype >protected
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::RecurrentLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
LSTMLayer(const LayerParameter &param) (defined in caffe::LSTMLayer< Dtype >)caffe::LSTMLayer< Dtype >inlineexplicit
MaxBottomBlobs() constcaffe::RecurrentLayer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::RecurrentLayer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
N_caffe::RecurrentLayer< Dtype >protected
output_blobs_ (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >protected
OutputBlobNames(vector< string > *names) constcaffe::LSTMLayer< Dtype >protectedvirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
recur_input_blobs_ (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >protected
recur_output_blobs_ (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >protected
RecurrentInputBlobNames(vector< string > *names) constcaffe::LSTMLayer< Dtype >protectedvirtual
RecurrentInputShapes(vector< BlobShape > *shapes) constcaffe::LSTMLayer< Dtype >protectedvirtual
RecurrentLayer(const LayerParameter &param) (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >inlineexplicit
RecurrentOutputBlobNames(vector< string > *names) constcaffe::LSTMLayer< Dtype >protectedvirtual
Reset() (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >virtual
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::RecurrentLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
static_input_caffe::RecurrentLayer< Dtype >protected
T_caffe::RecurrentLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::LSTMLayer< Dtype >inlinevirtual
unrolled_net_caffe::RecurrentLayer< Dtype >protected
x_input_blob_ (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >protected
x_static_input_blob_ (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >protected
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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+ + diff --git a/doxygen/classcaffe_1_1LSTMLayer.html b/doxygen/classcaffe_1_1LSTMLayer.html new file mode 100644 index 00000000000..ce4d47af03d --- /dev/null +++ b/doxygen/classcaffe_1_1LSTMLayer.html @@ -0,0 +1,312 @@ + + + + + + + +Caffe: caffe::LSTMLayer< Dtype > Class Template Reference + + + + + + + + + +
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caffe::LSTMLayer< Dtype > Class Template Reference
+
+
+ +

Processes sequential inputs using a "Long Short-Term Memory" (LSTM) [1] style recurrent neural network (RNN). Implemented by unrolling the LSTM computation through time. + More...

+ +

#include <lstm_layer.hpp>

+
+Inheritance diagram for caffe::LSTMLayer< Dtype >:
+
+
+ + +caffe::RecurrentLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ LSTMLayer (const LayerParameter &param)
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::RecurrentLayer< Dtype >
+ RecurrentLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual void Reset ()
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void FillUnrolledNet (NetParameter *net_param) const
 Fills net_param with the recurrent network architecture. Subclasses should define this – see RNNLayer and LSTMLayer for examples.
 
+virtual void RecurrentInputBlobNames (vector< string > *names) const
 Fills names with the names of the 0th timestep recurrent input Blob&s. Subclasses should define this – see RNNLayer and LSTMLayer for examples.
 
+virtual void RecurrentOutputBlobNames (vector< string > *names) const
 Fills names with the names of the Tth timestep recurrent output Blob&s. Subclasses should define this – see RNNLayer and LSTMLayer for examples.
 
+virtual void RecurrentInputShapes (vector< BlobShape > *shapes) const
 Fills shapes with the shapes of the recurrent input Blob&s. Subclasses should define this – see RNNLayer and LSTMLayer for examples.
 
+virtual void OutputBlobNames (vector< string > *names) const
 Fills names with the names of the output blobs, concatenated across all timesteps. Should return a name for each top Blob. Subclasses should define this – see RNNLayer and LSTMLayer for examples.
 
- Protected Member Functions inherited from caffe::RecurrentLayer< Dtype >
virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::RecurrentLayer< Dtype >
+shared_ptr< Net< Dtype > > unrolled_net_
 A Net to implement the Recurrent functionality.
 
+int N_
 The number of independent streams to process simultaneously.
 
+int T_
 The number of timesteps in the layer's input, and the number of timesteps over which to backpropagate through time.
 
+bool static_input_
 Whether the layer has a "static" input copied across all timesteps.
 
+int last_layer_index_
 The last layer to run in the network. (Any later layers are losses added to force the recurrent net to do backprop.)
 
+bool expose_hidden_
 Whether the layer's hidden state at the first and last timesteps are layer inputs and outputs, respectively.
 
+vector< Blob< Dtype > *> recur_input_blobs_
 
+vector< Blob< Dtype > *> recur_output_blobs_
 
+vector< Blob< Dtype > *> output_blobs_
 
+Blob< Dtype > * x_input_blob_
 
+Blob< Dtype > * x_static_input_blob_
 
+Blob< Dtype > * cont_input_blob_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::LSTMLayer< Dtype >

+ +

Processes sequential inputs using a "Long Short-Term Memory" (LSTM) [1] style recurrent neural network (RNN). Implemented by unrolling the LSTM computation through time.

+

The specific architecture used in this implementation is as described in "Learning to Execute" [2], reproduced below: i_t := [ W_{hi} * h_{t-1} + W_{xi} * x_t + b_i ] f_t := [ W_{hf} * h_{t-1} + W_{xf} * x_t + b_f ] o_t := [ W_{ho} * h_{t-1} + W_{xo} * x_t + b_o ] g_t := [ W_{hg} * h_{t-1} + W_{xg} * x_t + b_g ] c_t := (f_t .* c_{t-1}) + (i_t .* g_t) h_t := o_t .* [c_t] In the implementation, the i, f, o, and g computations are performed as a single inner product.

+

Notably, this implementation lacks the "diagonal" gates, as used in the LSTM architectures described by Alex Graves [3] and others.

+

[1] Hochreiter, Sepp, and Schmidhuber, Jürgen. "Long short-term memory." Neural Computation 9, no. 8 (1997): 1735-1780.

+

[2] Zaremba, Wojciech, and Sutskever, Ilya. "Learning to execute." arXiv preprint arXiv:1410.4615 (2014).

+

[3] Graves, Alex. "Generating sequences with recurrent neural networks." arXiv preprint arXiv:1308.0850 (2013).

+
The documentation for this class was generated from the following files:
    +
  • include/caffe/layers/lstm_layer.hpp
    • +
  • src/caffe/layers/lstm_layer.cpp
    • +
+
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+Generated on Mon Nov 21 2016 11:23:43 for Caffe by   +doxygen + 1.8.12 +
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caffe::LSTMUnitLayer< Dtype > Member List
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This is the complete list of members for caffe::LSTMUnitLayer< Dtype >, including all inherited members.

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AllowForceBackward(const int bottom_index) constcaffe::LSTMUnitLayer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::LSTMUnitLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::LSTMUnitLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::LSTMUnitLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::LSTMUnitLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::LSTMUnitLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::LSTMUnitLayer< Dtype >protectedvirtual
hidden_dim_caffe::LSTMUnitLayer< Dtype >protected
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlinevirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
LSTMUnitLayer(const LayerParameter &param) (defined in caffe::LSTMUnitLayer< Dtype >)caffe::LSTMUnitLayer< Dtype >inlineexplicit
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::LSTMUnitLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::LSTMUnitLayer< Dtype >inlinevirtual
X_acts_ (defined in caffe::LSTMUnitLayer< Dtype >)caffe::LSTMUnitLayer< Dtype >protected
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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+
+
caffe::LSTMUnitLayer< Dtype > Class Template Reference
+
+
+ +

A helper for LSTMLayer: computes a single timestep of the non-linearity of the LSTM, producing the updated cell and hidden states. + More...

+ +

#include <lstm_layer.hpp>

+
+Inheritance diagram for caffe::LSTMUnitLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ LSTMUnitLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the error gradient w.r.t. the LSTMUnit inputs. More...
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + +

+Protected Attributes

+int hidden_dim_
 The hidden and output dimension.
 
+Blob< Dtype > X_acts_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::LSTMUnitLayer< Dtype >

+ +

A helper for LSTMLayer: computes a single timestep of the non-linearity of the LSTM, producing the updated cell and hidden states.

+

Member Function Documentation

+ +

§ AllowForceBackward()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
virtual bool caffe::LSTMUnitLayer< Dtype >::AllowForceBackward (const int bottom_index) const
+ 		+inlinevirtual
+
+ +

Return whether to allow force_backward for a given bottom blob index.

+

If AllowForceBackward(i) == false, we will ignore the force_backward setting and backpropagate to blob i only if it needs gradient information (as is done when force_backward == false).

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::LSTMUnitLayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the error gradient w.r.t. the LSTMUnit inputs.

+
Parameters
+ + + + +
topoutput Blob vector (length 2), providing the error gradient with respect to the outputs
    +
  1. $ (1 \times N \times D) $: containing error gradients $ \frac{\partial E}{\partial c_t} $ with respect to the updated cell state $ c_t $
    2. +
  2. $ (1 \times N \times D) $: containing error gradients $ \frac{\partial E}{\partial h_t} $ with respect to the updated cell state $ h_t $
    3. +
+
propagate_downsee Layer::Backward.
bottominput Blob vector (length 3), into which the error gradients with respect to the LSTMUnit inputs $ c_{t-1} $ and the gate inputs are computed. Computatation of the error gradients w.r.t. the sequence indicators is not implemented.
    +
  1. $ (1 \times N \times D) $ the error gradient w.r.t. the previous timestep cell state $ c_{t-1} $
    2. +
  2. $ (1 \times N \times 4D) $ the error gradient w.r.t. the "gate inputs" $ [ \frac{\partial E}{\partial i_t} \frac{\partial E}{\partial f_t} \frac{\partial E}{\partial o_t} \frac{\partial E}{\partial g_t} ] $
    3. +
  3. $ (1 \times 1 \times N) $ the gradient w.r.t. the sequence continuation indicators $ \delta_t $ is currently not computed.
    4. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::LSTMUnitLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::LSTMUnitLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::LSTMUnitLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+
Parameters
+ + + +
bottominput Blob vector (length 3)
    +
  1. $ (1 \times N \times D) $ the previous timestep cell state $ c_{t-1} $
    2. +
  2. $ (1 \times N \times 4D) $ the "gate inputs" $ [i_t', f_t', o_t', g_t'] $
    3. +
  3. $ (1 \times N) $ the sequence continuation indicators $ \delta_t $
    4. +
+
topoutput Blob vector (length 2)
    +
  1. $ (1 \times N \times D) $ the updated cell state $ c_t $, computed as: i_t := [i_t'] f_t := [f_t'] o_t := [o_t'] g_t := [g_t'] c_t := cont_t * (f_t .* c_{t-1}) + (i_t .* g_t)
    2. +
  2. $ (1 \times N \times D) $ the updated hidden state $ h_t $, computed as: h_t := o_t .* [c_t]
    3. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::LSTMUnitLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/layers/lstm_layer.hpp
    • +
  • src/caffe/layers/lstm_unit_layer.cpp
    • +
+
+ +
+Generated on Mon Nov 21 2016 11:23:43 for Caffe by   +doxygen + 1.8.12 +
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caffe::Layer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::Layer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)=0caffe::Layer< Dtype >protectedpure virtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)=0caffe::Layer< Dtype >protectedpure virtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlinevirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)=0caffe::Layer< Dtype >pure virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::Layer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:43 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1Layer.html b/doxygen/classcaffe_1_1Layer.html new file mode 100644 index 00000000000..fce2b75557e --- /dev/null +++ b/doxygen/classcaffe_1_1Layer.html @@ -0,0 +1,1068 @@ + + + + + + + +Caffe: caffe::Layer< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
+
caffe::Layer< Dtype > Class Template Referenceabstract
+
+
+ +

An interface for the units of computation which can be composed into a Net. + More...

+ +

#include <layer.hpp>

+
+Inheritance diagram for caffe::Layer< Dtype >:
+
+
+ + +caffe::AccuracyLayer< Dtype > +caffe::ArgMaxLayer< Dtype > +caffe::BaseConvolutionLayer< Dtype > +caffe::BaseDataLayer< Dtype > +caffe::BatchNormLayer< Dtype > +caffe::BatchReindexLayer< Dtype > +caffe::BiasLayer< Dtype > +caffe::ConcatLayer< Dtype > +caffe::CropLayer< Dtype > +caffe::DummyDataLayer< Dtype > +caffe::EltwiseLayer< Dtype > +caffe::EmbedLayer< Dtype > +caffe::FilterLayer< Dtype > +caffe::FlattenLayer< Dtype > +caffe::HDF5DataLayer< Dtype > +caffe::HDF5OutputLayer< Dtype > +caffe::Im2colLayer< Dtype > +caffe::InnerProductLayer< Dtype > +caffe::InputLayer< Dtype > +caffe::LossLayer< Dtype > +caffe::LRNLayer< Dtype > +caffe::LSTMUnitLayer< Dtype > +caffe::MVNLayer< Dtype > +caffe::NeuronLayer< Dtype > +caffe::ParameterLayer< Dtype > +caffe::PoolingLayer< Dtype > +caffe::PythonLayer< Dtype > +caffe::RecurrentLayer< Dtype > +caffe::ReductionLayer< Dtype > +caffe::ReshapeLayer< Dtype > +caffe::ScaleLayer< Dtype > +caffe::SilenceLayer< Dtype > +caffe::SliceLayer< Dtype > +caffe::SoftmaxLayer< Dtype > +caffe::SplitLayer< Dtype > +caffe::SPPLayer< Dtype > +caffe::TileLayer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)=0
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)=0
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)=0
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + +

+Protected Attributes

LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::Layer< Dtype >

+ +

An interface for the units of computation which can be composed into a Net.

+

Layers must implement a Forward function, in which they take their input (bottom) Blobs (if any) and compute their output Blobs (if any). They may also implement a Backward function, in which they compute the error gradients with respect to their input Blobs, given the error gradients with their output Blobs.

+

Constructor & Destructor Documentation

+ +

§ Layer()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
caffe::Layer< Dtype >::Layer (const LayerParameter & param)
+ 		+inlineexplicit
+
+

You should not implement your own constructor. Any set up code should go to SetUp(), where the dimensions of the bottom blobs are provided to the layer.

+ +
+
+

Member Function Documentation

+ +

§ AllowForceBackward()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
virtual bool caffe::Layer< Dtype >::AllowForceBackward (const int bottom_index) const
+ 		+inlinevirtual
+
+ +

Return whether to allow force_backward for a given bottom blob index.

+

If AllowForceBackward(i) == false, we will ignore the force_backward setting and backpropagate to blob i only if it needs gradient information (as is done when force_backward == false).

+ +

Reimplemented in caffe::LSTMUnitLayer< Dtype >, caffe::RecurrentLayer< Dtype >, caffe::EuclideanLossLayer< Dtype >, caffe::ContrastiveLossLayer< Dtype >, and caffe::LossLayer< Dtype >.

+ +
+
+ +

§ AutoTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual bool caffe::Layer< Dtype >::AutoTopBlobs () const
+ 		+inlinevirtual
+
+ +

Return whether "anonymous" top blobs are created automatically by the layer.

+

If this method returns true, Net::Init will create enough "anonymous" top blobs to fulfill the requirement specified by ExactNumTopBlobs() or MinTopBlobs().

+ +

Reimplemented in caffe::LossLayer< Dtype >.

+ +
+
+ +

§ Backward()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::Layer< Dtype >::Backward (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+inline
+
+ +

Given the top blob error gradients, compute the bottom blob error gradients.

+
Parameters
+ + + + +
topthe output blobs, whose diff fields store the gradient of the error with respect to themselves
propagate_downa vector with equal length to bottom, with each index indicating whether to propagate the error gradients down to the bottom blob at the corresponding index
bottomthe input blobs, whose diff fields will store the gradient of the error with respect to themselves after Backward is run
+
+
+

The Backward wrapper calls the relevant device wrapper function (Backward_cpu or Backward_gpu) to compute the bottom blob diffs given the top blob diffs.

+

Your layer should implement Backward_cpu and (optionally) Backward_gpu.

+ +
+
+ +

§ CheckBlobCounts()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
virtual void caffe::Layer< Dtype >::CheckBlobCounts (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+inlineprotectedvirtual
+
+

Called by the parent Layer's SetUp to check that the number of bottom and top Blobs provided as input match the expected numbers specified by the {ExactNum,Min,Max}{Bottom,Top}Blobs() functions.

+ +
+
+ +

§ EqualNumBottomTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual bool caffe::Layer< Dtype >::EqualNumBottomTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns true if the layer requires an equal number of bottom and top blobs.

+

This method should be overridden to return true if your layer expects an equal number of bottom and top blobs.

+ +

Reimplemented in caffe::BaseConvolutionLayer< Dtype >.

+ +
+
+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::Layer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented in caffe::LSTMUnitLayer< Dtype >, caffe::InfogainLossLayer< Dtype >, caffe::BatchNormLayer< Dtype >, caffe::ArgMaxLayer< Dtype >, caffe::ContrastiveLossLayer< Dtype >, caffe::HDF5OutputLayer< Dtype >, caffe::AccuracyLayer< Dtype >, caffe::HDF5DataLayer< Dtype >, caffe::AbsValLayer< Dtype >, caffe::LRNLayer< Dtype >, caffe::WindowDataLayer< Dtype >, caffe::DummyDataLayer< Dtype >, caffe::ImageDataLayer< Dtype >, caffe::InputLayer< Dtype >, caffe::LossLayer< Dtype >, caffe::CropLayer< Dtype >, caffe::FlattenLayer< Dtype >, caffe::EmbedLayer< Dtype >, caffe::Im2colLayer< Dtype >, caffe::ReductionLayer< Dtype >, caffe::BatchReindexLayer< Dtype >, caffe::InnerProductLayer< Dtype >, caffe::ParameterLayer< Dtype >, caffe::ReshapeLayer< Dtype >, caffe::SliceLayer< Dtype >, caffe::SPPLayer< Dtype >, caffe::MemoryDataLayer< Dtype >, caffe::PoolingLayer< Dtype >, caffe::DataLayer< Dtype >, caffe::SplitLayer< Dtype >, caffe::MVNLayer< Dtype >, caffe::NeuronLayer< Dtype >, caffe::SoftmaxLayer< Dtype >, and caffe::TileLayer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::Layer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented in caffe::LSTMUnitLayer< Dtype >, caffe::SoftmaxWithLossLayer< Dtype >, caffe::BatchNormLayer< Dtype >, caffe::ArgMaxLayer< Dtype >, caffe::RecurrentLayer< Dtype >, caffe::LossLayer< Dtype >, caffe::HDF5OutputLayer< Dtype >, caffe::ScaleLayer< Dtype >, caffe::AbsValLayer< Dtype >, caffe::BiasLayer< Dtype >, caffe::LRNLayer< Dtype >, caffe::WindowDataLayer< Dtype >, caffe::ImageDataLayer< Dtype >, caffe::CropLayer< Dtype >, caffe::FlattenLayer< Dtype >, caffe::EmbedLayer< Dtype >, caffe::Im2colLayer< Dtype >, caffe::ReductionLayer< Dtype >, caffe::BatchReindexLayer< Dtype >, caffe::EltwiseLayer< Dtype >, caffe::InnerProductLayer< Dtype >, caffe::ParameterLayer< Dtype >, caffe::ReshapeLayer< Dtype >, caffe::SPPLayer< Dtype >, caffe::MemoryDataLayer< Dtype >, caffe::ConcatLayer< Dtype >, caffe::MVNLayer< Dtype >, caffe::NeuronLayer< Dtype >, caffe::SoftmaxLayer< Dtype >, caffe::SilenceLayer< Dtype >, and caffe::TileLayer< Dtype >.

+ +
+
+ +

§ Forward()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
Dtype caffe::Layer< Dtype >::Forward (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+inline
+
+ +

Given the bottom blobs, compute the top blobs and the loss.

+
Parameters
+ + + +
bottomthe input blobs, whose data fields store the input data for this layer
topthe preshaped output blobs, whose data fields will store this layers' outputs
+
+
+
Returns
The total loss from the layer.
+

The Forward wrapper calls the relevant device wrapper function (Forward_cpu or Forward_gpu) to compute the top blob values given the bottom blobs. If the layer has any non-zero loss_weights, the wrapper then computes and returns the loss.

+

Your layer should implement Forward_cpu and (optionally) Forward_gpu.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
virtual void caffe::Layer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+inlinevirtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented in caffe::BasePrefetchingDataLayer< Dtype >, caffe::SoftmaxWithLossLayer< Dtype >, caffe::InfogainLossLayer< Dtype >, caffe::SigmoidCrossEntropyLossLayer< Dtype >, caffe::BatchNormLayer< Dtype >, caffe::ContrastiveLossLayer< Dtype >, caffe::ArgMaxLayer< Dtype >, caffe::DropoutLayer< Dtype >, caffe::PReLULayer< Dtype >, caffe::ExpLayer< Dtype >, caffe::LogLayer< Dtype >, caffe::AccuracyLayer< Dtype >, caffe::PowerLayer< Dtype >, caffe::RecurrentLayer< Dtype >, caffe::ScaleLayer< Dtype >, caffe::AbsValLayer< Dtype >, caffe::HDF5OutputLayer< Dtype >, caffe::ThresholdLayer< Dtype >, caffe::HDF5DataLayer< Dtype >, caffe::BaseDataLayer< Dtype >, caffe::LossLayer< Dtype >, caffe::LRNLayer< Dtype >, caffe::BiasLayer< Dtype >, caffe::CropLayer< Dtype >, caffe::EmbedLayer< Dtype >, caffe::Im2colLayer< Dtype >, caffe::ReductionLayer< Dtype >, caffe::DummyDataLayer< Dtype >, caffe::EltwiseLayer< Dtype >, caffe::FilterLayer< Dtype >, caffe::InnerProductLayer< Dtype >, caffe::InputLayer< Dtype >, caffe::ReshapeLayer< Dtype >, caffe::SliceLayer< Dtype >, caffe::SPPLayer< Dtype >, caffe::BaseConvolutionLayer< Dtype >, caffe::PoolingLayer< Dtype >, caffe::ConcatLayer< Dtype >, caffe::PythonLayer< Dtype >, and caffe::ParameterLayer< Dtype >.

+ +
+
+ +

§ MaxBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::Layer< Dtype >::MaxBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some maximum number of bottom blobs.

+ +

Reimplemented in caffe::InfogainLossLayer< Dtype >, caffe::RecurrentLayer< Dtype >, caffe::ScaleLayer< Dtype >, and caffe::BiasLayer< Dtype >.

+ +
+
+ +

§ MaxTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::Layer< Dtype >::MaxTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some maximum number of top blobs.

+ +

Reimplemented in caffe::SoftmaxWithLossLayer< Dtype >, caffe::AccuracyLayer< Dtype >, caffe::PoolingLayer< Dtype >, and caffe::DataLayer< Dtype >.

+ +
+
+ +

§ MinBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::Layer< Dtype >::MinBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of bottom blobs.

+ +

Reimplemented in caffe::InfogainLossLayer< Dtype >, caffe::RecurrentLayer< Dtype >, caffe::ScaleLayer< Dtype >, caffe::BiasLayer< Dtype >, caffe::EltwiseLayer< Dtype >, caffe::FilterLayer< Dtype >, caffe::BaseConvolutionLayer< Dtype >, caffe::ConcatLayer< Dtype >, and caffe::SilenceLayer< Dtype >.

+ +
+
+ +

§ MinTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::Layer< Dtype >::MinTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of top blobs.

+ +

Reimplemented in caffe::SoftmaxWithLossLayer< Dtype >, caffe::AccuracyLayer< Dtype >, caffe::HDF5DataLayer< Dtype >, caffe::DummyDataLayer< Dtype >, caffe::InputLayer< Dtype >, caffe::FilterLayer< Dtype >, caffe::SliceLayer< Dtype >, caffe::PoolingLayer< Dtype >, caffe::BaseConvolutionLayer< Dtype >, caffe::DataLayer< Dtype >, and caffe::SplitLayer< Dtype >.

+ +
+
+ +

§ param_propagate_down()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
bool caffe::Layer< Dtype >::param_propagate_down (const int param_id)
+ 		+inline
+
+ +

Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.

+

You can safely ignore false values and always compute gradients for all parameters, but possibly with wasteful computation.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
virtual void caffe::Layer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+pure virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implemented in caffe::LSTMUnitLayer< Dtype >, caffe::SoftmaxWithLossLayer< Dtype >, caffe::InfogainLossLayer< Dtype >, caffe::SigmoidCrossEntropyLossLayer< Dtype >, caffe::MultinomialLogisticLossLayer< Dtype >, caffe::BatchNormLayer< Dtype >, caffe::EuclideanLossLayer< Dtype >, caffe::ArgMaxLayer< Dtype >, caffe::PReLULayer< Dtype >, caffe::DropoutLayer< Dtype >, caffe::BaseDataLayer< Dtype >, caffe::HDF5OutputLayer< Dtype >, caffe::AccuracyLayer< Dtype >, caffe::HDF5DataLayer< Dtype >, caffe::PythonLayer< Dtype >, caffe::RecurrentLayer< Dtype >, caffe::ScaleLayer< Dtype >, caffe::LossLayer< Dtype >, caffe::LRNLayer< Dtype >, caffe::BiasLayer< Dtype >, caffe::DummyDataLayer< Dtype >, caffe::InputLayer< Dtype >, caffe::CropLayer< Dtype >, caffe::FlattenLayer< Dtype >, caffe::EmbedLayer< Dtype >, caffe::Im2colLayer< Dtype >, caffe::ParameterLayer< Dtype >, caffe::ReductionLayer< Dtype >, caffe::BatchReindexLayer< Dtype >, caffe::EltwiseLayer< Dtype >, caffe::FilterLayer< Dtype >, caffe::InnerProductLayer< Dtype >, caffe::ReshapeLayer< Dtype >, caffe::SliceLayer< Dtype >, caffe::SPPLayer< Dtype >, caffe::BaseConvolutionLayer< Dtype >, caffe::PoolingLayer< Dtype >, caffe::ConcatLayer< Dtype >, caffe::NeuronLayer< Dtype >, caffe::SplitLayer< Dtype >, caffe::MVNLayer< Dtype >, caffe::SoftmaxLayer< Dtype >, caffe::SilenceLayer< Dtype >, and caffe::TileLayer< Dtype >.

+ +
+
+ +

§ SetLossWeights()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
void caffe::Layer< Dtype >::SetLossWeights (const vector< Blob< Dtype > *> & top)
+ 		+inlineprotected
+
+

Called by SetUp to initialize the weights associated with any top blobs in the loss function. Store non-zero loss weights in the diff blob.

+ +
+
+ +

§ SetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::Layer< Dtype >::SetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+inline
+
+ +

Implements common layer setup functionality.

+
Parameters
+ + + +
bottomthe preshaped input blobs
topthe allocated but unshaped output blobs, to be shaped by Reshape
+
+
+

Checks that the number of bottom and top blobs is correct. Calls LayerSetUp to do special layer setup for individual layer types, followed by Reshape to set up sizes of top blobs and internal buffers. Sets up the loss weight multiplier blobs for any non-zero loss weights. This method may not be overridden.

+ +
+
+

Member Data Documentation

+ +

§ blobs_

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + +
vector<shared_ptr<Blob<Dtype> > > caffe::Layer< Dtype >::blobs_
+ 		+protected
+
+

The vector that stores the learnable parameters as a set of blobs.

+ +
+
+ +

§ layer_param_

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + +
LayerParameter caffe::Layer< Dtype >::layer_param_
+ 		+protected
+
+

The protobuf that stores the layer parameters

+ +
+
+ +

§ loss_

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + +
vector<Dtype> caffe::Layer< Dtype >::loss_
+ 		+protected
+
+

The vector that indicates whether each top blob has a non-zero weight in the objective function.

+ +
+
+ +

§ param_propagate_down_

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + +
vector<bool> caffe::Layer< Dtype >::param_propagate_down_
+ 		+protected
+
+

Vector indicating whether to compute the diff of each param blob.

+ +
+
+ +

§ phase_

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + +
Phase caffe::Layer< Dtype >::phase_
+ 		+protected
+
+

The phase: TRAIN or TEST

+ +
+
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/layer.hpp
    • +
  • src/caffe/layer.cpp
    • +
+
+ +
+Generated on Mon Nov 21 2016 11:23:43 for Caffe by   +doxygen + 1.8.12 +
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caffe::LayerRegisterer< Dtype > Member List
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This is the complete list of members for caffe::LayerRegisterer< Dtype >, including all inherited members.

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LayerRegisterer(const string &type, shared_ptr< Layer< Dtype > >(*creator)(const LayerParameter &)) (defined in caffe::LayerRegisterer< Dtype >)caffe::LayerRegisterer< Dtype >inline
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+ LayerRegisterer (const string &type, shared_ptr< Layer< Dtype > >(*creator)(const LayerParameter &))
 
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caffe::LayerRegistry< Dtype > Member List
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This is the complete list of members for caffe::LayerRegistry< Dtype >, including all inherited members.

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AddCreator(const string &type, Creator creator) (defined in caffe::LayerRegistry< Dtype >)caffe::LayerRegistry< Dtype >inlinestatic
CreateLayer(const LayerParameter &param) (defined in caffe::LayerRegistry< Dtype >)caffe::LayerRegistry< Dtype >inlinestatic
Creator typedef (defined in caffe::LayerRegistry< Dtype >)caffe::LayerRegistry< Dtype >
CreatorRegistry typedef (defined in caffe::LayerRegistry< Dtype >)caffe::LayerRegistry< Dtype >
LayerTypeList() (defined in caffe::LayerRegistry< Dtype >)caffe::LayerRegistry< Dtype >inlinestatic
Registry() (defined in caffe::LayerRegistry< Dtype >)caffe::LayerRegistry< Dtype >inlinestatic
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caffe::LayerRegistry< Dtype > Class Template Reference
+
+
+ + + + + + +

+Public Types

+typedef shared_ptr< Layer< Dtype > >(* Creator) (const LayerParameter &)
 
+typedef std::map< string, Creator > CreatorRegistry
 
+ + + + + + + + + +

+Static Public Member Functions

+static CreatorRegistry & Registry ()
 
+static void AddCreator (const string &type, Creator creator)
 
+static shared_ptr< Layer< Dtype > > CreateLayer (const LayerParameter &param)
 
+static vector< string > LayerTypeList ()
 
+
The documentation for this class was generated from the following file: +
+ +
+Generated on Mon Nov 21 2016 11:23:43 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1LogLayer-members.html b/doxygen/classcaffe_1_1LogLayer-members.html new file mode 100644 index 00000000000..f149fb3cd7b --- /dev/null +++ b/doxygen/classcaffe_1_1LogLayer-members.html @@ -0,0 +1,124 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::LogLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::LogLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::LogLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::LogLayer< Dtype >protectedvirtual
backward_num_scale_ (defined in caffe::LogLayer< Dtype >)caffe::LogLayer< Dtype >protected
base_scale_ (defined in caffe::LogLayer< Dtype >)caffe::LogLayer< Dtype >protected
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::LogLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::LogLayer< Dtype >protectedvirtual
input_scale_ (defined in caffe::LogLayer< Dtype >)caffe::LogLayer< Dtype >protected
input_shift_ (defined in caffe::LogLayer< Dtype >)caffe::LogLayer< Dtype >protected
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::LogLayer< Dtype >virtual
LogLayer(const LayerParameter &param)caffe::LogLayer< Dtype >inlineexplicit
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
NeuronLayer(const LayerParameter &param) (defined in caffe::NeuronLayer< Dtype >)caffe::NeuronLayer< Dtype >inlineexplicit
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::NeuronLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::LogLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
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+ + diff --git a/doxygen/classcaffe_1_1LogLayer.html b/doxygen/classcaffe_1_1LogLayer.html new file mode 100644 index 00000000000..ab80557406a --- /dev/null +++ b/doxygen/classcaffe_1_1LogLayer.html @@ -0,0 +1,457 @@ + + + + + + + +Caffe: caffe::LogLayer< Dtype > Class Template Reference + + + + + + + + + +
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caffe::LogLayer< Dtype > Class Template Reference
+
+
+ +

Computes $ y = log_{\gamma}(\alpha x + \beta) $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $. + More...

+ +

#include <log_layer.hpp>

+
+Inheritance diagram for caffe::LogLayer< Dtype >:
+
+
+ + +caffe::NeuronLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

 LogLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::NeuronLayer< Dtype >
+ NeuronLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the error gradient w.r.t. the exp inputs. More...
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+Dtype base_scale_
 
+Dtype input_scale_
 
+Dtype input_shift_
 
+Dtype backward_num_scale_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::LogLayer< Dtype >

+ +

Computes $ y = log_{\gamma}(\alpha x + \beta) $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $.

+

Constructor & Destructor Documentation

+ +

§ LogLayer()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
caffe::LogLayer< Dtype >::LogLayer (const LayerParameter & param)
+ 		+inlineexplicit
+
+
Parameters
+ + +
paramprovides LogParameter log_param, with LogLayer options:
    +
  • scale (optional, default 1) the scale $ \alpha $
    • +
  • shift (optional, default 0) the shift $ \beta $
    • +
  • base (optional, default -1 for a value of $ e \approx 2.718 $) the base $ \gamma $
    • +
+
+
+
+ +
+
+

Member Function Documentation

+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::LogLayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the error gradient w.r.t. the exp inputs.

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
    +
  1. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
    2. +
+
propagate_downsee Layer::Backward.
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} y \alpha \log_e(gamma) $ if propagate_down[0]
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::LogLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+
Parameters
+ + + +
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $
    2. +
+
topoutput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the computed outputs $ y = log_{\gamma}(\alpha x + \beta) $
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::LogLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/layers/log_layer.hpp
    • +
  • src/caffe/layers/log_layer.cpp
    • +
+
+ +
+Generated on Mon Nov 21 2016 11:23:43 for Caffe by   +doxygen + 1.8.12 +
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caffe::LossLayer< Dtype > Member List
+
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This is the complete list of members for caffe::LossLayer< Dtype >, including all inherited members.

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AllowForceBackward(const int bottom_index) constcaffe::LossLayer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)=0caffe::Layer< Dtype >protectedpure virtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)=0caffe::Layer< Dtype >protectedpure virtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::LossLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
LossLayer(const LayerParameter &param) (defined in caffe::LossLayer< Dtype >)caffe::LossLayer< Dtype >inlineexplicit
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::LossLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::Layer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
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+ + diff --git a/doxygen/classcaffe_1_1LossLayer.html b/doxygen/classcaffe_1_1LossLayer.html new file mode 100644 index 00000000000..1b9c0cc309e --- /dev/null +++ b/doxygen/classcaffe_1_1LossLayer.html @@ -0,0 +1,447 @@ + + + + + + + +Caffe: caffe::LossLayer< Dtype > Class Template Reference + + + + + + + + + +
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caffe::LossLayer< Dtype > Class Template Reference
+
+
+ +

An interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth labels – and output a singleton Blob representing the loss. + More...

+ +

#include <loss_layer.hpp>

+
+Inheritance diagram for caffe::LossLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > +caffe::ContrastiveLossLayer< Dtype > +caffe::EuclideanLossLayer< Dtype > +caffe::HingeLossLayer< Dtype > +caffe::InfogainLossLayer< Dtype > +caffe::MultinomialLogisticLossLayer< Dtype > +caffe::SigmoidCrossEntropyLossLayer< Dtype > +caffe::SoftmaxWithLossLayer< Dtype > + +
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+Public Member Functions

+ LossLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
+virtual bool AutoTopBlobs () const
 For convenience and backwards compatibility, instruct the Net to automatically allocate a single top Blob for LossLayers, into which they output their singleton loss, (even if the user didn't specify one in the prototxt, etc.).
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)=0
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)=0
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::LossLayer< Dtype >

+ +

An interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth labels – and output a singleton Blob representing the loss.

+

LossLayers are typically only capable of backpropagating to their first input – the predictions.

+

Member Function Documentation

+ +

§ AllowForceBackward()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
virtual bool caffe::LossLayer< Dtype >::AllowForceBackward (const int bottom_index) const
+ 		+inlinevirtual
+
+

We usually cannot backpropagate to the labels; ignore force_backward for these inputs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +

Reimplemented in caffe::EuclideanLossLayer< Dtype >, and caffe::ContrastiveLossLayer< Dtype >.

+ +
+
+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::LossLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +

Reimplemented in caffe::InfogainLossLayer< Dtype >, and caffe::ContrastiveLossLayer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::LossLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +

Reimplemented in caffe::SoftmaxWithLossLayer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::LossLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +

Reimplemented in caffe::SoftmaxWithLossLayer< Dtype >, caffe::InfogainLossLayer< Dtype >, caffe::SigmoidCrossEntropyLossLayer< Dtype >, and caffe::ContrastiveLossLayer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::LossLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +

Reimplemented in caffe::SoftmaxWithLossLayer< Dtype >, caffe::InfogainLossLayer< Dtype >, caffe::SigmoidCrossEntropyLossLayer< Dtype >, caffe::MultinomialLogisticLossLayer< Dtype >, and caffe::EuclideanLossLayer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/layers/loss_layer.hpp
    • +
  • src/caffe/layers/loss_layer.cpp
    • +
+
+ +
+Generated on Mon Nov 21 2016 11:23:43 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1LossLayer.png b/doxygen/classcaffe_1_1LossLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..15bb9ea6f27a037e7320b78d6b4fd697d154ea80 GIT binary patch literal 4169 zcmdT{X;f257RFHl%hnBWYPi!FjNuJIF*23iz$B);*jy5O*SL)Lh8O(bG`WXZ<`OilJR9NMkq!6oM zLjOB@KT3;=P_5>zSzAYRi!_H zl;)k|P*Q1Oqi~sPofP^Yu{KnXDUJ3+O>ztA2y*~vy|W+fVqg#E88}9|IIw?+u~d*U zBQ*^x8|Rh|z^cUHjoVhlt;Z58Z(1ZKL`ocAC}E>b##pG%_==xV83(WM?Z_5LI(M^|{bS!Lu=S)Nj=e*vxfC|K6xUIj2E+c&CiX-DY)vka53x#_P5$ zSa--JhqkXyS7mrQ%U=2EVDlkOn9OlIuG{hvDQ#_KK3h{p;J}A9-5T0b$@j-sq(-y` zB{}pf5PFX+rM?Y^t?h~1EJNEiK>z=MOMKBg6;=kcwfZb;tjRyKafMkT(NWR(sEVv` zqKkrG{M@=iNX>-rYw-TdixSrYJ%C+a5S^1>ZI5{)3b z>lO~;(g?KY6vRDW8&Rfw&%Hrd6WwHLY z{NnnUy0EX>=g2CI!O)Svq$UWquB}0ztD20o%Ad2|uD1eIPmtC@r)zJoEtIJM#KHL3 zjWVn?)hEQS6yyQ;->3jYT@}U8RRH2eBQ*g2bMK11|3@7LpF=*>&wgjnpbXE^ex%=U z-d@H1VOqXXgR-FwFmo-vP1(K4C{MrPi9R+LJJJ`qP0LD)`Z_-uWN4#pbuIk@7MKO4 zA+4~*vi&5yBk+k1+_B1LcxN(EWVhXh^?Hq z#_D0rV@O!x(Ym4~SZ1a19RB=A)^>lxmxPkh?wp7yYX%SEW{V9$;BipGWqaY39pD*Z zhI2@_ft9T~Jllvrwga#2Kiv|Cw~?o4wsi)eLwAX$wu>EHFfWJDsmy}rj-R4NqcU$L zy^+Z8WtL;0J=v#wHwq;MLiv&rpeIM-4 z@NOjs&^UgXm$#aWjD*qFCiOvX{lq$+?@V=9%;CG;TB$g%U$_Q&szVTq<%#12wZkS) ziFgEt?%ON2&kd!=S0170h9E<%%|%11WWTe7?8Czl-&c}tgv%y-+ONssDRd3=oVX)n zd3gh#XKIC>S+)_SyaBXkMN3B;C>1{Up zlS<@son`Idz?Ws1EXtTwF)WGY39!iPZVeoUWP$dEa(!D;GrHJ)BGx|CiZew!5{c!S zODrbuNcDAE*o{N13F*89RBp*_yrt+5#}5{ywtHK+yHe;#1^(pBq#pgbvox;g?9Gs4 zO94G7P^doG>1XwJ)5{+4)D~tk3A(E{a|Uh}fh~k#(%bw6uG7jBDU7A3UbJx^YjVSj zA6iLa)b_nquOOP2Gn=Z3HR4OLsoMQt(nr7PIp+3;xVM~N+Mm|-xJZly!?6n-xP65ljz(iF4sqVExb+<&sGBVgaqgqHn<6EfcvA*> zmoK&+%6`B>_zmC{NMu1XL%LWx?RL zWFFjm1T#BbIWk!y5UQG+!9USb5&xc?_UKEr`HY`8b$TeI=ug?_s}_7QD3_(ea?xC+ zL8@Yo>6?Xape~Wno++q^?;^oZ(=}*VEP+nW(GAG5m(n$$U+R^fD&<^vZhv~eXd;oR_Q4xc-fE!_k{Fd?b+P<^0X`vJ|UVZ`0-u-!t=@{lOxr$KCHfs zCT7z-Bl83^tp%P!f+;Ve&hqYROa6EQmf6u>SWZcpL0sdLwarJ_SdU&WCdy*C2Ki=m zTGIa@uCl(Z4 + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::MSRAFiller< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::MSRAFiller< Dtype >, including all inherited members.

+ + + + + + +
Fill(Blob< Dtype > *blob) (defined in caffe::MSRAFiller< Dtype >)caffe::MSRAFiller< Dtype >inlinevirtual
Filler(const FillerParameter &param) (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >inlineexplicit
filler_param_ (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >protected
MSRAFiller(const FillerParameter &param) (defined in caffe::MSRAFiller< Dtype >)caffe::MSRAFiller< Dtype >inlineexplicit
~Filler() (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:44 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1MSRAFiller.html b/doxygen/classcaffe_1_1MSRAFiller.html new file mode 100644 index 00000000000..07c0c740588 --- /dev/null +++ b/doxygen/classcaffe_1_1MSRAFiller.html @@ -0,0 +1,126 @@ + + + + + + + +Caffe: caffe::MSRAFiller< Dtype > Class Template Reference + + + + + + + + + +
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caffe::MSRAFiller< Dtype > Class Template Reference
+
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+ +

Fills a Blob with values $ x \sim N(0, \sigma^2) $ where $ \sigma^2 $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average. + More...

+ +

#include <filler.hpp>

+
+Inheritance diagram for caffe::MSRAFiller< Dtype >:
+
+
+ + +caffe::Filler< Dtype > + +
+ + + + + + + + + +

+Public Member Functions

+ MSRAFiller (const FillerParameter &param)
 
+virtual void Fill (Blob< Dtype > *blob)
 
- Public Member Functions inherited from caffe::Filler< Dtype >
+ Filler (const FillerParameter &param)
 
+ + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Filler< Dtype >
+FillerParameter filler_param_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::MSRAFiller< Dtype >

+ +

Fills a Blob with values $ x \sim N(0, \sigma^2) $ where $ \sigma^2 $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average.

+

A Filler based on the paper [He, Zhang, Ren and Sun 2015]: Specifically accounts for ReLU nonlinearities.

+

Aside: for another perspective on the scaling factor, see the derivation of [Saxe, McClelland, and Ganguli 2013 (v3)].

+

It fills the incoming matrix by randomly sampling Gaussian data with std = sqrt(2 / n) where n is the fan_in, fan_out, or their average, depending on the variance_norm option. You should make sure the input blob has shape (num, a, b, c) where a * b * c = fan_in and num * b * c = fan_out. Note that this is currently not the case for inner product layers.

+
The documentation for this class was generated from the following file: +
+ +
+Generated on Mon Nov 21 2016 11:23:44 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1MSRAFiller.png b/doxygen/classcaffe_1_1MSRAFiller.png new file mode 100644 index 0000000000000000000000000000000000000000..e3665d8761bf2666247637dd71f4fb15b9d324e4 GIT binary patch literal 741 zcmeAS@N?(olHy`uVBq!ia0vp^D}gwGgBeJMPFs2gNJ#|vgt-3y{~ySF@#br3|Dg#$ z78oBmaDcV*jy#adQ4-`A%m7pb0#{Fk7%?y~m3X>1hEy=Vo%^=$wE_=oyYR~Q|4(es z<+#D=d1LDOs4Z)X#2gRI`_cR_HF8=i$3~A!46~L@5?Sr3<+<^dx^L#x`0Bs5$KOs| zGVRm8)3%dJ7Uo@=-rX#{xxl$9%W2ZC64za=iM?s3KJR(uk?rIC!se1yv~S9T>qjak zF7b|?Al6-G5G?GveFM|mRKLx4a)yMZEN!ule5$A zWxbOKZqA$g&Lg*I&0g(m)tB79oqv5L^|#8hogKY*`Oe+H{*ra&&4;hI?7QP-xxz2^ z{dI#JbBEoFv)+3f`S0~#J$J{w=}Z1Ej{nP6)3ilotxD-@<|WI5Rab_~pWLVNH)3+g zq$E>MRn0K}OF*v*vIwkWKOi90_T{4lYpKK~^%n{&9#1%TkGujW!k z7bXdZ$s!72Ae_GBd|37A_iG>Tmi&A)@%}Tj+OyZ#7c4E>WFDlxB%kYm?M%()kSTxi z-+yoWa)sByd1}e;Bc`*?f7Q)czU12dd0UQcX*s8y8ohe;+XhF50xh;vdDb&tX04oa zV?}OCPD>2q4d!>!f9FM+s@3EM&EK}S*nL*-j;zhx?w6V$E3f>%H`#W=w6=o2)sge7 z4hL|Q8=Af_TWK9M+u`-)W#=cGZF--aJFCiGZvUfzi*IL4%Q3yRLH6=xtFPjR`aOF^ z#QnUB7k^gXlRGe27?n)jyUtKEU;S3XLAkX`cZ6m!NaZI6C& v)dPO + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::MVNLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::MVNLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::MVNLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::MVNLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
eps_ (defined in caffe::MVNLayer< Dtype >)caffe::MVNLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::MVNLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::MVNLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::MVNLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::MVNLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlinevirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
mean_ (defined in caffe::MVNLayer< Dtype >)caffe::MVNLayer< Dtype >protected
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MVNLayer(const LayerParameter &param) (defined in caffe::MVNLayer< Dtype >)caffe::MVNLayer< Dtype >inlineexplicit
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::MVNLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
sum_multiplier_caffe::MVNLayer< Dtype >protected
temp_ (defined in caffe::MVNLayer< Dtype >)caffe::MVNLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::MVNLayer< Dtype >inlinevirtual
variance_ (defined in caffe::MVNLayer< Dtype >)caffe::MVNLayer< Dtype >protected
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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caffe::MVNLayer< Dtype > Class Template Reference
+
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Normalizes the input to have 0-mean and/or unit (1) variance. + More...

+ +

#include <mvn_layer.hpp>

+
+Inheritance diagram for caffe::MVNLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
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+Public Member Functions

+ MVNLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+Blob< Dtype > mean_
 
+Blob< Dtype > variance_
 
+Blob< Dtype > temp_
 
+Blob< Dtype > sum_multiplier_
 sum_multiplier is used to carry out sum using BLAS
 
+Dtype eps_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::MVNLayer< Dtype >

+ +

Normalizes the input to have 0-mean and/or unit (1) variance.

+

TODO(dox): thorough documentation for Forward, Backward, and proto params.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::MVNLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::MVNLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::MVNLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/layers/mvn_layer.hpp
    • +
  • src/caffe/layers/mvn_layer.cpp
    • +
+
+ +
+Generated on Mon Nov 21 2016 11:23:44 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1MVNLayer.png b/doxygen/classcaffe_1_1MVNLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..e708a7a2b92f99a22fc22ab8e8033da46f6cf809 GIT binary patch literal 716 zcmeAS@N?(olHy`uVBq!ia0vp^%YZn5gBeIJnfuQXNJ#|vgt-3y{~ySF@#br3|Dg#$ z78oBmaDcV*jy#adQ4-`A%m7pb0#{Fk7%?y~MSHq9hEy=Vo%^=$wE_=oyKv_H{}c10 zoi=!S9G)8PyLC;KnX2ix2mE!Lbxs%6Z}hly*VIr|(@Qvb(xMVx4wC z+qv&e^tzbak*DgDUxnR^nKZ}G;PrL3&qt0#z72YB__we4g87V{pM70d&!5}5HN$<~ z!7WQS+U;KOe1^NFm09HOi=sOx`zA+KKmWh^=G(L8Gmf45o|Y_iVRqRi-+5WCzO}DE z-13~BV)ymlnte=av6I+L3zYlfza-9BJZ1htnPqV=D&rjdAi~cTc>Rsm|efZ5W28)2BI?qp6*6gWYdwsj^ zGco1&<}+SzTU5kzKG-i(@5uE!Vfh1@#R-e9wnwJv6g`}w9?Vq0UE_-jQ zoqRiKR>tnX=UTgW9sgo2bwM^QFE4n@tGy~$t`#!hf4lwm?aCa3w=;7cg4@|XYw#-1 z*qvseTs+4&e%`Wneo#1y9cmt%`KVS6}3g!CtBBdPuV5L&nv&D z@s{LXVpKAH@a>T6|7b*TGBTMOGA=R!;xBtbgS}q9`^7GhsGQVQYhMXWdJLYfelF{r G5}E*?oKL&} literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1MemoryDataLayer-members.html b/doxygen/classcaffe_1_1MemoryDataLayer-members.html new file mode 100644 index 00000000000..948f1806016 --- /dev/null +++ b/doxygen/classcaffe_1_1MemoryDataLayer-members.html @@ -0,0 +1,143 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
caffe::MemoryDataLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::MemoryDataLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AddDatumVector(const vector< Datum > &datum_vector) (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >virtual
added_data_ (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >protected
added_label_ (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >protected
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BaseDataLayer< Dtype >inlinevirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BaseDataLayer< Dtype >inlinevirtual
BaseDataLayer(const LayerParameter &param) (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >explicit
batch_size() (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >inline
batch_size_ (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >protected
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
channels() (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >inline
channels_ (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
data_ (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >protected
data_transformer_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected
DataLayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >virtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::MemoryDataLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::MemoryDataLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::MemoryDataLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
has_new_data_ (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >protected
height() (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >inline
height_ (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >protected
IsShared() constcaffe::Layer< Dtype >inline
labels_ (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >protected
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BaseDataLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MemoryDataLayer(const LayerParameter &param) (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >inlineexplicit
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
n_ (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >protected
output_labels_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
pos_ (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >protected
Reset(Dtype *data, Dtype *label, int n) (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BaseDataLayer< Dtype >inlinevirtual
set_batch_size(int new_size) (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::BaseDataLayer< Dtype >inlinevirtual
size_ (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
transform_param_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected
type() constcaffe::MemoryDataLayer< Dtype >inlinevirtual
width() (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >inline
width_ (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >protected
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:44 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1MemoryDataLayer.html b/doxygen/classcaffe_1_1MemoryDataLayer.html new file mode 100644 index 00000000000..6ad0189ab0a --- /dev/null +++ b/doxygen/classcaffe_1_1MemoryDataLayer.html @@ -0,0 +1,379 @@ + + + + + + + +Caffe: caffe::MemoryDataLayer< Dtype > Class Template Reference + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
+
caffe::MemoryDataLayer< Dtype > Class Template Reference
+
+
+ +

Provides data to the Net from memory. + More...

+ +

#include <memory_data_layer.hpp>

+
+Inheritance diagram for caffe::MemoryDataLayer< Dtype >:
+
+
+ + +caffe::BaseDataLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ MemoryDataLayer (const LayerParameter &param)
 
+virtual void DataLayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
+virtual void AddDatumVector (const vector< Datum > &datum_vector)
 
+void Reset (Dtype *data, Dtype *label, int n)
 
+void set_batch_size (int new_size)
 
+int batch_size ()
 
+int channels ()
 
+int height ()
 
+int width ()
 
- Public Member Functions inherited from caffe::BaseDataLayer< Dtype >
+ BaseDataLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+int batch_size_
 
+int channels_
 
+int height_
 
+int width_
 
+int size_
 
+Dtype * data_
 
+Dtype * labels_
 
+int n_
 
+size_t pos_
 
+Blob< Dtype > added_data_
 
+Blob< Dtype > added_label_
 
+bool has_new_data_
 
- Protected Attributes inherited from caffe::BaseDataLayer< Dtype >
+TransformationParameter transform_param_
 
+shared_ptr< DataTransformer< Dtype > > data_transformer_
 
+bool output_labels_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::MemoryDataLayer< Dtype >

+ +

Provides data to the Net from memory.

+

TODO(dox): thorough documentation for Forward and proto params.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::MemoryDataLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::MemoryDataLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:44 for Caffe by   +doxygen + 1.8.12 +
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caffe::MultinomialLogisticLossLayer< Dtype > Member List
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+ +

This is the complete list of members for caffe::MultinomialLogisticLossLayer< Dtype >, including all inherited members.

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AllowForceBackward(const int bottom_index) constcaffe::LossLayer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::MultinomialLogisticLossLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::MultinomialLogisticLossLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::LossLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
LossLayer(const LayerParameter &param) (defined in caffe::LossLayer< Dtype >)caffe::LossLayer< Dtype >inlineexplicit
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MultinomialLogisticLossLayer(const LayerParameter &param) (defined in caffe::MultinomialLogisticLossLayer< Dtype >)caffe::MultinomialLogisticLossLayer< Dtype >inlineexplicit
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::MultinomialLogisticLossLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::MultinomialLogisticLossLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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+Generated on Mon Nov 21 2016 11:23:44 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1MultinomialLogisticLossLayer.html b/doxygen/classcaffe_1_1MultinomialLogisticLossLayer.html new file mode 100644 index 00000000000..dc08724950a --- /dev/null +++ b/doxygen/classcaffe_1_1MultinomialLogisticLossLayer.html @@ -0,0 +1,427 @@ + + + + + + + +Caffe: caffe::MultinomialLogisticLossLayer< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +List of all members
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+
caffe::MultinomialLogisticLossLayer< Dtype > Class Template Reference
+
+
+ +

Computes the multinomial logistic loss for a one-of-many classification task, directly taking a predicted probability distribution as input. + More...

+ +

#include <multinomial_logistic_loss_layer.hpp>

+
+Inheritance diagram for caffe::MultinomialLogisticLossLayer< Dtype >:
+
+
+ + +caffe::LossLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ MultinomialLogisticLossLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::LossLayer< Dtype >
+ LossLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
+virtual bool AutoTopBlobs () const
 For convenience and backwards compatibility, instruct the Net to automatically allocate a single top Blob for LossLayers, into which they output their singleton loss, (even if the user didn't specify one in the prototxt, etc.).
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Computes the multinomial logistic loss for a one-of-many classification task, directly taking a predicted probability distribution as input. More...
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the multinomial logistic loss error gradient w.r.t. the predictions. More...
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::MultinomialLogisticLossLayer< Dtype >

+ +

Computes the multinomial logistic loss for a one-of-many classification task, directly taking a predicted probability distribution as input.

+

When predictions are not already a probability distribution, you should instead use the SoftmaxWithLossLayer, which maps predictions to a distribution using the SoftmaxLayer, before computing the multinomial logistic loss. The SoftmaxWithLossLayer should be preferred over separate SoftmaxLayer + MultinomialLogisticLossLayer as its gradient computation is more numerically stable.

+
Parameters
+ + + +
bottominput Blob vector (length 2)
    +
  1. $ (N \times C \times H \times W) $ the predictions $ \hat{p} $, a Blob with values in $ [0, 1] $ indicating the predicted probability of each of the $ K = CHW $ classes. Each prediction vector $ \hat{p}_n $ should sum to 1 as in a probability distribution: $ \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 $.
    2. +
  2. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
    3. +
+
topoutput Blob vector (length 1)
    +
  1. $ (1 \times 1 \times 1 \times 1) $ the computed multinomial logistic loss: $ E = \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n}) $
    2. +
+
+
+
+

Member Function Documentation

+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::MultinomialLogisticLossLayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the multinomial logistic loss error gradient w.r.t. the predictions.

+

Gradients cannot be computed with respect to the label inputs (bottom[1]), so this method ignores bottom[1] and requires !propagate_down[1], crashing if propagate_down[1] is set.

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
    +
  1. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
    2. +
+
propagate_downsee Layer::Backward. propagate_down[1] must be false as we can't compute gradients with respect to the labels.
bottominput Blob vector (length 2)
    +
  1. $ (N \times C \times H \times W) $ the predictions $ \hat{p} $; Backward computes diff $ \frac{\partial E}{\partial \hat{p}} $
    2. +
  2. $ (N \times 1 \times 1 \times 1) $ the labels – ignored as we can't compute their error gradients
    3. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::MultinomialLogisticLossLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+ +

Computes the multinomial logistic loss for a one-of-many classification task, directly taking a predicted probability distribution as input.

+

When predictions are not already a probability distribution, you should instead use the SoftmaxWithLossLayer, which maps predictions to a distribution using the SoftmaxLayer, before computing the multinomial logistic loss. The SoftmaxWithLossLayer should be preferred over separate SoftmaxLayer + MultinomialLogisticLossLayer as its gradient computation is more numerically stable.

+
Parameters
+ + + +
bottominput Blob vector (length 2)
    +
  1. $ (N \times C \times H \times W) $ the predictions $ \hat{p} $, a Blob with values in $ [0, 1] $ indicating the predicted probability of each of the $ K = CHW $ classes. Each prediction vector $ \hat{p}_n $ should sum to 1 as in a probability distribution: $ \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 $.
    2. +
  2. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
    3. +
+
topoutput Blob vector (length 1)
    +
  1. $ (1 \times 1 \times 1 \times 1) $ the computed multinomial logistic loss: $ E = \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n}) $
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::MultinomialLogisticLossLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Reimplemented from caffe::LossLayer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:44 for Caffe by   +doxygen + 1.8.12 +
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caffe::NesterovSolver< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::NesterovSolver< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
action_request_function_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
add_callback(Callback *value) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
ApplyUpdate() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
callbacks() const (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
callbacks_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
CheckSnapshotWritePermissions() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
ClipGradients() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
ComputeUpdateValue(int param_id, Dtype rate) (defined in caffe::NesterovSolver< Dtype >)caffe::NesterovSolver< Dtype >protectedvirtual
current_step_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
DISABLE_COPY_AND_ASSIGN(NesterovSolver) (defined in caffe::NesterovSolver< Dtype >)caffe::NesterovSolver< Dtype >protected
DISABLE_COPY_AND_ASSIGN(SGDSolver) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
DISABLE_COPY_AND_ASSIGN(Solver) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
DisplayOutputBlobs(const int net_id) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
GetLearningRate() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
GetRequestedAction() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
history() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >inline
history_ (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
Init(const SolverParameter &param) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
InitTestNets() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
InitTrainNet() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
iter() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
iter_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
losses_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
NesterovSolver(const SolverParameter &param) (defined in caffe::NesterovSolver< Dtype >)caffe::NesterovSolver< Dtype >inlineexplicit
NesterovSolver(const string &param_file) (defined in caffe::NesterovSolver< Dtype >)caffe::NesterovSolver< Dtype >inlineexplicit
net() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
net_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Normalize(int param_id) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
param() const (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
param_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
PreSolve() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
Regularize(int param_id) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
requested_early_exit_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Restore(const char *resume_file) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
RestoreSolverStateFromBinaryProto(const string &state_file) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
RestoreSolverStateFromHDF5(const string &state_file) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
root_solver_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SetActionFunction(ActionCallback func) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
SGDSolver(const SolverParameter &param) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >inlineexplicit
SGDSolver(const string &param_file) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >inlineexplicit
smoothed_loss_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Snapshot() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
SnapshotFilename(const string extension) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SnapshotSolverState(const string &model_filename) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
SnapshotSolverStateToBinaryProto(const string &model_filename) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
SnapshotSolverStateToHDF5(const string &model_filename) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
SnapshotToBinaryProto() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SnapshotToHDF5() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Solve(const char *resume_file=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >virtual
Solve(const string resume_file) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
Solver(const SolverParameter &param, const Solver *root_solver=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >explicit
Solver(const string &param_file, const Solver *root_solver=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >explicit
Step(int iters) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
temp_ (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
Test(const int test_net_id=0) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
test_nets() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
test_nets_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
TestAll() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
type() constcaffe::NesterovSolver< Dtype >inlinevirtual
update_ (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
UpdateSmoothedLoss(Dtype loss, int start_iter, int average_loss) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
~Solver() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inlinevirtual
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+Generated on Mon Nov 21 2016 11:23:44 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1NesterovSolver.html b/doxygen/classcaffe_1_1NesterovSolver.html new file mode 100644 index 00000000000..3cf9f680e6d --- /dev/null +++ b/doxygen/classcaffe_1_1NesterovSolver.html @@ -0,0 +1,295 @@ + + + + + + + +Caffe: caffe::NesterovSolver< Dtype > Class Template Reference + + + + + + + + + +
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caffe::NesterovSolver< Dtype > Class Template Reference
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+Inheritance diagram for caffe::NesterovSolver< Dtype >:
+
+
+ + +caffe::SGDSolver< Dtype > +caffe::Solver< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ NesterovSolver (const SolverParameter &param)
 
+ NesterovSolver (const string &param_file)
 
+virtual const char * type () const
 Returns the solver type.
 
- Public Member Functions inherited from caffe::SGDSolver< Dtype >
+ SGDSolver (const SolverParameter &param)
 
+ SGDSolver (const string &param_file)
 
+const vector< shared_ptr< Blob< Dtype > > > & history ()
 
- Public Member Functions inherited from caffe::Solver< Dtype >
+ Solver (const SolverParameter &param, const Solver *root_solver=NULL)
 
+ Solver (const string &param_file, const Solver *root_solver=NULL)
 
+void Init (const SolverParameter &param)
 
+void InitTrainNet ()
 
+void InitTestNets ()
 
+void SetActionFunction (ActionCallback func)
 
+SolverAction::Enum GetRequestedAction ()
 
+virtual void Solve (const char *resume_file=NULL)
 
+void Solve (const string resume_file)
 
+void Step (int iters)
 
+void Restore (const char *resume_file)
 
+void Snapshot ()
 
+const SolverParameter & param () const
 
+shared_ptr< Net< Dtype > > net ()
 
+const vector< shared_ptr< Net< Dtype > > > & test_nets ()
 
+int iter ()
 
+const vector< Callback * > & callbacks () const
 
+void add_callback (Callback *value)
 
+void CheckSnapshotWritePermissions ()
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void ComputeUpdateValue (int param_id, Dtype rate)
 
+ DISABLE_COPY_AND_ASSIGN (NesterovSolver)
 
- Protected Member Functions inherited from caffe::SGDSolver< Dtype >
+void PreSolve ()
 
+Dtype GetLearningRate ()
 
+virtual void ApplyUpdate ()
 
+virtual void Normalize (int param_id)
 
+virtual void Regularize (int param_id)
 
+virtual void ClipGradients ()
 
+virtual void SnapshotSolverState (const string &model_filename)
 
+virtual void SnapshotSolverStateToBinaryProto (const string &model_filename)
 
+virtual void SnapshotSolverStateToHDF5 (const string &model_filename)
 
+virtual void RestoreSolverStateFromHDF5 (const string &state_file)
 
+virtual void RestoreSolverStateFromBinaryProto (const string &state_file)
 
+ DISABLE_COPY_AND_ASSIGN (SGDSolver)
 
- Protected Member Functions inherited from caffe::Solver< Dtype >
+string SnapshotFilename (const string extension)
 
+string SnapshotToBinaryProto ()
 
+string SnapshotToHDF5 ()
 
+void TestAll ()
 
+void Test (const int test_net_id=0)
 
+void DisplayOutputBlobs (const int net_id)
 
+void UpdateSmoothedLoss (Dtype loss, int start_iter, int average_loss)
 
+ DISABLE_COPY_AND_ASSIGN (Solver)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::SGDSolver< Dtype >
+vector< shared_ptr< Blob< Dtype > > > history_
 
+vector< shared_ptr< Blob< Dtype > > > update_
 
+vector< shared_ptr< Blob< Dtype > > > temp_
 
- Protected Attributes inherited from caffe::Solver< Dtype >
+SolverParameter param_
 
+int iter_
 
+int current_step_
 
+shared_ptr< Net< Dtype > > net_
 
+vector< shared_ptr< Net< Dtype > > > test_nets_
 
+vector< Callback * > callbacks_
 
+vector< Dtype > losses_
 
+Dtype smoothed_loss_
 
+const Solver *const root_solver_
 
+ActionCallback action_request_function_
 
+bool requested_early_exit_
 
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:44 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1NesterovSolver.png b/doxygen/classcaffe_1_1NesterovSolver.png new file mode 100644 index 0000000000000000000000000000000000000000..28ee7e1d91af544879ece5a5f5b337a6d6ad05a0 GIT binary patch literal 1149 zcmeAS@N?(olHy`uVBq!ia0vp^2Y|SPgBeJ+af+A#DTx4|5ZC|z{{xvX-h3_XKQsZz z0^gW-E1~I!ezr`LJ9fx4J^Lt9l|?*6sj{}w;P7a<+b`C9S^L^wTv zh3qc?N}ShX@H`d5=n=H+9m8jv^UT~2R_|i$S#yywZ~H+`H})OB?adB;U?Q;>vj6G`(87&&-S=v{g!!+Vq}v2 z-dkpyUPr&KbiMygXY#IM-3#|Fmp&_AT-sLXFsW}V_XG31-`k%(I?LD~Yxr#1;@C61 zzp`FM-HVx2%f|=~(TmF2mA_5|Dy*@uW~e(Rc}VlXq}XHUH?)1dE$>ls=v=?5B16QF z4UgpxNgjxMlJqQ>@l6MNLGX9~12b-w{5s7k^X!1%%ZzZ1X{xv7uYa(-z<#!={v2a; ztIwC3NW;|o^5xpEbw#7H-c0-bPuAeu|L|57P4D@2m+E*GMAAL2uJ<#}_@wA5{-rqF zW0GAUJRsO-Jg=0CV2HoUn~>7WSFoGc&%o-y-Txao+t^oVu}oNvGgPq!ditzonNpEH z43A-vV8yf}{d@4eZU2wOHQ3s|KH_>iOnCjwc}zA|=FGpf=f4w4i(#<1Bb(qZo1hw> zRR6wyTP}lm^yS^NwtY+fV45E|`%cwkp@WydPPxwXe@>zeb3qpOkFUM_>z{23GRZoB zdgWWDzxOO}TiiRe#z$R^77RL`DSW%u z?<>*U{-kvOHHn?omwA&Nel^T|l;_5<;ln<;FPpzIR5RXUYB{{=`FqA|bM7$hSaR2% zadwH}>hJy!4A>8)wOc-T*ZOy(-wvnE@7^`m2&{YBu-3_}YFX8cnm3^ + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::Net< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::Net< Dtype >, including all inherited members.

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AppendBottom(const NetParameter &param, const int layer_id, const int bottom_id, set< string > *available_blobs, map< string, int > *blob_name_to_idx)caffe::Net< Dtype >protected
AppendParam(const NetParameter &param, const int layer_id, const int param_id)caffe::Net< Dtype >protected
AppendTop(const NetParameter &param, const int layer_id, const int top_id, set< string > *available_blobs, map< string, int > *blob_name_to_idx)caffe::Net< Dtype >protected
Backward()caffe::Net< Dtype >
BackwardDebugInfo(const int layer_id)caffe::Net< Dtype >protected
BackwardFrom(int start) (defined in caffe::Net< Dtype >)caffe::Net< Dtype >
BackwardFromTo(int start, int end) (defined in caffe::Net< Dtype >)caffe::Net< Dtype >
BackwardTo(int end) (defined in caffe::Net< Dtype >)caffe::Net< Dtype >
blob_by_name(const string &blob_name) const (defined in caffe::Net< Dtype >)caffe::Net< Dtype >
blob_loss_weights() const (defined in caffe::Net< Dtype >)caffe::Net< Dtype >inline
blob_loss_weights_caffe::Net< Dtype >protected
blob_names() constcaffe::Net< Dtype >inline
blob_names_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
blob_names_index_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
blob_need_backward_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
blobs() constcaffe::Net< Dtype >inline
blobs_caffe::Net< Dtype >protected
bottom_id_vecs_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
bottom_ids(int i) constcaffe::Net< Dtype >inline
bottom_need_backward() const (defined in caffe::Net< Dtype >)caffe::Net< Dtype >inline
bottom_need_backward_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
bottom_vecs() constcaffe::Net< Dtype >inline
bottom_vecs_caffe::Net< Dtype >protected
ClearParamDiffs()caffe::Net< Dtype >
CopyTrainedLayersFrom(const NetParameter &param)caffe::Net< Dtype >
CopyTrainedLayersFrom(const string trained_filename) (defined in caffe::Net< Dtype >)caffe::Net< Dtype >
CopyTrainedLayersFromBinaryProto(const string trained_filename) (defined in caffe::Net< Dtype >)caffe::Net< Dtype >
CopyTrainedLayersFromHDF5(const string trained_filename) (defined in caffe::Net< Dtype >)caffe::Net< Dtype >
debug_info_caffe::Net< Dtype >protected
DISABLE_COPY_AND_ASSIGN(Net) (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
FilterNet(const NetParameter &param, NetParameter *param_filtered)caffe::Net< Dtype >static
Forward(Dtype *loss=NULL)caffe::Net< Dtype >
Forward(const vector< Blob< Dtype > * > &bottom, Dtype *loss=NULL)caffe::Net< Dtype >
ForwardBackward() (defined in caffe::Net< Dtype >)caffe::Net< Dtype >inline
ForwardDebugInfo(const int layer_id)caffe::Net< Dtype >protected
ForwardFrom(int start) (defined in caffe::Net< Dtype >)caffe::Net< Dtype >
ForwardFromTo(int start, int end)caffe::Net< Dtype >
ForwardPrefilled(Dtype *loss=NULL)caffe::Net< Dtype >inline
ForwardTo(int end) (defined in caffe::Net< Dtype >)caffe::Net< Dtype >
has_blob(const string &blob_name) const (defined in caffe::Net< Dtype >)caffe::Net< Dtype >
has_layer(const string &layer_name) const (defined in caffe::Net< Dtype >)caffe::Net< Dtype >
has_params_decay() const (defined in caffe::Net< Dtype >)caffe::Net< Dtype >inline
has_params_decay_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
has_params_lr() const (defined in caffe::Net< Dtype >)caffe::Net< Dtype >inline
has_params_lr_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
Init(const NetParameter &param)caffe::Net< Dtype >
input_blob_indices() const (defined in caffe::Net< Dtype >)caffe::Net< Dtype >inline
input_blobs() const (defined in caffe::Net< Dtype >)caffe::Net< Dtype >inline
layer_by_name(const string &layer_name) const (defined in caffe::Net< Dtype >)caffe::Net< Dtype >
layer_names() constcaffe::Net< Dtype >inline
layer_names_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
layer_names_index_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
layer_need_backward() const (defined in caffe::Net< Dtype >)caffe::Net< Dtype >inline
layer_need_backward_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
layers() constcaffe::Net< Dtype >inline
layers_caffe::Net< Dtype >protected
learnable_param_ids_caffe::Net< Dtype >protected
learnable_params() const (defined in caffe::Net< Dtype >)caffe::Net< Dtype >inline
learnable_params_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
memory_used_caffe::Net< Dtype >protected
name() constcaffe::Net< Dtype >inline
name_caffe::Net< Dtype >protected
Net(const NetParameter &param, const Net *root_net=NULL) (defined in caffe::Net< Dtype >)caffe::Net< Dtype >explicit
Net(const string &param_file, Phase phase, const int level=0, const vector< string > *stages=NULL, const Net *root_net=NULL) (defined in caffe::Net< Dtype >)caffe::Net< Dtype >explicit
net_input_blob_indices_caffe::Net< Dtype >protected
net_input_blobs_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
net_output_blob_indices_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
net_output_blobs_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
num_inputs() constcaffe::Net< Dtype >inline
num_outputs() const (defined in caffe::Net< Dtype >)caffe::Net< Dtype >inline
output_blob_indices() const (defined in caffe::Net< Dtype >)caffe::Net< Dtype >inline
output_blobs() const (defined in caffe::Net< Dtype >)caffe::Net< Dtype >inline
param_display_names() const (defined in caffe::Net< Dtype >)caffe::Net< Dtype >inline
param_display_names_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
param_id_vecs_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
param_layer_indices_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
param_names_index() const (defined in caffe::Net< Dtype >)caffe::Net< Dtype >inline
param_names_index_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
param_owners() const (defined in caffe::Net< Dtype >)caffe::Net< Dtype >inline
param_owners_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
params() constcaffe::Net< Dtype >inline
params_caffe::Net< Dtype >protected
params_lr() constcaffe::Net< Dtype >inline
params_lr_caffe::Net< Dtype >protected
params_weight_decay() constcaffe::Net< Dtype >inline
params_weight_decay_caffe::Net< Dtype >protected
phase() constcaffe::Net< Dtype >inline
phase_caffe::Net< Dtype >protected
Reshape()caffe::Net< Dtype >
root_net_caffe::Net< Dtype >protected
set_debug_info(const bool value) (defined in caffe::Net< Dtype >)caffe::Net< Dtype >inline
ShareTrainedLayersWith(const Net *other)caffe::Net< Dtype >
ShareWeights()caffe::Net< Dtype >
StateMeetsRule(const NetState &state, const NetStateRule &rule, const string &layer_name)caffe::Net< Dtype >static
ToHDF5(const string &filename, bool write_diff=false) constcaffe::Net< Dtype >
top_id_vecs_ (defined in caffe::Net< Dtype >)caffe::Net< Dtype >protected
top_ids(int i) constcaffe::Net< Dtype >inline
top_vecs() constcaffe::Net< Dtype >inline
top_vecs_caffe::Net< Dtype >protected
ToProto(NetParameter *param, bool write_diff=false) constcaffe::Net< Dtype >
Update()caffe::Net< Dtype >
UpdateDebugInfo(const int param_id)caffe::Net< Dtype >protected
~Net() (defined in caffe::Net< Dtype >)caffe::Net< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:44 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1Net.html b/doxygen/classcaffe_1_1Net.html new file mode 100644 index 00000000000..62bfd95ef33 --- /dev/null +++ b/doxygen/classcaffe_1_1Net.html @@ -0,0 +1,628 @@ + + + + + + + +Caffe: caffe::Net< Dtype > Class Template Reference + + + + + + + + + +
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caffe::Net< Dtype > Class Template Reference
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+ +

Connects Layers together into a directed acyclic graph (DAG) specified by a NetParameter. + More...

+ +

#include <net.hpp>

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+Public Member Functions

+ Net (const NetParameter &param, const Net *root_net=NULL)
 
+ Net (const string &param_file, Phase phase, const int level=0, const vector< string > *stages=NULL, const Net *root_net=NULL)
 
+void Init (const NetParameter &param)
 Initialize a network with a NetParameter.
 
+const vector< Blob< Dtype > * > & Forward (Dtype *loss=NULL)
 Run Forward and return the result.
 
+const vector< Blob< Dtype > * > & ForwardPrefilled (Dtype *loss=NULL)
 DEPRECATED; use Forward() instead.
 
Dtype ForwardFromTo (int start, int end)
 
+Dtype ForwardFrom (int start)
 
+Dtype ForwardTo (int end)
 
+const vector< Blob< Dtype > * > & Forward (const vector< Blob< Dtype > * > &bottom, Dtype *loss=NULL)
 DEPRECATED; set input blobs then use Forward() instead.
 
+void ClearParamDiffs ()
 Zeroes out the diffs of all net parameters. Should be run before Backward.
 
void Backward ()
 
+void BackwardFromTo (int start, int end)
 
+void BackwardFrom (int start)
 
+void BackwardTo (int end)
 
void Reshape ()
 Reshape all layers from bottom to top. More...
 
+Dtype ForwardBackward ()
 
+void Update ()
 Updates the network weights based on the diff values computed.
 
void ShareWeights ()
 Shares weight data of owner blobs with shared blobs. More...
 
+void ShareTrainedLayersWith (const Net *other)
 For an already initialized net, implicitly copies (i.e., using no additional memory) the pre-trained layers from another Net.
 
+void CopyTrainedLayersFrom (const NetParameter &param)
 For an already initialized net, copies the pre-trained layers from another Net.
 
+void CopyTrainedLayersFrom (const string trained_filename)
 
+void CopyTrainedLayersFromBinaryProto (const string trained_filename)
 
+void CopyTrainedLayersFromHDF5 (const string trained_filename)
 
+void ToProto (NetParameter *param, bool write_diff=false) const
 Writes the net to a proto.
 
+void ToHDF5 (const string &filename, bool write_diff=false) const
 Writes the net to an HDF5 file.
 
+const string & name () const
 returns the network name.
 
+const vector< string > & layer_names () const
 returns the layer names
 
+const vector< string > & blob_names () const
 returns the blob names
 
+const vector< shared_ptr< Blob< Dtype > > > & blobs () const
 returns the blobs
 
+const vector< shared_ptr< Layer< Dtype > > > & layers () const
 returns the layers
 
+Phase phase () const
 returns the phase: TRAIN or TEST
 
+const vector< vector< Blob< Dtype > * > > & bottom_vecs () const
 returns the bottom vecs for each layer – usually you won't need this unless you do per-layer checks such as gradients.
 
+const vector< vector< Blob< Dtype > * > > & top_vecs () const
 returns the top vecs for each layer – usually you won't need this unless you do per-layer checks such as gradients.
 
+const vector< int > & top_ids (int i) const
 returns the ids of the top blobs of layer i
 
+const vector< int > & bottom_ids (int i) const
 returns the ids of the bottom blobs of layer i
 
+const vector< vector< bool > > & bottom_need_backward () const
 
+const vector< Dtype > & blob_loss_weights () const
 
+const vector< bool > & layer_need_backward () const
 
+const vector< shared_ptr< Blob< Dtype > > > & params () const
 returns the parameters
 
+const vector< Blob< Dtype > * > & learnable_params () const
 
+const vector< float > & params_lr () const
 returns the learnable parameter learning rate multipliers
 
+const vector< bool > & has_params_lr () const
 
+const vector< float > & params_weight_decay () const
 returns the learnable parameter decay multipliers
 
+const vector< bool > & has_params_decay () const
 
+const map< string, int > & param_names_index () const
 
+const vector< int > & param_owners () const
 
+const vector< string > & param_display_names () const
 
+int num_inputs () const
 Input and output blob numbers.
 
+int num_outputs () const
 
+const vector< Blob< Dtype > * > & input_blobs () const
 
+const vector< Blob< Dtype > * > & output_blobs () const
 
+const vector< int > & input_blob_indices () const
 
+const vector< int > & output_blob_indices () const
 
+bool has_blob (const string &blob_name) const
 
+const shared_ptr< Blob< Dtype > > blob_by_name (const string &blob_name) const
 
+bool has_layer (const string &layer_name) const
 
+const shared_ptr< Layer< Dtype > > layer_by_name (const string &layer_name) const
 
+void set_debug_info (const bool value)
 
+ + + + + + + +

+Static Public Member Functions

+static void FilterNet (const NetParameter &param, NetParameter *param_filtered)
 Remove layers that the user specified should be excluded given the current phase, level, and stage.
 
+static bool StateMeetsRule (const NetState &state, const NetStateRule &rule, const string &layer_name)
 return whether NetState state meets NetStateRule rule
 
+ + + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+void AppendTop (const NetParameter &param, const int layer_id, const int top_id, set< string > *available_blobs, map< string, int > *blob_name_to_idx)
 Append a new top blob to the net.
 
+int AppendBottom (const NetParameter &param, const int layer_id, const int bottom_id, set< string > *available_blobs, map< string, int > *blob_name_to_idx)
 Append a new bottom blob to the net.
 
+void AppendParam (const NetParameter &param, const int layer_id, const int param_id)
 Append a new parameter blob to the net.
 
+void ForwardDebugInfo (const int layer_id)
 Helper for displaying debug info in Forward.
 
+void BackwardDebugInfo (const int layer_id)
 Helper for displaying debug info in Backward.
 
+void UpdateDebugInfo (const int param_id)
 Helper for displaying debug info in Update.
 
+ DISABLE_COPY_AND_ASSIGN (Net)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+string name_
 The network name.
 
+Phase phase_
 The phase: TRAIN or TEST.
 
+vector< shared_ptr< Layer< Dtype > > > layers_
 Individual layers in the net.
 
+vector< string > layer_names_
 
+map< string, int > layer_names_index_
 
+vector< bool > layer_need_backward_
 
+vector< shared_ptr< Blob< Dtype > > > blobs_
 the blobs storing intermediate results between the layer.
 
+vector< string > blob_names_
 
+map< string, int > blob_names_index_
 
+vector< bool > blob_need_backward_
 
vector< vector< Blob< Dtype > * > > bottom_vecs_
 
+vector< vector< int > > bottom_id_vecs_
 
+vector< vector< bool > > bottom_need_backward_
 
+vector< vector< Blob< Dtype > * > > top_vecs_
 top_vecs stores the vectors containing the output for each layer
 
+vector< vector< int > > top_id_vecs_
 
vector< Dtype > blob_loss_weights_
 
+vector< vector< int > > param_id_vecs_
 
+vector< int > param_owners_
 
+vector< string > param_display_names_
 
+vector< pair< int, int > > param_layer_indices_
 
+map< string, int > param_names_index_
 
+vector< int > net_input_blob_indices_
 blob indices for the input and the output of the net
 
+vector< int > net_output_blob_indices_
 
+vector< Blob< Dtype > * > net_input_blobs_
 
+vector< Blob< Dtype > * > net_output_blobs_
 
+vector< shared_ptr< Blob< Dtype > > > params_
 The parameters in the network.
 
+vector< Blob< Dtype > * > learnable_params_
 
vector< int > learnable_param_ids_
 
+vector< float > params_lr_
 the learning rate multipliers for learnable_params_
 
+vector< bool > has_params_lr_
 
+vector< float > params_weight_decay_
 the weight decay multipliers for learnable_params_
 
+vector< bool > has_params_decay_
 
+size_t memory_used_
 The bytes of memory used by this net.
 
+bool debug_info_
 Whether to compute and display debug info for the net.
 
+const Net *const root_net_
 The root net that actually holds the shared layers in data parallelism.
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::Net< Dtype >

+ +

Connects Layers together into a directed acyclic graph (DAG) specified by a NetParameter.

+

TODO(dox): more thorough description.

+

Member Function Documentation

+ +

§ Backward()

+ +
+
+
+template<typename Dtype >
+ + + + + + + +
void caffe::Net< Dtype >::Backward ()
+
+

The network backward should take no input and output, since it solely computes the gradient w.r.t the parameters, and the data has already been provided during the forward pass.

+ +
+
+ +

§ ForwardFromTo()

+ +
+
+
+template<typename Dtype >
+ + + + + + + + + + + + + + + + + + +
Dtype caffe::Net< Dtype >::ForwardFromTo (int start,
int end 
)
+
+

The From and To variants of Forward and Backward operate on the (topological) ordering by which the net is specified. For general DAG networks, note that (1) computing from one layer to another might entail extra computation on unrelated branches, and (2) computation starting in the middle may be incorrect if all of the layers of a fan-in are not included.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + + + +
void caffe::Net< Dtype >::Reshape ()
+
+ +

Reshape all layers from bottom to top.

+

This is useful to propagate changes to layer sizes without running a forward pass, e.g. to compute output feature size.

+ +
+
+ +

§ ShareWeights()

+ +
+
+
+template<typename Dtype >
+ + + + + + + +
void caffe::Net< Dtype >::ShareWeights ()
+
+ +

Shares weight data of owner blobs with shared blobs.

+

Note: this is called by Net::Init, and thus should normally not be called manually.

+ +
+
+

Member Data Documentation

+ +

§ blob_loss_weights_

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + +
vector<Dtype> caffe::Net< Dtype >::blob_loss_weights_
+ 		+protected
+
+

Vector of weight in the loss (or objective) function of each net blob, indexed by blob_id.

+ +
+
+ +

§ bottom_vecs_

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + +
vector<vector<Blob<Dtype>*> > caffe::Net< Dtype >::bottom_vecs_
+ 		+protected
+
+

bottom_vecs stores the vectors containing the input for each layer. They don't actually host the blobs (blobs_ does), so we simply store pointers.

+ +
+
+ +

§ learnable_param_ids_

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + +
vector<int> caffe::Net< Dtype >::learnable_param_ids_
+ 		+protected
+
+

The mapping from params_ -> learnable_params_: we have learnable_param_ids_.size() == params_.size(), and learnable_params_[learnable_param_ids_[i]] == params_[i].get() if and only if params_[i] is an "owner"; otherwise, params_[i] is a sharer and learnable_params_[learnable_param_ids_[i]] gives its owner.

+ +
+
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/net.hpp
    • +
  • src/caffe/net.cpp
    • +
+
+ +
+Generated on Mon Nov 21 2016 11:23:44 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1NeuronLayer-members.html b/doxygen/classcaffe_1_1NeuronLayer-members.html new file mode 100644 index 00000000000..e178cac0115 --- /dev/null +++ b/doxygen/classcaffe_1_1NeuronLayer-members.html @@ -0,0 +1,119 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
caffe::NeuronLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::NeuronLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)=0caffe::Layer< Dtype >protectedpure virtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)=0caffe::Layer< Dtype >protectedpure virtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlinevirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
NeuronLayer(const LayerParameter &param) (defined in caffe::NeuronLayer< Dtype >)caffe::NeuronLayer< Dtype >inlineexplicit
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::NeuronLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::Layer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:44 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1NeuronLayer.html b/doxygen/classcaffe_1_1NeuronLayer.html new file mode 100644 index 00000000000..08b8e4977e5 --- /dev/null +++ b/doxygen/classcaffe_1_1NeuronLayer.html @@ -0,0 +1,366 @@ + + + + + + + +Caffe: caffe::NeuronLayer< Dtype > Class Template Reference + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+Public Member Functions | +List of all members
+
+
caffe::NeuronLayer< Dtype > Class Template Reference
+
+
+ +

An interface for layers that take one blob as input ( $ x $) and produce one equally-sized blob as output ( $ y $), where each element of the output depends only on the corresponding input element. + More...

+ +

#include <neuron_layer.hpp>

+
+Inheritance diagram for caffe::NeuronLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > +caffe::AbsValLayer< Dtype > +caffe::BNLLLayer< Dtype > +caffe::DropoutLayer< Dtype > +caffe::ELULayer< Dtype > +caffe::ExpLayer< Dtype > +caffe::LogLayer< Dtype > +caffe::PowerLayer< Dtype > +caffe::PReLULayer< Dtype > +caffe::ReLULayer< Dtype > +caffe::SigmoidLayer< Dtype > +caffe::TanHLayer< Dtype > +caffe::ThresholdLayer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ NeuronLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)=0
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)=0
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::NeuronLayer< Dtype >

+ +

An interface for layers that take one blob as input ( $ x $) and produce one equally-sized blob as output ( $ y $), where each element of the output depends only on the corresponding input element.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::NeuronLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +

Reimplemented in caffe::AbsValLayer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::NeuronLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +

Reimplemented in caffe::AbsValLayer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::NeuronLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +

Reimplemented in caffe::PReLULayer< Dtype >, and caffe::DropoutLayer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:44 for Caffe by   +doxygen + 1.8.12 +
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caffe::P2PSync< Dtype > Member List
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This is the complete list of members for caffe::P2PSync< Dtype >, including all inherited members.

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children_ (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >protected
configure(Solver< Dtype > *solver) const (defined in caffe::GPUParams< Dtype >)caffe::GPUParams< Dtype >
data() const (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inline
data_ (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
diff() const (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inline
diff_ (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
DISABLE_COPY_AND_ASSIGN(Params) (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
GPUParams(shared_ptr< Solver< Dtype > > root_solver, int device) (defined in caffe::GPUParams< Dtype >)caffe::GPUParams< Dtype >
initial_iter() const (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >inline
initial_iter_ (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >protected
InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadinline
InternalThreadEntry() (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >protectedvirtual
is_started() const (defined in caffe::InternalThread)caffe::InternalThread
must_stop() (defined in caffe::InternalThread)caffe::InternalThreadprotected
on_gradients_ready() (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >protectedvirtual
on_start() (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >protectedvirtual
P2PSync(shared_ptr< Solver< Dtype > > root_solver, P2PSync< Dtype > *parent, const SolverParameter &param) (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >explicit
Params(shared_ptr< Solver< Dtype > > root_solver) (defined in caffe::Params< Dtype >)caffe::Params< Dtype >explicit
parent_ (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >protected
parent_grads_ (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >protected
Prepare(const vector< int > &gpus, vector< shared_ptr< P2PSync< Dtype > > > *syncs) (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >
queue_ (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >protected
Run(const vector< int > &gpus) (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >
size() const (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inline
size_ (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
solver() const (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >inline
solver_ (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >protected
StartInternalThread()caffe::InternalThread
StopInternalThread()caffe::InternalThread
~GPUParams() (defined in caffe::GPUParams< Dtype >)caffe::GPUParams< Dtype >virtual
~InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadvirtual
~P2PSync() (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >virtual
~Params() (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inlinevirtual
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+Generated on Mon Nov 21 2016 11:23:45 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1P2PSync.html b/doxygen/classcaffe_1_1P2PSync.html new file mode 100644 index 00000000000..ebb212f150d --- /dev/null +++ b/doxygen/classcaffe_1_1P2PSync.html @@ -0,0 +1,196 @@ + + + + + + + +Caffe: caffe::P2PSync< Dtype > Class Template Reference + + + + + + + + + +
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caffe::P2PSync< Dtype > Class Template Reference
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+Inheritance diagram for caffe::P2PSync< Dtype >:
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+ + +caffe::GPUParams< Dtype > +caffe::Solver< Dtype >::Callback +caffe::InternalThread +caffe::Params< Dtype > + +
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+Public Member Functions

+ P2PSync (shared_ptr< Solver< Dtype > > root_solver, P2PSync< Dtype > *parent, const SolverParameter &param)
 
+const shared_ptr< Solver< Dtype > > & solver () const
 
+void Run (const vector< int > &gpus)
 
+void Prepare (const vector< int > &gpus, vector< shared_ptr< P2PSync< Dtype > > > *syncs)
 
+const int initial_iter () const
 
- Public Member Functions inherited from caffe::GPUParams< Dtype >
+ GPUParams (shared_ptr< Solver< Dtype > > root_solver, int device)
 
+void configure (Solver< Dtype > *solver) const
 
- Public Member Functions inherited from caffe::Params< Dtype >
+ Params (shared_ptr< Solver< Dtype > > root_solver)
 
+size_t size () const
 
+Dtype * data () const
 
+Dtype * diff () const
 
- Public Member Functions inherited from caffe::InternalThread
void StartInternalThread ()
 
void StopInternalThread ()
 
+bool is_started () const
 
+ + + + + + + + + + + + + +

+Protected Member Functions

+void on_start ()
 
+void on_gradients_ready ()
 
+void InternalThreadEntry ()
 
- Protected Member Functions inherited from caffe::Params< Dtype >
+ DISABLE_COPY_AND_ASSIGN (Params)
 
- Protected Member Functions inherited from caffe::InternalThread
+bool must_stop ()
 
+ + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+P2PSync< Dtype > * parent_
 
+vector< P2PSync< Dtype > * > children_
 
+BlockingQueue< P2PSync< Dtype > * > queue_
 
+const int initial_iter_
 
+Dtype * parent_grads_
 
+shared_ptr< Solver< Dtype > > solver_
 
- Protected Attributes inherited from caffe::Params< Dtype >
+const size_t size_
 
+Dtype * data_
 
+Dtype * diff_
 
+
The documentation for this class was generated from the following files: +
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+Generated on Mon Nov 21 2016 11:23:45 for Caffe by   +doxygen + 1.8.12 +
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caffe::PReLULayer< Dtype > Member List
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This is the complete list of members for caffe::PReLULayer< Dtype >, including all inherited members.

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AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
backward_buff_ (defined in caffe::PReLULayer< Dtype >)caffe::PReLULayer< Dtype >protected
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::PReLULayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::PReLULayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
bottom_memory_ (defined in caffe::PReLULayer< Dtype >)caffe::PReLULayer< Dtype >protected
channel_shared_ (defined in caffe::PReLULayer< Dtype >)caffe::PReLULayer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::PReLULayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::PReLULayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::PReLULayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
multiplier_ (defined in caffe::PReLULayer< Dtype >)caffe::PReLULayer< Dtype >protected
NeuronLayer(const LayerParameter &param) (defined in caffe::NeuronLayer< Dtype >)caffe::NeuronLayer< Dtype >inlineexplicit
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
PReLULayer(const LayerParameter &param)caffe::PReLULayer< Dtype >inlineexplicit
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::PReLULayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::PReLULayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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+Generated on Mon Nov 21 2016 11:23:45 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1PReLULayer.html b/doxygen/classcaffe_1_1PReLULayer.html new file mode 100644 index 00000000000..09226e444ea --- /dev/null +++ b/doxygen/classcaffe_1_1PReLULayer.html @@ -0,0 +1,506 @@ + + + + + + + +Caffe: caffe::PReLULayer< Dtype > Class Template Reference + + + + + + + + + +
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Caffe +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
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caffe::PReLULayer< Dtype > Class Template Reference
+
+
+ +

Parameterized Rectified Linear Unit non-linearity $ y_i = \max(0, x_i) + a_i \min(0, x_i) $. The differences from ReLULayer are 1) negative slopes are learnable though backprop and 2) negative slopes can vary across channels. The number of axes of input blob should be greater than or equal to 2. The 1st axis (0-based) is seen as channels. + More...

+ +

#include <prelu_layer.hpp>

+
+Inheritance diagram for caffe::PReLULayer< Dtype >:
+
+
+ + +caffe::NeuronLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

 PReLULayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::NeuronLayer< Dtype >
+ NeuronLayer (const LayerParameter &param)
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the error gradient w.r.t. the PReLU inputs. More...
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+bool channel_shared_
 
+Blob< Dtype > multiplier_
 
+Blob< Dtype > backward_buff_
 
+Blob< Dtype > bottom_memory_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::PReLULayer< Dtype >

+ +

Parameterized Rectified Linear Unit non-linearity $ y_i = \max(0, x_i) + a_i \min(0, x_i) $. The differences from ReLULayer are 1) negative slopes are learnable though backprop and 2) negative slopes can vary across channels. The number of axes of input blob should be greater than or equal to 2. The 1st axis (0-based) is seen as channels.

+

Constructor & Destructor Documentation

+ +

§ PReLULayer()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
caffe::PReLULayer< Dtype >::PReLULayer (const LayerParameter & param)
+ 		+inlineexplicit
+
+
Parameters
+ + +
paramprovides PReLUParameter prelu_param, with PReLULayer options:
    +
  • filler (optional, FillerParameter, default {'type': constant 'value':0.25}).
    • +
  • channel_shared (optional, default false). negative slopes are shared across channels.
    • +
+
+
+
+ +
+
+

Member Function Documentation

+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::PReLULayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the error gradient w.r.t. the PReLU inputs.

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
    +
  1. $ (N \times C \times ...) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
    2. +
+
propagate_downsee Layer::Backward.
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times ...) $ the inputs $ x $; For each channel $i$, backward fills their diff with gradients $ \frac{\partial E}{\partial x_i} = \left\{ \begin{array}{lr} a_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\ \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i > 0 \end{array} \right. $. If param_propagate_down_[0] is true, it fills the diff with gradients $ \frac{\partial E}{\partial a_i} = \left\{ \begin{array}{lr} \sum_{x_i} x_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\ 0 & \mathrm{if} \; x_i > 0 \end{array} \right. $.
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::PReLULayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+
Parameters
+ + + +
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times ...) $ the inputs $ x $
    2. +
+
topoutput Blob vector (length 1)
    +
  1. $ (N \times C \times ...) $ the computed outputs for each channel $i$ $ y_i = \max(0, x_i) + a_i \min(0, x_i) $.
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::PReLULayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::PReLULayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Reimplemented from caffe::NeuronLayer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:45 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1PReLULayer.png b/doxygen/classcaffe_1_1PReLULayer.png new file mode 100644 index 0000000000000000000000000000000000000000..4b1446e86c0a1cc3614bf05a22713b20b5d15642 GIT binary patch literal 1066 zcmeAS@N?(olHy`uVBq!ia0vp^n}N84gBeK9F4tNQq$C1-LR|m<{|{uoc=NTi|Ih>= z3ycpOIKbL@M;^%KC<*clW&kPzfvcxNj2IZ0cY3-whEy=Vo%^uwvlb7lI{V7^|4(ec zJ0XE1=kVWGp%Y`Cny4CnOX&GMeM(T2kBVoM*Fhc6e_UyvpRS91-u8CYJkLw9x$Dn= z(eb=$diW1l38&d{n-XQu@~>?_Jxo}KecYKF(8FMj)t z=hwYG%k%m0#?&?&@v<7}%UL0_y@TA}&pds;?Wg$UQ?E8@niktW=KE^VgSc@N~Ba0Bm2 z`{$j}T)Jf%L(Lb42p?`Ed%4$+`X~Ol#PL>np4AqnI%Fl9dhuRw7pJZ}PR$BPg z&zUn#6Y6~T@7VFavaeDiV3L*And3>{dGagg%ujpX{`}9oka?dzUv|rX)8~4<;P|({ zEO(FJ^cCHExF+iMY{_@?ez`rnnE9vsJWr+Gn;-9XX`X&>EUwPGxz;Ww;`6gdYd(eN z-k-X2-o=3U8Q+-`w{5Q2UK$|DJJa9#^=jL@yqmwKEf#+@ZRhmK+Qqd2?Po#{PH+7) zDZM)Fmh;W?pDS*^PFQwIeP-|eUGtnERJtlRx zRNgMOjWDa_<>7Zwef(%nVt!gi#M`|NW@nh|B|pX7_?YXnIxzQMqMp66YV?1$S@YcR^X{s3j_0PY)y}Tk4@#mtHKVIi_0~S!bKP0E;zFBuy`JFv z@4O7+zAihyzW>C2X7h>o12(;Xcq`d{tbnDl@c;L= + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::ParameterLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::ParameterLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ParameterLayer< Dtype >inlineprotectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::ParameterLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::ParameterLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ParameterLayer< Dtype >inlineprotectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ParameterLayer< Dtype >inlinevirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
ParameterLayer(const LayerParameter &param) (defined in caffe::ParameterLayer< Dtype >)caffe::ParameterLayer< Dtype >inlineexplicit
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ParameterLayer< Dtype >inlinevirtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::ParameterLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:45 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ParameterLayer.html b/doxygen/classcaffe_1_1ParameterLayer.html new file mode 100644 index 00000000000..585e06095f1 --- /dev/null +++ b/doxygen/classcaffe_1_1ParameterLayer.html @@ -0,0 +1,391 @@ + + + + + + + +Caffe: caffe::ParameterLayer< Dtype > Class Template Reference + + + + + + + + + +
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caffe::ParameterLayer< Dtype > Class Template Reference
+
+
+
+Inheritance diagram for caffe::ParameterLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ ParameterLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::ParameterLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::ParameterLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
virtual void caffe::ParameterLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+inlinevirtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
virtual void caffe::ParameterLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+inlinevirtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following file: +
+ +
+Generated on Mon Nov 21 2016 11:23:45 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ParameterLayer.png b/doxygen/classcaffe_1_1ParameterLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..a4994f712561996b5bd6d0c9b432d999c1859bad GIT binary patch literal 756 zcmeAS@N?(olHy`uVBq!ia0vp^hk-bNgBeIp4mKw+)YG`}}=GCa0H)^&5>DFyZJ z(krG5k~Vm5HMG`jlX$oGUG3>QraSjCpB#)hJ*_5CZr1aMoXv$lKLudgaD0m6I_~{{|%O-KFPwX#rc>r`y}_u9k2Uyf-0H;-Tzk1<(4W2@{t5B`U`0%x~wu3)mhhmJ}BVma}dW?ITn-~-J5sx3N4Dx#UNQp5f z>L7zbJP7Ny-A?>}ZDkXqP552m1>wgH_U@J9d42YBbK`;iNf~|&5Bj*A+wL0`?EZgu zn|6HD^U_;uf3AO>A6wVstagU8V)lW&Sn0IBhP{0Z?YIBUcxLEs-JbMA?&#O`v2P8( zdCD*NdAoJH?VP)o*P)bE977OmG~{4bP0 Hl+XkKzJO*< literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1Params-members.html b/doxygen/classcaffe_1_1Params-members.html new file mode 100644 index 00000000000..a7bcd1410cc --- /dev/null +++ b/doxygen/classcaffe_1_1Params-members.html @@ -0,0 +1,89 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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+
caffe::Params< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::Params< Dtype >, including all inherited members.

+ + + + + + + + + + +
data() const (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inline
data_ (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
diff() const (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inline
diff_ (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
DISABLE_COPY_AND_ASSIGN(Params) (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
Params(shared_ptr< Solver< Dtype > > root_solver) (defined in caffe::Params< Dtype >)caffe::Params< Dtype >explicit
size() const (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inline
size_ (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
~Params() (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:45 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1Params.html b/doxygen/classcaffe_1_1Params.html new file mode 100644 index 00000000000..ae109b17611 --- /dev/null +++ b/doxygen/classcaffe_1_1Params.html @@ -0,0 +1,130 @@ + + + + + + + +Caffe: caffe::Params< Dtype > Class Template Reference + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
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caffe::Params< Dtype > Class Template Reference
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+Inheritance diagram for caffe::Params< Dtype >:
+
+
+ + +caffe::GPUParams< Dtype > +caffe::P2PSync< Dtype > + +
+ + + + + + + + + + +

+Public Member Functions

+ Params (shared_ptr< Solver< Dtype > > root_solver)
 
+size_t size () const
 
+Dtype * data () const
 
+Dtype * diff () const
 
+ + + +

+Protected Member Functions

+ DISABLE_COPY_AND_ASSIGN (Params)
 
+ + + + + + + +

+Protected Attributes

+const size_t size_
 
+Dtype * data_
 
+Dtype * diff_
 
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:45 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1Params.png b/doxygen/classcaffe_1_1Params.png new file mode 100644 index 0000000000000000000000000000000000000000..e263d2cb32f118809afea4abfded6e480426f9fd GIT binary patch literal 1072 zcmeAS@N?(olHy`uVBq!ia0vp^tAV(KgBeJ!=X<>wNJ#|vgt-3y{~ySF@#br3|Dg#$ z78oBmaDcV*jy#adQ4-`A%m7pb0#{Fk7%?y~@Aq_Z45?szJNMzFPYOJ){L>}x|DR~T zRhK1dOX=qMr+sAJZM*2erh^}aG5X(bE$BW^3K&xqfoXJcCW zY>w26AmQ2n%~tQ9BD>ml?Ynhy7xyLEu4w#Q)^KH&<=?`%>lf5cWMA@s_0{I}g1FVI zHNW(~zWS|k*YBdUvu)Od@A_(P%5Wv{cXsTT_F9Gq_Aj#f|0!j!&1ew*CAWI#0S5Ii zXQdc+EJ|Sg5Wa!w$Lot-!CskMj6Ytp8FZ>8?Y;Y~ac;-gJ}cfgK5H(rGt@bQ!T_pP zJA(0#=o*H9Twx6LtlACxVL=3!K53a=ez)k{&Da#rSxyJFJb@}bKV9!IUFtgNjK}x$ zzbfqJ-w%zRbZ+$-E9=q&Q64H+18=bTds?kGdTsT3+ey{MnOk!Yow_|?t4daEa`jz5 zuOQj(y)01^<=YY`rno2S-usq#M)%3?KYAxhpBe9&8*gA1CcSNA)c3xvdY3lNdb=i5 z{#v%MyB8!tU)-G{`&wvQ+6kHV*qZtA)!%2eZhQLc_N|S#lHa_0mGxx}yJ^9WABx$N zuB-$F>hxXPE|xDzf4x<+>3jtH*QnUDkKVrm1?$P8*{e;hKL7i^X~IfI&(bUZva>zuF~@;;^9D~7 z!Pjri_}{p!DZi4Lke<|Q$M8WKXE`u^OPlfU$J=ZT5x@hznJIQ8;{o5M|}?Y@#sf#*zC>6=TF*GyN3Ci*T)%| z*Iq}?-+%w>xu2q|J;Q1xHv1-=Jhy3nTI;Jlb@}GW*R6`~h2Lw~y02_a?B2CAw)#z1 zz7g&r{i|auulWMmjd~2K+xD8@_1>H`d;XzYEpH>%7MI&v-(r02$opn{af04C>uZT| z{d4u7lt!$V-z0Z6yY!~l->SX0>;jte?k?~ST`3%FW<8@;eg*GNtNKgqDleDsx?-;X zC;SKNwbv`+lm9Y&t*)H9Bt8)2yQa`de@fHOT0Xb@!ryQ1>iOcDVJk3uGkCiCxvX + + + + + + +Caffe: Member List + + + + + + + + + + +
+
+
caffe::PoolingLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::PoolingLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::PoolingLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::PoolingLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
channels_ (defined in caffe::PoolingLayer< Dtype >)caffe::PoolingLayer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::PoolingLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::PoolingLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::PoolingLayer< Dtype >protectedvirtual
global_pooling_ (defined in caffe::PoolingLayer< Dtype >)caffe::PoolingLayer< Dtype >protected
height_ (defined in caffe::PoolingLayer< Dtype >)caffe::PoolingLayer< Dtype >protected
IsShared() constcaffe::Layer< Dtype >inline
kernel_h_ (defined in caffe::PoolingLayer< Dtype >)caffe::PoolingLayer< Dtype >protected
kernel_w_ (defined in caffe::PoolingLayer< Dtype >)caffe::PoolingLayer< Dtype >protected
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::PoolingLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
max_idx_ (defined in caffe::PoolingLayer< Dtype >)caffe::PoolingLayer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::PoolingLayer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::PoolingLayer< Dtype >inlinevirtual
pad_h_ (defined in caffe::PoolingLayer< Dtype >)caffe::PoolingLayer< Dtype >protected
pad_w_ (defined in caffe::PoolingLayer< Dtype >)caffe::PoolingLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
pooled_height_ (defined in caffe::PoolingLayer< Dtype >)caffe::PoolingLayer< Dtype >protected
pooled_width_ (defined in caffe::PoolingLayer< Dtype >)caffe::PoolingLayer< Dtype >protected
PoolingLayer(const LayerParameter &param) (defined in caffe::PoolingLayer< Dtype >)caffe::PoolingLayer< Dtype >inlineexplicit
rand_idx_ (defined in caffe::PoolingLayer< Dtype >)caffe::PoolingLayer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::PoolingLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
stride_h_ (defined in caffe::PoolingLayer< Dtype >)caffe::PoolingLayer< Dtype >protected
stride_w_ (defined in caffe::PoolingLayer< Dtype >)caffe::PoolingLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::PoolingLayer< Dtype >inlinevirtual
width_ (defined in caffe::PoolingLayer< Dtype >)caffe::PoolingLayer< Dtype >protected
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:45 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1PoolingLayer.html b/doxygen/classcaffe_1_1PoolingLayer.html new file mode 100644 index 00000000000..ff59a2048b1 --- /dev/null +++ b/doxygen/classcaffe_1_1PoolingLayer.html @@ -0,0 +1,478 @@ + + + + + + + +Caffe: caffe::PoolingLayer< Dtype > Class Template Reference + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
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+
+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
+
caffe::PoolingLayer< Dtype > Class Template Reference
+
+
+ +

Pools the input image by taking the max, average, etc. within regions. + More...

+ +

#include <pooling_layer.hpp>

+
+Inheritance diagram for caffe::PoolingLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ PoolingLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+int kernel_h_
 
+int kernel_w_
 
+int stride_h_
 
+int stride_w_
 
+int pad_h_
 
+int pad_w_
 
+int channels_
 
+int height_
 
+int width_
 
+int pooled_height_
 
+int pooled_width_
 
+bool global_pooling_
 
+Blob< Dtype > rand_idx_
 
+Blob< int > max_idx_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::PoolingLayer< Dtype >

+ +

Pools the input image by taking the max, average, etc. within regions.

+

TODO(dox): thorough documentation for Forward, Backward, and proto params.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::PoolingLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::PoolingLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MaxTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::PoolingLayer< Dtype >::MaxTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some maximum number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::PoolingLayer< Dtype >::MinTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::PoolingLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
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The documentation for this class was generated from the following files: +
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caffe::PositiveUnitballFiller< Dtype > Member List
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This is the complete list of members for caffe::PositiveUnitballFiller< Dtype >, including all inherited members.

+ + + + + + +
Fill(Blob< Dtype > *blob) (defined in caffe::PositiveUnitballFiller< Dtype >)caffe::PositiveUnitballFiller< Dtype >inlinevirtual
Filler(const FillerParameter &param) (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >inlineexplicit
filler_param_ (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >protected
PositiveUnitballFiller(const FillerParameter &param) (defined in caffe::PositiveUnitballFiller< Dtype >)caffe::PositiveUnitballFiller< Dtype >inlineexplicit
~Filler() (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:45 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1PositiveUnitballFiller.html b/doxygen/classcaffe_1_1PositiveUnitballFiller.html new file mode 100644 index 00000000000..62b655acbde --- /dev/null +++ b/doxygen/classcaffe_1_1PositiveUnitballFiller.html @@ -0,0 +1,123 @@ + + + + + + + +Caffe: caffe::PositiveUnitballFiller< Dtype > Class Template Reference + + + + + + + + + +
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caffe::PositiveUnitballFiller< Dtype > Class Template Reference
+
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+ +

Fills a Blob with values $ x \in [0, 1] $ such that $ \forall i \sum_j x_{ij} = 1 $. + More...

+ +

#include <filler.hpp>

+
+Inheritance diagram for caffe::PositiveUnitballFiller< Dtype >:
+
+
+ + +caffe::Filler< Dtype > + +
+ + + + + + + + + +

+Public Member Functions

+ PositiveUnitballFiller (const FillerParameter &param)
 
+virtual void Fill (Blob< Dtype > *blob)
 
- Public Member Functions inherited from caffe::Filler< Dtype >
+ Filler (const FillerParameter &param)
 
+ + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Filler< Dtype >
+FillerParameter filler_param_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::PositiveUnitballFiller< Dtype >

+ +

Fills a Blob with values $ x \in [0, 1] $ such that $ \forall i \sum_j x_{ij} = 1 $.

+
The documentation for this class was generated from the following file: +
+ +
+Generated on Mon Nov 21 2016 11:23:45 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1PositiveUnitballFiller.png b/doxygen/classcaffe_1_1PositiveUnitballFiller.png new file mode 100644 index 0000000000000000000000000000000000000000..2fd334e3fbd4b5ebb8b75419669cc0994f4dd2b3 GIT binary patch literal 802 zcmeAS@N?(olHy`uVBq!ia0vp^w}3c+gBeJAyqFyhq$C1-LR|m<{|{uoc=NTi|Ih>= z3ycpOIKbL@M;^%KC<*clW&kPzfvcxNj2IZ0HhQ`^hEy=VoqN0QwE~Z;xOLF~|C9YM z$82caGIvS(GPjT%*M&za9&)euxoq-PQ_b^LAhYkJMVSjcgG^%Gy)!4ypI!g`xtYq! zrKNW-n@%cmn|?8EzSjOTN4U2*3r{NXO~0_MIquQaYu`(+EquD-?ZJ;cr*9`Esm_T% zqc^F@kn@HDXzZbbvxy8*%?)oh61I;Vewr0GEWBgsS*4EKjyJqcE`|U-4 zR!41?HIn~wP5pMRO1Xz;+LLPC+dmKUX?p&5pTBp{e$HHd8_!+q^qq{~dQQ@Ny;>+} zlHKw~HP4GC7gRK7z00_4;v38L;Cl)4i{QVD?&jUy^tHQ8{q@wf$y+WeGCs;&!02+> zfw^N@Bb$IPGq1udHYo=aUXz9lsSF072@Jq6LQ(L5*-LY&oh#FeK$v3Lg?E3Sb`I5N zy))ywEnC8|WTo@l!qyhAzRGOfd-s0tQN~*~KTpNl^WO+uZ|hXOiRnc0_8-Mt-ug_d zxcglGy_w(5AKOX{H{4OUz3z4S?)7<7Z!UH?onKwBYVwBLl8?`wWQ)3Y=4Yn)*>_G^ zy^l6NnH?LnE@$CW6o13fkCXY&0CYiHqDVy^doK}PjMMi~vYsM{1GP&A+Zhf^qmdKI;Vst038c@ + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::PowerLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::PowerLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::PowerLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::PowerLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
diff_scale_caffe::PowerLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::PowerLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::PowerLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::PowerLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
NeuronLayer(const LayerParameter &param) (defined in caffe::NeuronLayer< Dtype >)caffe::NeuronLayer< Dtype >inlineexplicit
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
power_caffe::PowerLayer< Dtype >protected
PowerLayer(const LayerParameter &param)caffe::PowerLayer< Dtype >inlineexplicit
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::NeuronLayer< Dtype >virtual
scale_caffe::PowerLayer< Dtype >protected
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
shift_caffe::PowerLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::PowerLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:45 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1PowerLayer.html b/doxygen/classcaffe_1_1PowerLayer.html new file mode 100644 index 00000000000..df57df31e6c --- /dev/null +++ b/doxygen/classcaffe_1_1PowerLayer.html @@ -0,0 +1,461 @@ + + + + + + + +Caffe: caffe::PowerLayer< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
+
caffe::PowerLayer< Dtype > Class Template Reference
+
+
+ +

Computes $ y = (\alpha x + \beta) ^ \gamma $, as specified by the scale $ \alpha $, shift $ \beta $, and power $ \gamma $. + More...

+ +

#include <power_layer.hpp>

+
+Inheritance diagram for caffe::PowerLayer< Dtype >:
+
+
+ + +caffe::NeuronLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

 PowerLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::NeuronLayer< Dtype >
+ NeuronLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the error gradient w.r.t. the power inputs. More...
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+Dtype power_
 $ \gamma $ from layer_param_.power_param()
 
+Dtype scale_
 $ \alpha $ from layer_param_.power_param()
 
+Dtype shift_
 $ \beta $ from layer_param_.power_param()
 
+Dtype diff_scale_
 Result of $ \alpha \gamma $.
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::PowerLayer< Dtype >

+ +

Computes $ y = (\alpha x + \beta) ^ \gamma $, as specified by the scale $ \alpha $, shift $ \beta $, and power $ \gamma $.

+

Constructor & Destructor Documentation

+ +

§ PowerLayer()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
caffe::PowerLayer< Dtype >::PowerLayer (const LayerParameter & param)
+ 		+inlineexplicit
+
+
Parameters
+ + +
paramprovides PowerParameter power_param, with PowerLayer options:
    +
  • scale (optional, default 1) the scale $ \alpha $
    • +
  • shift (optional, default 0) the shift $ \beta $
    • +
  • power (optional, default 1) the power $ \gamma $
    • +
+
+
+
+ +
+
+

Member Function Documentation

+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::PowerLayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the error gradient w.r.t. the power inputs.

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
    +
  1. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
    2. +
+
propagate_downsee Layer::Backward.
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} \alpha \gamma (\alpha x + \beta) ^ {\gamma - 1} = \frac{\partial E}{\partial y} \frac{\alpha \gamma y}{\alpha x + \beta} $ if propagate_down[0]
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::PowerLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+
Parameters
+ + + +
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $
    2. +
+
topoutput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the computed outputs $ y = (\alpha x + \beta) ^ \gamma $
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::PowerLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
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+Generated on Mon Nov 21 2016 11:23:45 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1PowerLayer.png b/doxygen/classcaffe_1_1PowerLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..ed1ee01d6070adecaac707dde2bf5f753952c74a GIT binary patch literal 1068 zcmeAS@N?(olHy`uVBq!ia0vp^n}N84gBeK9F4tNQq$C1-LR|m<{|{uoc=NTi|Ih>= z3ycpOIKbL@M;^%KC<*clW&kPzfvcxNj2IZ0cYC@xhEy=VoqMtGwE~X|zwpZc|4(ke zJ0XFi!6P3Kdv;-PuE3?y>_p%^W3%i%dgq< zR99A|&4}lY6`P$bw`<~(-M?CYa-5cXIOX=v?Mdg9+xrTx6;x-JPxg)H-8@O<@3QY_ zmdF22p0%?$XQtx$UfKNQsI6JWp08e%Kl^mkV87wYHG1tq#@CJC1>8@*x4zHv$D=Pv zNxHYJZyVfp-fUODaNp58-EsF-(hH_?H(N+wo|Git_1R#4eWF=dsh#<<`RCH3&3#x- zADnHXvGl+-Ue9NmUw8iaRdRR25>MZdd;kBw|CISG@`--JJhy*s7T%M#H$_hRBYI}i zllAJKx8>fBV)}5AIl)l(#Gi^a?0XeH3s+jc5S_U=kKs>iljMQ;gFFw`C$N18H(>su zJ%bUbj{z7Q4E3zW4f|20pWfV-8|{-bY0DJ2`GVje`#f#Q#&%jzo5B`^OpWxoIJs^s#VU2WEV@6#{toqIQAb@>ec zY+&H+UcPVQORL`dKh8{eeyp$jy6?@p`8zE#XXO4bNIrYwXynuNR{OMzZ5J=xXYgM9 zkWBi=yw3}aq>Sd5T??Tc&T4fBtdf z_3oKE^Ha?Jy{gIcdH;0tT4^=Crxv-jcDdGV-vWF!HL^m%aNw z+CT80kv?&ML#5PD=|?)A@*xJ^llFtW{x#(uFeJPg?5Y@ama)#1@Bg|$Jf%)apM9@V z@fF^N{Q(4n5-Fq?*k~@@GQ)$xTN4`iRfqrVnQy;KM6$tr7T2Md1xeRt{JdH(x%+Wl zU*!%Fhj`J?8=u-9I=^uKw%(k`N5_|~DR*0Kac1+|mh7Hy*Y;g`s5SrH2bZ<|#eIJb z&)$h-J6)h?t!(yT8a!Ek)+_#*GvoD_Z1YQ|a;^uIzRY+n8U9M{^SM;Mtv(wbdIrp^ z(%&lmdgy`HyVQJl8Yk-1W-n z_Q5LttuKA-ZtqH56wJ5P?~T^FZM91rHCeYh?za;UH!fv9;21phd*5FB&pMyP4g8hs qo#!yxt%jz$*P#c3y)rfbGJosyxDXcij1QQ#89ZJ6T-G@yGywo$fDUf} literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1PythonLayer-members.html b/doxygen/classcaffe_1_1PythonLayer-members.html new file mode 100644 index 00000000000..c89a3b9354a --- /dev/null +++ b/doxygen/classcaffe_1_1PythonLayer-members.html @@ -0,0 +1,119 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::PythonLayer< Dtype > Member List
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This is the complete list of members for caffe::PythonLayer< Dtype >, including all inherited members.

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AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::PythonLayer< Dtype >inlineprotectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::PythonLayer< Dtype >inlineprotectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::PythonLayer< Dtype >inlinevirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
PythonLayer(PyObject *self, const LayerParameter &param) (defined in caffe::PythonLayer< Dtype >)caffe::PythonLayer< Dtype >inline
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::PythonLayer< Dtype >inlinevirtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::PythonLayer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::PythonLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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+ + diff --git a/doxygen/classcaffe_1_1PythonLayer.html b/doxygen/classcaffe_1_1PythonLayer.html new file mode 100644 index 00000000000..062c7dbb485 --- /dev/null +++ b/doxygen/classcaffe_1_1PythonLayer.html @@ -0,0 +1,327 @@ + + + + + + + +Caffe: caffe::PythonLayer< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +List of all members
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+
caffe::PythonLayer< Dtype > Class Template Reference
+
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+
+Inheritance diagram for caffe::PythonLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ PythonLayer (PyObject *self, const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Member Function Documentation

+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
virtual void caffe::PythonLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+inlinevirtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
virtual void caffe::PythonLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+inlinevirtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following file: +
+ +
+Generated on Mon Nov 21 2016 11:23:46 for Caffe by   +doxygen + 1.8.12 +
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caffe::RMSPropSolver< Dtype > Member List
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This is the complete list of members for caffe::RMSPropSolver< Dtype >, including all inherited members.

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action_request_function_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
add_callback(Callback *value) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
ApplyUpdate() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
callbacks() const (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
callbacks_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
CheckSnapshotWritePermissions() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
ClipGradients() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
ComputeUpdateValue(int param_id, Dtype rate) (defined in caffe::RMSPropSolver< Dtype >)caffe::RMSPropSolver< Dtype >protectedvirtual
constructor_sanity_check() (defined in caffe::RMSPropSolver< Dtype >)caffe::RMSPropSolver< Dtype >inlineprotected
current_step_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
DISABLE_COPY_AND_ASSIGN(RMSPropSolver) (defined in caffe::RMSPropSolver< Dtype >)caffe::RMSPropSolver< Dtype >protected
DISABLE_COPY_AND_ASSIGN(SGDSolver) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
DISABLE_COPY_AND_ASSIGN(Solver) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
DisplayOutputBlobs(const int net_id) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
GetLearningRate() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
GetRequestedAction() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
history() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >inline
history_ (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
Init(const SolverParameter &param) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
InitTestNets() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
InitTrainNet() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
iter() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
iter_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
losses_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
net() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
net_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Normalize(int param_id) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
param() const (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
param_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
PreSolve() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
Regularize(int param_id) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
requested_early_exit_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Restore(const char *resume_file) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
RestoreSolverStateFromBinaryProto(const string &state_file) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
RestoreSolverStateFromHDF5(const string &state_file) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
RMSPropSolver(const SolverParameter &param) (defined in caffe::RMSPropSolver< Dtype >)caffe::RMSPropSolver< Dtype >inlineexplicit
RMSPropSolver(const string &param_file) (defined in caffe::RMSPropSolver< Dtype >)caffe::RMSPropSolver< Dtype >inlineexplicit
root_solver_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SetActionFunction(ActionCallback func) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
SGDSolver(const SolverParameter &param) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >inlineexplicit
SGDSolver(const string &param_file) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >inlineexplicit
smoothed_loss_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Snapshot() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
SnapshotFilename(const string extension) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SnapshotSolverState(const string &model_filename) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
SnapshotSolverStateToBinaryProto(const string &model_filename) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
SnapshotSolverStateToHDF5(const string &model_filename) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
SnapshotToBinaryProto() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SnapshotToHDF5() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Solve(const char *resume_file=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >virtual
Solve(const string resume_file) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
Solver(const SolverParameter &param, const Solver *root_solver=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >explicit
Solver(const string &param_file, const Solver *root_solver=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >explicit
Step(int iters) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
temp_ (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
Test(const int test_net_id=0) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
test_nets() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
test_nets_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
TestAll() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
type() constcaffe::RMSPropSolver< Dtype >inlinevirtual
update_ (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
UpdateSmoothedLoss(Dtype loss, int start_iter, int average_loss) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
~Solver() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inlinevirtual
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caffe::RMSPropSolver< Dtype > Class Template Reference
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+Inheritance diagram for caffe::RMSPropSolver< Dtype >:
+
+
+ + +caffe::SGDSolver< Dtype > +caffe::Solver< Dtype > + +
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+Public Member Functions

+ RMSPropSolver (const SolverParameter &param)
 
+ RMSPropSolver (const string &param_file)
 
+virtual const char * type () const
 Returns the solver type.
 
- Public Member Functions inherited from caffe::SGDSolver< Dtype >
+ SGDSolver (const SolverParameter &param)
 
+ SGDSolver (const string &param_file)
 
+const vector< shared_ptr< Blob< Dtype > > > & history ()
 
- Public Member Functions inherited from caffe::Solver< Dtype >
+ Solver (const SolverParameter &param, const Solver *root_solver=NULL)
 
+ Solver (const string &param_file, const Solver *root_solver=NULL)
 
+void Init (const SolverParameter &param)
 
+void InitTrainNet ()
 
+void InitTestNets ()
 
+void SetActionFunction (ActionCallback func)
 
+SolverAction::Enum GetRequestedAction ()
 
+virtual void Solve (const char *resume_file=NULL)
 
+void Solve (const string resume_file)
 
+void Step (int iters)
 
+void Restore (const char *resume_file)
 
+void Snapshot ()
 
+const SolverParameter & param () const
 
+shared_ptr< Net< Dtype > > net ()
 
+const vector< shared_ptr< Net< Dtype > > > & test_nets ()
 
+int iter ()
 
+const vector< Callback * > & callbacks () const
 
+void add_callback (Callback *value)
 
+void CheckSnapshotWritePermissions ()
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void ComputeUpdateValue (int param_id, Dtype rate)
 
+void constructor_sanity_check ()
 
+ DISABLE_COPY_AND_ASSIGN (RMSPropSolver)
 
- Protected Member Functions inherited from caffe::SGDSolver< Dtype >
+void PreSolve ()
 
+Dtype GetLearningRate ()
 
+virtual void ApplyUpdate ()
 
+virtual void Normalize (int param_id)
 
+virtual void Regularize (int param_id)
 
+virtual void ClipGradients ()
 
+virtual void SnapshotSolverState (const string &model_filename)
 
+virtual void SnapshotSolverStateToBinaryProto (const string &model_filename)
 
+virtual void SnapshotSolverStateToHDF5 (const string &model_filename)
 
+virtual void RestoreSolverStateFromHDF5 (const string &state_file)
 
+virtual void RestoreSolverStateFromBinaryProto (const string &state_file)
 
+ DISABLE_COPY_AND_ASSIGN (SGDSolver)
 
- Protected Member Functions inherited from caffe::Solver< Dtype >
+string SnapshotFilename (const string extension)
 
+string SnapshotToBinaryProto ()
 
+string SnapshotToHDF5 ()
 
+void TestAll ()
 
+void Test (const int test_net_id=0)
 
+void DisplayOutputBlobs (const int net_id)
 
+void UpdateSmoothedLoss (Dtype loss, int start_iter, int average_loss)
 
+ DISABLE_COPY_AND_ASSIGN (Solver)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::SGDSolver< Dtype >
+vector< shared_ptr< Blob< Dtype > > > history_
 
+vector< shared_ptr< Blob< Dtype > > > update_
 
+vector< shared_ptr< Blob< Dtype > > > temp_
 
- Protected Attributes inherited from caffe::Solver< Dtype >
+SolverParameter param_
 
+int iter_
 
+int current_step_
 
+shared_ptr< Net< Dtype > > net_
 
+vector< shared_ptr< Net< Dtype > > > test_nets_
 
+vector< Callback * > callbacks_
 
+vector< Dtype > losses_
 
+Dtype smoothed_loss_
 
+const Solver *const root_solver_
 
+ActionCallback action_request_function_
 
+bool requested_early_exit_
 
+
The documentation for this class was generated from the following files: +
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+Generated on Mon Nov 21 2016 11:23:46 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1RMSPropSolver.png b/doxygen/classcaffe_1_1RMSPropSolver.png new file mode 100644 index 0000000000000000000000000000000000000000..d7f8b0e2de57ac255b7d74640b9adf3d696893db GIT binary patch literal 1154 zcmeAS@N?(olHy`uVBq!ia0vp^hk>|*gBeKX+O0PSQW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;|gWES2Z*Exn!MLY|fpQ;)cYlQ&r|(R&1NI=_kvw?>Ex++3bzK`on8i?S{`8 zWuaj+a#dcg=rj1S!mA+FqW#tky?|5YFC^Bil&&oHjJg`w_Uneh4x^h18BVVZe~2E} zJZEqwHN$RFTC*8n_SJ8VZ3jP@*L~OYGx%b3d)kXh2PMCpKR9Eb`Z<9=j<#!zeq5XU z`N7PtqLVdi@2A6$WQ3l$5Oz`%6y=L z1iTI-5KDU!XQg?r+W+!rx_sTgV&R*9lgvWH-p%$3TJ6lUo{oojIAc-z6Lo@dv)uFAhwl;{i!+1Vo2JjEMl21uKmmDKCx%9PdK zR+M5pegDmkr8m-EJYBDQM76p0vE-dg|12efqoe*;@U8xOAye&Xbe8ngx%ss}s#fUt z9=?C2`K`BF`@V}aULTlWo%wyKj@QhR_29sc|E}sOwe<4#yI&<{_33(A?~-YLsWz#^ zMf}e+m6ubR948&qmQcyuGml4eX-Pg~O&EW}?AP29a{a%ZYZwm;Kl~Pi%gAZ*^-iBYj%P6uuPBTyH|HzAK5S2clOYdKk2pG?=DWuT69DZF~0Qtn!-{?N_TJ48AbM80Z+rvS0^fhz%o2%H ze;-)p#%q0V-gqYUQtrvE+j{;k+{f}^`!DI^?0LIa8_&4Ld*SL)$!Wdv3of!tGW-*V jrQW}HrH>gTe~DWM4f@E{@u literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1RNNLayer-members.html b/doxygen/classcaffe_1_1RNNLayer-members.html new file mode 100644 index 00000000000..0d5862d9132 --- /dev/null +++ b/doxygen/classcaffe_1_1RNNLayer-members.html @@ -0,0 +1,138 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::RNNLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::RNNLayer< Dtype >, including all inherited members.

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AllowForceBackward(const int bottom_index) constcaffe::RecurrentLayer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::RecurrentLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
cont_input_blob_ (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::RecurrentLayer< Dtype >inlinevirtual
expose_hidden_caffe::RecurrentLayer< Dtype >protected
FillUnrolledNet(NetParameter *net_param) constcaffe::RNNLayer< Dtype >protectedvirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::RecurrentLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::RecurrentLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
last_layer_index_caffe::RecurrentLayer< Dtype >protected
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::RecurrentLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::RecurrentLayer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::RecurrentLayer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
N_caffe::RecurrentLayer< Dtype >protected
output_blobs_ (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >protected
OutputBlobNames(vector< string > *names) constcaffe::RNNLayer< Dtype >protectedvirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
recur_input_blobs_ (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >protected
recur_output_blobs_ (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >protected
RecurrentInputBlobNames(vector< string > *names) constcaffe::RNNLayer< Dtype >protectedvirtual
RecurrentInputShapes(vector< BlobShape > *shapes) constcaffe::RNNLayer< Dtype >protectedvirtual
RecurrentLayer(const LayerParameter &param) (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >inlineexplicit
RecurrentOutputBlobNames(vector< string > *names) constcaffe::RNNLayer< Dtype >protectedvirtual
Reset() (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >virtual
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::RecurrentLayer< Dtype >virtual
RNNLayer(const LayerParameter &param) (defined in caffe::RNNLayer< Dtype >)caffe::RNNLayer< Dtype >inlineexplicit
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
static_input_caffe::RecurrentLayer< Dtype >protected
T_caffe::RecurrentLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::RNNLayer< Dtype >inlinevirtual
unrolled_net_caffe::RecurrentLayer< Dtype >protected
x_input_blob_ (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >protected
x_static_input_blob_ (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >protected
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:46 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1RNNLayer.html b/doxygen/classcaffe_1_1RNNLayer.html new file mode 100644 index 00000000000..13a5aa32862 --- /dev/null +++ b/doxygen/classcaffe_1_1RNNLayer.html @@ -0,0 +1,308 @@ + + + + + + + +Caffe: caffe::RNNLayer< Dtype > Class Template Reference + + + + + + + + + +
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caffe::RNNLayer< Dtype > Class Template Reference
+
+
+ +

Processes time-varying inputs using a simple recurrent neural network (RNN). Implemented as a network unrolling the RNN computation in time. + More...

+ +

#include <rnn_layer.hpp>

+
+Inheritance diagram for caffe::RNNLayer< Dtype >:
+
+
+ + +caffe::RecurrentLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ RNNLayer (const LayerParameter &param)
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::RecurrentLayer< Dtype >
+ RecurrentLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual void Reset ()
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void FillUnrolledNet (NetParameter *net_param) const
 Fills net_param with the recurrent network architecture. Subclasses should define this – see RNNLayer and LSTMLayer for examples.
 
+virtual void RecurrentInputBlobNames (vector< string > *names) const
 Fills names with the names of the 0th timestep recurrent input Blob&s. Subclasses should define this – see RNNLayer and LSTMLayer for examples.
 
+virtual void RecurrentOutputBlobNames (vector< string > *names) const
 Fills names with the names of the Tth timestep recurrent output Blob&s. Subclasses should define this – see RNNLayer and LSTMLayer for examples.
 
+virtual void RecurrentInputShapes (vector< BlobShape > *shapes) const
 Fills shapes with the shapes of the recurrent input Blob&s. Subclasses should define this – see RNNLayer and LSTMLayer for examples.
 
+virtual void OutputBlobNames (vector< string > *names) const
 Fills names with the names of the output blobs, concatenated across all timesteps. Should return a name for each top Blob. Subclasses should define this – see RNNLayer and LSTMLayer for examples.
 
- Protected Member Functions inherited from caffe::RecurrentLayer< Dtype >
virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::RecurrentLayer< Dtype >
+shared_ptr< Net< Dtype > > unrolled_net_
 A Net to implement the Recurrent functionality.
 
+int N_
 The number of independent streams to process simultaneously.
 
+int T_
 The number of timesteps in the layer's input, and the number of timesteps over which to backpropagate through time.
 
+bool static_input_
 Whether the layer has a "static" input copied across all timesteps.
 
+int last_layer_index_
 The last layer to run in the network. (Any later layers are losses added to force the recurrent net to do backprop.)
 
+bool expose_hidden_
 Whether the layer's hidden state at the first and last timesteps are layer inputs and outputs, respectively.
 
+vector< Blob< Dtype > *> recur_input_blobs_
 
+vector< Blob< Dtype > *> recur_output_blobs_
 
+vector< Blob< Dtype > *> output_blobs_
 
+Blob< Dtype > * x_input_blob_
 
+Blob< Dtype > * x_static_input_blob_
 
+Blob< Dtype > * cont_input_blob_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::RNNLayer< Dtype >

+ +

Processes time-varying inputs using a simple recurrent neural network (RNN). Implemented as a network unrolling the RNN computation in time.

+

Given time-varying inputs $ x_t $, computes hidden state $ h_t := \tanh[ W_{hh} h_{t_1} + W_{xh} x_t + b_h ] $, and outputs $ o_t := \tanh[ W_{ho} h_t + b_o ] $.

+
The documentation for this class was generated from the following files:
    +
  • include/caffe/layers/rnn_layer.hpp
    • +
  • src/caffe/layers/rnn_layer.cpp
    • +
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This is the complete list of members for caffe::ReLULayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ReLULayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ReLULayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ReLULayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ReLULayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlinevirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
NeuronLayer(const LayerParameter &param) (defined in caffe::NeuronLayer< Dtype >)caffe::NeuronLayer< Dtype >inlineexplicit
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
ReLULayer(const LayerParameter &param)caffe::ReLULayer< Dtype >inlineexplicit
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::NeuronLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::ReLULayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:46 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ReLULayer.html b/doxygen/classcaffe_1_1ReLULayer.html new file mode 100644 index 00000000000..654776a9ff2 --- /dev/null +++ b/doxygen/classcaffe_1_1ReLULayer.html @@ -0,0 +1,392 @@ + + + + + + + +Caffe: caffe::ReLULayer< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +List of all members
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+
caffe::ReLULayer< Dtype > Class Template Reference
+
+
+ +

Rectified Linear Unit non-linearity $ y = \max(0, x) $. The simple max is fast to compute, and the function does not saturate. + More...

+ +

#include <relu_layer.hpp>

+
+Inheritance diagram for caffe::ReLULayer< Dtype >:
+
+
+ + +caffe::NeuronLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

 ReLULayer (const LayerParameter &param)
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::NeuronLayer< Dtype >
+ NeuronLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the error gradient w.r.t. the ReLU inputs. More...
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::ReLULayer< Dtype >

+ +

Rectified Linear Unit non-linearity $ y = \max(0, x) $. The simple max is fast to compute, and the function does not saturate.

+

Constructor & Destructor Documentation

+ +

§ ReLULayer()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
caffe::ReLULayer< Dtype >::ReLULayer (const LayerParameter & param)
+ 		+inlineexplicit
+
+
Parameters
+ + +
paramprovides ReLUParameter relu_param, with ReLULayer options:
    +
  • negative_slope (optional, default 0). the value $ \nu $ by which negative values are multiplied.
    • +
+
+
+
+ +
+
+

Member Function Documentation

+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
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void caffe::ReLULayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the error gradient w.r.t. the ReLU inputs.

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
    +
  1. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
    2. +
+
propagate_downsee Layer::Backward.
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} 0 & \mathrm{if} \; x \le 0 \\ \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0 \end{array} \right. $ if propagate_down[0], by default. If a non-zero negative_slope $ \nu $ is provided, the computed gradients are $ \frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} \nu \frac{\partial E}{\partial y} & \mathrm{if} \; x \le 0 \\ \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0 \end{array} \right. $.
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
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+template<typename Dtype >
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void caffe::ReLULayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+
Parameters
+ + + +
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $
    2. +
+
topoutput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the computed outputs $ y = \max(0, x) $ by default. If a non-zero negative_slope $ \nu $ is provided, the computed outputs are $ y = \max(0, x) + \nu \min(0, x) $.
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/layers/relu_layer.hpp
    • +
  • src/caffe/layers/relu_layer.cpp
    • +
+
+ +
+Generated on Mon Nov 21 2016 11:23:46 for Caffe by   +doxygen + 1.8.12 +
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caffe::RecurrentLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::RecurrentLayer< Dtype >, including all inherited members.

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AllowForceBackward(const int bottom_index) constcaffe::RecurrentLayer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::RecurrentLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
cont_input_blob_ (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::RecurrentLayer< Dtype >inlinevirtual
expose_hidden_caffe::RecurrentLayer< Dtype >protected
FillUnrolledNet(NetParameter *net_param) const =0caffe::RecurrentLayer< Dtype >protectedpure virtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::RecurrentLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::RecurrentLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
last_layer_index_caffe::RecurrentLayer< Dtype >protected
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::RecurrentLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::RecurrentLayer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::RecurrentLayer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
N_caffe::RecurrentLayer< Dtype >protected
output_blobs_ (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >protected
OutputBlobNames(vector< string > *names) const =0caffe::RecurrentLayer< Dtype >protectedpure virtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
recur_input_blobs_ (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >protected
recur_output_blobs_ (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >protected
RecurrentInputBlobNames(vector< string > *names) const =0caffe::RecurrentLayer< Dtype >protectedpure virtual
RecurrentInputShapes(vector< BlobShape > *shapes) const =0caffe::RecurrentLayer< Dtype >protectedpure virtual
RecurrentLayer(const LayerParameter &param) (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >inlineexplicit
RecurrentOutputBlobNames(vector< string > *names) const =0caffe::RecurrentLayer< Dtype >protectedpure virtual
Reset() (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >virtual
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::RecurrentLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
static_input_caffe::RecurrentLayer< Dtype >protected
T_caffe::RecurrentLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::RecurrentLayer< Dtype >inlinevirtual
unrolled_net_caffe::RecurrentLayer< Dtype >protected
x_input_blob_ (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >protected
x_static_input_blob_ (defined in caffe::RecurrentLayer< Dtype >)caffe::RecurrentLayer< Dtype >protected
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:46 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1RecurrentLayer.html b/doxygen/classcaffe_1_1RecurrentLayer.html new file mode 100644 index 00000000000..c9e1454ff08 --- /dev/null +++ b/doxygen/classcaffe_1_1RecurrentLayer.html @@ -0,0 +1,594 @@ + + + + + + + +Caffe: caffe::RecurrentLayer< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
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caffe::RecurrentLayer< Dtype > Class Template Referenceabstract
+
+
+ +

An abstract class for implementing recurrent behavior inside of an unrolled network. This Layer type cannot be instantiated – instead, you should use one of its implementations which defines the recurrent architecture, such as RNNLayer or LSTMLayer. + More...

+ +

#include <recurrent_layer.hpp>

+
+Inheritance diagram for caffe::RecurrentLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > +caffe::LSTMLayer< Dtype > +caffe::RNNLayer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ RecurrentLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual void Reset ()
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void FillUnrolledNet (NetParameter *net_param) const =0
 Fills net_param with the recurrent network architecture. Subclasses should define this – see RNNLayer and LSTMLayer for examples.
 
+virtual void RecurrentInputBlobNames (vector< string > *names) const =0
 Fills names with the names of the 0th timestep recurrent input Blob&s. Subclasses should define this – see RNNLayer and LSTMLayer for examples.
 
+virtual void RecurrentInputShapes (vector< BlobShape > *shapes) const =0
 Fills shapes with the shapes of the recurrent input Blob&s. Subclasses should define this – see RNNLayer and LSTMLayer for examples.
 
+virtual void RecurrentOutputBlobNames (vector< string > *names) const =0
 Fills names with the names of the Tth timestep recurrent output Blob&s. Subclasses should define this – see RNNLayer and LSTMLayer for examples.
 
+virtual void OutputBlobNames (vector< string > *names) const =0
 Fills names with the names of the output blobs, concatenated across all timesteps. Should return a name for each top Blob. Subclasses should define this – see RNNLayer and LSTMLayer for examples.
 
virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+shared_ptr< Net< Dtype > > unrolled_net_
 A Net to implement the Recurrent functionality.
 
+int N_
 The number of independent streams to process simultaneously.
 
+int T_
 The number of timesteps in the layer's input, and the number of timesteps over which to backpropagate through time.
 
+bool static_input_
 Whether the layer has a "static" input copied across all timesteps.
 
+int last_layer_index_
 The last layer to run in the network. (Any later layers are losses added to force the recurrent net to do backprop.)
 
+bool expose_hidden_
 Whether the layer's hidden state at the first and last timesteps are layer inputs and outputs, respectively.
 
+vector< Blob< Dtype > *> recur_input_blobs_
 
+vector< Blob< Dtype > *> recur_output_blobs_
 
+vector< Blob< Dtype > *> output_blobs_
 
+Blob< Dtype > * x_input_blob_
 
+Blob< Dtype > * x_static_input_blob_
 
+Blob< Dtype > * cont_input_blob_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::RecurrentLayer< Dtype >

+ +

An abstract class for implementing recurrent behavior inside of an unrolled network. This Layer type cannot be instantiated – instead, you should use one of its implementations which defines the recurrent architecture, such as RNNLayer or LSTMLayer.

+

Member Function Documentation

+ +

§ AllowForceBackward()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
virtual bool caffe::RecurrentLayer< Dtype >::AllowForceBackward (const int bottom_index) const
+ 		+inlinevirtual
+
+ +

Return whether to allow force_backward for a given bottom blob index.

+

If AllowForceBackward(i) == false, we will ignore the force_backward setting and backpropagate to blob i only if it needs gradient information (as is done when force_backward == false).

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::RecurrentLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::RecurrentLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+
Parameters
+ + +
bottominput Blob vector (length 2-3)
+
+
+
    +
  1. $ (T \times N \times ...) $ the time-varying input $ x $. After the first two axes, whose dimensions must correspond to the number of timesteps $ T $ and the number of independent streams $ N $, respectively, its dimensions may be arbitrary. Note that the ordering of dimensions – $ (T \times N \times ...) $, rather than $ (N \times T \times ...) $ – means that the $ N $ independent input streams must be "interleaved".
    2. +
  2. $ (T \times N) $ the sequence continuation indicators $ \delta $. These inputs should be binary (0 or 1) indicators, where $ \delta_{t,n} = 0 $ means that timestep $ t $ of stream $ n $ is the beginning of a new sequence, and hence the previous hidden state $ h_{t-1} $ is multiplied by $ \delta_t = 0 $ and has no effect on the cell's output at timestep $ t $, and a value of $ \delta_{t,n} = 1 $ means that timestep $ t $ of stream $ n $ is a continuation from the previous timestep $ t-1 $, and the previous hidden state $ h_{t-1} $ affects the updated hidden state and output.
    3. +
  3. $ (N \times ...) $ (optional) the static (non-time-varying) input $ x_{static} $. After the first axis, whose dimension must be the number of independent streams, its dimensions may be arbitrary. This is mathematically equivalent to using a time-varying input of $ x'_t = [x_t; x_{static}] $ – i.e., tiling the static input across the $ T $ timesteps and concatenating with the time-varying input. Note that if this input is used, all timesteps in a single batch within a particular one of the $ N $ streams must share the same static input, even if the sequence continuation indicators suggest that difference sequences are ending and beginning within a single batch. This may require padding and/or truncation for uniform length.
    4. +
+
Parameters
+ + +
topoutput Blob vector (length 1)
    +
  1. $ (T \times N \times D) $ the time-varying output $ y $, where $ D $ is recurrent_param.num_output(). Refer to documentation for particular RecurrentLayer implementations (such as RNNLayer and LSTMLayer) for the definition of $ y $.
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::RecurrentLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MaxBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::RecurrentLayer< Dtype >::MaxBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some maximum number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::RecurrentLayer< Dtype >::MinBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::RecurrentLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:46 for Caffe by   +doxygen + 1.8.12 +
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+
+ + + + + + +
+
Caffe +
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caffe::ReductionLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::ReductionLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
axis_caffe::ReductionLayer< Dtype >protected
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ReductionLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ReductionLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
coeff_caffe::ReductionLayer< Dtype >protected
dim_caffe::ReductionLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::ReductionLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::ReductionLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ReductionLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ReductionLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ReductionLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
num_caffe::ReductionLayer< Dtype >protected
op_caffe::ReductionLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
ReductionLayer(const LayerParameter &param) (defined in caffe::ReductionLayer< Dtype >)caffe::ReductionLayer< Dtype >inlineexplicit
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ReductionLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
sum_multiplier_caffe::ReductionLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::ReductionLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:46 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ReductionLayer.html b/doxygen/classcaffe_1_1ReductionLayer.html new file mode 100644 index 00000000000..446c7b29aa0 --- /dev/null +++ b/doxygen/classcaffe_1_1ReductionLayer.html @@ -0,0 +1,428 @@ + + + + + + + +Caffe: caffe::ReductionLayer< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
+
caffe::ReductionLayer< Dtype > Class Template Reference
+
+
+ +

Compute "reductions" – operations that return a scalar output Blob for an input Blob of arbitrary size, such as the sum, absolute sum, and sum of squares. + More...

+ +

#include <reduction_layer.hpp>

+
+Inheritance diagram for caffe::ReductionLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ ReductionLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+ReductionParameter_ReductionOp op_
 the reduction operation performed by the layer
 
+Dtype coeff_
 a scalar coefficient applied to all outputs
 
+int axis_
 the index of the first input axis to reduce
 
+int num_
 the number of reductions performed
 
+int dim_
 the input size of each reduction
 
+Blob< Dtype > sum_multiplier_
 a helper Blob used for summation (op_ == SUM)
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::ReductionLayer< Dtype >

+ +

Compute "reductions" – operations that return a scalar output Blob for an input Blob of arbitrary size, such as the sum, absolute sum, and sum of squares.

+

TODO(dox): thorough documentation for Forward, Backward, and proto params.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::ReductionLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::ReductionLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ReductionLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ReductionLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:46 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ReductionLayer.png b/doxygen/classcaffe_1_1ReductionLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..efdf662a2e121897aaa533b93ea19f1b735d8f1c GIT binary patch literal 748 zcmeAS@N?(olHy`uVBq!ia0vp^2Z1<%gBeIpU-r-gNJ#|vgt-3y{~ySF@#br3|Dg#$ z78oBmaDcV*jy#adQ4-`A%m7pb0#{Fk7%?y~ReQQPhEy=VoqMt9u>y}P|8?V^|NZY- z9CKru-1=@=i=@@Q(uGb1DvJ9z<=!^idP5~MNtek}YPG9^XYyC!CIP;kmY!LmH|lqV zdtU0TbJ&m{opaYcpj6p&ZkDozbD4|%k-|BpEFbNcp9}cCSZ?uQUz5A+Y3iO?A;Jf@ zcGu2KoUrxxoFQl;tkN{prk}zf=)kQn|4$cXlL$ z{8i?JEMNZ*6=(VkY!3=26t(>14AWBBeo@WuKpYm5xPt0?#Lkjc@AU7iPbsDId+^VI9hH;t3NZ{u0(Kh0C$ z_3GFS)h9gLdZ10UYmKo}`{Ffwe zRS6!@UzvQoE&nz{Q(VJ(mdK II;Vst0LU~>Hvj+t literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1ReshapeLayer-members.html b/doxygen/classcaffe_1_1ReshapeLayer-members.html new file mode 100644 index 00000000000..cddcd8a8bd8 --- /dev/null +++ b/doxygen/classcaffe_1_1ReshapeLayer-members.html @@ -0,0 +1,122 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
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+
caffe::ReshapeLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::ReshapeLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ReshapeLayer< Dtype >inlineprotectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ReshapeLayer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
constant_count_caffe::ReshapeLayer< Dtype >protected
copy_axes_caffe::ReshapeLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::ReshapeLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::ReshapeLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ReshapeLayer< Dtype >inlineprotectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ReshapeLayer< Dtype >inlineprotectedvirtual
inferred_axis_caffe::ReshapeLayer< Dtype >protected
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ReshapeLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ReshapeLayer< Dtype >virtual
ReshapeLayer(const LayerParameter &param) (defined in caffe::ReshapeLayer< Dtype >)caffe::ReshapeLayer< Dtype >inlineexplicit
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::ReshapeLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:46 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ReshapeLayer.html b/doxygen/classcaffe_1_1ReshapeLayer.html new file mode 100644 index 00000000000..a099c4baf23 --- /dev/null +++ b/doxygen/classcaffe_1_1ReshapeLayer.html @@ -0,0 +1,405 @@ + + + + + + + +Caffe: caffe::ReshapeLayer< Dtype > Class Template Reference + + + + + + + + + +
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+
+
+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
+
caffe::ReshapeLayer< Dtype > Class Template Reference
+
+
+
+Inheritance diagram for caffe::ReshapeLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ ReshapeLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+vector< int > copy_axes_
 vector of axes indices whose dimensions we'll copy from the bottom
 
+int inferred_axis_
 the index of the axis whose dimension we infer, or -1 if none
 
+int constant_count_
 the product of the "constant" output dimensions
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::ReshapeLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::ReshapeLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ReshapeLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ReshapeLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:46 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ReshapeLayer.png b/doxygen/classcaffe_1_1ReshapeLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..1fd5cc49ae37b3a2ef2a2b496fe933b29895e2b8 GIT binary patch literal 735 zcmeAS@N?(olHy`uVBq!ia0vp^yMQ==gBeK9= z3ycpOIKbL@M;^%KC<*clW&kPzfvcxNj2IZ0@;qG}Ln;{G&b^p-Sb@it-~7_Q|C6ul zsoYR0S+F*2;*Fx)PQ8{pE#7J0-j-Y3{ErMDA%<}a1< z$(^z+(Yi;ac{v2@Ba5zYiR-I4}i zfHh=ny&!-7eZSsbj)Lo}t>ufJp1HSsRpY-odzP-Ie+8V zohtTD-&s=crlthXw>tB+=Xb_^^Y&jk4ZF{=q;IJ;K5X+cxhQm|rG3>*D{u&I+w}7T z*It#C7ivwd41^00?mqkS(zW!cg}+5b|FG5`H*SyKbbWHEWW?@s6HmVk3$I<^v|TRr z?L&+8Tz}8a+I4Mi@OHMl&(2iuWXjx;=k)iXX>9(rU!r@!e4JY zEKz;xp_#SM%-=t^pFVeLQ=9p%@|UkuKkwsX{!qTx&ul~J@%~_yUm8E3FW{eid=axt lCN#)i-~1ZvmFZT?>|v|EYW~)V>w&3}!PC{xWt~$(697flWj6o- literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1SGDSolver-members.html b/doxygen/classcaffe_1_1SGDSolver-members.html new file mode 100644 index 00000000000..fdc9837a323 --- /dev/null +++ b/doxygen/classcaffe_1_1SGDSolver-members.html @@ -0,0 +1,139 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::SGDSolver< Dtype > Member List
+
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This is the complete list of members for caffe::SGDSolver< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
action_request_function_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
add_callback(Callback *value) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
ApplyUpdate() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
callbacks() const (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
callbacks_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
CheckSnapshotWritePermissions() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
ClipGradients() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
ComputeUpdateValue(int param_id, Dtype rate) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
current_step_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
DISABLE_COPY_AND_ASSIGN(SGDSolver) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
DISABLE_COPY_AND_ASSIGN(Solver) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
DisplayOutputBlobs(const int net_id) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
GetLearningRate() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
GetRequestedAction() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
history() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >inline
history_ (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
Init(const SolverParameter &param) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
InitTestNets() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
InitTrainNet() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
iter() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
iter_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
losses_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
net() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
net_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Normalize(int param_id) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
param() const (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
param_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
PreSolve() (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
Regularize(int param_id) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
requested_early_exit_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Restore(const char *resume_file) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
RestoreSolverStateFromBinaryProto(const string &state_file) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
RestoreSolverStateFromHDF5(const string &state_file) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
root_solver_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SetActionFunction(ActionCallback func) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
SGDSolver(const SolverParameter &param) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >inlineexplicit
SGDSolver(const string &param_file) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >inlineexplicit
smoothed_loss_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Snapshot() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
SnapshotFilename(const string extension) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SnapshotSolverState(const string &model_filename) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
SnapshotSolverStateToBinaryProto(const string &model_filename) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
SnapshotSolverStateToHDF5(const string &model_filename) (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protectedvirtual
SnapshotToBinaryProto() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SnapshotToHDF5() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Solve(const char *resume_file=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >virtual
Solve(const string resume_file) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
Solver(const SolverParameter &param, const Solver *root_solver=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >explicit
Solver(const string &param_file, const Solver *root_solver=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >explicit
Step(int iters) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
temp_ (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
Test(const int test_net_id=0) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
test_nets() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
test_nets_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
TestAll() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
type() constcaffe::SGDSolver< Dtype >inlinevirtual
update_ (defined in caffe::SGDSolver< Dtype >)caffe::SGDSolver< Dtype >protected
UpdateSmoothedLoss(Dtype loss, int start_iter, int average_loss) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
~Solver() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:47 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1SGDSolver.html b/doxygen/classcaffe_1_1SGDSolver.html new file mode 100644 index 00000000000..ddce89e5eff --- /dev/null +++ b/doxygen/classcaffe_1_1SGDSolver.html @@ -0,0 +1,298 @@ + + + + + + + +Caffe: caffe::SGDSolver< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
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caffe::SGDSolver< Dtype > Class Template Reference
+
+
+ +

Optimizes the parameters of a Net using stochastic gradient descent (SGD) with momentum. + More...

+ +

#include <sgd_solvers.hpp>

+
+Inheritance diagram for caffe::SGDSolver< Dtype >:
+
+
+ + +caffe::Solver< Dtype > +caffe::AdaDeltaSolver< Dtype > +caffe::AdaGradSolver< Dtype > +caffe::AdamSolver< Dtype > +caffe::NesterovSolver< Dtype > +caffe::RMSPropSolver< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ SGDSolver (const SolverParameter &param)
 
+ SGDSolver (const string &param_file)
 
+virtual const char * type () const
 Returns the solver type.
 
+const vector< shared_ptr< Blob< Dtype > > > & history ()
 
- Public Member Functions inherited from caffe::Solver< Dtype >
+ Solver (const SolverParameter &param, const Solver *root_solver=NULL)
 
+ Solver (const string &param_file, const Solver *root_solver=NULL)
 
+void Init (const SolverParameter &param)
 
+void InitTrainNet ()
 
+void InitTestNets ()
 
+void SetActionFunction (ActionCallback func)
 
+SolverAction::Enum GetRequestedAction ()
 
+virtual void Solve (const char *resume_file=NULL)
 
+void Solve (const string resume_file)
 
+void Step (int iters)
 
+void Restore (const char *resume_file)
 
+void Snapshot ()
 
+const SolverParameter & param () const
 
+shared_ptr< Net< Dtype > > net ()
 
+const vector< shared_ptr< Net< Dtype > > > & test_nets ()
 
+int iter ()
 
+const vector< Callback * > & callbacks () const
 
+void add_callback (Callback *value)
 
+void CheckSnapshotWritePermissions ()
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+void PreSolve ()
 
+Dtype GetLearningRate ()
 
+virtual void ApplyUpdate ()
 
+virtual void Normalize (int param_id)
 
+virtual void Regularize (int param_id)
 
+virtual void ComputeUpdateValue (int param_id, Dtype rate)
 
+virtual void ClipGradients ()
 
+virtual void SnapshotSolverState (const string &model_filename)
 
+virtual void SnapshotSolverStateToBinaryProto (const string &model_filename)
 
+virtual void SnapshotSolverStateToHDF5 (const string &model_filename)
 
+virtual void RestoreSolverStateFromHDF5 (const string &state_file)
 
+virtual void RestoreSolverStateFromBinaryProto (const string &state_file)
 
+ DISABLE_COPY_AND_ASSIGN (SGDSolver)
 
- Protected Member Functions inherited from caffe::Solver< Dtype >
+string SnapshotFilename (const string extension)
 
+string SnapshotToBinaryProto ()
 
+string SnapshotToHDF5 ()
 
+void TestAll ()
 
+void Test (const int test_net_id=0)
 
+void DisplayOutputBlobs (const int net_id)
 
+void UpdateSmoothedLoss (Dtype loss, int start_iter, int average_loss)
 
+ DISABLE_COPY_AND_ASSIGN (Solver)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+vector< shared_ptr< Blob< Dtype > > > history_
 
+vector< shared_ptr< Blob< Dtype > > > update_
 
+vector< shared_ptr< Blob< Dtype > > > temp_
 
- Protected Attributes inherited from caffe::Solver< Dtype >
+SolverParameter param_
 
+int iter_
 
+int current_step_
 
+shared_ptr< Net< Dtype > > net_
 
+vector< shared_ptr< Net< Dtype > > > test_nets_
 
+vector< Callback * > callbacks_
 
+vector< Dtype > losses_
 
+Dtype smoothed_loss_
 
+const Solver *const root_solver_
 
+ActionCallback action_request_function_
 
+bool requested_early_exit_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::SGDSolver< Dtype >

+ +

Optimizes the parameters of a Net using stochastic gradient descent (SGD) with momentum.

+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:47 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1SGDSolver.png b/doxygen/classcaffe_1_1SGDSolver.png new file mode 100644 index 0000000000000000000000000000000000000000..c339d83987fa94808623701b45578304794b1ad9 GIT binary patch literal 2486 zcmd5;X;4#H77j!)3bKu|6$S)atGmHOi3$S27ErJSEa0(8K%j#iKo|s(BoIVVpp~sH zK!eCkJA(a?MbZ$KEVQ7sY!TB48b|~r$QmIeF@%tL$<)lBu9>R&*RSe*_uPBFd+)jD zp6}HwIO}(6=MIYn;F0 zka-pUslkBcK;x1i+$|{_vR07VTRu)c2Wlq|@=_Ml!|5l6@jrui?3qEbG z*Ako)7#(uym3E0N+)5o47D-n9Bp`NDcBe^K(w3oEp68_fo#)0Q=Q!heK189Fk-4s?{Ncl=wsXdi2u2k zgddgcAYDl}Z{xSqDZf}G z6WN(lxPMhtcPF2XV2;(QOD>$i+aI#r%Bhx#$$Waki}}Jzp{BUbCi7bZa(59ekvEo!Rgl$)L> zc@!vJ0iIkv;$rPtKd+un@49B(uA9+Vma^+8_> zwPx;Ty8(xa;1z76k0{coqZ{dNUe9i~NBG z5H#|+3qgSl0H7@Z06Ih+sAEH`1LHl7Yam!AWjoz}3`*M~2`nb1{34Y*Gsr{M#itA~ z-ICtnN(^$t3^Im4$iuLhPVBCQ+4+@`zWaWojbxR?HYm=qU+z}XKWHDGAbyy#OcWGd zP9d_|sJQ-UKJ%^7WYsA2#awN(wnC)diL07#55rC=^J0|e-B{uGmdrPn7fwAMNH6dI z-5C`(Xiw_p&h`=*>?dZm_yxQ)BKT3EvxG!u5pmV1*;K2+<3eu#2=PXEk>oXxXL!IL z<535OHv=W8oA;7AO8a2iES`w&WEs|(FVP$`pB@sEsZs2zfQxvl9{cRQ2lhNZ3U_g8 zqXZKosz7~D;fm+h`L-#+&anGo0!Oyudi3Dg7R2n;(AT9c5FMwz*);^3OE`x7TGdvz8`_6^M}(5CH~Yzpp1s7XDOG^&Vms=Sjr z%pu(vPwcSh-;fL~u4mqz8DeLMX*5^qCiiJW87ckdDZYDtlQh$f9h|O=#FLsCl!h0m z7bKSC>y#v>d!ewS>HaqQ=@cf9m4;>W8Mwa)f#l_cgZN-06JCf*`9iE{paGTJzR`Md z8;A3HR#_W^tC+rU;p56?6+(PhxVJvC?Yox9jq~^pS_FLq*qX%KI9r;;5QK-elGt=) zvV#lH=$)U)9_P!I=FRg(Y~#3xmhL5w`W2k#@xNzZx~VU+@(T5CewtX%P*wGHlgEZR-w_v; zwFzmXie%2GJ!S1jrIq_lvMH^HCc=`&Sqn=4-E;HG{t>ak=K8qg0@JB!To;waZpl(& za1k%EADD#mVw;1^E5CLt5MV@4gDpgO0?Y>U?jgs0fITdErOs$Y3|PuWJCdn4YvmZ zhiz6^V7&FAPDr_S8u>m~K)Po829{`=XJ@#W!r!uY3$5T^NDBqIq1U0GESQg%pJ&5~ H^Vj|fqTa-G literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1SPPLayer-members.html b/doxygen/classcaffe_1_1SPPLayer-members.html new file mode 100644 index 00000000000..a37a9a1c8f5 --- /dev/null +++ b/doxygen/classcaffe_1_1SPPLayer-members.html @@ -0,0 +1,141 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::SPPLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::SPPLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::SPPLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
bottom_h_ (defined in caffe::SPPLayer< Dtype >)caffe::SPPLayer< Dtype >protected
bottom_w_ (defined in caffe::SPPLayer< Dtype >)caffe::SPPLayer< Dtype >protected
channels_ (defined in caffe::SPPLayer< Dtype >)caffe::SPPLayer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
concat_bottom_vec_caffe::SPPLayer< Dtype >protected
concat_layer_caffe::SPPLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::SPPLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::SPPLayer< Dtype >inlinevirtual
flatten_layers_caffe::SPPLayer< Dtype >protected
flatten_outputs_caffe::SPPLayer< Dtype >protected
flatten_top_vecs_caffe::SPPLayer< Dtype >protected
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SPPLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
GetPoolingParam(const int pyramid_level, const int bottom_h, const int bottom_w, const SPPParameter spp_param) (defined in caffe::SPPLayer< Dtype >)caffe::SPPLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
kernel_h_ (defined in caffe::SPPLayer< Dtype >)caffe::SPPLayer< Dtype >protected
kernel_w_ (defined in caffe::SPPLayer< Dtype >)caffe::SPPLayer< Dtype >protected
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SPPLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
num_ (defined in caffe::SPPLayer< Dtype >)caffe::SPPLayer< Dtype >protected
pad_h_ (defined in caffe::SPPLayer< Dtype >)caffe::SPPLayer< Dtype >protected
pad_w_ (defined in caffe::SPPLayer< Dtype >)caffe::SPPLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
pooling_bottom_vecs_caffe::SPPLayer< Dtype >protected
pooling_layers_caffe::SPPLayer< Dtype >protected
pooling_outputs_caffe::SPPLayer< Dtype >protected
pooling_top_vecs_caffe::SPPLayer< Dtype >protected
pyramid_height_ (defined in caffe::SPPLayer< Dtype >)caffe::SPPLayer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SPPLayer< Dtype >virtual
reshaped_first_time_ (defined in caffe::SPPLayer< Dtype >)caffe::SPPLayer< Dtype >protected
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
split_layer_caffe::SPPLayer< Dtype >protected
split_top_vec_caffe::SPPLayer< Dtype >protected
SPPLayer(const LayerParameter &param) (defined in caffe::SPPLayer< Dtype >)caffe::SPPLayer< Dtype >inlineexplicit
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::SPPLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:48 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1SPPLayer.html b/doxygen/classcaffe_1_1SPPLayer.html new file mode 100644 index 00000000000..a47a9443141 --- /dev/null +++ b/doxygen/classcaffe_1_1SPPLayer.html @@ -0,0 +1,480 @@ + + + + + + + +Caffe: caffe::SPPLayer< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
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+
caffe::SPPLayer< Dtype > Class Template Reference
+
+
+ +

Does spatial pyramid pooling on the input image by taking the max, average, etc. within regions so that the result vector of different sized images are of the same size. + More...

+ +

#include <spp_layer.hpp>

+
+Inheritance diagram for caffe::SPPLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ SPPLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual LayerParameter GetPoolingParam (const int pyramid_level, const int bottom_h, const int bottom_w, const SPPParameter spp_param)
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+int pyramid_height_
 
+int bottom_h_
 
+int bottom_w_
 
+int num_
 
+int channels_
 
+int kernel_h_
 
+int kernel_w_
 
+int pad_h_
 
+int pad_w_
 
+bool reshaped_first_time_
 
+shared_ptr< SplitLayer< Dtype > > split_layer_
 the internal Split layer that feeds the pooling layers
 
+vector< Blob< Dtype > * > split_top_vec_
 top vector holder used in call to the underlying SplitLayer::Forward
 
+vector< vector< Blob< Dtype > * > * > pooling_bottom_vecs_
 bottom vector holder used in call to the underlying PoolingLayer::Forward
 
+vector< shared_ptr< PoolingLayer< Dtype > > > pooling_layers_
 the internal Pooling layers of different kernel sizes
 
+vector< vector< Blob< Dtype > * > * > pooling_top_vecs_
 top vector holders used in call to the underlying PoolingLayer::Forward
 
+vector< Blob< Dtype > * > pooling_outputs_
 pooling_outputs stores the outputs of the PoolingLayers
 
+vector< FlattenLayer< Dtype > * > flatten_layers_
 the internal Flatten layers that the Pooling layers feed into
 
+vector< vector< Blob< Dtype > * > * > flatten_top_vecs_
 top vector holders used in call to the underlying FlattenLayer::Forward
 
+vector< Blob< Dtype > * > flatten_outputs_
 flatten_outputs stores the outputs of the FlattenLayers
 
+vector< Blob< Dtype > * > concat_bottom_vec_
 bottom vector holder used in call to the underlying ConcatLayer::Forward
 
+shared_ptr< ConcatLayer< Dtype > > concat_layer_
 the internal Concat layers that the Flatten layers feed into
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::SPPLayer< Dtype >

+ +

Does spatial pyramid pooling on the input image by taking the max, average, etc. within regions so that the result vector of different sized images are of the same size.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::SPPLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::SPPLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::SPPLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::SPPLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/layers/spp_layer.hpp
    • +
  • src/caffe/layers/spp_layer.cpp
    • +
+
+ +
+Generated on Mon Nov 21 2016 11:23:48 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1SPPLayer.png b/doxygen/classcaffe_1_1SPPLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..264a6896b3565b9178ea4fc56849fd92ec2e0d2b GIT binary patch literal 695 zcmeAS@N?(olHy`uVBq!ia0vp^3xPO*gBeJ=IX<-jQW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;e ze4k!>TK1)d+dtD~dC$(O&Rp&t@oz1MzR#Sinh)k(Sid7-kIEgcJ4-g~u$URqy=$|U z(`ji)4pxl zt8>!ksh!Tgq`f+x{9hj|tF9>D-}yoF!EsJ`<~qB!NnhM&wM-JZtTJWEjJTH5n{<@9 zAFTbwBoTF3xUROh&%)**cR|6CkDTfH%r2&!j7hUv7=Zq6n4zZV;4@i3VVNg~z-1Md zj!Yq@BPK}F|J)Bv36hFZX=sFL`pOZn`$czw&HcGs%lFGNzSGj*wRe^CjXM_Bd3_A! zB^PYf&$!=S^u22S<0o^!d+^_OwhWO=ot^ux{Bmg$2g7d}KE7=0885fJ6iqySc;7ME z^S|zBpJKmu^ym+LKP{*5dEuou>=xhu;PWCpq(}WwZusO?T3^gof1NQs?8iB0*9_s% zx1}j(gtsYOcr(Q~G4k=JQbFhBs;nJlJiAMOcsp&rbv-e)YCS02G^T{i{Jmk~-8*}q zRzJU`{XOyK<2ZfhJ)ZnF_jc_5-+%}?hWsl~k8im*O>?QqhJTE&rz!<{>Py-IQyGJ& LtDnm{r-UW|6E{02 literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1ScaleLayer-members.html b/doxygen/classcaffe_1_1ScaleLayer-members.html new file mode 100644 index 00000000000..89c27d61778 --- /dev/null +++ b/doxygen/classcaffe_1_1ScaleLayer-members.html @@ -0,0 +1,130 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
caffe::ScaleLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::ScaleLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
axis_ (defined in caffe::ScaleLayer< Dtype >)caffe::ScaleLayer< Dtype >protected
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ScaleLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ScaleLayer< Dtype >protectedvirtual
bias_bottom_vec_ (defined in caffe::ScaleLayer< Dtype >)caffe::ScaleLayer< Dtype >protected
bias_layer_ (defined in caffe::ScaleLayer< Dtype >)caffe::ScaleLayer< Dtype >protected
bias_param_id_ (defined in caffe::ScaleLayer< Dtype >)caffe::ScaleLayer< Dtype >protected
bias_propagate_down_ (defined in caffe::ScaleLayer< Dtype >)caffe::ScaleLayer< Dtype >protected
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::ScaleLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ScaleLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ScaleLayer< Dtype >protectedvirtual
inner_dim_ (defined in caffe::ScaleLayer< Dtype >)caffe::ScaleLayer< Dtype >protected
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ScaleLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::ScaleLayer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::ScaleLayer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
outer_dim_ (defined in caffe::ScaleLayer< Dtype >)caffe::ScaleLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ScaleLayer< Dtype >virtual
scale_dim_ (defined in caffe::ScaleLayer< Dtype >)caffe::ScaleLayer< Dtype >protected
ScaleLayer(const LayerParameter &param) (defined in caffe::ScaleLayer< Dtype >)caffe::ScaleLayer< Dtype >inlineexplicit
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
sum_multiplier_ (defined in caffe::ScaleLayer< Dtype >)caffe::ScaleLayer< Dtype >protected
sum_result_ (defined in caffe::ScaleLayer< Dtype >)caffe::ScaleLayer< Dtype >protected
temp_ (defined in caffe::ScaleLayer< Dtype >)caffe::ScaleLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::ScaleLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:47 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ScaleLayer.html b/doxygen/classcaffe_1_1ScaleLayer.html new file mode 100644 index 00000000000..af05d9212ee --- /dev/null +++ b/doxygen/classcaffe_1_1ScaleLayer.html @@ -0,0 +1,522 @@ + + + + + + + +Caffe: caffe::ScaleLayer< Dtype > Class Template Reference + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
+
caffe::ScaleLayer< Dtype > Class Template Reference
+
+
+ +

Computes the elementwise product of two input Blobs, with the shape of the latter Blob "broadcast" to match the shape of the former. Equivalent to tiling the latter Blob, then computing the elementwise product. Note: for efficiency and convenience, this layer can additionally perform a "broadcast" sum too when bias_term: true is set. + More...

+ +

#include <scale_layer.hpp>

+
+Inheritance diagram for caffe::ScaleLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ ScaleLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+shared_ptr< Layer< Dtype > > bias_layer_
 
+vector< Blob< Dtype > * > bias_bottom_vec_
 
+vector< bool > bias_propagate_down_
 
+int bias_param_id_
 
+Blob< Dtype > sum_multiplier_
 
+Blob< Dtype > sum_result_
 
+Blob< Dtype > temp_
 
+int axis_
 
+int outer_dim_
 
+int scale_dim_
 
+int inner_dim_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::ScaleLayer< Dtype >

+ +

Computes the elementwise product of two input Blobs, with the shape of the latter Blob "broadcast" to match the shape of the former. Equivalent to tiling the latter Blob, then computing the elementwise product. Note: for efficiency and convenience, this layer can additionally perform a "broadcast" sum too when bias_term: true is set.

+

The latter, scale input may be omitted, in which case it's learned as parameter of the layer (as is the bias, if it is included).

+

Member Function Documentation

+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::ScaleLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ScaleLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+

In the below shape specifications, $ i $ denotes the value of the axis field given by this->layer_param_.scale_param().axis(), after canonicalization (i.e., conversion from negative to positive index, if applicable).

+
Parameters
+ + + +
bottominput Blob vector (length 2)
    +
  1. $ (d_0 \times ... \times d_i \times ... \times d_j \times ... \times d_n) $ the first factor $ x $
    2. +
  2. $ (d_i \times ... \times d_j) $ the second factor $ y $
    3. +
+
topoutput Blob vector (length 1)
    +
  1. $ (d_0 \times ... \times d_i \times ... \times d_j \times ... \times d_n) $ the product $ z = x y $ computed after "broadcasting" y. Equivalent to tiling $ y $ to have the same shape as $ x $, then computing the elementwise product.
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ScaleLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MaxBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::ScaleLayer< Dtype >::MaxBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some maximum number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::ScaleLayer< Dtype >::MinBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ScaleLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:47 for Caffe by   +doxygen + 1.8.12 +
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caffe::SigmoidCrossEntropyLossLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::SigmoidCrossEntropyLossLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::LossLayer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::SigmoidCrossEntropyLossLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::SigmoidCrossEntropyLossLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SigmoidCrossEntropyLossLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SigmoidCrossEntropyLossLayer< Dtype >protectedvirtual
get_normalizer(LossParameter_NormalizationMode normalization_mode, int valid_count)caffe::SigmoidCrossEntropyLossLayer< Dtype >protectedvirtual
has_ignore_label_caffe::SigmoidCrossEntropyLossLayer< Dtype >protected
ignore_label_caffe::SigmoidCrossEntropyLossLayer< Dtype >protected
inner_num_ (defined in caffe::SigmoidCrossEntropyLossLayer< Dtype >)caffe::SigmoidCrossEntropyLossLayer< Dtype >protected
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SigmoidCrossEntropyLossLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
LossLayer(const LayerParameter &param) (defined in caffe::LossLayer< Dtype >)caffe::LossLayer< Dtype >inlineexplicit
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
normalization_caffe::SigmoidCrossEntropyLossLayer< Dtype >protected
normalizer_ (defined in caffe::SigmoidCrossEntropyLossLayer< Dtype >)caffe::SigmoidCrossEntropyLossLayer< Dtype >protected
outer_num_ (defined in caffe::SigmoidCrossEntropyLossLayer< Dtype >)caffe::SigmoidCrossEntropyLossLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SigmoidCrossEntropyLossLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
sigmoid_bottom_vec_caffe::SigmoidCrossEntropyLossLayer< Dtype >protected
sigmoid_layer_caffe::SigmoidCrossEntropyLossLayer< Dtype >protected
sigmoid_output_caffe::SigmoidCrossEntropyLossLayer< Dtype >protected
sigmoid_top_vec_caffe::SigmoidCrossEntropyLossLayer< Dtype >protected
SigmoidCrossEntropyLossLayer(const LayerParameter &param) (defined in caffe::SigmoidCrossEntropyLossLayer< Dtype >)caffe::SigmoidCrossEntropyLossLayer< Dtype >inlineexplicit
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::SigmoidCrossEntropyLossLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:47 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1SigmoidCrossEntropyLossLayer.html b/doxygen/classcaffe_1_1SigmoidCrossEntropyLossLayer.html new file mode 100644 index 00000000000..4637913aa37 --- /dev/null +++ b/doxygen/classcaffe_1_1SigmoidCrossEntropyLossLayer.html @@ -0,0 +1,556 @@ + + + + + + + +Caffe: caffe::SigmoidCrossEntropyLossLayer< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
+
caffe::SigmoidCrossEntropyLossLayer< Dtype > Class Template Reference
+
+
+ +

Computes the cross-entropy (logistic) loss $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $, often used for predicting targets interpreted as probabilities. + More...

+ +

#include <sigmoid_cross_entropy_loss_layer.hpp>

+
+Inheritance diagram for caffe::SigmoidCrossEntropyLossLayer< Dtype >:
+
+
+ + +caffe::LossLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ SigmoidCrossEntropyLossLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::LossLayer< Dtype >
+ LossLayer (const LayerParameter &param)
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
+virtual bool AutoTopBlobs () const
 For convenience and backwards compatibility, instruct the Net to automatically allocate a single top Blob for LossLayers, into which they output their singleton loss, (even if the user didn't specify one in the prototxt, etc.).
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Computes the cross-entropy (logistic) loss $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $, often used for predicting targets interpreted as probabilities. More...
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the sigmoid cross-entropy loss error gradient w.r.t. the predictions. More...
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual Dtype get_normalizer (LossParameter_NormalizationMode normalization_mode, int valid_count)
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+shared_ptr< SigmoidLayer< Dtype > > sigmoid_layer_
 The internal SigmoidLayer used to map predictions to probabilities.
 
+shared_ptr< Blob< Dtype > > sigmoid_output_
 sigmoid_output stores the output of the SigmoidLayer.
 
+vector< Blob< Dtype > * > sigmoid_bottom_vec_
 bottom vector holder to call the underlying SigmoidLayer::Forward
 
+vector< Blob< Dtype > * > sigmoid_top_vec_
 top vector holder to call the underlying SigmoidLayer::Forward
 
+bool has_ignore_label_
 Whether to ignore instances with a certain label.
 
+int ignore_label_
 The label indicating that an instance should be ignored.
 
+LossParameter_NormalizationMode normalization_
 How to normalize the loss.
 
+Dtype normalizer_
 
+int outer_num_
 
+int inner_num_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::SigmoidCrossEntropyLossLayer< Dtype >

+ +

Computes the cross-entropy (logistic) loss $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $, often used for predicting targets interpreted as probabilities.

+

This layer is implemented rather than separate SigmoidLayer + CrossEntropyLayer as its gradient computation is more numerically stable. At test time, this layer can be replaced simply by a SigmoidLayer.

+
Parameters
+ + + +
bottominput Blob vector (length 2)
    +
  1. $ (N \times C \times H \times W) $ the scores $ x \in [-\infty, +\infty]$, which this layer maps to probability predictions $ \hat{p}_n = \sigma(x_n) \in [0, 1] $ using the sigmoid function $ \sigma(.) $ (see SigmoidLayer).
    2. +
  2. $ (N \times C \times H \times W) $ the targets $ y \in [0, 1] $
    3. +
+
topoutput Blob vector (length 1)
    +
  1. $ (1 \times 1 \times 1 \times 1) $ the computed cross-entropy loss: $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $
    2. +
+
+
+
+

Member Function Documentation

+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::SigmoidCrossEntropyLossLayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the sigmoid cross-entropy loss error gradient w.r.t. the predictions.

+

Gradients cannot be computed with respect to the target inputs (bottom[1]), so this method ignores bottom[1] and requires !propagate_down[1], crashing if propagate_down[1] is set.

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
    +
  1. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
    2. +
+
propagate_downsee Layer::Backward. propagate_down[1] must be false as gradient computation with respect to the targets is not implemented.
bottominput Blob vector (length 2)
    +
  1. $ (N \times C \times H \times W) $ the predictions $x$; Backward computes diff $ \frac{\partial E}{\partial x} = \frac{1}{n} \sum\limits_{n=1}^N (\hat{p}_n - p_n) $
    2. +
  2. $ (N \times 1 \times 1 \times 1) $ the labels – ignored as we can't compute their error gradients
    3. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::SigmoidCrossEntropyLossLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+ +

Computes the cross-entropy (logistic) loss $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $, often used for predicting targets interpreted as probabilities.

+

This layer is implemented rather than separate SigmoidLayer + CrossEntropyLayer as its gradient computation is more numerically stable. At test time, this layer can be replaced simply by a SigmoidLayer.

+
Parameters
+ + + +
bottominput Blob vector (length 2)
    +
  1. $ (N \times C \times H \times W) $ the scores $ x \in [-\infty, +\infty]$, which this layer maps to probability predictions $ \hat{p}_n = \sigma(x_n) \in [0, 1] $ using the sigmoid function $ \sigma(.) $ (see SigmoidLayer).
    2. +
  2. $ (N \times C \times H \times W) $ the targets $ y \in [0, 1] $
    3. +
+
topoutput Blob vector (length 1)
    +
  1. $ (1 \times 1 \times 1 \times 1) $ the computed cross-entropy loss: $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ get_normalizer()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
Dtype caffe::SigmoidCrossEntropyLossLayer< Dtype >::get_normalizer (LossParameter_NormalizationMode normalization_mode,
int valid_count 
)
+ 		+protectedvirtual
+
+

Read the normalization mode parameter and compute the normalizer based on the blob size. If normalization_mode is VALID, the count of valid outputs will be read from valid_count, unless it is -1 in which case all outputs are assumed to be valid.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::SigmoidCrossEntropyLossLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::LossLayer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::SigmoidCrossEntropyLossLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Reimplemented from caffe::LossLayer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:47 for Caffe by   +doxygen + 1.8.12 +
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caffe::SigmoidLayer< Dtype > Member List
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+ +

This is the complete list of members for caffe::SigmoidLayer< Dtype >, including all inherited members.

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AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::SigmoidLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::SigmoidLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SigmoidLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SigmoidLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlinevirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
NeuronLayer(const LayerParameter &param) (defined in caffe::NeuronLayer< Dtype >)caffe::NeuronLayer< Dtype >inlineexplicit
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::NeuronLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
SigmoidLayer(const LayerParameter &param) (defined in caffe::SigmoidLayer< Dtype >)caffe::SigmoidLayer< Dtype >inlineexplicit
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::SigmoidLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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+ + diff --git a/doxygen/classcaffe_1_1SigmoidLayer.html b/doxygen/classcaffe_1_1SigmoidLayer.html new file mode 100644 index 00000000000..0407e9273a4 --- /dev/null +++ b/doxygen/classcaffe_1_1SigmoidLayer.html @@ -0,0 +1,356 @@ + + + + + + + +Caffe: caffe::SigmoidLayer< Dtype > Class Template Reference + + + + + + + + + +
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caffe::SigmoidLayer< Dtype > Class Template Reference
+
+
+ +

Sigmoid function non-linearity $ y = (1 + \exp(-x))^{-1} $, a classic choice in neural networks. + More...

+ +

#include <sigmoid_layer.hpp>

+
+Inheritance diagram for caffe::SigmoidLayer< Dtype >:
+
+
+ + +caffe::NeuronLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ SigmoidLayer (const LayerParameter &param)
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::NeuronLayer< Dtype >
+ NeuronLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the error gradient w.r.t. the sigmoid inputs. More...
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::SigmoidLayer< Dtype >

+ +

Sigmoid function non-linearity $ y = (1 + \exp(-x))^{-1} $, a classic choice in neural networks.

+

Note that the gradient vanishes as the values move away from 0. The ReLULayer is often a better choice for this reason.

+

Member Function Documentation

+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::SigmoidLayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the error gradient w.r.t. the sigmoid inputs.

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
    +
  1. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
    2. +
+
propagate_downsee Layer::Backward.
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} y (1 - y) $ if propagate_down[0]
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::SigmoidLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+
Parameters
+ + + +
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $
    2. +
+
topoutput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the computed outputs $ y = (1 + \exp(-x))^{-1} $
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
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+ + diff --git a/doxygen/classcaffe_1_1SigmoidLayer.png b/doxygen/classcaffe_1_1SigmoidLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..d16ee5bab4e5b865c84c81100e79d00f6174dfa8 GIT binary patch literal 1099 zcmeAS@N?(olHy`uVBq!ia0vp^TY= z3ycpOIKbL@M;^%KC<*clW&kPzfvcxNj2IZ0Z+W^nhEy=Vo%`_8D+L}G|LKzV|4&>W z6~&|*weMog%+AZ9nwb_VihsSmG=0@4srW8iFil0Cb*swHjGn5G5Zm zwYo|=YqrJf&$8c5Uv1`?H~Wd#I;};**~w8{Z<9B^yY^K&D>?LfY4We1ySCXLaoM=! zzKC-s_qKKqqwts4k6wRX-J!ypwLtj)&)Uz0uL57pUm(}^uQ$T`(sj`*lb$RF`Rne= zV6RLw#u`uahLw)$!1f_9fcb~U3dS0dD-3pASq$>5 zrVaf~QU}}}c^@oBk#;CuI^|M{lINuqt}M?_fdSr=`gdJ9y=m31^nI!;*K3=lt^A$- zVx`>I!v_zZEWMw6_vcXhu{+zh)z06g$@n60`hMQ6d+*zAlYLeYbSW0=}H#f*@Gv@>EU5q}U@7{4^hV)a${ppPB zvU0igZJD3F3YdQ=w#>%oZmiG)m)5G=@1&mo{%g8U?ZNJ+v%1x9vqfy$a5iFnsvh&T ziq{#jMzihM+PF4cf0wX*n|<1hMlX$GUeObG-&uaXl>IIFaKPHlzc+BMc$EIjJ->tPBGhq($Fq zdaKy5(`&!p>$um749QaYCmG(CKHU1j{nfA1AO6Eqr0uSs fn{-a6{9^x_=6Qgz{_h-M&S&s+^>bP0l+XkKqxdG{ literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1SignalHandler-members.html b/doxygen/classcaffe_1_1SignalHandler-members.html new file mode 100644 index 00000000000..6c88d8d619c --- /dev/null +++ b/doxygen/classcaffe_1_1SignalHandler-members.html @@ -0,0 +1,83 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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GetActionFunction() (defined in caffe::SignalHandler)caffe::SignalHandler
SignalHandler(SolverAction::Enum SIGINT_action, SolverAction::Enum SIGHUP_action) (defined in caffe::SignalHandler)caffe::SignalHandler
~SignalHandler() (defined in caffe::SignalHandler)caffe::SignalHandler
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+Public Member Functions

+ SignalHandler (SolverAction::Enum SIGINT_action, SolverAction::Enum SIGHUP_action)
 
+ActionCallback GetActionFunction ()
 
+
The documentation for this class was generated from the following files: +
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This is the complete list of members for caffe::SilenceLayer< Dtype >, including all inherited members.

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AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::SilenceLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::SilenceLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::SilenceLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SilenceLayer< Dtype >inlineprotectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SilenceLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlinevirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::SilenceLayer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SilenceLayer< Dtype >inlinevirtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
SilenceLayer(const LayerParameter &param) (defined in caffe::SilenceLayer< Dtype >)caffe::SilenceLayer< Dtype >inlineexplicit
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::SilenceLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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caffe::SilenceLayer< Dtype > Class Template Reference
+
+
+ +

Ignores bottom blobs while producing no top blobs. (This is useful to suppress outputs during testing.) + More...

+ +

#include <silence_layer.hpp>

+
+Inheritance diagram for caffe::SilenceLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ SilenceLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::SilenceLayer< Dtype >

+ +

Ignores bottom blobs while producing no top blobs. (This is useful to suppress outputs during testing.)

+

Member Function Documentation

+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::SilenceLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::SilenceLayer< Dtype >::MinBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
virtual void caffe::SilenceLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+inlinevirtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:47 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1SilenceLayer.png b/doxygen/classcaffe_1_1SilenceLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..c82068f06d8f0d73e2220d3e13a6cd288512ea72 GIT binary patch literal 734 zcmeAS@N?(olHy`uVBq!ia0vp^n}IlhgBeJsN^N-yq$C1-LR|m<{|{uoc=NTi|Ih>= z3ycpOIKbL@M;^%KC<*clW&kPzfvcxNj2IZ0ay?xfLn;{G&b`?8T7k!fUpVvs|H=8g z6%VQ{3SvEuNuinRYnWM5UEd417PI^Kt0 z(QJ|3X3@=eOHDc z*gIo-YUD8?hn_kHw#yA??Cl>r@}KzQB*$0f^6aKL!#}6Vk_vIeV~ubxOMN4e1CY;M?*xB=O@tL!%jD8_Awoi{W{xd7S zIBU{g_h+g_m(CWaUaER`>(a}I+jMezzhA#Mlj++n-ZRUl&z4-*E%ofJwtdmk%OB3@ zsj<%Yt*tm3^YmlNX&>A8K)Ew>?kE0^TBf#cPs-6favYoIUj6pw-p|!tJAJH-Ux&_r z*L5>5f9tP(=jZ*@QU98#d9VGzopr0D_8S!~g&Q literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1SliceLayer-members.html b/doxygen/classcaffe_1_1SliceLayer-members.html new file mode 100644 index 00000000000..61cd784c2ff --- /dev/null +++ b/doxygen/classcaffe_1_1SliceLayer-members.html @@ -0,0 +1,124 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
caffe::SliceLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::SliceLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::SliceLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::SliceLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
count_ (defined in caffe::SliceLayer< Dtype >)caffe::SliceLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::SliceLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SliceLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SliceLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SliceLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::SliceLayer< Dtype >inlinevirtual
num_slices_ (defined in caffe::SliceLayer< Dtype >)caffe::SliceLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SliceLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
slice_axis_ (defined in caffe::SliceLayer< Dtype >)caffe::SliceLayer< Dtype >protected
slice_point_ (defined in caffe::SliceLayer< Dtype >)caffe::SliceLayer< Dtype >protected
slice_size_ (defined in caffe::SliceLayer< Dtype >)caffe::SliceLayer< Dtype >protected
SliceLayer(const LayerParameter &param) (defined in caffe::SliceLayer< Dtype >)caffe::SliceLayer< Dtype >inlineexplicit
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::SliceLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:47 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1SliceLayer.html b/doxygen/classcaffe_1_1SliceLayer.html new file mode 100644 index 00000000000..a1114803edc --- /dev/null +++ b/doxygen/classcaffe_1_1SliceLayer.html @@ -0,0 +1,419 @@ + + + + + + + +Caffe: caffe::SliceLayer< Dtype > Class Template Reference + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
+
caffe::SliceLayer< Dtype > Class Template Reference
+
+
+ +

Takes a Blob and slices it along either the num or channel dimension, outputting multiple sliced Blob results. + More...

+ +

#include <slice_layer.hpp>

+
+Inheritance diagram for caffe::SliceLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ SliceLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+int count_
 
+int num_slices_
 
+int slice_size_
 
+int slice_axis_
 
+vector< int > slice_point_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::SliceLayer< Dtype >

+ +

Takes a Blob and slices it along either the num or channel dimension, outputting multiple sliced Blob results.

+

TODO(dox): thorough documentation for Forward, Backward, and proto params.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::SliceLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::SliceLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::SliceLayer< Dtype >::MinTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::SliceLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:47 for Caffe by   +doxygen + 1.8.12 +
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caffe::SoftmaxLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::SoftmaxLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::SoftmaxLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::SoftmaxLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::SoftmaxLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::SoftmaxLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SoftmaxLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SoftmaxLayer< Dtype >protectedvirtual
inner_num_ (defined in caffe::SoftmaxLayer< Dtype >)caffe::SoftmaxLayer< Dtype >protected
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlinevirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
outer_num_ (defined in caffe::SoftmaxLayer< Dtype >)caffe::SoftmaxLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SoftmaxLayer< Dtype >virtual
scale_caffe::SoftmaxLayer< Dtype >protected
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
softmax_axis_ (defined in caffe::SoftmaxLayer< Dtype >)caffe::SoftmaxLayer< Dtype >protected
SoftmaxLayer(const LayerParameter &param) (defined in caffe::SoftmaxLayer< Dtype >)caffe::SoftmaxLayer< Dtype >inlineexplicit
sum_multiplier_caffe::SoftmaxLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::SoftmaxLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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+ + diff --git a/doxygen/classcaffe_1_1SoftmaxLayer.html b/doxygen/classcaffe_1_1SoftmaxLayer.html new file mode 100644 index 00000000000..571c8e07894 --- /dev/null +++ b/doxygen/classcaffe_1_1SoftmaxLayer.html @@ -0,0 +1,371 @@ + + + + + + + +Caffe: caffe::SoftmaxLayer< Dtype > Class Template Reference + + + + + + + + + +
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caffe::SoftmaxLayer< Dtype > Class Template Reference
+
+
+ +

Computes the softmax function. + More...

+ +

#include <softmax_layer.hpp>

+
+Inheritance diagram for caffe::SoftmaxLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ SoftmaxLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+int outer_num_
 
+int inner_num_
 
+int softmax_axis_
 
+Blob< Dtype > sum_multiplier_
 sum_multiplier is used to carry out sum using BLAS
 
+Blob< Dtype > scale_
 scale is an intermediate Blob to hold temporary results.
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::SoftmaxLayer< Dtype >

+ +

Computes the softmax function.

+

TODO(dox): thorough documentation for Forward, Backward, and proto params.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::SoftmaxLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::SoftmaxLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::SoftmaxLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:47 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1SoftmaxLayer.png b/doxygen/classcaffe_1_1SoftmaxLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..f18e15cf791ad67eaf2481da9b55e049638570f9 GIT binary patch literal 738 zcmeAS@N?(olHy`uVBq!ia0vp^TY)%$gBeI>upC(hq$C1-LR|m<{|{uoc=NTi|Ih>= z3ycpOIKbL@M;^%KC<*clW&kPzfvcxNj2IZ03O!vMLn;{G&b`>ztRUdZe|^dS|0i#+ zI>F;K)z{nW(6QUPWjkcr6Khf&m1n?vtY?{aVp$)#CeZp32?Oiod4k zC#ft~dv!f%`O{k~R+~*Ki4MPFXc|53u-(1)R6XjSUFV$aE6PC7I7SLvr;HT)BoxS5tbs^6b)=XaOuuke+#7xqug_WZR=ZL7-Hi%X_0 zv8xKwKfS3(twH}KqrsfEC3QA#N8~2`;lhq{&Epj!Gm=IP7;1B4F_TJfxW*feZm=EM)|MrUTIeptha63 z1%uU_KD>VRvi-~siO=@0ysZz!bz0fIoA&hi-_o4T)iuS2tD9#=71@dezxuc>a7*;@ z^^FUe-jqtnT>Lg)GOv4@({UTJ6J#ka z_sHjMddeO3S8o$$=$_rSBBdthXk3V@_3LT#zx3Qndv=Gx>&ntKPuiC`{axntEj7k9 zq$t;E^4Z(KAl=n?OMbiY*1|8Pb6fAN&c5yJlXhv^th!t&XPfqo&N9A_G{k2=^NRNS zYqhp|eLF) + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::SoftmaxWithLossLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::SoftmaxWithLossLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::LossLayer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::SoftmaxWithLossLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::SoftmaxWithLossLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::LossLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::SoftmaxWithLossLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SoftmaxWithLossLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SoftmaxWithLossLayer< Dtype >protectedvirtual
get_normalizer(LossParameter_NormalizationMode normalization_mode, int valid_count)caffe::SoftmaxWithLossLayer< Dtype >protectedvirtual
has_ignore_label_caffe::SoftmaxWithLossLayer< Dtype >protected
ignore_label_caffe::SoftmaxWithLossLayer< Dtype >protected
inner_num_ (defined in caffe::SoftmaxWithLossLayer< Dtype >)caffe::SoftmaxWithLossLayer< Dtype >protected
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SoftmaxWithLossLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
LossLayer(const LayerParameter &param) (defined in caffe::LossLayer< Dtype >)caffe::LossLayer< Dtype >inlineexplicit
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::SoftmaxWithLossLayer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::SoftmaxWithLossLayer< Dtype >inlinevirtual
normalization_caffe::SoftmaxWithLossLayer< Dtype >protected
outer_num_ (defined in caffe::SoftmaxWithLossLayer< Dtype >)caffe::SoftmaxWithLossLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
prob_caffe::SoftmaxWithLossLayer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SoftmaxWithLossLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
softmax_axis_ (defined in caffe::SoftmaxWithLossLayer< Dtype >)caffe::SoftmaxWithLossLayer< Dtype >protected
softmax_bottom_vec_caffe::SoftmaxWithLossLayer< Dtype >protected
softmax_layer_caffe::SoftmaxWithLossLayer< Dtype >protected
softmax_top_vec_caffe::SoftmaxWithLossLayer< Dtype >protected
SoftmaxWithLossLayer(const LayerParameter &param)caffe::SoftmaxWithLossLayer< Dtype >inlineexplicit
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::SoftmaxWithLossLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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+ + diff --git a/doxygen/classcaffe_1_1SoftmaxWithLossLayer.html b/doxygen/classcaffe_1_1SoftmaxWithLossLayer.html new file mode 100644 index 00000000000..085a1d4d50e --- /dev/null +++ b/doxygen/classcaffe_1_1SoftmaxWithLossLayer.html @@ -0,0 +1,634 @@ + + + + + + + +Caffe: caffe::SoftmaxWithLossLayer< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
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caffe::SoftmaxWithLossLayer< Dtype > Class Template Reference
+
+
+ +

Computes the multinomial logistic loss for a one-of-many classification task, passing real-valued predictions through a softmax to get a probability distribution over classes. + More...

+ +

#include <softmax_loss_layer.hpp>

+
+Inheritance diagram for caffe::SoftmaxWithLossLayer< Dtype >:
+
+
+ + +caffe::LossLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

 SoftmaxWithLossLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
- Public Member Functions inherited from caffe::LossLayer< Dtype >
+ LossLayer (const LayerParameter &param)
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
+virtual bool AutoTopBlobs () const
 For convenience and backwards compatibility, instruct the Net to automatically allocate a single top Blob for LossLayers, into which they output their singleton loss, (even if the user didn't specify one in the prototxt, etc.).
 
virtual bool AllowForceBackward (const int bottom_index) const
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the softmax loss error gradient w.r.t. the predictions. More...
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual Dtype get_normalizer (LossParameter_NormalizationMode normalization_mode, int valid_count)
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+shared_ptr< Layer< Dtype > > softmax_layer_
 The internal SoftmaxLayer used to map predictions to a distribution.
 
+Blob< Dtype > prob_
 prob stores the output probability predictions from the SoftmaxLayer.
 
+vector< Blob< Dtype > * > softmax_bottom_vec_
 bottom vector holder used in call to the underlying SoftmaxLayer::Forward
 
+vector< Blob< Dtype > * > softmax_top_vec_
 top vector holder used in call to the underlying SoftmaxLayer::Forward
 
+bool has_ignore_label_
 Whether to ignore instances with a certain label.
 
+int ignore_label_
 The label indicating that an instance should be ignored.
 
+LossParameter_NormalizationMode normalization_
 How to normalize the output loss.
 
+int softmax_axis_
 
+int outer_num_
 
+int inner_num_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::SoftmaxWithLossLayer< Dtype >

+ +

Computes the multinomial logistic loss for a one-of-many classification task, passing real-valued predictions through a softmax to get a probability distribution over classes.

+

This layer should be preferred over separate SoftmaxLayer + MultinomialLogisticLossLayer as its gradient computation is more numerically stable. At test time, this layer can be replaced simply by a SoftmaxLayer.

+
Parameters
+ + + +
bottominput Blob vector (length 2)
    +
  1. $ (N \times C \times H \times W) $ the predictions $ x $, a Blob with values in $ [-\infty, +\infty] $ indicating the predicted score for each of the $ K = CHW $ classes. This layer maps these scores to a probability distribution over classes using the softmax function $ \hat{p}_{nk} = \exp(x_{nk}) / \left[\sum_{k'} \exp(x_{nk'})\right] $ (see SoftmaxLayer).
    2. +
  2. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
    3. +
+
topoutput Blob vector (length 1)
    +
  1. $ (1 \times 1 \times 1 \times 1) $ the computed cross-entropy classification loss: $ E = \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n}) $, for softmax output class probabilites $ \hat{p} $
    2. +
+
+
+
+

Constructor & Destructor Documentation

+ +

§ SoftmaxWithLossLayer()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
caffe::SoftmaxWithLossLayer< Dtype >::SoftmaxWithLossLayer (const LayerParameter & param)
+ 		+inlineexplicit
+
+
Parameters
+ + +
paramprovides LossParameter loss_param, with options:
    +
  • ignore_label (optional) Specify a label value that should be ignored when computing the loss.
    • +
  • normalize (optional, default true) If true, the loss is normalized by the number of (nonignored) labels present; otherwise the loss is simply summed over spatial locations.
    • +
+
+
+
+ +
+
+

Member Function Documentation

+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::SoftmaxWithLossLayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the softmax loss error gradient w.r.t. the predictions.

+

Gradients cannot be computed with respect to the label inputs (bottom[1]), so this method ignores bottom[1] and requires !propagate_down[1], crashing if propagate_down[1] is set.

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
    +
  1. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
    2. +
+
propagate_downsee Layer::Backward. propagate_down[1] must be false as we can't compute gradients with respect to the labels.
bottominput Blob vector (length 2)
    +
  1. $ (N \times C \times H \times W) $ the predictions $ x $; Backward computes diff $ \frac{\partial E}{\partial x} $
    2. +
  2. $ (N \times 1 \times 1 \times 1) $ the labels – ignored as we can't compute their error gradients
    3. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::SoftmaxWithLossLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::LossLayer< Dtype >.

+ +
+
+ +

§ get_normalizer()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
Dtype caffe::SoftmaxWithLossLayer< Dtype >::get_normalizer (LossParameter_NormalizationMode normalization_mode,
int valid_count 
)
+ 		+protectedvirtual
+
+

Read the normalization mode parameter and compute the normalizer based on the blob size. If normalization_mode is VALID, the count of valid outputs will be read from valid_count, unless it is -1 in which case all outputs are assumed to be valid.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::SoftmaxWithLossLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::LossLayer< Dtype >.

+ +
+
+ +

§ MaxTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::SoftmaxWithLossLayer< Dtype >::MaxTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some maximum number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::SoftmaxWithLossLayer< Dtype >::MinTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::SoftmaxWithLossLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Reimplemented from caffe::LossLayer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:48 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1SoftmaxWithLossLayer.png b/doxygen/classcaffe_1_1SoftmaxWithLossLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..cd388cc540b64b2ce303c969b1441c3acca304ce GIT binary patch literal 1160 zcmeAS@N?(olHy`uVBq!ia0vp^&w;pugBeI_cks*wQW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;TdUmY$xok4m!Do_4TyyZGM;QfPK{9(gjh9Bgo2g>Ey5#rk zd8(f0U)s(we{RP2t?-?mvgi8O2G6DbSx(NB)c2Qt{weM1EHi$S{%5ITmv0|*v|ESo^t&Z*UD`O_X?p@C%hW(@$GE_#dh~YQy>>(!zZ|YsZpJC614Hi;!7z#}+7=hu!bmZa#mX5_}3jQz2oV852!*kN1Y`7(l=V+cSIH>s4aY&L$M0Sd$oAJ=?6wsvE~(}XqU$*4UbMH&>^UL3&C-($&hEG&6CXIW;KeuMvOrUIt9EuV`l*%*!It~^i|q^(@r5C_K>jd)N|2Yj0H?wpSWMh{yvJj zs}T;l&TKzzZn1&SOcDDxJn3ZB&`o4-tToovS2-lwad zS$MNPe{}Cl^ygzscK@BJbKigS+2Fro5BygiUZ1AHQ2aL%k@lJP1i}*Y-F@pcmx{#w aXDA4a^AV_jbp%*!FnGH9xvX + + + + + + +Caffe: Member List + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
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+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
caffe::Solver< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::Solver< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
action_request_function_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
add_callback(Callback *value) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
ApplyUpdate()=0 (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protectedpure virtual
callbacks() const (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
callbacks_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
CheckSnapshotWritePermissions() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
current_step_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
DISABLE_COPY_AND_ASSIGN(Solver) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
DisplayOutputBlobs(const int net_id) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
GetRequestedAction() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
Init(const SolverParameter &param) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
InitTestNets() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
InitTrainNet() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
iter() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
iter_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
losses_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
net() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
net_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
param() const (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
param_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
requested_early_exit_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Restore(const char *resume_file) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
RestoreSolverStateFromBinaryProto(const string &state_file)=0 (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protectedpure virtual
RestoreSolverStateFromHDF5(const string &state_file)=0 (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protectedpure virtual
root_solver_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SetActionFunction(ActionCallback func) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
smoothed_loss_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Snapshot() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
SnapshotFilename(const string extension) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SnapshotSolverState(const string &model_filename)=0 (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protectedpure virtual
SnapshotToBinaryProto() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SnapshotToHDF5() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Solve(const char *resume_file=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >virtual
Solve(const string resume_file) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
Solver(const SolverParameter &param, const Solver *root_solver=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >explicit
Solver(const string &param_file, const Solver *root_solver=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >explicit
Step(int iters) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
Test(const int test_net_id=0) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
test_nets() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
test_nets_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
TestAll() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
type() constcaffe::Solver< Dtype >inlinevirtual
UpdateSmoothedLoss(Dtype loss, int start_iter, int average_loss) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
~Solver() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:48 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1Solver.html b/doxygen/classcaffe_1_1Solver.html new file mode 100644 index 00000000000..a7393611986 --- /dev/null +++ b/doxygen/classcaffe_1_1Solver.html @@ -0,0 +1,258 @@ + + + + + + + +Caffe: caffe::Solver< Dtype > Class Template Reference + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
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+Classes | +Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
+
caffe::Solver< Dtype > Class Template Referenceabstract
+
+
+ +

An interface for classes that perform optimization on Nets. + More...

+ +

#include <solver.hpp>

+
+Inheritance diagram for caffe::Solver< Dtype >:
+
+
+ + +caffe::SGDSolver< Dtype > +caffe::WorkerSolver< Dtype > +caffe::AdaDeltaSolver< Dtype > +caffe::AdaGradSolver< Dtype > +caffe::AdamSolver< Dtype > +caffe::NesterovSolver< Dtype > +caffe::RMSPropSolver< Dtype > + +
+ + + + +

+Classes

class  Callback
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ Solver (const SolverParameter &param, const Solver *root_solver=NULL)
 
+ Solver (const string &param_file, const Solver *root_solver=NULL)
 
+void Init (const SolverParameter &param)
 
+void InitTrainNet ()
 
+void InitTestNets ()
 
+void SetActionFunction (ActionCallback func)
 
+SolverAction::Enum GetRequestedAction ()
 
+virtual void Solve (const char *resume_file=NULL)
 
+void Solve (const string resume_file)
 
+void Step (int iters)
 
+void Restore (const char *resume_file)
 
+void Snapshot ()
 
+const SolverParameter & param () const
 
+shared_ptr< Net< Dtype > > net ()
 
+const vector< shared_ptr< Net< Dtype > > > & test_nets ()
 
+int iter ()
 
+const vector< Callback * > & callbacks () const
 
+void add_callback (Callback *value)
 
+void CheckSnapshotWritePermissions ()
 
+virtual const char * type () const
 Returns the solver type.
 
+ + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void ApplyUpdate ()=0
 
+string SnapshotFilename (const string extension)
 
+string SnapshotToBinaryProto ()
 
+string SnapshotToHDF5 ()
 
+void TestAll ()
 
+void Test (const int test_net_id=0)
 
+virtual void SnapshotSolverState (const string &model_filename)=0
 
+virtual void RestoreSolverStateFromHDF5 (const string &state_file)=0
 
+virtual void RestoreSolverStateFromBinaryProto (const string &state_file)=0
 
+void DisplayOutputBlobs (const int net_id)
 
+void UpdateSmoothedLoss (Dtype loss, int start_iter, int average_loss)
 
+ DISABLE_COPY_AND_ASSIGN (Solver)
 
+ + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+SolverParameter param_
 
+int iter_
 
+int current_step_
 
+shared_ptr< Net< Dtype > > net_
 
+vector< shared_ptr< Net< Dtype > > > test_nets_
 
+vector< Callback * > callbacks_
 
+vector< Dtype > losses_
 
+Dtype smoothed_loss_
 
+const Solver *const root_solver_
 
+ActionCallback action_request_function_
 
+bool requested_early_exit_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::Solver< Dtype >

+ +

An interface for classes that perform optimization on Nets.

+

Requires implementation of ApplyUpdate to compute a parameter update given the current state of the Net parameters.

+
The documentation for this class was generated from the following files:
    +
  • include/caffe/solver.hpp
    • +
  • src/caffe/solver.cpp
    • +
+
+ +
+Generated on Mon Nov 21 2016 11:23:48 for Caffe by   +doxygen + 1.8.12 +
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caffe::SolverRegisterer< Dtype > Member List
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This is the complete list of members for caffe::SolverRegisterer< Dtype >, including all inherited members.

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SolverRegisterer(const string &type, Solver< Dtype > *(*creator)(const SolverParameter &)) (defined in caffe::SolverRegisterer< Dtype >)caffe::SolverRegisterer< Dtype >inline
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caffe::SolverRegisterer< Dtype > Class Template Reference
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+ SolverRegisterer (const string &type, Solver< Dtype > *(*creator)(const SolverParameter &))
 
+
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caffe::SolverRegistry< Dtype > Member List
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This is the complete list of members for caffe::SolverRegistry< Dtype >, including all inherited members.

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AddCreator(const string &type, Creator creator) (defined in caffe::SolverRegistry< Dtype >)caffe::SolverRegistry< Dtype >inlinestatic
CreateSolver(const SolverParameter &param) (defined in caffe::SolverRegistry< Dtype >)caffe::SolverRegistry< Dtype >inlinestatic
Creator typedef (defined in caffe::SolverRegistry< Dtype >)caffe::SolverRegistry< Dtype >
CreatorRegistry typedef (defined in caffe::SolverRegistry< Dtype >)caffe::SolverRegistry< Dtype >
Registry() (defined in caffe::SolverRegistry< Dtype >)caffe::SolverRegistry< Dtype >inlinestatic
SolverTypeList() (defined in caffe::SolverRegistry< Dtype >)caffe::SolverRegistry< Dtype >inlinestatic
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caffe::SolverRegistry< Dtype > Class Template Reference
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+typedef Solver< Dtype > *(* Creator) (const SolverParameter &)
 
+typedef std::map< string, Creator > CreatorRegistry
 
+ + + + + + + + + +

+Static Public Member Functions

+static CreatorRegistry & Registry ()
 
+static void AddCreator (const string &type, Creator creator)
 
+static Solver< Dtype > * CreateSolver (const SolverParameter &param)
 
+static vector< string > SolverTypeList ()
 
+
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This is the complete list of members for caffe::Solver< Dtype >::Callback, including all inherited members.

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on_gradients_ready()=0 (defined in caffe::Solver< Dtype >::Callback)caffe::Solver< Dtype >::Callbackprotectedpure virtual
on_start()=0 (defined in caffe::Solver< Dtype >::Callback)caffe::Solver< Dtype >::Callbackprotectedpure virtual
Solver (defined in caffe::Solver< Dtype >::Callback)caffe::Solver< Dtype >::Callbackfriend
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caffe::Solver< Dtype >::Callback Class Referenceabstract
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+virtual void on_start ()=0
 
+virtual void on_gradients_ready ()=0
 
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+Friends

+template<typename T >
class Solver
 
+
The documentation for this class was generated from the following file: +
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+ + diff --git a/doxygen/classcaffe_1_1Solver_1_1Callback.png b/doxygen/classcaffe_1_1Solver_1_1Callback.png new file mode 100644 index 0000000000000000000000000000000000000000..0ac96400656fe731a88b8e4ea897c8dfbb1d3865 GIT binary patch literal 792 zcmeAS@N?(olHy`uVBq!ia0vp^M}RnhgBeH`_G|F~DTx4|5ZC|z{{xvX-h3_XKQsZz z0^#8tTZ`oe9!TES?UxqhWU}U_|yiMU*S3QTu^~HiSRx+&L zQqmW@On;7!ctd#Q4#vXzh`k&i-!$C#BjOOfb+WR+`6as92KMzmxly0){#wN{L-^fZ z-NjFXE87a01yc4%swYnSod5UNhN2QZ$<>cPT-fwv?F)r9E2SepPbhwVjFkaPumw9M@)1-oqu+}O1SM{(4?%X zoRhvR<8!cSn(|~_I>T#k>pP<1W;XnX(tiF^U%s1f5o6$?-RDj2cMJ4?-~7*L+LCE; zPBXqtt516oJn!?)hF$t1kH79*y5)YB}T-d;Ch% zhj(QOZ<->MPCMRQ#A|+T>iLYef{!d5*)mm!xqEB{df$c=o3(WpvTocdl>69!&5<U$CY9`hdo-iXLvv~OYxyTHG`s)o7heWU#tfdWelF{r5}E)PwO^$G literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1SplitLayer-members.html b/doxygen/classcaffe_1_1SplitLayer-members.html new file mode 100644 index 00000000000..0b7d223133a --- /dev/null +++ b/doxygen/classcaffe_1_1SplitLayer-members.html @@ -0,0 +1,120 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::SplitLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::SplitLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
count_ (defined in caffe::SplitLayer< Dtype >)caffe::SplitLayer< Dtype >protected
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::SplitLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SplitLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SplitLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlinevirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::SplitLayer< Dtype >inlinevirtual
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::SplitLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
SplitLayer(const LayerParameter &param) (defined in caffe::SplitLayer< Dtype >)caffe::SplitLayer< Dtype >inlineexplicit
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::SplitLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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caffe::SplitLayer< Dtype > Class Template Reference
+
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+ +

Creates a "split" path in the network by copying the bottom Blob into multiple top Blobs to be used by multiple consuming layers. + More...

+ +

#include <split_layer.hpp>

+
+Inheritance diagram for caffe::SplitLayer< Dtype >:
+
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+ + +caffe::Layer< Dtype > + +
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+Public Member Functions

+ SplitLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + +

+Protected Attributes

+int count_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::SplitLayer< Dtype >

+ +

Creates a "split" path in the network by copying the bottom Blob into multiple top Blobs to be used by multiple consuming layers.

+

TODO(dox): thorough documentation for Forward, Backward, and proto params.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::SplitLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ MinTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::SplitLayer< Dtype >::MinTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required.

+

This method should be overridden to return a non-negative value if your layer expects some minimum number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::SplitLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:48 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1SplitLayer.png b/doxygen/classcaffe_1_1SplitLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..edac5e1e677ea34c738ba30c0251b025668fb429 GIT binary patch literal 714 zcmeAS@N?(olHy`uVBq!ia0vp^3xPO*gBeJ=IX<-jQW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;mJbWH2wx`|wdhwy@%m`hy1@kA=@gvUOw$FCWK5dX!T|Jp!wfY=2cO9T3d=k> z1TL$vbYvn)|Lt$lTxzn#gMm34Dr%)%({~}-A@==AqjjZ>cY@{4zv253sNS-^eYgCq z13#n9A7)lGFozy0+A?SF^UvS!rR8oubF+5y&EHvHHMCZK(s&wkw6HgcgJJhJhS^*H z8r=SK=@#RJGrKwhYRhKoFUxy=R@&)$g5Ne<+imMVeU$vm{k`Sh;>0_G&)m$GW(B=h zsycM`YSlL3>jJlg&EIR?%;)Ggx?uK`Gx2NT*Ig$T8s6o-Q^@E4dxN6Sg*C}ekKg{@ zV7T`>C=|uAR=%puNdNh@R69e~-}-bz|JHg8>AE+sp51JnD7`pUHx3vIVCg!04`2TjsO4v literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1SyncedMemory-members.html b/doxygen/classcaffe_1_1SyncedMemory-members.html new file mode 100644 index 00000000000..d0b733d7dc7 --- /dev/null +++ b/doxygen/classcaffe_1_1SyncedMemory-members.html @@ -0,0 +1,97 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::SyncedMemory Member List
+
+
+ +

This is the complete list of members for caffe::SyncedMemory, including all inherited members.

+ + + + + + + + + + + + + + + + + + +
async_gpu_push(const cudaStream_t &stream) (defined in caffe::SyncedMemory)caffe::SyncedMemory
cpu_data() (defined in caffe::SyncedMemory)caffe::SyncedMemory
gpu_data() (defined in caffe::SyncedMemory)caffe::SyncedMemory
head() (defined in caffe::SyncedMemory)caffe::SyncedMemoryinline
HEAD_AT_CPU enum value (defined in caffe::SyncedMemory)caffe::SyncedMemory
HEAD_AT_GPU enum value (defined in caffe::SyncedMemory)caffe::SyncedMemory
mutable_cpu_data() (defined in caffe::SyncedMemory)caffe::SyncedMemory
mutable_gpu_data() (defined in caffe::SyncedMemory)caffe::SyncedMemory
set_cpu_data(void *data) (defined in caffe::SyncedMemory)caffe::SyncedMemory
set_gpu_data(void *data) (defined in caffe::SyncedMemory)caffe::SyncedMemory
size() (defined in caffe::SyncedMemory)caffe::SyncedMemoryinline
SYNCED enum value (defined in caffe::SyncedMemory)caffe::SyncedMemory
SyncedHead enum name (defined in caffe::SyncedMemory)caffe::SyncedMemory
SyncedMemory() (defined in caffe::SyncedMemory)caffe::SyncedMemoryinline
SyncedMemory(size_t size) (defined in caffe::SyncedMemory)caffe::SyncedMemoryinlineexplicit
UNINITIALIZED enum value (defined in caffe::SyncedMemory)caffe::SyncedMemory
~SyncedMemory() (defined in caffe::SyncedMemory)caffe::SyncedMemory
+ +
+Generated on Mon Nov 21 2016 11:23:48 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1SyncedMemory.html b/doxygen/classcaffe_1_1SyncedMemory.html new file mode 100644 index 00000000000..00f8ae2eb19 --- /dev/null +++ b/doxygen/classcaffe_1_1SyncedMemory.html @@ -0,0 +1,136 @@ + + + + + + + +Caffe: caffe::SyncedMemory Class Reference + + + + + + + + + +
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+Public Types | +Public Member Functions | +List of all members
+
+
caffe::SyncedMemory Class Reference
+
+
+ +

Manages memory allocation and synchronization between the host (CPU) and device (GPU). + More...

+ +

#include <syncedmem.hpp>

+ + + + +

+Public Types

enum  SyncedHead { UNINITIALIZED, +HEAD_AT_CPU, +HEAD_AT_GPU, +SYNCED + }
 
+ + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ SyncedMemory (size_t size)
 
+const void * cpu_data ()
 
+void set_cpu_data (void *data)
 
+const void * gpu_data ()
 
+void set_gpu_data (void *data)
 
+void * mutable_cpu_data ()
 
+void * mutable_gpu_data ()
 
+SyncedHead head ()
 
+size_t size ()
 
+void async_gpu_push (const cudaStream_t &stream)
 
+

Detailed Description

+

Manages memory allocation and synchronization between the host (CPU) and device (GPU).

+

TODO(dox): more thorough description.

+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:48 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1TanHLayer-members.html b/doxygen/classcaffe_1_1TanHLayer-members.html new file mode 100644 index 00000000000..dd48f82e215 --- /dev/null +++ b/doxygen/classcaffe_1_1TanHLayer-members.html @@ -0,0 +1,120 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::TanHLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::TanHLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::TanHLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::TanHLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::TanHLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::TanHLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlinevirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
NeuronLayer(const LayerParameter &param) (defined in caffe::NeuronLayer< Dtype >)caffe::NeuronLayer< Dtype >inlineexplicit
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::NeuronLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
TanHLayer(const LayerParameter &param) (defined in caffe::TanHLayer< Dtype >)caffe::TanHLayer< Dtype >inlineexplicit
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::TanHLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:48 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1TanHLayer.html b/doxygen/classcaffe_1_1TanHLayer.html new file mode 100644 index 00000000000..a3a4b8529d3 --- /dev/null +++ b/doxygen/classcaffe_1_1TanHLayer.html @@ -0,0 +1,356 @@ + + + + + + + +Caffe: caffe::TanHLayer< Dtype > Class Template Reference + + + + + + + + + +
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caffe::TanHLayer< Dtype > Class Template Reference
+
+
+ +

TanH hyperbolic tangent non-linearity $ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $, popular in auto-encoders. + More...

+ +

#include <tanh_layer.hpp>

+
+Inheritance diagram for caffe::TanHLayer< Dtype >:
+
+
+ + +caffe::NeuronLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ TanHLayer (const LayerParameter &param)
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::NeuronLayer< Dtype >
+ NeuronLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Computes the error gradient w.r.t. the sigmoid inputs. More...
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::TanHLayer< Dtype >

+ +

TanH hyperbolic tangent non-linearity $ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $, popular in auto-encoders.

+

Note that the gradient vanishes as the values move away from 0. The ReLULayer is often a better choice for this reason.

+

Member Function Documentation

+ +

§ Backward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
void caffe::TanHLayer< Dtype >::Backward_cpu (const vector< Blob< Dtype > *> & top,
const vector< bool > & propagate_down,
const vector< Blob< Dtype > *> & bottom 
)
+ 		+protectedvirtual
+
+ +

Computes the error gradient w.r.t. the sigmoid inputs.

+
Parameters
+ + + + +
topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
    +
  1. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
    2. +
+
propagate_downsee Layer::Backward.
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} \left(1 - \left[\frac{\exp(2x) - 1}{exp(2x) + 1} \right]^2 \right) = \frac{\partial E}{\partial y} (1 - y^2) $ if propagate_down[0]
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::TanHLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+
Parameters
+ + + +
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $
    2. +
+
topoutput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the computed outputs $ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/layers/tanh_layer.hpp
    • +
  • src/caffe/layers/tanh_layer.cpp
    • +
+
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+Generated on Mon Nov 21 2016 11:23:48 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1TanHLayer.png b/doxygen/classcaffe_1_1TanHLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..2da52ff88364f15433a72643c87f554e59cce43e GIT binary patch literal 1068 zcmeAS@N?(olHy`uVBq!ia0vp^n}N84gBeK9F4tNQq$C1-LR|m<{|{uoc=NTi|Ih>= z3ycpOIKbL@M;^%KC<*clW&kPzfvcxNj2IZ0cYC@xhEy=Vo%^uwvlb7lI{V7^|4(es z^)P5z^04mfDv!9QCESK-8+w0FpAt07N5ylN*Fhc6e_UyvpRNmh-n3@bGnJR?rd_}P zLCf>1@u5GrUTnB2@p+}R=dRWKXQCsnap+ghN!Nb%C+)J>IjKC^wRxLfW+#+QTH?Pe z?cU#8^=UKI&+8gLsoU`O@{F*>w_+w){W=q~&+Lr2+SIFxnx@5jAMt&)efIU$j3eU9 z*@d$&-n-d$Q|;`pKMu9sJH_|z^IZ2}m6+e793St_TFrCIzy4{w8FBUNof&nz=jGlv z5Y*{3PYsA_d@DWaOz^Mbk6+*JQ}k?>da>vK_v=q@pILmu{(w!-AMV0wD*3FNReoyE zP(2y%{|x9$Zw9+62A^e|Gv)h#E>KUYQ}SoGRWAO*+ps^tfcb~^48}jAXBdD%z);U> z+_1k%@<9ASo(Jm_*gk}#NH4I_Txv4YW0J|#rpQS^Et8(CH-45H8G3f6zo*r8S?SGJ zKj)`h<@}ue^Wf3i^T}tHOM3;mw^mxek$R}w|2d~@U*);?WlvS#uP^(4EYG@xcl*-! zdtX|e%UgBV`poXFf79DmFW)zD=B-}+nzR#t6KztbXf#@jzH56&`YPoKJ9 ztJeKlm8)}YLY#h!XZpvy*9(lSQt!Q8v;JF|`I&38@9quQerE3snVGMxi! zo%42XrhcmZx#yYd&5c9tPUQRn1?aui>9@^&V!W(!>+j`Sx0$V6CVFF^;z^sO_TuT^ zf3>OX%v=zE{|E06;b(zQ)YPE8M$7wtMBQ&HvInR@_sAzGpVUlpLSbF zUp^fAPv-gMs(0cLFmPgg&ebxsLQ E0I8}S + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::ThresholdLayer< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::ThresholdLayer< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::ThresholdLayer< Dtype >inlineprotectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inlineprotectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::NeuronLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ThresholdLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ThresholdLayer< Dtype >protectedvirtual
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::ThresholdLayer< Dtype >virtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
NeuronLayer(const LayerParameter &param) (defined in caffe::NeuronLayer< Dtype >)caffe::NeuronLayer< Dtype >inlineexplicit
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::NeuronLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
threshold_ (defined in caffe::ThresholdLayer< Dtype >)caffe::ThresholdLayer< Dtype >protected
ThresholdLayer(const LayerParameter &param)caffe::ThresholdLayer< Dtype >inlineexplicit
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::ThresholdLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:48 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1ThresholdLayer.html b/doxygen/classcaffe_1_1ThresholdLayer.html new file mode 100644 index 00000000000..1be34f69206 --- /dev/null +++ b/doxygen/classcaffe_1_1ThresholdLayer.html @@ -0,0 +1,385 @@ + + + + + + + +Caffe: caffe::ThresholdLayer< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +Protected Attributes | +List of all members
+
+
caffe::ThresholdLayer< Dtype > Class Template Reference
+
+
+ +

Tests whether the input exceeds a threshold: outputs 1 for inputs above threshold; 0 otherwise. + More...

+ +

#include <threshold_layer.hpp>

+
+Inheritance diagram for caffe::ThresholdLayer< Dtype >:
+
+
+ + +caffe::NeuronLayer< Dtype > +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

 ThresholdLayer (const LayerParameter &param)
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual const char * type () const
 Returns the layer type.
 
- Public Member Functions inherited from caffe::NeuronLayer< Dtype >
+ NeuronLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + +

+Protected Member Functions

virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Not implemented (non-differentiable function)
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + +

+Protected Attributes

+Dtype threshold_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::ThresholdLayer< Dtype >

+ +

Tests whether the input exceeds a threshold: outputs 1 for inputs above threshold; 0 otherwise.

+

Constructor & Destructor Documentation

+ +

§ ThresholdLayer()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + +
caffe::ThresholdLayer< Dtype >::ThresholdLayer (const LayerParameter & param)
+ 		+inlineexplicit
+
+
Parameters
+ + +
paramprovides ThresholdParameter threshold_param, with ThresholdLayer options:
    +
  • threshold (optional, default 0). the threshold value $ t $ to which the input values are compared.
    • +
+
+
+
+ +
+
+

Member Function Documentation

+ +

§ Forward_cpu()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ThresholdLayer< Dtype >::Forward_cpu (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+protectedvirtual
+
+
Parameters
+ + + +
bottominput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the inputs $ x $
    2. +
+
topoutput Blob vector (length 1)
    +
  1. $ (N \times C \times H \times W) $ the computed outputs $ y = \left\{ \begin{array}{lr} 0 & \mathrm{if} \; x \le t \\ 1 & \mathrm{if} \; x > t \end{array} \right. $
    2. +
+
+
+
+ +

Implements caffe::Layer< Dtype >.

+ +
+
+ +

§ LayerSetUp()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::ThresholdLayer< Dtype >::LayerSetUp (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Does layer-specific setup: your layer should implement this function as well as Reshape.

+
Parameters
+ + + +
bottomthe preshaped input blobs, whose data fields store the input data for this layer
topthe allocated but unshaped output blobs
+
+
+

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Mon Nov 21 2016 11:23:48 for Caffe by   +doxygen + 1.8.12 +
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caffe::TileLayer< Dtype > Member List
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This is the complete list of members for caffe::TileLayer< Dtype >, including all inherited members.

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AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
axis_ (defined in caffe::TileLayer< Dtype >)caffe::TileLayer< Dtype >protected
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::TileLayer< Dtype >protectedvirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::TileLayer< Dtype >protectedvirtual
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::TileLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::TileLayer< Dtype >inlinevirtual
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::TileLayer< Dtype >protectedvirtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::TileLayer< Dtype >protectedvirtual
inner_dim_ (defined in caffe::TileLayer< Dtype >)caffe::TileLayer< Dtype >protected
IsShared() constcaffe::Layer< Dtype >inline
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlinevirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
outer_dim_ (defined in caffe::TileLayer< Dtype >)caffe::TileLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::TileLayer< Dtype >virtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::Layer< Dtype >inlinevirtual
TileLayer(const LayerParameter &param) (defined in caffe::TileLayer< Dtype >)caffe::TileLayer< Dtype >inlineexplicit
tiles_ (defined in caffe::TileLayer< Dtype >)caffe::TileLayer< Dtype >protected
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
type() constcaffe::TileLayer< Dtype >inlinevirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
+ +
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+ + diff --git a/doxygen/classcaffe_1_1TileLayer.html b/doxygen/classcaffe_1_1TileLayer.html new file mode 100644 index 00000000000..17a42c71de1 --- /dev/null +++ b/doxygen/classcaffe_1_1TileLayer.html @@ -0,0 +1,365 @@ + + + + + + + +Caffe: caffe::TileLayer< Dtype > Class Template Reference + + + + + + + + + +
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caffe::TileLayer< Dtype > Class Template Reference
+
+
+ +

Copy a Blob along specified dimensions. + More...

+ +

#include <tile_layer.hpp>

+
+Inheritance diagram for caffe::TileLayer< Dtype >:
+
+
+ + +caffe::Layer< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ TileLayer (const LayerParameter &param)
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
+ + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
+ + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+unsigned int axis_
 
+unsigned int tiles_
 
+unsigned int outer_dim_
 
+unsigned int inner_dim_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::TileLayer< Dtype >

+ +

Copy a Blob along specified dimensions.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::TileLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::TileLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ Reshape()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void caffe::TileLayer< Dtype >::Reshape (const vector< Blob< Dtype > *> & bottom,
const vector< Blob< Dtype > *> & top 
)
+ 		+virtual
+
+ +

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

+
Parameters
+ + + +
bottomthe input blobs, with the requested input shapes
topthe top blobs, which should be reshaped as needed
+
+
+

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

+ +

Implements caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/layers/tile_layer.hpp
    • +
  • src/caffe/layers/tile_layer.cpp
    • +
+
+ +
+Generated on Mon Nov 21 2016 11:23:49 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1TileLayer.png b/doxygen/classcaffe_1_1TileLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..f8c5d1688458eb695018853cf5abbe565bbadf21 GIT binary patch literal 691 zcmeAS@N?(olHy`uVBq!ia0vp^bAdR3gBeJg^tJZ^DTx4|5ZC|z{{xvX-h3_XKQsZz z0^aLzwrnt@cNArE1)0=p-#XO}#Ci_iFnpHAMwHeTZS+{i{l@;@mC==-rAwaP?|RqUx!Fqf*5%56wfoY$*3R_3+4cM6k*F)dAs25t z-Ix(xle=qmPt5Eme#KLBo}8O8jqT;R*v;?lE=X$MFQ1yZ=lz>Av(`-6G*fAs;TB`< zlQpMm!;_AthfT7&S~$7%?&#+u*BFIM;b?+W+F&vyHh0t}bEj$h^dO#N;Id(BBM(zE%x0)MOoeCi5vQ^JEvej3oUl zx}b?&$cr7xi=iX^>0S%N{`6tQs#S%`}}u)FW1hG zyEs)r^sehWo4Mu1^WLRS|0g=v+Rt^n={n7czB`v{x1Bp%b#-#+#hZ_Qe3yw_|6-lr z{WEt@>b65d?U#^A@%1;m`={Domh=aP+snQCHXCOf1$bSVadU4+D$m<9OwWSf?H2lf z?+jCoqwK}JJE!B9!9$MygDWfmUgypT@@jr1zwe1+dFHW{5?~5r@O1TaS?83{1OPk7 BQ-c5i literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1Timer-members.html b/doxygen/classcaffe_1_1Timer-members.html new file mode 100644 index 00000000000..66208fda338 --- /dev/null +++ b/doxygen/classcaffe_1_1Timer-members.html @@ -0,0 +1,100 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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This is the complete list of members for caffe::Timer, including all inherited members.

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elapsed_microseconds_ (defined in caffe::Timer)caffe::Timerprotected
elapsed_milliseconds_ (defined in caffe::Timer)caffe::Timerprotected
has_run_at_least_once() (defined in caffe::Timer)caffe::Timerinline
has_run_at_least_once_ (defined in caffe::Timer)caffe::Timerprotected
Init() (defined in caffe::Timer)caffe::Timerprotected
initted() (defined in caffe::Timer)caffe::Timerinline
initted_ (defined in caffe::Timer)caffe::Timerprotected
MicroSeconds() (defined in caffe::Timer)caffe::Timervirtual
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running_ (defined in caffe::Timer)caffe::Timerprotected
Seconds() (defined in caffe::Timer)caffe::Timervirtual
Start() (defined in caffe::Timer)caffe::Timervirtual
start_cpu_ (defined in caffe::Timer)caffe::Timerprotected
start_gpu_ (defined in caffe::Timer)caffe::Timerprotected
Stop() (defined in caffe::Timer)caffe::Timervirtual
stop_cpu_ (defined in caffe::Timer)caffe::Timerprotected
stop_gpu_ (defined in caffe::Timer)caffe::Timerprotected
Timer() (defined in caffe::Timer)caffe::Timer
~Timer() (defined in caffe::Timer)caffe::Timervirtual
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+Public Member Functions

+virtual void Start ()
 
+virtual void Stop ()
 
+virtual float MilliSeconds ()
 
+virtual float MicroSeconds ()
 
+virtual float Seconds ()
 
+bool initted ()
 
+bool running ()
 
+bool has_run_at_least_once ()
 
+ + + +

+Protected Member Functions

+void Init ()
 
+ + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+bool initted_
 
+bool running_
 
+bool has_run_at_least_once_
 
+cudaEvent_t start_gpu_
 
+cudaEvent_t stop_gpu_
 
+boost::posix_time::ptime start_cpu_
 
+boost::posix_time::ptime stop_cpu_
 
+float elapsed_milliseconds_
 
+float elapsed_microseconds_
 
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/util/benchmark.hpp
    • +
  • src/caffe/util/benchmark.cpp
    • +
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+
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This is the complete list of members for caffe::UniformFiller< Dtype >, including all inherited members.

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Fill(Blob< Dtype > *blob) (defined in caffe::UniformFiller< Dtype >)caffe::UniformFiller< Dtype >inlinevirtual
Filler(const FillerParameter &param) (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >inlineexplicit
filler_param_ (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >protected
UniformFiller(const FillerParameter &param) (defined in caffe::UniformFiller< Dtype >)caffe::UniformFiller< Dtype >inlineexplicit
~Filler() (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >inlinevirtual
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caffe::UniformFiller< Dtype > Class Template Reference
+
+
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Fills a Blob with uniformly distributed values $ x\sim U(a, b) $. + More...

+ +

#include <filler.hpp>

+
+Inheritance diagram for caffe::UniformFiller< Dtype >:
+
+
+ + +caffe::Filler< Dtype > + +
+ + + + + + + + + +

+Public Member Functions

+ UniformFiller (const FillerParameter &param)
 
+virtual void Fill (Blob< Dtype > *blob)
 
- Public Member Functions inherited from caffe::Filler< Dtype >
+ Filler (const FillerParameter &param)
 
+ + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Filler< Dtype >
+FillerParameter filler_param_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::UniformFiller< Dtype >

+ +

Fills a Blob with uniformly distributed values $ x\sim U(a, b) $.

+
The documentation for this class was generated from the following file: +
+ +
+Generated on Mon Nov 21 2016 11:23:49 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1UniformFiller.png b/doxygen/classcaffe_1_1UniformFiller.png new file mode 100644 index 0000000000000000000000000000000000000000..af11ba30d9fd29f97336b5ef93739bcdcd8f29d8 GIT binary patch literal 742 zcmeAS@N?(olHy`uVBq!ia0vp^8-O@~gBeKn>GHn=QW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;Se?8^@fA#&r zj!{QnezQC4cIePurfc579gV*Rj^+jG}i?gx{mm~PiR|9KVjI>~!NFHc^(`AVtyys)tA zB)zXv6~#IAXRbWCqPyl!w4`Oi>kIoIo>5)7G&`aGUf4R5{sX&z=q9}XF80ax!P~g0 z#_QwyCC#iiY%D+UZN-}X+E1bn^iHx^pBT=hE_seM@6Wv>f7eUZG3Myh@Lq3UdglMO zw%q9}JT><&s(qO?eL{-c+rwAS&oAfn?7DcoUNN5O9#_<)dzZ=^&ZbUVQvPM_qzOy< zO&U}+gP0>GDSc&`w=DCO2ZLf2!!1g2yj@m~>SZ zFxE)%E?CGFQ?~2bx1IaWd|p*-XCEEERc-U~qqBMAjdRxYSx3h{72jMfzKd~BlRSJ%b$)NLw2ex0N`CM?_3rGHZH<2wquv_bVJWNGX)-(d^T+-Ja*6FV4EOZB zEl<4qEBnB??JVCL&-V>$m8EaZIm@>rP^eexAH$8G8R-^>C0dx@-Ie+$o$&a~^@7?R zv2CxVw#6=bx5cC>N}<}M_`l-J!fUDw##!>~6yKL}HmrYrEdGQ)V?HZy!J>F3JF|#} txpk{m81zgU7?n&xxcudmpjYny8Qy!Sc7|W~Ukyx@44$rjF6*2UngGy!SY-eJ literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1WindowDataLayer-members.html b/doxygen/classcaffe_1_1WindowDataLayer-members.html new file mode 100644 index 00000000000..4edbbe2cee0 --- /dev/null +++ b/doxygen/classcaffe_1_1WindowDataLayer-members.html @@ -0,0 +1,159 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::WindowDataLayer< Dtype > Member List
+
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+ +

This is the complete list of members for caffe::WindowDataLayer< Dtype >, including all inherited members.

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AllowForceBackward(const int bottom_index) constcaffe::Layer< Dtype >inlinevirtual
AutoTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
Backward(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::Layer< Dtype >inline
Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BaseDataLayer< Dtype >inlinevirtual
Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)caffe::BaseDataLayer< Dtype >inlinevirtual
BaseDataLayer(const LayerParameter &param) (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >explicit
BasePrefetchingDataLayer(const LayerParameter &param) (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >explicit
bg_windows_ (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected
blobs()caffe::Layer< Dtype >inline
blobs_caffe::Layer< Dtype >protected
cache_images_ (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected
CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual
data_mean_ (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected
data_transformer_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected
DataLayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >virtual
EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
ExactNumBottomBlobs() constcaffe::WindowDataLayer< Dtype >inlinevirtual
ExactNumTopBlobs() constcaffe::WindowDataLayer< Dtype >inlinevirtual
fg_windows_ (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected
Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BasePrefetchingDataLayer< Dtype >virtual
Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BasePrefetchingDataLayer< Dtype >virtual
has_mean_file_ (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected
has_mean_values_ (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected
image_database_ (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected
image_database_cache_ (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected
IMAGE_INDEX enum value (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected
InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadinline
InternalThreadEntry() (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protectedvirtual
is_started() const (defined in caffe::InternalThread)caffe::InternalThread
IsShared() constcaffe::Layer< Dtype >inline
LABEL enum value (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected
Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit
layer_param() constcaffe::Layer< Dtype >inline
layer_param_caffe::Layer< Dtype >protected
LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BasePrefetchingDataLayer< Dtype >virtual
load_batch(Batch< Dtype > *batch) (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protectedvirtual
loss(const int top_index) constcaffe::Layer< Dtype >inline
loss_caffe::Layer< Dtype >protected
MaxBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
mean_values_ (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected
MinBottomBlobs() constcaffe::Layer< Dtype >inlinevirtual
MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual
must_stop() (defined in caffe::InternalThread)caffe::InternalThreadprotected
NUM enum value (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected
output_labels_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected
OVERLAP enum value (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected
param_propagate_down(const int param_id)caffe::Layer< Dtype >inline
param_propagate_down_caffe::Layer< Dtype >protected
phase_caffe::Layer< Dtype >protected
prefetch_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected
PREFETCH_COUNT (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >static
prefetch_free_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected
prefetch_full_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected
prefetch_rng_ (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected
PrefetchRand() (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protectedvirtual
Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::BaseDataLayer< Dtype >inlinevirtual
set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline
set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline
SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected
SetShared(bool is_shared)caffe::Layer< Dtype >inline
SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline
ShareInParallel() constcaffe::BaseDataLayer< Dtype >inlinevirtual
StartInternalThread()caffe::InternalThread
StopInternalThread()caffe::InternalThread
ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual
transform_param_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected
transformed_data_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected
type() constcaffe::WindowDataLayer< Dtype >inlinevirtual
WindowDataLayer(const LayerParameter &param) (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >inlineexplicit
WindowField enum name (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected
X1 enum value (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected
X2 enum value (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected
Y1 enum value (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected
Y2 enum value (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected
~InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadvirtual
~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
~WindowDataLayer() (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >virtual
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caffe::WindowDataLayer< Dtype > Class Template Reference
+
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+ +

Provides data to the Net from windows of images files, specified by a window data file. + More...

+ +

#include <window_data_layer.hpp>

+
+Inheritance diagram for caffe::WindowDataLayer< Dtype >:
+
+
+ + +caffe::BasePrefetchingDataLayer< Dtype > +caffe::BaseDataLayer< Dtype > +caffe::InternalThread +caffe::Layer< Dtype > + +
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+Public Member Functions

+ WindowDataLayer (const LayerParameter &param)
 
+virtual void DataLayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
+virtual const char * type () const
 Returns the layer type.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
- Public Member Functions inherited from caffe::BasePrefetchingDataLayer< Dtype >
+ BasePrefetchingDataLayer (const LayerParameter &param)
 
void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
+virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the CPU device, compute the layer output.
 
+virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
- Public Member Functions inherited from caffe::BaseDataLayer< Dtype >
+ BaseDataLayer (const LayerParameter &param)
 
+virtual bool ShareInParallel () const
 Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
+virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
+virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
+bool IsShared () const
 Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true.
 
+void SetShared (bool is_shared)
 Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true.
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
+vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
+const LayerParameter & layer_param () const
 Returns the layer parameter.
 
+virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
+Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
+void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
+void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 
- Public Member Functions inherited from caffe::InternalThread
void StartInternalThread ()
 
void StopInternalThread ()
 
+bool is_started () const
 
+ + + +

+Protected Types

enum  WindowField {
+  IMAGE_INDEX, +LABEL, +OVERLAP, +X1, +
+  Y1, +X2, +Y2, +NUM +
+ }
 
+ + + + + + + + + + + + + + + + +

+Protected Member Functions

+virtual unsigned int PrefetchRand ()
 
+virtual void load_batch (Batch< Dtype > *batch)
 
- Protected Member Functions inherited from caffe::BasePrefetchingDataLayer< Dtype >
+virtual void InternalThreadEntry ()
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 
- Protected Member Functions inherited from caffe::InternalThread
+bool must_stop ()
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+shared_ptr< Caffe::RNG > prefetch_rng_
 
+vector< std::pair< std::string, vector< int > > > image_database_
 
+vector< vector< float > > fg_windows_
 
+vector< vector< float > > bg_windows_
 
+Blob< Dtype > data_mean_
 
+vector< Dtype > mean_values_
 
+bool has_mean_file_
 
+bool has_mean_values_
 
+bool cache_images_
 
+vector< std::pair< std::string, Datum > > image_database_cache_
 
- Protected Attributes inherited from caffe::BasePrefetchingDataLayer< Dtype >
+Batch< Dtype > prefetch_ [PREFETCH_COUNT]
 
+BlockingQueue< Batch< Dtype > * > prefetch_free_
 
+BlockingQueue< Batch< Dtype > * > prefetch_full_
 
+Blob< Dtype > transformed_data_
 
- Protected Attributes inherited from caffe::BaseDataLayer< Dtype >
+TransformationParameter transform_param_
 
+shared_ptr< DataTransformer< Dtype > > data_transformer_
 
+bool output_labels_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 
+ + + + +

+Additional Inherited Members

- Static Public Attributes inherited from caffe::BasePrefetchingDataLayer< Dtype >
+static const int PREFETCH_COUNT = 3
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::WindowDataLayer< Dtype >

+ +

Provides data to the Net from windows of images files, specified by a window data file.

+

TODO(dox): thorough documentation for Forward and proto params.

+

Member Function Documentation

+ +

§ ExactNumBottomBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::WindowDataLayer< Dtype >::ExactNumBottomBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+ +

§ ExactNumTopBlobs()

+ +
+
+
+template<typename Dtype >
+ + + + + +
+ + + + + + + +
virtual int caffe::WindowDataLayer< Dtype >::ExactNumTopBlobs () const
+ 		+inlinevirtual
+
+ +

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

+

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

+ +

Reimplemented from caffe::Layer< Dtype >.

+ +
+
+
The documentation for this class was generated from the following file: +
+ +
+Generated on Mon Nov 21 2016 11:23:49 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1WindowDataLayer.png b/doxygen/classcaffe_1_1WindowDataLayer.png new file mode 100644 index 0000000000000000000000000000000000000000..1a71cc36c0d70f106837ecc46774546c7a4c06aa GIT binary patch literal 2286 zcmb`Jc~}!?9>*sFjfqwe4MYT$5>_2W0Y#!BAc7J(8bdBXASf1zEkUjY2@-io5k-WQ zOA$eY3Q0(VU^zl!2*kJ?O1WHIuAl@EtH>cBg>*vQeYU&*?X&YdGxPl&&&)gT`+2_a zq~JYIe2Fwd0s!zO&efR!0IM`wx>^gm5~f9Wpc@+RPIOVL)sP%}I~#RVmJX?vQ!16} zr1Wit!qt%kPcHy!(j48*L~AG&k8?gsjDh`{Kk@MXZatTre{k#7W(ijQW5?b1E8M5D z7S~=F*3rA|GMyuNzqm__wk1J~@iXMnQ2bSnG7vy(R3Zs*y(4gXaichxQ6#Z3Qow^_xYvxa(|{Zk$Mc zveF%uJ#x)K8GWh7FT!V0p0l9F+w_mT;mp{RmEWS8&y~o20Lywnb`UE3`i65tWC31z zSKR5Z+sh=G_vA(|Wh1TRP4>?-8bGEayT_kF*_5_Cruc!c$IXs-;td;OK09?U|KkK- zNH*>AAx9ICl(YV*~I?A%YAWdc_=Jr56uML8=Z9NEaf3CmKaV0s8TS zbCfTn05g9x3s!T843qYG)b#2AXHh`w`9wd!63V1n185cx%A}H3V(LnHt={#24>dK% z-B9-*)z7U!#h*eb&}usK9}l{mrdhsMuDdhF%*X4TQJC?%m9g;?i}BF<36bQZgdOj> z?NMe6y-QznjJn=8&yv^Rd@3R%O0zKEeF#C_zVgy`A@t5W@$7W(-Hq36Noa}0sG-t^ zJ1FMKR0{KP6MMcay6|J^JCi5V^#8^HFvVsyepHknJm>qaz9GW)1rJ@uFzh+zJPsP1e$z1^1jcsJwcPX#Y6 zBuSwv?)V-`PO;$~flT6BMpIN#*DYo`g;&6!!)}{AkW4gH zlFJB6*BJXE8PCxs+SzXxZ(AAfyoWDmKqW-J3QxB4FWRDe^_<(+HXX1Etp7lg=VS~Y*W)VhrrQnyDgj{YOv8+TySUhmc!@?2SK)xRe}2rbGg3#DcQ!yn)&i$AGv~5 z_}r#rwSWBi0xDd6Ojmtc2lvU)IG5UKfIB2z09`xO83kU-QUv)Z=h@H;BKF%vQhM>e zd9jugXW5k806~d^h=IWTy!bo7nfSqg+(M_pxg|Di9<&8sxnhB|CTn$`Ul_(1D9q%Y z7`9Ik4QaC4_-ihj)<&D9KD@MxtmGpB;5~vN9%Ut_*X_&R%el7 z?#c9?&eD${E>8H>#EXSd@b_Hd!Am$g4b=m-ib6C_w6&U!m_GfeYK@!aqnjp8ixs_L zk0UJyN(Q{HW2Nla=~0^kQEY4BvZtFaPe=$%@rAIQrk5P5ZK!fC_mnkuJe1I9{4u>o zLtUXC%p)s5oe?&J08txt61xl4qlg_)bZ-)uId$HstM*~3o^}kqX??VC1<#OFA@jgg zH=~ZQlnA?R*(L|OU9)7B;kCZ(z8b5CSYgNuMw#Assa`v+scZ|BPo98$um0&S#_Zf^0JO0N(WE~BWsUM$AR+0=)7w3rI?(lx&4V{@-UmZ^&4b%8*vp$Rt8qX zj^-t2pqX;*s6Jnwfw4J=uMN2lxrOX~X?plIjCcql3`R2yc#izF$pUAwE)R0L=nre) zNtk$q2ChHv4;sWa0a}w$p}-eIZy=9~uvLCzxZ-yA5z#Aj>}v*SfFSMvV|f4Vobw0k z1QUX_tW6}KW)HccVsZwy!mYt)tvK^bKTo&oi2G~X8v>E?C-0GVYt}*DpO`wtLK7WQ_7HyK<)#go-Y-Z+ce_C_sDyVBVhcF%fT;a zeZeN@tm7YjzTkyH{ + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::WorkerSolver< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::WorkerSolver< Dtype >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
action_request_function_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
add_callback(Callback *value) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
ApplyUpdate() (defined in caffe::WorkerSolver< Dtype >)caffe::WorkerSolver< Dtype >inlineprotectedvirtual
callbacks() const (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
callbacks_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
CheckSnapshotWritePermissions() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
current_step_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
DISABLE_COPY_AND_ASSIGN(Solver) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
DisplayOutputBlobs(const int net_id) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
GetRequestedAction() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
Init(const SolverParameter &param) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
InitTestNets() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
InitTrainNet() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
iter() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
iter_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
losses_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
net() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
net_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
param() const (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
param_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
requested_early_exit_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Restore(const char *resume_file) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
RestoreSolverStateFromBinaryProto(const string &state_file) (defined in caffe::WorkerSolver< Dtype >)caffe::WorkerSolver< Dtype >inlineprotectedvirtual
RestoreSolverStateFromHDF5(const string &state_file) (defined in caffe::WorkerSolver< Dtype >)caffe::WorkerSolver< Dtype >inlineprotectedvirtual
root_solver_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SetActionFunction(ActionCallback func) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
smoothed_loss_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Snapshot() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
SnapshotFilename(const string extension) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SnapshotSolverState(const string &model_filename) (defined in caffe::WorkerSolver< Dtype >)caffe::WorkerSolver< Dtype >inlineprotectedvirtual
SnapshotToBinaryProto() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
SnapshotToHDF5() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
Solve(const char *resume_file=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >virtual
Solve(const string resume_file) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
Solver(const SolverParameter &param, const Solver *root_solver=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >explicit
Solver(const string &param_file, const Solver *root_solver=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >explicit
Step(int iters) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
Test(const int test_net_id=0) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
test_nets() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
test_nets_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
TestAll() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
type() constcaffe::Solver< Dtype >inlinevirtual
UpdateSmoothedLoss(Dtype loss, int start_iter, int average_loss) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
WorkerSolver(const SolverParameter &param, const Solver< Dtype > *root_solver=NULL) (defined in caffe::WorkerSolver< Dtype >)caffe::WorkerSolver< Dtype >inlineexplicit
~Solver() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:49 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1WorkerSolver.html b/doxygen/classcaffe_1_1WorkerSolver.html new file mode 100644 index 00000000000..82f9eb96a15 --- /dev/null +++ b/doxygen/classcaffe_1_1WorkerSolver.html @@ -0,0 +1,249 @@ + + + + + + + +Caffe: caffe::WorkerSolver< Dtype > Class Template Reference + + + + + + + + + +
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+Public Member Functions | +Protected Member Functions | +List of all members
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+
caffe::WorkerSolver< Dtype > Class Template Reference
+
+
+ +

Solver that only computes gradients, used as worker for multi-GPU training. + More...

+ +

#include <solver.hpp>

+
+Inheritance diagram for caffe::WorkerSolver< Dtype >:
+
+
+ + +caffe::Solver< Dtype > + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ WorkerSolver (const SolverParameter &param, const Solver< Dtype > *root_solver=NULL)
 
- Public Member Functions inherited from caffe::Solver< Dtype >
+ Solver (const SolverParameter &param, const Solver *root_solver=NULL)
 
+ Solver (const string &param_file, const Solver *root_solver=NULL)
 
+void Init (const SolverParameter &param)
 
+void InitTrainNet ()
 
+void InitTestNets ()
 
+void SetActionFunction (ActionCallback func)
 
+SolverAction::Enum GetRequestedAction ()
 
+virtual void Solve (const char *resume_file=NULL)
 
+void Solve (const string resume_file)
 
+void Step (int iters)
 
+void Restore (const char *resume_file)
 
+void Snapshot ()
 
+const SolverParameter & param () const
 
+shared_ptr< Net< Dtype > > net ()
 
+const vector< shared_ptr< Net< Dtype > > > & test_nets ()
 
+int iter ()
 
+const vector< Callback * > & callbacks () const
 
+void add_callback (Callback *value)
 
+void CheckSnapshotWritePermissions ()
 
+virtual const char * type () const
 Returns the solver type.
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Member Functions

+void ApplyUpdate ()
 
+void SnapshotSolverState (const string &model_filename)
 
+void RestoreSolverStateFromBinaryProto (const string &state_file)
 
+void RestoreSolverStateFromHDF5 (const string &state_file)
 
- Protected Member Functions inherited from caffe::Solver< Dtype >
+string SnapshotFilename (const string extension)
 
+string SnapshotToBinaryProto ()
 
+string SnapshotToHDF5 ()
 
+void TestAll ()
 
+void Test (const int test_net_id=0)
 
+void DisplayOutputBlobs (const int net_id)
 
+void UpdateSmoothedLoss (Dtype loss, int start_iter, int average_loss)
 
+ DISABLE_COPY_AND_ASSIGN (Solver)
 
+ + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Solver< Dtype >
+SolverParameter param_
 
+int iter_
 
+int current_step_
 
+shared_ptr< Net< Dtype > > net_
 
+vector< shared_ptr< Net< Dtype > > > test_nets_
 
+vector< Callback * > callbacks_
 
+vector< Dtype > losses_
 
+Dtype smoothed_loss_
 
+const Solver *const root_solver_
 
+ActionCallback action_request_function_
 
+bool requested_early_exit_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::WorkerSolver< Dtype >

+ +

Solver that only computes gradients, used as worker for multi-GPU training.

+
The documentation for this class was generated from the following file: +
+ +
+Generated on Mon Nov 21 2016 11:23:49 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1WorkerSolver.png b/doxygen/classcaffe_1_1WorkerSolver.png new file mode 100644 index 0000000000000000000000000000000000000000..d8d2158dfe50635fdc1aa19cd1f24aecdff9039d GIT binary patch literal 750 zcmeAS@N?(olHy`uVBq!ia0vp^+kiNLgBeIR8`rM{QW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;SZ+_|C|B3sz zWgak7S^C>7Ol4Wz$|d5(1v&5aZ*R-}-03?h?ed{%O9G>AOfvC$pRjz3chJuzx93NC zURwI@U;FE=p_BZ+uzmYG=hYgA3yrl>UX!XyduvtF&KFm$%dPvo{kcD@rux;-Z&%xf z+)M16xa9h-Bi|Hes_U2UN?7}%xY05vyHq=W`jWP|r0MG!Z%_ThTDBk~@wcq)gV-C@ z;xo6)Cmmb7R(o6Ffo?QDJf*sq!k?0@aI)^@#ZKU!E?-@9h< zpZZPP-l`Z{esMnRCvDB|f2?@s?PLEpPj;D97k>Zm@z2|Dq*i!clKvH(bNSPxFKz#J zc&NOz278P9s^DR?j?$m??_i@|J|8mq(mzP@t47cC;k z{O$9LuiDHB_P+~WY>G=Ve{t^sAB*gb<>^c&AshOCzpLD5TfZjvmQLBZbIJ~Xj~uh% zxfgQ3#@_h7fenM7Wkdgaj>G)({&6JEmw9^iR;ZUkvsQ0 z<+JKHL|uE|ma}}|s?O*89CTRLf8+dfK?)Bur1id414H!N-Pn7JkLN92dU2)P-Mya4 zD>ue+Z`=G + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::XavierFiller< Dtype > Member List
+
+
+ +

This is the complete list of members for caffe::XavierFiller< Dtype >, including all inherited members.

+ + + + + + +
Fill(Blob< Dtype > *blob) (defined in caffe::XavierFiller< Dtype >)caffe::XavierFiller< Dtype >inlinevirtual
Filler(const FillerParameter &param) (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >inlineexplicit
filler_param_ (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >protected
XavierFiller(const FillerParameter &param) (defined in caffe::XavierFiller< Dtype >)caffe::XavierFiller< Dtype >inlineexplicit
~Filler() (defined in caffe::Filler< Dtype >)caffe::Filler< Dtype >inlinevirtual
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+ + diff --git a/doxygen/classcaffe_1_1XavierFiller.html b/doxygen/classcaffe_1_1XavierFiller.html new file mode 100644 index 00000000000..b65b5ef9741 --- /dev/null +++ b/doxygen/classcaffe_1_1XavierFiller.html @@ -0,0 +1,126 @@ + + + + + + + +Caffe: caffe::XavierFiller< Dtype > Class Template Reference + + + + + + + + + +
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caffe::XavierFiller< Dtype > Class Template Reference
+
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+ +

Fills a Blob with values $ x \sim U(-a, +a) $ where $ a $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average. + More...

+ +

#include <filler.hpp>

+
+Inheritance diagram for caffe::XavierFiller< Dtype >:
+
+
+ + +caffe::Filler< Dtype > + +
+ + + + + + + + + +

+Public Member Functions

+ XavierFiller (const FillerParameter &param)
 
+virtual void Fill (Blob< Dtype > *blob)
 
- Public Member Functions inherited from caffe::Filler< Dtype >
+ Filler (const FillerParameter &param)
 
+ + + + +

+Additional Inherited Members

- Protected Attributes inherited from caffe::Filler< Dtype >
+FillerParameter filler_param_
 
+

Detailed Description

+

template<typename Dtype>
+class caffe::XavierFiller< Dtype >

+ +

Fills a Blob with values $ x \sim U(-a, +a) $ where $ a $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average.

+

A Filler based on the paper [Bengio and Glorot 2010]: Understanding the difficulty of training deep feedforward neuralnetworks.

+

It fills the incoming matrix by randomly sampling uniform data from [-scale, scale] where scale = sqrt(3 / n) where n is the fan_in, fan_out, or their average, depending on the variance_norm option. You should make sure the input blob has shape (num, a, b, c) where a * b * c = fan_in and num * b * c = fan_out. Note that this is currently not the case for inner product layers.

+

TODO(dox): make notation in above comment consistent with rest & use LaTeX.

+
The documentation for this class was generated from the following file: +
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+ + diff --git a/doxygen/classcaffe_1_1XavierFiller.png b/doxygen/classcaffe_1_1XavierFiller.png new file mode 100644 index 0000000000000000000000000000000000000000..8510583128b7d8dff334660215a69d8f639efc9b GIT binary patch literal 736 zcmeAS@N?(olHy`uVBq!ia0vp^tARLxgBeH$CYOE$QW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;q|C8G@ zg*G%Uxw$)TtH+udy-qR3No>!jrbbS4Jfh_3oUzF(Xph=zm7}lO*J$Q`Dt>+ca}|&0 ztIYo0JrkE`-%XDd&lEEf&X4d9_PAslyGd^84Xs(aUT>Zz z>bdKhX!f~G{b$!gF8Su1xM|*ZRqf0Ef`^l?ypUaeKS4YF@U*Q_yNKYeY)Teh1u)3=yMSj$;_Uw!-YHY*_U_0K6)v?htc9M*C(lO;`g|8|Hc1(T=I!$pG{rz`xnb<%gvKYqUV2XQ284( zIdsw^(@0g#IR9%vzZ$X#tY<%e z*NT&C7oGok{jXj0#&_qr{MJo4dA<6^?PZG#_buXOX$ba95N~U_9Js)CSLfM<%O;&; zJASyxaGKG|Kl(8hM_+9}x9aW#yVV;LPv6qHyXn_e?OSc8@;uuMMCTS3+0M~kwM**9 zx4qTtL)OkJUy}5;V!_w!iJa^Gy)JXDdleP5m3>qE_2B*UHax4}`(Yn@*8Z^dwddDw z^d@+?{ ms8|g1_153Zrd$&E#V%T`lHb2%{}W(xWbkzLb6Mw<&;$TPifsu1 literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1db_1_1Cursor-members.html b/doxygen/classcaffe_1_1db_1_1Cursor-members.html new file mode 100644 index 00000000000..cdce9e2a480 --- /dev/null +++ b/doxygen/classcaffe_1_1db_1_1Cursor-members.html @@ -0,0 +1,88 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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Cursor() (defined in caffe::db::Cursor)caffe::db::Cursorinline
DISABLE_COPY_AND_ASSIGN(Cursor) (defined in caffe::db::Cursor)caffe::db::Cursor
key()=0 (defined in caffe::db::Cursor)caffe::db::Cursorpure virtual
Next()=0 (defined in caffe::db::Cursor)caffe::db::Cursorpure virtual
SeekToFirst()=0 (defined in caffe::db::Cursor)caffe::db::Cursorpure virtual
valid()=0 (defined in caffe::db::Cursor)caffe::db::Cursorpure virtual
value()=0 (defined in caffe::db::Cursor)caffe::db::Cursorpure virtual
~Cursor() (defined in caffe::db::Cursor)caffe::db::Cursorinlinevirtual
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caffe::db::Cursor Class Referenceabstract
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+Public Member Functions

+virtual void SeekToFirst ()=0
 
+virtual void Next ()=0
 
+virtual string key ()=0
 
+virtual string value ()=0
 
+virtual bool valid ()=0
 
+ DISABLE_COPY_AND_ASSIGN (Cursor)
 
+
The documentation for this class was generated from the following file: +
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Close()=0 (defined in caffe::db::DB)caffe::db::DBpure virtual
DB() (defined in caffe::db::DB)caffe::db::DBinline
DISABLE_COPY_AND_ASSIGN(DB) (defined in caffe::db::DB)caffe::db::DB
NewCursor()=0 (defined in caffe::db::DB)caffe::db::DBpure virtual
NewTransaction()=0 (defined in caffe::db::DB)caffe::db::DBpure virtual
Open(const string &source, Mode mode)=0 (defined in caffe::db::DB)caffe::db::DBpure virtual
~DB() (defined in caffe::db::DB)caffe::db::DBinlinevirtual
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caffe::db::DB Class Referenceabstract
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+Public Member Functions

+virtual void Open (const string &source, Mode mode)=0
 
+virtual void Close ()=0
 
+virtual Cursor * NewCursor ()=0
 
+virtual Transaction * NewTransaction ()=0
 
+ DISABLE_COPY_AND_ASSIGN (DB)
 
+
The documentation for this class was generated from the following file: +
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+ + diff --git a/doxygen/classcaffe_1_1db_1_1DB.png b/doxygen/classcaffe_1_1db_1_1DB.png new file mode 100644 index 0000000000000000000000000000000000000000..d494a69e11b2457e4ac7516c1ba1b09832b5e69e GIT binary patch literal 686 zcmV;f0#W^mP)vTJkN^MxkN^Mxkifve1&Q1r00008bW%=J0RR90|NsC0)yh;d0006#NklfT?53yXiDNjR-Kfc2tk%_w4wzaIx$#R9yc7}6pjQc5THQo!KbcI*8^v15g^ckMF`h143 zl%*fZI4Q(joj172(m9J=<2?-UcepS>KU^4~DJ~4q4;Kb#iVFkuV;F{^GXN)K1h^A- z6CTH#8Q)F#*Y{q=w-fd(4RCE-7~smdFu;{@VSp>+!T|r9aU93*0nnMRu8|7^G{%Ji zdf~zVjd5XsUbrwoV_X=Z7cLCY7#9ZUg$n~T=B&aYgzyn#2qApLcLxuW)DOHwafIDLj8BB{8h>P1_KPQQr$styW!Xg^SKo<9))l zAtbZMG_8&LB0Nt@{EFpbhNjx715@{$*VsCfODTw*!iPdw3NJ@p3Y!S$A5%(!89o}qitw8=Z1$!d%|7vNwA3z*`s}27g%5-<+t#e` z%6{6|avLeq{QIHs=OI)JZyq3%@NHUn39?L(wORUaLii+0n{_JkZIL2#3Mo@*PL;Vu z)qD6*2v)fMJuFGh*!Lcmqz1UKBsIo`C8-xKEJ=-VVM*%69fd;(p)W=_gb@1j1K8a$ Uj>{r>_y7O^07*qoM6N<$g4qZ<3jhEB literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1db_1_1LMDB-members.html b/doxygen/classcaffe_1_1db_1_1LMDB-members.html new file mode 100644 index 00000000000..b566f7a9c35 --- /dev/null +++ b/doxygen/classcaffe_1_1db_1_1LMDB-members.html @@ -0,0 +1,114 @@ + + + + + + +Caffe: Member List + + + + + + + + + + +
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caffe::db::LMDB Member List
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Close() (defined in caffe::db::LMDB)caffe::db::LMDBinlinevirtual
DB() (defined in caffe::db::DB)caffe::db::DBinline
DISABLE_COPY_AND_ASSIGN(DB) (defined in caffe::db::DB)caffe::db::DB
LMDB() (defined in caffe::db::LMDB)caffe::db::LMDBinline
NewCursor() (defined in caffe::db::LMDB)caffe::db::LMDBvirtual
NewTransaction() (defined in caffe::db::LMDB)caffe::db::LMDBvirtual
Open(const string &source, Mode mode) (defined in caffe::db::LMDB)caffe::db::LMDBvirtual
~DB() (defined in caffe::db::DB)caffe::db::DBinlinevirtual
~LMDB() (defined in caffe::db::LMDB)caffe::db::LMDBinlinevirtual
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+Public Member Functions

+virtual void Open (const string &source, Mode mode)
 
+virtual void Close ()
 
+virtual LMDBCursor * NewCursor ()
 
+virtual LMDBTransaction * NewTransaction ()
 
- Public Member Functions inherited from caffe::db::DB
+ DISABLE_COPY_AND_ASSIGN (DB)
 
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/util/db_lmdb.hpp
    • +
  • src/caffe/util/db_lmdb.cpp
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+ + diff --git a/doxygen/classcaffe_1_1db_1_1LMDB.png b/doxygen/classcaffe_1_1db_1_1LMDB.png new file mode 100644 index 0000000000000000000000000000000000000000..6d7f806ccfcd0429cc45f6f25ae0648b9b2d9fe6 GIT binary patch literal 488 zcmeAS@N?(olHy`uVBq!ia0vp^X+Rvn!3-qtD1CSUq$C1-LR|m<{|{uoc=NTi|Ih>= z3ycpOIKbL@M;^%KC<*clW&kPzfvcxNj2IXg%ROBjLn;{G&b_#9jRFrt`=lTL=huX= zMDy>u{pOJE&X;Y0iH$3l?weeE?y+KbM~|9`l9J!QF14ji9W%D^-mZLo-loFjc2nPy z#fF>Ldt1#iUR9)7EBwf8*W0DXP6bche@#a4=ImE;YZuLmP8MGL(xs?&_V;tk{4_Vu zcG9oAw&UKi$y;Xc;j@@+JoEX@nz+nrM_F#~y;nt6d9616c(Pns=@YAyveF|Jkgd-$ zi_TSAFvN*9d^~ee=d{RqnX_3lAI@Cz^SqX>2}7I{^8*zV20t`va*)?!MMj{r8T|fj zOk3M4dtgiMk4xd(|GwY5A&b*Kd)vX?_Pb^k-ETI29WCK|BS~ZWC3|MA-EV%c*eLDG z{d|?>pKr@{xvySPmRIcZ(F@`Q%h`R)%FVROqZTi-%bxXk?e>={Z==NJv-8fog|4=H z9)EG>-T1JmvnA4NlVUTss^v_oz0Z5VoRha<@&yKqnae#jmp=T%kQ5~5CUK^0DKJ_X NJYD@<);T3K0RZ?^+9?14 literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1db_1_1LMDBCursor-members.html b/doxygen/classcaffe_1_1db_1_1LMDBCursor-members.html new file mode 100644 index 00000000000..28a8a258741 --- /dev/null +++ b/doxygen/classcaffe_1_1db_1_1LMDBCursor-members.html @@ -0,0 +1,115 @@ + + + + + + +Caffe: Member List + + + + + + + + + + +
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caffe::db::LMDBCursor Member List
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This is the complete list of members for caffe::db::LMDBCursor, including all inherited members.

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Cursor() (defined in caffe::db::Cursor)caffe::db::Cursorinline
DISABLE_COPY_AND_ASSIGN(Cursor) (defined in caffe::db::Cursor)caffe::db::Cursor
key() (defined in caffe::db::LMDBCursor)caffe::db::LMDBCursorinlinevirtual
LMDBCursor(MDB_txn *mdb_txn, MDB_cursor *mdb_cursor) (defined in caffe::db::LMDBCursor)caffe::db::LMDBCursorinlineexplicit
Next() (defined in caffe::db::LMDBCursor)caffe::db::LMDBCursorinlinevirtual
SeekToFirst() (defined in caffe::db::LMDBCursor)caffe::db::LMDBCursorinlinevirtual
valid() (defined in caffe::db::LMDBCursor)caffe::db::LMDBCursorinlinevirtual
value() (defined in caffe::db::LMDBCursor)caffe::db::LMDBCursorinlinevirtual
~Cursor() (defined in caffe::db::Cursor)caffe::db::Cursorinlinevirtual
~LMDBCursor() (defined in caffe::db::LMDBCursor)caffe::db::LMDBCursorinlinevirtual
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+Public Member Functions

+ LMDBCursor (MDB_txn *mdb_txn, MDB_cursor *mdb_cursor)
 
+virtual void SeekToFirst ()
 
+virtual void Next ()
 
+virtual string key ()
 
+virtual string value ()
 
+virtual bool valid ()
 
- Public Member Functions inherited from caffe::db::Cursor
+ DISABLE_COPY_AND_ASSIGN (Cursor)
 
+
The documentation for this class was generated from the following file: +
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This is the complete list of members for caffe::db::LMDBTransaction, including all inherited members.

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Commit() (defined in caffe::db::LMDBTransaction)caffe::db::LMDBTransactioninlinevirtual
DISABLE_COPY_AND_ASSIGN(Transaction) (defined in caffe::db::Transaction)caffe::db::Transaction
LMDBTransaction(MDB_dbi *mdb_dbi, MDB_txn *mdb_txn) (defined in caffe::db::LMDBTransaction)caffe::db::LMDBTransactioninlineexplicit
Put(const string &key, const string &value) (defined in caffe::db::LMDBTransaction)caffe::db::LMDBTransactionvirtual
Transaction() (defined in caffe::db::Transaction)caffe::db::Transactioninline
~Transaction() (defined in caffe::db::Transaction)caffe::db::Transactioninlinevirtual
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+Inheritance diagram for caffe::db::LMDBTransaction:
+
+
+ + +caffe::db::Transaction + +
+ + + + + + + + + + + +

+Public Member Functions

+ LMDBTransaction (MDB_dbi *mdb_dbi, MDB_txn *mdb_txn)
 
+virtual void Put (const string &key, const string &value)
 
+virtual void Commit ()
 
- Public Member Functions inherited from caffe::db::Transaction
+ DISABLE_COPY_AND_ASSIGN (Transaction)
 
+
The documentation for this class was generated from the following files:
    +
  • include/caffe/util/db_lmdb.hpp
    • +
  • src/caffe/util/db_lmdb.cpp
    • +
+
+ +
+Generated on Tue Jul 21 2015 22:14:09 for Caffe by   +doxygen + 1.8.9.1 +
+ + diff --git a/doxygen/classcaffe_1_1db_1_1LMDBTransaction.png b/doxygen/classcaffe_1_1db_1_1LMDBTransaction.png new file mode 100644 index 0000000000000000000000000000000000000000..58f4754193cfe769d7c164158dcd8968f7f30c54 GIT binary patch literal 678 zcmeAS@N?(olHy`uVBq!ia0vp^D}XqFgBeKf3OV-)NJ#|vgt-3y{~ySF@#br3|Dg#$ z78oBmaDcV*jy#adQ4-`A%m7pb0#{Fk7%?y~nR>c7hEy=VoqN0QwE~Z;xOLF~|C7(F zOgIvAX-V;)0F!k$7T!5?=!VT^ozq6m5h^c#i!PY-WF^B?6@ObcUrq0z&};9qZhf1) zL_I6a`%>$?rghKS)PAp(3$8Qy&YMwqCT#8eJL~Rj{kCp*sNR|XpFQG&j`iARe|wSr z#>42T)RrF`*7FCuWQMd1$Qf+$LU`{*vLKY4a@_w22a z_f>-TU%O>}Z-UnAdUh?(`-?%2W?i9DspX)0GT69L@AM`g1%~py3~3j17OcOnvSf3^ zzk+j)R=gQ0whil+d zl^OmC-m#^htuFkkTFaK6nN-41)w1mGwwzp--UsaqRGG{4X0zuz@}Dvfw>OCYUdv#$ zKQ^rJJQV-{ literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1db_1_1LevelDB-members.html b/doxygen/classcaffe_1_1db_1_1LevelDB-members.html new file mode 100644 index 00000000000..fbef9878904 --- /dev/null +++ b/doxygen/classcaffe_1_1db_1_1LevelDB-members.html @@ -0,0 +1,114 @@ + + + + + + +Caffe: Member List + + + + + + + + + + +
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caffe::db::LevelDB Member List
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This is the complete list of members for caffe::db::LevelDB, including all inherited members.

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Close() (defined in caffe::db::LevelDB)caffe::db::LevelDBinlinevirtual
DB() (defined in caffe::db::DB)caffe::db::DBinline
DISABLE_COPY_AND_ASSIGN(DB) (defined in caffe::db::DB)caffe::db::DB
LevelDB() (defined in caffe::db::LevelDB)caffe::db::LevelDBinline
NewCursor() (defined in caffe::db::LevelDB)caffe::db::LevelDBinlinevirtual
NewTransaction() (defined in caffe::db::LevelDB)caffe::db::LevelDBinlinevirtual
Open(const string &source, Mode mode) (defined in caffe::db::LevelDB)caffe::db::LevelDBvirtual
~DB() (defined in caffe::db::DB)caffe::db::DBinlinevirtual
~LevelDB() (defined in caffe::db::LevelDB)caffe::db::LevelDBinlinevirtual
+ +
+Generated on Tue Jul 21 2015 22:14:08 for Caffe by   +doxygen + 1.8.9.1 +
+ + diff --git a/doxygen/classcaffe_1_1db_1_1LevelDB.html b/doxygen/classcaffe_1_1db_1_1LevelDB.html new file mode 100644 index 00000000000..7546162de60 --- /dev/null +++ b/doxygen/classcaffe_1_1db_1_1LevelDB.html @@ -0,0 +1,138 @@ + + + + + + +Caffe: caffe::db::LevelDB Class Reference + + + + + + + + + + +
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+Inheritance diagram for caffe::db::LevelDB:
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+Public Member Functions

+virtual void Open (const string &source, Mode mode)
 
+virtual void Close ()
 
+virtual LevelDBCursor * NewCursor ()
 
+virtual LevelDBTransaction * NewTransaction ()
 
- Public Member Functions inherited from caffe::db::DB
+ DISABLE_COPY_AND_ASSIGN (DB)
 
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Tue Jul 21 2015 22:14:08 for Caffe by   +doxygen + 1.8.9.1 +
+ + diff --git a/doxygen/classcaffe_1_1db_1_1LevelDB.png b/doxygen/classcaffe_1_1db_1_1LevelDB.png new file mode 100644 index 0000000000000000000000000000000000000000..da84c4a11e2b3aceda8daf5c16645d6f0930e521 GIT binary patch literal 507 zcmVvTJkN^MxkN^Mxkifve1&Q1r00008bW%=J0RR90|NsC0)yh;d0004sNklrf+Oi1_fu&Xfuw zV%jon=iNW%hlt1n&pDUX!wA2eX7-MB*V*$ekJ2~EFQZFfHCBGLOins$uJ%iHYip?X zp3|P6W{}q7K3}-cHoq+0d^*yx^YwiW&UWYcz3;Q1dB4vO*X>%KHA<=8cG9YDTTy-N z{S*=T9N`oZ@gbZdB0gN?%sJ<|0KCQxU@{jtZ@2l<3!Hc5bm%ZEwf1 za~a(Gw;3niPXIT<3E)FG0erZ~nNmt~`2jIQ83QfPXJr5Y002ovPDHLkV1g~#>s0^% literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1db_1_1LevelDBCursor-members.html b/doxygen/classcaffe_1_1db_1_1LevelDBCursor-members.html new file mode 100644 index 00000000000..5239e4dc544 --- /dev/null +++ b/doxygen/classcaffe_1_1db_1_1LevelDBCursor-members.html @@ -0,0 +1,115 @@ + + + + + + +Caffe: Member List + + + + + + + + + + +
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caffe::db::LevelDBCursor Member List
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This is the complete list of members for caffe::db::LevelDBCursor, including all inherited members.

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Cursor() (defined in caffe::db::Cursor)caffe::db::Cursorinline
DISABLE_COPY_AND_ASSIGN(Cursor) (defined in caffe::db::Cursor)caffe::db::Cursor
key() (defined in caffe::db::LevelDBCursor)caffe::db::LevelDBCursorinlinevirtual
LevelDBCursor(leveldb::Iterator *iter) (defined in caffe::db::LevelDBCursor)caffe::db::LevelDBCursorinlineexplicit
Next() (defined in caffe::db::LevelDBCursor)caffe::db::LevelDBCursorinlinevirtual
SeekToFirst() (defined in caffe::db::LevelDBCursor)caffe::db::LevelDBCursorinlinevirtual
valid() (defined in caffe::db::LevelDBCursor)caffe::db::LevelDBCursorinlinevirtual
value() (defined in caffe::db::LevelDBCursor)caffe::db::LevelDBCursorinlinevirtual
~Cursor() (defined in caffe::db::Cursor)caffe::db::Cursorinlinevirtual
~LevelDBCursor() (defined in caffe::db::LevelDBCursor)caffe::db::LevelDBCursorinline
+ +
+Generated on Tue Jul 21 2015 22:14:08 for Caffe by   +doxygen + 1.8.9.1 +
+ + diff --git a/doxygen/classcaffe_1_1db_1_1LevelDBCursor.html b/doxygen/classcaffe_1_1db_1_1LevelDBCursor.html new file mode 100644 index 00000000000..fe9453b8b7a --- /dev/null +++ b/doxygen/classcaffe_1_1db_1_1LevelDBCursor.html @@ -0,0 +1,143 @@ + + + + + + +Caffe: caffe::db::LevelDBCursor Class Reference + + + + + + + + + + +
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+Inheritance diagram for caffe::db::LevelDBCursor:
+
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+ + + + + + + + + + + + + + + + + +

+Public Member Functions

+ LevelDBCursor (leveldb::Iterator *iter)
 
+virtual void SeekToFirst ()
 
+virtual void Next ()
 
+virtual string key ()
 
+virtual string value ()
 
+virtual bool valid ()
 
- Public Member Functions inherited from caffe::db::Cursor
+ DISABLE_COPY_AND_ASSIGN (Cursor)
 
+
The documentation for this class was generated from the following file: +
+ +
+Generated on Tue Jul 21 2015 22:14:08 for Caffe by   +doxygen + 1.8.9.1 +
+ + diff --git a/doxygen/classcaffe_1_1db_1_1LevelDBCursor.png b/doxygen/classcaffe_1_1db_1_1LevelDBCursor.png new file mode 100644 index 0000000000000000000000000000000000000000..6368450c740ebf649ee3ea495ce6479570e568e6 GIT binary patch literal 644 zcmeAS@N?(olHy`uVBq!ia0vp^vw%2&gBeI(3!3vCNJ#|vgt-3y{~ySF@#br3|Dg#$ z78oBmaDcV*jy#adQ4-`A%m7pb0#{Fk7%?y~346LYhEy=VoqMtGwE_=oyYR~Y|4(k8 zBH+X1yJt7Q$93&xx+(`641b^6mV0~F2G2`fEwfZAIZZu3Jqq<+sjyOeXXGT!mw(kg z?T@XTrY`MQ5%~K`-P5x5+w}i^o@(#4_x7Yavuu5Ce_XA%Y|o{(ThS_+ht}S*EBT+g zs(R&&qM+AuSH(PMee_t%KPxrC{_fUW6)*KYndMeb4Za(eqx36cZO)bW)x1nroj=XD z{*;(h@~Aw0t?%`7w=|t!KYZ`^{QioYJi3;to7VWH7;oXaHeq_Z+5%l?)l9!Xizlh* zFI4l4bMl?E2jt?))w#E~>2e;p|B)$Ta_QB$^{IESg;vjA`)b>Us1%Kc`2oxygt8do zoOlm-uVAd;G-c3V$o4?>3d5cjsfOtY$8g|cdRf13PmQ{5zDiQo z_V#P{npFocX4l{Bxw_A%R9`fB+1~jpOP#My>yDD=IGyls-R-@%>W;AOPP_N&*uE80 z6@5~ozU*5mYdkxSpE=6>m0zyFh2p$3Wovn_OUbX|eO^;`?M!OHoW6@)zkL48TKM(D z_xF5PbJ}KYE#G-9HA8ma`6rLx?b>_&R01fV9?Od!IM0Fb>yzKcOQ(cAk(cFDRFXd} RWe-d;44$rjF6*2UngAGcE+GH_ literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1db_1_1LevelDBTransaction-members.html b/doxygen/classcaffe_1_1db_1_1LevelDBTransaction-members.html new file mode 100644 index 00000000000..16efdef44c4 --- /dev/null +++ b/doxygen/classcaffe_1_1db_1_1LevelDBTransaction-members.html @@ -0,0 +1,111 @@ + + + + + + +Caffe: Member List + + + + + + + + + + +
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caffe::db::LevelDBTransaction Member List
+
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This is the complete list of members for caffe::db::LevelDBTransaction, including all inherited members.

+ + + + + + + +
Commit() (defined in caffe::db::LevelDBTransaction)caffe::db::LevelDBTransactioninlinevirtual
DISABLE_COPY_AND_ASSIGN(Transaction) (defined in caffe::db::Transaction)caffe::db::Transaction
LevelDBTransaction(leveldb::DB *db) (defined in caffe::db::LevelDBTransaction)caffe::db::LevelDBTransactioninlineexplicit
Put(const string &key, const string &value) (defined in caffe::db::LevelDBTransaction)caffe::db::LevelDBTransactioninlinevirtual
Transaction() (defined in caffe::db::Transaction)caffe::db::Transactioninline
~Transaction() (defined in caffe::db::Transaction)caffe::db::Transactioninlinevirtual
+ +
+Generated on Tue Jul 21 2015 22:14:08 for Caffe by   +doxygen + 1.8.9.1 +
+ + diff --git a/doxygen/classcaffe_1_1db_1_1LevelDBTransaction.html b/doxygen/classcaffe_1_1db_1_1LevelDBTransaction.html new file mode 100644 index 00000000000..0f55e88b519 --- /dev/null +++ b/doxygen/classcaffe_1_1db_1_1LevelDBTransaction.html @@ -0,0 +1,134 @@ + + + + + + +Caffe: caffe::db::LevelDBTransaction Class Reference + + + + + + + + + + +
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caffe::db::LevelDBTransaction Class Reference
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+Inheritance diagram for caffe::db::LevelDBTransaction:
+
+
+ + +caffe::db::Transaction + +
+ + + + + + + + + + + +

+Public Member Functions

+ LevelDBTransaction (leveldb::DB *db)
 
+virtual void Put (const string &key, const string &value)
 
+virtual void Commit ()
 
- Public Member Functions inherited from caffe::db::Transaction
+ DISABLE_COPY_AND_ASSIGN (Transaction)
 
+
The documentation for this class was generated from the following file: +
+ +
+Generated on Tue Jul 21 2015 22:14:08 for Caffe by   +doxygen + 1.8.9.1 +
+ + diff --git a/doxygen/classcaffe_1_1db_1_1LevelDBTransaction.png b/doxygen/classcaffe_1_1db_1_1LevelDBTransaction.png new file mode 100644 index 0000000000000000000000000000000000000000..093aff3f892ded2d5e46d4bd7d7bc6ec24c9fca7 GIT binary patch literal 698 zcmeAS@N?(olHy`uVBq!ia0vp^TY)%$gBeI>upC(hq$C1-LR|m<{|{uoc=NTi|Ih>= z3ycpOIKbL@M;^%KC<*clW&kPzfvcxNj2IZ0JUv|;Ln;{G&b{6D+JM7#dij!{|NZSI zKTF#1SZ(UE7ZFKM=Jw=oaB57SmKwQCxLd_{*^+5XBviMm9DNb|D#*;UC^@$5cwGIY zFD_?uJ*!spuMEFE{af|^`3W^Ge>$r8Z@%7Yw{0i)lU=)Zl^wkM?|#`f?ce)0rk|ac z-7D*vRqDOh{rdhhCGzcASIV=Bw|kWR2ru`WRYR3T)&z=zhC*)?>+yRv!dTrFTH;C zaQj_Uoz0vNynivy@Cy$5 z|L07Ht?F0(FD*BUgVOmKJA#6kk7%r9Oqz0q0jQxtLeVe+UoimwZUZp1K{#YD5mzVoNXH>rVmDi6l9xG?1ud!p0 z%P83vbbUcif9^Y`&0kNJuHL+RzFo}1wAFLZts%WnOjx>oPPovqccjOV`Hy!T4l!>iL{g5HM)*T0&@_kP9q;^+EN z%oe96aL3oZWq#Golj&U|@;d9>PnqEIZ@UiIFOqw<^<|3Q{#*TDeILj2zg+qL^RK2` zV(U9+yk=Y1F7xzd_1VDebk?7BcT+n)rv9BTduIERc~S{t$RUxr(pxjt>@V|kMYVZx TWl;{mgvQ|M>gTe~DWM4fdjM0q literal 0 HcmV?d00001 diff --git a/doxygen/classcaffe_1_1db_1_1Transaction-members.html b/doxygen/classcaffe_1_1db_1_1Transaction-members.html new file mode 100644 index 00000000000..54940573df9 --- /dev/null +++ b/doxygen/classcaffe_1_1db_1_1Transaction-members.html @@ -0,0 +1,85 @@ + + + + + + + +Caffe: Member List + + + + + + + + + +
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caffe::db::Transaction Member List
+
+
+ +

This is the complete list of members for caffe::db::Transaction, including all inherited members.

+ + + + + + +
Commit()=0 (defined in caffe::db::Transaction)caffe::db::Transactionpure virtual
DISABLE_COPY_AND_ASSIGN(Transaction) (defined in caffe::db::Transaction)caffe::db::Transaction
Put(const string &key, const string &value)=0 (defined in caffe::db::Transaction)caffe::db::Transactionpure virtual
Transaction() (defined in caffe::db::Transaction)caffe::db::Transactioninline
~Transaction() (defined in caffe::db::Transaction)caffe::db::Transactioninlinevirtual
+ +
+Generated on Mon Nov 21 2016 11:23:49 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1db_1_1Transaction.html b/doxygen/classcaffe_1_1db_1_1Transaction.html new file mode 100644 index 00000000000..10d3288afdc --- /dev/null +++ b/doxygen/classcaffe_1_1db_1_1Transaction.html @@ -0,0 +1,96 @@ + + + + + + + +Caffe: caffe::db::Transaction Class Reference + + + + + + + + + +
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caffe::db::Transaction Class Referenceabstract
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+Public Member Functions

+virtual void Put (const string &key, const string &value)=0
 
+virtual void Commit ()=0
 
+ DISABLE_COPY_AND_ASSIGN (Transaction)
 
+
The documentation for this class was generated from the following file: +
+ +
+Generated on Mon Nov 21 2016 11:23:49 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/classcaffe_1_1db_1_1Transaction.png b/doxygen/classcaffe_1_1db_1_1Transaction.png new file mode 100644 index 0000000000000000000000000000000000000000..88f1e6f067fe75f5a635901668590f929a204d25 GIT binary patch literal 955 zcmeAS@N?(olHy`uVBq!ia0y~yU@QT$12~w0q}}(ruRuy7z$e7@|Ns9$=8HF9OZyK^ z0J6aNz<~p-opiH zW|lw0QL|XRS!e4R-UobTt2mYKIbTY?EMx23&g!s7_4>7QH+)XKrThG-$-H%bja@7E z{)v016MOBq+C1Lf@v9#(T>10<()FFOa%W!d%6($F-^;v~eM59*#x2G#KUiO;a&DB_ zGrelr_1faeHAP>P7^?nseR;Zj2lw{*C#PR}z3=JP348$)wq7yb8{8Q6^6cNVTkRzW za{RNa!meF=H1ErGQ`;r}r3uzP|92)yxPIXqkGggqVux?UJ2V)0a3u6X@TsdhbuNoy^gx zOQ!wGR9`w@=1AGwU3=zx&)+3eX05%KV_Ny7FKm0az4x@Y?g)LQ^K;f7>6bsR>g|5I zr(*BrtD1{;Zad$%^y{&*ZG4M;>pHiX-JVx7qm6UB{dVoYs~xXg`d@c%Ye}-EJSYNZ ztiAm^^V71M3OCnw25hTV`+M7QO`qQB-yT&vf9bG0XPtUj{Ic3}%kN#mdw=z_eewEb z-fS`9U-;*Dp7HiFJN=$){it()>)m~8KAF7Gz1*BW_4>XE{~juA`q=&HoritI%(+Kz z|NJm}_xefsk4!H=cYZma|B>#?`Oj-tEqfkN+N;$bS@$jX-1^D&vC{V^|NE=3?)2_y zdG`C$_T_~pM|=D{yuWtJHL=vEx1)2ke{V3Gc7D_O`$}(~t}3}c`QKfIH5al! + + + + + + +Caffe: Class Index + + + + + + + + + +
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+Generated on Mon Nov 21 2016 11:23:49 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/closed.png b/doxygen/closed.png new file mode 100644 index 0000000000000000000000000000000000000000..98cc2c909da37a6df914fbf67780eebd99c597f5 GIT binary patch literal 132 zcmeAS@N?(olHy`uVBq!ia0vp^oFL4>1|%O$WD@{V-kvUwAr*{o@8{^CZMh(5KoB^r_<4^zF@3)Cp&&t3hdujKf f*?bjBoY!V+E))@{xMcbjXe@)LtDnm{r-UW|*e5JT literal 0 HcmV?d00001 diff --git a/doxygen/common_8hpp_source.html b/doxygen/common_8hpp_source.html new file mode 100644 index 00000000000..ff41c098c0c --- /dev/null +++ b/doxygen/common_8hpp_source.html @@ -0,0 +1,82 @@ + + + + + + + +Caffe: include/caffe/common.hpp Source File + + + + + + + + + +
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+ + + + + + +
+
Caffe +
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common.hpp
+
+
+
1 #ifndef CAFFE_COMMON_HPP_
2 #define CAFFE_COMMON_HPP_
3 
4 #include <boost/shared_ptr.hpp>
5 #include <gflags/gflags.h>
6 #include <glog/logging.h>
7 
8 #include <climits>
9 #include <cmath>
10 #include <fstream> // NOLINT(readability/streams)
11 #include <iostream> // NOLINT(readability/streams)
12 #include <map>
13 #include <set>
14 #include <sstream>
15 #include <string>
16 #include <utility> // pair
17 #include <vector>
18 
19 #include "caffe/util/device_alternate.hpp"
20 
21 // Convert macro to string
22 #define STRINGIFY(m) #m
23 #define AS_STRING(m) STRINGIFY(m)
24 
25 // gflags 2.1 issue: namespace google was changed to gflags without warning.
26 // Luckily we will be able to use GFLAGS_GFLAGS_H_ to detect if it is version
27 // 2.1. If yes, we will add a temporary solution to redirect the namespace.
28 // TODO(Yangqing): Once gflags solves the problem in a more elegant way, let's
29 // remove the following hack.
30 #ifndef GFLAGS_GFLAGS_H_
31 namespace gflags = google;
32 #endif // GFLAGS_GFLAGS_H_
33 
34 // Disable the copy and assignment operator for a class.
35 #define DISABLE_COPY_AND_ASSIGN(classname) \
36 private:\
37  classname(const classname&);\
38  classname& operator=(const classname&)
39 
40 // Instantiate a class with float and double specifications.
41 #define INSTANTIATE_CLASS(classname) \
42  char gInstantiationGuard##classname; \
43  template class classname<float>; \
44  template class classname<double>
45 
46 #define INSTANTIATE_LAYER_GPU_FORWARD(classname) \
47  template void classname<float>::Forward_gpu( \
48  const std::vector<Blob<float>*>& bottom, \
49  const std::vector<Blob<float>*>& top); \
50  template void classname<double>::Forward_gpu( \
51  const std::vector<Blob<double>*>& bottom, \
52  const std::vector<Blob<double>*>& top);
53 
54 #define INSTANTIATE_LAYER_GPU_BACKWARD(classname) \
55  template void classname<float>::Backward_gpu( \
56  const std::vector<Blob<float>*>& top, \
57  const std::vector<bool>& propagate_down, \
58  const std::vector<Blob<float>*>& bottom); \
59  template void classname<double>::Backward_gpu( \
60  const std::vector<Blob<double>*>& top, \
61  const std::vector<bool>& propagate_down, \
62  const std::vector<Blob<double>*>& bottom)
63 
64 #define INSTANTIATE_LAYER_GPU_FUNCS(classname) \
65  INSTANTIATE_LAYER_GPU_FORWARD(classname); \
66  INSTANTIATE_LAYER_GPU_BACKWARD(classname)
67 
68 // A simple macro to mark codes that are not implemented, so that when the code
69 // is executed we will see a fatal log.
70 #define NOT_IMPLEMENTED LOG(FATAL) << "Not Implemented Yet"
71 
72 // See PR #1236
73 namespace cv { class Mat; }
74 
75 namespace caffe {
76 
77 // We will use the boost shared_ptr instead of the new C++11 one mainly
78 // because cuda does not work (at least now) well with C++11 features.
79 using boost::shared_ptr;
80 
81 // Common functions and classes from std that caffe often uses.
82 using std::fstream;
83 using std::ios;
84 using std::isnan;
85 using std::isinf;
86 using std::iterator;
87 using std::make_pair;
88 using std::map;
89 using std::ostringstream;
90 using std::pair;
91 using std::set;
92 using std::string;
93 using std::stringstream;
94 using std::vector;
95 
96 // A global initialization function that you should call in your main function.
97 // Currently it initializes google flags and google logging.
98 void GlobalInit(int* pargc, char*** pargv);
99 
100 // A singleton class to hold common caffe stuff, such as the handler that
101 // caffe is going to use for cublas, curand, etc.
102 class Caffe {
103  public:
104  ~Caffe();
105 
106  // Thread local context for Caffe. Moved to common.cpp instead of
107  // including boost/thread.hpp to avoid a boost/NVCC issues (#1009, #1010)
108  // on OSX. Also fails on Linux with CUDA 7.0.18.
109  static Caffe& Get();
110 
111  enum Brew { CPU, GPU };
112 
113  // This random number generator facade hides boost and CUDA rng
114  // implementation from one another (for cross-platform compatibility).
115  class RNG {
116  public:
117  RNG();
118  explicit RNG(unsigned int seed);
119  explicit RNG(const RNG&);
120  RNG& operator=(const RNG&);
121  void* generator();
122  private:
123  class Generator;
124  shared_ptr<Generator> generator_;
125  };
126 
127  // Getters for boost rng, curand, and cublas handles
128  inline static RNG& rng_stream() {
129  if (!Get().random_generator_) {
130  Get().random_generator_.reset(new RNG());
131  }
132  return *(Get().random_generator_);
133  }
134 #ifndef CPU_ONLY
135  inline static cublasHandle_t cublas_handle() { return Get().cublas_handle_; }
136  inline static curandGenerator_t curand_generator() {
137  return Get().curand_generator_;
138  }
139 #endif
140 
141  // Returns the mode: running on CPU or GPU.
142  inline static Brew mode() { return Get().mode_; }
143  // The setters for the variables
144  // Sets the mode. It is recommended that you don't change the mode halfway
145  // into the program since that may cause allocation of pinned memory being
146  // freed in a non-pinned way, which may cause problems - I haven't verified
147  // it personally but better to note it here in the header file.
148  inline static void set_mode(Brew mode) { Get().mode_ = mode; }
149  // Sets the random seed of both boost and curand
150  static void set_random_seed(const unsigned int seed);
151  // Sets the device. Since we have cublas and curand stuff, set device also
152  // requires us to reset those values.
153  static void SetDevice(const int device_id);
154  // Prints the current GPU status.
155  static void DeviceQuery();
156  // Check if specified device is available
157  static bool CheckDevice(const int device_id);
158  // Search from start_id to the highest possible device ordinal,
159  // return the ordinal of the first available device.
160  static int FindDevice(const int start_id = 0);
161  // Parallel training info
162  inline static int solver_count() { return Get().solver_count_; }
163  inline static void set_solver_count(int val) { Get().solver_count_ = val; }
164  inline static bool root_solver() { return Get().root_solver_; }
165  inline static void set_root_solver(bool val) { Get().root_solver_ = val; }
166 
167  protected:
168 #ifndef CPU_ONLY
169  cublasHandle_t cublas_handle_;
170  curandGenerator_t curand_generator_;
171 #endif
172  shared_ptr<RNG> random_generator_;
173 
174  Brew mode_;
175  int solver_count_;
176  bool root_solver_;
177 
178  private:
179  // The private constructor to avoid duplicate instantiation.
180  Caffe();
181 
182  DISABLE_COPY_AND_ASSIGN(Caffe);
183 };
184 
185 } // namespace caffe
186 
187 #endif // CAFFE_COMMON_HPP_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
cv
Definition: common.hpp:73
+
caffe::Caffe::RNG
Definition: common.hpp:115
+
caffe::Caffe::RNG::Generator
Definition: common.cpp:238
+
caffe::Caffe
Definition: common.hpp:102
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/common__layers_8hpp_source.html b/doxygen/common__layers_8hpp_source.html new file mode 100644 index 00000000000..cb906af5d9d --- /dev/null +++ b/doxygen/common__layers_8hpp_source.html @@ -0,0 +1,776 @@ + + + + + + +Caffe: include/caffe/common_layers.hpp Source File + + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
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+ +
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+
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+
common_layers.hpp
+
+
+
1 #ifndef CAFFE_COMMON_LAYERS_HPP_
+
2 #define CAFFE_COMMON_LAYERS_HPP_
+
3 
+
4 #include <string>
+
5 #include <utility>
+
6 #include <vector>
+
7 
+
8 #include "caffe/blob.hpp"
+
9 #include "caffe/common.hpp"
+
10 #include "caffe/data_layers.hpp"
+
11 #include "caffe/layer.hpp"
+
12 #include "caffe/loss_layers.hpp"
+
13 #include "caffe/neuron_layers.hpp"
+
14 #include "caffe/proto/caffe.pb.h"
+
15 
+
16 namespace caffe {
+
17 
+
29 template <typename Dtype>
+
30 class ArgMaxLayer : public Layer<Dtype> {
+
31  public:
+
44  explicit ArgMaxLayer(const LayerParameter& param)
+
45  : Layer<Dtype>(param) {}
+
46  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
47  const vector<Blob<Dtype>*>& top);
+
48  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
49  const vector<Blob<Dtype>*>& top);
+
50 
+
51  virtual inline const char* type() const { return "ArgMax"; }
+
52  virtual inline int ExactNumBottomBlobs() const { return 1; }
+
53  virtual inline int ExactNumTopBlobs() const { return 1; }
+
54 
+
55  protected:
+
68  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
69  const vector<Blob<Dtype>*>& top);
+
71  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
72  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+
73  NOT_IMPLEMENTED;
+
74  }
+
75  bool out_max_val_;
+
76  size_t top_k_;
+
77  bool has_axis_;
+
78  int axis_;
+
79 };
+
80 
+
88 template <typename Dtype>
+
89 class BatchReindexLayer : public Layer<Dtype> {
+
90  public:
+
91  explicit BatchReindexLayer(const LayerParameter& param)
+
92  : Layer<Dtype>(param) {}
+
93  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
94  const vector<Blob<Dtype>*>& top);
+
95 
+
96  virtual inline const char* type() const { return "BatchReindex"; }
+
97  virtual inline int ExactNumBottomBlobs() const { return 2; }
+
98  virtual inline int ExactNumTopBlobs() const { return 1; }
+
99 
+
100  protected:
+
113  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
114  const vector<Blob<Dtype>*>& top);
+
115  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
116  const vector<Blob<Dtype>*>& top);
+
117 
+
133  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
134  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
135  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
136  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
137 
+
138  private:
+
139  struct pair_sort_first {
+
140  bool operator()(const std::pair<int, int> &left,
+
141  const std::pair<int, int> &right) {
+
142  return left.first < right.first;
+
143  }
+
144  };
+
145  void check_batch_reindex(int initial_num, int final_num,
+
146  const Dtype* ridx_data);
+
147 };
+
148 
+
149 
+
154 template <typename Dtype>
+
155 class ConcatLayer : public Layer<Dtype> {
+
156  public:
+
157  explicit ConcatLayer(const LayerParameter& param)
+
158  : Layer<Dtype>(param) {}
+
159  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
160  const vector<Blob<Dtype>*>& top);
+
161  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
162  const vector<Blob<Dtype>*>& top);
+
163 
+
164  virtual inline const char* type() const { return "Concat"; }
+
165  virtual inline int MinBottomBlobs() const { return 1; }
+
166  virtual inline int ExactNumTopBlobs() const { return 1; }
+
167 
+
168  protected:
+
185  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
186  const vector<Blob<Dtype>*>& top);
+
187  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
188  const vector<Blob<Dtype>*>& top);
+
189 
+
212  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
213  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
214  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
215  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
216 
+
217  int count_;
+
218  int num_concats_;
+
219  int concat_input_size_;
+
220  int concat_axis_;
+
221 };
+
222 
+
229 template <typename Dtype>
+
230 class EltwiseLayer : public Layer<Dtype> {
+
231  public:
+
232  explicit EltwiseLayer(const LayerParameter& param)
+
233  : Layer<Dtype>(param) {}
+
234  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
235  const vector<Blob<Dtype>*>& top);
+
236  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
237  const vector<Blob<Dtype>*>& top);
+
238 
+
239  virtual inline const char* type() const { return "Eltwise"; }
+
240  virtual inline int MinBottomBlobs() const { return 2; }
+
241  virtual inline int ExactNumTopBlobs() const { return 1; }
+
242 
+
243  protected:
+
244  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
245  const vector<Blob<Dtype>*>& top);
+
246  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
247  const vector<Blob<Dtype>*>& top);
+
248  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
249  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
250  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
251  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
252 
+
253  EltwiseParameter_EltwiseOp op_;
+
254  vector<Dtype> coeffs_;
+
255  Blob<int> max_idx_;
+
256 
+
257  bool stable_prod_grad_;
+
258 };
+
259 
+
267 template <typename Dtype>
+
268 class EmbedLayer : public Layer<Dtype> {
+
269  public:
+
270  explicit EmbedLayer(const LayerParameter& param)
+
271  : Layer<Dtype>(param) {}
+
272  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
273  const vector<Blob<Dtype>*>& top);
+
274  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
275  const vector<Blob<Dtype>*>& top);
+
276 
+
277  virtual inline const char* type() const { return "Embed"; }
+
278  virtual inline int ExactNumBottomBlobs() const { return 1; }
+
279  virtual inline int ExactNumTopBlobs() const { return 1; }
+
280 
+
281  protected:
+
282  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
283  const vector<Blob<Dtype>*>& top);
+
284  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
285  const vector<Blob<Dtype>*>& top);
+
286  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
287  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
288  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
289  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
290 
+
291  int M_;
+
292  int K_;
+
293  int N_;
+
294  bool bias_term_;
+
295  Blob<Dtype> bias_multiplier_;
+
296 };
+
297 
+
304 template <typename Dtype>
+
305 class FilterLayer : public Layer<Dtype> {
+
306  public:
+
307  explicit FilterLayer(const LayerParameter& param)
+
308  : Layer<Dtype>(param) {}
+
309  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
310  const vector<Blob<Dtype>*>& top);
+
311  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
312  const vector<Blob<Dtype>*>& top);
+
313 
+
314  virtual inline const char* type() const { return "Filter"; }
+
315  virtual inline int MinBottomBlobs() const { return 2; }
+
316  virtual inline int MinTopBlobs() const { return 1; }
+
317 
+
318  protected:
+
338  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
339  const vector<Blob<Dtype>*>& top);
+
340  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
341  const vector<Blob<Dtype>*>& top);
+
342 
+
352  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
353  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
354  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
355  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
356 
+
357  bool first_reshape_;
+
358  vector<int> indices_to_forward_;
+
359 };
+
360 
+
371 template <typename Dtype>
+
372 class FlattenLayer : public Layer<Dtype> {
+
373  public:
+
374  explicit FlattenLayer(const LayerParameter& param)
+
375  : Layer<Dtype>(param) {}
+
376  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
377  const vector<Blob<Dtype>*>& top);
+
378 
+
379  virtual inline const char* type() const { return "Flatten"; }
+
380  virtual inline int ExactNumBottomBlobs() const { return 1; }
+
381  virtual inline int ExactNumTopBlobs() const { return 1; }
+
382 
+
383  protected:
+
392  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
393  const vector<Blob<Dtype>*>& top);
+
394 
+
404  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
405  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
406 };
+
407 
+
414 template <typename Dtype>
+
415 class InnerProductLayer : public Layer<Dtype> {
+
416  public:
+
417  explicit InnerProductLayer(const LayerParameter& param)
+
418  : Layer<Dtype>(param) {}
+
419  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
420  const vector<Blob<Dtype>*>& top);
+
421  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
422  const vector<Blob<Dtype>*>& top);
+
423 
+
424  virtual inline const char* type() const { return "InnerProduct"; }
+
425  virtual inline int ExactNumBottomBlobs() const { return 1; }
+
426  virtual inline int ExactNumTopBlobs() const { return 1; }
+
427 
+
428  protected:
+
429  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
430  const vector<Blob<Dtype>*>& top);
+
431  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
432  const vector<Blob<Dtype>*>& top);
+
433  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
434  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
435  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
436  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
437 
+
438  int M_;
+
439  int K_;
+
440  int N_;
+
441  bool bias_term_;
+
442  Blob<Dtype> bias_multiplier_;
+
443 };
+
444 
+
450 template <typename Dtype>
+
451 class MVNLayer : public Layer<Dtype> {
+
452  public:
+
453  explicit MVNLayer(const LayerParameter& param)
+
454  : Layer<Dtype>(param) {}
+
455  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
456  const vector<Blob<Dtype>*>& top);
+
457 
+
458  virtual inline const char* type() const { return "MVN"; }
+
459  virtual inline int ExactNumBottomBlobs() const { return 1; }
+
460  virtual inline int ExactNumTopBlobs() const { return 1; }
+
461 
+
462  protected:
+
463  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
464  const vector<Blob<Dtype>*>& top);
+
465  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
466  const vector<Blob<Dtype>*>& top);
+
467  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
468  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
469  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
470  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
471 
+
472  Blob<Dtype> mean_, variance_, temp_;
+
473 
+
475  Blob<Dtype> sum_multiplier_;
+
476  Dtype eps_;
+
477 };
+
478 
+
479 /*
+
480  * @brief Reshapes the input Blob into an arbitrary-sized output Blob.
+
481  *
+
482  * Note: similarly to FlattenLayer, this layer does not change the input values
+
483  * (see FlattenLayer, Blob::ShareData and Blob::ShareDiff).
+
484  */
+
485 template <typename Dtype>
+
486 class ReshapeLayer : public Layer<Dtype> {
+
487  public:
+
488  explicit ReshapeLayer(const LayerParameter& param)
+
489  : Layer<Dtype>(param) {}
+
490  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
491  const vector<Blob<Dtype>*>& top);
+
492  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
493  const vector<Blob<Dtype>*>& top);
+
494 
+
495  virtual inline const char* type() const { return "Reshape"; }
+
496  virtual inline int ExactNumBottomBlobs() const { return 1; }
+
497  virtual inline int ExactNumTopBlobs() const { return 1; }
+
498 
+
499  protected:
+
500  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
501  const vector<Blob<Dtype>*>& top) {}
+
502  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
503  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
504  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
505  const vector<Blob<Dtype>*>& top) {}
+
506  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
507  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
508 
+
510  vector<int> copy_axes_;
+
512  int inferred_axis_;
+
514  int constant_count_;
+
515 };
+
516 
+
524 template <typename Dtype>
+
525 class ReductionLayer : public Layer<Dtype> {
+
526  public:
+
527  explicit ReductionLayer(const LayerParameter& param)
+
528  : Layer<Dtype>(param) {}
+
529  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
530  const vector<Blob<Dtype>*>& top);
+
531  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
532  const vector<Blob<Dtype>*>& top);
+
533 
+
534  virtual inline const char* type() const { return "Reduction"; }
+
535  virtual inline int ExactNumBottomBlobs() const { return 1; }
+
536  virtual inline int ExactNumTopBlobs() const { return 1; }
+
537 
+
538  protected:
+
539  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
540  const vector<Blob<Dtype>*>& top);
+
541  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
542  const vector<Blob<Dtype>*>& top);
+
543  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
544  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
545  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
546  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
547 
+
549  ReductionParameter_ReductionOp op_;
+
551  Dtype coeff_;
+
553  int axis_;
+
555  int num_;
+
557  int dim_;
+
559  Blob<Dtype> sum_multiplier_;
+
560 };
+
561 
+
566 template <typename Dtype>
+
567 class SilenceLayer : public Layer<Dtype> {
+
568  public:
+
569  explicit SilenceLayer(const LayerParameter& param)
+
570  : Layer<Dtype>(param) {}
+
571  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
572  const vector<Blob<Dtype>*>& top) {}
+
573 
+
574  virtual inline const char* type() const { return "Silence"; }
+
575  virtual inline int MinBottomBlobs() const { return 1; }
+
576  virtual inline int ExactNumTopBlobs() const { return 0; }
+
577 
+
578  protected:
+
579  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
580  const vector<Blob<Dtype>*>& top) {}
+
581  // We can't define Forward_gpu here, since STUB_GPU will provide
+
582  // its own definition for CPU_ONLY mode.
+
583  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
584  const vector<Blob<Dtype>*>& top);
+
585  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
586  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
587  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
588  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
589 };
+
590 
+
596 template <typename Dtype>
+
597 class SoftmaxLayer : public Layer<Dtype> {
+
598  public:
+
599  explicit SoftmaxLayer(const LayerParameter& param)
+
600  : Layer<Dtype>(param) {}
+
601  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
602  const vector<Blob<Dtype>*>& top);
+
603 
+
604  virtual inline const char* type() const { return "Softmax"; }
+
605  virtual inline int ExactNumBottomBlobs() const { return 1; }
+
606  virtual inline int ExactNumTopBlobs() const { return 1; }
+
607 
+
608  protected:
+
609  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
610  const vector<Blob<Dtype>*>& top);
+
611  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
612  const vector<Blob<Dtype>*>& top);
+
613  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
614  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
615  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
616  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
617 
+
618  int outer_num_;
+
619  int inner_num_;
+
620  int softmax_axis_;
+
622  Blob<Dtype> sum_multiplier_;
+
624  Blob<Dtype> scale_;
+
625 };
+
626 
+
627 #ifdef USE_CUDNN
+
628 
+
632 template <typename Dtype>
+
633 class CuDNNSoftmaxLayer : public SoftmaxLayer<Dtype> {
+
634  public:
+
635  explicit CuDNNSoftmaxLayer(const LayerParameter& param)
+
636  : SoftmaxLayer<Dtype>(param), handles_setup_(false) {}
+
637  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
638  const vector<Blob<Dtype>*>& top);
+
639  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
640  const vector<Blob<Dtype>*>& top);
+
641  virtual ~CuDNNSoftmaxLayer();
+
642 
+
643  protected:
+
644  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
645  const vector<Blob<Dtype>*>& top);
+
646  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
647  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
648 
+
649  bool handles_setup_;
+
650  cudnnHandle_t handle_;
+
651  cudnnTensorDescriptor_t bottom_desc_;
+
652  cudnnTensorDescriptor_t top_desc_;
+
653 };
+
654 #endif
+
655 
+
662 template <typename Dtype>
+
663 class SplitLayer : public Layer<Dtype> {
+
664  public:
+
665  explicit SplitLayer(const LayerParameter& param)
+
666  : Layer<Dtype>(param) {}
+
667  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
668  const vector<Blob<Dtype>*>& top);
+
669 
+
670  virtual inline const char* type() const { return "Split"; }
+
671  virtual inline int ExactNumBottomBlobs() const { return 1; }
+
672  virtual inline int MinTopBlobs() const { return 1; }
+
673 
+
674  protected:
+
675  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
676  const vector<Blob<Dtype>*>& top);
+
677  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
678  const vector<Blob<Dtype>*>& top);
+
679  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
680  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
681  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
682  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
683 
+
684  int count_;
+
685 };
+
686 
+
693 template <typename Dtype>
+
694 class SliceLayer : public Layer<Dtype> {
+
695  public:
+
696  explicit SliceLayer(const LayerParameter& param)
+
697  : Layer<Dtype>(param) {}
+
698  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
699  const vector<Blob<Dtype>*>& top);
+
700  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
701  const vector<Blob<Dtype>*>& top);
+
702 
+
703  virtual inline const char* type() const { return "Slice"; }
+
704  virtual inline int ExactNumBottomBlobs() const { return 1; }
+
705  virtual inline int MinTopBlobs() const { return 1; }
+
706 
+
707  protected:
+
708  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
709  const vector<Blob<Dtype>*>& top);
+
710  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
711  const vector<Blob<Dtype>*>& top);
+
712  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
713  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
714  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
715  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
716 
+
717  int count_;
+
718  int num_slices_;
+
719  int slice_size_;
+
720  int slice_axis_;
+
721  vector<int> slice_point_;
+
722 };
+
723 
+
727 template <typename Dtype>
+
728 class TileLayer : public Layer<Dtype> {
+
729  public:
+
730  explicit TileLayer(const LayerParameter& param)
+
731  : Layer<Dtype>(param) {}
+
732  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
733  const vector<Blob<Dtype>*>& top);
+
734 
+
735  virtual inline const char* type() const { return "Tile"; }
+
736  virtual inline int ExactNumBottomBlobs() const { return 1; }
+
737  virtual inline int ExactNumTopBlobs() const { return 1; }
+
738 
+
739  protected:
+
740  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
741  const vector<Blob<Dtype>*>& top);
+
742  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
743  const vector<Blob<Dtype>*>& top);
+
744 
+
745  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
746  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
747  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
748  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
749 
+
750  unsigned int axis_, tiles_, outer_dim_, inner_dim_;
+
751 };
+
752 
+
753 } // namespace caffe
+
754 
+
755 #endif // CAFFE_COMMON_LAYERS_HPP_
+
caffe::ReshapeLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: common_layers.hpp:506
+
caffe::SplitLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the CPU device, compute the layer output.
Definition: split_layer.cpp:27
+
caffe::EltwiseLayer::MinBottomBlobs
virtual int MinBottomBlobs() const
Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
Definition: common_layers.hpp:240
+
caffe::MVNLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:460
+
caffe::EmbedLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the CPU device, compute the layer output.
Definition: embed_layer.cpp:68
+
caffe::ArgMaxLayer::type
virtual const char * type() const
Returns the layer type.
Definition: common_layers.hpp:51
+
caffe::InnerProductLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: inner_product_layer.cpp:13
+
caffe::ReductionLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::TileLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: tile_layer.cpp:39
+
caffe::FlattenLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:381
+
caffe::EmbedLayer::type
virtual const char * type() const
Returns the layer type.
Definition: common_layers.hpp:277
+
caffe::TileLayer
Copy a Blob along specified dimensions.
Definition: common_layers.hpp:728
+
caffe::BatchReindexLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: batch_reindex_layer.cpp:10
+
caffe::ReshapeLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
Definition: common_layers.hpp:504
+
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers could be called...
Definition: blob.hpp:15
+
caffe::ConcatLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Definition: concat_layer.cpp:58
+
caffe::EmbedLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: embed_layer.cpp:52
+
caffe::ReductionLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: reduction_layer.cpp:80
+
caffe::ReductionLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the CPU device, compute the layer output.
Definition: reduction_layer.cpp:45
+
caffe::InnerProductLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:426
+
caffe::SoftmaxLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:605
+
caffe::SoftmaxLayer::sum_multiplier_
Blob< Dtype > sum_multiplier_
sum_multiplier is used to carry out sum using BLAS
Definition: common_layers.hpp:622
+
caffe::SliceLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:704
+
caffe::FlattenLayer
Reshapes the input Blob into flat vectors.
Definition: common_layers.hpp:372
+
caffe::ReshapeLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: common_layers.hpp:502
+
caffe::InnerProductLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: inner_product_layer.cpp:55
+
caffe::SoftmaxLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: softmax_layer.cpp:64
+
caffe::ReductionLayer::dim_
int dim_
the input size of each reduction
Definition: common_layers.hpp:557
+
caffe::SliceLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: slice_layer.cpp:23
+
caffe::SilenceLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the CPU device, compute the layer output.
Definition: common_layers.hpp:579
+
caffe::ReshapeLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: reshape_layer.cpp:9
+
caffe::FlattenLayer::type
virtual const char * type() const
Returns the layer type.
Definition: common_layers.hpp:379
+
caffe::MVNLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:459
+
caffe::SoftmaxLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:606
+
caffe::SilenceLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::TileLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the CPU device, compute the layer output.
Definition: tile_layer.cpp:25
+
caffe::InnerProductLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::SliceLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::ReshapeLayer::constant_count_
int constant_count_
the product of the "constant" output dimensions
Definition: common_layers.hpp:514
+
caffe::FilterLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: filter_layer.cpp:18
+
caffe::ArgMaxLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: argmax_layer.cpp:34
+
caffe::ConcatLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Computes the error gradient w.r.t. the concatenate inputs.
Definition: concat_layer.cpp:78
+
caffe::SliceLayer::MinTopBlobs
virtual int MinTopBlobs() const
Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
Definition: common_layers.hpp:705
+
caffe::SoftmaxLayer::type
virtual const char * type() const
Returns the layer type.
Definition: common_layers.hpp:604
+
caffe::BatchReindexLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:97
+
caffe::ReshapeLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: reshape_layer.cpp:31
+
caffe::EmbedLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:278
+
caffe::SilenceLayer::MinBottomBlobs
virtual int MinBottomBlobs() const
Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
Definition: common_layers.hpp:575
+
caffe::FilterLayer::MinBottomBlobs
virtual int MinBottomBlobs() const
Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
Definition: common_layers.hpp:315
+
caffe::ConcatLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: concat_layer.cpp:10
+
caffe::EltwiseLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: eltwise_layer.cpp:11
+
caffe::ReshapeLayer::inferred_axis_
int inferred_axis_
the index of the axis whose dimension we infer, or -1 if none
Definition: common_layers.hpp:512
+
caffe::EmbedLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: embed_layer.cpp:13
+
caffe::EmbedLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: embed_layer.cpp:89
+
caffe::ReductionLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: reduction_layer.cpp:18
+
caffe::MVNLayer
Normalizes the input to have 0-mean and/or unit (1) variance.
Definition: common_layers.hpp:451
+
caffe::EmbedLayer
A layer for learning "embeddings" of one-hot vector input. Equivalent to an InnerProductLayer with on...
Definition: common_layers.hpp:268
+
caffe::ReductionLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:535
+
caffe::FilterLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Definition: filter_layer.cpp:63
+
caffe::FilterLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Computes the error gradient w.r.t. the forwarded inputs.
Definition: filter_layer.cpp:81
+
caffe::ArgMaxLayer
Compute the index of the max values for each datum across all dimensions .
Definition: common_layers.hpp:30
+
caffe::ArgMaxLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: argmax_layer.cpp:12
+
caffe::SoftmaxLayer
Computes the softmax function.
Definition: common_layers.hpp:597
+
caffe::SliceLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::ReductionLayer::type
virtual const char * type() const
Returns the layer type.
Definition: common_layers.hpp:534
+
caffe::FilterLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::TileLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::SilenceLayer::type
virtual const char * type() const
Returns the layer type.
Definition: common_layers.hpp:574
+
caffe::InnerProductLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the CPU device, compute the layer output.
Definition: inner_product_layer.cpp:81
+
caffe::MVNLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::InnerProductLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::EmbedLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::EmbedLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::SilenceLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: silence_layer.cpp:10
+
caffe::ConcatLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::EltwiseLayer
Compute elementwise operations, such as product and sum, along multiple input Blobs.
Definition: common_layers.hpp:230
+
caffe::InnerProductLayer::type
virtual const char * type() const
Returns the layer type.
Definition: common_layers.hpp:424
+
caffe::ReshapeLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:497
+
caffe::FlattenLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:380
+
caffe::ArgMaxLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:53
+
caffe::BatchReindexLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::TileLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:736
+
caffe::SilenceLayer
Ignores bottom blobs while producing no top blobs. (This is useful to suppress outputs during testing...
Definition: common_layers.hpp:567
+
caffe::ReductionLayer::sum_multiplier_
Blob< Dtype > sum_multiplier_
a helper Blob used for summation (op_ == SUM)
Definition: common_layers.hpp:559
+
caffe::SilenceLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:576
+
caffe::ConcatLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::EltwiseLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: eltwise_layer.cpp:32
+
caffe::SliceLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the CPU device, compute the layer output.
Definition: slice_layer.cpp:77
+
caffe::TileLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::MVNLayer::type
virtual const char * type() const
Returns the layer type.
Definition: common_layers.hpp:458
+
caffe::ArgMaxLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Not implemented (non-differentiable function)
Definition: common_layers.hpp:71
+
caffe::ReshapeLayer
Definition: common_layers.hpp:486
+
caffe::EltwiseLayer::type
virtual const char * type() const
Returns the layer type.
Definition: common_layers.hpp:239
+
caffe::SplitLayer::type
virtual const char * type() const
Returns the layer type.
Definition: common_layers.hpp:670
+
caffe::SliceLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: slice_layer.cpp:98
+
caffe::InnerProductLayer
Also known as a "fully-connected" layer, computes an inner product with a set of learned weights...
Definition: common_layers.hpp:415
+
caffe::ReshapeLayer::copy_axes_
vector< int > copy_axes_
vector of axes indices whose dimensions we'll copy from the bottom
Definition: common_layers.hpp:510
+
caffe::FlattenLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: flatten_layer.cpp:10
+
caffe::FilterLayer::type
virtual const char * type() const
Returns the layer type.
Definition: common_layers.hpp:314
+
caffe::InnerProductLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: inner_product_layer.cpp:96
+
caffe::FilterLayer::MinTopBlobs
virtual int MinTopBlobs() const
Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
Definition: common_layers.hpp:316
+
caffe::ReductionLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:536
+
caffe::SplitLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::ReductionLayer::op_
ReductionParameter_ReductionOp op_
the reduction operation performed by the layer
Definition: common_layers.hpp:549
+
caffe::ReductionLayer
Compute "reductions" – operations that return a scalar output Blob for an input Blob of arbitrary si...
Definition: common_layers.hpp:525
+
caffe::FilterLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::BatchReindexLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Definition: batch_reindex_layer.cpp:34
+
caffe::TileLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:737
+
caffe::SplitLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: split_layer.cpp:35
+
caffe::EltwiseLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::BatchReindexLayer::type
virtual const char * type() const
Returns the layer type.
Definition: common_layers.hpp:96
+
caffe::EmbedLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:279
+
caffe::ReductionLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: reduction_layer.cpp:12
+
caffe::MVNLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::MVNLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: mvn_layer.cpp:11
+
caffe::BatchReindexLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Computes the error gradient w.r.t. the reordered input.
Definition: batch_reindex_layer.cpp:53
+
caffe::TileLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: tile_layer.cpp:10
+
caffe::ArgMaxLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:52
+
caffe::EltwiseLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:241
+
caffe::EltwiseLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::MVNLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: mvn_layer.cpp:76
+
caffe::ReductionLayer::coeff_
Dtype coeff_
a scalar coefficient applied to all outputs
Definition: common_layers.hpp:551
+
caffe::ConcatLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: concat_layer.cpp:18
+
caffe::SoftmaxLayer::scale_
Blob< Dtype > scale_
scale is an intermediate Blob to hold temporary results.
Definition: common_layers.hpp:624
+
caffe::ConcatLayer::MinBottomBlobs
virtual int MinBottomBlobs() const
Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
Definition: common_layers.hpp:165
+
caffe::SoftmaxLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::SoftmaxLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the CPU device, compute the layer output.
Definition: softmax_layer.cpp:28
+
caffe::SilenceLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: common_layers.hpp:571
+
caffe::ConcatLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:166
+
caffe::SoftmaxLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::SplitLayer
Creates a "split" path in the network by copying the bottom Blob into multiple top Blobs to be used b...
Definition: common_layers.hpp:663
+
caffe::SplitLayer::MinTopBlobs
virtual int MinTopBlobs() const
Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
Definition: common_layers.hpp:672
+
caffe::ConcatLayer::type
virtual const char * type() const
Returns the layer type.
Definition: common_layers.hpp:164
+
caffe::BatchReindexLayer
Index into the input blob along its first axis.
Definition: common_layers.hpp:89
+
caffe::SliceLayer
Takes a Blob and slices it along either the num or channel dimension, outputting multiple sliced Blob...
Definition: common_layers.hpp:694
+
caffe::TileLayer::type
virtual const char * type() const
Returns the layer type.
Definition: common_layers.hpp:735
+
caffe::SilenceLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::SplitLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:671
+
caffe::ReductionLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::FlattenLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Definition: flatten_layer.cpp:30
+
caffe::EltwiseLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: eltwise_layer.cpp:101
+
caffe::ReshapeLayer::type
virtual const char * type() const
Returns the layer type.
Definition: common_layers.hpp:495
+
caffe::SliceLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: slice_layer.cpp:11
+
caffe::ArgMaxLayer::ArgMaxLayer
ArgMaxLayer(const LayerParameter &param)
Definition: common_layers.hpp:44
+
caffe::EltwiseLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the CPU device, compute the layer output.
Definition: eltwise_layer.cpp:46
+
caffe::BatchReindexLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::MVNLayer::sum_multiplier_
Blob< Dtype > sum_multiplier_
sum_multiplier is used to carry out sum using BLAS
Definition: common_layers.hpp:475
+
caffe::SoftmaxLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: softmax_layer.cpp:11
+
caffe::MVNLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the CPU device, compute the layer output.
Definition: mvn_layer.cpp:33
+
caffe::ReshapeLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the CPU device, compute the layer output.
Definition: common_layers.hpp:500
+
caffe::SplitLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::ReshapeLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:496
+
caffe::ReductionLayer::num_
int num_
the number of reductions performed
Definition: common_layers.hpp:555
+
caffe::FlattenLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Computes the error gradient w.r.t. the concatenate inputs.
Definition: flatten_layer.cpp:36
+
caffe::SplitLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: split_layer.cpp:10
+
caffe::SliceLayer::type
virtual const char * type() const
Returns the layer type.
Definition: common_layers.hpp:703
+
caffe::FilterLayer
Takes two+ Blobs, interprets last Blob as a selector and filter remaining Blobs accordingly with sele...
Definition: common_layers.hpp:305
+
caffe::BatchReindexLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:98
+
caffe::InnerProductLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: common_layers.hpp:425
+
caffe::ArgMaxLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Definition: argmax_layer.cpp:54
+
caffe::ReductionLayer::axis_
int axis_
the index of the first input axis to reduce
Definition: common_layers.hpp:553
+
caffe::FilterLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: filter_layer.cpp:11
+
caffe::ConcatLayer
Takes at least two Blobs and concatenates them along either the num or channel dimension, outputting the result.
Definition: common_layers.hpp:155
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:25
+
+ +
+Generated on Mon Oct 19 2015 15:25:08 for Caffe by   +doxygen + 1.8.9.1 +
+ + diff --git a/doxygen/concat__layer_8hpp_source.html b/doxygen/concat__layer_8hpp_source.html new file mode 100644 index 00000000000..304ab001279 --- /dev/null +++ b/doxygen/concat__layer_8hpp_source.html @@ -0,0 +1,90 @@ + + + + + + + +Caffe: include/caffe/layers/concat_layer.hpp Source File + + + + + + + + + +
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Caffe +
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concat_layer.hpp
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1 #ifndef CAFFE_CONCAT_LAYER_HPP_
2 #define CAFFE_CONCAT_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 namespace caffe {
11 
16 template <typename Dtype>
17 class ConcatLayer : public Layer<Dtype> {
18  public:
19  explicit ConcatLayer(const LayerParameter& param)
20  : Layer<Dtype>(param) {}
21  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
22  const vector<Blob<Dtype>*>& top);
23  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
24  const vector<Blob<Dtype>*>& top);
25 
26  virtual inline const char* type() const { return "Concat"; }
27  virtual inline int MinBottomBlobs() const { return 1; }
28  virtual inline int ExactNumTopBlobs() const { return 1; }
29 
30  protected:
47  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
48  const vector<Blob<Dtype>*>& top);
49  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
50  const vector<Blob<Dtype>*>& top);
51 
74  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
75  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
76  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
77  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
78 
79  int count_;
80  int num_concats_;
81  int concat_input_size_;
82  int concat_axis_;
83 };
84 
85 } // namespace caffe
86 
87 #endif // CAFFE_CONCAT_LAYER_HPP_
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::ConcatLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Definition: concat_layer.cpp:57
+
caffe::ConcatLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Computes the error gradient w.r.t. the concatenate inputs.
Definition: concat_layer.cpp:77
+
caffe::ConcatLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::ConcatLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: concat_layer.hpp:28
+
caffe::ConcatLayer::type
virtual const char * type() const
Returns the layer type.
Definition: concat_layer.hpp:26
+
caffe::ConcatLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::ConcatLayer::MinBottomBlobs
virtual int MinBottomBlobs() const
Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
Definition: concat_layer.hpp:27
+
caffe::ConcatLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: concat_layer.cpp:17
+
caffe::ConcatLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: concat_layer.cpp:9
+
caffe::ConcatLayer
Takes at least two Blobs and concatenates them along either the num or channel dimension, outputting the result.
Definition: concat_layer.hpp:17
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/contrastive__loss__layer_8hpp_source.html b/doxygen/contrastive__loss__layer_8hpp_source.html new file mode 100644 index 00000000000..7e4026c96f0 --- /dev/null +++ b/doxygen/contrastive__loss__layer_8hpp_source.html @@ -0,0 +1,89 @@ + + + + + + + +Caffe: include/caffe/layers/contrastive_loss_layer.hpp Source File + + + + + + + + + +
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Caffe +
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contrastive_loss_layer.hpp
+
+
+
1 #ifndef CAFFE_CONTRASTIVE_LOSS_LAYER_HPP_
2 #define CAFFE_CONTRASTIVE_LOSS_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/loss_layer.hpp"
11 
12 namespace caffe {
13 
38 template <typename Dtype>
39 class ContrastiveLossLayer : public LossLayer<Dtype> {
40  public:
41  explicit ContrastiveLossLayer(const LayerParameter& param)
42  : LossLayer<Dtype>(param), diff_() {}
43  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
44  const vector<Blob<Dtype>*>& top);
45 
46  virtual inline int ExactNumBottomBlobs() const { return 3; }
47  virtual inline const char* type() const { return "ContrastiveLoss"; }
52  virtual inline bool AllowForceBackward(const int bottom_index) const {
53  return bottom_index != 2;
54  }
55 
56  protected:
58  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
59  const vector<Blob<Dtype>*>& top);
60  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
61  const vector<Blob<Dtype>*>& top);
62 
88  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
89  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
90  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
91  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
92 
93  Blob<Dtype> diff_; // cached for backward pass
94  Blob<Dtype> dist_sq_; // cached for backward pass
95  Blob<Dtype> diff_sq_; // tmp storage for gpu forward pass
96  Blob<Dtype> summer_vec_; // tmp storage for gpu forward pass
97 };
98 
99 } // namespace caffe
100 
101 #endif // CAFFE_CONTRASTIVE_LOSS_LAYER_HPP_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::ContrastiveLossLayer::type
virtual const char * type() const
Returns the layer type.
Definition: contrastive_loss_layer.hpp:47
+
caffe::ContrastiveLossLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: contrastive_loss_layer.hpp:46
+
caffe::ContrastiveLossLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::ContrastiveLossLayer::AllowForceBackward
virtual bool AllowForceBackward(const int bottom_index) const
Definition: contrastive_loss_layer.hpp:52
+
caffe::ContrastiveLossLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Computes the contrastive loss where . This can be used to train siamese networks.
Definition: contrastive_loss_layer.cpp:31
+
caffe::ContrastiveLossLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::LossLayer
An interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth ...
Definition: loss_layer.hpp:23
+
caffe::ContrastiveLossLayer
Computes the contrastive loss where . This can be used to train siamese networks.
Definition: contrastive_loss_layer.hpp:39
+
caffe::ContrastiveLossLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: contrastive_loss_layer.cpp:10
+
caffe::ContrastiveLossLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Computes the Contrastive error gradient w.r.t. the inputs.
Definition: contrastive_loss_layer.cpp:65
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/conv__layer_8hpp_source.html b/doxygen/conv__layer_8hpp_source.html new file mode 100644 index 00000000000..b7856149b01 --- /dev/null +++ b/doxygen/conv__layer_8hpp_source.html @@ -0,0 +1,87 @@ + + + + + + + +Caffe: include/caffe/layers/conv_layer.hpp Source File + + + + + + + + + +
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Caffe +
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conv_layer.hpp
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1 #ifndef CAFFE_CONV_LAYER_HPP_
2 #define CAFFE_CONV_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/base_conv_layer.hpp"
11 
12 namespace caffe {
13 
30 template <typename Dtype>
31 class ConvolutionLayer : public BaseConvolutionLayer<Dtype> {
32  public:
64  explicit ConvolutionLayer(const LayerParameter& param)
65  : BaseConvolutionLayer<Dtype>(param) {}
66 
67  virtual inline const char* type() const { return "Convolution"; }
68 
69  protected:
70  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
71  const vector<Blob<Dtype>*>& top);
72  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
73  const vector<Blob<Dtype>*>& top);
74  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
75  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
76  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
77  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
78  virtual inline bool reverse_dimensions() { return false; }
79  virtual void compute_output_shape();
80 };
81 
82 } // namespace caffe
83 
84 #endif // CAFFE_CONV_LAYER_HPP_
caffe::ConvolutionLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::ConvolutionLayer::type
virtual const char * type() const
Returns the layer type.
Definition: conv_layer.hpp:67
+
caffe::ConvolutionLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: conv_layer.cpp:43
+
caffe::BaseConvolutionLayer
Abstract base class that factors out the BLAS code common to ConvolutionLayer and DeconvolutionLayer...
Definition: base_conv_layer.hpp:18
+
caffe::ConvolutionLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::ConvolutionLayer::ConvolutionLayer
ConvolutionLayer(const LayerParameter &param)
Definition: conv_layer.hpp:64
+
caffe::ConvolutionLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the CPU device, compute the layer output.
Definition: conv_layer.cpp:25
+
caffe::ConvolutionLayer
Convolves the input image with a bank of learned filters, and (optionally) adds biases.
Definition: conv_layer.hpp:31
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/crop__layer_8hpp_source.html b/doxygen/crop__layer_8hpp_source.html new file mode 100644 index 00000000000..24ec76381a8 --- /dev/null +++ b/doxygen/crop__layer_8hpp_source.html @@ -0,0 +1,90 @@ + + + + + + + +Caffe: include/caffe/layers/crop_layer.hpp Source File + + + + + + + + + +
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Caffe +
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crop_layer.hpp
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1 #ifndef CAFFE_CROP_LAYER_HPP_
2 #define CAFFE_CROP_LAYER_HPP_
3 
4 #include <utility>
5 #include <vector>
6 
7 #include "caffe/blob.hpp"
8 #include "caffe/layer.hpp"
9 #include "caffe/proto/caffe.pb.h"
10 
11 namespace caffe {
12 
20 template <typename Dtype>
21 class CropLayer : public Layer<Dtype> {
22  public:
23  explicit CropLayer(const LayerParameter& param)
24  : Layer<Dtype>(param) {}
25  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
26  const vector<Blob<Dtype>*>& top);
27  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
28  const vector<Blob<Dtype>*>& top);
29 
30  virtual inline const char* type() const { return "Crop"; }
31  virtual inline int ExactNumBottomBlobs() const { return 2; }
32  virtual inline int ExactNumTopBlobs() const { return 1; }
33 
34  protected:
35  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
36  const vector<Blob<Dtype>*>& top);
37  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
38  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
39  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
40  const vector<Blob<Dtype>*>& top);
41  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
42  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
43 
44  vector<int> offsets;
45 
46  private:
47  // Recursive copy function.
48  void crop_copy(const vector<Blob<Dtype>*>& bottom,
49  const vector<Blob<Dtype>*>& top,
50  const vector<int>& offsets,
51  vector<int> indices,
52  int cur_dim,
53  const Dtype* src_data,
54  Dtype* dest_data,
55  bool is_forward);
56 
57  // Recursive copy function: this is similar to crop_copy() but loops over all
58  // but the last two dimensions to allow for ND cropping while still relying on
59  // a CUDA kernel for the innermost two dimensions for performance reasons. An
60  // alterantive implementation could rely on the kernel more by passing
61  // offsets, but this is problematic because of its variable length.
62  // Since in the standard (N,C,W,H) case N,C are usually not cropped a speedup
63  // could be achieved by not looping the application of the copy_kernel around
64  // these dimensions.
65  void crop_copy_gpu(const vector<Blob<Dtype>*>& bottom,
66  const vector<Blob<Dtype>*>& top,
67  const vector<int>& offsets,
68  vector<int> indices,
69  int cur_dim,
70  const Dtype* src_data,
71  Dtype* dest_data,
72  bool is_forward);
73 };
74 } // namespace caffe
75 
76 #endif // CAFFE_CROP_LAYER_HPP_
caffe::CropLayer::type
virtual const char * type() const
Returns the layer type.
Definition: crop_layer.hpp:30
+
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::CropLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::CropLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: crop_layer.hpp:32
+
caffe::CropLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: crop_layer.hpp:31
+
caffe::CropLayer
Takes a Blob and crop it, to the shape specified by the second input Blob, across all dimensions afte...
Definition: crop_layer.hpp:21
+
caffe::CropLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: crop_layer.cpp:36
+
caffe::CropLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::CropLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the CPU device, compute the layer output.
Definition: crop_layer.cpp:115
+
caffe::CropLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: crop_layer.cpp:124
+
caffe::CropLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: crop_layer.cpp:16
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/cudnn_8hpp_source.html b/doxygen/cudnn_8hpp_source.html new file mode 100644 index 00000000000..5e59602490a --- /dev/null +++ b/doxygen/cudnn_8hpp_source.html @@ -0,0 +1,78 @@ + + + + + + + +Caffe: include/caffe/util/cudnn.hpp Source File + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
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+ + + + + + + + +
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cudnn.hpp
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1 #ifndef CAFFE_UTIL_CUDNN_H_
2 #define CAFFE_UTIL_CUDNN_H_
3 #ifdef USE_CUDNN
4 
5 #include <cudnn.h>
6 
7 #include "caffe/common.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #define CUDNN_VERSION_MIN(major, minor, patch) \
11  (CUDNN_VERSION >= (major * 1000 + minor * 100 + patch))
12 
13 #define CUDNN_CHECK(condition) \
14  do { \
15  cudnnStatus_t status = condition; \
16  CHECK_EQ(status, CUDNN_STATUS_SUCCESS) << " "\
17  << cudnnGetErrorString(status); \
18  } while (0)
19 
20 inline const char* cudnnGetErrorString(cudnnStatus_t status) {
21  switch (status) {
22  case CUDNN_STATUS_SUCCESS:
23  return "CUDNN_STATUS_SUCCESS";
24  case CUDNN_STATUS_NOT_INITIALIZED:
25  return "CUDNN_STATUS_NOT_INITIALIZED";
26  case CUDNN_STATUS_ALLOC_FAILED:
27  return "CUDNN_STATUS_ALLOC_FAILED";
28  case CUDNN_STATUS_BAD_PARAM:
29  return "CUDNN_STATUS_BAD_PARAM";
30  case CUDNN_STATUS_INTERNAL_ERROR:
31  return "CUDNN_STATUS_INTERNAL_ERROR";
32  case CUDNN_STATUS_INVALID_VALUE:
33  return "CUDNN_STATUS_INVALID_VALUE";
34  case CUDNN_STATUS_ARCH_MISMATCH:
35  return "CUDNN_STATUS_ARCH_MISMATCH";
36  case CUDNN_STATUS_MAPPING_ERROR:
37  return "CUDNN_STATUS_MAPPING_ERROR";
38  case CUDNN_STATUS_EXECUTION_FAILED:
39  return "CUDNN_STATUS_EXECUTION_FAILED";
40  case CUDNN_STATUS_NOT_SUPPORTED:
41  return "CUDNN_STATUS_NOT_SUPPORTED";
42  case CUDNN_STATUS_LICENSE_ERROR:
43  return "CUDNN_STATUS_LICENSE_ERROR";
44  }
45  return "Unknown cudnn status";
46 }
47 
48 namespace caffe {
49 
50 namespace cudnn {
51 
52 template <typename Dtype> class dataType;
53 template<> class dataType<float> {
54  public:
55  static const cudnnDataType_t type = CUDNN_DATA_FLOAT;
56  static float oneval, zeroval;
57  static const void *one, *zero;
58 };
59 template<> class dataType<double> {
60  public:
61  static const cudnnDataType_t type = CUDNN_DATA_DOUBLE;
62  static double oneval, zeroval;
63  static const void *one, *zero;
64 };
65 
66 template <typename Dtype>
67 inline void createTensor4dDesc(cudnnTensorDescriptor_t* desc) {
68  CUDNN_CHECK(cudnnCreateTensorDescriptor(desc));
69 }
70 
71 template <typename Dtype>
72 inline void setTensor4dDesc(cudnnTensorDescriptor_t* desc,
73  int n, int c, int h, int w,
74  int stride_n, int stride_c, int stride_h, int stride_w) {
75  CUDNN_CHECK(cudnnSetTensor4dDescriptorEx(*desc, dataType<Dtype>::type,
76  n, c, h, w, stride_n, stride_c, stride_h, stride_w));
77 }
78 
79 template <typename Dtype>
80 inline void setTensor4dDesc(cudnnTensorDescriptor_t* desc,
81  int n, int c, int h, int w) {
82  const int stride_w = 1;
83  const int stride_h = w * stride_w;
84  const int stride_c = h * stride_h;
85  const int stride_n = c * stride_c;
86  setTensor4dDesc<Dtype>(desc, n, c, h, w,
87  stride_n, stride_c, stride_h, stride_w);
88 }
89 
90 template <typename Dtype>
91 inline void createFilterDesc(cudnnFilterDescriptor_t* desc,
92  int n, int c, int h, int w) {
93  CUDNN_CHECK(cudnnCreateFilterDescriptor(desc));
94 #if CUDNN_VERSION_MIN(5, 0, 0)
95  CUDNN_CHECK(cudnnSetFilter4dDescriptor(*desc, dataType<Dtype>::type,
96  CUDNN_TENSOR_NCHW, n, c, h, w));
97 #else
98  CUDNN_CHECK(cudnnSetFilter4dDescriptor_v4(*desc, dataType<Dtype>::type,
99  CUDNN_TENSOR_NCHW, n, c, h, w));
100 #endif
101 }
102 
103 template <typename Dtype>
104 inline void createConvolutionDesc(cudnnConvolutionDescriptor_t* conv) {
105  CUDNN_CHECK(cudnnCreateConvolutionDescriptor(conv));
106 }
107 
108 template <typename Dtype>
109 inline void setConvolutionDesc(cudnnConvolutionDescriptor_t* conv,
110  cudnnTensorDescriptor_t bottom, cudnnFilterDescriptor_t filter,
111  int pad_h, int pad_w, int stride_h, int stride_w) {
112  CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv,
113  pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION));
114 }
115 
116 template <typename Dtype>
117 inline void createPoolingDesc(cudnnPoolingDescriptor_t* pool_desc,
118  PoolingParameter_PoolMethod poolmethod, cudnnPoolingMode_t* mode,
119  int h, int w, int pad_h, int pad_w, int stride_h, int stride_w) {
120  switch (poolmethod) {
121  case PoolingParameter_PoolMethod_MAX:
122  *mode = CUDNN_POOLING_MAX;
123  break;
124  case PoolingParameter_PoolMethod_AVE:
125  *mode = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
126  break;
127  default:
128  LOG(FATAL) << "Unknown pooling method.";
129  }
130  CUDNN_CHECK(cudnnCreatePoolingDescriptor(pool_desc));
131 #if CUDNN_VERSION_MIN(5, 0, 0)
132  CUDNN_CHECK(cudnnSetPooling2dDescriptor(*pool_desc, *mode,
133  CUDNN_PROPAGATE_NAN, h, w, pad_h, pad_w, stride_h, stride_w));
134 #else
135  CUDNN_CHECK(cudnnSetPooling2dDescriptor_v4(*pool_desc, *mode,
136  CUDNN_PROPAGATE_NAN, h, w, pad_h, pad_w, stride_h, stride_w));
137 #endif
138 }
139 
140 template <typename Dtype>
141 inline void createActivationDescriptor(cudnnActivationDescriptor_t* activ_desc,
142  cudnnActivationMode_t mode) {
143  CUDNN_CHECK(cudnnCreateActivationDescriptor(activ_desc));
144  CUDNN_CHECK(cudnnSetActivationDescriptor(*activ_desc, mode,
145  CUDNN_PROPAGATE_NAN, Dtype(0)));
146 }
147 
148 } // namespace cudnn
149 
150 } // namespace caffe
151 
152 #endif // USE_CUDNN
153 #endif // CAFFE_UTIL_CUDNN_H_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/cudnn__conv__layer_8hpp_source.html b/doxygen/cudnn__conv__layer_8hpp_source.html new file mode 100644 index 00000000000..c74a3f57ca8 --- /dev/null +++ b/doxygen/cudnn__conv__layer_8hpp_source.html @@ -0,0 +1,78 @@ + + + + + + + +Caffe: include/caffe/layers/cudnn_conv_layer.hpp Source File + + + + + + + + + +
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Caffe +
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cudnn_conv_layer.hpp
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+
+
1 #ifndef CAFFE_CUDNN_CONV_LAYER_HPP_
2 #define CAFFE_CUDNN_CONV_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/conv_layer.hpp"
11 
12 namespace caffe {
13 
14 #ifdef USE_CUDNN
15 /*
16  * @brief cuDNN implementation of ConvolutionLayer.
17  * Fallback to ConvolutionLayer for CPU mode.
18  *
19  * cuDNN accelerates convolution through forward kernels for filtering and bias
20  * plus backward kernels for the gradient w.r.t. the filters, biases, and
21  * inputs. Caffe + cuDNN further speeds up the computation through forward
22  * parallelism across groups and backward parallelism across gradients.
23  *
24  * The CUDNN engine does not have memory overhead for matrix buffers. For many
25  * input and filter regimes the CUDNN engine is faster than the CAFFE engine,
26  * but for fully-convolutional models and large inputs the CAFFE engine can be
27  * faster as long as it fits in memory.
28 */
29 template <typename Dtype>
30 class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
31  public:
32  explicit CuDNNConvolutionLayer(const LayerParameter& param)
33  : ConvolutionLayer<Dtype>(param), handles_setup_(false) {}
34  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
35  const vector<Blob<Dtype>*>& top);
36  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
37  const vector<Blob<Dtype>*>& top);
38  virtual ~CuDNNConvolutionLayer();
39 
40  protected:
41  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
42  const vector<Blob<Dtype>*>& top);
43  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
44  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
45 
46  bool handles_setup_;
47  cudnnHandle_t* handle_;
48  cudaStream_t* stream_;
49 
50  // algorithms for forward and backwards convolutions
51  cudnnConvolutionFwdAlgo_t *fwd_algo_;
52  cudnnConvolutionBwdFilterAlgo_t *bwd_filter_algo_;
53  cudnnConvolutionBwdDataAlgo_t *bwd_data_algo_;
54 
55  vector<cudnnTensorDescriptor_t> bottom_descs_, top_descs_;
56  cudnnTensorDescriptor_t bias_desc_;
57  cudnnFilterDescriptor_t filter_desc_;
58  vector<cudnnConvolutionDescriptor_t> conv_descs_;
59  int bottom_offset_, top_offset_, bias_offset_;
60 
61  size_t *workspace_fwd_sizes_;
62  size_t *workspace_bwd_data_sizes_;
63  size_t *workspace_bwd_filter_sizes_;
64  size_t workspaceSizeInBytes; // size of underlying storage
65  void *workspaceData; // underlying storage
66  void **workspace; // aliases into workspaceData
67 };
68 #endif
69 
70 } // namespace caffe
71 
72 #endif // CAFFE_CUDNN_CONV_LAYER_HPP_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
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+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/cudnn__lcn__layer_8hpp_source.html b/doxygen/cudnn__lcn__layer_8hpp_source.html new file mode 100644 index 00000000000..1979015737a --- /dev/null +++ b/doxygen/cudnn__lcn__layer_8hpp_source.html @@ -0,0 +1,78 @@ + + + + + + + +Caffe: include/caffe/layers/cudnn_lcn_layer.hpp Source File + + + + + + + + + +
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cudnn_lcn_layer.hpp
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1 #ifndef CAFFE_CUDNN_LCN_LAYER_HPP_
2 #define CAFFE_CUDNN_LCN_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/lrn_layer.hpp"
11 #include "caffe/layers/power_layer.hpp"
12 
13 namespace caffe {
14 
15 #ifdef USE_CUDNN
16 template <typename Dtype>
17 class CuDNNLCNLayer : public LRNLayer<Dtype> {
18  public:
19  explicit CuDNNLCNLayer(const LayerParameter& param)
20  : LRNLayer<Dtype>(param), handles_setup_(false), tempDataSize(0),
21  tempData1(NULL), tempData2(NULL) {}
22  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
23  const vector<Blob<Dtype>*>& top);
24  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
25  const vector<Blob<Dtype>*>& top);
26  virtual ~CuDNNLCNLayer();
27 
28  protected:
29  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
30  const vector<Blob<Dtype>*>& top);
31  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
32  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
33 
34  bool handles_setup_;
35  cudnnHandle_t handle_;
36  cudnnLRNDescriptor_t norm_desc_;
37  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
38 
39  int size_, pre_pad_;
40  Dtype alpha_, beta_, k_;
41 
42  size_t tempDataSize;
43  void *tempData1, *tempData2;
44 };
45 #endif
46 
47 } // namespace caffe
48 
49 #endif // CAFFE_CUDNN_LCN_LAYER_HPP_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
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+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/cudnn__lrn__layer_8hpp_source.html b/doxygen/cudnn__lrn__layer_8hpp_source.html new file mode 100644 index 00000000000..7429338d678 --- /dev/null +++ b/doxygen/cudnn__lrn__layer_8hpp_source.html @@ -0,0 +1,78 @@ + + + + + + + +Caffe: include/caffe/layers/cudnn_lrn_layer.hpp Source File + + + + + + + + + +
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cudnn_lrn_layer.hpp
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1 #ifndef CAFFE_CUDNN_LRN_LAYER_HPP_
2 #define CAFFE_CUDNN_LRN_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/lrn_layer.hpp"
11 
12 namespace caffe {
13 
14 #ifdef USE_CUDNN
15 template <typename Dtype>
16 class CuDNNLRNLayer : public LRNLayer<Dtype> {
17  public:
18  explicit CuDNNLRNLayer(const LayerParameter& param)
19  : LRNLayer<Dtype>(param), handles_setup_(false) {}
20  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
21  const vector<Blob<Dtype>*>& top);
22  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
23  const vector<Blob<Dtype>*>& top);
24  virtual ~CuDNNLRNLayer();
25 
26  protected:
27  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
28  const vector<Blob<Dtype>*>& top);
29  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
30  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
31 
32  bool handles_setup_;
33  cudnnHandle_t handle_;
34  cudnnLRNDescriptor_t norm_desc_;
35  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
36 
37  int size_;
38  Dtype alpha_, beta_, k_;
39 };
40 #endif
41 
42 } // namespace caffe
43 
44 #endif // CAFFE_CUDNN_LRN_LAYER_HPP_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
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+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/cudnn__pooling__layer_8hpp_source.html b/doxygen/cudnn__pooling__layer_8hpp_source.html new file mode 100644 index 00000000000..f0e0be00f23 --- /dev/null +++ b/doxygen/cudnn__pooling__layer_8hpp_source.html @@ -0,0 +1,78 @@ + + + + + + + +Caffe: include/caffe/layers/cudnn_pooling_layer.hpp Source File + + + + + + + + + +
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cudnn_pooling_layer.hpp
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1 #ifndef CAFFE_CUDNN_POOLING_LAYER_HPP_
2 #define CAFFE_CUDNN_POOLING_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/pooling_layer.hpp"
11 
12 namespace caffe {
13 
14 #ifdef USE_CUDNN
15 /*
16  * @brief cuDNN implementation of PoolingLayer.
17  * Fallback to PoolingLayer for CPU mode.
18 */
19 template <typename Dtype>
20 class CuDNNPoolingLayer : public PoolingLayer<Dtype> {
21  public:
22  explicit CuDNNPoolingLayer(const LayerParameter& param)
23  : PoolingLayer<Dtype>(param), handles_setup_(false) {}
24  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
25  const vector<Blob<Dtype>*>& top);
26  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
27  const vector<Blob<Dtype>*>& top);
28  virtual ~CuDNNPoolingLayer();
29  // Currently, cuDNN does not support the extra top blob.
30  virtual inline int MinTopBlobs() const { return -1; }
31  virtual inline int ExactNumTopBlobs() const { return 1; }
32 
33  protected:
34  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
35  const vector<Blob<Dtype>*>& top);
36  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
37  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
38 
39  bool handles_setup_;
40  cudnnHandle_t handle_;
41  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
42  cudnnPoolingDescriptor_t pooling_desc_;
43  cudnnPoolingMode_t mode_;
44 };
45 #endif
46 
47 } // namespace caffe
48 
49 #endif // CAFFE_CUDNN_POOLING_LAYER_HPP_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/cudnn__relu__layer_8hpp_source.html b/doxygen/cudnn__relu__layer_8hpp_source.html new file mode 100644 index 00000000000..d051a49116c --- /dev/null +++ b/doxygen/cudnn__relu__layer_8hpp_source.html @@ -0,0 +1,78 @@ + + + + + + + +Caffe: include/caffe/layers/cudnn_relu_layer.hpp Source File + + + + + + + + + +
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cudnn_relu_layer.hpp
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+
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1 #ifndef CAFFE_CUDNN_RELU_LAYER_HPP_
2 #define CAFFE_CUDNN_RELU_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/neuron_layer.hpp"
11 #include "caffe/layers/relu_layer.hpp"
12 
13 namespace caffe {
14 
15 #ifdef USE_CUDNN
16 
19 template <typename Dtype>
20 class CuDNNReLULayer : public ReLULayer<Dtype> {
21  public:
22  explicit CuDNNReLULayer(const LayerParameter& param)
23  : ReLULayer<Dtype>(param), handles_setup_(false) {}
24  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
25  const vector<Blob<Dtype>*>& top);
26  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
27  const vector<Blob<Dtype>*>& top);
28  virtual ~CuDNNReLULayer();
29 
30  protected:
31  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
32  const vector<Blob<Dtype>*>& top);
33  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
34  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
35 
36  bool handles_setup_;
37  cudnnHandle_t handle_;
38  cudnnTensorDescriptor_t bottom_desc_;
39  cudnnTensorDescriptor_t top_desc_;
40  cudnnActivationDescriptor_t activ_desc_;
41 };
42 #endif
43 
44 } // namespace caffe
45 
46 #endif // CAFFE_CUDNN_RELU_LAYER_HPP_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/cudnn__sigmoid__layer_8hpp_source.html b/doxygen/cudnn__sigmoid__layer_8hpp_source.html new file mode 100644 index 00000000000..7c4993c930d --- /dev/null +++ b/doxygen/cudnn__sigmoid__layer_8hpp_source.html @@ -0,0 +1,78 @@ + + + + + + + +Caffe: include/caffe/layers/cudnn_sigmoid_layer.hpp Source File + + + + + + + + + +
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cudnn_sigmoid_layer.hpp
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1 #ifndef CAFFE_CUDNN_SIGMOID_LAYER_HPP_
2 #define CAFFE_CUDNN_SIGMOID_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/neuron_layer.hpp"
11 #include "caffe/layers/sigmoid_layer.hpp"
12 
13 namespace caffe {
14 
15 #ifdef USE_CUDNN
16 
19 template <typename Dtype>
20 class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
21  public:
22  explicit CuDNNSigmoidLayer(const LayerParameter& param)
23  : SigmoidLayer<Dtype>(param), handles_setup_(false) {}
24  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
25  const vector<Blob<Dtype>*>& top);
26  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
27  const vector<Blob<Dtype>*>& top);
28  virtual ~CuDNNSigmoidLayer();
29 
30  protected:
31  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
32  const vector<Blob<Dtype>*>& top);
33  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
34  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
35 
36  bool handles_setup_;
37  cudnnHandle_t handle_;
38  cudnnTensorDescriptor_t bottom_desc_;
39  cudnnTensorDescriptor_t top_desc_;
40  cudnnActivationDescriptor_t activ_desc_;
41 };
42 #endif
43 
44 } // namespace caffe
45 
46 #endif // CAFFE_CUDNN_SIGMOID_LAYER_HPP_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/cudnn__softmax__layer_8hpp_source.html b/doxygen/cudnn__softmax__layer_8hpp_source.html new file mode 100644 index 00000000000..2e9156eaeea --- /dev/null +++ b/doxygen/cudnn__softmax__layer_8hpp_source.html @@ -0,0 +1,78 @@ + + + + + + + +Caffe: include/caffe/layers/cudnn_softmax_layer.hpp Source File + + + + + + + + + +
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cudnn_softmax_layer.hpp
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1 #ifndef CAFFE_CUDNN_SOFTMAX_LAYER_HPP_
2 #define CAFFE_CUDNN_SOFTMAX_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/softmax_layer.hpp"
11 
12 namespace caffe {
13 
14 #ifdef USE_CUDNN
15 
19 template <typename Dtype>
20 class CuDNNSoftmaxLayer : public SoftmaxLayer<Dtype> {
21  public:
22  explicit CuDNNSoftmaxLayer(const LayerParameter& param)
23  : SoftmaxLayer<Dtype>(param), handles_setup_(false) {}
24  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
25  const vector<Blob<Dtype>*>& top);
26  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
27  const vector<Blob<Dtype>*>& top);
28  virtual ~CuDNNSoftmaxLayer();
29 
30  protected:
31  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
32  const vector<Blob<Dtype>*>& top);
33  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
34  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
35 
36  bool handles_setup_;
37  cudnnHandle_t handle_;
38  cudnnTensorDescriptor_t bottom_desc_;
39  cudnnTensorDescriptor_t top_desc_;
40 };
41 #endif
42 
43 } // namespace caffe
44 
45 #endif // CAFFE_CUDNN_SOFTMAX_LAYER_HPP_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
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+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/cudnn__tanh__layer_8hpp_source.html b/doxygen/cudnn__tanh__layer_8hpp_source.html new file mode 100644 index 00000000000..583a2157408 --- /dev/null +++ b/doxygen/cudnn__tanh__layer_8hpp_source.html @@ -0,0 +1,78 @@ + + + + + + + +Caffe: include/caffe/layers/cudnn_tanh_layer.hpp Source File + + + + + + + + + +
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cudnn_tanh_layer.hpp
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1 #ifndef CAFFE_CUDNN_TANH_LAYER_HPP_
2 #define CAFFE_CUDNN_TANH_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/neuron_layer.hpp"
11 #include "caffe/layers/tanh_layer.hpp"
12 
13 namespace caffe {
14 
15 #ifdef USE_CUDNN
16 
19 template <typename Dtype>
20 class CuDNNTanHLayer : public TanHLayer<Dtype> {
21  public:
22  explicit CuDNNTanHLayer(const LayerParameter& param)
23  : TanHLayer<Dtype>(param), handles_setup_(false) {}
24  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
25  const vector<Blob<Dtype>*>& top);
26  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
27  const vector<Blob<Dtype>*>& top);
28  virtual ~CuDNNTanHLayer();
29 
30  protected:
31  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
32  const vector<Blob<Dtype>*>& top);
33  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
34  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
35 
36  bool handles_setup_;
37  cudnnHandle_t handle_;
38  cudnnTensorDescriptor_t bottom_desc_;
39  cudnnTensorDescriptor_t top_desc_;
40  cudnnActivationDescriptor_t activ_desc_;
41 };
42 #endif
43 
44 } // namespace caffe
45 
46 #endif // CAFFE_CUDNN_TANH_LAYER_HPP_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
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+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/data__layer_8hpp_source.html b/doxygen/data__layer_8hpp_source.html new file mode 100644 index 00000000000..ef5cce2c697 --- /dev/null +++ b/doxygen/data__layer_8hpp_source.html @@ -0,0 +1,88 @@ + + + + + + + +Caffe: include/caffe/layers/data_layer.hpp Source File + + + + + + + + + +
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data_layer.hpp
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1 #ifndef CAFFE_DATA_LAYER_HPP_
2 #define CAFFE_DATA_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/data_reader.hpp"
8 #include "caffe/data_transformer.hpp"
9 #include "caffe/internal_thread.hpp"
10 #include "caffe/layer.hpp"
11 #include "caffe/layers/base_data_layer.hpp"
12 #include "caffe/proto/caffe.pb.h"
13 #include "caffe/util/db.hpp"
14 
15 namespace caffe {
16 
17 template <typename Dtype>
18 class DataLayer : public BasePrefetchingDataLayer<Dtype> {
19  public:
20  explicit DataLayer(const LayerParameter& param);
21  virtual ~DataLayer();
22  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
23  const vector<Blob<Dtype>*>& top);
24  // DataLayer uses DataReader instead for sharing for parallelism
25  virtual inline bool ShareInParallel() const { return false; }
26  virtual inline const char* type() const { return "Data"; }
27  virtual inline int ExactNumBottomBlobs() const { return 0; }
28  virtual inline int MinTopBlobs() const { return 1; }
29  virtual inline int MaxTopBlobs() const { return 2; }
30 
31  protected:
32  virtual void load_batch(Batch<Dtype>* batch);
33 
34  DataReader reader_;
35 };
36 
37 } // namespace caffe
38 
39 #endif // CAFFE_DATA_LAYER_HPP_
caffe::Batch
Definition: base_data_layer.hpp:49
+
caffe::BasePrefetchingDataLayer
Definition: base_data_layer.hpp:55
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::DataLayer::ShareInParallel
virtual bool ShareInParallel() const
Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
Definition: data_layer.hpp:25
+
caffe::DataLayer
Definition: data_layer.hpp:18
+
caffe::DataLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: data_layer.hpp:27
+
caffe::DataLayer::type
virtual const char * type() const
Returns the layer type.
Definition: data_layer.hpp:26
+
caffe::DataReader
Reads data from a source to queues available to data layers. A single reading thread is created per s...
Definition: data_reader.hpp:23
+
caffe::DataLayer::MaxTopBlobs
virtual int MaxTopBlobs() const
Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required...
Definition: data_layer.hpp:29
+
caffe::DataLayer::MinTopBlobs
virtual int MinTopBlobs() const
Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
Definition: data_layer.hpp:28
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
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+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/data__layers_8hpp_source.html b/doxygen/data__layers_8hpp_source.html new file mode 100644 index 00000000000..bda072cae87 --- /dev/null +++ b/doxygen/data__layers_8hpp_source.html @@ -0,0 +1,475 @@ + + + + + + +Caffe: include/caffe/data_layers.hpp Source File + + + + + + + + + + +
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data_layers.hpp
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1 #ifndef CAFFE_DATA_LAYERS_HPP_
+
2 #define CAFFE_DATA_LAYERS_HPP_
+
3 
+
4 #include <string>
+
5 #include <utility>
+
6 #include <vector>
+
7 #include "hdf5.h"
+
8 
+
9 #include "caffe/blob.hpp"
+
10 #include "caffe/common.hpp"
+
11 #include "caffe/data_reader.hpp"
+
12 #include "caffe/data_transformer.hpp"
+
13 #include "caffe/filler.hpp"
+
14 #include "caffe/internal_thread.hpp"
+
15 #include "caffe/layer.hpp"
+
16 #include "caffe/proto/caffe.pb.h"
+
17 #include "caffe/util/blocking_queue.hpp"
+
18 #include "caffe/util/db.hpp"
+
19 
+
20 namespace caffe {
+
21 
+
27 template <typename Dtype>
+
28 class BaseDataLayer : public Layer<Dtype> {
+
29  public:
+
30  explicit BaseDataLayer(const LayerParameter& param);
+
31  // LayerSetUp: implements common data layer setup functionality, and calls
+
32  // DataLayerSetUp to do special data layer setup for individual layer types.
+
33  // This method may not be overridden except by the BasePrefetchingDataLayer.
+
34  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
35  const vector<Blob<Dtype>*>& top);
+
36  // Data layers should be shared by multiple solvers in parallel
+
37  virtual inline bool ShareInParallel() const { return true; }
+
38  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
39  const vector<Blob<Dtype>*>& top) {}
+
40  // Data layers have no bottoms, so reshaping is trivial.
+
41  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
42  const vector<Blob<Dtype>*>& top) {}
+
43 
+
44  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
45  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
46  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
47  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
48 
+
49  protected:
+
50  TransformationParameter transform_param_;
+
51  shared_ptr<DataTransformer<Dtype> > data_transformer_;
+
52  bool output_labels_;
+
53 };
+
54 
+
55 template <typename Dtype>
+
56 class Batch {
+
57  public:
+
58  Blob<Dtype> data_, label_;
+
59 };
+
60 
+
61 template <typename Dtype>
+
62 class BasePrefetchingDataLayer :
+
63  public BaseDataLayer<Dtype>, public InternalThread {
+
64  public:
+
65  explicit BasePrefetchingDataLayer(const LayerParameter& param);
+
66  // LayerSetUp: implements common data layer setup functionality, and calls
+
67  // DataLayerSetUp to do special data layer setup for individual layer types.
+
68  // This method may not be overridden.
+
69  void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
70  const vector<Blob<Dtype>*>& top);
+
71 
+
72  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
73  const vector<Blob<Dtype>*>& top);
+
74  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
75  const vector<Blob<Dtype>*>& top);
+
76 
+
77  // Prefetches batches (asynchronously if to GPU memory)
+
78  static const int PREFETCH_COUNT = 3;
+
79 
+
80  protected:
+
81  virtual void InternalThreadEntry();
+
82  virtual void load_batch(Batch<Dtype>* batch) = 0;
+
83 
+
84  Batch<Dtype> prefetch_[PREFETCH_COUNT];
+
85  BlockingQueue<Batch<Dtype>*> prefetch_free_;
+
86  BlockingQueue<Batch<Dtype>*> prefetch_full_;
+
87 
+
88  Blob<Dtype> transformed_data_;
+
89 };
+
90 
+
91 template <typename Dtype>
+
92 class DataLayer : public BasePrefetchingDataLayer<Dtype> {
+
93  public:
+
94  explicit DataLayer(const LayerParameter& param);
+
95  virtual ~DataLayer();
+
96  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
97  const vector<Blob<Dtype>*>& top);
+
98  // DataLayer uses DataReader instead for sharing for parallelism
+
99  virtual inline bool ShareInParallel() const { return false; }
+
100  virtual inline const char* type() const { return "Data"; }
+
101  virtual inline int ExactNumBottomBlobs() const { return 0; }
+
102  virtual inline int MinTopBlobs() const { return 1; }
+
103  virtual inline int MaxTopBlobs() const { return 2; }
+
104 
+
105  protected:
+
106  virtual void load_batch(Batch<Dtype>* batch);
+
107 
+
108  DataReader reader_;
+
109 };
+
110 
+
116 template <typename Dtype>
+
117 class DummyDataLayer : public Layer<Dtype> {
+
118  public:
+
119  explicit DummyDataLayer(const LayerParameter& param)
+
120  : Layer<Dtype>(param) {}
+
121  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
122  const vector<Blob<Dtype>*>& top);
+
123  // Data layers should be shared by multiple solvers in parallel
+
124  virtual inline bool ShareInParallel() const { return true; }
+
125  // Data layers have no bottoms, so reshaping is trivial.
+
126  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
127  const vector<Blob<Dtype>*>& top) {}
+
128 
+
129  virtual inline const char* type() const { return "DummyData"; }
+
130  virtual inline int ExactNumBottomBlobs() const { return 0; }
+
131  virtual inline int MinTopBlobs() const { return 1; }
+
132 
+
133  protected:
+
134  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
135  const vector<Blob<Dtype>*>& top);
+
136  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
137  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
138  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
139  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
140 
+
141  vector<shared_ptr<Filler<Dtype> > > fillers_;
+
142  vector<bool> refill_;
+
143 };
+
144 
+
150 template <typename Dtype>
+
151 class HDF5DataLayer : public Layer<Dtype> {
+
152  public:
+
153  explicit HDF5DataLayer(const LayerParameter& param)
+
154  : Layer<Dtype>(param) {}
+
155  virtual ~HDF5DataLayer();
+
156  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
157  const vector<Blob<Dtype>*>& top);
+
158  // Data layers should be shared by multiple solvers in parallel
+
159  virtual inline bool ShareInParallel() const { return true; }
+
160  // Data layers have no bottoms, so reshaping is trivial.
+
161  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
162  const vector<Blob<Dtype>*>& top) {}
+
163 
+
164  virtual inline const char* type() const { return "HDF5Data"; }
+
165  virtual inline int ExactNumBottomBlobs() const { return 0; }
+
166  virtual inline int MinTopBlobs() const { return 1; }
+
167 
+
168  protected:
+
169  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
170  const vector<Blob<Dtype>*>& top);
+
171  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
172  const vector<Blob<Dtype>*>& top);
+
173  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
174  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
175  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
176  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
177  virtual void LoadHDF5FileData(const char* filename);
+
178 
+
179  std::vector<std::string> hdf_filenames_;
+
180  unsigned int num_files_;
+
181  unsigned int current_file_;
+
182  hsize_t current_row_;
+
183  std::vector<shared_ptr<Blob<Dtype> > > hdf_blobs_;
+
184  std::vector<unsigned int> data_permutation_;
+
185  std::vector<unsigned int> file_permutation_;
+
186 };
+
187 
+
193 template <typename Dtype>
+
194 class HDF5OutputLayer : public Layer<Dtype> {
+
195  public:
+
196  explicit HDF5OutputLayer(const LayerParameter& param)
+
197  : Layer<Dtype>(param), file_opened_(false) {}
+
198  virtual ~HDF5OutputLayer();
+
199  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
200  const vector<Blob<Dtype>*>& top);
+
201  // Data layers should be shared by multiple solvers in parallel
+
202  virtual inline bool ShareInParallel() const { return true; }
+
203  // Data layers have no bottoms, so reshaping is trivial.
+
204  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
205  const vector<Blob<Dtype>*>& top) {}
+
206 
+
207  virtual inline const char* type() const { return "HDF5Output"; }
+
208  // TODO: no limit on the number of blobs
+
209  virtual inline int ExactNumBottomBlobs() const { return 2; }
+
210  virtual inline int ExactNumTopBlobs() const { return 0; }
+
211 
+
212  inline std::string file_name() const { return file_name_; }
+
213 
+
214  protected:
+
215  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
216  const vector<Blob<Dtype>*>& top);
+
217  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
218  const vector<Blob<Dtype>*>& top);
+
219  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
220  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
221  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
222  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
223  virtual void SaveBlobs();
+
224 
+
225  bool file_opened_;
+
226  std::string file_name_;
+
227  hid_t file_id_;
+
228  Blob<Dtype> data_blob_;
+
229  Blob<Dtype> label_blob_;
+
230 };
+
231 
+
237 template <typename Dtype>
+
238 class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
+
239  public:
+
240  explicit ImageDataLayer(const LayerParameter& param)
+
241  : BasePrefetchingDataLayer<Dtype>(param) {}
+
242  virtual ~ImageDataLayer();
+
243  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
244  const vector<Blob<Dtype>*>& top);
+
245 
+
246  virtual inline const char* type() const { return "ImageData"; }
+
247  virtual inline int ExactNumBottomBlobs() const { return 0; }
+
248  virtual inline int ExactNumTopBlobs() const { return 2; }
+
249 
+
250  protected:
+
251  shared_ptr<Caffe::RNG> prefetch_rng_;
+
252  virtual void ShuffleImages();
+
253  virtual void load_batch(Batch<Dtype>* batch);
+
254 
+
255  vector<std::pair<std::string, int> > lines_;
+
256  int lines_id_;
+
257 };
+
258 
+
264 template <typename Dtype>
+
265 class MemoryDataLayer : public BaseDataLayer<Dtype> {
+
266  public:
+
267  explicit MemoryDataLayer(const LayerParameter& param)
+
268  : BaseDataLayer<Dtype>(param), has_new_data_(false) {}
+
269  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
270  const vector<Blob<Dtype>*>& top);
+
271 
+
272  virtual inline const char* type() const { return "MemoryData"; }
+
273  virtual inline int ExactNumBottomBlobs() const { return 0; }
+
274  virtual inline int ExactNumTopBlobs() const { return 2; }
+
275 
+
276  virtual void AddDatumVector(const vector<Datum>& datum_vector);
+
277 #ifdef USE_OPENCV
+
278  virtual void AddMatVector(const vector<cv::Mat>& mat_vector,
+
279  const vector<int>& labels);
+
280 #endif // USE_OPENCV
+
281 
+
282  // Reset should accept const pointers, but can't, because the memory
+
283  // will be given to Blob, which is mutable
+
284  void Reset(Dtype* data, Dtype* label, int n);
+
285  void set_batch_size(int new_size);
+
286 
+
287  int batch_size() { return batch_size_; }
+
288  int channels() { return channels_; }
+
289  int height() { return height_; }
+
290  int width() { return width_; }
+
291 
+
292  protected:
+
293  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
294  const vector<Blob<Dtype>*>& top);
+
295 
+
296  int batch_size_, channels_, height_, width_, size_;
+
297  Dtype* data_;
+
298  Dtype* labels_;
+
299  int n_;
+
300  size_t pos_;
+
301  Blob<Dtype> added_data_;
+
302  Blob<Dtype> added_label_;
+
303  bool has_new_data_;
+
304 };
+
305 
+
312 template <typename Dtype>
+
313 class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
+
314  public:
+
315  explicit WindowDataLayer(const LayerParameter& param)
+
316  : BasePrefetchingDataLayer<Dtype>(param) {}
+
317  virtual ~WindowDataLayer();
+
318  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
319  const vector<Blob<Dtype>*>& top);
+
320 
+
321  virtual inline const char* type() const { return "WindowData"; }
+
322  virtual inline int ExactNumBottomBlobs() const { return 0; }
+
323  virtual inline int ExactNumTopBlobs() const { return 2; }
+
324 
+
325  protected:
+
326  virtual unsigned int PrefetchRand();
+
327  virtual void load_batch(Batch<Dtype>* batch);
+
328 
+
329  shared_ptr<Caffe::RNG> prefetch_rng_;
+
330  vector<std::pair<std::string, vector<int> > > image_database_;
+
331  enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM };
+
332  vector<vector<float> > fg_windows_;
+
333  vector<vector<float> > bg_windows_;
+
334  Blob<Dtype> data_mean_;
+
335  vector<Dtype> mean_values_;
+
336  bool has_mean_file_;
+
337  bool has_mean_values_;
+
338  bool cache_images_;
+
339  vector<std::pair<std::string, Datum > > image_database_cache_;
+
340 };
+
341 
+
342 } // namespace caffe
+
343 
+
344 #endif // CAFFE_DATA_LAYERS_HPP_
+
caffe::DataLayer::ShareInParallel
virtual bool ShareInParallel() const
Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
Definition: data_layers.hpp:99
+
caffe::Batch
Definition: data_layers.hpp:56
+
caffe::BasePrefetchingDataLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::HDF5DataLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: data_layers.hpp:175
+
caffe::BasePrefetchingDataLayer
Definition: data_layers.hpp:62
+
caffe::HDF5DataLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: data_layers.hpp:165
+
caffe::HDF5DataLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: hdf5_data_layer.cpp:73
+
caffe::HDF5OutputLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe::DummyDataLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: data_layers.hpp:136
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers could be called...
Definition: blob.hpp:15
+
caffe::WindowDataLayer
Provides data to the Net from windows of images files, specified by a window data file...
Definition: data_layers.hpp:313
+
caffe::HDF5OutputLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: data_layers.hpp:210
+
caffe::HDF5OutputLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: hdf5_output_layer.cpp:15
+
caffe::DummyDataLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: data_layers.hpp:126
+
caffe::HDF5OutputLayer
Write blobs to disk as HDF5 files.
Definition: data_layers.hpp:194
+
caffe::HDF5DataLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the CPU device, compute the layer output.
Definition: hdf5_data_layer.cpp:129
+
caffe::BaseDataLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: base_data_layer.cpp:18
+
caffe::DummyDataLayer
Provides data to the Net generated by a Filler.
Definition: data_layers.hpp:117
+
caffe::DataLayer
Definition: data_layers.hpp:92
+
caffe::DataLayer::MinTopBlobs
virtual int MinTopBlobs() const
Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
Definition: data_layers.hpp:102
+
caffe::HDF5OutputLayer::type
virtual const char * type() const
Returns the layer type.
Definition: data_layers.hpp:207
+
caffe::MemoryDataLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the CPU device, compute the layer output.
Definition: memory_data_layer.cpp:110
+
caffe::HDF5OutputLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::DataLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: data_layers.hpp:101
+
caffe::BaseDataLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: data_layers.hpp:44
+
caffe::MemoryDataLayer::type
virtual const char * type() const
Returns the layer type.
Definition: data_layers.hpp:272
+
caffe::BaseDataLayer
Provides base for data layers that feed blobs to the Net.
Definition: data_layers.hpp:28
+
caffe::DummyDataLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: data_layers.hpp:130
+
caffe::HDF5OutputLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the CPU device, compute the layer output.
Definition: hdf5_output_layer.cpp:44
+
caffe::BasePrefetchingDataLayer::LayerSetUp
void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: base_data_layer.cpp:43
+
caffe::BaseDataLayer::ShareInParallel
virtual bool ShareInParallel() const
Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
Definition: data_layers.hpp:37
+
caffe::ImageDataLayer
Provides data to the Net from image files.
Definition: data_layers.hpp:238
+
caffe::WindowDataLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: data_layers.hpp:322
+
caffe::ImageDataLayer::type
virtual const char * type() const
Returns the layer type.
Definition: data_layers.hpp:246
+
caffe::HDF5OutputLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: data_layers.hpp:209
+
caffe::WindowDataLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: data_layers.hpp:323
+
caffe::MemoryDataLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: data_layers.hpp:273
+
caffe::DummyDataLayer::type
virtual const char * type() const
Returns the layer type.
Definition: data_layers.hpp:129
+
caffe::HDF5DataLayer
Provides data to the Net from HDF5 files.
Definition: data_layers.hpp:151
+
caffe::DummyDataLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: data_layers.hpp:138
+
caffe::HDF5DataLayer::type
virtual const char * type() const
Returns the layer type.
Definition: data_layers.hpp:164
+
caffe::HDF5DataLayer::MinTopBlobs
virtual int MinTopBlobs() const
Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
Definition: data_layers.hpp:166
+
caffe::HDF5DataLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::DummyDataLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: dummy_data_layer.cpp:10
+
caffe::DataLayer::MaxTopBlobs
virtual int MaxTopBlobs() const
Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required...
Definition: data_layers.hpp:103
+
caffe::HDF5OutputLayer::ShareInParallel
virtual bool ShareInParallel() const
Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
Definition: data_layers.hpp:202
+
caffe::MemoryDataLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: data_layers.hpp:274
+
caffe::DummyDataLayer::MinTopBlobs
virtual int MinTopBlobs() const
Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
Definition: data_layers.hpp:131
+
caffe::DataLayer::type
virtual const char * type() const
Returns the layer type.
Definition: data_layers.hpp:100
+
caffe::InternalThread
Definition: internal_thread.hpp:19
+
caffe::HDF5DataLayer::ShareInParallel
virtual bool ShareInParallel() const
Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
Definition: data_layers.hpp:159
+
caffe::HDF5OutputLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: hdf5_output_layer.cpp:65
+
caffe::BaseDataLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: data_layers.hpp:41
+
caffe::ImageDataLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: data_layers.hpp:248
+
caffe::BlockingQueue
Definition: blocking_queue.hpp:12
+
caffe::WindowDataLayer::type
virtual const char * type() const
Returns the layer type.
Definition: data_layers.hpp:321
+
caffe::DummyDataLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the CPU device, compute the layer output.
Definition: dummy_data_layer.cpp:102
+
caffe::HDF5DataLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: data_layers.hpp:161
+
caffe::DataReader
Reads data from a source to queues available to data layers. A single reading thread is created per s...
Definition: data_reader.hpp:23
+
caffe::BasePrefetchingDataLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the CPU device, compute the layer output.
Definition: base_data_layer.cpp:104
+
caffe::DummyDataLayer::ShareInParallel
virtual bool ShareInParallel() const
Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
Definition: data_layers.hpp:124
+
caffe::BaseDataLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: data_layers.hpp:46
+
caffe::HDF5DataLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: data_layers.hpp:173
+
caffe::ImageDataLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: data_layers.hpp:247
+
caffe::MemoryDataLayer
Provides data to the Net from memory.
Definition: data_layers.hpp:265
+
caffe::HDF5OutputLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: data_layers.hpp:204
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:25
+
+ +
+Generated on Mon Oct 19 2015 15:25:08 for Caffe by   +doxygen + 1.8.9.1 +
+ + diff --git a/doxygen/data__reader_8hpp_source.html b/doxygen/data__reader_8hpp_source.html new file mode 100644 index 00000000000..3f7708f5d00 --- /dev/null +++ b/doxygen/data__reader_8hpp_source.html @@ -0,0 +1,84 @@ + + + + + + + +Caffe: include/caffe/data_reader.hpp Source File + + + + + + + + + +
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Caffe +
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data_reader.hpp
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1 #ifndef CAFFE_DATA_READER_HPP_
2 #define CAFFE_DATA_READER_HPP_
3 
4 #include <map>
5 #include <string>
6 #include <vector>
7 
8 #include "caffe/common.hpp"
9 #include "caffe/internal_thread.hpp"
10 #include "caffe/util/blocking_queue.hpp"
11 #include "caffe/util/db.hpp"
12 
13 namespace caffe {
14 
23 class DataReader {
24  public:
25  explicit DataReader(const LayerParameter& param);
26  ~DataReader();
27 
28  inline BlockingQueue<Datum*>& free() const {
29  return queue_pair_->free_;
30  }
31  inline BlockingQueue<Datum*>& full() const {
32  return queue_pair_->full_;
33  }
34 
35  protected:
36  // Queue pairs are shared between a body and its readers
37  class QueuePair {
38  public:
39  explicit QueuePair(int size);
40  ~QueuePair();
41 
42  BlockingQueue<Datum*> free_;
43  BlockingQueue<Datum*> full_;
44 
45  DISABLE_COPY_AND_ASSIGN(QueuePair);
46  };
47 
48  // A single body is created per source
49  class Body : public InternalThread {
50  public:
51  explicit Body(const LayerParameter& param);
52  virtual ~Body();
53 
54  protected:
55  void InternalThreadEntry();
56  void read_one(db::Cursor* cursor, QueuePair* qp);
57 
58  const LayerParameter param_;
59  BlockingQueue<shared_ptr<QueuePair> > new_queue_pairs_;
60 
61  friend class DataReader;
62 
63  DISABLE_COPY_AND_ASSIGN(Body);
64  };
65 
66  // A source is uniquely identified by its layer name + path, in case
67  // the same database is read from two different locations in the net.
68  static inline string source_key(const LayerParameter& param) {
69  return param.name() + ":" + param.data_param().source();
70  }
71 
72  const shared_ptr<QueuePair> queue_pair_;
73  shared_ptr<Body> body_;
74 
75  static map<const string, boost::weak_ptr<DataReader::Body> > bodies_;
76 
77 DISABLE_COPY_AND_ASSIGN(DataReader);
78 };
79 
80 } // namespace caffe
81 
82 #endif // CAFFE_DATA_READER_HPP_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::DataReader::Body
Definition: data_reader.hpp:49
+
caffe::DataReader::QueuePair
Definition: data_reader.hpp:37
+
caffe::InternalThread
Definition: internal_thread.hpp:19
+
caffe::BlockingQueue
Definition: blocking_queue.hpp:10
+
caffe::DataReader
Reads data from a source to queues available to data layers. A single reading thread is created per s...
Definition: data_reader.hpp:23
+
caffe::db::Cursor
Definition: db.hpp:13
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/data__transformer_8hpp_source.html b/doxygen/data__transformer_8hpp_source.html new file mode 100644 index 00000000000..d4c44c5e820 --- /dev/null +++ b/doxygen/data__transformer_8hpp_source.html @@ -0,0 +1,84 @@ + + + + + + + +Caffe: include/caffe/data_transformer.hpp Source File + + + + + + + + + +
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data_transformer.hpp
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1 #ifndef CAFFE_DATA_TRANSFORMER_HPP
2 #define CAFFE_DATA_TRANSFORMER_HPP
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/common.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 namespace caffe {
11 
16 template <typename Dtype>
17 class DataTransformer {
18  public:
19  explicit DataTransformer(const TransformationParameter& param, Phase phase);
20  virtual ~DataTransformer() {}
21 
26  void InitRand();
27 
38  void Transform(const Datum& datum, Blob<Dtype>* transformed_blob);
39 
50  void Transform(const vector<Datum> & datum_vector,
51  Blob<Dtype>* transformed_blob);
52 
53 #ifdef USE_OPENCV
54 
64  void Transform(const vector<cv::Mat> & mat_vector,
65  Blob<Dtype>* transformed_blob);
66 
77  void Transform(const cv::Mat& cv_img, Blob<Dtype>* transformed_blob);
78 #endif // USE_OPENCV
79 
91  void Transform(Blob<Dtype>* input_blob, Blob<Dtype>* transformed_blob);
92 
100  vector<int> InferBlobShape(const Datum& datum);
109  vector<int> InferBlobShape(const vector<Datum> & datum_vector);
118 #ifdef USE_OPENCV
119  vector<int> InferBlobShape(const vector<cv::Mat> & mat_vector);
127  vector<int> InferBlobShape(const cv::Mat& cv_img);
128 #endif // USE_OPENCV
129 
130  protected:
139  virtual int Rand(int n);
140 
141  void Transform(const Datum& datum, Dtype* transformed_data);
142  // Tranformation parameters
143  TransformationParameter param_;
144 
145 
146  shared_ptr<Caffe::RNG> rng_;
147  Phase phase_;
148  Blob<Dtype> data_mean_;
149  vector<Dtype> mean_values_;
150 };
151 
152 } // namespace caffe
153 
154 #endif // CAFFE_DATA_TRANSFORMER_HPP_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::DataTransformer::InferBlobShape
vector< int > InferBlobShape(const Datum &datum)
Infers the shape of transformed_blob will have when the transformation is applied to the data...
Definition: data_transformer.cpp:442
+
caffe::DataTransformer
Applies common transformations to the input data, such as scaling, mirroring, substracting the image ...
Definition: data_transformer.hpp:17
+
caffe::DataTransformer::InitRand
void InitRand()
Initialize the Random number generations if needed by the transformation.
Definition: data_transformer.cpp:523
+
caffe::DataTransformer::Rand
virtual int Rand(int n)
Infers the shape of transformed_blob will have when the transformation is applied to the data...
Definition: data_transformer.cpp:535
+
caffe::DataTransformer::Transform
void Transform(const Datum &datum, Blob< Dtype > *transformed_blob)
Applies the transformation defined in the data layer&#39;s transform_param block to the data...
Definition: data_transformer.cpp:131
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/db_8hpp_source.html b/doxygen/db_8hpp_source.html new file mode 100644 index 00000000000..3b740057f87 --- /dev/null +++ b/doxygen/db_8hpp_source.html @@ -0,0 +1,81 @@ + + + + + + + +Caffe: include/caffe/util/db.hpp Source File + + + + + + + + + +
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Caffe +
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db.hpp
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1 #ifndef CAFFE_UTIL_DB_HPP
2 #define CAFFE_UTIL_DB_HPP
3 
4 #include <string>
5 
6 #include "caffe/common.hpp"
7 #include "caffe/proto/caffe.pb.h"
8 
9 namespace caffe { namespace db {
10 
11 enum Mode { READ, WRITE, NEW };
12 
13 class Cursor {
14  public:
15  Cursor() { }
16  virtual ~Cursor() { }
17  virtual void SeekToFirst() = 0;
18  virtual void Next() = 0;
19  virtual string key() = 0;
20  virtual string value() = 0;
21  virtual bool valid() = 0;
22 
23  DISABLE_COPY_AND_ASSIGN(Cursor);
24 };
25 
26 class Transaction {
27  public:
28  Transaction() { }
29  virtual ~Transaction() { }
30  virtual void Put(const string& key, const string& value) = 0;
31  virtual void Commit() = 0;
32 
33  DISABLE_COPY_AND_ASSIGN(Transaction);
34 };
35 
36 class DB {
37  public:
38  DB() { }
39  virtual ~DB() { }
40  virtual void Open(const string& source, Mode mode) = 0;
41  virtual void Close() = 0;
42  virtual Cursor* NewCursor() = 0;
43  virtual Transaction* NewTransaction() = 0;
44 
45  DISABLE_COPY_AND_ASSIGN(DB);
46 };
47 
48 DB* GetDB(DataParameter::DB backend);
49 DB* GetDB(const string& backend);
50 
51 } // namespace db
52 } // namespace caffe
53 
54 #endif // CAFFE_UTIL_DB_HPP
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::db::Transaction
Definition: db.hpp:26
+
caffe::db::DB
Definition: db.hpp:36
+
caffe::db::Cursor
Definition: db.hpp:13
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/db__leveldb_8hpp_source.html b/doxygen/db__leveldb_8hpp_source.html new file mode 100644 index 00000000000..188ad073382 --- /dev/null +++ b/doxygen/db__leveldb_8hpp_source.html @@ -0,0 +1,78 @@ + + + + + + + +Caffe: include/caffe/util/db_leveldb.hpp Source File + + + + + + + + + +
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Caffe +
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db_leveldb.hpp
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1 #ifdef USE_LEVELDB
2 #ifndef CAFFE_UTIL_DB_LEVELDB_HPP
3 #define CAFFE_UTIL_DB_LEVELDB_HPP
4 
5 #include <string>
6 
7 #include "leveldb/db.h"
8 #include "leveldb/write_batch.h"
9 
10 #include "caffe/util/db.hpp"
11 
12 namespace caffe { namespace db {
13 
14 class LevelDBCursor : public Cursor {
15  public:
16  explicit LevelDBCursor(leveldb::Iterator* iter)
17  : iter_(iter) { SeekToFirst(); }
18  ~LevelDBCursor() { delete iter_; }
19  virtual void SeekToFirst() { iter_->SeekToFirst(); }
20  virtual void Next() { iter_->Next(); }
21  virtual string key() { return iter_->key().ToString(); }
22  virtual string value() { return iter_->value().ToString(); }
23  virtual bool valid() { return iter_->Valid(); }
24 
25  private:
26  leveldb::Iterator* iter_;
27 };
28 
29 class LevelDBTransaction : public Transaction {
30  public:
31  explicit LevelDBTransaction(leveldb::DB* db) : db_(db) { CHECK_NOTNULL(db_); }
32  virtual void Put(const string& key, const string& value) {
33  batch_.Put(key, value);
34  }
35  virtual void Commit() {
36  leveldb::Status status = db_->Write(leveldb::WriteOptions(), &batch_);
37  CHECK(status.ok()) << "Failed to write batch to leveldb "
38  << std::endl << status.ToString();
39  }
40 
41  private:
42  leveldb::DB* db_;
43  leveldb::WriteBatch batch_;
44 
45  DISABLE_COPY_AND_ASSIGN(LevelDBTransaction);
46 };
47 
48 class LevelDB : public DB {
49  public:
50  LevelDB() : db_(NULL) { }
51  virtual ~LevelDB() { Close(); }
52  virtual void Open(const string& source, Mode mode);
53  virtual void Close() {
54  if (db_ != NULL) {
55  delete db_;
56  db_ = NULL;
57  }
58  }
59  virtual LevelDBCursor* NewCursor() {
60  return new LevelDBCursor(db_->NewIterator(leveldb::ReadOptions()));
61  }
62  virtual LevelDBTransaction* NewTransaction() {
63  return new LevelDBTransaction(db_);
64  }
65 
66  private:
67  leveldb::DB* db_;
68 };
69 
70 
71 } // namespace db
72 } // namespace caffe
73 
74 #endif // CAFFE_UTIL_DB_LEVELDB_HPP
75 #endif // USE_LEVELDB
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/db__lmdb_8hpp_source.html b/doxygen/db__lmdb_8hpp_source.html new file mode 100644 index 00000000000..588d4dec474 --- /dev/null +++ b/doxygen/db__lmdb_8hpp_source.html @@ -0,0 +1,78 @@ + + + + + + + +Caffe: include/caffe/util/db_lmdb.hpp Source File + + + + + + + + + +
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Caffe +
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db_lmdb.hpp
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+
+
1 #ifdef USE_LMDB
2 #ifndef CAFFE_UTIL_DB_LMDB_HPP
3 #define CAFFE_UTIL_DB_LMDB_HPP
4 
5 #include <string>
6 #include <vector>
7 
8 #include "lmdb.h"
9 
10 #include "caffe/util/db.hpp"
11 
12 namespace caffe { namespace db {
13 
14 inline void MDB_CHECK(int mdb_status) {
15  CHECK_EQ(mdb_status, MDB_SUCCESS) << mdb_strerror(mdb_status);
16 }
17 
18 class LMDBCursor : public Cursor {
19  public:
20  explicit LMDBCursor(MDB_txn* mdb_txn, MDB_cursor* mdb_cursor)
21  : mdb_txn_(mdb_txn), mdb_cursor_(mdb_cursor), valid_(false) {
22  SeekToFirst();
23  }
24  virtual ~LMDBCursor() {
25  mdb_cursor_close(mdb_cursor_);
26  mdb_txn_abort(mdb_txn_);
27  }
28  virtual void SeekToFirst() { Seek(MDB_FIRST); }
29  virtual void Next() { Seek(MDB_NEXT); }
30  virtual string key() {
31  return string(static_cast<const char*>(mdb_key_.mv_data), mdb_key_.mv_size);
32  }
33  virtual string value() {
34  return string(static_cast<const char*>(mdb_value_.mv_data),
35  mdb_value_.mv_size);
36  }
37  virtual bool valid() { return valid_; }
38 
39  private:
40  void Seek(MDB_cursor_op op) {
41  int mdb_status = mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, op);
42  if (mdb_status == MDB_NOTFOUND) {
43  valid_ = false;
44  } else {
45  MDB_CHECK(mdb_status);
46  valid_ = true;
47  }
48  }
49 
50  MDB_txn* mdb_txn_;
51  MDB_cursor* mdb_cursor_;
52  MDB_val mdb_key_, mdb_value_;
53  bool valid_;
54 };
55 
56 class LMDBTransaction : public Transaction {
57  public:
58  explicit LMDBTransaction(MDB_env* mdb_env)
59  : mdb_env_(mdb_env) { }
60  virtual void Put(const string& key, const string& value);
61  virtual void Commit();
62 
63  private:
64  MDB_env* mdb_env_;
65  vector<string> keys, values;
66 
67  void DoubleMapSize();
68 
69  DISABLE_COPY_AND_ASSIGN(LMDBTransaction);
70 };
71 
72 class LMDB : public DB {
73  public:
74  LMDB() : mdb_env_(NULL) { }
75  virtual ~LMDB() { Close(); }
76  virtual void Open(const string& source, Mode mode);
77  virtual void Close() {
78  if (mdb_env_ != NULL) {
79  mdb_dbi_close(mdb_env_, mdb_dbi_);
80  mdb_env_close(mdb_env_);
81  mdb_env_ = NULL;
82  }
83  }
84  virtual LMDBCursor* NewCursor();
85  virtual LMDBTransaction* NewTransaction();
86 
87  private:
88  MDB_env* mdb_env_;
89  MDB_dbi mdb_dbi_;
90 };
91 
92 } // namespace db
93 } // namespace caffe
94 
95 #endif // CAFFE_UTIL_DB_LMDB_HPP
96 #endif // USE_LMDB
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/deconv__layer_8hpp_source.html b/doxygen/deconv__layer_8hpp_source.html new file mode 100644 index 00000000000..6286bd2f33f --- /dev/null +++ b/doxygen/deconv__layer_8hpp_source.html @@ -0,0 +1,86 @@ + + + + + + + +Caffe: include/caffe/layers/deconv_layer.hpp Source File + + + + + + + + + +
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Caffe +
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deconv_layer.hpp
+
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+
1 #ifndef CAFFE_DECONV_LAYER_HPP_
2 #define CAFFE_DECONV_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/base_conv_layer.hpp"
11 
12 namespace caffe {
13 
28 template <typename Dtype>
29 class DeconvolutionLayer : public BaseConvolutionLayer<Dtype> {
30  public:
31  explicit DeconvolutionLayer(const LayerParameter& param)
32  : BaseConvolutionLayer<Dtype>(param) {}
33 
34  virtual inline const char* type() const { return "Deconvolution"; }
35 
36  protected:
37  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
38  const vector<Blob<Dtype>*>& top);
39  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
40  const vector<Blob<Dtype>*>& top);
41  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
42  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
43  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
44  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
45  virtual inline bool reverse_dimensions() { return true; }
46  virtual void compute_output_shape();
47 };
48 
49 } // namespace caffe
50 
51 #endif // CAFFE_DECONV_LAYER_HPP_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::DeconvolutionLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the CPU device, compute the layer output.
Definition: deconv_layer.cpp:25
+
caffe::DeconvolutionLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::BaseConvolutionLayer
Abstract base class that factors out the BLAS code common to ConvolutionLayer and DeconvolutionLayer...
Definition: base_conv_layer.hpp:18
+
caffe::DeconvolutionLayer::type
virtual const char * type() const
Returns the layer type.
Definition: deconv_layer.hpp:34
+
caffe::DeconvolutionLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::DeconvolutionLayer
Convolve the input with a bank of learned filters, and (optionally) add biases, treating filters and ...
Definition: deconv_layer.hpp:29
+
caffe::DeconvolutionLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: deconv_layer.cpp:43
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
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device_alternate.hpp
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1 #ifndef CAFFE_UTIL_DEVICE_ALTERNATE_H_
2 #define CAFFE_UTIL_DEVICE_ALTERNATE_H_
3 
4 #ifdef CPU_ONLY // CPU-only Caffe.
5 
6 #include <vector>
7 
8 // Stub out GPU calls as unavailable.
9 
10 #define NO_GPU LOG(FATAL) << "Cannot use GPU in CPU-only Caffe: check mode."
11 
12 #define STUB_GPU(classname) \
13 template <typename Dtype> \
14 void classname<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom, \
15  const vector<Blob<Dtype>*>& top) { NO_GPU; } \
16 template <typename Dtype> \
17 void classname<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top, \
18  const vector<bool>& propagate_down, \
19  const vector<Blob<Dtype>*>& bottom) { NO_GPU; } \
20 
21 #define STUB_GPU_FORWARD(classname, funcname) \
22 template <typename Dtype> \
23 void classname<Dtype>::funcname##_##gpu(const vector<Blob<Dtype>*>& bottom, \
24  const vector<Blob<Dtype>*>& top) { NO_GPU; } \
25 
26 #define STUB_GPU_BACKWARD(classname, funcname) \
27 template <typename Dtype> \
28 void classname<Dtype>::funcname##_##gpu(const vector<Blob<Dtype>*>& top, \
29  const vector<bool>& propagate_down, \
30  const vector<Blob<Dtype>*>& bottom) { NO_GPU; } \
31 
32 #else // Normal GPU + CPU Caffe.
33 
34 #include <cublas_v2.h>
35 #include <cuda.h>
36 #include <cuda_runtime.h>
37 #include <curand.h>
38 #include <driver_types.h> // cuda driver types
39 #ifdef USE_CUDNN // cuDNN acceleration library.
40 #include "caffe/util/cudnn.hpp"
41 #endif
42 
43 //
44 // CUDA macros
45 //
46 
47 // CUDA: various checks for different function calls.
48 #define CUDA_CHECK(condition) \
49  /* Code block avoids redefinition of cudaError_t error */ \
50  do { \
51  cudaError_t error = condition; \
52  CHECK_EQ(error, cudaSuccess) << " " << cudaGetErrorString(error); \
53  } while (0)
54 
55 #define CUBLAS_CHECK(condition) \
56  do { \
57  cublasStatus_t status = condition; \
58  CHECK_EQ(status, CUBLAS_STATUS_SUCCESS) << " " \
59  << caffe::cublasGetErrorString(status); \
60  } while (0)
61 
62 #define CURAND_CHECK(condition) \
63  do { \
64  curandStatus_t status = condition; \
65  CHECK_EQ(status, CURAND_STATUS_SUCCESS) << " " \
66  << caffe::curandGetErrorString(status); \
67  } while (0)
68 
69 // CUDA: grid stride looping
70 #define CUDA_KERNEL_LOOP(i, n) \
71  for (int i = blockIdx.x * blockDim.x + threadIdx.x; \
72  i < (n); \
73  i += blockDim.x * gridDim.x)
74 
75 // CUDA: check for error after kernel execution and exit loudly if there is one.
76 #define CUDA_POST_KERNEL_CHECK CUDA_CHECK(cudaPeekAtLastError())
77 
78 namespace caffe {
79 
80 // CUDA: library error reporting.
81 const char* cublasGetErrorString(cublasStatus_t error);
82 const char* curandGetErrorString(curandStatus_t error);
83 
84 // CUDA: use 512 threads per block
85 const int CAFFE_CUDA_NUM_THREADS = 512;
86 
87 // CUDA: number of blocks for threads.
88 inline int CAFFE_GET_BLOCKS(const int N) {
89  return (N + CAFFE_CUDA_NUM_THREADS - 1) / CAFFE_CUDA_NUM_THREADS;
90 }
91 
92 } // namespace caffe
93 
94 #endif // CPU_ONLY
95 
96 #endif // CAFFE_UTIL_DEVICE_ALTERNATE_H_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
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+ border-left: 2px solid #9CAFD4; + margin: 0 24px 0 4px; + padding: 0 12px 0 16px; +} + +/* @end */ + +/* +.search { + color: #003399; + font-weight: bold; +} + +form.search { + margin-bottom: 0px; + margin-top: 0px; +} + +input.search { + font-size: 75%; + color: #000080; + font-weight: normal; + background-color: #e8eef2; +} +*/ + +td.tiny { + font-size: 75%; +} + +.dirtab { + padding: 4px; + border-collapse: collapse; + border: 1px solid #A3B4D7; +} + +th.dirtab { + background: #EBEFF6; + font-weight: bold; +} + +hr { + height: 0px; + border: none; + border-top: 1px solid #4A6AAA; +} + +hr.footer { + height: 1px; +} + +/* @group Member Descriptions */ + +table.memberdecls { + border-spacing: 0px; + padding: 0px; +} + +.memberdecls td, .fieldtable tr { + -webkit-transition-property: background-color, box-shadow; + -webkit-transition-duration: 0.5s; + -moz-transition-property: background-color, box-shadow; + -moz-transition-duration: 0.5s; + -ms-transition-property: background-color, box-shadow; + -ms-transition-duration: 0.5s; + -o-transition-property: background-color, box-shadow; + -o-transition-duration: 0.5s; + transition-property: background-color, box-shadow; + transition-duration: 0.5s; +} + +.memberdecls td.glow, .fieldtable tr.glow { + background-color: cyan; + box-shadow: 0 0 15px cyan; +} + +.mdescLeft, .mdescRight, +.memItemLeft, .memItemRight, +.memTemplItemLeft, .memTemplItemRight, .memTemplParams { + background-color: #F9FAFC; + border: none; + margin: 4px; + padding: 1px 0 0 8px; +} + +.mdescLeft, .mdescRight { + padding: 0px 8px 4px 8px; + color: #555; +} + +.memSeparator { + border-bottom: 1px solid #DEE4F0; + line-height: 1px; + margin: 0px; + padding: 0px; +} + +.memItemLeft, .memTemplItemLeft { + white-space: nowrap; +} + +.memItemRight { + width: 100%; +} + +.memTemplParams { + color: #4665A2; + white-space: nowrap; + font-size: 80%; +} + +/* @end */ + +/* @group Member Details */ + +/* Styles for detailed member documentation */ + +.memtitle { + padding: 8px; + border-top: 1px solid #A8B8D9; + border-left: 1px solid #A8B8D9; + border-right: 1px solid #A8B8D9; + border-top-right-radius: 4px; + border-top-left-radius: 4px; + margin-bottom: -1px; + background-image: url('nav_f.png'); + background-repeat: repeat-x; + background-color: #E2E8F2; + line-height: 1.25; + font-weight: 300; + float:left; +} + +.permalink +{ + font-size: 65%; + display: inline-block; + vertical-align: middle; +} + +.memtemplate { + font-size: 80%; + color: #4665A2; + font-weight: normal; + margin-left: 9px; +} + +.memnav { + background-color: #EBEFF6; + border: 1px solid #A3B4D7; + text-align: center; + margin: 2px; + margin-right: 15px; + padding: 2px; +} + +.mempage { + width: 100%; +} + +.memitem { + padding: 0; + margin-bottom: 10px; + margin-right: 5px; + -webkit-transition: box-shadow 0.5s linear; + -moz-transition: box-shadow 0.5s linear; + -ms-transition: box-shadow 0.5s linear; + -o-transition: box-shadow 0.5s linear; + transition: box-shadow 0.5s linear; + display: table !important; + width: 100%; +} + +.memitem.glow { + box-shadow: 0 0 15px cyan; +} + +.memname { + font-weight: 400; + margin-left: 6px; +} + +.memname td { + vertical-align: bottom; +} + +.memproto, dl.reflist dt { + border-top: 1px solid #A8B8D9; + border-left: 1px solid #A8B8D9; + border-right: 1px solid #A8B8D9; + padding: 6px 0px 6px 0px; + color: #253555; + font-weight: bold; + text-shadow: 0px 1px 1px rgba(255, 255, 255, 0.9); + background-color: #DFE5F1; + /* opera specific markup */ + box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); + border-top-right-radius: 4px; + /* firefox specific markup */ + -moz-box-shadow: rgba(0, 0, 0, 0.15) 5px 5px 5px; + -moz-border-radius-topright: 4px; + /* webkit specific markup */ + -webkit-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); + -webkit-border-top-right-radius: 4px; + +} + +.overload { + font-family: "courier new",courier,monospace; + font-size: 65%; +} + +.memdoc, dl.reflist dd { + border-bottom: 1px solid #A8B8D9; + border-left: 1px solid #A8B8D9; + border-right: 1px solid #A8B8D9; + padding: 6px 10px 2px 10px; + background-color: #FBFCFD; + border-top-width: 0; + background-image:url('nav_g.png'); + background-repeat:repeat-x; + background-color: #FFFFFF; + /* opera specific markup */ + border-bottom-left-radius: 4px; + border-bottom-right-radius: 4px; + box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); + /* firefox specific markup */ + -moz-border-radius-bottomleft: 4px; + -moz-border-radius-bottomright: 4px; + -moz-box-shadow: rgba(0, 0, 0, 0.15) 5px 5px 5px; + /* webkit specific markup */ + -webkit-border-bottom-left-radius: 4px; + -webkit-border-bottom-right-radius: 4px; + -webkit-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); +} + +dl.reflist dt { + padding: 5px; +} + +dl.reflist dd { + margin: 0px 0px 10px 0px; + padding: 5px; +} + +.paramkey { + text-align: right; +} + +.paramtype { + white-space: nowrap; +} + +.paramname { + color: #602020; + white-space: nowrap; +} +.paramname em { + font-style: normal; +} +.paramname code { + line-height: 14px; +} + +.params, .retval, .exception, .tparams { + margin-left: 0px; + padding-left: 0px; +} + +.params .paramname, .retval .paramname { + font-weight: bold; + vertical-align: top; +} + +.params .paramtype { + font-style: italic; + vertical-align: top; +} + +.params .paramdir { + font-family: "courier new",courier,monospace; + vertical-align: top; +} + +table.mlabels { + border-spacing: 0px; +} + +td.mlabels-left { + width: 100%; + padding: 0px; +} + +td.mlabels-right { + vertical-align: bottom; + padding: 0px; + white-space: nowrap; +} + +span.mlabels { + margin-left: 8px; +} + +span.mlabel { + background-color: #728DC1; + border-top:1px solid #5373B4; + border-left:1px solid #5373B4; + border-right:1px solid #C4CFE5; + border-bottom:1px solid #C4CFE5; + text-shadow: none; + color: white; + margin-right: 4px; + padding: 2px 3px; + border-radius: 3px; + font-size: 7pt; + white-space: nowrap; + vertical-align: middle; +} + + + +/* @end */ + +/* these are for tree view inside a (index) page */ + +div.directory { + margin: 10px 0px; + border-top: 1px solid #9CAFD4; + border-bottom: 1px solid #9CAFD4; + width: 100%; +} + +.directory table { + border-collapse:collapse; +} + +.directory td { + margin: 0px; + padding: 0px; + vertical-align: top; +} + +.directory td.entry { + white-space: nowrap; + padding-right: 6px; + padding-top: 3px; +} + +.directory td.entry a { + outline:none; +} + +.directory td.entry a img { + border: none; +} + +.directory td.desc { + width: 100%; + padding-left: 6px; + padding-right: 6px; + padding-top: 3px; + border-left: 1px solid rgba(0,0,0,0.05); +} + +.directory tr.even { + padding-left: 6px; + background-color: #F7F8FB; +} + +.directory img { + vertical-align: -30%; +} + +.directory .levels { + white-space: nowrap; + width: 100%; + text-align: right; + font-size: 9pt; +} + +.directory .levels span { + cursor: pointer; + padding-left: 2px; + padding-right: 2px; + color: #3D578C; +} + +.arrow { + color: #9CAFD4; + -webkit-user-select: none; + -khtml-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; + cursor: pointer; + font-size: 80%; + display: inline-block; + width: 16px; + height: 22px; +} + +.icon { + font-family: Arial, Helvetica; + font-weight: bold; + font-size: 12px; + height: 14px; + width: 16px; + display: inline-block; + background-color: #728DC1; + color: white; + text-align: center; + border-radius: 4px; + margin-left: 2px; + margin-right: 2px; +} + +.icona { + width: 24px; + height: 22px; + display: inline-block; +} + +.iconfopen { + width: 24px; + height: 18px; + margin-bottom: 4px; + background-image:url('folderopen.png'); + background-position: 0px -4px; + background-repeat: repeat-y; + vertical-align:top; + display: inline-block; +} + +.iconfclosed { + width: 24px; + height: 18px; + margin-bottom: 4px; + background-image:url('folderclosed.png'); + background-position: 0px -4px; + background-repeat: repeat-y; + vertical-align:top; + display: inline-block; +} + +.icondoc { + width: 24px; + height: 18px; + margin-bottom: 4px; + background-image:url('doc.png'); + background-position: 0px -4px; + background-repeat: repeat-y; + vertical-align:top; + display: inline-block; +} + +table.directory { + font: 400 14px Roboto,sans-serif; +} + +/* @end */ + +div.dynheader { + margin-top: 8px; + -webkit-touch-callout: none; + -webkit-user-select: none; + -khtml-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +address { + font-style: normal; + color: #2A3D61; +} + +table.doxtable caption { + caption-side: top; +} + +table.doxtable { + border-collapse:collapse; + margin-top: 4px; + margin-bottom: 4px; +} + +table.doxtable td, table.doxtable th { + border: 1px solid #2D4068; + padding: 3px 7px 2px; +} + +table.doxtable th { + background-color: #374F7F; + color: #FFFFFF; + font-size: 110%; + padding-bottom: 4px; + padding-top: 5px; +} + +table.fieldtable { + /*width: 100%;*/ + margin-bottom: 10px; + border: 1px solid #A8B8D9; + border-spacing: 0px; + -moz-border-radius: 4px; + -webkit-border-radius: 4px; + border-radius: 4px; + -moz-box-shadow: rgba(0, 0, 0, 0.15) 2px 2px 2px; + -webkit-box-shadow: 2px 2px 2px rgba(0, 0, 0, 0.15); + box-shadow: 2px 2px 2px rgba(0, 0, 0, 0.15); +} + +.fieldtable td, .fieldtable th { + padding: 3px 7px 2px; +} + +.fieldtable td.fieldtype, .fieldtable td.fieldname { + white-space: nowrap; + border-right: 1px solid #A8B8D9; + border-bottom: 1px solid #A8B8D9; + vertical-align: top; +} + +.fieldtable td.fieldname { + padding-top: 3px; +} + +.fieldtable td.fielddoc { + border-bottom: 1px solid #A8B8D9; + /*width: 100%;*/ +} + +.fieldtable td.fielddoc p:first-child { + margin-top: 0px; +} + +.fieldtable td.fielddoc p:last-child { + margin-bottom: 2px; +} + +.fieldtable tr:last-child td { + border-bottom: none; +} + +.fieldtable th { + background-image:url('nav_f.png'); + background-repeat:repeat-x; + background-color: #E2E8F2; + font-size: 90%; + color: #253555; + padding-bottom: 4px; + padding-top: 5px; + text-align:left; + font-weight: 400; + -moz-border-radius-topleft: 4px; + -moz-border-radius-topright: 4px; + -webkit-border-top-left-radius: 4px; + -webkit-border-top-right-radius: 4px; + border-top-left-radius: 4px; + border-top-right-radius: 4px; + border-bottom: 1px solid #A8B8D9; +} + + +.tabsearch { + top: 0px; + left: 10px; + height: 36px; + background-image: url('tab_b.png'); + z-index: 101; + overflow: hidden; + font-size: 13px; +} + +.navpath ul +{ + font-size: 11px; + background-image:url('tab_b.png'); + background-repeat:repeat-x; + background-position: 0 -5px; + height:30px; + line-height:30px; + color:#8AA0CC; + border:solid 1px #C2CDE4; + overflow:hidden; + margin:0px; + padding:0px; +} + +.navpath li +{ + list-style-type:none; + float:left; + padding-left:10px; + padding-right:15px; + background-image:url('bc_s.png'); + background-repeat:no-repeat; + background-position:right; + color:#364D7C; +} + +.navpath li.navelem a +{ + height:32px; + display:block; + text-decoration: none; + outline: none; + color: #283A5D; + font-family: 'Lucida Grande',Geneva,Helvetica,Arial,sans-serif; + text-shadow: 0px 1px 1px rgba(255, 255, 255, 0.9); + text-decoration: none; +} + +.navpath li.navelem a:hover +{ + color:#6884BD; +} + +.navpath li.footer +{ + list-style-type:none; + float:right; + padding-left:10px; + padding-right:15px; + background-image:none; + background-repeat:no-repeat; + background-position:right; + color:#364D7C; + font-size: 8pt; +} + + +div.summary +{ + float: right; + font-size: 8pt; + padding-right: 5px; + width: 50%; + text-align: right; +} + +div.summary a +{ + white-space: nowrap; +} + +table.classindex +{ + margin: 10px; + white-space: nowrap; + margin-left: 3%; + margin-right: 3%; + width: 94%; + border: 0; + border-spacing: 0; + padding: 0; +} + +div.ingroups +{ + font-size: 8pt; + width: 50%; + text-align: left; +} + +div.ingroups a +{ + white-space: nowrap; +} + +div.header +{ + background-image:url('nav_h.png'); + background-repeat:repeat-x; + background-color: #F9FAFC; + margin: 0px; + border-bottom: 1px solid #C4CFE5; +} + +div.headertitle +{ + padding: 5px 5px 5px 10px; +} + +dl +{ + padding: 0 0 0 10px; +} + +/* dl.note, dl.warning, dl.attention, dl.pre, dl.post, dl.invariant, dl.deprecated, dl.todo, dl.test, dl.bug */ +dl.section +{ + margin-left: 0px; + padding-left: 0px; +} + +dl.note +{ + margin-left:-7px; + padding-left: 3px; + border-left:4px solid; + border-color: #D0C000; +} + +dl.warning, dl.attention +{ + margin-left:-7px; + padding-left: 3px; + border-left:4px solid; + border-color: #FF0000; +} + +dl.pre, dl.post, dl.invariant +{ + margin-left:-7px; + padding-left: 3px; + border-left:4px solid; + border-color: #00D000; +} + +dl.deprecated +{ + margin-left:-7px; + padding-left: 3px; + border-left:4px solid; + border-color: #505050; +} + +dl.todo +{ + margin-left:-7px; + padding-left: 3px; + border-left:4px solid; + border-color: #00C0E0; +} + +dl.test +{ + margin-left:-7px; + padding-left: 3px; + border-left:4px solid; + border-color: #3030E0; +} + +dl.bug +{ + margin-left:-7px; + padding-left: 3px; + border-left:4px solid; + border-color: #C08050; +} + +dl.section dd { + margin-bottom: 6px; +} + + +#projectlogo +{ + text-align: center; + vertical-align: bottom; + border-collapse: separate; +} + +#projectlogo img +{ + border: 0px none; +} + +#projectalign +{ + vertical-align: middle; +} + +#projectname +{ + font: 300% Tahoma, Arial,sans-serif; + margin: 0px; + padding: 2px 0px; +} + +#projectbrief +{ + font: 120% Tahoma, Arial,sans-serif; + margin: 0px; + padding: 0px; +} + +#projectnumber +{ + font: 50% Tahoma, Arial,sans-serif; + margin: 0px; + padding: 0px; +} + +#titlearea +{ + padding: 0px; + margin: 0px; + width: 100%; + border-bottom: 1px solid #5373B4; +} + +.image +{ + text-align: center; +} + +.dotgraph +{ + text-align: center; +} + +.mscgraph +{ + text-align: center; +} + +.diagraph +{ + text-align: center; +} + +.caption +{ + font-weight: bold; +} + +div.zoom +{ + border: 1px solid #90A5CE; +} + +dl.citelist { + margin-bottom:50px; +} + +dl.citelist dt { + color:#334975; + float:left; + font-weight:bold; + margin-right:10px; + padding:5px; +} + +dl.citelist dd { + margin:2px 0; + padding:5px 0; +} + +div.toc { + padding: 14px 25px; + background-color: #F4F6FA; + border: 1px solid #D8DFEE; + border-radius: 7px 7px 7px 7px; + float: right; + height: auto; + margin: 0 8px 10px 10px; + width: 200px; +} + +div.toc li { + background: url("bdwn.png") no-repeat scroll 0 5px transparent; + font: 10px/1.2 Verdana,DejaVu Sans,Geneva,sans-serif; + margin-top: 5px; + padding-left: 10px; + padding-top: 2px; +} + +div.toc h3 { + font: bold 12px/1.2 Arial,FreeSans,sans-serif; + color: #4665A2; + border-bottom: 0 none; + margin: 0; +} + +div.toc ul { + list-style: none outside none; + border: medium none; + padding: 0px; +} + +div.toc li.level1 { + margin-left: 0px; +} + +div.toc li.level2 { + margin-left: 15px; +} + +div.toc li.level3 { + margin-left: 30px; +} + +div.toc li.level4 { + margin-left: 45px; +} + +.inherit_header { + font-weight: bold; + color: gray; + cursor: pointer; + -webkit-touch-callout: none; + -webkit-user-select: none; + -khtml-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +.inherit_header td { + padding: 6px 0px 2px 5px; +} + +.inherit { + display: none; +} + +tr.heading h2 { + margin-top: 12px; + margin-bottom: 4px; +} + +/* tooltip related style info */ + +.ttc { + position: absolute; + display: none; +} + +#powerTip { + cursor: default; + white-space: nowrap; + background-color: white; + border: 1px solid gray; + border-radius: 4px 4px 4px 4px; + box-shadow: 1px 1px 7px gray; + display: none; + font-size: smaller; + max-width: 80%; + opacity: 0.9; + padding: 1ex 1em 1em; + position: absolute; + z-index: 2147483647; +} + +#powerTip div.ttdoc { + color: grey; + font-style: italic; +} + +#powerTip div.ttname a { + font-weight: bold; +} + +#powerTip div.ttname { + font-weight: bold; +} + +#powerTip div.ttdeci { + color: #006318; +} + +#powerTip div { + margin: 0px; + padding: 0px; + font: 12px/16px Roboto,sans-serif; +} + +#powerTip:before, #powerTip:after { + content: ""; + position: absolute; + margin: 0px; +} + +#powerTip.n:after, #powerTip.n:before, +#powerTip.s:after, #powerTip.s:before, +#powerTip.w:after, #powerTip.w:before, +#powerTip.e:after, #powerTip.e:before, +#powerTip.ne:after, #powerTip.ne:before, +#powerTip.se:after, #powerTip.se:before, +#powerTip.nw:after, #powerTip.nw:before, +#powerTip.sw:after, #powerTip.sw:before { + border: solid transparent; + content: " "; + height: 0; + width: 0; + position: absolute; +} + +#powerTip.n:after, #powerTip.s:after, +#powerTip.w:after, #powerTip.e:after, +#powerTip.nw:after, #powerTip.ne:after, +#powerTip.sw:after, #powerTip.se:after { + border-color: rgba(255, 255, 255, 0); +} + +#powerTip.n:before, #powerTip.s:before, +#powerTip.w:before, #powerTip.e:before, +#powerTip.nw:before, #powerTip.ne:before, +#powerTip.sw:before, #powerTip.se:before { + border-color: rgba(128, 128, 128, 0); +} + +#powerTip.n:after, #powerTip.n:before, +#powerTip.ne:after, #powerTip.ne:before, +#powerTip.nw:after, #powerTip.nw:before { + top: 100%; +} + +#powerTip.n:after, #powerTip.ne:after, #powerTip.nw:after { + border-top-color: #ffffff; + border-width: 10px; + margin: 0px -10px; +} +#powerTip.n:before { + border-top-color: #808080; + border-width: 11px; + margin: 0px -11px; +} +#powerTip.n:after, #powerTip.n:before { + left: 50%; +} + +#powerTip.nw:after, #powerTip.nw:before { + right: 14px; +} + +#powerTip.ne:after, #powerTip.ne:before { + left: 14px; +} + +#powerTip.s:after, #powerTip.s:before, +#powerTip.se:after, #powerTip.se:before, +#powerTip.sw:after, #powerTip.sw:before { + bottom: 100%; +} + +#powerTip.s:after, #powerTip.se:after, #powerTip.sw:after { + border-bottom-color: #ffffff; + border-width: 10px; + margin: 0px -10px; +} + +#powerTip.s:before, #powerTip.se:before, #powerTip.sw:before { + border-bottom-color: #808080; + border-width: 11px; + margin: 0px -11px; +} + +#powerTip.s:after, #powerTip.s:before { + left: 50%; +} + +#powerTip.sw:after, #powerTip.sw:before { + right: 14px; +} + +#powerTip.se:after, #powerTip.se:before { + left: 14px; +} + +#powerTip.e:after, #powerTip.e:before { + left: 100%; +} +#powerTip.e:after { + border-left-color: #ffffff; 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+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
dropout_layer.hpp
+
+
+
1 #ifndef CAFFE_DROPOUT_LAYER_HPP_
2 #define CAFFE_DROPOUT_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/neuron_layer.hpp"
11 
12 namespace caffe {
13 
25 template <typename Dtype>
26 class DropoutLayer : public NeuronLayer<Dtype> {
27  public:
34  explicit DropoutLayer(const LayerParameter& param)
35  : NeuronLayer<Dtype>(param) {}
36  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
37  const vector<Blob<Dtype>*>& top);
38  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
39  const vector<Blob<Dtype>*>& top);
40 
41  virtual inline const char* type() const { return "Dropout"; }
42 
43  protected:
60  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
61  const vector<Blob<Dtype>*>& top);
62  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
63  const vector<Blob<Dtype>*>& top);
64  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
65  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
66  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
67  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
68 
70  Blob<unsigned int> rand_vec_;
72  Dtype threshold_;
74  Dtype scale_;
75  unsigned int uint_thres_;
76 };
77 
78 } // namespace caffe
79 
80 #endif // CAFFE_DROPOUT_LAYER_HPP_
caffe::DropoutLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::DropoutLayer::rand_vec_
Blob< unsigned int > rand_vec_
when divided by UINT_MAX, the randomly generated values
Definition: dropout_layer.hpp:70
+
caffe::DropoutLayer::scale_
Dtype scale_
the scale for undropped inputs at train time
Definition: dropout_layer.hpp:74
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::DropoutLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: dropout_layer.cpp:22
+
caffe::DropoutLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Definition: dropout_layer.cpp:31
+
caffe::DropoutLayer::DropoutLayer
DropoutLayer(const LayerParameter &param)
Definition: dropout_layer.hpp:34
+
caffe::DropoutLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::NeuronLayer
An interface for layers that take one blob as input ( ) and produce one equally-sized blob as output ...
Definition: neuron_layer.hpp:19
+
caffe::DropoutLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: dropout_layer.cpp:11
+
caffe::DropoutLayer::type
virtual const char * type() const
Returns the layer type.
Definition: dropout_layer.hpp:41
+
caffe::DropoutLayer
During training only, sets a random portion of to 0, adjusting the rest of the vector magnitude acco...
Definition: dropout_layer.hpp:26
+
caffe::DropoutLayer::threshold_
Dtype threshold_
the probability of dropping any input
Definition: dropout_layer.hpp:72
+
caffe::DropoutLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: dropout_layer.cpp:49
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/dummy__data__layer_8hpp_source.html b/doxygen/dummy__data__layer_8hpp_source.html new file mode 100644 index 00000000000..98d80cf59f4 --- /dev/null +++ b/doxygen/dummy__data__layer_8hpp_source.html @@ -0,0 +1,90 @@ + + + + + + + +Caffe: include/caffe/layers/dummy_data_layer.hpp Source File + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
dummy_data_layer.hpp
+
+
+
1 #ifndef CAFFE_DUMMY_DATA_LAYER_HPP_
2 #define CAFFE_DUMMY_DATA_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/filler.hpp"
8 #include "caffe/layer.hpp"
9 #include "caffe/proto/caffe.pb.h"
10 
11 namespace caffe {
12 
18 template <typename Dtype>
19 class DummyDataLayer : public Layer<Dtype> {
20  public:
21  explicit DummyDataLayer(const LayerParameter& param)
22  : Layer<Dtype>(param) {}
23  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
24  const vector<Blob<Dtype>*>& top);
25  // Data layers should be shared by multiple solvers in parallel
26  virtual inline bool ShareInParallel() const { return true; }
27  // Data layers have no bottoms, so reshaping is trivial.
28  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
29  const vector<Blob<Dtype>*>& top) {}
30 
31  virtual inline const char* type() const { return "DummyData"; }
32  virtual inline int ExactNumBottomBlobs() const { return 0; }
33  virtual inline int MinTopBlobs() const { return 1; }
34 
35  protected:
36  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
37  const vector<Blob<Dtype>*>& top);
38  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
39  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
40  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
41  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
42 
43  vector<shared_ptr<Filler<Dtype> > > fillers_;
44  vector<bool> refill_;
45 };
46 
47 } // namespace caffe
48 
49 #endif // CAFFE_DUMMY_DATA_LAYER_HPP_
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::DummyDataLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: dummy_data_layer.hpp:40
+
caffe::DummyDataLayer
Provides data to the Net generated by a Filler.
Definition: dummy_data_layer.hpp:19
+
caffe::DummyDataLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: dummy_data_layer.hpp:28
+
caffe::DummyDataLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: dummy_data_layer.cpp:9
+
caffe::DummyDataLayer::MinTopBlobs
virtual int MinTopBlobs() const
Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
Definition: dummy_data_layer.hpp:33
+
caffe::DummyDataLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: dummy_data_layer.hpp:32
+
caffe::DummyDataLayer::ShareInParallel
virtual bool ShareInParallel() const
Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
Definition: dummy_data_layer.hpp:26
+
caffe::DummyDataLayer::type
virtual const char * type() const
Returns the layer type.
Definition: dummy_data_layer.hpp:31
+
caffe::DummyDataLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: dummy_data_layer.hpp:38
+
caffe::DummyDataLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the CPU device, compute the layer output.
Definition: dummy_data_layer.cpp:101
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/dynsections.js b/doxygen/dynsections.js new file mode 100644 index 00000000000..85e18369095 --- /dev/null +++ b/doxygen/dynsections.js @@ -0,0 +1,97 @@ +function toggleVisibility(linkObj) +{ + var base = $(linkObj).attr('id'); + var summary = $('#'+base+'-summary'); + var content = $('#'+base+'-content'); + var trigger = $('#'+base+'-trigger'); + var src=$(trigger).attr('src'); + if (content.is(':visible')===true) { + content.hide(); + summary.show(); + $(linkObj).addClass('closed').removeClass('opened'); + $(trigger).attr('src',src.substring(0,src.length-8)+'closed.png'); + } else { + content.show(); + summary.hide(); + $(linkObj).removeClass('closed').addClass('opened'); + $(trigger).attr('src',src.substring(0,src.length-10)+'open.png'); + } + return false; +} + +function updateStripes() +{ + $('table.directory tr'). + removeClass('even').filter(':visible:even').addClass('even'); +} + +function toggleLevel(level) +{ + $('table.directory tr').each(function() { + var l = this.id.split('_').length-1; + var i = $('#img'+this.id.substring(3)); + var a = $('#arr'+this.id.substring(3)); + if (l + + + + + + +Caffe: include/caffe/layers/eltwise_layer.hpp Source File + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
eltwise_layer.hpp
+
+
+
1 #ifndef CAFFE_ELTWISE_LAYER_HPP_
2 #define CAFFE_ELTWISE_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 namespace caffe {
11 
18 template <typename Dtype>
19 class EltwiseLayer : public Layer<Dtype> {
20  public:
21  explicit EltwiseLayer(const LayerParameter& param)
22  : Layer<Dtype>(param) {}
23  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
24  const vector<Blob<Dtype>*>& top);
25  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
26  const vector<Blob<Dtype>*>& top);
27 
28  virtual inline const char* type() const { return "Eltwise"; }
29  virtual inline int MinBottomBlobs() const { return 2; }
30  virtual inline int ExactNumTopBlobs() const { return 1; }
31 
32  protected:
33  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
34  const vector<Blob<Dtype>*>& top);
35  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
36  const vector<Blob<Dtype>*>& top);
37  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
38  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
39  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
40  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
41 
42  EltwiseParameter_EltwiseOp op_;
43  vector<Dtype> coeffs_;
44  Blob<int> max_idx_;
45 
46  bool stable_prod_grad_;
47 };
48 
49 } // namespace caffe
50 
51 #endif // CAFFE_ELTWISE_LAYER_HPP_
caffe::EltwiseLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: eltwise_layer.cpp:100
+
caffe::EltwiseLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: eltwise_layer.hpp:30
+
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::EltwiseLayer::type
virtual const char * type() const
Returns the layer type.
Definition: eltwise_layer.hpp:28
+
caffe::EltwiseLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::EltwiseLayer
Compute elementwise operations, such as product and sum, along multiple input Blobs.
Definition: eltwise_layer.hpp:19
+
caffe::EltwiseLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: eltwise_layer.cpp:31
+
caffe::EltwiseLayer::MinBottomBlobs
virtual int MinBottomBlobs() const
Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
Definition: eltwise_layer.hpp:29
+
caffe::EltwiseLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the CPU device, compute the layer output.
Definition: eltwise_layer.cpp:45
+
caffe::EltwiseLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::EltwiseLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: eltwise_layer.cpp:10
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/elu__layer_8hpp_source.html b/doxygen/elu__layer_8hpp_source.html new file mode 100644 index 00000000000..7f68dd6aa30 --- /dev/null +++ b/doxygen/elu__layer_8hpp_source.html @@ -0,0 +1,87 @@ + + + + + + + +Caffe: include/caffe/layers/elu_layer.hpp Source File + + + + + + + + + +
+
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+
Caffe +
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elu_layer.hpp
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+
+
1 #ifndef CAFFE_ELU_LAYER_HPP_
2 #define CAFFE_ELU_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/neuron_layer.hpp"
11 
12 namespace caffe {
13 
23 template <typename Dtype>
24 class ELULayer : public NeuronLayer<Dtype> {
25  public:
32  explicit ELULayer(const LayerParameter& param)
33  : NeuronLayer<Dtype>(param) {}
34 
35  virtual inline const char* type() const { return "ELU"; }
36 
37  protected:
52  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
53  const vector<Blob<Dtype>*>& top);
54  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
55  const vector<Blob<Dtype>*>& top);
56 
77  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
78  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
79  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
80  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
81 };
82 
83 
84 } // namespace caffe
85 
86 #endif // CAFFE_ELU_LAYER_HPP_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::ELULayer::type
virtual const char * type() const
Returns the layer type.
Definition: elu_layer.hpp:35
+
caffe::ELULayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Definition: elu_layer.cpp:9
+
caffe::NeuronLayer
An interface for layers that take one blob as input ( ) and produce one equally-sized blob as output ...
Definition: neuron_layer.hpp:19
+
caffe::ELULayer::ELULayer
ELULayer(const LayerParameter &param)
Definition: elu_layer.hpp:32
+
caffe::ELULayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Computes the error gradient w.r.t. the ELU inputs.
Definition: elu_layer.cpp:22
+
caffe::ELULayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::ELULayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::ELULayer
Exponential Linear Unit non-linearity .
Definition: elu_layer.hpp:24
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/embed__layer_8hpp_source.html b/doxygen/embed__layer_8hpp_source.html new file mode 100644 index 00000000000..f64c218da1d --- /dev/null +++ b/doxygen/embed__layer_8hpp_source.html @@ -0,0 +1,90 @@ + + + + + + + +Caffe: include/caffe/layers/embed_layer.hpp Source File + + + + + + + + + +
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Caffe +
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embed_layer.hpp
+
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1 #ifndef CAFFE_EMBED_LAYER_HPP_
2 #define CAFFE_EMBED_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 namespace caffe {
11 
19 template <typename Dtype>
20 class EmbedLayer : public Layer<Dtype> {
21  public:
22  explicit EmbedLayer(const LayerParameter& param)
23  : Layer<Dtype>(param) {}
24  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
25  const vector<Blob<Dtype>*>& top);
26  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
27  const vector<Blob<Dtype>*>& top);
28 
29  virtual inline const char* type() const { return "Embed"; }
30  virtual inline int ExactNumBottomBlobs() const { return 1; }
31  virtual inline int ExactNumTopBlobs() const { return 1; }
32 
33  protected:
34  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
35  const vector<Blob<Dtype>*>& top);
36  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
37  const vector<Blob<Dtype>*>& top);
38  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
39  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
40  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
41  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
42 
43  int M_;
44  int K_;
45  int N_;
46  bool bias_term_;
47  Blob<Dtype> bias_multiplier_;
48 };
49 
50 } // namespace caffe
51 
52 #endif // CAFFE_EMBED_LAYER_HPP_
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::EmbedLayer::type
virtual const char * type() const
Returns the layer type.
Definition: embed_layer.hpp:29
+
caffe::EmbedLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: embed_layer.cpp:49
+
caffe::EmbedLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: embed_layer.hpp:30
+
caffe::EmbedLayer
A layer for learning "embeddings" of one-hot vector input. Equivalent to an InnerProductLayer with on...
Definition: embed_layer.hpp:20
+
caffe::EmbedLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::EmbedLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: embed_layer.hpp:31
+
caffe::EmbedLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the CPU device, compute the layer output.
Definition: embed_layer.cpp:65
+
caffe::EmbedLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::EmbedLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: embed_layer.cpp:86
+
caffe::EmbedLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: embed_layer.cpp:10
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/euclidean__loss__layer_8hpp_source.html b/doxygen/euclidean__loss__layer_8hpp_source.html new file mode 100644 index 00000000000..d05a64fe7d6 --- /dev/null +++ b/doxygen/euclidean__loss__layer_8hpp_source.html @@ -0,0 +1,88 @@ + + + + + + + +Caffe: include/caffe/layers/euclidean_loss_layer.hpp Source File + + + + + + + + + +
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Caffe +
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+
euclidean_loss_layer.hpp
+
+
+
1 #ifndef CAFFE_EUCLIDEAN_LOSS_LAYER_HPP_
2 #define CAFFE_EUCLIDEAN_LOSS_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/loss_layer.hpp"
11 
12 namespace caffe {
13 
40 template <typename Dtype>
41 class EuclideanLossLayer : public LossLayer<Dtype> {
42  public:
43  explicit EuclideanLossLayer(const LayerParameter& param)
44  : LossLayer<Dtype>(param), diff_() {}
45  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
46  const vector<Blob<Dtype>*>& top);
47 
48  virtual inline const char* type() const { return "EuclideanLoss"; }
53  virtual inline bool AllowForceBackward(const int bottom_index) const {
54  return true;
55  }
56 
57  protected:
59  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
60  const vector<Blob<Dtype>*>& top);
61  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
62  const vector<Blob<Dtype>*>& top);
63 
97  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
98  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
99  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
100  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
101 
102  Blob<Dtype> diff_;
103 };
104 
105 } // namespace caffe
106 
107 #endif // CAFFE_EUCLIDEAN_LOSS_LAYER_HPP_
caffe::EuclideanLossLayer::AllowForceBackward
virtual bool AllowForceBackward(const int bottom_index) const
Definition: euclidean_loss_layer.hpp:53
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::EuclideanLossLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: euclidean_loss_layer.cpp:9
+
caffe::EuclideanLossLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Computes the Euclidean error gradient w.r.t. the inputs.
Definition: euclidean_loss_layer.cpp:32
+
caffe::EuclideanLossLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Computes the Euclidean (L2) loss for real-valued regression tasks.
Definition: euclidean_loss_layer.cpp:18
+
caffe::EuclideanLossLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::EuclideanLossLayer::type
virtual const char * type() const
Returns the layer type.
Definition: euclidean_loss_layer.hpp:48
+
caffe::LossLayer
An interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth ...
Definition: loss_layer.hpp:23
+
caffe::EuclideanLossLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::EuclideanLossLayer
Computes the Euclidean (L2) loss for real-valued regression tasks.
Definition: euclidean_loss_layer.hpp:41
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/exp__layer_8hpp_source.html b/doxygen/exp__layer_8hpp_source.html new file mode 100644 index 00000000000..2c0da9c7b4c --- /dev/null +++ b/doxygen/exp__layer_8hpp_source.html @@ -0,0 +1,88 @@ + + + + + + + +Caffe: include/caffe/layers/exp_layer.hpp Source File + + + + + + + + + +
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Caffe +
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+ + +
+ +
+ + +
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+
+
exp_layer.hpp
+
+
+
1 #ifndef CAFFE_EXP_LAYER_HPP_
2 #define CAFFE_EXP_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/neuron_layer.hpp"
11 
12 namespace caffe {
13 
19 template <typename Dtype>
20 class ExpLayer : public NeuronLayer<Dtype> {
21  public:
30  explicit ExpLayer(const LayerParameter& param)
31  : NeuronLayer<Dtype>(param) {}
32  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
33  const vector<Blob<Dtype>*>& top);
34 
35  virtual inline const char* type() const { return "Exp"; }
36 
37  protected:
48  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
49  const vector<Blob<Dtype>*>& top);
50  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
51  const vector<Blob<Dtype>*>& top);
52 
70  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
71  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
72  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
73  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
74 
75  Dtype inner_scale_, outer_scale_;
76 };
77 
78 } // namespace caffe
79 
80 #endif // CAFFE_EXP_LAYER_HPP_
caffe::ExpLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: exp_layer.cpp:9
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::ExpLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Computes the error gradient w.r.t. the exp inputs.
Definition: exp_layer.cpp:48
+
caffe::ExpLayer::type
virtual const char * type() const
Returns the layer type.
Definition: exp_layer.hpp:35
+
caffe::ExpLayer
Computes , as specified by the scale , shift , and base .
Definition: exp_layer.hpp:20
+
caffe::ExpLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Definition: exp_layer.cpp:31
+
caffe::ExpLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::ExpLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::NeuronLayer
An interface for layers that take one blob as input ( ) and produce one equally-sized blob as output ...
Definition: neuron_layer.hpp:19
+
caffe::ExpLayer::ExpLayer
ExpLayer(const LayerParameter &param)
Definition: exp_layer.hpp:30
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
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Here is a list of all documented files with brief descriptions:
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 ▼ include
 ▼ caffe
 ► layers
 ► util
 blob.hpp
 caffe.hpp
 common.hpp
 data_reader.hpp
 data_transformer.hpp
 filler.hpp
 internal_thread.hpp
 layer.hpp
 layer_factory.hpp
 net.hpp
 parallel.hpp
 sgd_solvers.hpp
 solver.hpp
 solver_factory.hpp
 syncedmem.hpp
+
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+Generated on Mon Nov 21 2016 11:23:49 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/filler_8hpp_source.html b/doxygen/filler_8hpp_source.html new file mode 100644 index 00000000000..d983532a825 --- /dev/null +++ b/doxygen/filler_8hpp_source.html @@ -0,0 +1,93 @@ + + + + + + + +Caffe: include/caffe/filler.hpp Source File + + + + + + + + + +
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Caffe +
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filler.hpp
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1 // Fillers are random number generators that fills a blob using the specified
2 // algorithm. The expectation is that they are only going to be used during
3 // initialization time and will not involve any GPUs.
4 
5 #ifndef CAFFE_FILLER_HPP
6 #define CAFFE_FILLER_HPP
7 
8 #include <string>
9 
10 #include "caffe/blob.hpp"
11 #include "caffe/proto/caffe.pb.h"
12 #include "caffe/syncedmem.hpp"
13 #include "caffe/util/math_functions.hpp"
14 
15 namespace caffe {
16 
18 template <typename Dtype>
19 class Filler {
20  public:
21  explicit Filler(const FillerParameter& param) : filler_param_(param) {}
22  virtual ~Filler() {}
23  virtual void Fill(Blob<Dtype>* blob) = 0;
24  protected:
25  FillerParameter filler_param_;
26 }; // class Filler
27 
28 
30 template <typename Dtype>
31 class ConstantFiller : public Filler<Dtype> {
32  public:
33  explicit ConstantFiller(const FillerParameter& param)
34  : Filler<Dtype>(param) {}
35  virtual void Fill(Blob<Dtype>* blob) {
36  Dtype* data = blob->mutable_cpu_data();
37  const int count = blob->count();
38  const Dtype value = this->filler_param_.value();
39  CHECK(count);
40  for (int i = 0; i < count; ++i) {
41  data[i] = value;
42  }
43  CHECK_EQ(this->filler_param_.sparse(), -1)
44  << "Sparsity not supported by this Filler.";
45  }
46 };
47 
49 template <typename Dtype>
50 class UniformFiller : public Filler<Dtype> {
51  public:
52  explicit UniformFiller(const FillerParameter& param)
53  : Filler<Dtype>(param) {}
54  virtual void Fill(Blob<Dtype>* blob) {
55  CHECK(blob->count());
56  caffe_rng_uniform<Dtype>(blob->count(), Dtype(this->filler_param_.min()),
57  Dtype(this->filler_param_.max()), blob->mutable_cpu_data());
58  CHECK_EQ(this->filler_param_.sparse(), -1)
59  << "Sparsity not supported by this Filler.";
60  }
61 };
62 
64 template <typename Dtype>
65 class GaussianFiller : public Filler<Dtype> {
66  public:
67  explicit GaussianFiller(const FillerParameter& param)
68  : Filler<Dtype>(param) {}
69  virtual void Fill(Blob<Dtype>* blob) {
70  Dtype* data = blob->mutable_cpu_data();
71  CHECK(blob->count());
72  caffe_rng_gaussian<Dtype>(blob->count(), Dtype(this->filler_param_.mean()),
73  Dtype(this->filler_param_.std()), blob->mutable_cpu_data());
74  int sparse = this->filler_param_.sparse();
75  CHECK_GE(sparse, -1);
76  if (sparse >= 0) {
77  // Sparse initialization is implemented for "weight" blobs; i.e. matrices.
78  // These have num == channels == 1; width is number of inputs; height is
79  // number of outputs. The 'sparse' variable specifies the mean number
80  // of non-zero input weights for a given output.
81  CHECK_GE(blob->num_axes(), 1);
82  const int num_outputs = blob->shape(0);
83  Dtype non_zero_probability = Dtype(sparse) / Dtype(num_outputs);
84  rand_vec_.reset(new SyncedMemory(blob->count() * sizeof(int)));
85  int* mask = reinterpret_cast<int*>(rand_vec_->mutable_cpu_data());
86  caffe_rng_bernoulli(blob->count(), non_zero_probability, mask);
87  for (int i = 0; i < blob->count(); ++i) {
88  data[i] *= mask[i];
89  }
90  }
91  }
92 
93  protected:
94  shared_ptr<SyncedMemory> rand_vec_;
95 };
96 
100 template <typename Dtype>
101 class PositiveUnitballFiller : public Filler<Dtype> {
102  public:
103  explicit PositiveUnitballFiller(const FillerParameter& param)
104  : Filler<Dtype>(param) {}
105  virtual void Fill(Blob<Dtype>* blob) {
106  Dtype* data = blob->mutable_cpu_data();
107  DCHECK(blob->count());
108  caffe_rng_uniform<Dtype>(blob->count(), 0, 1, blob->mutable_cpu_data());
109  // We expect the filler to not be called very frequently, so we will
110  // just use a simple implementation
111  int dim = blob->count() / blob->num();
112  CHECK(dim);
113  for (int i = 0; i < blob->num(); ++i) {
114  Dtype sum = 0;
115  for (int j = 0; j < dim; ++j) {
116  sum += data[i * dim + j];
117  }
118  for (int j = 0; j < dim; ++j) {
119  data[i * dim + j] /= sum;
120  }
121  }
122  CHECK_EQ(this->filler_param_.sparse(), -1)
123  << "Sparsity not supported by this Filler.";
124  }
125 };
126 
143 template <typename Dtype>
144 class XavierFiller : public Filler<Dtype> {
145  public:
146  explicit XavierFiller(const FillerParameter& param)
147  : Filler<Dtype>(param) {}
148  virtual void Fill(Blob<Dtype>* blob) {
149  CHECK(blob->count());
150  int fan_in = blob->count() / blob->num();
151  int fan_out = blob->count() / blob->channels();
152  Dtype n = fan_in; // default to fan_in
153  if (this->filler_param_.variance_norm() ==
154  FillerParameter_VarianceNorm_AVERAGE) {
155  n = (fan_in + fan_out) / Dtype(2);
156  } else if (this->filler_param_.variance_norm() ==
157  FillerParameter_VarianceNorm_FAN_OUT) {
158  n = fan_out;
159  }
160  Dtype scale = sqrt(Dtype(3) / n);
161  caffe_rng_uniform<Dtype>(blob->count(), -scale, scale,
162  blob->mutable_cpu_data());
163  CHECK_EQ(this->filler_param_.sparse(), -1)
164  << "Sparsity not supported by this Filler.";
165  }
166 };
167 
185 template <typename Dtype>
186 class MSRAFiller : public Filler<Dtype> {
187  public:
188  explicit MSRAFiller(const FillerParameter& param)
189  : Filler<Dtype>(param) {}
190  virtual void Fill(Blob<Dtype>* blob) {
191  CHECK(blob->count());
192  int fan_in = blob->count() / blob->num();
193  int fan_out = blob->count() / blob->channels();
194  Dtype n = fan_in; // default to fan_in
195  if (this->filler_param_.variance_norm() ==
196  FillerParameter_VarianceNorm_AVERAGE) {
197  n = (fan_in + fan_out) / Dtype(2);
198  } else if (this->filler_param_.variance_norm() ==
199  FillerParameter_VarianceNorm_FAN_OUT) {
200  n = fan_out;
201  }
202  Dtype std = sqrt(Dtype(2) / n);
203  caffe_rng_gaussian<Dtype>(blob->count(), Dtype(0), std,
204  blob->mutable_cpu_data());
205  CHECK_EQ(this->filler_param_.sparse(), -1)
206  << "Sparsity not supported by this Filler.";
207  }
208 };
209 
243 template <typename Dtype>
244 class BilinearFiller : public Filler<Dtype> {
245  public:
246  explicit BilinearFiller(const FillerParameter& param)
247  : Filler<Dtype>(param) {}
248  virtual void Fill(Blob<Dtype>* blob) {
249  CHECK_EQ(blob->num_axes(), 4) << "Blob must be 4 dim.";
250  CHECK_EQ(blob->width(), blob->height()) << "Filter must be square";
251  Dtype* data = blob->mutable_cpu_data();
252  int f = ceil(blob->width() / 2.);
253  float c = (2 * f - 1 - f % 2) / (2. * f);
254  for (int i = 0; i < blob->count(); ++i) {
255  float x = i % blob->width();
256  float y = (i / blob->width()) % blob->height();
257  data[i] = (1 - fabs(x / f - c)) * (1 - fabs(y / f - c));
258  }
259  CHECK_EQ(this->filler_param_.sparse(), -1)
260  << "Sparsity not supported by this Filler.";
261  }
262 };
263 
270 template <typename Dtype>
271 Filler<Dtype>* GetFiller(const FillerParameter& param) {
272  const std::string& type = param.type();
273  if (type == "constant") {
274  return new ConstantFiller<Dtype>(param);
275  } else if (type == "gaussian") {
276  return new GaussianFiller<Dtype>(param);
277  } else if (type == "positive_unitball") {
278  return new PositiveUnitballFiller<Dtype>(param);
279  } else if (type == "uniform") {
280  return new UniformFiller<Dtype>(param);
281  } else if (type == "xavier") {
282  return new XavierFiller<Dtype>(param);
283  } else if (type == "msra") {
284  return new MSRAFiller<Dtype>(param);
285  } else if (type == "bilinear") {
286  return new BilinearFiller<Dtype>(param);
287  } else {
288  CHECK(false) << "Unknown filler name: " << param.type();
289  }
290  return (Filler<Dtype>*)(NULL);
291 }
292 
293 } // namespace caffe
294 
295 #endif // CAFFE_FILLER_HPP_
caffe::Blob::channels
int channels() const
Deprecated legacy shape accessor channels: use shape(1) instead.
Definition: blob.hpp:134
+
caffe::Filler
Fills a Blob with constant or randomly-generated data.
Definition: filler.hpp:19
+
caffe::PositiveUnitballFiller
Fills a Blob with values such that .
Definition: filler.hpp:101
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::Blob::height
int height() const
Deprecated legacy shape accessor height: use shape(2) instead.
Definition: blob.hpp:136
+
caffe::BilinearFiller
Fills a Blob with coefficients for bilinear interpolation.
Definition: filler.hpp:244
+
caffe::GetFiller
Filler< Dtype > * GetFiller(const FillerParameter &param)
Get a specific filler from the specification given in FillerParameter.
Definition: filler.hpp:271
+
caffe::XavierFiller
Fills a Blob with values where is set inversely proportional to number of incoming nodes...
Definition: filler.hpp:144
+
caffe::UniformFiller
Fills a Blob with uniformly distributed values .
Definition: filler.hpp:50
+
caffe::GaussianFiller
Fills a Blob with Gaussian-distributed values .
Definition: filler.hpp:65
+
caffe::SyncedMemory
Manages memory allocation and synchronization between the host (CPU) and device (GPU).
Definition: syncedmem.hpp:45
+
caffe::MSRAFiller
Fills a Blob with values where is set inversely proportional to number of incoming nodes...
Definition: filler.hpp:186
+
caffe::Blob::num
int num() const
Deprecated legacy shape accessor num: use shape(0) instead.
Definition: blob.hpp:132
+
caffe::Blob::width
int width() const
Deprecated legacy shape accessor width: use shape(3) instead.
Definition: blob.hpp:138
+
caffe::ConstantFiller
Fills a Blob with constant values .
Definition: filler.hpp:31
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/filter__layer_8hpp_source.html b/doxygen/filter__layer_8hpp_source.html new file mode 100644 index 00000000000..d5c5eda1b0c --- /dev/null +++ b/doxygen/filter__layer_8hpp_source.html @@ -0,0 +1,90 @@ + + + + + + + +Caffe: include/caffe/layers/filter_layer.hpp Source File + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
filter_layer.hpp
+
+
+
1 #ifndef CAFFE_FILTER_LAYER_HPP_
2 #define CAFFE_FILTER_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 namespace caffe {
11 
18 template <typename Dtype>
19 class FilterLayer : public Layer<Dtype> {
20  public:
21  explicit FilterLayer(const LayerParameter& param)
22  : Layer<Dtype>(param) {}
23  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
24  const vector<Blob<Dtype>*>& top);
25  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
26  const vector<Blob<Dtype>*>& top);
27 
28  virtual inline const char* type() const { return "Filter"; }
29  virtual inline int MinBottomBlobs() const { return 2; }
30  virtual inline int MinTopBlobs() const { return 1; }
31 
32  protected:
52  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
53  const vector<Blob<Dtype>*>& top);
54  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
55  const vector<Blob<Dtype>*>& top);
56 
66  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
67  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
68  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
69  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
70 
71  bool first_reshape_;
72  vector<int> indices_to_forward_;
73 };
74 
75 } // namespace caffe
76 
77 #endif // CAFFE_FILTER_LAYER_HPP_
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::FilterLayer::type
virtual const char * type() const
Returns the layer type.
Definition: filter_layer.hpp:28
+
caffe::FilterLayer::MinTopBlobs
virtual int MinTopBlobs() const
Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
Definition: filter_layer.hpp:30
+
caffe::FilterLayer::MinBottomBlobs
virtual int MinBottomBlobs() const
Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
Definition: filter_layer.hpp:29
+
caffe::FilterLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: filter_layer.cpp:16
+
caffe::FilterLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::FilterLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Definition: filter_layer.cpp:61
+
caffe::FilterLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: filter_layer.cpp:9
+
caffe::FilterLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Computes the error gradient w.r.t. the forwarded inputs.
Definition: filter_layer.cpp:79
+
caffe::FilterLayer
Takes two+ Blobs, interprets last Blob as a selector and filter remaining Blobs accordingly with sele...
Definition: filter_layer.hpp:19
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
caffe::FilterLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/flatten__layer_8hpp_source.html b/doxygen/flatten__layer_8hpp_source.html new file mode 100644 index 00000000000..bb318d1932a --- /dev/null +++ b/doxygen/flatten__layer_8hpp_source.html @@ -0,0 +1,87 @@ + + + + + + + +Caffe: include/caffe/layers/flatten_layer.hpp Source File + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
flatten_layer.hpp
+
+
+
1 #ifndef CAFFE_FLATTEN_LAYER_HPP_
2 #define CAFFE_FLATTEN_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 namespace caffe {
11 
22 template <typename Dtype>
23 class FlattenLayer : public Layer<Dtype> {
24  public:
25  explicit FlattenLayer(const LayerParameter& param)
26  : Layer<Dtype>(param) {}
27  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
28  const vector<Blob<Dtype>*>& top);
29 
30  virtual inline const char* type() const { return "Flatten"; }
31  virtual inline int ExactNumBottomBlobs() const { return 1; }
32  virtual inline int ExactNumTopBlobs() const { return 1; }
33 
34  protected:
43  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
44  const vector<Blob<Dtype>*>& top);
45 
55  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
56  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
57 };
58 
59 } // namespace caffe
60 
61 #endif // CAFFE_FLATTEN_LAYER_HPP_
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::FlattenLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Definition: flatten_layer.cpp:30
+
caffe::FlattenLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: flatten_layer.cpp:8
+
caffe::FlattenLayer
Reshapes the input Blob into flat vectors.
Definition: flatten_layer.hpp:23
+
caffe::FlattenLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: flatten_layer.hpp:31
+
caffe::FlattenLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: flatten_layer.hpp:32
+
caffe::FlattenLayer::type
virtual const char * type() const
Returns the layer type.
Definition: flatten_layer.hpp:30
+
caffe::FlattenLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Computes the error gradient w.r.t. the concatenate inputs.
Definition: flatten_layer.cpp:36
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
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+
+ + + + + + +
+
Caffe +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
format.hpp
+
+
+
1 #ifndef CAFFE_UTIL_FORMAT_H_
2 #define CAFFE_UTIL_FORMAT_H_
3 
4 #include <iomanip> // NOLINT(readability/streams)
5 #include <sstream> // NOLINT(readability/streams)
6 #include <string>
7 
8 namespace caffe {
9 
10 inline std::string format_int(int n, int numberOfLeadingZeros = 0 ) {
11  std::ostringstream s;
12  s << std::setw(numberOfLeadingZeros) << std::setfill('0') << n;
13  return s.str();
14 }
15 
16 }
17 
18 #endif // CAFFE_UTIL_FORMAT_H_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/formula.repository b/doxygen/formula.repository new file mode 100644 index 00000000000..9b558c46cde --- /dev/null +++ b/doxygen/formula.repository @@ -0,0 +1,180 @@ +\form#0:$ K $ +\form#1:$ (C \times H \times W) $ +\form#2:$ (N \times C \times H \times W) $ +\form#3:$ x $ +\form#4:$ (N \times 1 \times K \times 1) $ +\form#5:$ (N \times 2 \times K \times 1) $ +\form#6:$ y_n = \arg\max\limits_i x_{ni} $ +\form#7:$ K = 1 $ +\form#8:$ x_1 $ +\form#9:$ x_2 $ +\form#10:$ x_K $ +\form#11:$ (KN \times C \times H \times W) $ +\form#12:$ (N \times KC \times H \times W) $ +\form#13:$ y = [\begin{array}{cccc} x_1 & x_2 & ... & x_K \end{array}] $ +\form#14:$ \frac{\partial E}{\partial y} $ +\form#15:$ y $ +\form#16:$ \left[ \begin{array}{cccc} \frac{\partial E}{\partial x_1} & \frac{\partial E}{\partial x_2} & ... & \frac{\partial E}{\partial x_K} \end{array} \right] = \frac{\partial E}{\partial y} $ +\form#17:$ (N \times CHW \times 1 \times 1) $ +\form#18:$ x = 0 $ +\form#19:$ x\sim U(a, b) $ +\form#20:$ x = a $ +\form#21:$ x \in [0, 1] $ +\form#22:$ \forall i \sum_j x_{ij} = 1 $ +\form#23:$ x \sim U(-a, +a) $ +\form#24:$ a $ +\form#25:$ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d + \left(1-y\right) \max \left(margin-d, 0\right) $ +\form#26:$ d = \left| \left| a_n - b_n \right| \right|_2^2 $ +\form#27:$ (N \times C \times 1 \times 1) $ +\form#28:$ a \in [-\infty, +\infty]$ +\form#29:$ b \in [-\infty, +\infty]$ +\form#30:$ (N \times 1 \times 1 \times 1) $ +\form#31:$ s \in [0, 1]$ +\form#32:$ (1 \times 1 \times 1 \times 1) $ +\form#33:$ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ +\form#34:$ \hat{y} \in [-\infty, +\infty]$ +\form#35:$ y \in [-\infty, +\infty]$ +\form#36:$ E = \frac{1}{2n} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ +\form#37:$ t $ +\form#38:$ [-\infty, +\infty] $ +\form#39:$ K = CHW $ +\form#40:$ X^T W $ +\form#41:$ X \in \mathcal{R}^{D \times N} $ +\form#42:$ W \in \mathcal{R}^{D \times K} $ +\form#43:$ l $ +\form#44:$ l_n \in [0, 1, 2, ..., K - 1] $ +\form#45:$ E = \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p $ +\form#46:$ L^p $ +\form#47:$ p = 1 $ +\form#48:$ \delta\{\mathrm{condition}\} = \left\{ \begin{array}{lr} 1 & \mbox{if condition} \\ -1 & \mbox{otherwise} \end{array} \right. $ +\form#49:$ t \in \mathcal{R}^{N \times K} $ +\form#50:$k$ +\form#51:$ \hat{p} $ +\form#52:$ [0, 1] $ +\form#53:$ \hat{p}_n $ +\form#54:$ \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 $ +\form#55:$ (1 \times 1 \times K \times K) $ +\form#56:$ H $ +\form#57:$ H = I $ +\form#58:$ E = \frac{-1}{N} \sum\limits_{n=1}^N H_{l_n} \log(\hat{p}_n) = \frac{-1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^{K} H_{l_n,k} \log(\hat{p}_{n,k}) $ +\form#59:$ H_{l_n} $ +\form#60:$l_n$ +\form#61:$H$ +\form#62:$ E = \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n}) $ +\form#63:$ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $ +\form#64:$ x \in [-\infty, +\infty]$ +\form#65:$ \hat{p}_n = \sigma(x_n) \in [0, 1] $ +\form#66:$ \sigma(.) $ +\form#67:$ y \in [0, 1] $ +\form#68:$ \hat{p}_{nk} = \exp(x_{nk}) / \left[\sum_{k'} \exp(x_{nk'})\right] $ +\form#69:$ k $ +\form#70:$ k = 5 $ +\form#71:$ x_n $ +\form#72:$ \hat{l}_n $ +\form#73:$ \hat{l}_n = \arg\max\limits_k x_{nk} $ +\form#74:$ \frac{1}{N} \sum\limits_{n=1}^N \delta\{ \hat{l}_n = l_n \} $ +\form#75:$ \delta\{\mathrm{condition}\} = \left\{ \begin{array}{lr} 1 & \mbox{if condition} \\ 0 & \mbox{otherwise} \end{array} \right. $ +\form#76:$ \lambda $ +\form#77:$\ell_i$ +\form#78:$ E = \lambda_i \ell_i + \mbox{other loss terms}$ +\form#79:$ \frac{\partial E}{\partial \ell_i} = \lambda_i $ +\form#80:$a$ +\form#81:$b$ +\form#82:$\hat{y}$ +\form#83:$ \frac{\partial E}{\partial \hat{y}} = \frac{1}{n} \sum\limits_{n=1}^N (\hat{y}_n - y_n) $ +\form#84:$y$ +\form#85:$ \frac{\partial E}{\partial y} = \frac{1}{n} \sum\limits_{n=1}^N (y_n - \hat{y}_n) $ +\form#86:$t$ +\form#87:$ \frac{\partial E}{\partial t} $ +\form#88:$ \frac{\partial E}{\partial \hat{p}} $ +\form#89:$x$ +\form#90:$ \frac{\partial E}{\partial x} = \frac{1}{n} \sum\limits_{n=1}^N (\hat{p}_n - p_n) $ +\form#91:$ \frac{\partial E}{\partial x} $ +\form#92:$ y = |x| $ +\form#93:$ y = x + \log(1 + \exp(-x)) $ +\form#94:$ x > 0 $ +\form#95:$ y = \log(1 + \exp(x)) $ +\form#96:$ y = \left\{ \begin{array}{ll} x + \log(1 + \exp(-x)) & \mbox{if } x > 0 \\ \log(1 + \exp(x)) & \mbox{otherwise} \end{array} \right. $ +\form#97:$ y = \gamma ^ {\alpha x + \beta} $ +\form#98:$ \alpha $ +\form#99:$ \beta $ +\form#100:$ \gamma $ +\form#101:$ y = (\alpha x + \beta) ^ \gamma $ +\form#102:$ y = \max(0, x) $ +\form#103:$ y = (1 + \exp(-x))^{-1} $ +\form#104:$ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $ +\form#105:$ \frac{\partial E}{\partial x} = \mathrm{sign}(x) \frac{\partial E}{\partial y} $ +\form#106:$ p $ +\form#107:$ y_{\mbox{train}} = \left\{ \begin{array}{ll} \frac{x}{1 - p} & \mbox{if } u > p \\ 0 & \mbox{otherwise} \end{array} \right. $ +\form#108:$ u \sim U(0, 1)$ +\form#109:$ y_{\mbox{test}} = \mathbb{E}[y_{\mbox{train}}] = x $ +\form#110:$u\sim U(0,1)$ +\form#111:$ 1 / (1 - p) $ +\form#112:$ e \approx 2.718 $ +\form#113:$ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} y \alpha \log_e(gamma) $ +\form#114:$ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} \alpha \gamma (\alpha x + \beta) ^ {\gamma - 1} = \frac{\partial E}{\partial y} \frac{\alpha \gamma y}{\alpha x + \beta} $ +\form#115:$ \alpha \gamma $ +\form#116:$ \nu $ +\form#117:$ y = \max(0, x) + \nu \min(0, x) $ +\form#118:$ \frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} 0 & \mathrm{if} \; x \le 0 \\ \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0 \end{array} \right. $ +\form#119:$ \frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} \nu \frac{\partial E}{\partial y} & \mathrm{if} \; x \le 0 \\ \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0 \end{array} \right. $ +\form#120:$ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} y (1 - y) $ +\form#121:$ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} \left(1 - \left[\frac{\exp(2x) - 1}{exp(2x) + 1} \right]^2 \right) = \frac{\partial E}{\partial y} (1 - y^2) $ +\form#122:$ y = \left\{ \begin{array}{lr} 0 & \mathrm{if} \; x \le t \\ 1 & \mathrm{if} \; x > t \end{array} \right. $ +\form#123:$ \geq 1 $ +\form#124:$ x \sim N(0, \sigma^2) $ +\form#125:$ \sigma^2 $ +\form#126:$ y_i = \max(0, x_i) + a_i \min(0, x_i) $ +\form#127:$ (N \times C \times ...) $ +\form#128:$i$ +\form#129:$ \frac{\partial E}{\partial x_i} = \left\{ \begin{array}{lr} a_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\ \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i > 0 \end{array} \right. $ +\form#130:$ \frac{\partial E}{\partial a_i} = \left\{ \begin{array}{lr} \sum_{x_i} x_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\ 0 & \mathrm{if} \; x_i > 0 \end{array} \right. $ +\form#131:$ (S \times C \times H \times W) $ +\form#132:$ y = log_{\gamma}(\alpha x + \beta) $ +\form#133:$ (N \times K \times H \times W) $ +\form#134:$ (N \times ...) $ +\form#135:$ (M) $ +\form#136:$ (M \times ...) $ +\form#137:$ y = x_1[x_2] $ +\form#138:$ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ +\form#139:$ d = \left| \left| a_n - b_n \right| \right|_2 $ +\form#140:$ (N \times 1 \times K) $ +\form#141:$ (N \times 2 \times K) $ +\form#142:$ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ +\form#143:$ y = \left\{ \begin{array}{lr} x & \mathrm{if} \; x > 0 \\ \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 \end{array} \right. $ +\form#144:$ \frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} 1 & \mathrm{if} \; x > 0 \\ y + \alpha & \mathrm{if} \; x \le 0 \end{array} \right. $ +\form#145:$ i $ +\form#146:$ (d_0 \times ... \times d_i \times ... \times d_j \times ... \times d_n) $ +\form#147:$ (d_i \times ... \times d_j) $ +\form#148:$ z = x y $ +\form#149:$ (1 \times N \times D) $ +\form#150:$ c_{t-1} $ +\form#151:$ (1 \times N \times 4D) $ +\form#152:$ [i_t', f_t', o_t', g_t'] $ +\form#153:$ (1 \times N) $ +\form#154:$ \delta_t $ +\form#155:$ c_t $ +\form#156:$ h_t $ +\form#157:$ \frac{\partial E}{\partial c_t} $ +\form#158:$ \frac{\partial E}{\partial h_t} $ +\form#159:$ [ \frac{\partial E}{\partial i_t} \frac{\partial E}{\partial f_t} \frac{\partial E}{\partial o_t} \frac{\partial E}{\partial g_t} ] $ +\form#160:$ (1 \times 1 \times N) $ +\form#161:$ (T \times N \times ...) $ +\form#162:$ T $ +\form#163:$ N $ +\form#164:$ (N \times T \times ...) $ +\form#165:$ (T \times N) $ +\form#166:$ \delta $ +\form#167:$ \delta_{t,n} = 0 $ +\form#168:$ n $ +\form#169:$ h_{t-1} $ +\form#170:$ \delta_t = 0 $ +\form#171:$ \delta_{t,n} = 1 $ +\form#172:$ t-1 $ +\form#173:$ x_{static} $ +\form#174:$ x'_t = [x_t; x_{static}] $ +\form#175:$ (T \times N \times D) $ +\form#176:$ D $ +\form#177:$ x_t $ +\form#178:$ h_t := \tanh[ W_{hh} h_{t_1} + W_{xh} x_t + b_h ] $ +\form#179:$ o_t := \tanh[ W_{ho} h_t + b_o ] $ diff --git a/doxygen/functions.html b/doxygen/functions.html new file mode 100644 index 00000000000..93599772827 --- /dev/null +++ b/doxygen/functions.html @@ -0,0 +1,109 @@ + + + + + + + +Caffe: Class Members + + + + + + + + + +
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+ + diff --git a/doxygen/hdf5_8hpp_source.html b/doxygen/hdf5_8hpp_source.html new file mode 100644 index 00000000000..af57cd9ba7f --- /dev/null +++ b/doxygen/hdf5_8hpp_source.html @@ -0,0 +1,78 @@ + + + + + + + +Caffe: include/caffe/util/hdf5.hpp Source File + + + + + + + + + +
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Caffe +
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hdf5.hpp
+
+
+
1 #ifndef CAFFE_UTIL_HDF5_H_
2 #define CAFFE_UTIL_HDF5_H_
3 
4 #include <string>
5 
6 #include "hdf5.h"
7 #include "hdf5_hl.h"
8 
9 #include "caffe/blob.hpp"
10 
11 namespace caffe {
12 
13 template <typename Dtype>
14 void hdf5_load_nd_dataset_helper(
15  hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
16  Blob<Dtype>* blob);
17 
18 template <typename Dtype>
19 void hdf5_load_nd_dataset(
20  hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
21  Blob<Dtype>* blob);
22 
23 template <typename Dtype>
24 void hdf5_save_nd_dataset(
25  const hid_t file_id, const string& dataset_name, const Blob<Dtype>& blob,
26  bool write_diff = false);
27 
28 int hdf5_load_int(hid_t loc_id, const string& dataset_name);
29 void hdf5_save_int(hid_t loc_id, const string& dataset_name, int i);
30 string hdf5_load_string(hid_t loc_id, const string& dataset_name);
31 void hdf5_save_string(hid_t loc_id, const string& dataset_name,
32  const string& s);
33 
34 int hdf5_get_num_links(hid_t loc_id);
35 string hdf5_get_name_by_idx(hid_t loc_id, int idx);
36 
37 } // namespace caffe
38 
39 #endif // CAFFE_UTIL_HDF5_H_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/hdf5__data__layer_8hpp_source.html b/doxygen/hdf5__data__layer_8hpp_source.html new file mode 100644 index 00000000000..c90d37d3763 --- /dev/null +++ b/doxygen/hdf5__data__layer_8hpp_source.html @@ -0,0 +1,91 @@ + + + + + + + +Caffe: include/caffe/layers/hdf5_data_layer.hpp Source File + + + + + + + + + +
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Caffe +
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hdf5_data_layer.hpp
+
+
+
1 #ifndef CAFFE_HDF5_DATA_LAYER_HPP_
2 #define CAFFE_HDF5_DATA_LAYER_HPP_
3 
4 #include "hdf5.h"
5 
6 #include <string>
7 #include <vector>
8 
9 #include "caffe/blob.hpp"
10 #include "caffe/layer.hpp"
11 #include "caffe/proto/caffe.pb.h"
12 
13 #include "caffe/layers/base_data_layer.hpp"
14 
15 namespace caffe {
16 
22 template <typename Dtype>
23 class HDF5DataLayer : public Layer<Dtype> {
24  public:
25  explicit HDF5DataLayer(const LayerParameter& param)
26  : Layer<Dtype>(param) {}
27  virtual ~HDF5DataLayer();
28  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
29  const vector<Blob<Dtype>*>& top);
30  // Data layers should be shared by multiple solvers in parallel
31  virtual inline bool ShareInParallel() const { return true; }
32  // Data layers have no bottoms, so reshaping is trivial.
33  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
34  const vector<Blob<Dtype>*>& top) {}
35 
36  virtual inline const char* type() const { return "HDF5Data"; }
37  virtual inline int ExactNumBottomBlobs() const { return 0; }
38  virtual inline int MinTopBlobs() const { return 1; }
39 
40  protected:
41  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
42  const vector<Blob<Dtype>*>& top);
43  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
44  const vector<Blob<Dtype>*>& top);
45  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
46  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
47  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
48  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
49  virtual void LoadHDF5FileData(const char* filename);
50 
51  std::vector<std::string> hdf_filenames_;
52  unsigned int num_files_;
53  unsigned int current_file_;
54  hsize_t current_row_;
55  std::vector<shared_ptr<Blob<Dtype> > > hdf_blobs_;
56  std::vector<unsigned int> data_permutation_;
57  std::vector<unsigned int> file_permutation_;
58 };
59 
60 } // namespace caffe
61 
62 #endif // CAFFE_HDF5_DATA_LAYER_HPP_
caffe::HDF5DataLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::HDF5DataLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the CPU device, compute the layer output.
Definition: hdf5_data_layer.cpp:128
+
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::HDF5DataLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: hdf5_data_layer.hpp:47
+
caffe::HDF5DataLayer::ShareInParallel
virtual bool ShareInParallel() const
Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
Definition: hdf5_data_layer.hpp:31
+
caffe::HDF5DataLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: hdf5_data_layer.hpp:33
+
caffe::HDF5DataLayer
Provides data to the Net from HDF5 files.
Definition: hdf5_data_layer.hpp:23
+
caffe::HDF5DataLayer::MinTopBlobs
virtual int MinTopBlobs() const
Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
Definition: hdf5_data_layer.hpp:38
+
caffe::HDF5DataLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: hdf5_data_layer.cpp:72
+
caffe::HDF5DataLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: hdf5_data_layer.hpp:37
+
caffe::HDF5DataLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: hdf5_data_layer.hpp:45
+
caffe::HDF5DataLayer::type
virtual const char * type() const
Returns the layer type.
Definition: hdf5_data_layer.hpp:36
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/hdf5__output__layer_8hpp_source.html b/doxygen/hdf5__output__layer_8hpp_source.html new file mode 100644 index 00000000000..25ae0fe233a --- /dev/null +++ b/doxygen/hdf5__output__layer_8hpp_source.html @@ -0,0 +1,91 @@ + + + + + + + +Caffe: include/caffe/layers/hdf5_output_layer.hpp Source File + + + + + + + + + +
+
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Caffe +
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hdf5_output_layer.hpp
+
+
+
1 #ifndef CAFFE_HDF5_OUTPUT_LAYER_HPP_
2 #define CAFFE_HDF5_OUTPUT_LAYER_HPP_
3 
4 #include "hdf5.h"
5 
6 #include <string>
7 #include <vector>
8 
9 #include "caffe/blob.hpp"
10 #include "caffe/layer.hpp"
11 #include "caffe/proto/caffe.pb.h"
12 
13 namespace caffe {
14 
15 #define HDF5_DATA_DATASET_NAME "data"
16 #define HDF5_DATA_LABEL_NAME "label"
17 
23 template <typename Dtype>
24 class HDF5OutputLayer : public Layer<Dtype> {
25  public:
26  explicit HDF5OutputLayer(const LayerParameter& param)
27  : Layer<Dtype>(param), file_opened_(false) {}
28  virtual ~HDF5OutputLayer();
29  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
30  const vector<Blob<Dtype>*>& top);
31  // Data layers should be shared by multiple solvers in parallel
32  virtual inline bool ShareInParallel() const { return true; }
33  // Data layers have no bottoms, so reshaping is trivial.
34  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
35  const vector<Blob<Dtype>*>& top) {}
36 
37  virtual inline const char* type() const { return "HDF5Output"; }
38  // TODO: no limit on the number of blobs
39  virtual inline int ExactNumBottomBlobs() const { return 2; }
40  virtual inline int ExactNumTopBlobs() const { return 0; }
41 
42  inline std::string file_name() const { return file_name_; }
43 
44  protected:
45  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
46  const vector<Blob<Dtype>*>& top);
47  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
48  const vector<Blob<Dtype>*>& top);
49  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
50  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
51  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
52  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
53  virtual void SaveBlobs();
54 
55  bool file_opened_;
56  std::string file_name_;
57  hid_t file_id_;
58  Blob<Dtype> data_blob_;
59  Blob<Dtype> label_blob_;
60 };
61 
62 } // namespace caffe
63 
64 #endif // CAFFE_HDF5_OUTPUT_LAYER_HPP_
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::HDF5OutputLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the CPU device, compute the layer output.
Definition: hdf5_output_layer.cpp:41
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caffe::HDF5OutputLayer
Write blobs to disk as HDF5 files.
Definition: hdf5_output_layer.hpp:24
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caffe::HDF5OutputLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: hdf5_output_layer.hpp:34
+
caffe::HDF5OutputLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: hdf5_output_layer.hpp:40
+
caffe::HDF5OutputLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::HDF5OutputLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: hdf5_output_layer.cpp:62
+
caffe::HDF5OutputLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: hdf5_output_layer.hpp:39
+
caffe::HDF5OutputLayer::type
virtual const char * type() const
Returns the layer type.
Definition: hdf5_output_layer.hpp:37
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caffe::HDF5OutputLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: hdf5_output_layer.cpp:12
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caffe::HDF5OutputLayer::ShareInParallel
virtual bool ShareInParallel() const
Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
Definition: hdf5_output_layer.hpp:32
+
caffe::HDF5OutputLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
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+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/hierarchy.html b/doxygen/hierarchy.html new file mode 100644 index 00000000000..160299fd293 --- /dev/null +++ b/doxygen/hierarchy.html @@ -0,0 +1,197 @@ + + + + + + + +Caffe: Class Hierarchy + + + + + + + + + +
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Class Hierarchy
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This inheritance list is sorted roughly, but not completely, alphabetically:
+
[detail level 123]
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 Ccaffe::Batch< Dtype >
 Ccaffe::Blob< Dtype >A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers, Nets, and Solvers interact
 Ccaffe::Blob< int >
 Ccaffe::Blob< unsigned int >
 Ccaffe::BlockingQueue< T >
 Ccaffe::BlockingQueue< caffe::Batch< Dtype > *>
 Ccaffe::BlockingQueue< caffe::P2PSync< Dtype > *>
 Ccaffe::BlockingQueue< Datum *>
 Ccaffe::BlockingQueue< shared_ptr< caffe::DataReader::QueuePair > >
 Ccaffe::Caffe
 ▼Ccaffe::Solver< Dtype >::Callback
 Ccaffe::P2PSync< Dtype >
 Ccaffe::db::Cursor
 Ccaffe::DataReaderReads data from a source to queues available to data layers. A single reading thread is created per source, even if multiple solvers are running in parallel, e.g. for multi-GPU training. This makes sure databases are read sequentially, and that each solver accesses a different subset of the database. Data is distributed to solvers in a round-robin way to keep parallel training deterministic
 Ccaffe::DataTransformer< Dtype >Applies common transformations to the input data, such as scaling, mirroring, substracting the image mean..
 Ccaffe::db::DB
 Ccaffe::DevicePair
 ▼Ccaffe::Filler< Dtype >Fills a Blob with constant or randomly-generated data
 Ccaffe::BilinearFiller< Dtype >Fills a Blob with coefficients for bilinear interpolation
 Ccaffe::ConstantFiller< Dtype >Fills a Blob with constant values $ x = 0 $
 Ccaffe::GaussianFiller< Dtype >Fills a Blob with Gaussian-distributed values $ x = a $
 Ccaffe::MSRAFiller< Dtype >Fills a Blob with values $ x \sim N(0, \sigma^2) $ where $ \sigma^2 $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average
 Ccaffe::PositiveUnitballFiller< Dtype >Fills a Blob with values $ x \in [0, 1] $ such that $ \forall i \sum_j x_{ij} = 1 $
 Ccaffe::UniformFiller< Dtype >Fills a Blob with uniformly distributed values $ x\sim U(a, b) $
 Ccaffe::XavierFiller< Dtype >Fills a Blob with values $ x \sim U(-a, +a) $ where $ a $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average
 Ccaffe::Caffe::RNG::Generator
 ▼Ccaffe::InternalThread
 ►Ccaffe::BasePrefetchingDataLayer< Dtype >
 Ccaffe::DataReader::Body
 Ccaffe::P2PSync< Dtype >
 ▼Ccaffe::Layer< Dtype >An interface for the units of computation which can be composed into a Net
 Ccaffe::AccuracyLayer< Dtype >Computes the classification accuracy for a one-of-many classification task
 Ccaffe::ArgMaxLayer< Dtype >Compute the index of the $ K $ max values for each datum across all dimensions $ (C \times H \times W) $
 ►Ccaffe::BaseConvolutionLayer< Dtype >Abstract base class that factors out the BLAS code common to ConvolutionLayer and DeconvolutionLayer
 ►Ccaffe::BaseDataLayer< Dtype >Provides base for data layers that feed blobs to the Net
 Ccaffe::BatchNormLayer< Dtype >Normalizes the input to have 0-mean and/or unit (1) variance across the batch
 Ccaffe::BatchReindexLayer< Dtype >Index into the input blob along its first axis
 Ccaffe::BiasLayer< Dtype >Computes a sum of two input Blobs, with the shape of the latter Blob "broadcast" to match the shape of the former. Equivalent to tiling the latter Blob, then computing the elementwise sum
 Ccaffe::ConcatLayer< Dtype >Takes at least two Blobs and concatenates them along either the num or channel dimension, outputting the result
 Ccaffe::CropLayer< Dtype >Takes a Blob and crop it, to the shape specified by the second input Blob, across all dimensions after the specified axis
 Ccaffe::DummyDataLayer< Dtype >Provides data to the Net generated by a Filler
 Ccaffe::EltwiseLayer< Dtype >Compute elementwise operations, such as product and sum, along multiple input Blobs
 Ccaffe::EmbedLayer< Dtype >A layer for learning "embeddings" of one-hot vector input. Equivalent to an InnerProductLayer with one-hot vectors as input, but for efficiency the input is the "hot" index of each column itself
 Ccaffe::FilterLayer< Dtype >Takes two+ Blobs, interprets last Blob as a selector and filter remaining Blobs accordingly with selector data (0 means that the corresponding item has to be filtered, non-zero means that corresponding item needs to stay)
 Ccaffe::FlattenLayer< Dtype >Reshapes the input Blob into flat vectors
 Ccaffe::HDF5DataLayer< Dtype >Provides data to the Net from HDF5 files
 Ccaffe::HDF5OutputLayer< Dtype >Write blobs to disk as HDF5 files
 Ccaffe::Im2colLayer< Dtype >A helper for image operations that rearranges image regions into column vectors. Used by ConvolutionLayer to perform convolution by matrix multiplication
 Ccaffe::InnerProductLayer< Dtype >Also known as a "fully-connected" layer, computes an inner product with a set of learned weights, and (optionally) adds biases
 Ccaffe::InputLayer< Dtype >Provides data to the Net by assigning tops directly
 ►Ccaffe::LossLayer< Dtype >An interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth labels – and output a singleton Blob representing the loss
 Ccaffe::LRNLayer< Dtype >Normalize the input in a local region across or within feature maps
 Ccaffe::LSTMUnitLayer< Dtype >A helper for LSTMLayer: computes a single timestep of the non-linearity of the LSTM, producing the updated cell and hidden states
 Ccaffe::MVNLayer< Dtype >Normalizes the input to have 0-mean and/or unit (1) variance
 ►Ccaffe::NeuronLayer< Dtype >An interface for layers that take one blob as input ( $ x $) and produce one equally-sized blob as output ( $ y $), where each element of the output depends only on the corresponding input element
 Ccaffe::ParameterLayer< Dtype >
 Ccaffe::PoolingLayer< Dtype >Pools the input image by taking the max, average, etc. within regions
 Ccaffe::PythonLayer< Dtype >
 ►Ccaffe::RecurrentLayer< Dtype >An abstract class for implementing recurrent behavior inside of an unrolled network. This Layer type cannot be instantiated – instead, you should use one of its implementations which defines the recurrent architecture, such as RNNLayer or LSTMLayer
 Ccaffe::ReductionLayer< Dtype >Compute "reductions" – operations that return a scalar output Blob for an input Blob of arbitrary size, such as the sum, absolute sum, and sum of squares
 Ccaffe::ReshapeLayer< Dtype >
 Ccaffe::ScaleLayer< Dtype >Computes the elementwise product of two input Blobs, with the shape of the latter Blob "broadcast" to match the shape of the former. Equivalent to tiling the latter Blob, then computing the elementwise product. Note: for efficiency and convenience, this layer can additionally perform a "broadcast" sum too when bias_term: true is set
 Ccaffe::SilenceLayer< Dtype >Ignores bottom blobs while producing no top blobs. (This is useful to suppress outputs during testing.)
 Ccaffe::SliceLayer< Dtype >Takes a Blob and slices it along either the num or channel dimension, outputting multiple sliced Blob results
 Ccaffe::SoftmaxLayer< Dtype >Computes the softmax function
 Ccaffe::SplitLayer< Dtype >Creates a "split" path in the network by copying the bottom Blob into multiple top Blobs to be used by multiple consuming layers
 Ccaffe::SPPLayer< Dtype >Does spatial pyramid pooling on the input image by taking the max, average, etc. within regions so that the result vector of different sized images are of the same size
 Ccaffe::TileLayer< Dtype >Copy a Blob along specified dimensions
 Ccaffe::LayerRegisterer< Dtype >
 Ccaffe::LayerRegistry< Dtype >
 Ccaffe::Net< Dtype >Connects Layers together into a directed acyclic graph (DAG) specified by a NetParameter
 ▼Ccaffe::Params< Dtype >
 ►Ccaffe::GPUParams< Dtype >
 Ccaffe::DataReader::QueuePair
 Ccaffe::Caffe::RNG
 Ccaffe::SignalHandler
 ▼Ccaffe::Solver< Dtype >An interface for classes that perform optimization on Nets
 ►Ccaffe::SGDSolver< Dtype >Optimizes the parameters of a Net using stochastic gradient descent (SGD) with momentum
 Ccaffe::WorkerSolver< Dtype >Solver that only computes gradients, used as worker for multi-GPU training
 Ccaffe::SolverRegisterer< Dtype >
 Ccaffe::SolverRegistry< Dtype >
 Ccaffe::BlockingQueue< T >::sync
 Ccaffe::SyncedMemoryManages memory allocation and synchronization between the host (CPU) and device (GPU)
 ▼Ccaffe::Timer
 Ccaffe::CPUTimer
 Ccaffe::db::Transaction
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+Generated on Mon Nov 21 2016 11:23:49 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/hinge__loss__layer_8hpp_source.html b/doxygen/hinge__loss__layer_8hpp_source.html new file mode 100644 index 00000000000..8ce898cdd26 --- /dev/null +++ b/doxygen/hinge__loss__layer_8hpp_source.html @@ -0,0 +1,84 @@ + + + + + + + +Caffe: include/caffe/layers/hinge_loss_layer.hpp Source File + + + + + + + + + +
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hinge_loss_layer.hpp
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1 #ifndef CAFFE_HINGE_LOSS_LAYER_HPP_
2 #define CAFFE_HINGE_LOSS_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/loss_layer.hpp"
11 
12 namespace caffe {
13 
57 template <typename Dtype>
58 class HingeLossLayer : public LossLayer<Dtype> {
59  public:
60  explicit HingeLossLayer(const LayerParameter& param)
61  : LossLayer<Dtype>(param) {}
62 
63  virtual inline const char* type() const { return "HingeLoss"; }
64 
65  protected:
67  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
68  const vector<Blob<Dtype>*>& top);
69 
97  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
98  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
99 };
100 
101 
102 } // namespace caffe
103 
104 #endif // CAFFE_HINGE_LOSS_LAYER_HPP_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::HingeLossLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Computes the hinge loss for a one-of-many classification task.
Definition: hinge_loss_layer.cpp:10
+
caffe::HingeLossLayer
Computes the hinge loss for a one-of-many classification task.
Definition: hinge_loss_layer.hpp:58
+
caffe::HingeLossLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Computes the hinge loss error gradient w.r.t. the predictions.
Definition: hinge_loss_layer.cpp:43
+
caffe::LossLayer
An interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth ...
Definition: loss_layer.hpp:23
+
caffe::HingeLossLayer::type
virtual const char * type() const
Returns the layer type.
Definition: hinge_loss_layer.hpp:63
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
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+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/im2col_8hpp_source.html b/doxygen/im2col_8hpp_source.html new file mode 100644 index 00000000000..5c25d899b5e --- /dev/null +++ b/doxygen/im2col_8hpp_source.html @@ -0,0 +1,78 @@ + + + + + + + +Caffe: include/caffe/util/im2col.hpp Source File + + + + + + + + + +
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im2col.hpp
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1 #ifndef _CAFFE_UTIL_IM2COL_HPP_
2 #define _CAFFE_UTIL_IM2COL_HPP_
3 
4 namespace caffe {
5 
6 template <typename Dtype>
7 void im2col_nd_cpu(const Dtype* data_im, const int num_spatial_axes,
8  const int* im_shape, const int* col_shape,
9  const int* kernel_shape, const int* pad, const int* stride,
10  const int* dilation, Dtype* data_col);
11 
12 template <typename Dtype>
13 void im2col_cpu(const Dtype* data_im, const int channels,
14  const int height, const int width, const int kernel_h, const int kernel_w,
15  const int pad_h, const int pad_w, const int stride_h,
16  const int stride_w, const int dilation_h, const int dilation_w,
17  Dtype* data_col);
18 
19 template <typename Dtype>
20 void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes,
21  const int* im_shape, const int* col_shape,
22  const int* kernel_shape, const int* pad, const int* stride,
23  const int* dilation, Dtype* data_im);
24 
25 template <typename Dtype>
26 void col2im_cpu(const Dtype* data_col, const int channels,
27  const int height, const int width, const int kernel_h, const int kernel_w,
28  const int pad_h, const int pad_w, const int stride_h,
29  const int stride_w, const int dilation_h, const int dilation_w,
30  Dtype* data_im);
31 
32 template <typename Dtype>
33 void im2col_nd_gpu(const Dtype* data_im, const int num_spatial_axes,
34  const int col_size, const int* im_shape, const int* col_shape,
35  const int* kernel_shape, const int* pad, const int* stride,
36  const int* dilation, Dtype* data_col);
37 
38 template <typename Dtype>
39 void im2col_gpu(const Dtype* data_im, const int channels,
40  const int height, const int width, const int kernel_h, const int kernel_w,
41  const int pad_h, const int pad_w, const int stride_h,
42  const int stride_w, const int dilation_h, const int dilation_w,
43  Dtype* data_col);
44 
45 template <typename Dtype>
46 void col2im_nd_gpu(const Dtype* data_col, const int num_spatial_axes,
47  const int im_size, const int* im_shape, const int* col_shape,
48  const int* kernel_shape, const int* pad, const int* stride,
49  const int* dilation, Dtype* data_im);
50 
51 template <typename Dtype>
52 void col2im_gpu(const Dtype* data_col, const int channels,
53  const int height, const int width, const int kernel_h, const int kernel_w,
54  const int pad_h, const int pad_w, const int stride_h,
55  const int stride_w, const int dilation_h, const int dilation_w,
56  Dtype* data_im);
57 
58 } // namespace caffe
59 
60 #endif // CAFFE_UTIL_IM2COL_HPP_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
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+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/im2col__layer_8hpp_source.html b/doxygen/im2col__layer_8hpp_source.html new file mode 100644 index 00000000000..2f317af955f --- /dev/null +++ b/doxygen/im2col__layer_8hpp_source.html @@ -0,0 +1,94 @@ + + + + + + + +Caffe: include/caffe/layers/im2col_layer.hpp Source File + + + + + + + + + +
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im2col_layer.hpp
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1 #ifndef CAFFE_IM2COL_LAYER_HPP_
2 #define CAFFE_IM2COL_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 namespace caffe {
11 
19 template <typename Dtype>
20 class Im2colLayer : public Layer<Dtype> {
21  public:
22  explicit Im2colLayer(const LayerParameter& param)
23  : Layer<Dtype>(param) {}
24  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
25  const vector<Blob<Dtype>*>& top);
26  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
27  const vector<Blob<Dtype>*>& top);
28 
29  virtual inline const char* type() const { return "Im2col"; }
30  virtual inline int ExactNumBottomBlobs() const { return 1; }
31  virtual inline int ExactNumTopBlobs() const { return 1; }
32 
33  protected:
34  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
35  const vector<Blob<Dtype>*>& top);
36  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
37  const vector<Blob<Dtype>*>& top);
38  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
39  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
40  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
41  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
42 
44  Blob<int> kernel_shape_;
46  Blob<int> stride_;
48  Blob<int> pad_;
50  Blob<int> dilation_;
51 
52  int num_spatial_axes_;
53  int bottom_dim_;
54  int top_dim_;
55 
56  int channel_axis_;
57  int num_;
58  int channels_;
59 
60  bool force_nd_im2col_;
61 };
62 
63 } // namespace caffe
64 
65 #endif // CAFFE_IM2COL_LAYER_HPP_
caffe::Im2colLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::Im2colLayer
A helper for image operations that rearranges image regions into column vectors. Used by ConvolutionL...
Definition: im2col_layer.hpp:20
+
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::Im2colLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::Im2colLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: im2col_layer.hpp:30
+
caffe::Im2colLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the CPU device, compute the layer output.
Definition: im2col_layer.cpp:131
+
caffe::Im2colLayer::pad_
Blob< int > pad_
The spatial dimensions of the padding.
Definition: im2col_layer.hpp:48
+
caffe::Im2colLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: im2col_layer.hpp:31
+
caffe::Im2colLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: im2col_layer.cpp:9
+
caffe::Im2colLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: im2col_layer.cpp:162
+
caffe::Im2colLayer::kernel_shape_
Blob< int > kernel_shape_
The spatial dimensions of a filter kernel.
Definition: im2col_layer.hpp:44
+
caffe::Im2colLayer::type
virtual const char * type() const
Returns the layer type.
Definition: im2col_layer.hpp:29
+
caffe::Im2colLayer::dilation_
Blob< int > dilation_
The spatial dimensions of the dilation.
Definition: im2col_layer.hpp:50
+
caffe::Im2colLayer::stride_
Blob< int > stride_
The spatial dimensions of the stride.
Definition: im2col_layer.hpp:46
+
caffe::Im2colLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: im2col_layer.cpp:107
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/image__data__layer_8hpp_source.html b/doxygen/image__data__layer_8hpp_source.html new file mode 100644 index 00000000000..395fb30fa5b --- /dev/null +++ b/doxygen/image__data__layer_8hpp_source.html @@ -0,0 +1,85 @@ + + + + + + + +Caffe: include/caffe/layers/image_data_layer.hpp Source File + + + + + + + + + +
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Caffe +
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image_data_layer.hpp
+
+
+
1 #ifndef CAFFE_IMAGE_DATA_LAYER_HPP_
2 #define CAFFE_IMAGE_DATA_LAYER_HPP_
3 
4 #include <string>
5 #include <utility>
6 #include <vector>
7 
8 #include "caffe/blob.hpp"
9 #include "caffe/data_transformer.hpp"
10 #include "caffe/internal_thread.hpp"
11 #include "caffe/layer.hpp"
12 #include "caffe/layers/base_data_layer.hpp"
13 #include "caffe/proto/caffe.pb.h"
14 
15 namespace caffe {
16 
22 template <typename Dtype>
23 class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
24  public:
25  explicit ImageDataLayer(const LayerParameter& param)
26  : BasePrefetchingDataLayer<Dtype>(param) {}
27  virtual ~ImageDataLayer();
28  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
29  const vector<Blob<Dtype>*>& top);
30 
31  virtual inline const char* type() const { return "ImageData"; }
32  virtual inline int ExactNumBottomBlobs() const { return 0; }
33  virtual inline int ExactNumTopBlobs() const { return 2; }
34 
35  protected:
36  shared_ptr<Caffe::RNG> prefetch_rng_;
37  virtual void ShuffleImages();
38  virtual void load_batch(Batch<Dtype>* batch);
39 
40  vector<std::pair<std::string, int> > lines_;
41  int lines_id_;
42 };
43 
44 
45 } // namespace caffe
46 
47 #endif // CAFFE_IMAGE_DATA_LAYER_HPP_
caffe::Batch
Definition: base_data_layer.hpp:49
+
caffe::BasePrefetchingDataLayer
Definition: base_data_layer.hpp:55
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::ImageDataLayer
Provides data to the Net from image files.
Definition: image_data_layer.hpp:23
+
caffe::ImageDataLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: image_data_layer.hpp:33
+
caffe::ImageDataLayer::type
virtual const char * type() const
Returns the layer type.
Definition: image_data_layer.hpp:31
+
caffe::ImageDataLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: image_data_layer.hpp:32
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
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+ + diff --git a/doxygen/infogain__loss__layer_8hpp_source.html b/doxygen/infogain__loss__layer_8hpp_source.html new file mode 100644 index 00000000000..15efbc887ed --- /dev/null +++ b/doxygen/infogain__loss__layer_8hpp_source.html @@ -0,0 +1,89 @@ + + + + + + + +Caffe: include/caffe/layers/infogain_loss_layer.hpp Source File + + + + + + + + + +
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infogain_loss_layer.hpp
+
+
+
1 #ifndef CAFFE_INFOGAIN_LOSS_LAYER_HPP_
2 #define CAFFE_INFOGAIN_LOSS_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/loss_layer.hpp"
11 
12 namespace caffe {
13 
46 template <typename Dtype>
47 class InfogainLossLayer : public LossLayer<Dtype> {
48  public:
49  explicit InfogainLossLayer(const LayerParameter& param)
50  : LossLayer<Dtype>(param), infogain_() {}
51  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
52  const vector<Blob<Dtype>*>& top);
53  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
54  const vector<Blob<Dtype>*>& top);
55 
56  // InfogainLossLayer takes 2-3 bottom Blobs; if there are 3 the third should
57  // be the infogain matrix. (Otherwise the infogain matrix is loaded from a
58  // file specified by LayerParameter.)
59  virtual inline int ExactNumBottomBlobs() const { return -1; }
60  virtual inline int MinBottomBlobs() const { return 2; }
61  virtual inline int MaxBottomBlobs() const { return 3; }
62 
63  virtual inline const char* type() const { return "InfogainLoss"; }
64 
65  protected:
67  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
68  const vector<Blob<Dtype>*>& top);
69 
102  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
103  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
104 
105  Blob<Dtype> infogain_;
106 };
107 
108 } // namespace caffe
109 
110 #endif // CAFFE_INFOGAIN_LOSS_LAYER_HPP_
caffe::InfogainLossLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: infogain_loss_layer.cpp:11
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::InfogainLossLayer::type
virtual const char * type() const
Returns the layer type.
Definition: infogain_loss_layer.hpp:63
+
caffe::InfogainLossLayer
A generalization of MultinomialLogisticLossLayer that takes an "information gain" (infogain) matrix s...
Definition: infogain_loss_layer.hpp:47
+
caffe::InfogainLossLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Computes the infogain loss error gradient w.r.t. the predictions.
Definition: infogain_loss_layer.cpp:71
+
caffe::InfogainLossLayer::MaxBottomBlobs
virtual int MaxBottomBlobs() const
Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is requi...
Definition: infogain_loss_layer.hpp:61
+
caffe::InfogainLossLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
A generalization of MultinomialLogisticLossLayer that takes an "information gain" (infogain) matrix s...
Definition: infogain_loss_layer.cpp:47
+
caffe::InfogainLossLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: infogain_loss_layer.hpp:59
+
caffe::LossLayer
An interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth ...
Definition: loss_layer.hpp:23
+
caffe::InfogainLossLayer::MinBottomBlobs
virtual int MinBottomBlobs() const
Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
Definition: infogain_loss_layer.hpp:60
+
caffe::InfogainLossLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: infogain_loss_layer.cpp:25
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/inner__product__layer_8hpp_source.html b/doxygen/inner__product__layer_8hpp_source.html new file mode 100644 index 00000000000..94913238876 --- /dev/null +++ b/doxygen/inner__product__layer_8hpp_source.html @@ -0,0 +1,91 @@ + + + + + + + +Caffe: include/caffe/layers/inner_product_layer.hpp Source File + + + + + + + + + +
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Caffe +
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inner_product_layer.hpp
+
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+
1 #ifndef CAFFE_INNER_PRODUCT_LAYER_HPP_
2 #define CAFFE_INNER_PRODUCT_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 namespace caffe {
11 
18 template <typename Dtype>
19 class InnerProductLayer : public Layer<Dtype> {
20  public:
21  explicit InnerProductLayer(const LayerParameter& param)
22  : Layer<Dtype>(param) {}
23  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
24  const vector<Blob<Dtype>*>& top);
25  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
26  const vector<Blob<Dtype>*>& top);
27 
28  virtual inline const char* type() const { return "InnerProduct"; }
29  virtual inline int ExactNumBottomBlobs() const { return 1; }
30  virtual inline int ExactNumTopBlobs() const { return 1; }
31 
32  protected:
33  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
34  const vector<Blob<Dtype>*>& top);
35  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
36  const vector<Blob<Dtype>*>& top);
37  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
38  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
39  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
40  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
41 
42  int M_;
43  int K_;
44  int N_;
45  bool bias_term_;
46  Blob<Dtype> bias_multiplier_;
47  bool transpose_;
48 };
49 
50 } // namespace caffe
51 
52 #endif // CAFFE_INNER_PRODUCT_LAYER_HPP_
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::InnerProductLayer::type
virtual const char * type() const
Returns the layer type.
Definition: inner_product_layer.hpp:28
+
caffe::InnerProductLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: inner_product_layer.hpp:30
+
caffe::InnerProductLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: inner_product_layer.cpp:58
+
caffe::InnerProductLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::InnerProductLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::InnerProductLayer::transpose_
bool transpose_
if true, assume transposed weights
Definition: inner_product_layer.hpp:47
+
caffe::InnerProductLayer
Also known as a "fully-connected" layer, computes an inner product with a set of learned weights...
Definition: inner_product_layer.hpp:19
+
caffe::InnerProductLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: inner_product_layer.hpp:29
+
caffe::InnerProductLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: inner_product_layer.cpp:10
+
caffe::InnerProductLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the CPU device, compute the layer output.
Definition: inner_product_layer.cpp:84
+
caffe::InnerProductLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: inner_product_layer.cpp:100
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/input__layer_8hpp_source.html b/doxygen/input__layer_8hpp_source.html new file mode 100644 index 00000000000..c709dacf3a7 --- /dev/null +++ b/doxygen/input__layer_8hpp_source.html @@ -0,0 +1,89 @@ + + + + + + + +Caffe: include/caffe/layers/input_layer.hpp Source File + + + + + + + + + +
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Caffe +
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input_layer.hpp
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+
+
1 #ifndef CAFFE_INPUT_LAYER_HPP_
2 #define CAFFE_INPUT_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 namespace caffe {
11 
18 template <typename Dtype>
19 class InputLayer : public Layer<Dtype> {
20  public:
21  explicit InputLayer(const LayerParameter& param)
22  : Layer<Dtype>(param) {}
23  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
24  const vector<Blob<Dtype>*>& top);
25  // Data layers should be shared by multiple solvers in parallel
26  virtual inline bool ShareInParallel() const { return true; }
27  // Data layers have no bottoms, so reshaping is trivial.
28  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
29  const vector<Blob<Dtype>*>& top) {}
30 
31  virtual inline const char* type() const { return "Input"; }
32  virtual inline int ExactNumBottomBlobs() const { return 0; }
33  virtual inline int MinTopBlobs() const { return 1; }
34 
35  protected:
36  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
37  const vector<Blob<Dtype>*>& top) {}
38  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
39  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
40 };
41 
42 } // namespace caffe
43 
44 #endif // CAFFE_INPUT_LAYER_HPP_
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::InputLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: input_layer.hpp:32
+
caffe::InputLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: input_layer.cpp:8
+
caffe::InputLayer::ShareInParallel
virtual bool ShareInParallel() const
Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
Definition: input_layer.hpp:26
+
caffe::InputLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the CPU device, compute the layer output.
Definition: input_layer.hpp:36
+
caffe::InputLayer::MinTopBlobs
virtual int MinTopBlobs() const
Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
Definition: input_layer.hpp:33
+
caffe::InputLayer
Provides data to the Net by assigning tops directly.
Definition: input_layer.hpp:19
+
caffe::InputLayer::type
virtual const char * type() const
Returns the layer type.
Definition: input_layer.hpp:31
+
caffe::InputLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: input_layer.hpp:38
+
caffe::InputLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: input_layer.hpp:28
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/insert__splits_8hpp_source.html b/doxygen/insert__splits_8hpp_source.html new file mode 100644 index 00000000000..fbdaa672d6d --- /dev/null +++ b/doxygen/insert__splits_8hpp_source.html @@ -0,0 +1,78 @@ + + + + + + + +Caffe: include/caffe/util/insert_splits.hpp Source File + + + + + + + + + +
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Caffe +
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insert_splits.hpp
+
+
+
1 #ifndef _CAFFE_UTIL_INSERT_SPLITS_HPP_
2 #define _CAFFE_UTIL_INSERT_SPLITS_HPP_
3 
4 #include <string>
5 
6 #include "caffe/proto/caffe.pb.h"
7 
8 namespace caffe {
9 
10 // Copy NetParameters with SplitLayers added to replace any shared bottom
11 // blobs with unique bottom blobs provided by the SplitLayer.
12 void InsertSplits(const NetParameter& param, NetParameter* param_split);
13 
14 void ConfigureSplitLayer(const string& layer_name, const string& blob_name,
15  const int blob_idx, const int split_count, const float loss_weight,
16  LayerParameter* split_layer_param);
17 
18 string SplitLayerName(const string& layer_name, const string& blob_name,
19  const int blob_idx);
20 
21 string SplitBlobName(const string& layer_name, const string& blob_name,
22  const int blob_idx, const int split_idx);
23 
24 } // namespace caffe
25 
26 #endif // CAFFE_UTIL_INSERT_SPLITS_HPP_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/internal__thread_8hpp_source.html b/doxygen/internal__thread_8hpp_source.html new file mode 100644 index 00000000000..7424a280348 --- /dev/null +++ b/doxygen/internal__thread_8hpp_source.html @@ -0,0 +1,80 @@ + + + + + + + +Caffe: include/caffe/internal_thread.hpp Source File + + + + + + + + + +
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Caffe +
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internal_thread.hpp
+
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1 #ifndef CAFFE_INTERNAL_THREAD_HPP_
2 #define CAFFE_INTERNAL_THREAD_HPP_
3 
4 #include "caffe/common.hpp"
5 
10 namespace boost { class thread; }
11 
12 namespace caffe {
13 
19 class InternalThread {
20  public:
21  InternalThread() : thread_() {}
22  virtual ~InternalThread();
23 
29  void StartInternalThread();
30 
32  void StopInternalThread();
33 
34  bool is_started() const;
35 
36  protected:
37  /* Implement this method in your subclass
38  with the code you want your thread to run. */
39  virtual void InternalThreadEntry() {}
40 
41  /* Should be tested when running loops to exit when requested. */
42  bool must_stop();
43 
44  private:
45  void entry(int device, Caffe::Brew mode, int rand_seed, int solver_count,
46  bool root_solver);
47 
48  shared_ptr<boost::thread> thread_;
49 };
50 
51 } // namespace caffe
52 
53 #endif // CAFFE_INTERNAL_THREAD_HPP_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
boost
Definition: internal_thread.hpp:10
+
caffe::InternalThread
Definition: internal_thread.hpp:19
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/io_8hpp_source.html b/doxygen/io_8hpp_source.html new file mode 100644 index 00000000000..2852419fff1 --- /dev/null +++ b/doxygen/io_8hpp_source.html @@ -0,0 +1,78 @@ + + + + + + + +Caffe: include/caffe/util/io.hpp Source File + + + + + + + + + +
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io.hpp
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1 #ifndef CAFFE_UTIL_IO_H_
2 #define CAFFE_UTIL_IO_H_
3 
4 #include <boost/filesystem.hpp>
5 #include <iomanip>
6 #include <iostream> // NOLINT(readability/streams)
7 #include <string>
8 
9 #include "google/protobuf/message.h"
10 
11 #include "caffe/common.hpp"
12 #include "caffe/proto/caffe.pb.h"
13 #include "caffe/util/format.hpp"
14 
15 #ifndef CAFFE_TMP_DIR_RETRIES
16 #define CAFFE_TMP_DIR_RETRIES 100
17 #endif
18 
19 namespace caffe {
20 
21 using ::google::protobuf::Message;
22 using ::boost::filesystem::path;
23 
24 inline void MakeTempDir(string* temp_dirname) {
25  temp_dirname->clear();
26  const path& model =
27  boost::filesystem::temp_directory_path()/"caffe_test.%%%%-%%%%";
28  for ( int i = 0; i < CAFFE_TMP_DIR_RETRIES; i++ ) {
29  const path& dir = boost::filesystem::unique_path(model).string();
30  bool done = boost::filesystem::create_directory(dir);
31  if ( done ) {
32  *temp_dirname = dir.string();
33  return;
34  }
35  }
36  LOG(FATAL) << "Failed to create a temporary directory.";
37 }
38 
39 inline void MakeTempFilename(string* temp_filename) {
40  static path temp_files_subpath;
41  static uint64_t next_temp_file = 0;
42  temp_filename->clear();
43  if ( temp_files_subpath.empty() ) {
44  string path_string="";
45  MakeTempDir(&path_string);
46  temp_files_subpath = path_string;
47  }
48  *temp_filename =
49  (temp_files_subpath/caffe::format_int(next_temp_file++, 9)).string();
50 }
51 
52 bool ReadProtoFromTextFile(const char* filename, Message* proto);
53 
54 inline bool ReadProtoFromTextFile(const string& filename, Message* proto) {
55  return ReadProtoFromTextFile(filename.c_str(), proto);
56 }
57 
58 inline void ReadProtoFromTextFileOrDie(const char* filename, Message* proto) {
59  CHECK(ReadProtoFromTextFile(filename, proto));
60 }
61 
62 inline void ReadProtoFromTextFileOrDie(const string& filename, Message* proto) {
63  ReadProtoFromTextFileOrDie(filename.c_str(), proto);
64 }
65 
66 void WriteProtoToTextFile(const Message& proto, const char* filename);
67 inline void WriteProtoToTextFile(const Message& proto, const string& filename) {
68  WriteProtoToTextFile(proto, filename.c_str());
69 }
70 
71 bool ReadProtoFromBinaryFile(const char* filename, Message* proto);
72 
73 inline bool ReadProtoFromBinaryFile(const string& filename, Message* proto) {
74  return ReadProtoFromBinaryFile(filename.c_str(), proto);
75 }
76 
77 inline void ReadProtoFromBinaryFileOrDie(const char* filename, Message* proto) {
78  CHECK(ReadProtoFromBinaryFile(filename, proto));
79 }
80 
81 inline void ReadProtoFromBinaryFileOrDie(const string& filename,
82  Message* proto) {
83  ReadProtoFromBinaryFileOrDie(filename.c_str(), proto);
84 }
85 
86 
87 void WriteProtoToBinaryFile(const Message& proto, const char* filename);
88 inline void WriteProtoToBinaryFile(
89  const Message& proto, const string& filename) {
90  WriteProtoToBinaryFile(proto, filename.c_str());
91 }
92 
93 bool ReadFileToDatum(const string& filename, const int label, Datum* datum);
94 
95 inline bool ReadFileToDatum(const string& filename, Datum* datum) {
96  return ReadFileToDatum(filename, -1, datum);
97 }
98 
99 bool ReadImageToDatum(const string& filename, const int label,
100  const int height, const int width, const bool is_color,
101  const std::string & encoding, Datum* datum);
102 
103 inline bool ReadImageToDatum(const string& filename, const int label,
104  const int height, const int width, const bool is_color, Datum* datum) {
105  return ReadImageToDatum(filename, label, height, width, is_color,
106  "", datum);
107 }
108 
109 inline bool ReadImageToDatum(const string& filename, const int label,
110  const int height, const int width, Datum* datum) {
111  return ReadImageToDatum(filename, label, height, width, true, datum);
112 }
113 
114 inline bool ReadImageToDatum(const string& filename, const int label,
115  const bool is_color, Datum* datum) {
116  return ReadImageToDatum(filename, label, 0, 0, is_color, datum);
117 }
118 
119 inline bool ReadImageToDatum(const string& filename, const int label,
120  Datum* datum) {
121  return ReadImageToDatum(filename, label, 0, 0, true, datum);
122 }
123 
124 inline bool ReadImageToDatum(const string& filename, const int label,
125  const std::string & encoding, Datum* datum) {
126  return ReadImageToDatum(filename, label, 0, 0, true, encoding, datum);
127 }
128 
129 bool DecodeDatumNative(Datum* datum);
130 bool DecodeDatum(Datum* datum, bool is_color);
131 
132 #ifdef USE_OPENCV
133 cv::Mat ReadImageToCVMat(const string& filename,
134  const int height, const int width, const bool is_color);
135 
136 cv::Mat ReadImageToCVMat(const string& filename,
137  const int height, const int width);
138 
139 cv::Mat ReadImageToCVMat(const string& filename,
140  const bool is_color);
141 
142 cv::Mat ReadImageToCVMat(const string& filename);
143 
144 cv::Mat DecodeDatumToCVMatNative(const Datum& datum);
145 cv::Mat DecodeDatumToCVMat(const Datum& datum, bool is_color);
146 
147 void CVMatToDatum(const cv::Mat& cv_img, Datum* datum);
148 #endif // USE_OPENCV
149 
150 } // namespace caffe
151 
152 #endif // CAFFE_UTIL_IO_H_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
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0},disableSelection:function(){return this.bind((a.support.selectstart?"selectstart":"mousedown")+".ui-disableSelection",function(e){e.preventDefault()})},enableSelection:function(){return this.unbind(".ui-disableSelection")}});a.each(["Width","Height"],function(g,e){var f=e==="Width"?["Left","Right"]:["Top","Bottom"],h=e.toLowerCase(),k={innerWidth:a.fn.innerWidth,innerHeight:a.fn.innerHeight,outerWidth:a.fn.outerWidth,outerHeight:a.fn.outerHeight};function j(m,l,i,n){a.each(f,function(){l-=parseFloat(a.curCSS(m,"padding"+this,true))||0;if(i){l-=parseFloat(a.curCSS(m,"border"+this+"Width",true))||0}if(n){l-=parseFloat(a.curCSS(m,"margin"+this,true))||0}});return l}a.fn["inner"+e]=function(i){if(i===d){return k["inner"+e].call(this)}return this.each(function(){a(this).css(h,j(this,i)+"px")})};a.fn["outer"+e]=function(i,l){if(typeof i!=="number"){return k["outer"+e].call(this,i)}return this.each(function(){a(this).css(h,j(this,i,true,l)+"px")})}});function c(g,e){var j=g.nodeName.toLowerCase();if("area"===j){var i=g.parentNode,h=i.name,f;if(!g.href||!h||i.nodeName.toLowerCase()!=="map"){return false}f=a("img[usemap=#"+h+"]")[0];return !!f&&b(f)}return(/input|select|textarea|button|object/.test(j)?!g.disabled:"a"==j?g.href||e:e)&&b(g)}function b(e){return !a(e).parents().andSelf().filter(function(){return a.curCSS(this,"visibility")==="hidden"||a.expr.filters.hidden(this)}).length}a.extend(a.expr[":"],{data:function(g,f,e){return !!a.data(g,e[3])},focusable:function(e){return c(e,!isNaN(a.attr(e,"tabindex")))},tabbable:function(g){var e=a.attr(g,"tabindex"),f=isNaN(e);return(f||e>=0)&&c(g,!f)}});a(function(){var e=document.body,f=e.appendChild(f=document.createElement("div"));f.offsetHeight;a.extend(f.style,{minHeight:"100px",height:"auto",padding:0,borderWidth:0});a.support.minHeight=f.offsetHeight===100;a.support.selectstart="onselectstart" in f;e.removeChild(f).style.display="none"});a.extend(a.ui,{plugin:{add:function(f,g,j){var h=a.ui[f].prototype;for(var e in j){h.plugins[e]=h.plugins[e]||[];h.plugins[e].push([g,j[e]])}},call:function(e,g,f){var j=e.plugins[g];if(!j||!e.element[0].parentNode){return}for(var h=0;h0){return true}h[e]=1;g=(h[e]>0);h[e]=0;return g},isOverAxis:function(f,e,g){return(f>e)&&(f<(e+g))},isOver:function(j,f,i,h,e,g){return a.ui.isOverAxis(j,i,e)&&a.ui.isOverAxis(f,h,g)}})})(jQuery);/*! + * jQuery UI Widget 1.8.18 + * + * Copyright 2011, AUTHORS.txt (http://jqueryui.com/about) + * Dual licensed under the MIT or GPL Version 2 licenses. + * http://jquery.org/license + * + * http://docs.jquery.com/UI/Widget + */ +(function(b,d){if(b.cleanData){var c=b.cleanData;b.cleanData=function(f){for(var g=0,h;(h=f[g])!=null;g++){try{b(h).triggerHandler("remove")}catch(j){}}c(f)}}else{var a=b.fn.remove;b.fn.remove=function(e,f){return this.each(function(){if(!f){if(!e||b.filter(e,[this]).length){b("*",this).add([this]).each(function(){try{b(this).triggerHandler("remove")}catch(g){}})}}return a.call(b(this),e,f)})}}b.widget=function(f,h,e){var g=f.split(".")[0],j;f=f.split(".")[1];j=g+"-"+f;if(!e){e=h;h=b.Widget}b.expr[":"][j]=function(k){return !!b.data(k,f)};b[g]=b[g]||{};b[g][f]=function(k,l){if(arguments.length){this._createWidget(k,l)}};var i=new h();i.options=b.extend(true,{},i.options);b[g][f].prototype=b.extend(true,i,{namespace:g,widgetName:f,widgetEventPrefix:b[g][f].prototype.widgetEventPrefix||f,widgetBaseClass:j},e);b.widget.bridge(f,b[g][f])};b.widget.bridge=function(f,e){b.fn[f]=function(i){var g=typeof i==="string",h=Array.prototype.slice.call(arguments,1),j=this;i=!g&&h.length?b.extend.apply(null,[true,i].concat(h)):i;if(g&&i.charAt(0)==="_"){return j}if(g){this.each(function(){var k=b.data(this,f),l=k&&b.isFunction(k[i])?k[i].apply(k,h):k;if(l!==k&&l!==d){j=l;return false}})}else{this.each(function(){var k=b.data(this,f);if(k){k.option(i||{})._init()}else{b.data(this,f,new e(i,this))}})}return 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http://jquery.org/license + * + * http://docs.jquery.com/UI/Mouse + * + * Depends: + * jquery.ui.widget.js + */ +(function(b,c){var a=false;b(document).mouseup(function(d){a=false});b.widget("ui.mouse",{options:{cancel:":input,option",distance:1,delay:0},_mouseInit:function(){var d=this;this.element.bind("mousedown."+this.widgetName,function(e){return d._mouseDown(e)}).bind("click."+this.widgetName,function(e){if(true===b.data(e.target,d.widgetName+".preventClickEvent")){b.removeData(e.target,d.widgetName+".preventClickEvent");e.stopImmediatePropagation();return false}});this.started=false},_mouseDestroy:function(){this.element.unbind("."+this.widgetName)},_mouseDown:function(f){if(a){return}(this._mouseStarted&&this._mouseUp(f));this._mouseDownEvent=f;var e=this,g=(f.which==1),d=(typeof this.options.cancel=="string"&&f.target.nodeName?b(f.target).closest(this.options.cancel).length:false);if(!g||d||!this._mouseCapture(f)){return true}this.mouseDelayMet=!this.options.delay;if(!this.mouseDelayMet){this._mouseDelayTimer=setTimeout(function(){e.mouseDelayMet=true},this.options.delay)}if(this._mouseDistanceMet(f)&&this._mouseDelayMet(f)){this._mouseStarted=(this._mouseStart(f)!==false);if(!this._mouseStarted){f.preventDefault();return true}}if(true===b.data(f.target,this.widgetName+".preventClickEvent")){b.removeData(f.target,this.widgetName+".preventClickEvent")}this._mouseMoveDelegate=function(h){return e._mouseMove(h)};this._mouseUpDelegate=function(h){return e._mouseUp(h)};b(document).bind("mousemove."+this.widgetName,this._mouseMoveDelegate).bind("mouseup."+this.widgetName,this._mouseUpDelegate);f.preventDefault();a=true;return true},_mouseMove:function(d){if(b.browser.msie&&!(document.documentMode>=9)&&!d.button){return this._mouseUp(d)}if(this._mouseStarted){this._mouseDrag(d);return d.preventDefault()}if(this._mouseDistanceMet(d)&&this._mouseDelayMet(d)){this._mouseStarted=(this._mouseStart(this._mouseDownEvent,d)!==false);(this._mouseStarted?this._mouseDrag(d):this._mouseUp(d))}return !this._mouseStarted},_mouseUp:function(d){b(document).unbind("mousemove."+this.widgetName,this._mouseMoveDelegate).unbind("mouseup."+this.widgetName,this._mouseUpDelegate);if(this._mouseStarted){this._mouseStarted=false;if(d.target==this._mouseDownEvent.target){b.data(d.target,this.widgetName+".preventClickEvent",true)}this._mouseStop(d)}return false},_mouseDistanceMet:function(d){return(Math.max(Math.abs(this._mouseDownEvent.pageX-d.pageX),Math.abs(this._mouseDownEvent.pageY-d.pageY))>=this.options.distance)},_mouseDelayMet:function(d){return this.mouseDelayMet},_mouseStart:function(d){},_mouseDrag:function(d){},_mouseStop:function(d){},_mouseCapture:function(d){return true}})})(jQuery);(function(c,d){c.widget("ui.resizable",c.ui.mouse,{widgetEventPrefix:"resize",options:{alsoResize:false,animate:false,animateDuration:"slow",animateEasing:"swing",aspectRatio:false,autoHide:false,containment:false,ghost:false,grid:false,handles:"e,s,se",helper:false,maxHeight:null,maxWidth:null,minHeight:10,minWidth:10,zIndex:1000},_create:function(){var f=this,k=this.options;this.element.addClass("ui-resizable");c.extend(this,{_aspectRatio:!!(k.aspectRatio),aspectRatio:k.aspectRatio,originalElement:this.element,_proportionallyResizeElements:[],_helper:k.helper||k.ghost||k.animate?k.helper||"ui-resizable-helper":null});if(this.element[0].nodeName.match(/canvas|textarea|input|select|button|img/i)){this.element.wrap(c('
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');if(/sw|se|ne|nw/.test(j)){h.css({zIndex:++k.zIndex})}if("se"==j){h.addClass("ui-icon ui-icon-gripsmall-diagonal-se")}this.handles[j]=".ui-resizable-"+j;this.element.append(h)}}this._renderAxis=function(q){q=q||this.element;for(var n in this.handles){if(this.handles[n].constructor==String){this.handles[n]=c(this.handles[n],this.element).show()}if(this.elementIsWrapper&&this.originalElement[0].nodeName.match(/textarea|input|select|button/i)){var o=c(this.handles[n],this.element),p=0;p=/sw|ne|nw|se|n|s/.test(n)?o.outerHeight():o.outerWidth();var m=["padding",/ne|nw|n/.test(n)?"Top":/se|sw|s/.test(n)?"Bottom":/^e$/.test(n)?"Right":"Left"].join("");q.css(m,p);this._proportionallyResize()}if(!c(this.handles[n]).length){continue}}};this._renderAxis(this.element);this._handles=c(".ui-resizable-handle",this.element).disableSelection();this._handles.mouseover(function(){if(!f.resizing){if(this.className){var i=this.className.match(/ui-resizable-(se|sw|ne|nw|n|e|s|w)/i)}f.axis=i&&i[1]?i[1]:"se"}});if(k.autoHide){this._handles.hide();c(this.element).addClass("ui-resizable-autohide").hover(function(){if(k.disabled){return}c(this).removeClass("ui-resizable-autohide");f._handles.show()},function(){if(k.disabled){return}if(!f.resizing){c(this).addClass("ui-resizable-autohide");f._handles.hide()}})}this._mouseInit()},destroy:function(){this._mouseDestroy();var e=function(g){c(g).removeClass("ui-resizable ui-resizable-disabled ui-resizable-resizing").removeData("resizable").unbind(".resizable").find(".ui-resizable-handle").remove()};if(this.elementIsWrapper){e(this.element);var f=this.element;f.after(this.originalElement.css({position:f.css("position"),width:f.outerWidth(),height:f.outerHeight(),top:f.css("top"),left:f.css("left")})).remove()}this.originalElement.css("resize",this.originalResizeStyle);e(this.originalElement);return this},_mouseCapture:function(f){var g=false;for(var e in this.handles){if(c(this.handles[e])[0]==f.target){g=true}}return !this.options.disabled&&g},_mouseStart:function(g){var j=this.options,f=this.element.position(),e=this.element;this.resizing=true;this.documentScroll={top:c(document).scrollTop(),left:c(document).scrollLeft()};if(e.is(".ui-draggable")||(/absolute/).test(e.css("position"))){e.css({position:"absolute",top:f.top,left:f.left})}this._renderProxy();var k=b(this.helper.css("left")),h=b(this.helper.css("top"));if(j.containment){k+=c(j.containment).scrollLeft()||0;h+=c(j.containment).scrollTop()||0}this.offset=this.helper.offset();this.position={left:k,top:h};this.size=this._helper?{width:e.outerWidth(),height:e.outerHeight()}:{width:e.width(),height:e.height()};this.originalSize=this._helper?{width:e.outerWidth(),height:e.outerHeight()}:{width:e.width(),height:e.height()};this.originalPosition={left:k,top:h};this.sizeDiff={width:e.outerWidth()-e.width(),height:e.outerHeight()-e.height()};this.originalMousePosition={left:g.pageX,top:g.pageY};this.aspectRatio=(typeof j.aspectRatio=="number")?j.aspectRatio:((this.originalSize.width/this.originalSize.height)||1);var i=c(".ui-resizable-"+this.axis).css("cursor");c("body").css("cursor",i=="auto"?this.axis+"-resize":i);e.addClass("ui-resizable-resizing");this._propagate("start",g);return true},_mouseDrag:function(e){var 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+
+
layer.hpp
+
+
+
1 #ifndef CAFFE_LAYER_H_
2 #define CAFFE_LAYER_H_
3 
4 #include <algorithm>
5 #include <string>
6 #include <vector>
7 
8 #include "caffe/blob.hpp"
9 #include "caffe/common.hpp"
10 #include "caffe/layer_factory.hpp"
11 #include "caffe/proto/caffe.pb.h"
12 #include "caffe/util/math_functions.hpp"
13 
18 namespace boost { class mutex; }
19 
20 namespace caffe {
21 
32 template <typename Dtype>
33 class Layer {
34  public:
40  explicit Layer(const LayerParameter& param)
41  : layer_param_(param), is_shared_(false) {
42  // Set phase and copy blobs (if there are any).
43  phase_ = param.phase();
44  if (layer_param_.blobs_size() > 0) {
45  blobs_.resize(layer_param_.blobs_size());
46  for (int i = 0; i < layer_param_.blobs_size(); ++i) {
47  blobs_[i].reset(new Blob<Dtype>());
48  blobs_[i]->FromProto(layer_param_.blobs(i));
49  }
50  }
51  }
52  virtual ~Layer() {}
53 
67  void SetUp(const vector<Blob<Dtype>*>& bottom,
68  const vector<Blob<Dtype>*>& top) {
69  InitMutex();
70  CheckBlobCounts(bottom, top);
71  LayerSetUp(bottom, top);
72  Reshape(bottom, top);
73  SetLossWeights(top);
74  }
75 
92  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
93  const vector<Blob<Dtype>*>& top) {}
94 
101  virtual inline bool ShareInParallel() const { return false; }
102 
107  inline bool IsShared() const { return is_shared_; }
108 
113  inline void SetShared(bool is_shared) {
114  CHECK(ShareInParallel() || !is_shared)
115  << type() << "Layer does not support sharing.";
116  is_shared_ = is_shared;
117  }
118 
131  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
132  const vector<Blob<Dtype>*>& top) = 0;
133 
151  inline Dtype Forward(const vector<Blob<Dtype>*>& bottom,
152  const vector<Blob<Dtype>*>& top);
153 
175  inline void Backward(const vector<Blob<Dtype>*>& top,
176  const vector<bool>& propagate_down,
177  const vector<Blob<Dtype>*>& bottom);
178 
182  vector<shared_ptr<Blob<Dtype> > >& blobs() {
183  return blobs_;
184  }
185 
189  const LayerParameter& layer_param() const { return layer_param_; }
190 
194  virtual void ToProto(LayerParameter* param, bool write_diff = false);
195 
199  inline Dtype loss(const int top_index) const {
200  return (loss_.size() > top_index) ? loss_[top_index] : Dtype(0);
201  }
202 
206  inline void set_loss(const int top_index, const Dtype value) {
207  if (loss_.size() <= top_index) {
208  loss_.resize(top_index + 1, Dtype(0));
209  }
210  loss_[top_index] = value;
211  }
212 
216  virtual inline const char* type() const { return ""; }
217 
225  virtual inline int ExactNumBottomBlobs() const { return -1; }
233  virtual inline int MinBottomBlobs() const { return -1; }
241  virtual inline int MaxBottomBlobs() const { return -1; }
249  virtual inline int ExactNumTopBlobs() const { return -1; }
257  virtual inline int MinTopBlobs() const { return -1; }
265  virtual inline int MaxTopBlobs() const { return -1; }
273  virtual inline bool EqualNumBottomTopBlobs() const { return false; }
274 
283  virtual inline bool AutoTopBlobs() const { return false; }
284 
293  virtual inline bool AllowForceBackward(const int bottom_index) const {
294  return true;
295  }
296 
304  inline bool param_propagate_down(const int param_id) {
305  return (param_propagate_down_.size() > param_id) ?
306  param_propagate_down_[param_id] : false;
307  }
312  inline void set_param_propagate_down(const int param_id, const bool value) {
313  if (param_propagate_down_.size() <= param_id) {
314  param_propagate_down_.resize(param_id + 1, true);
315  }
316  param_propagate_down_[param_id] = value;
317  }
318 
319 
320  protected:
322  LayerParameter layer_param_;
324  Phase phase_;
326  vector<shared_ptr<Blob<Dtype> > > blobs_;
328  vector<bool> param_propagate_down_;
329 
332  vector<Dtype> loss_;
333 
335  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
336  const vector<Blob<Dtype>*>& top) = 0;
341  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
342  const vector<Blob<Dtype>*>& top) {
343  // LOG(WARNING) << "Using CPU code as backup.";
344  return Forward_cpu(bottom, top);
345  }
346 
351  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
352  const vector<bool>& propagate_down,
353  const vector<Blob<Dtype>*>& bottom) = 0;
359  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
360  const vector<bool>& propagate_down,
361  const vector<Blob<Dtype>*>& bottom) {
362  // LOG(WARNING) << "Using CPU code as backup.";
363  Backward_cpu(top, propagate_down, bottom);
364  }
365 
371  virtual void CheckBlobCounts(const vector<Blob<Dtype>*>& bottom,
372  const vector<Blob<Dtype>*>& top) {
373  if (ExactNumBottomBlobs() >= 0) {
374  CHECK_EQ(ExactNumBottomBlobs(), bottom.size())
375  << type() << " Layer takes " << ExactNumBottomBlobs()
376  << " bottom blob(s) as input.";
377  }
378  if (MinBottomBlobs() >= 0) {
379  CHECK_LE(MinBottomBlobs(), bottom.size())
380  << type() << " Layer takes at least " << MinBottomBlobs()
381  << " bottom blob(s) as input.";
382  }
383  if (MaxBottomBlobs() >= 0) {
384  CHECK_GE(MaxBottomBlobs(), bottom.size())
385  << type() << " Layer takes at most " << MaxBottomBlobs()
386  << " bottom blob(s) as input.";
387  }
388  if (ExactNumTopBlobs() >= 0) {
389  CHECK_EQ(ExactNumTopBlobs(), top.size())
390  << type() << " Layer produces " << ExactNumTopBlobs()
391  << " top blob(s) as output.";
392  }
393  if (MinTopBlobs() >= 0) {
394  CHECK_LE(MinTopBlobs(), top.size())
395  << type() << " Layer produces at least " << MinTopBlobs()
396  << " top blob(s) as output.";
397  }
398  if (MaxTopBlobs() >= 0) {
399  CHECK_GE(MaxTopBlobs(), top.size())
400  << type() << " Layer produces at most " << MaxTopBlobs()
401  << " top blob(s) as output.";
402  }
403  if (EqualNumBottomTopBlobs()) {
404  CHECK_EQ(bottom.size(), top.size())
405  << type() << " Layer produces one top blob as output for each "
406  << "bottom blob input.";
407  }
408  }
409 
414  inline void SetLossWeights(const vector<Blob<Dtype>*>& top) {
415  const int num_loss_weights = layer_param_.loss_weight_size();
416  if (num_loss_weights) {
417  CHECK_EQ(top.size(), num_loss_weights) << "loss_weight must be "
418  "unspecified or specified once per top blob.";
419  for (int top_id = 0; top_id < top.size(); ++top_id) {
420  const Dtype loss_weight = layer_param_.loss_weight(top_id);
421  if (loss_weight == Dtype(0)) { continue; }
422  this->set_loss(top_id, loss_weight);
423  const int count = top[top_id]->count();
424  Dtype* loss_multiplier = top[top_id]->mutable_cpu_diff();
425  caffe_set(count, loss_weight, loss_multiplier);
426  }
427  }
428  }
429 
430  private:
432  bool is_shared_;
433 
435  shared_ptr<boost::mutex> forward_mutex_;
436 
438  void InitMutex();
440  void Lock();
442  void Unlock();
443 
444  DISABLE_COPY_AND_ASSIGN(Layer);
445 }; // class Layer
446 
447 // Forward and backward wrappers. You should implement the cpu and
448 // gpu specific implementations instead, and should not change these
449 // functions.
450 template <typename Dtype>
451 inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
452  const vector<Blob<Dtype>*>& top) {
453  // Lock during forward to ensure sequential forward
454  Lock();
455  Dtype loss = 0;
456  Reshape(bottom, top);
457  switch (Caffe::mode()) {
458  case Caffe::CPU:
459  Forward_cpu(bottom, top);
460  for (int top_id = 0; top_id < top.size(); ++top_id) {
461  if (!this->loss(top_id)) { continue; }
462  const int count = top[top_id]->count();
463  const Dtype* data = top[top_id]->cpu_data();
464  const Dtype* loss_weights = top[top_id]->cpu_diff();
465  loss += caffe_cpu_dot(count, data, loss_weights);
466  }
467  break;
468  case Caffe::GPU:
469  Forward_gpu(bottom, top);
470 #ifndef CPU_ONLY
471  for (int top_id = 0; top_id < top.size(); ++top_id) {
472  if (!this->loss(top_id)) { continue; }
473  const int count = top[top_id]->count();
474  const Dtype* data = top[top_id]->gpu_data();
475  const Dtype* loss_weights = top[top_id]->gpu_diff();
476  Dtype blob_loss = 0;
477  caffe_gpu_dot(count, data, loss_weights, &blob_loss);
478  loss += blob_loss;
479  }
480 #endif
481  break;
482  default:
483  LOG(FATAL) << "Unknown caffe mode.";
484  }
485  Unlock();
486  return loss;
487 }
488 
489 template <typename Dtype>
490 inline void Layer<Dtype>::Backward(const vector<Blob<Dtype>*>& top,
491  const vector<bool>& propagate_down,
492  const vector<Blob<Dtype>*>& bottom) {
493  switch (Caffe::mode()) {
494  case Caffe::CPU:
495  Backward_cpu(top, propagate_down, bottom);
496  break;
497  case Caffe::GPU:
498  Backward_gpu(top, propagate_down, bottom);
499  break;
500  default:
501  LOG(FATAL) << "Unknown caffe mode.";
502  }
503 }
504 
505 // Serialize LayerParameter to protocol buffer
506 template <typename Dtype>
507 void Layer<Dtype>::ToProto(LayerParameter* param, bool write_diff) {
508  param->Clear();
509  param->CopyFrom(layer_param_);
510  param->clear_blobs();
511  for (int i = 0; i < blobs_.size(); ++i) {
512  blobs_[i]->ToProto(param->add_blobs(), write_diff);
513  }
514 }
515 
516 } // namespace caffe
517 
518 #endif // CAFFE_LAYER_H_
caffe::Layer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: layer.hpp:359
+
caffe::Layer::loss_
vector< Dtype > loss_
Definition: layer.hpp:332
+
caffe::Layer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
Definition: layer.hpp:341
+
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
boost
Definition: internal_thread.hpp:10
+
caffe::Layer::blobs_
vector< shared_ptr< Blob< Dtype > > > blobs_
Definition: layer.hpp:326
+
caffe::Layer::type
virtual const char * type() const
Returns the layer type.
Definition: layer.hpp:216
+
caffe::Layer::blobs
vector< shared_ptr< Blob< Dtype > > > & blobs()
Returns the vector of learnable parameter blobs.
Definition: layer.hpp:182
+
caffe::Layer::MaxTopBlobs
virtual int MaxTopBlobs() const
Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required...
Definition: layer.hpp:265
+
caffe::Layer::SetShared
void SetShared(bool is_shared)
Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more t...
Definition: layer.hpp:113
+
caffe::Layer::SetLossWeights
void SetLossWeights(const vector< Blob< Dtype > *> &top)
Definition: layer.hpp:414
+
caffe::Layer::param_propagate_down_
vector< bool > param_propagate_down_
Definition: layer.hpp:328
+
caffe::Layer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: layer.hpp:225
+
caffe::Layer::MaxBottomBlobs
virtual int MaxBottomBlobs() const
Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is requi...
Definition: layer.hpp:241
+
caffe::Layer::set_param_propagate_down
void set_param_propagate_down(const int param_id, const bool value)
Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by par...
Definition: layer.hpp:312
+
caffe::Layer::ShareInParallel
virtual bool ShareInParallel() const
Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
Definition: layer.hpp:101
+
caffe::Layer::layer_param
const LayerParameter & layer_param() const
Returns the layer parameter.
Definition: layer.hpp:189
+
caffe::Layer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: layer.hpp:249
+
caffe::Layer::SetUp
void SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Implements common layer setup functionality.
Definition: layer.hpp:67
+
caffe::Layer::Layer
Layer(const LayerParameter &param)
Definition: layer.hpp:40
+
caffe::Layer::AllowForceBackward
virtual bool AllowForceBackward(const int bottom_index) const
Return whether to allow force_backward for a given bottom blob index.
Definition: layer.hpp:293
+
caffe::Layer::AutoTopBlobs
virtual bool AutoTopBlobs() const
Return whether "anonymous" top blobs are created automatically by the layer.
Definition: layer.hpp:283
+
caffe::Layer::MinTopBlobs
virtual int MinTopBlobs() const
Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
Definition: layer.hpp:257
+
caffe::Layer::ToProto
virtual void ToProto(LayerParameter *param, bool write_diff=false)
Writes the layer parameter to a protocol buffer.
Definition: layer.hpp:507
+
caffe::Layer::phase_
Phase phase_
Definition: layer.hpp:324
+
caffe::Layer::layer_param_
LayerParameter layer_param_
Definition: layer.hpp:322
+
caffe::Layer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: layer.hpp:92
+
caffe::Layer::set_loss
void set_loss(const int top_index, const Dtype value)
Sets the loss associated with a top blob at a given index.
Definition: layer.hpp:206
+
caffe::Layer::loss
Dtype loss(const int top_index) const
Returns the scalar loss associated with a top blob at a given index.
Definition: layer.hpp:199
+
caffe::Layer::MinBottomBlobs
virtual int MinBottomBlobs() const
Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
Definition: layer.hpp:233
+
caffe::Layer::IsShared
bool IsShared() const
Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using mo...
Definition: layer.hpp:107
+
caffe::Layer::CheckBlobCounts
virtual void CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Definition: layer.hpp:371
+
caffe::Layer::param_propagate_down
bool param_propagate_down(const int param_id)
Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given b...
Definition: layer.hpp:304
+
caffe::Layer::EqualNumBottomTopBlobs
virtual bool EqualNumBottomTopBlobs() const
Returns true if the layer requires an equal number of bottom and top blobs.
Definition: layer.hpp:273
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/layer__factory_8hpp_source.html b/doxygen/layer__factory_8hpp_source.html new file mode 100644 index 00000000000..7dda3d93a3e --- /dev/null +++ b/doxygen/layer__factory_8hpp_source.html @@ -0,0 +1,81 @@ + + + + + + + +Caffe: include/caffe/layer_factory.hpp Source File + + + + + + + + + +
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Caffe +
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layer_factory.hpp
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1 
39 #ifndef CAFFE_LAYER_FACTORY_H_
40 #define CAFFE_LAYER_FACTORY_H_
41 
42 #include <map>
43 #include <string>
44 #include <vector>
45 
46 #include "caffe/common.hpp"
47 #include "caffe/layer.hpp"
48 #include "caffe/proto/caffe.pb.h"
49 
50 namespace caffe {
51 
52 template <typename Dtype>
53 class Layer;
54 
55 template <typename Dtype>
56 class LayerRegistry {
57  public:
58  typedef shared_ptr<Layer<Dtype> > (*Creator)(const LayerParameter&);
59  typedef std::map<string, Creator> CreatorRegistry;
60 
61  static CreatorRegistry& Registry() {
62  static CreatorRegistry* g_registry_ = new CreatorRegistry();
63  return *g_registry_;
64  }
65 
66  // Adds a creator.
67  static void AddCreator(const string& type, Creator creator) {
68  CreatorRegistry& registry = Registry();
69  CHECK_EQ(registry.count(type), 0)
70  << "Layer type " << type << " already registered.";
71  registry[type] = creator;
72  }
73 
74  // Get a layer using a LayerParameter.
75  static shared_ptr<Layer<Dtype> > CreateLayer(const LayerParameter& param) {
76  if (Caffe::root_solver()) {
77  LOG(INFO) << "Creating layer " << param.name();
78  }
79  const string& type = param.type();
80  CreatorRegistry& registry = Registry();
81  CHECK_EQ(registry.count(type), 1) << "Unknown layer type: " << type
82  << " (known types: " << LayerTypeListString() << ")";
83  return registry[type](param);
84  }
85 
86  static vector<string> LayerTypeList() {
87  CreatorRegistry& registry = Registry();
88  vector<string> layer_types;
89  for (typename CreatorRegistry::iterator iter = registry.begin();
90  iter != registry.end(); ++iter) {
91  layer_types.push_back(iter->first);
92  }
93  return layer_types;
94  }
95 
96  private:
97  // Layer registry should never be instantiated - everything is done with its
98  // static variables.
99  LayerRegistry() {}
100 
101  static string LayerTypeListString() {
102  vector<string> layer_types = LayerTypeList();
103  string layer_types_str;
104  for (vector<string>::iterator iter = layer_types.begin();
105  iter != layer_types.end(); ++iter) {
106  if (iter != layer_types.begin()) {
107  layer_types_str += ", ";
108  }
109  layer_types_str += *iter;
110  }
111  return layer_types_str;
112  }
113 };
114 
115 
116 template <typename Dtype>
117 class LayerRegisterer {
118  public:
119  LayerRegisterer(const string& type,
120  shared_ptr<Layer<Dtype> > (*creator)(const LayerParameter&)) {
121  // LOG(INFO) << "Registering layer type: " << type;
122  LayerRegistry<Dtype>::AddCreator(type, creator);
123  }
124 };
125 
126 
127 #define REGISTER_LAYER_CREATOR(type, creator) \
128  static LayerRegisterer<float> g_creator_f_##type(#type, creator<float>); \
129  static LayerRegisterer<double> g_creator_d_##type(#type, creator<double>) \
130 
131 #define REGISTER_LAYER_CLASS(type) \
132  template <typename Dtype> \
133  shared_ptr<Layer<Dtype> > Creator_##type##Layer(const LayerParameter& param) \
134  { \
135  return shared_ptr<Layer<Dtype> >(new type##Layer<Dtype>(param)); \
136  } \
137  REGISTER_LAYER_CREATOR(type, Creator_##type##Layer)
138 
139 } // namespace caffe
140 
141 #endif // CAFFE_LAYER_FACTORY_H_
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::LayerRegisterer
Definition: layer_factory.hpp:117
+
caffe::LayerRegistry
Definition: layer_factory.hpp:56
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/log__layer_8hpp_source.html b/doxygen/log__layer_8hpp_source.html new file mode 100644 index 00000000000..b504f684a03 --- /dev/null +++ b/doxygen/log__layer_8hpp_source.html @@ -0,0 +1,88 @@ + + + + + + + +Caffe: include/caffe/layers/log_layer.hpp Source File + + + + + + + + + +
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+ + + + + + +
+
Caffe +
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+
log_layer.hpp
+
+
+
1 #ifndef CAFFE_LOG_LAYER_HPP_
2 #define CAFFE_LOG_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/neuron_layer.hpp"
11 
12 namespace caffe {
13 
19 template <typename Dtype>
20 class LogLayer : public NeuronLayer<Dtype> {
21  public:
30  explicit LogLayer(const LayerParameter& param)
31  : NeuronLayer<Dtype>(param) {}
32  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
33  const vector<Blob<Dtype>*>& top);
34 
35  virtual inline const char* type() const { return "Log"; }
36 
37  protected:
48  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
49  const vector<Blob<Dtype>*>& top);
50  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
51  const vector<Blob<Dtype>*>& top);
52 
70  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
71  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
72  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
73  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
74 
75  Dtype base_scale_;
76  Dtype input_scale_, input_shift_;
77  Dtype backward_num_scale_;
78 };
79 
80 } // namespace caffe
81 
82 #endif // CAFFE_LOG_LAYER_HPP_
caffe::LogLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Definition: log_layer.cpp:34
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::LogLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::LogLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: log_layer.cpp:9
+
caffe::LogLayer
Computes , as specified by the scale , shift , and base .
Definition: log_layer.hpp:20
+
caffe::LogLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::LogLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Computes the error gradient w.r.t. the exp inputs.
Definition: log_layer.cpp:57
+
caffe::NeuronLayer
An interface for layers that take one blob as input ( ) and produce one equally-sized blob as output ...
Definition: neuron_layer.hpp:19
+
caffe::LogLayer::LogLayer
LogLayer(const LayerParameter &param)
Definition: log_layer.hpp:30
+
caffe::LogLayer::type
virtual const char * type() const
Returns the layer type.
Definition: log_layer.hpp:35
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/loss__layer_8hpp_source.html b/doxygen/loss__layer_8hpp_source.html new file mode 100644 index 00000000000..b949ec7eddb --- /dev/null +++ b/doxygen/loss__layer_8hpp_source.html @@ -0,0 +1,87 @@ + + + + + + + +Caffe: include/caffe/layers/loss_layer.hpp Source File + + + + + + + + + +
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Caffe +
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loss_layer.hpp
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1 #ifndef CAFFE_LOSS_LAYER_HPP_
2 #define CAFFE_LOSS_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 namespace caffe {
11 
12 const float kLOG_THRESHOLD = 1e-20;
13 
22 template <typename Dtype>
23 class LossLayer : public Layer<Dtype> {
24  public:
25  explicit LossLayer(const LayerParameter& param)
26  : Layer<Dtype>(param) {}
27  virtual void LayerSetUp(
28  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
29  virtual void Reshape(
30  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
31 
32  virtual inline int ExactNumBottomBlobs() const { return 2; }
33 
40  virtual inline bool AutoTopBlobs() const { return true; }
41  virtual inline int ExactNumTopBlobs() const { return 1; }
46  virtual inline bool AllowForceBackward(const int bottom_index) const {
47  return bottom_index != 1;
48  }
49 };
50 
51 } // namespace caffe
52 
53 #endif // CAFFE_LOSS_LAYER_HPP_
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::LossLayer::AutoTopBlobs
virtual bool AutoTopBlobs() const
For convenience and backwards compatibility, instruct the Net to automatically allocate a single top ...
Definition: loss_layer.hpp:40
+
caffe::LossLayer::AllowForceBackward
virtual bool AllowForceBackward(const int bottom_index) const
Definition: loss_layer.hpp:46
+
caffe::LossLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: loss_layer.hpp:32
+
caffe::LossLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: loss_layer.cpp:17
+
caffe::LossLayer
An interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth ...
Definition: loss_layer.hpp:23
+
caffe::LossLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: loss_layer.cpp:8
+
caffe::LossLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: loss_layer.hpp:41
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/loss__layers_8hpp_source.html b/doxygen/loss__layers_8hpp_source.html new file mode 100644 index 00000000000..16f743e7f36 --- /dev/null +++ b/doxygen/loss__layers_8hpp_source.html @@ -0,0 +1,456 @@ + + + + + + +Caffe: include/caffe/loss_layers.hpp Source File + + + + + + + + + + +
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loss_layers.hpp
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+
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1 #ifndef CAFFE_LOSS_LAYERS_HPP_
+
2 #define CAFFE_LOSS_LAYERS_HPP_
+
3 
+
4 #include <string>
+
5 #include <utility>
+
6 #include <vector>
+
7 
+
8 #include "caffe/blob.hpp"
+
9 #include "caffe/common.hpp"
+
10 #include "caffe/layer.hpp"
+
11 #include "caffe/neuron_layers.hpp"
+
12 #include "caffe/proto/caffe.pb.h"
+
13 
+
14 namespace caffe {
+
15 
+
16 const float kLOG_THRESHOLD = 1e-20;
+
17 
+
22 template <typename Dtype>
+
23 class AccuracyLayer : public Layer<Dtype> {
+
24  public:
+
33  explicit AccuracyLayer(const LayerParameter& param)
+
34  : Layer<Dtype>(param) {}
+
35  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
36  const vector<Blob<Dtype>*>& top);
+
37  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
38  const vector<Blob<Dtype>*>& top);
+
39 
+
40  virtual inline const char* type() const { return "Accuracy"; }
+
41  virtual inline int ExactNumBottomBlobs() const { return 2; }
+
42 
+
43  // If there are two top blobs, then the second blob will contain
+
44  // accuracies per class.
+
45  virtual inline int MinTopBlobs() const { return 1; }
+
46  virtual inline int MaxTopBlos() const { return 2; }
+
47 
+
48  protected:
+
73  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
74  const vector<Blob<Dtype>*>& top);
+
75 
+
76 
+
78  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
79  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+
80  for (int i = 0; i < propagate_down.size(); ++i) {
+
81  if (propagate_down[i]) { NOT_IMPLEMENTED; }
+
82  }
+
83  }
+
84 
+
85  int label_axis_, outer_num_, inner_num_;
+
86 
+
87  int top_k_;
+
88 
+
90  bool has_ignore_label_;
+
92  int ignore_label_;
+
94  Blob<Dtype> nums_buffer_;
+
95 };
+
96 
+
105 template <typename Dtype>
+
106 class LossLayer : public Layer<Dtype> {
+
107  public:
+
108  explicit LossLayer(const LayerParameter& param)
+
109  : Layer<Dtype>(param) {}
+
110  virtual void LayerSetUp(
+
111  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
+
112  virtual void Reshape(
+
113  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
+
114 
+
115  virtual inline int ExactNumBottomBlobs() const { return 2; }
+
116 
+
123  virtual inline bool AutoTopBlobs() const { return true; }
+
124  virtual inline int ExactNumTopBlobs() const { return 1; }
+
129  virtual inline bool AllowForceBackward(const int bottom_index) const {
+
130  return bottom_index != 1;
+
131  }
+
132 };
+
133 
+
158 template <typename Dtype>
+
159 class ContrastiveLossLayer : public LossLayer<Dtype> {
+
160  public:
+
161  explicit ContrastiveLossLayer(const LayerParameter& param)
+
162  : LossLayer<Dtype>(param), diff_() {}
+
163  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
164  const vector<Blob<Dtype>*>& top);
+
165 
+
166  virtual inline int ExactNumBottomBlobs() const { return 3; }
+
167  virtual inline const char* type() const { return "ContrastiveLoss"; }
+
172  virtual inline bool AllowForceBackward(const int bottom_index) const {
+
173  return bottom_index != 2;
+
174  }
+
175 
+
176  protected:
+
178  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
179  const vector<Blob<Dtype>*>& top);
+
180  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
181  const vector<Blob<Dtype>*>& top);
+
182 
+
208  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
209  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
210  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
211  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
212 
+
213  Blob<Dtype> diff_; // cached for backward pass
+
214  Blob<Dtype> dist_sq_; // cached for backward pass
+
215  Blob<Dtype> diff_sq_; // tmp storage for gpu forward pass
+
216  Blob<Dtype> summer_vec_; // tmp storage for gpu forward pass
+
217 };
+
218 
+
245 template <typename Dtype>
+
246 class EuclideanLossLayer : public LossLayer<Dtype> {
+
247  public:
+
248  explicit EuclideanLossLayer(const LayerParameter& param)
+
249  : LossLayer<Dtype>(param), diff_() {}
+
250  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
251  const vector<Blob<Dtype>*>& top);
+
252 
+
253  virtual inline const char* type() const { return "EuclideanLoss"; }
+
258  virtual inline bool AllowForceBackward(const int bottom_index) const {
+
259  return true;
+
260  }
+
261 
+
262  protected:
+
264  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
265  const vector<Blob<Dtype>*>& top);
+
266  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
267  const vector<Blob<Dtype>*>& top);
+
268 
+
302  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
303  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
304  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
305  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
306 
+
307  Blob<Dtype> diff_;
+
308 };
+
309 
+
353 template <typename Dtype>
+
354 class HingeLossLayer : public LossLayer<Dtype> {
+
355  public:
+
356  explicit HingeLossLayer(const LayerParameter& param)
+
357  : LossLayer<Dtype>(param) {}
+
358 
+
359  virtual inline const char* type() const { return "HingeLoss"; }
+
360 
+
361  protected:
+
363  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
364  const vector<Blob<Dtype>*>& top);
+
365 
+
393  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
394  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
395 };
+
396 
+
429 template <typename Dtype>
+
430 class InfogainLossLayer : public LossLayer<Dtype> {
+
431  public:
+
432  explicit InfogainLossLayer(const LayerParameter& param)
+
433  : LossLayer<Dtype>(param), infogain_() {}
+
434  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
435  const vector<Blob<Dtype>*>& top);
+
436  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
437  const vector<Blob<Dtype>*>& top);
+
438 
+
439  // InfogainLossLayer takes 2-3 bottom Blobs; if there are 3 the third should
+
440  // be the infogain matrix. (Otherwise the infogain matrix is loaded from a
+
441  // file specified by LayerParameter.)
+
442  virtual inline int ExactNumBottomBlobs() const { return -1; }
+
443  virtual inline int MinBottomBlobs() const { return 2; }
+
444  virtual inline int MaxBottomBlobs() const { return 3; }
+
445 
+
446  virtual inline const char* type() const { return "InfogainLoss"; }
+
447 
+
448  protected:
+
450  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
451  const vector<Blob<Dtype>*>& top);
+
452 
+
485  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
486  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
487 
+
488  Blob<Dtype> infogain_;
+
489 };
+
490 
+
520 template <typename Dtype>
+
521 class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
+
522  public:
+
523  explicit MultinomialLogisticLossLayer(const LayerParameter& param)
+
524  : LossLayer<Dtype>(param) {}
+
525  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
526  const vector<Blob<Dtype>*>& top);
+
527 
+
528  virtual inline const char* type() const { return "MultinomialLogisticLoss"; }
+
529 
+
530  protected:
+
532  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
533  const vector<Blob<Dtype>*>& top);
+
534 
+
563  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
564  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
565 };
+
566 
+
596 template <typename Dtype>
+
597 class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
+
598  public:
+
599  explicit SigmoidCrossEntropyLossLayer(const LayerParameter& param)
+
600  : LossLayer<Dtype>(param),
+
601  sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
+
602  sigmoid_output_(new Blob<Dtype>()) {}
+
603  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
604  const vector<Blob<Dtype>*>& top);
+
605  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
606  const vector<Blob<Dtype>*>& top);
+
607 
+
608  virtual inline const char* type() const { return "SigmoidCrossEntropyLoss"; }
+
609 
+
610  protected:
+
612  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
613  const vector<Blob<Dtype>*>& top);
+
614 
+
645  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
646  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
647  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
648  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
649 
+
651  shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
+
653  shared_ptr<Blob<Dtype> > sigmoid_output_;
+
655  vector<Blob<Dtype>*> sigmoid_bottom_vec_;
+
657  vector<Blob<Dtype>*> sigmoid_top_vec_;
+
658 };
+
659 
+
660 // Forward declare SoftmaxLayer for use in SoftmaxWithLossLayer.
+
661 template <typename Dtype> class SoftmaxLayer;
+
662 
+
691 template <typename Dtype>
+
692 class SoftmaxWithLossLayer : public LossLayer<Dtype> {
+
693  public:
+
702  explicit SoftmaxWithLossLayer(const LayerParameter& param)
+
703  : LossLayer<Dtype>(param) {}
+
704  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+
705  const vector<Blob<Dtype>*>& top);
+
706  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+
707  const vector<Blob<Dtype>*>& top);
+
708 
+
709  virtual inline const char* type() const { return "SoftmaxWithLoss"; }
+
710  virtual inline int ExactNumTopBlobs() const { return -1; }
+
711  virtual inline int MinTopBlobs() const { return 1; }
+
712  virtual inline int MaxTopBlobs() const { return 2; }
+
713 
+
714  protected:
+
715  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+
716  const vector<Blob<Dtype>*>& top);
+
717  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+
718  const vector<Blob<Dtype>*>& top);
+
746  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+
747  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
748  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+
749  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
750 
+
751 
+
753  shared_ptr<Layer<Dtype> > softmax_layer_;
+
755  Blob<Dtype> prob_;
+
757  vector<Blob<Dtype>*> softmax_bottom_vec_;
+
759  vector<Blob<Dtype>*> softmax_top_vec_;
+
761  bool has_ignore_label_;
+
763  int ignore_label_;
+
766  bool normalize_;
+
767 
+
768  int softmax_axis_, outer_num_, inner_num_;
+
769 };
+
770 
+
771 } // namespace caffe
+
772 
+
773 #endif // CAFFE_LOSS_LAYERS_HPP_
+
caffe::LossLayer::AutoTopBlobs
virtual bool AutoTopBlobs() const
For convenience and backwards compatibility, instruct the Net to automatically allocate a single top ...
Definition: loss_layers.hpp:123
+
caffe::SigmoidCrossEntropyLossLayer::sigmoid_bottom_vec_
vector< Blob< Dtype > * > sigmoid_bottom_vec_
bottom vector holder to call the underlying SigmoidLayer::Forward
Definition: loss_layers.hpp:655
+
caffe::SoftmaxWithLossLayer::softmax_layer_
shared_ptr< Layer< Dtype > > softmax_layer_
The internal SoftmaxLayer used to map predictions to a distribution.
Definition: loss_layers.hpp:753
+
caffe::ContrastiveLossLayer::AllowForceBackward
virtual bool AllowForceBackward(const int bottom_index) const
Definition: loss_layers.hpp:172
+
caffe::MultinomialLogisticLossLayer::type
virtual const char * type() const
Returns the layer type.
Definition: loss_layers.hpp:528
+
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers could be called...
Definition: blob.hpp:15
+
caffe::AccuracyLayer::MinTopBlobs
virtual int MinTopBlobs() const
Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
Definition: loss_layers.hpp:45
+
caffe::AccuracyLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: accuracy_layer.cpp:26
+
caffe::SigmoidLayer
Sigmoid function non-linearity , a classic choice in neural networks.
Definition: neuron_layers.hpp:515
+
caffe::SigmoidCrossEntropyLossLayer::sigmoid_output_
shared_ptr< Blob< Dtype > > sigmoid_output_
sigmoid_output stores the output of the SigmoidLayer.
Definition: loss_layers.hpp:653
+
caffe::SigmoidCrossEntropyLossLayer::sigmoid_top_vec_
vector< Blob< Dtype > * > sigmoid_top_vec_
top vector holder to call the underlying SigmoidLayer::Forward
Definition: loss_layers.hpp:657
+
caffe::InfogainLossLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: infogain_loss_layer.cpp:14
+
caffe::ContrastiveLossLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Computes the Contrastive error gradient w.r.t. the inputs.
Definition: contrastive_loss_layer.cpp:66
+
caffe::LossLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: loss_layers.hpp:115
+
caffe::InfogainLossLayer::type
virtual const char * type() const
Returns the layer type.
Definition: loss_layers.hpp:446
+
caffe::HingeLossLayer
Computes the hinge loss for a one-of-many classification task.
Definition: loss_layers.hpp:354
+
caffe::InfogainLossLayer
A generalization of MultinomialLogisticLossLayer that takes an "information gain" (infogain) matrix s...
Definition: loss_layers.hpp:430
+
caffe::SigmoidCrossEntropyLossLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: sigmoid_cross_entropy_loss_layer.cpp:23
+
caffe::AccuracyLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Not implemented – AccuracyLayer cannot be used as a loss.
Definition: loss_layers.hpp:78
+
caffe::LossLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: loss_layer.cpp:23
+
caffe::EuclideanLossLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Computes the Euclidean (L2) loss for real-valued regression tasks.
Definition: euclidean_loss_layer.cpp:20
+
caffe::HingeLossLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Computes the hinge loss for a one-of-many classification task.
Definition: hinge_loss_layer.cpp:14
+
caffe::SoftmaxWithLossLayer::SoftmaxWithLossLayer
SoftmaxWithLossLayer(const LayerParameter &param)
Definition: loss_layers.hpp:702
+
caffe::SoftmaxWithLossLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: loss_layers.hpp:710
+
caffe::SoftmaxWithLossLayer::MinTopBlobs
virtual int MinTopBlobs() const
Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
Definition: loss_layers.hpp:711
+
caffe::SigmoidCrossEntropyLossLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Computes the sigmoid cross-entropy loss error gradient w.r.t. the predictions.
Definition: sigmoid_cross_entropy_loss_layer.cpp:52
+
caffe::LossLayer::AllowForceBackward
virtual bool AllowForceBackward(const int bottom_index) const
Definition: loss_layers.hpp:129
+
caffe::SoftmaxWithLossLayer::softmax_top_vec_
vector< Blob< Dtype > * > softmax_top_vec_
top vector holder used in call to the underlying SoftmaxLayer::Forward
Definition: loss_layers.hpp:759
+
caffe::AccuracyLayer::ignore_label_
int ignore_label_
The label indicating that an instance should be ignored.
Definition: loss_layers.hpp:92
+
caffe::AccuracyLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: accuracy_layer.cpp:14
+
caffe::SigmoidCrossEntropyLossLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::SigmoidCrossEntropyLossLayer::sigmoid_layer_
shared_ptr< SigmoidLayer< Dtype > > sigmoid_layer_
The internal SigmoidLayer used to map predictions to probabilities.
Definition: loss_layers.hpp:651
+
caffe::SoftmaxWithLossLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: softmax_loss_layer.cpp:13
+
caffe::AccuracyLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Definition: accuracy_layer.cpp:51
+
caffe::AccuracyLayer::type
virtual const char * type() const
Returns the layer type.
Definition: loss_layers.hpp:40
+
caffe::SoftmaxLayer
Computes the softmax function.
Definition: common_layers.hpp:597
+
caffe::InfogainLossLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: infogain_loss_layer.cpp:28
+
caffe::SoftmaxWithLossLayer::MaxTopBlobs
virtual int MaxTopBlobs() const
Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required...
Definition: loss_layers.hpp:712
+
caffe::AccuracyLayer
Computes the classification accuracy for a one-of-many classification task.
Definition: loss_layers.hpp:23
+
caffe::HingeLossLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Computes the hinge loss error gradient w.r.t. the predictions.
Definition: hinge_loss_layer.cpp:47
+
caffe::EuclideanLossLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: euclidean_loss_layer.cpp:11
+
caffe::EuclideanLossLayer::type
virtual const char * type() const
Returns the layer type.
Definition: loss_layers.hpp:253
+
caffe::AccuracyLayer::has_ignore_label_
bool has_ignore_label_
Whether to ignore instances with a certain label.
Definition: loss_layers.hpp:90
+
caffe::InfogainLossLayer::MinBottomBlobs
virtual int MinBottomBlobs() const
Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
Definition: loss_layers.hpp:443
+
caffe::SigmoidCrossEntropyLossLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: sigmoid_cross_entropy_loss_layer.cpp:12
+
caffe::LossLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: loss_layer.cpp:14
+
caffe::EuclideanLossLayer::AllowForceBackward
virtual bool AllowForceBackward(const int bottom_index) const
Definition: loss_layers.hpp:258
+
caffe::EuclideanLossLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::MultinomialLogisticLossLayer
Computes the multinomial logistic loss for a one-of-many classification task, directly taking a predi...
Definition: loss_layers.hpp:521
+
caffe::ContrastiveLossLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Computes the contrastive loss where . This can be used to train siamese networks.
Definition: contrastive_loss_layer.cpp:33
+
caffe::AccuracyLayer::AccuracyLayer
AccuracyLayer(const LayerParameter &param)
Definition: loss_layers.hpp:33
+
caffe::SoftmaxWithLossLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::SigmoidCrossEntropyLossLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Computes the cross-entropy (logistic) loss , often used for predicting targets interpreted as probabi...
Definition: sigmoid_cross_entropy_loss_layer.cpp:32
+
caffe::SigmoidCrossEntropyLossLayer
Computes the cross-entropy (logistic) loss , often used for predicting targets interpreted as probabi...
Definition: loss_layers.hpp:597
+
caffe::SigmoidCrossEntropyLossLayer::type
virtual const char * type() const
Returns the layer type.
Definition: loss_layers.hpp:608
+
caffe::SoftmaxWithLossLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Computes the softmax loss error gradient w.r.t. the predictions.
Definition: softmax_loss_layer.cpp:87
+
caffe::SoftmaxWithLossLayer
Computes the multinomial logistic loss for a one-of-many classification task, passing real-valued pre...
Definition: loss_layers.hpp:692
+
caffe::SoftmaxWithLossLayer::softmax_bottom_vec_
vector< Blob< Dtype > * > softmax_bottom_vec_
bottom vector holder used in call to the underlying SoftmaxLayer::Forward
Definition: loss_layers.hpp:757
+
caffe::SoftmaxWithLossLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: softmax_loss_layer.cpp:34
+
caffe::SoftmaxWithLossLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::ContrastiveLossLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::ContrastiveLossLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: loss_layers.hpp:166
+
caffe::ContrastiveLossLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::SoftmaxWithLossLayer::normalize_
bool normalize_
Definition: loss_layers.hpp:766
+
caffe::EuclideanLossLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Computes the Euclidean error gradient w.r.t. the inputs.
Definition: euclidean_loss_layer.cpp:34
+
caffe::SoftmaxWithLossLayer::ignore_label_
int ignore_label_
The label indicating that an instance should be ignored.
Definition: loss_layers.hpp:763
+
caffe::InfogainLossLayer::MaxBottomBlobs
virtual int MaxBottomBlobs() const
Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is requi...
Definition: loss_layers.hpp:444
+
caffe::LossLayer
An interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth ...
Definition: loss_layers.hpp:106
+
caffe::SoftmaxWithLossLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the CPU device, compute the layer output.
Definition: softmax_loss_layer.cpp:54
+
caffe::InfogainLossLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
A generalization of MultinomialLogisticLossLayer that takes an "information gain" (infogain) matrix s...
Definition: infogain_loss_layer.cpp:50
+
caffe::ContrastiveLossLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: contrastive_loss_layer.cpp:12
+
caffe::SoftmaxWithLossLayer::type
virtual const char * type() const
Returns the layer type.
Definition: loss_layers.hpp:709
+
caffe::EuclideanLossLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::LossLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: loss_layers.hpp:124
+
caffe::ContrastiveLossLayer
Computes the contrastive loss where . This can be used to train siamese networks.
Definition: loss_layers.hpp:159
+
caffe::InfogainLossLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Computes the infogain loss error gradient w.r.t. the predictions.
Definition: infogain_loss_layer.cpp:74
+
caffe::AccuracyLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: loss_layers.hpp:41
+
caffe::AccuracyLayer::nums_buffer_
Blob< Dtype > nums_buffer_
Keeps counts of the number of samples per class.
Definition: loss_layers.hpp:94
+
caffe::HingeLossLayer::type
virtual const char * type() const
Returns the layer type.
Definition: loss_layers.hpp:359
+
caffe::InfogainLossLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: loss_layers.hpp:442
+
caffe::MultinomialLogisticLossLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Computes the multinomial logistic loss for a one-of-many classification task, directly taking a predi...
Definition: multinomial_logistic_loss_layer.cpp:23
+
caffe::EuclideanLossLayer
Computes the Euclidean (L2) loss for real-valued regression tasks.
Definition: loss_layers.hpp:246
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caffe::SoftmaxWithLossLayer::prob_
Blob< Dtype > prob_
prob stores the output probability predictions from the SoftmaxLayer.
Definition: loss_layers.hpp:755
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caffe::MultinomialLogisticLossLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: multinomial_logistic_loss_layer.cpp:14
+
caffe::ContrastiveLossLayer::type
virtual const char * type() const
Returns the layer type.
Definition: loss_layers.hpp:167
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caffe::MultinomialLogisticLossLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Computes the multinomial logistic loss error gradient w.r.t. the predictions.
Definition: multinomial_logistic_loss_layer.cpp:40
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:25
+
caffe::SoftmaxWithLossLayer::has_ignore_label_
bool has_ignore_label_
Whether to ignore instances with a certain label.
Definition: loss_layers.hpp:761
+
+ +
+Generated on Mon Oct 19 2015 15:25:08 for Caffe by   +doxygen + 1.8.9.1 +
+ + diff --git a/doxygen/lrn__layer_8hpp_source.html b/doxygen/lrn__layer_8hpp_source.html new file mode 100644 index 00000000000..4d742d0e6e3 --- /dev/null +++ b/doxygen/lrn__layer_8hpp_source.html @@ -0,0 +1,90 @@ + + + + + + + +Caffe: include/caffe/layers/lrn_layer.hpp Source File + + + + + + + + + +
+
+ + + + + + +
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Caffe +
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lrn_layer.hpp
+
+
+
1 #ifndef CAFFE_LRN_LAYER_HPP_
2 #define CAFFE_LRN_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/eltwise_layer.hpp"
11 #include "caffe/layers/pooling_layer.hpp"
12 #include "caffe/layers/power_layer.hpp"
13 #include "caffe/layers/split_layer.hpp"
14 
15 namespace caffe {
16 
22 template <typename Dtype>
23 class LRNLayer : public Layer<Dtype> {
24  public:
25  explicit LRNLayer(const LayerParameter& param)
26  : Layer<Dtype>(param) {}
27  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
28  const vector<Blob<Dtype>*>& top);
29  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
30  const vector<Blob<Dtype>*>& top);
31 
32  virtual inline const char* type() const { return "LRN"; }
33  virtual inline int ExactNumBottomBlobs() const { return 1; }
34  virtual inline int ExactNumTopBlobs() const { return 1; }
35 
36  protected:
37  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
38  const vector<Blob<Dtype>*>& top);
39  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
40  const vector<Blob<Dtype>*>& top);
41  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
42  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
43  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
44  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
45 
46  virtual void CrossChannelForward_cpu(const vector<Blob<Dtype>*>& bottom,
47  const vector<Blob<Dtype>*>& top);
48  virtual void CrossChannelForward_gpu(const vector<Blob<Dtype>*>& bottom,
49  const vector<Blob<Dtype>*>& top);
50  virtual void WithinChannelForward(const vector<Blob<Dtype>*>& bottom,
51  const vector<Blob<Dtype>*>& top);
52  virtual void CrossChannelBackward_cpu(const vector<Blob<Dtype>*>& top,
53  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
54  virtual void CrossChannelBackward_gpu(const vector<Blob<Dtype>*>& top,
55  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
56  virtual void WithinChannelBackward(const vector<Blob<Dtype>*>& top,
57  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
58 
59  int size_;
60  int pre_pad_;
61  Dtype alpha_;
62  Dtype beta_;
63  Dtype k_;
64  int num_;
65  int channels_;
66  int height_;
67  int width_;
68 
69  // Fields used for normalization ACROSS_CHANNELS
70  // scale_ stores the intermediate summing results
71  Blob<Dtype> scale_;
72 
73  // Fields used for normalization WITHIN_CHANNEL
74  shared_ptr<SplitLayer<Dtype> > split_layer_;
75  vector<Blob<Dtype>*> split_top_vec_;
76  shared_ptr<PowerLayer<Dtype> > square_layer_;
77  Blob<Dtype> square_input_;
78  Blob<Dtype> square_output_;
79  vector<Blob<Dtype>*> square_bottom_vec_;
80  vector<Blob<Dtype>*> square_top_vec_;
81  shared_ptr<PoolingLayer<Dtype> > pool_layer_;
82  Blob<Dtype> pool_output_;
83  vector<Blob<Dtype>*> pool_top_vec_;
84  shared_ptr<PowerLayer<Dtype> > power_layer_;
85  Blob<Dtype> power_output_;
86  vector<Blob<Dtype>*> power_top_vec_;
87  shared_ptr<EltwiseLayer<Dtype> > product_layer_;
88  Blob<Dtype> product_input_;
89  vector<Blob<Dtype>*> product_bottom_vec_;
90 };
91 
92 } // namespace caffe
93 
94 #endif // CAFFE_LRN_LAYER_HPP_
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
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caffe::LRNLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: lrn_layer.hpp:34
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caffe::LRNLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
+
caffe::LRNLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: lrn_layer.cpp:9
+
caffe::LRNLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: lrn_layer.cpp:69
+
caffe::LRNLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: lrn_layer.cpp:165
+
caffe::LRNLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: lrn_layer.hpp:33
+
caffe::LRNLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the CPU device, compute the layer output.
Definition: lrn_layer.cpp:93
+
caffe::LRNLayer::type
virtual const char * type() const
Returns the layer type.
Definition: lrn_layer.hpp:32
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caffe::LRNLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::LRNLayer
Normalize the input in a local region across or within feature maps.
Definition: lrn_layer.hpp:23
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/lstm__layer_8hpp_source.html b/doxygen/lstm__layer_8hpp_source.html new file mode 100644 index 00000000000..3830807f85e --- /dev/null +++ b/doxygen/lstm__layer_8hpp_source.html @@ -0,0 +1,99 @@ + + + + + + + +Caffe: include/caffe/layers/lstm_layer.hpp Source File + + + + + + + + + +
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Caffe +
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lstm_layer.hpp
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+
+
1 #ifndef CAFFE_LSTM_LAYER_HPP_
2 #define CAFFE_LSTM_LAYER_HPP_
3 
4 #include <string>
5 #include <utility>
6 #include <vector>
7 
8 #include "caffe/blob.hpp"
9 #include "caffe/common.hpp"
10 #include "caffe/layer.hpp"
11 #include "caffe/layers/recurrent_layer.hpp"
12 #include "caffe/net.hpp"
13 #include "caffe/proto/caffe.pb.h"
14 
15 namespace caffe {
16 
17 template <typename Dtype> class RecurrentLayer;
18 
47 template <typename Dtype>
48 class LSTMLayer : public RecurrentLayer<Dtype> {
49  public:
50  explicit LSTMLayer(const LayerParameter& param)
51  : RecurrentLayer<Dtype>(param) {}
52 
53  virtual inline const char* type() const { return "LSTM"; }
54 
55  protected:
56  virtual void FillUnrolledNet(NetParameter* net_param) const;
57  virtual void RecurrentInputBlobNames(vector<string>* names) const;
58  virtual void RecurrentOutputBlobNames(vector<string>* names) const;
59  virtual void RecurrentInputShapes(vector<BlobShape>* shapes) const;
60  virtual void OutputBlobNames(vector<string>* names) const;
61 };
62 
68 template <typename Dtype>
69 class LSTMUnitLayer : public Layer<Dtype> {
70  public:
71  explicit LSTMUnitLayer(const LayerParameter& param)
72  : Layer<Dtype>(param) {}
73  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
74  const vector<Blob<Dtype>*>& top);
75 
76  virtual inline const char* type() const { return "LSTMUnit"; }
77  virtual inline int ExactNumBottomBlobs() const { return 3; }
78  virtual inline int ExactNumTopBlobs() const { return 2; }
79 
80  virtual inline bool AllowForceBackward(const int bottom_index) const {
81  // Can't propagate to sequence continuation indicators.
82  return bottom_index != 2;
83  }
84 
85  protected:
106  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
107  const vector<Blob<Dtype>*>& top);
108  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
109  const vector<Blob<Dtype>*>& top);
110 
142  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
143  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
144  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
145  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
146 
148  int hidden_dim_;
149  Blob<Dtype> X_acts_;
150 };
151 
152 } // namespace caffe
153 
154 #endif // CAFFE_LSTM_LAYER_HPP_
caffe::Layer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: layer.hpp:359
+
caffe::LSTMLayer
Processes sequential inputs using a "Long Short-Term Memory" (LSTM) [1] style recurrent neural networ...
Definition: lstm_layer.hpp:48
+
caffe::LSTMLayer::RecurrentInputShapes
virtual void RecurrentInputShapes(vector< BlobShape > *shapes) const
Fills shapes with the shapes of the recurrent input Blob&s. Subclasses should define this – see RNNL...
Definition: lstm_layer.cpp:28
+
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
+
caffe::LSTMLayer::RecurrentOutputBlobNames
virtual void RecurrentOutputBlobNames(vector< string > *names) const
Fills names with the names of the Tth timestep recurrent output Blob&s. Subclasses should define this...
Definition: lstm_layer.cpp:21
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::RecurrentLayer
An abstract class for implementing recurrent behavior inside of an unrolled network. This Layer type cannot be instantiated – instead, you should use one of its implementations which defines the recurrent architecture, such as RNNLayer or LSTMLayer.
Definition: lstm_layer.hpp:17
+
caffe::RecurrentLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Definition: recurrent_layer.cpp:245
+
caffe::RecurrentLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
+
caffe::LSTMLayer::RecurrentInputBlobNames
virtual void RecurrentInputBlobNames(vector< string > *names) const
Fills names with the names of the 0th timestep recurrent input Blob&s. Subclasses should define this ...
Definition: lstm_layer.cpp:14
+
caffe::LSTMLayer::FillUnrolledNet
virtual void FillUnrolledNet(NetParameter *net_param) const
Fills net_param with the recurrent network architecture. Subclasses should define this – see RNNLaye...
Definition: lstm_layer.cpp:47
+
caffe::LSTMUnitLayer::type
virtual const char * type() const
Returns the layer type.
Definition: lstm_layer.hpp:76
+
caffe::LSTMUnitLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: lstm_layer.hpp:77
+
caffe::LSTMUnitLayer::AllowForceBackward
virtual bool AllowForceBackward(const int bottom_index) const
Return whether to allow force_backward for a given bottom blob index.
Definition: lstm_layer.hpp:80
+
caffe::LSTMUnitLayer::hidden_dim_
int hidden_dim_
The hidden and output dimension.
Definition: lstm_layer.hpp:148
+
caffe::LSTMLayer::OutputBlobNames
virtual void OutputBlobNames(vector< string > *names) const
Fills names with the names of the output blobs, concatenated across all timesteps. Should return a name for each top Blob. Subclasses should define this – see RNNLayer and LSTMLayer for examples.
Definition: lstm_layer.cpp:41
+
caffe::LSTMLayer::type
virtual const char * type() const
Returns the layer type.
Definition: lstm_layer.hpp:53
+
caffe::RecurrentLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: recurrent_layer.cpp:183
+
caffe::RecurrentLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: recurrent_layer.cpp:277
+
caffe::LSTMUnitLayer
A helper for LSTMLayer: computes a single timestep of the non-linearity of the LSTM, producing the updated cell and hidden states.
Definition: lstm_layer.hpp:69
+
caffe::LSTMUnitLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: lstm_layer.hpp:78
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/math__functions_8hpp_source.html b/doxygen/math__functions_8hpp_source.html new file mode 100644 index 00000000000..3de1e659ad6 --- /dev/null +++ b/doxygen/math__functions_8hpp_source.html @@ -0,0 +1,78 @@ + + + + + + + +Caffe: include/caffe/util/math_functions.hpp Source File + + + + + + + + + +
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Caffe +
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math_functions.hpp
+
+
+
1 #ifndef CAFFE_UTIL_MATH_FUNCTIONS_H_
2 #define CAFFE_UTIL_MATH_FUNCTIONS_H_
3 
4 #include <stdint.h>
5 #include <cmath> // for std::fabs and std::signbit
6 
7 #include "glog/logging.h"
8 
9 #include "caffe/common.hpp"
10 #include "caffe/util/device_alternate.hpp"
11 #include "caffe/util/mkl_alternate.hpp"
12 
13 namespace caffe {
14 
15 // Caffe gemm provides a simpler interface to the gemm functions, with the
16 // limitation that the data has to be contiguous in memory.
17 template <typename Dtype>
18 void caffe_cpu_gemm(const CBLAS_TRANSPOSE TransA,
19  const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
20  const Dtype alpha, const Dtype* A, const Dtype* B, const Dtype beta,
21  Dtype* C);
22 
23 template <typename Dtype>
24 void caffe_cpu_gemv(const CBLAS_TRANSPOSE TransA, const int M, const int N,
25  const Dtype alpha, const Dtype* A, const Dtype* x, const Dtype beta,
26  Dtype* y);
27 
28 template <typename Dtype>
29 void caffe_axpy(const int N, const Dtype alpha, const Dtype* X,
30  Dtype* Y);
31 
32 template <typename Dtype>
33 void caffe_cpu_axpby(const int N, const Dtype alpha, const Dtype* X,
34  const Dtype beta, Dtype* Y);
35 
36 template <typename Dtype>
37 void caffe_copy(const int N, const Dtype *X, Dtype *Y);
38 
39 template <typename Dtype>
40 void caffe_set(const int N, const Dtype alpha, Dtype *X);
41 
42 inline void caffe_memset(const size_t N, const int alpha, void* X) {
43  memset(X, alpha, N); // NOLINT(caffe/alt_fn)
44 }
45 
46 template <typename Dtype>
47 void caffe_add_scalar(const int N, const Dtype alpha, Dtype *X);
48 
49 template <typename Dtype>
50 void caffe_scal(const int N, const Dtype alpha, Dtype *X);
51 
52 template <typename Dtype>
53 void caffe_sqr(const int N, const Dtype* a, Dtype* y);
54 
55 template <typename Dtype>
56 void caffe_add(const int N, const Dtype* a, const Dtype* b, Dtype* y);
57 
58 template <typename Dtype>
59 void caffe_sub(const int N, const Dtype* a, const Dtype* b, Dtype* y);
60 
61 template <typename Dtype>
62 void caffe_mul(const int N, const Dtype* a, const Dtype* b, Dtype* y);
63 
64 template <typename Dtype>
65 void caffe_div(const int N, const Dtype* a, const Dtype* b, Dtype* y);
66 
67 template <typename Dtype>
68 void caffe_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
69 
70 unsigned int caffe_rng_rand();
71 
72 template <typename Dtype>
73 Dtype caffe_nextafter(const Dtype b);
74 
75 template <typename Dtype>
76 void caffe_rng_uniform(const int n, const Dtype a, const Dtype b, Dtype* r);
77 
78 template <typename Dtype>
79 void caffe_rng_gaussian(const int n, const Dtype mu, const Dtype sigma,
80  Dtype* r);
81 
82 template <typename Dtype>
83 void caffe_rng_bernoulli(const int n, const Dtype p, int* r);
84 
85 template <typename Dtype>
86 void caffe_rng_bernoulli(const int n, const Dtype p, unsigned int* r);
87 
88 template <typename Dtype>
89 void caffe_exp(const int n, const Dtype* a, Dtype* y);
90 
91 template <typename Dtype>
92 void caffe_log(const int n, const Dtype* a, Dtype* y);
93 
94 template <typename Dtype>
95 void caffe_abs(const int n, const Dtype* a, Dtype* y);
96 
97 template <typename Dtype>
98 Dtype caffe_cpu_dot(const int n, const Dtype* x, const Dtype* y);
99 
100 template <typename Dtype>
101 Dtype caffe_cpu_strided_dot(const int n, const Dtype* x, const int incx,
102  const Dtype* y, const int incy);
103 
104 // Returns the sum of the absolute values of the elements of vector x
105 template <typename Dtype>
106 Dtype caffe_cpu_asum(const int n, const Dtype* x);
107 
108 // the branchless, type-safe version from
109 // http://stackoverflow.com/questions/1903954/is-there-a-standard-sign-function-signum-sgn-in-c-c
110 template<typename Dtype>
111 inline int8_t caffe_sign(Dtype val) {
112  return (Dtype(0) < val) - (val < Dtype(0));
113 }
114 
115 // The following two macros are modifications of DEFINE_VSL_UNARY_FUNC
116 // in include/caffe/util/mkl_alternate.hpp authored by @Rowland Depp.
117 // Please refer to commit 7e8ef25c7 of the boost-eigen branch.
118 // Git cherry picking that commit caused a conflict hard to resolve and
119 // copying that file in convenient for code reviewing.
120 // So they have to be pasted here temporarily.
121 #define DEFINE_CAFFE_CPU_UNARY_FUNC(name, operation) \
122  template<typename Dtype> \
123  void caffe_cpu_##name(const int n, const Dtype* x, Dtype* y) { \
124  CHECK_GT(n, 0); CHECK(x); CHECK(y); \
125  for (int i = 0; i < n; ++i) { \
126  operation; \
127  } \
128  }
129 
130 // output is 1 for the positives, 0 for zero, and -1 for the negatives
131 DEFINE_CAFFE_CPU_UNARY_FUNC(sign, y[i] = caffe_sign<Dtype>(x[i]));
132 
133 // This returns a nonzero value if the input has its sign bit set.
134 // The name sngbit is meant to avoid conflicts with std::signbit in the macro.
135 // The extra parens are needed because CUDA < 6.5 defines signbit as a macro,
136 // and we don't want that to expand here when CUDA headers are also included.
137 DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, \
138  y[i] = static_cast<bool>((std::signbit)(x[i])));
139 
140 DEFINE_CAFFE_CPU_UNARY_FUNC(fabs, y[i] = std::fabs(x[i]));
141 
142 template <typename Dtype>
143 void caffe_cpu_scale(const int n, const Dtype alpha, const Dtype *x, Dtype* y);
144 
145 #ifndef CPU_ONLY // GPU
146 
147 // Decaf gpu gemm provides an interface that is almost the same as the cpu
148 // gemm function - following the c convention and calling the fortran-order
149 // gpu code under the hood.
150 template <typename Dtype>
151 void caffe_gpu_gemm(const CBLAS_TRANSPOSE TransA,
152  const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
153  const Dtype alpha, const Dtype* A, const Dtype* B, const Dtype beta,
154  Dtype* C);
155 
156 template <typename Dtype>
157 void caffe_gpu_gemv(const CBLAS_TRANSPOSE TransA, const int M, const int N,
158  const Dtype alpha, const Dtype* A, const Dtype* x, const Dtype beta,
159  Dtype* y);
160 
161 template <typename Dtype>
162 void caffe_gpu_axpy(const int N, const Dtype alpha, const Dtype* X,
163  Dtype* Y);
164 
165 template <typename Dtype>
166 void caffe_gpu_axpby(const int N, const Dtype alpha, const Dtype* X,
167  const Dtype beta, Dtype* Y);
168 
169 void caffe_gpu_memcpy(const size_t N, const void *X, void *Y);
170 
171 template <typename Dtype>
172 void caffe_gpu_set(const int N, const Dtype alpha, Dtype *X);
173 
174 inline void caffe_gpu_memset(const size_t N, const int alpha, void* X) {
175 #ifndef CPU_ONLY
176  CUDA_CHECK(cudaMemset(X, alpha, N)); // NOLINT(caffe/alt_fn)
177 #else
178  NO_GPU;
179 #endif
180 }
181 
182 template <typename Dtype>
183 void caffe_gpu_add_scalar(const int N, const Dtype alpha, Dtype *X);
184 
185 template <typename Dtype>
186 void caffe_gpu_scal(const int N, const Dtype alpha, Dtype *X);
187 
188 template <typename Dtype>
189 void caffe_gpu_add(const int N, const Dtype* a, const Dtype* b, Dtype* y);
190 
191 template <typename Dtype>
192 void caffe_gpu_sub(const int N, const Dtype* a, const Dtype* b, Dtype* y);
193 
194 template <typename Dtype>
195 void caffe_gpu_mul(const int N, const Dtype* a, const Dtype* b, Dtype* y);
196 
197 template <typename Dtype>
198 void caffe_gpu_div(const int N, const Dtype* a, const Dtype* b, Dtype* y);
199 
200 template <typename Dtype>
201 void caffe_gpu_abs(const int n, const Dtype* a, Dtype* y);
202 
203 template <typename Dtype>
204 void caffe_gpu_exp(const int n, const Dtype* a, Dtype* y);
205 
206 template <typename Dtype>
207 void caffe_gpu_log(const int n, const Dtype* a, Dtype* y);
208 
209 template <typename Dtype>
210 void caffe_gpu_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
211 
212 // caffe_gpu_rng_uniform with two arguments generates integers in the range
213 // [0, UINT_MAX].
214 void caffe_gpu_rng_uniform(const int n, unsigned int* r);
215 
216 // caffe_gpu_rng_uniform with four arguments generates floats in the range
217 // (a, b] (strictly greater than a, less than or equal to b) due to the
218 // specification of curandGenerateUniform. With a = 0, b = 1, just calls
219 // curandGenerateUniform; with other limits will shift and scale the outputs
220 // appropriately after calling curandGenerateUniform.
221 template <typename Dtype>
222 void caffe_gpu_rng_uniform(const int n, const Dtype a, const Dtype b, Dtype* r);
223 
224 template <typename Dtype>
225 void caffe_gpu_rng_gaussian(const int n, const Dtype mu, const Dtype sigma,
226  Dtype* r);
227 
228 template <typename Dtype>
229 void caffe_gpu_rng_bernoulli(const int n, const Dtype p, int* r);
230 
231 template <typename Dtype>
232 void caffe_gpu_dot(const int n, const Dtype* x, const Dtype* y, Dtype* out);
233 
234 template <typename Dtype>
235 void caffe_gpu_asum(const int n, const Dtype* x, Dtype* y);
236 
237 template<typename Dtype>
238 void caffe_gpu_sign(const int n, const Dtype* x, Dtype* y);
239 
240 template<typename Dtype>
241 void caffe_gpu_sgnbit(const int n, const Dtype* x, Dtype* y);
242 
243 template <typename Dtype>
244 void caffe_gpu_fabs(const int n, const Dtype* x, Dtype* y);
245 
246 template <typename Dtype>
247 void caffe_gpu_scale(const int n, const Dtype alpha, const Dtype *x, Dtype* y);
248 
249 #define DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(name, operation) \
250 template<typename Dtype> \
251 __global__ void name##_kernel(const int n, const Dtype* x, Dtype* y) { \
252  CUDA_KERNEL_LOOP(index, n) { \
253  operation; \
254  } \
255 } \
256 template <> \
257 void caffe_gpu_##name<float>(const int n, const float* x, float* y) { \
258  /* NOLINT_NEXT_LINE(whitespace/operators) */ \
259  name##_kernel<float><<<CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS>>>( \
260  n, x, y); \
261 } \
262 template <> \
263 void caffe_gpu_##name<double>(const int n, const double* x, double* y) { \
264  /* NOLINT_NEXT_LINE(whitespace/operators) */ \
265  name##_kernel<double><<<CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS>>>( \
266  n, x, y); \
267 }
268 
269 #endif // !CPU_ONLY
270 
271 } // namespace caffe
272 
273 #endif // CAFFE_UTIL_MATH_FUNCTIONS_H_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/memory__data__layer_8hpp_source.html b/doxygen/memory__data__layer_8hpp_source.html new file mode 100644 index 00000000000..2138456a3cb --- /dev/null +++ b/doxygen/memory__data__layer_8hpp_source.html @@ -0,0 +1,85 @@ + + + + + + + +Caffe: include/caffe/layers/memory_data_layer.hpp Source File + + + + + + + + + +
+
+ + + + + + +
+
Caffe +
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+ + + + + + + + +
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+ + +
+
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+
memory_data_layer.hpp
+
+
+
1 #ifndef CAFFE_MEMORY_DATA_LAYER_HPP_
2 #define CAFFE_MEMORY_DATA_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 #include "caffe/layers/base_data_layer.hpp"
11 
12 namespace caffe {
13 
19 template <typename Dtype>
20 class MemoryDataLayer : public BaseDataLayer<Dtype> {
21  public:
22  explicit MemoryDataLayer(const LayerParameter& param)
23  : BaseDataLayer<Dtype>(param), has_new_data_(false) {}
24  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
25  const vector<Blob<Dtype>*>& top);
26 
27  virtual inline const char* type() const { return "MemoryData"; }
28  virtual inline int ExactNumBottomBlobs() const { return 0; }
29  virtual inline int ExactNumTopBlobs() const { return 2; }
30 
31  virtual void AddDatumVector(const vector<Datum>& datum_vector);
32 #ifdef USE_OPENCV
33  virtual void AddMatVector(const vector<cv::Mat>& mat_vector,
34  const vector<int>& labels);
35 #endif // USE_OPENCV
36 
37  // Reset should accept const pointers, but can't, because the memory
38  // will be given to Blob, which is mutable
39  void Reset(Dtype* data, Dtype* label, int n);
40  void set_batch_size(int new_size);
41 
42  int batch_size() { return batch_size_; }
43  int channels() { return channels_; }
44  int height() { return height_; }
45  int width() { return width_; }
46 
47  protected:
48  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
49  const vector<Blob<Dtype>*>& top);
50 
51  int batch_size_, channels_, height_, width_, size_;
52  Dtype* data_;
53  Dtype* labels_;
54  int n_;
55  size_t pos_;
56  Blob<Dtype> added_data_;
57  Blob<Dtype> added_label_;
58  bool has_new_data_;
59 };
60 
61 } // namespace caffe
62 
63 #endif // CAFFE_MEMORY_DATA_LAYER_HPP_
caffe::MemoryDataLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the CPU device, compute the layer output.
Definition: memory_data_layer.cpp:108
+
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
+
caffe::MemoryDataLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: memory_data_layer.hpp:28
+
caffe::BaseDataLayer
Provides base for data layers that feed blobs to the Net.
Definition: base_data_layer.hpp:21
+
caffe::MemoryDataLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
Definition: memory_data_layer.hpp:29
+
caffe::MemoryDataLayer
Provides data to the Net from memory.
Definition: memory_data_layer.hpp:20
+
caffe::MemoryDataLayer::type
virtual const char * type() const
Returns the layer type.
Definition: memory_data_layer.hpp:27
+
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24
+
+ +
+Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
+ + diff --git a/doxygen/menu.js b/doxygen/menu.js new file mode 100644 index 00000000000..97db4c23922 --- /dev/null +++ b/doxygen/menu.js @@ -0,0 +1,26 @@ +function initMenu(relPath,searchEnabled,serverSide,searchPage,search) { + function makeTree(data,relPath) { + var result=''; + if ('children' in data) { + result+=''; + } + return result; + } + + $('#main-nav').append(makeTree(menudata,relPath)); + $('#main-nav').children(':first').addClass('sm sm-dox').attr('id','main-menu'); + if (searchEnabled) { + if (serverSide) { + $('#main-menu').append('
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    +
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    mkl_alternate.hpp
    +
    +
    +
    1 #ifndef CAFFE_UTIL_MKL_ALTERNATE_H_
    2 #define CAFFE_UTIL_MKL_ALTERNATE_H_
    3 
    4 #ifdef USE_MKL
    5 
    6 #include <mkl.h>
    7 
    8 #else // If use MKL, simply include the MKL header
    9 
    10 #ifdef USE_ACCELERATE
    11 #include <Accelerate/Accelerate.h>
    12 #else
    13 extern "C" {
    14 #include <cblas.h>
    15 }
    16 #endif // USE_ACCELERATE
    17 
    18 #include <math.h>
    19 
    20 // Functions that caffe uses but are not present if MKL is not linked.
    21 
    22 // A simple way to define the vsl unary functions. The operation should
    23 // be in the form e.g. y[i] = sqrt(a[i])
    24 #define DEFINE_VSL_UNARY_FUNC(name, operation) \
    25  template<typename Dtype> \
    26  void v##name(const int n, const Dtype* a, Dtype* y) { \
    27  CHECK_GT(n, 0); CHECK(a); CHECK(y); \
    28  for (int i = 0; i < n; ++i) { operation; } \
    29  } \
    30  inline void vs##name( \
    31  const int n, const float* a, float* y) { \
    32  v##name<float>(n, a, y); \
    33  } \
    34  inline void vd##name( \
    35  const int n, const double* a, double* y) { \
    36  v##name<double>(n, a, y); \
    37  }
    38 
    39 DEFINE_VSL_UNARY_FUNC(Sqr, y[i] = a[i] * a[i]);
    40 DEFINE_VSL_UNARY_FUNC(Exp, y[i] = exp(a[i]));
    41 DEFINE_VSL_UNARY_FUNC(Ln, y[i] = log(a[i]));
    42 DEFINE_VSL_UNARY_FUNC(Abs, y[i] = fabs(a[i]));
    43 
    44 // A simple way to define the vsl unary functions with singular parameter b.
    45 // The operation should be in the form e.g. y[i] = pow(a[i], b)
    46 #define DEFINE_VSL_UNARY_FUNC_WITH_PARAM(name, operation) \
    47  template<typename Dtype> \
    48  void v##name(const int n, const Dtype* a, const Dtype b, Dtype* y) { \
    49  CHECK_GT(n, 0); CHECK(a); CHECK(y); \
    50  for (int i = 0; i < n; ++i) { operation; } \
    51  } \
    52  inline void vs##name( \
    53  const int n, const float* a, const float b, float* y) { \
    54  v##name<float>(n, a, b, y); \
    55  } \
    56  inline void vd##name( \
    57  const int n, const double* a, const float b, double* y) { \
    58  v##name<double>(n, a, b, y); \
    59  }
    60 
    61 DEFINE_VSL_UNARY_FUNC_WITH_PARAM(Powx, y[i] = pow(a[i], b));
    62 
    63 // A simple way to define the vsl binary functions. The operation should
    64 // be in the form e.g. y[i] = a[i] + b[i]
    65 #define DEFINE_VSL_BINARY_FUNC(name, operation) \
    66  template<typename Dtype> \
    67  void v##name(const int n, const Dtype* a, const Dtype* b, Dtype* y) { \
    68  CHECK_GT(n, 0); CHECK(a); CHECK(b); CHECK(y); \
    69  for (int i = 0; i < n; ++i) { operation; } \
    70  } \
    71  inline void vs##name( \
    72  const int n, const float* a, const float* b, float* y) { \
    73  v##name<float>(n, a, b, y); \
    74  } \
    75  inline void vd##name( \
    76  const int n, const double* a, const double* b, double* y) { \
    77  v##name<double>(n, a, b, y); \
    78  }
    79 
    80 DEFINE_VSL_BINARY_FUNC(Add, y[i] = a[i] + b[i]);
    81 DEFINE_VSL_BINARY_FUNC(Sub, y[i] = a[i] - b[i]);
    82 DEFINE_VSL_BINARY_FUNC(Mul, y[i] = a[i] * b[i]);
    83 DEFINE_VSL_BINARY_FUNC(Div, y[i] = a[i] / b[i]);
    84 
    85 // In addition, MKL comes with an additional function axpby that is not present
    86 // in standard blas. We will simply use a two-step (inefficient, of course) way
    87 // to mimic that.
    88 inline void cblas_saxpby(const int N, const float alpha, const float* X,
    89  const int incX, const float beta, float* Y,
    90  const int incY) {
    91  cblas_sscal(N, beta, Y, incY);
    92  cblas_saxpy(N, alpha, X, incX, Y, incY);
    93 }
    94 inline void cblas_daxpby(const int N, const double alpha, const double* X,
    95  const int incX, const double beta, double* Y,
    96  const int incY) {
    97  cblas_dscal(N, beta, Y, incY);
    98  cblas_daxpy(N, alpha, X, incX, Y, incY);
    99 }
    100 
    101 #endif // USE_MKL
    102 #endif // CAFFE_UTIL_MKL_ALTERNATE_H_
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/multinomial__logistic__loss__layer_8hpp_source.html b/doxygen/multinomial__logistic__loss__layer_8hpp_source.html new file mode 100644 index 00000000000..02524e1bc26 --- /dev/null +++ b/doxygen/multinomial__logistic__loss__layer_8hpp_source.html @@ -0,0 +1,85 @@ + + + + + + + +Caffe: include/caffe/layers/multinomial_logistic_loss_layer.hpp Source File + + + + + + + + + +
    +
    + + + + + + +
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    Caffe +
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    + + +
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    +
    +
    multinomial_logistic_loss_layer.hpp
    +
    +
    +
    1 #ifndef CAFFE_MULTINOMIAL_LOGISTIC_LOSS_LAYER_HPP_
    2 #define CAFFE_MULTINOMIAL_LOGISTIC_LOSS_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 #include "caffe/layers/loss_layer.hpp"
    11 
    12 namespace caffe {
    13 
    43 template <typename Dtype>
    44 class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
    45  public:
    46  explicit MultinomialLogisticLossLayer(const LayerParameter& param)
    47  : LossLayer<Dtype>(param) {}
    48  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    49  const vector<Blob<Dtype>*>& top);
    50 
    51  virtual inline const char* type() const { return "MultinomialLogisticLoss"; }
    52 
    53  protected:
    55  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    56  const vector<Blob<Dtype>*>& top);
    57 
    86  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    87  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    88 };
    89 
    90 } // namespace caffe
    91 
    92 #endif // CAFFE_MULTINOMIAL_LOGISTIC_LOSS_LAYER_HPP_
    caffe::MultinomialLogisticLossLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Computes the multinomial logistic loss for a one-of-many classification task, directly taking a predi...
    Definition: multinomial_logistic_loss_layer.cpp:20
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::MultinomialLogisticLossLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: multinomial_logistic_loss_layer.hpp:51
    +
    caffe::MultinomialLogisticLossLayer
    Computes the multinomial logistic loss for a one-of-many classification task, directly taking a predi...
    Definition: multinomial_logistic_loss_layer.hpp:44
    +
    caffe::MultinomialLogisticLossLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: multinomial_logistic_loss_layer.cpp:11
    +
    caffe::MultinomialLogisticLossLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Computes the multinomial logistic loss error gradient w.r.t. the predictions.
    Definition: multinomial_logistic_loss_layer.cpp:37
    +
    caffe::LossLayer
    An interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth ...
    Definition: loss_layer.hpp:23
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/mvn__layer_8hpp_source.html b/doxygen/mvn__layer_8hpp_source.html new file mode 100644 index 00000000000..bbed39c44a3 --- /dev/null +++ b/doxygen/mvn__layer_8hpp_source.html @@ -0,0 +1,90 @@ + + + + + + + +Caffe: include/caffe/layers/mvn_layer.hpp Source File + + + + + + + + + +
    +
    + + + + + + +
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    Caffe +
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    mvn_layer.hpp
    +
    +
    +
    1 #ifndef CAFFE_MVN_LAYER_HPP_
    2 #define CAFFE_MVN_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 namespace caffe {
    11 
    17 template <typename Dtype>
    18 class MVNLayer : public Layer<Dtype> {
    19  public:
    20  explicit MVNLayer(const LayerParameter& param)
    21  : Layer<Dtype>(param) {}
    22  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    23  const vector<Blob<Dtype>*>& top);
    24 
    25  virtual inline const char* type() const { return "MVN"; }
    26  virtual inline int ExactNumBottomBlobs() const { return 1; }
    27  virtual inline int ExactNumTopBlobs() const { return 1; }
    28 
    29  protected:
    30  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    31  const vector<Blob<Dtype>*>& top);
    32  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    33  const vector<Blob<Dtype>*>& top);
    34  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    35  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    36  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    37  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    38 
    39  Blob<Dtype> mean_, variance_, temp_;
    40 
    42  Blob<Dtype> sum_multiplier_;
    43  Dtype eps_;
    44 };
    45 
    46 } // namespace caffe
    47 
    48 #endif // CAFFE_MVN_LAYER_HPP_
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::MVNLayer
    Normalizes the input to have 0-mean and/or unit (1) variance.
    Definition: mvn_layer.hpp:18
    +
    caffe::MVNLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: mvn_layer.cpp:74
    +
    caffe::MVNLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::MVNLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: mvn_layer.hpp:27
    +
    caffe::MVNLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: mvn_layer.hpp:25
    +
    caffe::MVNLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the CPU device, compute the layer output.
    Definition: mvn_layer.cpp:31
    +
    caffe::MVNLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: mvn_layer.hpp:26
    +
    caffe::MVNLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::MVNLayer::sum_multiplier_
    Blob< Dtype > sum_multiplier_
    sum_multiplier is used to carry out sum using BLAS
    Definition: mvn_layer.hpp:42
    +
    caffe::MVNLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: mvn_layer.cpp:9
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/namespaceboost.html b/doxygen/namespaceboost.html new file mode 100644 index 00000000000..bf6b4dd4579 --- /dev/null +++ b/doxygen/namespaceboost.html @@ -0,0 +1,75 @@ + + + + + + + +Caffe: boost Namespace Reference + + + + + + + + + +
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    Caffe +
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    boost Namespace Reference
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    Detailed Description

    +

    Forward declare boost::thread instead of including boost/thread.hpp to avoid a boost/NVCC issues (#1009, #1010) on OSX.

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    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/namespacecaffe.html b/doxygen/namespacecaffe.html new file mode 100644 index 00000000000..c9fa050a416 --- /dev/null +++ b/doxygen/namespacecaffe.html @@ -0,0 +1,1666 @@ + + + + + + + +Caffe: caffe Namespace Reference + + + + + + + + + +
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    Caffe +
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    +Namespaces | +Classes | +Typedefs | +Enumerations | +Functions | +Variables
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    caffe Namespace Reference
    +
    +
    + +

    A layer factory that allows one to register layers. During runtime, registered layers can be called by passing a LayerParameter protobuffer to the CreateLayer function: +More...

    + + + + + +

    +Namespaces

     SolverAction
     Enumeration of actions that a client of the Solver may request by implementing the Solver's action request function, which a client may optionally provide in order to request early termination or saving a snapshot without exiting. In the executable caffe, this mechanism is used to allow the snapshot to be saved when stopping execution with a SIGINT (Ctrl-C).
     
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    +Classes

    class  AbsValLayer
     Computes $ y = |x| $. More...
     
    class  AccuracyLayer
     Computes the classification accuracy for a one-of-many classification task. More...
     
    class  AdaDeltaSolver
     
    class  AdaGradSolver
     
    class  AdamSolver
     AdamSolver, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. Described in [1]. More...
     
    class  ArgMaxLayer
     Compute the index of the $ K $ max values for each datum across all dimensions $ (C \times H \times W) $. More...
     
    class  BaseConvolutionLayer
     Abstract base class that factors out the BLAS code common to ConvolutionLayer and DeconvolutionLayer. More...
     
    class  BaseDataLayer
     Provides base for data layers that feed blobs to the Net. More...
     
    class  BasePrefetchingDataLayer
     
    class  Batch
     
    class  BatchNormLayer
     Normalizes the input to have 0-mean and/or unit (1) variance across the batch. More...
     
    class  BatchReindexLayer
     Index into the input blob along its first axis. More...
     
    class  BiasLayer
     Computes a sum of two input Blobs, with the shape of the latter Blob "broadcast" to match the shape of the former. Equivalent to tiling the latter Blob, then computing the elementwise sum. More...
     
    class  BilinearFiller
     Fills a Blob with coefficients for bilinear interpolation. More...
     
    class  Blob
     A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers, Nets, and Solvers interact. More...
     
    class  BlockingQueue
     
    class  BNLLLayer
     Computes $ y = x + \log(1 + \exp(-x)) $ if $ x > 0 $; $ y = \log(1 + \exp(x)) $ otherwise. More...
     
    class  Caffe
     
    class  ConcatLayer
     Takes at least two Blobs and concatenates them along either the num or channel dimension, outputting the result. More...
     
    class  ConstantFiller
     Fills a Blob with constant values $ x = 0 $. More...
     
    class  ContrastiveLossLayer
     Computes the contrastive loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks. More...
     
    class  ConvolutionLayer
     Convolves the input image with a bank of learned filters, and (optionally) adds biases. More...
     
    class  CPUTimer
     
    class  CropLayer
     Takes a Blob and crop it, to the shape specified by the second input Blob, across all dimensions after the specified axis. More...
     
    class  DataLayer
     
    class  DataReader
     Reads data from a source to queues available to data layers. A single reading thread is created per source, even if multiple solvers are running in parallel, e.g. for multi-GPU training. This makes sure databases are read sequentially, and that each solver accesses a different subset of the database. Data is distributed to solvers in a round-robin way to keep parallel training deterministic. More...
     
    class  DataTransformer
     Applies common transformations to the input data, such as scaling, mirroring, substracting the image mean... More...
     
    class  DeconvolutionLayer
     Convolve the input with a bank of learned filters, and (optionally) add biases, treating filters and convolution parameters in the opposite sense as ConvolutionLayer. More...
     
    class  DevicePair
     
    class  DropoutLayer
     During training only, sets a random portion of $x$ to 0, adjusting the rest of the vector magnitude accordingly. More...
     
    class  DummyDataLayer
     Provides data to the Net generated by a Filler. More...
     
    class  EltwiseLayer
     Compute elementwise operations, such as product and sum, along multiple input Blobs. More...
     
    class  ELULayer
     Exponential Linear Unit non-linearity $ y = \left\{ \begin{array}{lr} x & \mathrm{if} \; x > 0 \\ \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 \end{array} \right. $. More...
     
    class  EmbedLayer
     A layer for learning "embeddings" of one-hot vector input. Equivalent to an InnerProductLayer with one-hot vectors as input, but for efficiency the input is the "hot" index of each column itself. More...
     
    class  EuclideanLossLayer
     Computes the Euclidean (L2) loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ for real-valued regression tasks. More...
     
    class  ExpLayer
     Computes $ y = \gamma ^ {\alpha x + \beta} $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $. More...
     
    class  Filler
     Fills a Blob with constant or randomly-generated data. More...
     
    class  FilterLayer
     Takes two+ Blobs, interprets last Blob as a selector and filter remaining Blobs accordingly with selector data (0 means that the corresponding item has to be filtered, non-zero means that corresponding item needs to stay). More...
     
    class  FlattenLayer
     Reshapes the input Blob into flat vectors. More...
     
    class  GaussianFiller
     Fills a Blob with Gaussian-distributed values $ x = a $. More...
     
    class  GPUParams
     
    class  HDF5DataLayer
     Provides data to the Net from HDF5 files. More...
     
    class  HDF5OutputLayer
     Write blobs to disk as HDF5 files. More...
     
    class  HingeLossLayer
     Computes the hinge loss for a one-of-many classification task. More...
     
    class  Im2colLayer
     A helper for image operations that rearranges image regions into column vectors. Used by ConvolutionLayer to perform convolution by matrix multiplication. More...
     
    class  ImageDataLayer
     Provides data to the Net from image files. More...
     
    class  InfogainLossLayer
     A generalization of MultinomialLogisticLossLayer that takes an "information gain" (infogain) matrix specifying the "value" of all label pairs. More...
     
    class  InnerProductLayer
     Also known as a "fully-connected" layer, computes an inner product with a set of learned weights, and (optionally) adds biases. More...
     
    class  InputLayer
     Provides data to the Net by assigning tops directly. More...
     
    class  InternalThread
     
    class  Layer
     An interface for the units of computation which can be composed into a Net. More...
     
    class  LayerRegisterer
     
    class  LayerRegistry
     
    class  LogLayer
     Computes $ y = log_{\gamma}(\alpha x + \beta) $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $. More...
     
    class  LossLayer
     An interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth labels – and output a singleton Blob representing the loss. More...
     
    class  LRNLayer
     Normalize the input in a local region across or within feature maps. More...
     
    class  LSTMLayer
     Processes sequential inputs using a "Long Short-Term Memory" (LSTM) [1] style recurrent neural network (RNN). Implemented by unrolling the LSTM computation through time. More...
     
    class  LSTMUnitLayer
     A helper for LSTMLayer: computes a single timestep of the non-linearity of the LSTM, producing the updated cell and hidden states. More...
     
    class  MemoryDataLayer
     Provides data to the Net from memory. More...
     
    class  MSRAFiller
     Fills a Blob with values $ x \sim N(0, \sigma^2) $ where $ \sigma^2 $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average. More...
     
    class  MultinomialLogisticLossLayer
     Computes the multinomial logistic loss for a one-of-many classification task, directly taking a predicted probability distribution as input. More...
     
    class  MVNLayer
     Normalizes the input to have 0-mean and/or unit (1) variance. More...
     
    class  NesterovSolver
     
    class  Net
     Connects Layers together into a directed acyclic graph (DAG) specified by a NetParameter. More...
     
    class  NeuronLayer
     An interface for layers that take one blob as input ( $ x $) and produce one equally-sized blob as output ( $ y $), where each element of the output depends only on the corresponding input element. More...
     
    class  P2PSync
     
    class  ParameterLayer
     
    class  Params
     
    class  PoolingLayer
     Pools the input image by taking the max, average, etc. within regions. More...
     
    class  PositiveUnitballFiller
     Fills a Blob with values $ x \in [0, 1] $ such that $ \forall i \sum_j x_{ij} = 1 $. More...
     
    class  PowerLayer
     Computes $ y = (\alpha x + \beta) ^ \gamma $, as specified by the scale $ \alpha $, shift $ \beta $, and power $ \gamma $. More...
     
    class  PReLULayer
     Parameterized Rectified Linear Unit non-linearity $ y_i = \max(0, x_i) + a_i \min(0, x_i) $. The differences from ReLULayer are 1) negative slopes are learnable though backprop and 2) negative slopes can vary across channels. The number of axes of input blob should be greater than or equal to 2. The 1st axis (0-based) is seen as channels. More...
     
    class  PythonLayer
     
    class  RecurrentLayer
     An abstract class for implementing recurrent behavior inside of an unrolled network. This Layer type cannot be instantiated – instead, you should use one of its implementations which defines the recurrent architecture, such as RNNLayer or LSTMLayer. More...
     
    class  ReductionLayer
     Compute "reductions" – operations that return a scalar output Blob for an input Blob of arbitrary size, such as the sum, absolute sum, and sum of squares. More...
     
    class  ReLULayer
     Rectified Linear Unit non-linearity $ y = \max(0, x) $. The simple max is fast to compute, and the function does not saturate. More...
     
    class  ReshapeLayer
     
    class  RMSPropSolver
     
    class  RNNLayer
     Processes time-varying inputs using a simple recurrent neural network (RNN). Implemented as a network unrolling the RNN computation in time. More...
     
    class  ScaleLayer
     Computes the elementwise product of two input Blobs, with the shape of the latter Blob "broadcast" to match the shape of the former. Equivalent to tiling the latter Blob, then computing the elementwise product. Note: for efficiency and convenience, this layer can additionally perform a "broadcast" sum too when bias_term: true is set. More...
     
    class  SGDSolver
     Optimizes the parameters of a Net using stochastic gradient descent (SGD) with momentum. More...
     
    class  SigmoidCrossEntropyLossLayer
     Computes the cross-entropy (logistic) loss $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $, often used for predicting targets interpreted as probabilities. More...
     
    class  SigmoidLayer
     Sigmoid function non-linearity $ y = (1 + \exp(-x))^{-1} $, a classic choice in neural networks. More...
     
    class  SignalHandler
     
    class  SilenceLayer
     Ignores bottom blobs while producing no top blobs. (This is useful to suppress outputs during testing.) More...
     
    class  SliceLayer
     Takes a Blob and slices it along either the num or channel dimension, outputting multiple sliced Blob results. More...
     
    class  SoftmaxLayer
     Computes the softmax function. More...
     
    class  SoftmaxWithLossLayer
     Computes the multinomial logistic loss for a one-of-many classification task, passing real-valued predictions through a softmax to get a probability distribution over classes. More...
     
    class  Solver
     An interface for classes that perform optimization on Nets. More...
     
    class  SolverRegisterer
     
    class  SolverRegistry
     
    class  SplitLayer
     Creates a "split" path in the network by copying the bottom Blob into multiple top Blobs to be used by multiple consuming layers. More...
     
    class  SPPLayer
     Does spatial pyramid pooling on the input image by taking the max, average, etc. within regions so that the result vector of different sized images are of the same size. More...
     
    class  SyncedMemory
     Manages memory allocation and synchronization between the host (CPU) and device (GPU). More...
     
    class  TanHLayer
     TanH hyperbolic tangent non-linearity $ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $, popular in auto-encoders. More...
     
    class  ThresholdLayer
     Tests whether the input exceeds a threshold: outputs 1 for inputs above threshold; 0 otherwise. More...
     
    class  TileLayer
     Copy a Blob along specified dimensions. More...
     
    class  Timer
     
    class  UniformFiller
     Fills a Blob with uniformly distributed values $ x\sim U(a, b) $. More...
     
    class  WindowDataLayer
     Provides data to the Net from windows of images files, specified by a window data file. More...
     
    class  WorkerSolver
     Solver that only computes gradients, used as worker for multi-GPU training. More...
     
    class  XavierFiller
     Fills a Blob with values $ x \sim U(-a, +a) $ where $ a $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average. More...
     
    + + + + + + +

    +Typedefs

    +typedef boost::function< SolverAction::Enum()> ActionCallback
     Type of a function that returns a Solver Action enumeration.
     
    +typedef boost::mt19937 rng_t
     
    + + + +

    +Enumerations

    enum  Op {
    +  copy, +replace_cpu, +replace_gpu, +replace_cpu_diff, +
    +  replace_gpu_diff +
    + }
     
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    +Functions

    +void GlobalInit (int *pargc, char ***pargv)
     
    template<typename Dtype >
    Filler< Dtype > * GetFiller (const FillerParameter &param)
     Get a specific filler from the specification given in FillerParameter. More...
     
    +void CaffeMallocHost (void **ptr, size_t size, bool *use_cuda)
     
    +void CaffeFreeHost (void *ptr, bool use_cuda)
     
    +const char * cublasGetErrorString (cublasStatus_t error)
     
    +const char * curandGetErrorString (curandStatus_t error)
     
    +int CAFFE_GET_BLOCKS (const int N)
     
    +std::string format_int (int n, int numberOfLeadingZeros=0)
     
    +template<typename Dtype >
    void hdf5_load_nd_dataset_helper (hid_t file_id, const char *dataset_name_, int min_dim, int max_dim, Blob< Dtype > *blob)
     
    +template<typename Dtype >
    void hdf5_load_nd_dataset (hid_t file_id, const char *dataset_name_, int min_dim, int max_dim, Blob< Dtype > *blob)
     
    +template<typename Dtype >
    void hdf5_save_nd_dataset (const hid_t file_id, const string &dataset_name, const Blob< Dtype > &blob, bool write_diff=false)
     
    +int hdf5_load_int (hid_t loc_id, const string &dataset_name)
     
    +void hdf5_save_int (hid_t loc_id, const string &dataset_name, int i)
     
    +string hdf5_load_string (hid_t loc_id, const string &dataset_name)
     
    +void hdf5_save_string (hid_t loc_id, const string &dataset_name, const string &s)
     
    +int hdf5_get_num_links (hid_t loc_id)
     
    +string hdf5_get_name_by_idx (hid_t loc_id, int idx)
     
    +template<typename Dtype >
    void im2col_nd_cpu (const Dtype *data_im, const int num_spatial_axes, const int *im_shape, const int *col_shape, const int *kernel_shape, const int *pad, const int *stride, const int *dilation, Dtype *data_col)
     
    +template<typename Dtype >
    void im2col_cpu (const Dtype *data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, Dtype *data_col)
     
    +template<typename Dtype >
    void col2im_nd_cpu (const Dtype *data_col, const int num_spatial_axes, const int *im_shape, const int *col_shape, const int *kernel_shape, const int *pad, const int *stride, const int *dilation, Dtype *data_im)
     
    +template<typename Dtype >
    void col2im_cpu (const Dtype *data_col, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, Dtype *data_im)
     
    +template<typename Dtype >
    void im2col_nd_gpu (const Dtype *data_im, const int num_spatial_axes, const int col_size, const int *im_shape, const int *col_shape, const int *kernel_shape, const int *pad, const int *stride, const int *dilation, Dtype *data_col)
     
    +template<typename Dtype >
    void im2col_gpu (const Dtype *data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, Dtype *data_col)
     
    +template<typename Dtype >
    void col2im_nd_gpu (const Dtype *data_col, const int num_spatial_axes, const int im_size, const int *im_shape, const int *col_shape, const int *kernel_shape, const int *pad, const int *stride, const int *dilation, Dtype *data_im)
     
    +template<typename Dtype >
    void col2im_gpu (const Dtype *data_col, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, Dtype *data_im)
     
    +void InsertSplits (const NetParameter &param, NetParameter *param_split)
     
    +void ConfigureSplitLayer (const string &layer_name, const string &blob_name, const int blob_idx, const int split_count, const float loss_weight, LayerParameter *split_layer_param)
     
    +string SplitLayerName (const string &layer_name, const string &blob_name, const int blob_idx)
     
    +string SplitBlobName (const string &layer_name, const string &blob_name, const int blob_idx, const int split_idx)
     
    +void MakeTempDir (string *temp_dirname)
     
    +void MakeTempFilename (string *temp_filename)
     
    +bool ReadProtoFromTextFile (const char *filename, Message *proto)
     
    +bool ReadProtoFromTextFile (const string &filename, Message *proto)
     
    +void ReadProtoFromTextFileOrDie (const char *filename, Message *proto)
     
    +void ReadProtoFromTextFileOrDie (const string &filename, Message *proto)
     
    +void WriteProtoToTextFile (const Message &proto, const char *filename)
     
    +void WriteProtoToTextFile (const Message &proto, const string &filename)
     
    +bool ReadProtoFromBinaryFile (const char *filename, Message *proto)
     
    +bool ReadProtoFromBinaryFile (const string &filename, Message *proto)
     
    +void ReadProtoFromBinaryFileOrDie (const char *filename, Message *proto)
     
    +void ReadProtoFromBinaryFileOrDie (const string &filename, Message *proto)
     
    +void WriteProtoToBinaryFile (const Message &proto, const char *filename)
     
    +void WriteProtoToBinaryFile (const Message &proto, const string &filename)
     
    +bool ReadFileToDatum (const string &filename, const int label, Datum *datum)
     
    +bool ReadFileToDatum (const string &filename, Datum *datum)
     
    +bool ReadImageToDatum (const string &filename, const int label, const int height, const int width, const bool is_color, const std::string &encoding, Datum *datum)
     
    +bool ReadImageToDatum (const string &filename, const int label, const int height, const int width, const bool is_color, Datum *datum)
     
    +bool ReadImageToDatum (const string &filename, const int label, const int height, const int width, Datum *datum)
     
    +bool ReadImageToDatum (const string &filename, const int label, const bool is_color, Datum *datum)
     
    +bool ReadImageToDatum (const string &filename, const int label, Datum *datum)
     
    +bool ReadImageToDatum (const string &filename, const int label, const std::string &encoding, Datum *datum)
     
    +bool DecodeDatumNative (Datum *datum)
     
    +bool DecodeDatum (Datum *datum, bool is_color)
     
    +template<typename Dtype >
    void caffe_cpu_gemm (const CBLAS_TRANSPOSE TransA, const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K, const Dtype alpha, const Dtype *A, const Dtype *B, const Dtype beta, Dtype *C)
     
    +template<typename Dtype >
    void caffe_cpu_gemv (const CBLAS_TRANSPOSE TransA, const int M, const int N, const Dtype alpha, const Dtype *A, const Dtype *x, const Dtype beta, Dtype *y)
     
    +template<typename Dtype >
    void caffe_axpy (const int N, const Dtype alpha, const Dtype *X, Dtype *Y)
     
    +template<typename Dtype >
    void caffe_cpu_axpby (const int N, const Dtype alpha, const Dtype *X, const Dtype beta, Dtype *Y)
     
    +template<typename Dtype >
    void caffe_copy (const int N, const Dtype *X, Dtype *Y)
     
    +template<typename Dtype >
    void caffe_set (const int N, const Dtype alpha, Dtype *X)
     
    +void caffe_memset (const size_t N, const int alpha, void *X)
     
    +template<typename Dtype >
    void caffe_add_scalar (const int N, const Dtype alpha, Dtype *X)
     
    +template<typename Dtype >
    void caffe_scal (const int N, const Dtype alpha, Dtype *X)
     
    +template<typename Dtype >
    void caffe_sqr (const int N, const Dtype *a, Dtype *y)
     
    +template<typename Dtype >
    void caffe_add (const int N, const Dtype *a, const Dtype *b, Dtype *y)
     
    +template<typename Dtype >
    void caffe_sub (const int N, const Dtype *a, const Dtype *b, Dtype *y)
     
    +template<typename Dtype >
    void caffe_mul (const int N, const Dtype *a, const Dtype *b, Dtype *y)
     
    +template<typename Dtype >
    void caffe_div (const int N, const Dtype *a, const Dtype *b, Dtype *y)
     
    +template<typename Dtype >
    void caffe_powx (const int n, const Dtype *a, const Dtype b, Dtype *y)
     
    +unsigned int caffe_rng_rand ()
     
    +template<typename Dtype >
    Dtype caffe_nextafter (const Dtype b)
     
    +template<typename Dtype >
    void caffe_rng_uniform (const int n, const Dtype a, const Dtype b, Dtype *r)
     
    +template<typename Dtype >
    void caffe_rng_gaussian (const int n, const Dtype mu, const Dtype sigma, Dtype *r)
     
    +template<typename Dtype >
    void caffe_rng_bernoulli (const int n, const Dtype p, int *r)
     
    +template<typename Dtype >
    void caffe_rng_bernoulli (const int n, const Dtype p, unsigned int *r)
     
    +template<typename Dtype >
    void caffe_exp (const int n, const Dtype *a, Dtype *y)
     
    +template<typename Dtype >
    void caffe_log (const int n, const Dtype *a, Dtype *y)
     
    +template<typename Dtype >
    void caffe_abs (const int n, const Dtype *a, Dtype *y)
     
    +template<typename Dtype >
    Dtype caffe_cpu_dot (const int n, const Dtype *x, const Dtype *y)
     
    +template<typename Dtype >
    Dtype caffe_cpu_strided_dot (const int n, const Dtype *x, const int incx, const Dtype *y, const int incy)
     
    +template<typename Dtype >
    Dtype caffe_cpu_asum (const int n, const Dtype *x)
     
    +template<typename Dtype >
    int8_t caffe_sign (Dtype val)
     
    + DEFINE_CAFFE_CPU_UNARY_FUNC (sign, y[i]=caffe_sign< Dtype >(x[i]))
     
    + DEFINE_CAFFE_CPU_UNARY_FUNC (sgnbit, y[i]=static_cast< bool >((std::signbit)(x[i])))
     
    + DEFINE_CAFFE_CPU_UNARY_FUNC (fabs, y[i]=std::fabs(x[i]))
     
    +template<typename Dtype >
    void caffe_cpu_scale (const int n, const Dtype alpha, const Dtype *x, Dtype *y)
     
    +template<typename Dtype >
    void caffe_gpu_gemm (const CBLAS_TRANSPOSE TransA, const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K, const Dtype alpha, const Dtype *A, const Dtype *B, const Dtype beta, Dtype *C)
     
    +template<typename Dtype >
    void caffe_gpu_gemv (const CBLAS_TRANSPOSE TransA, const int M, const int N, const Dtype alpha, const Dtype *A, const Dtype *x, const Dtype beta, Dtype *y)
     
    +template<typename Dtype >
    void caffe_gpu_axpy (const int N, const Dtype alpha, const Dtype *X, Dtype *Y)
     
    +template<typename Dtype >
    void caffe_gpu_axpby (const int N, const Dtype alpha, const Dtype *X, const Dtype beta, Dtype *Y)
     
    +void caffe_gpu_memcpy (const size_t N, const void *X, void *Y)
     
    +template<typename Dtype >
    void caffe_gpu_set (const int N, const Dtype alpha, Dtype *X)
     
    +void caffe_gpu_memset (const size_t N, const int alpha, void *X)
     
    +template<typename Dtype >
    void caffe_gpu_add_scalar (const int N, const Dtype alpha, Dtype *X)
     
    +template<typename Dtype >
    void caffe_gpu_scal (const int N, const Dtype alpha, Dtype *X)
     
    +template<typename Dtype >
    void caffe_gpu_add (const int N, const Dtype *a, const Dtype *b, Dtype *y)
     
    +template<typename Dtype >
    void caffe_gpu_sub (const int N, const Dtype *a, const Dtype *b, Dtype *y)
     
    +template<typename Dtype >
    void caffe_gpu_mul (const int N, const Dtype *a, const Dtype *b, Dtype *y)
     
    +template<typename Dtype >
    void caffe_gpu_div (const int N, const Dtype *a, const Dtype *b, Dtype *y)
     
    +template<typename Dtype >
    void caffe_gpu_abs (const int n, const Dtype *a, Dtype *y)
     
    +template<typename Dtype >
    void caffe_gpu_exp (const int n, const Dtype *a, Dtype *y)
     
    +template<typename Dtype >
    void caffe_gpu_log (const int n, const Dtype *a, Dtype *y)
     
    +template<typename Dtype >
    void caffe_gpu_powx (const int n, const Dtype *a, const Dtype b, Dtype *y)
     
    +void caffe_gpu_rng_uniform (const int n, unsigned int *r)
     
    +template<typename Dtype >
    void caffe_gpu_rng_uniform (const int n, const Dtype a, const Dtype b, Dtype *r)
     
    +template<typename Dtype >
    void caffe_gpu_rng_gaussian (const int n, const Dtype mu, const Dtype sigma, Dtype *r)
     
    +template<typename Dtype >
    void caffe_gpu_rng_bernoulli (const int n, const Dtype p, int *r)
     
    +template<typename Dtype >
    void caffe_gpu_dot (const int n, const Dtype *x, const Dtype *y, Dtype *out)
     
    +template<typename Dtype >
    void caffe_gpu_asum (const int n, const Dtype *x, Dtype *y)
     
    +template<typename Dtype >
    void caffe_gpu_sign (const int n, const Dtype *x, Dtype *y)
     
    +template<typename Dtype >
    void caffe_gpu_sgnbit (const int n, const Dtype *x, Dtype *y)
     
    +template<typename Dtype >
    void caffe_gpu_fabs (const int n, const Dtype *x, Dtype *y)
     
    +template<typename Dtype >
    void caffe_gpu_scale (const int n, const Dtype alpha, const Dtype *x, Dtype *y)
     
    +rng_t * caffe_rng ()
     
    +template<class RandomAccessIterator , class RandomGenerator >
    void shuffle (RandomAccessIterator begin, RandomAccessIterator end, RandomGenerator *gen)
     
    +template<class RandomAccessIterator >
    void shuffle (RandomAccessIterator begin, RandomAccessIterator end)
     
    +bool NetNeedsUpgrade (const NetParameter &net_param)
     
    +bool UpgradeNetAsNeeded (const string &param_file, NetParameter *param)
     
    +void ReadNetParamsFromTextFileOrDie (const string &param_file, NetParameter *param)
     
    +void ReadNetParamsFromBinaryFileOrDie (const string &param_file, NetParameter *param)
     
    +bool NetNeedsV0ToV1Upgrade (const NetParameter &net_param)
     
    +bool UpgradeV0Net (const NetParameter &v0_net_param, NetParameter *net_param)
     
    +void UpgradeV0PaddingLayers (const NetParameter &param, NetParameter *param_upgraded_pad)
     
    +bool UpgradeV0LayerParameter (const V1LayerParameter &v0_layer_connection, V1LayerParameter *layer_param)
     
    +V1LayerParameter_LayerType UpgradeV0LayerType (const string &type)
     
    +bool NetNeedsDataUpgrade (const NetParameter &net_param)
     
    +void UpgradeNetDataTransformation (NetParameter *net_param)
     
    +bool NetNeedsV1ToV2Upgrade (const NetParameter &net_param)
     
    +bool UpgradeV1Net (const NetParameter &v1_net_param, NetParameter *net_param)
     
    +bool UpgradeV1LayerParameter (const V1LayerParameter &v1_layer_param, LayerParameter *layer_param)
     
    +const char * UpgradeV1LayerType (const V1LayerParameter_LayerType type)
     
    +bool NetNeedsInputUpgrade (const NetParameter &net_param)
     
    +void UpgradeNetInput (NetParameter *net_param)
     
    +bool NetNeedsBatchNormUpgrade (const NetParameter &net_param)
     
    +void UpgradeNetBatchNorm (NetParameter *net_param)
     
    +bool SolverNeedsTypeUpgrade (const SolverParameter &solver_param)
     
    +bool UpgradeSolverType (SolverParameter *solver_param)
     
    +bool UpgradeSolverAsNeeded (const string &param_file, SolverParameter *param)
     
    +void ReadSolverParamsFromTextFileOrDie (const string &param_file, SolverParameter *param)
     
    + INSTANTIATE_CLASS (Blob)
     
    +int64_t cluster_seedgen (void)
     
    + INSTANTIATE_CLASS (DataTransformer)
     
    + INSTANTIATE_CLASS (Layer)
     
    +template<typename Dtype >
    shared_ptr< Layer< Dtype > > GetConvolutionLayer (const LayerParameter &param)
     
    + REGISTER_LAYER_CREATOR (Convolution, GetConvolutionLayer)
     
    +template<typename Dtype >
    shared_ptr< Layer< Dtype > > GetPoolingLayer (const LayerParameter &param)
     
    + REGISTER_LAYER_CREATOR (Pooling, GetPoolingLayer)
     
    +template<typename Dtype >
    shared_ptr< Layer< Dtype > > GetLRNLayer (const LayerParameter &param)
     
    + REGISTER_LAYER_CREATOR (LRN, GetLRNLayer)
     
    +template<typename Dtype >
    shared_ptr< Layer< Dtype > > GetReLULayer (const LayerParameter &param)
     
    + REGISTER_LAYER_CREATOR (ReLU, GetReLULayer)
     
    +template<typename Dtype >
    shared_ptr< Layer< Dtype > > GetSigmoidLayer (const LayerParameter &param)
     
    + REGISTER_LAYER_CREATOR (Sigmoid, GetSigmoidLayer)
     
    +template<typename Dtype >
    shared_ptr< Layer< Dtype > > GetSoftmaxLayer (const LayerParameter &param)
     
    + REGISTER_LAYER_CREATOR (Softmax, GetSoftmaxLayer)
     
    +template<typename Dtype >
    shared_ptr< Layer< Dtype > > GetTanHLayer (const LayerParameter &param)
     
    + REGISTER_LAYER_CREATOR (TanH, GetTanHLayer)
     
    + INSTANTIATE_CLASS (AbsValLayer)
     
    + REGISTER_LAYER_CLASS (AbsVal)
     
    + INSTANTIATE_CLASS (AccuracyLayer)
     
    + REGISTER_LAYER_CLASS (Accuracy)
     
    + INSTANTIATE_CLASS (ArgMaxLayer)
     
    + REGISTER_LAYER_CLASS (ArgMax)
     
    + INSTANTIATE_CLASS (BaseConvolutionLayer)
     
    + INSTANTIATE_CLASS (BaseDataLayer)
     
    + INSTANTIATE_CLASS (BasePrefetchingDataLayer)
     
    + INSTANTIATE_CLASS (BatchNormLayer)
     
    + REGISTER_LAYER_CLASS (BatchNorm)
     
    + INSTANTIATE_CLASS (BatchReindexLayer)
     
    + REGISTER_LAYER_CLASS (BatchReindex)
     
    + INSTANTIATE_CLASS (BiasLayer)
     
    + REGISTER_LAYER_CLASS (Bias)
     
    + INSTANTIATE_CLASS (BNLLLayer)
     
    + REGISTER_LAYER_CLASS (BNLL)
     
    + INSTANTIATE_CLASS (ConcatLayer)
     
    + REGISTER_LAYER_CLASS (Concat)
     
    + INSTANTIATE_CLASS (ContrastiveLossLayer)
     
    + REGISTER_LAYER_CLASS (ContrastiveLoss)
     
    + INSTANTIATE_CLASS (ConvolutionLayer)
     
    + INSTANTIATE_CLASS (CropLayer)
     
    + REGISTER_LAYER_CLASS (Crop)
     
    + INSTANTIATE_CLASS (DataLayer)
     
    + REGISTER_LAYER_CLASS (Data)
     
    + INSTANTIATE_CLASS (DeconvolutionLayer)
     
    + REGISTER_LAYER_CLASS (Deconvolution)
     
    + INSTANTIATE_CLASS (DropoutLayer)
     
    + REGISTER_LAYER_CLASS (Dropout)
     
    + INSTANTIATE_CLASS (DummyDataLayer)
     
    + REGISTER_LAYER_CLASS (DummyData)
     
    + INSTANTIATE_CLASS (EltwiseLayer)
     
    + REGISTER_LAYER_CLASS (Eltwise)
     
    + INSTANTIATE_CLASS (ELULayer)
     
    + REGISTER_LAYER_CLASS (ELU)
     
    + INSTANTIATE_CLASS (EmbedLayer)
     
    + REGISTER_LAYER_CLASS (Embed)
     
    + INSTANTIATE_CLASS (EuclideanLossLayer)
     
    + REGISTER_LAYER_CLASS (EuclideanLoss)
     
    + INSTANTIATE_CLASS (ExpLayer)
     
    + REGISTER_LAYER_CLASS (Exp)
     
    + INSTANTIATE_CLASS (FilterLayer)
     
    + REGISTER_LAYER_CLASS (Filter)
     
    + INSTANTIATE_CLASS (FlattenLayer)
     
    + REGISTER_LAYER_CLASS (Flatten)
     
    + INSTANTIATE_CLASS (HDF5DataLayer)
     
    + REGISTER_LAYER_CLASS (HDF5Data)
     
    + INSTANTIATE_CLASS (HDF5OutputLayer)
     
    + REGISTER_LAYER_CLASS (HDF5Output)
     
    + INSTANTIATE_CLASS (HingeLossLayer)
     
    + REGISTER_LAYER_CLASS (HingeLoss)
     
    + INSTANTIATE_CLASS (Im2colLayer)
     
    + REGISTER_LAYER_CLASS (Im2col)
     
    + INSTANTIATE_CLASS (InfogainLossLayer)
     
    + REGISTER_LAYER_CLASS (InfogainLoss)
     
    + INSTANTIATE_CLASS (InnerProductLayer)
     
    + REGISTER_LAYER_CLASS (InnerProduct)
     
    + INSTANTIATE_CLASS (InputLayer)
     
    + REGISTER_LAYER_CLASS (Input)
     
    + INSTANTIATE_CLASS (LogLayer)
     
    + REGISTER_LAYER_CLASS (Log)
     
    + INSTANTIATE_CLASS (LossLayer)
     
    + INSTANTIATE_CLASS (LRNLayer)
     
    + INSTANTIATE_CLASS (LSTMLayer)
     
    + REGISTER_LAYER_CLASS (LSTM)
     
    +template<typename Dtype >
    Dtype sigmoid (Dtype x)
     
    +template<typename Dtype >
    Dtype tanh (Dtype x)
     
    + INSTANTIATE_CLASS (LSTMUnitLayer)
     
    + REGISTER_LAYER_CLASS (LSTMUnit)
     
    + INSTANTIATE_CLASS (MemoryDataLayer)
     
    + REGISTER_LAYER_CLASS (MemoryData)
     
    + INSTANTIATE_CLASS (MultinomialLogisticLossLayer)
     
    + REGISTER_LAYER_CLASS (MultinomialLogisticLoss)
     
    + INSTANTIATE_CLASS (MVNLayer)
     
    + REGISTER_LAYER_CLASS (MVN)
     
    + INSTANTIATE_CLASS (NeuronLayer)
     
    + INSTANTIATE_CLASS (ParameterLayer)
     
    + REGISTER_LAYER_CLASS (Parameter)
     
    + INSTANTIATE_CLASS (PoolingLayer)
     
    + INSTANTIATE_CLASS (PowerLayer)
     
    + REGISTER_LAYER_CLASS (Power)
     
    + INSTANTIATE_CLASS (PReLULayer)
     
    + REGISTER_LAYER_CLASS (PReLU)
     
    + INSTANTIATE_CLASS (RecurrentLayer)
     
    + INSTANTIATE_CLASS (ReductionLayer)
     
    + REGISTER_LAYER_CLASS (Reduction)
     
    + INSTANTIATE_CLASS (ReLULayer)
     
    + INSTANTIATE_CLASS (ReshapeLayer)
     
    + REGISTER_LAYER_CLASS (Reshape)
     
    + INSTANTIATE_CLASS (RNNLayer)
     
    + REGISTER_LAYER_CLASS (RNN)
     
    + INSTANTIATE_CLASS (ScaleLayer)
     
    + REGISTER_LAYER_CLASS (Scale)
     
    + INSTANTIATE_CLASS (SigmoidCrossEntropyLossLayer)
     
    + REGISTER_LAYER_CLASS (SigmoidCrossEntropyLoss)
     
    + INSTANTIATE_CLASS (SigmoidLayer)
     
    + INSTANTIATE_CLASS (SilenceLayer)
     
    + REGISTER_LAYER_CLASS (Silence)
     
    + INSTANTIATE_CLASS (SliceLayer)
     
    + REGISTER_LAYER_CLASS (Slice)
     
    + INSTANTIATE_CLASS (SoftmaxLayer)
     
    + INSTANTIATE_CLASS (SoftmaxWithLossLayer)
     
    + REGISTER_LAYER_CLASS (SoftmaxWithLoss)
     
    + INSTANTIATE_CLASS (SplitLayer)
     
    + REGISTER_LAYER_CLASS (Split)
     
    + INSTANTIATE_CLASS (SPPLayer)
     
    + REGISTER_LAYER_CLASS (SPP)
     
    + INSTANTIATE_CLASS (TanHLayer)
     
    + INSTANTIATE_CLASS (ThresholdLayer)
     
    + REGISTER_LAYER_CLASS (Threshold)
     
    + INSTANTIATE_CLASS (TileLayer)
     
    + REGISTER_LAYER_CLASS (Tile)
     
    + INSTANTIATE_CLASS (Net)
     
    + INSTANTIATE_CLASS (Params)
     
    + INSTANTIATE_CLASS (GPUParams)
     
    + INSTANTIATE_CLASS (P2PSync)
     
    + INSTANTIATE_CLASS (Solver)
     
    +template<typename Dtype >
    void adadelta_update_gpu (int N, Dtype *g, Dtype *h, Dtype *h2, Dtype momentum, Dtype delta, Dtype local_rate)
     
    + INSTANTIATE_CLASS (AdaDeltaSolver)
     
    + REGISTER_SOLVER_CLASS (AdaDelta)
     
    +template<typename Dtype >
    void adagrad_update_gpu (int N, Dtype *g, Dtype *h, Dtype delta, Dtype local_rate)
     
    + INSTANTIATE_CLASS (AdaGradSolver)
     
    + REGISTER_SOLVER_CLASS (AdaGrad)
     
    +template<typename Dtype >
    void adam_update_gpu (int N, Dtype *g, Dtype *m, Dtype *v, Dtype beta1, Dtype beta2, Dtype eps_hat, Dtype corrected_local_rate)
     
    + INSTANTIATE_CLASS (AdamSolver)
     
    + REGISTER_SOLVER_CLASS (Adam)
     
    +template<typename Dtype >
    void nesterov_update_gpu (int N, Dtype *g, Dtype *h, Dtype momentum, Dtype local_rate)
     
    + INSTANTIATE_CLASS (NesterovSolver)
     
    + REGISTER_SOLVER_CLASS (Nesterov)
     
    +template<typename Dtype >
    void rmsprop_update_gpu (int N, Dtype *g, Dtype *h, Dtype rms_decay, Dtype delta, Dtype local_rate)
     
    + INSTANTIATE_CLASS (RMSPropSolver)
     
    + REGISTER_SOLVER_CLASS (RMSProp)
     
    +template<typename Dtype >
    void sgd_update_gpu (int N, Dtype *g, Dtype *h, Dtype momentum, Dtype local_rate)
     
    + INSTANTIATE_CLASS (SGDSolver)
     
    + REGISTER_SOLVER_CLASS (SGD)
     
    +template<>
    void hdf5_load_nd_dataset< float > (hid_t file_id, const char *dataset_name_, int min_dim, int max_dim, Blob< float > *blob)
     
    +template<>
    void hdf5_load_nd_dataset< double > (hid_t file_id, const char *dataset_name_, int min_dim, int max_dim, Blob< double > *blob)
     
    +template<>
    void hdf5_save_nd_dataset< float > (const hid_t file_id, const string &dataset_name, const Blob< float > &blob, bool write_diff)
     
    +template<>
    void hdf5_save_nd_dataset< double > (hid_t file_id, const string &dataset_name, const Blob< double > &blob, bool write_diff)
     
    +bool is_a_ge_zero_and_a_lt_b (int a, int b)
     
    +template void im2col_cpu< float > (const float *data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, float *data_col)
     
    +template void im2col_cpu< double > (const double *data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, double *data_col)
     
    +template<typename Dtype >
    void im2col_nd_core_cpu (const Dtype *data_input, const bool im2col, const int num_spatial_axes, const int *im_shape, const int *col_shape, const int *kernel_shape, const int *pad, const int *stride, const int *dilation, Dtype *data_output)
     
    +template void im2col_nd_cpu< float > (const float *data_im, const int num_spatial_axes, const int *im_shape, const int *col_shape, const int *kernel_shape, const int *pad, const int *stride, const int *dilation, float *data_col)
     
    +template void im2col_nd_cpu< double > (const double *data_im, const int num_spatial_axes, const int *im_shape, const int *col_shape, const int *kernel_shape, const int *pad, const int *stride, const int *dilation, double *data_col)
     
    +template void col2im_cpu< float > (const float *data_col, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, float *data_im)
     
    +template void col2im_cpu< double > (const double *data_col, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, double *data_im)
     
    +template void col2im_nd_cpu< float > (const float *data_col, const int num_spatial_axes, const int *im_shape, const int *col_shape, const int *kernel_shape, const int *pad, const int *stride, const int *dilation, float *data_im)
     
    +template void col2im_nd_cpu< double > (const double *data_col, const int num_spatial_axes, const int *im_shape, const int *col_shape, const int *kernel_shape, const int *pad, const int *stride, const int *dilation, double *data_im)
     
    +template<>
    void caffe_cpu_gemm< float > (const CBLAS_TRANSPOSE TransA, const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K, const float alpha, const float *A, const float *B, const float beta, float *C)
     
    +template<>
    void caffe_cpu_gemm< double > (const CBLAS_TRANSPOSE TransA, const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K, const double alpha, const double *A, const double *B, const double beta, double *C)
     
    +template<>
    void caffe_cpu_gemv< float > (const CBLAS_TRANSPOSE TransA, const int M, const int N, const float alpha, const float *A, const float *x, const float beta, float *y)
     
    +template<>
    void caffe_cpu_gemv< double > (const CBLAS_TRANSPOSE TransA, const int M, const int N, const double alpha, const double *A, const double *x, const double beta, double *y)
     
    +template<>
    void caffe_axpy< float > (const int N, const float alpha, const float *X, float *Y)
     
    +template<>
    void caffe_axpy< double > (const int N, const double alpha, const double *X, double *Y)
     
    +template void caffe_set< int > (const int N, const int alpha, int *Y)
     
    +template void caffe_set< float > (const int N, const float alpha, float *Y)
     
    +template void caffe_set< double > (const int N, const double alpha, double *Y)
     
    +template<>
    void caffe_add_scalar (const int N, const float alpha, float *Y)
     
    +template<>
    void caffe_add_scalar (const int N, const double alpha, double *Y)
     
    +template void caffe_copy< int > (const int N, const int *X, int *Y)
     
    +template void caffe_copy< unsigned int > (const int N, const unsigned int *X, unsigned int *Y)
     
    +template void caffe_copy< float > (const int N, const float *X, float *Y)
     
    +template void caffe_copy< double > (const int N, const double *X, double *Y)
     
    +template<>
    void caffe_scal< float > (const int N, const float alpha, float *X)
     
    +template<>
    void caffe_scal< double > (const int N, const double alpha, double *X)
     
    +template<>
    void caffe_cpu_axpby< float > (const int N, const float alpha, const float *X, const float beta, float *Y)
     
    +template<>
    void caffe_cpu_axpby< double > (const int N, const double alpha, const double *X, const double beta, double *Y)
     
    +template<>
    void caffe_add< float > (const int n, const float *a, const float *b, float *y)
     
    +template<>
    void caffe_add< double > (const int n, const double *a, const double *b, double *y)
     
    +template<>
    void caffe_sub< float > (const int n, const float *a, const float *b, float *y)
     
    +template<>
    void caffe_sub< double > (const int n, const double *a, const double *b, double *y)
     
    +template<>
    void caffe_mul< float > (const int n, const float *a, const float *b, float *y)
     
    +template<>
    void caffe_mul< double > (const int n, const double *a, const double *b, double *y)
     
    +template<>
    void caffe_div< float > (const int n, const float *a, const float *b, float *y)
     
    +template<>
    void caffe_div< double > (const int n, const double *a, const double *b, double *y)
     
    +template<>
    void caffe_powx< float > (const int n, const float *a, const float b, float *y)
     
    +template<>
    void caffe_powx< double > (const int n, const double *a, const double b, double *y)
     
    +template<>
    void caffe_sqr< float > (const int n, const float *a, float *y)
     
    +template<>
    void caffe_sqr< double > (const int n, const double *a, double *y)
     
    +template<>
    void caffe_exp< float > (const int n, const float *a, float *y)
     
    +template<>
    void caffe_exp< double > (const int n, const double *a, double *y)
     
    +template<>
    void caffe_log< float > (const int n, const float *a, float *y)
     
    +template<>
    void caffe_log< double > (const int n, const double *a, double *y)
     
    +template<>
    void caffe_abs< float > (const int n, const float *a, float *y)
     
    +template<>
    void caffe_abs< double > (const int n, const double *a, double *y)
     
    +template float caffe_nextafter (const float b)
     
    +template double caffe_nextafter (const double b)
     
    +template void caffe_rng_uniform< float > (const int n, const float a, const float b, float *r)
     
    +template void caffe_rng_uniform< double > (const int n, const double a, const double b, double *r)
     
    +template void caffe_rng_gaussian< float > (const int n, const float mu, const float sigma, float *r)
     
    +template void caffe_rng_gaussian< double > (const int n, const double mu, const double sigma, double *r)
     
    +template void caffe_rng_bernoulli< double > (const int n, const double p, int *r)
     
    +template void caffe_rng_bernoulli< float > (const int n, const float p, int *r)
     
    +template void caffe_rng_bernoulli< double > (const int n, const double p, unsigned int *r)
     
    +template void caffe_rng_bernoulli< float > (const int n, const float p, unsigned int *r)
     
    +template<>
    float caffe_cpu_strided_dot< float > (const int n, const float *x, const int incx, const float *y, const int incy)
     
    +template<>
    double caffe_cpu_strided_dot< double > (const int n, const double *x, const int incx, const double *y, const int incy)
     
    +template float caffe_cpu_dot< float > (const int n, const float *x, const float *y)
     
    +template double caffe_cpu_dot< double > (const int n, const double *x, const double *y)
     
    +template<>
    float caffe_cpu_asum< float > (const int n, const float *x)
     
    +template<>
    double caffe_cpu_asum< double > (const int n, const double *x)
     
    +template<>
    void caffe_cpu_scale< float > (const int n, const float alpha, const float *x, float *y)
     
    +template<>
    void caffe_cpu_scale< double > (const int n, const double alpha, const double *x, double *y)
     
    + + + + + + + +

    +Variables

    +const float kLOG_THRESHOLD = 1e-20
     
    +const int CAFFE_CUDA_NUM_THREADS = 512
     
    +const float kBNLL_THRESHOLD = 50.
     
    +

    Detailed Description

    +

    A layer factory that allows one to register layers. During runtime, registered layers can be called by passing a LayerParameter protobuffer to the CreateLayer function:

    +

    A solver factory that allows one to register solvers, similar to layer factory. During runtime, registered solvers could be called by passing a SolverParameter protobuffer to the CreateSolver function:

    +

    LayerRegistry<Dtype>::CreateLayer(param);

    +

    There are two ways to register a layer. Assuming that we have a layer like:

    +

    template <typename dtype>=""> class MyAwesomeLayer : public Layer<Dtype> { // your implementations };

    +

    and its type is its C++ class name, but without the "Layer" at the end ("MyAwesomeLayer" -> "MyAwesome").

    +

    If the layer is going to be created simply by its constructor, in your c++ file, add the following line:

    +

    REGISTER_LAYER_CLASS(MyAwesome);

    +

    Or, if the layer is going to be created by another creator function, in the format of:

    +

    template <typename dtype>=""> Layer<Dtype*> GetMyAwesomeLayer(const LayerParameter& param) { // your implementation }

    +

    (for example, when your layer has multiple backends, see GetConvolutionLayer for a use case), then you can register the creator function instead, like

    +

    REGISTER_LAYER_CREATOR(MyAwesome, GetMyAwesomeLayer)

    +

    Note that each layer type should only be registered once.

    +

    SolverRegistry<Dtype>::CreateSolver(param);

    +

    There are two ways to register a solver. Assuming that we have a solver like:

    +

    template <typename dtype>=""> class MyAwesomeSolver : public Solver<Dtype> { // your implementations };

    +

    and its type is its C++ class name, but without the "Solver" at the end ("MyAwesomeSolver" -> "MyAwesome").

    +

    If the solver is going to be created simply by its constructor, in your C++ file, add the following line:

    +

    REGISTER_SOLVER_CLASS(MyAwesome);

    +

    Or, if the solver is going to be created by another creator function, in the format of:

    +

    template <typename dtype>=""> Solver<Dtype*> GetMyAwesomeSolver(const SolverParameter& param) { // your implementation }

    +

    then you can register the creator function instead, like

    +

    REGISTER_SOLVER_CREATOR(MyAwesome, GetMyAwesomeSolver)

    +

    Note that each solver type should only be registered once.

    +

    Function Documentation

    + +

    § GetFiller()

    + +
    +
    +
    +template<typename Dtype >
    + + + + + + + + +
    Filler<Dtype>* caffe::GetFiller (const FillerParameter & param)
    +
    + +

    Get a specific filler from the specification given in FillerParameter.

    +

    Ideally this would be replaced by a factory pattern, but we will leave it this way for now.

    + +
    +
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/namespacecaffe_1_1SolverAction.html b/doxygen/namespacecaffe_1_1SolverAction.html new file mode 100644 index 00000000000..992ec98eaa0 --- /dev/null +++ b/doxygen/namespacecaffe_1_1SolverAction.html @@ -0,0 +1,93 @@ + + + + + + + +Caffe: caffe::SolverAction Namespace Reference + + + + + + + + + +
    +
    + + + + + + +
    +
    Caffe +
    +
    +
    + + + + + + + + +
    +
    + + +
    + +
    + + +
    +
    +
    +Enumerations
    +
    +
    caffe::SolverAction Namespace Reference
    +
    +
    + +

    Enumeration of actions that a client of the Solver may request by implementing the Solver's action request function, which a client may optionally provide in order to request early termination or saving a snapshot without exiting. In the executable caffe, this mechanism is used to allow the snapshot to be saved when stopping execution with a SIGINT (Ctrl-C). +More...

    + + + + +

    +Enumerations

    enum  Enum { NONE = 0, +STOP = 1, +SNAPSHOT = 2 + }
     
    +

    Detailed Description

    +

    Enumeration of actions that a client of the Solver may request by implementing the Solver's action request function, which a client may optionally provide in order to request early termination or saving a snapshot without exiting. In the executable caffe, this mechanism is used to allow the snapshot to be saved when stopping execution with a SIGINT (Ctrl-C).

    +
    + +
    +Generated on Mon Nov 21 2016 11:23:49 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/namespacemembers.html b/doxygen/namespacemembers.html new file mode 100644 index 00000000000..5926044ba80 --- /dev/null +++ b/doxygen/namespacemembers.html @@ -0,0 +1,77 @@ + + + + + + + +Caffe: Namespace Members + + + + + + + + + +
    +
    + + + + + + +
    +
    Caffe +
    +
    +
    + + + + + + + +
    + +
    +
    + + +
    + +
    + +
    +
    Here is a list of all documented namespace members with links to the namespaces they belong to:
      +
    • ActionCallback +: caffe +
      • +
    • GetFiller() +: caffe +
      • +
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:49 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/namespacemembers_func.html b/doxygen/namespacemembers_func.html new file mode 100644 index 00000000000..237733ea5d0 --- /dev/null +++ b/doxygen/namespacemembers_func.html @@ -0,0 +1,74 @@ + + + + + + + +Caffe: Namespace Members + + + + + + + + + +
    +
    + + + + + + +
    +
    Caffe +
    +
    +
    + + + + + + + +
    + +
    +
    + + +
    + +
    + +
    + 
      +
    • GetFiller() +: caffe +
      • +
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:49 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/namespacemembers_type.html b/doxygen/namespacemembers_type.html new file mode 100644 index 00000000000..3820b419a99 --- /dev/null +++ b/doxygen/namespacemembers_type.html @@ -0,0 +1,74 @@ + + + + + + + +Caffe: Namespace Members + + + + + + + + + +
    +
    + + + + + + +
    +
    Caffe +
    +
    +
    + + + + + + + +
    + +
    +
    + + +
    + +
    + +
    + 
      +
    • ActionCallback +: caffe +
      • +
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:49 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/namespaces.html b/doxygen/namespaces.html new file mode 100644 index 00000000000..8ff81fae45c --- /dev/null +++ b/doxygen/namespaces.html @@ -0,0 +1,80 @@ + + + + + + + +Caffe: Namespace List + + + + + + + + + +
    +
    + + + + + + +
    +
    Caffe +
    +
    +
    + + + + + + + +
    + +
    +
    + + +
    + +
    + +
    +
    +
    Namespace List
    +
    +
    +
    Here is a list of all documented namespaces with brief descriptions:
    +
    [detail level 12]
    + + + +
     Nboost
     ▼NcaffeA layer factory that allows one to register layers. During runtime, registered layers can be called by passing a LayerParameter protobuffer to the CreateLayer function:
     NSolverActionEnumeration of actions that a client of the Solver may request by implementing the Solver's action request function, which a client may optionally provide in order to request early termination or saving a snapshot without exiting. In the executable caffe, this mechanism is used to allow the snapshot to be saved when stopping execution with a SIGINT (Ctrl-C)
    +
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:49 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/nav_f.png b/doxygen/nav_f.png new file mode 100644 index 0000000000000000000000000000000000000000..72a58a529ed3a9ed6aa0c51a79cf207e026deee2 GIT binary patch literal 153 zcmeAS@N?(olHy`uVBq!ia0vp^j6iI`!2~2XGqLUlQVE_ejv*C{Z|{2ZH7M}7UYxc) zn!W8uqtnIQ>_z8U literal 0 HcmV?d00001 diff --git a/doxygen/nav_g.png b/doxygen/nav_g.png new file mode 100644 index 0000000000000000000000000000000000000000..2093a237a94f6c83e19ec6e5fd42f7ddabdafa81 GIT binary patch literal 95 zcmeAS@N?(olHy`uVBq!ia0vp^j6lrB!3HFm1ilyoDK$?Q$B+ufw|5PB85lU25BhtE tr?otc=hd~V+ws&_A@j8Fiv!KF$B+ufw|5=67#uj90@pIL wZ=Q8~_Ju`#59=RjDrmm`tMD@M=!-l18IR?&vFVdQ&MBb@0HFXL + + + + + + +Caffe: include/caffe/net.hpp Source File + + + + + + + + + +
    +
    + + + + + + +
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    Caffe +
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    +
    net.hpp
    +
    +
    +
    1 #ifndef CAFFE_NET_HPP_
    2 #define CAFFE_NET_HPP_
    3 
    4 #include <map>
    5 #include <set>
    6 #include <string>
    7 #include <utility>
    8 #include <vector>
    9 
    10 #include "caffe/blob.hpp"
    11 #include "caffe/common.hpp"
    12 #include "caffe/layer.hpp"
    13 #include "caffe/proto/caffe.pb.h"
    14 
    15 namespace caffe {
    16 
    23 template <typename Dtype>
    24 class Net {
    25  public:
    26  explicit Net(const NetParameter& param, const Net* root_net = NULL);
    27  explicit Net(const string& param_file, Phase phase,
    28  const int level = 0, const vector<string>* stages = NULL,
    29  const Net* root_net = NULL);
    30  virtual ~Net() {}
    31 
    33  void Init(const NetParameter& param);
    34 
    39  const vector<Blob<Dtype>*>& Forward(Dtype* loss = NULL);
    41  const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL) {
    42  LOG_EVERY_N(WARNING, 1000) << "DEPRECATED: ForwardPrefilled() "
    43  << "will be removed in a future version. Use Forward().";
    44  return Forward(loss);
    45  }
    46 
    55  Dtype ForwardFromTo(int start, int end);
    56  Dtype ForwardFrom(int start);
    57  Dtype ForwardTo(int end);
    59  const vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom,
    60  Dtype* loss = NULL);
    61 
    66  void ClearParamDiffs();
    67 
    73  void Backward();
    74  void BackwardFromTo(int start, int end);
    75  void BackwardFrom(int start);
    76  void BackwardTo(int end);
    77 
    84  void Reshape();
    85 
    86  Dtype ForwardBackward() {
    87  Dtype loss;
    88  Forward(&loss);
    89  Backward();
    90  return loss;
    91  }
    92 
    94  void Update();
    101  void ShareWeights();
    102 
    107  void ShareTrainedLayersWith(const Net* other);
    108  // For an already initialized net, CopyTrainedLayersFrom() copies the already
    109  // trained layers from another net parameter instance.
    114  void CopyTrainedLayersFrom(const NetParameter& param);
    115  void CopyTrainedLayersFrom(const string trained_filename);
    116  void CopyTrainedLayersFromBinaryProto(const string trained_filename);
    117  void CopyTrainedLayersFromHDF5(const string trained_filename);
    119  void ToProto(NetParameter* param, bool write_diff = false) const;
    121  void ToHDF5(const string& filename, bool write_diff = false) const;
    122 
    124  inline const string& name() const { return name_; }
    126  inline const vector<string>& layer_names() const { return layer_names_; }
    128  inline const vector<string>& blob_names() const { return blob_names_; }
    130  inline const vector<shared_ptr<Blob<Dtype> > >& blobs() const {
    131  return blobs_;
    132  }
    134  inline const vector<shared_ptr<Layer<Dtype> > >& layers() const {
    135  return layers_;
    136  }
    138  inline Phase phase() const { return phase_; }
    143  inline const vector<vector<Blob<Dtype>*> >& bottom_vecs() const {
    144  return bottom_vecs_;
    145  }
    150  inline const vector<vector<Blob<Dtype>*> >& top_vecs() const {
    151  return top_vecs_;
    152  }
    154  inline const vector<int> & top_ids(int i) const {
    155  CHECK_GE(i, 0) << "Invalid layer id";
    156  CHECK_LT(i, top_id_vecs_.size()) << "Invalid layer id";
    157  return top_id_vecs_[i];
    158  }
    160  inline const vector<int> & bottom_ids(int i) const {
    161  CHECK_GE(i, 0) << "Invalid layer id";
    162  CHECK_LT(i, bottom_id_vecs_.size()) << "Invalid layer id";
    163  return bottom_id_vecs_[i];
    164  }
    165  inline const vector<vector<bool> >& bottom_need_backward() const {
    166  return bottom_need_backward_;
    167  }
    168  inline const vector<Dtype>& blob_loss_weights() const {
    169  return blob_loss_weights_;
    170  }
    171  inline const vector<bool>& layer_need_backward() const {
    172  return layer_need_backward_;
    173  }
    175  inline const vector<shared_ptr<Blob<Dtype> > >& params() const {
    176  return params_;
    177  }
    178  inline const vector<Blob<Dtype>*>& learnable_params() const {
    179  return learnable_params_;
    180  }
    182  inline const vector<float>& params_lr() const { return params_lr_; }
    183  inline const vector<bool>& has_params_lr() const { return has_params_lr_; }
    185  inline const vector<float>& params_weight_decay() const {
    186  return params_weight_decay_;
    187  }
    188  inline const vector<bool>& has_params_decay() const {
    189  return has_params_decay_;
    190  }
    191  const map<string, int>& param_names_index() const {
    192  return param_names_index_;
    193  }
    194  inline const vector<int>& param_owners() const { return param_owners_; }
    195  inline const vector<string>& param_display_names() const {
    196  return param_display_names_;
    197  }
    199  inline int num_inputs() const { return net_input_blobs_.size(); }
    200  inline int num_outputs() const { return net_output_blobs_.size(); }
    201  inline const vector<Blob<Dtype>*>& input_blobs() const {
    202  return net_input_blobs_;
    203  }
    204  inline const vector<Blob<Dtype>*>& output_blobs() const {
    205  return net_output_blobs_;
    206  }
    207  inline const vector<int>& input_blob_indices() const {
    208  return net_input_blob_indices_;
    209  }
    210  inline const vector<int>& output_blob_indices() const {
    211  return net_output_blob_indices_;
    212  }
    213  bool has_blob(const string& blob_name) const;
    214  const shared_ptr<Blob<Dtype> > blob_by_name(const string& blob_name) const;
    215  bool has_layer(const string& layer_name) const;
    216  const shared_ptr<Layer<Dtype> > layer_by_name(const string& layer_name) const;
    217 
    218  void set_debug_info(const bool value) { debug_info_ = value; }
    219 
    220  // Helpers for Init.
    225  static void FilterNet(const NetParameter& param,
    226  NetParameter* param_filtered);
    228  static bool StateMeetsRule(const NetState& state, const NetStateRule& rule,
    229  const string& layer_name);
    230 
    231  protected:
    232  // Helpers for Init.
    234  void AppendTop(const NetParameter& param, const int layer_id,
    235  const int top_id, set<string>* available_blobs,
    236  map<string, int>* blob_name_to_idx);
    238  int AppendBottom(const NetParameter& param, const int layer_id,
    239  const int bottom_id, set<string>* available_blobs,
    240  map<string, int>* blob_name_to_idx);
    242  void AppendParam(const NetParameter& param, const int layer_id,
    243  const int param_id);
    244 
    246  void ForwardDebugInfo(const int layer_id);
    248  void BackwardDebugInfo(const int layer_id);
    250  void UpdateDebugInfo(const int param_id);
    251 
    253  string name_;
    255  Phase phase_;
    257  vector<shared_ptr<Layer<Dtype> > > layers_;
    258  vector<string> layer_names_;
    259  map<string, int> layer_names_index_;
    260  vector<bool> layer_need_backward_;
    262  vector<shared_ptr<Blob<Dtype> > > blobs_;
    263  vector<string> blob_names_;
    264  map<string, int> blob_names_index_;
    265  vector<bool> blob_need_backward_;
    269  vector<vector<Blob<Dtype>*> > bottom_vecs_;
    270  vector<vector<int> > bottom_id_vecs_;
    271  vector<vector<bool> > bottom_need_backward_;
    273  vector<vector<Blob<Dtype>*> > top_vecs_;
    274  vector<vector<int> > top_id_vecs_;
    277  vector<Dtype> blob_loss_weights_;
    278  vector<vector<int> > param_id_vecs_;
    279  vector<int> param_owners_;
    280  vector<string> param_display_names_;
    281  vector<pair<int, int> > param_layer_indices_;
    282  map<string, int> param_names_index_;
    284  vector<int> net_input_blob_indices_;
    285  vector<int> net_output_blob_indices_;
    286  vector<Blob<Dtype>*> net_input_blobs_;
    287  vector<Blob<Dtype>*> net_output_blobs_;
    289  vector<shared_ptr<Blob<Dtype> > > params_;
    290  vector<Blob<Dtype>*> learnable_params_;
    298  vector<int> learnable_param_ids_;
    300  vector<float> params_lr_;
    301  vector<bool> has_params_lr_;
    303  vector<float> params_weight_decay_;
    304  vector<bool> has_params_decay_;
    306  size_t memory_used_;
    308  bool debug_info_;
    310  const Net* const root_net_;
    311  DISABLE_COPY_AND_ASSIGN(Net);
    312 };
    313 
    314 
    315 } // namespace caffe
    316 
    317 #endif // CAFFE_NET_HPP_
    caffe::Net::AppendTop
    void AppendTop(const NetParameter &param, const int layer_id, const int top_id, set< string > *available_blobs, map< string, int > *blob_name_to_idx)
    Append a new top blob to the net.
    Definition: net.cpp:384
    +
    caffe::Net::ForwardPrefilled
    const vector< Blob< Dtype > * > & ForwardPrefilled(Dtype *loss=NULL)
    DEPRECATED; use Forward() instead.
    Definition: net.hpp:41
    +
    caffe::Net::layers_
    vector< shared_ptr< Layer< Dtype > > > layers_
    Individual layers in the net.
    Definition: net.hpp:257
    +
    caffe::Net::num_inputs
    int num_inputs() const
    Input and output blob numbers.
    Definition: net.hpp:199
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::Net::blob_names
    const vector< string > & blob_names() const
    returns the blob names
    Definition: net.hpp:128
    +
    caffe::Net::blob_loss_weights_
    vector< Dtype > blob_loss_weights_
    Definition: net.hpp:277
    +
    caffe::Net::AppendParam
    void AppendParam(const NetParameter &param, const int layer_id, const int param_id)
    Append a new parameter blob to the net.
    Definition: net.cpp:449
    +
    caffe::Net::UpdateDebugInfo
    void UpdateDebugInfo(const int param_id)
    Helper for displaying debug info in Update.
    Definition: net.cpp:656
    +
    caffe::Net::Reshape
    void Reshape()
    Reshape all layers from bottom to top.
    Definition: net.cpp:743
    +
    caffe::Net::ForwardFromTo
    Dtype ForwardFromTo(int start, int end)
    Definition: net.cpp:544
    +
    caffe::Net::layers
    const vector< shared_ptr< Layer< Dtype > > > & layers() const
    returns the layers
    Definition: net.hpp:134
    +
    caffe::Net::memory_used_
    size_t memory_used_
    The bytes of memory used by this net.
    Definition: net.hpp:306
    +
    caffe::Net::Update
    void Update()
    Updates the network weights based on the diff values computed.
    Definition: net.cpp:925
    +
    caffe::Net::debug_info_
    bool debug_info_
    Whether to compute and display debug info for the net.
    Definition: net.hpp:308
    +
    caffe::Net::blobs
    const vector< shared_ptr< Blob< Dtype > > > & blobs() const
    returns the blobs
    Definition: net.hpp:130
    +
    caffe::Net::top_vecs_
    vector< vector< Blob< Dtype > * > > top_vecs_
    top_vecs stores the vectors containing the output for each layer
    Definition: net.hpp:273
    +
    caffe::Net::name
    const string & name() const
    returns the network name.
    Definition: net.hpp:124
    +
    caffe::Net::ClearParamDiffs
    void ClearParamDiffs()
    Zeroes out the diffs of all net parameters. Should be run before Backward.
    Definition: net.cpp:932
    +
    caffe::Net::BackwardDebugInfo
    void BackwardDebugInfo(const int layer_id)
    Helper for displaying debug info in Backward.
    Definition: net.cpp:629
    +
    caffe::Net::ShareWeights
    void ShareWeights()
    Shares weight data of owner blobs with shared blobs.
    Definition: net.cpp:953
    +
    caffe::Net::Init
    void Init(const NetParameter &param)
    Initialize a network with a NetParameter.
    Definition: net.cpp:49
    +
    caffe::Net::Backward
    void Backward()
    Definition: net.cpp:724
    +
    caffe::Net::top_vecs
    const vector< vector< Blob< Dtype > * > > & top_vecs() const
    returns the top vecs for each layer – usually you won&#39;t need this unless you do per-layer checks suc...
    Definition: net.hpp:150
    +
    caffe::Net::ShareTrainedLayersWith
    void ShareTrainedLayersWith(const Net *other)
    For an already initialized net, implicitly copies (i.e., using no additional memory) the pre-trained ...
    Definition: net.cpp:682
    +
    caffe::Net::AppendBottom
    int AppendBottom(const NetParameter &param, const int layer_id, const int bottom_id, set< string > *available_blobs, map< string, int > *blob_name_to_idx)
    Append a new bottom blob to the net.
    Definition: net.cpp:424
    +
    caffe::Net::params_weight_decay
    const vector< float > & params_weight_decay() const
    returns the learnable parameter decay multipliers
    Definition: net.hpp:185
    +
    caffe::Net::params_weight_decay_
    vector< float > params_weight_decay_
    the weight decay multipliers for learnable_params_
    Definition: net.hpp:303
    +
    caffe::Net::FilterNet
    static void FilterNet(const NetParameter &param, NetParameter *param_filtered)
    Remove layers that the user specified should be excluded given the current phase, level...
    Definition: net.cpp:287
    +
    caffe::Net::top_ids
    const vector< int > & top_ids(int i) const
    returns the ids of the top blobs of layer i
    Definition: net.hpp:154
    +
    caffe::Net::bottom_ids
    const vector< int > & bottom_ids(int i) const
    returns the ids of the bottom blobs of layer i
    Definition: net.hpp:160
    +
    caffe::Net::ToProto
    void ToProto(NetParameter *param, bool write_diff=false) const
    Writes the net to a proto.
    Definition: net.cpp:856
    +
    caffe::Net::ToHDF5
    void ToHDF5(const string &filename, bool write_diff=false) const
    Writes the net to an HDF5 file.
    Definition: net.cpp:868
    +
    caffe::Net::learnable_param_ids_
    vector< int > learnable_param_ids_
    Definition: net.hpp:298
    +
    caffe::Net::params_
    vector< shared_ptr< Blob< Dtype > > > params_
    The parameters in the network.
    Definition: net.hpp:289
    +
    caffe::Net::phase_
    Phase phase_
    The phase: TRAIN or TEST.
    Definition: net.hpp:255
    +
    caffe::Net::CopyTrainedLayersFrom
    void CopyTrainedLayersFrom(const NetParameter &param)
    For an already initialized net, copies the pre-trained layers from another Net.
    Definition: net.cpp:750
    +
    caffe::Net::StateMeetsRule
    static bool StateMeetsRule(const NetState &state, const NetStateRule &rule, const string &layer_name)
    return whether NetState state meets NetStateRule rule
    Definition: net.cpp:317
    +
    caffe::Net::Forward
    const vector< Blob< Dtype > * > & Forward(Dtype *loss=NULL)
    Run Forward and return the result.
    Definition: net.cpp:568
    +
    caffe::Net::params
    const vector< shared_ptr< Blob< Dtype > > > & params() const
    returns the parameters
    Definition: net.hpp:175
    +
    caffe::Net::ForwardDebugInfo
    void ForwardDebugInfo(const int layer_id)
    Helper for displaying debug info in Forward.
    Definition: net.cpp:603
    +
    caffe::Net::params_lr_
    vector< float > params_lr_
    the learning rate multipliers for learnable_params_
    Definition: net.hpp:300
    +
    caffe::Net::root_net_
    const Net *const root_net_
    The root net that actually holds the shared layers in data parallelism.
    Definition: net.hpp:310
    +
    caffe::Net::net_input_blob_indices_
    vector< int > net_input_blob_indices_
    blob indices for the input and the output of the net
    Definition: net.hpp:284
    +
    caffe::Net::layer_names
    const vector< string > & layer_names() const
    returns the layer names
    Definition: net.hpp:126
    +
    caffe::Net::params_lr
    const vector< float > & params_lr() const
    returns the learnable parameter learning rate multipliers
    Definition: net.hpp:182
    +
    caffe::Net::phase
    Phase phase() const
    returns the phase: TRAIN or TEST
    Definition: net.hpp:138
    +
    caffe::Net
    Connects Layers together into a directed acyclic graph (DAG) specified by a NetParameter.
    Definition: net.hpp:24
    +
    caffe::Net::blobs_
    vector< shared_ptr< Blob< Dtype > > > blobs_
    the blobs storing intermediate results between the layer.
    Definition: net.hpp:262
    +
    caffe::Net::bottom_vecs_
    vector< vector< Blob< Dtype > * > > bottom_vecs_
    Definition: net.hpp:269
    +
    caffe::Net::bottom_vecs
    const vector< vector< Blob< Dtype > * > > & bottom_vecs() const
    returns the bottom vecs for each layer – usually you won&#39;t need this unless you do per-layer checks ...
    Definition: net.hpp:143
    +
    caffe::Net::name_
    string name_
    The network name.
    Definition: net.hpp:253
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/neuron__layer_8hpp_source.html b/doxygen/neuron__layer_8hpp_source.html new file mode 100644 index 00000000000..fe276f33377 --- /dev/null +++ b/doxygen/neuron__layer_8hpp_source.html @@ -0,0 +1,84 @@ + + + + + + + +Caffe: include/caffe/layers/neuron_layer.hpp Source File + + + + + + + + + +
    +
    + + + + + + +
    +
    Caffe +
    +
    +
    + + + + + + + + +
    +
    + + +
    + +
    + + +
    +
    +
    +
    neuron_layer.hpp
    +
    +
    +
    1 #ifndef CAFFE_NEURON_LAYER_HPP_
    2 #define CAFFE_NEURON_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 namespace caffe {
    11 
    18 template <typename Dtype>
    19 class NeuronLayer : public Layer<Dtype> {
    20  public:
    21  explicit NeuronLayer(const LayerParameter& param)
    22  : Layer<Dtype>(param) {}
    23  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    24  const vector<Blob<Dtype>*>& top);
    25 
    26  virtual inline int ExactNumBottomBlobs() const { return 1; }
    27  virtual inline int ExactNumTopBlobs() const { return 1; }
    28 };
    29 
    30 } // namespace caffe
    31 
    32 #endif // CAFFE_NEURON_LAYER_HPP_
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::NeuronLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: neuron_layer.cpp:8
    +
    caffe::NeuronLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: neuron_layer.hpp:27
    +
    caffe::NeuronLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: neuron_layer.hpp:26
    +
    caffe::NeuronLayer
    An interface for layers that take one blob as input ( ) and produce one equally-sized blob as output ...
    Definition: neuron_layer.hpp:19
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/neuron__layers_8hpp_source.html b/doxygen/neuron__layers_8hpp_source.html new file mode 100644 index 00000000000..0b9c643b660 --- /dev/null +++ b/doxygen/neuron__layers_8hpp_source.html @@ -0,0 +1,569 @@ + + + + + + +Caffe: include/caffe/neuron_layers.hpp Source File + + + + + + + + + + +
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    + + + + + + +
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    Caffe +
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    +
    +
    neuron_layers.hpp
    +
    +
    +
    1 #ifndef CAFFE_NEURON_LAYERS_HPP_
    +
    2 #define CAFFE_NEURON_LAYERS_HPP_
    +
    3 
    +
    4 #include <string>
    +
    5 #include <utility>
    +
    6 #include <vector>
    +
    7 
    +
    8 #include "caffe/blob.hpp"
    +
    9 #include "caffe/common.hpp"
    +
    10 #include "caffe/layer.hpp"
    +
    11 #include "caffe/proto/caffe.pb.h"
    +
    12 
    +
    13 #define HDF5_DATA_DATASET_NAME "data"
    +
    14 #define HDF5_DATA_LABEL_NAME "label"
    +
    15 
    +
    16 namespace caffe {
    +
    17 
    +
    24 template <typename Dtype>
    +
    25 class NeuronLayer : public Layer<Dtype> {
    +
    26  public:
    +
    27  explicit NeuronLayer(const LayerParameter& param)
    +
    28  : Layer<Dtype>(param) {}
    +
    29  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    +
    30  const vector<Blob<Dtype>*>& top);
    +
    31 
    +
    32  virtual inline int ExactNumBottomBlobs() const { return 1; }
    +
    33  virtual inline int ExactNumTopBlobs() const { return 1; }
    +
    34 };
    +
    35 
    +
    46 template <typename Dtype>
    +
    47 class AbsValLayer : public NeuronLayer<Dtype> {
    +
    48  public:
    +
    49  explicit AbsValLayer(const LayerParameter& param)
    +
    50  : NeuronLayer<Dtype>(param) {}
    +
    51  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    +
    52  const vector<Blob<Dtype>*>& top);
    +
    53 
    +
    54  virtual inline const char* type() const { return "AbsVal"; }
    +
    55  virtual inline int ExactNumBottomBlobs() const { return 1; }
    +
    56  virtual inline int ExactNumTopBlobs() const { return 1; }
    +
    57 
    +
    58  protected:
    +
    60  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    +
    61  const vector<Blob<Dtype>*>& top);
    +
    62  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    63  const vector<Blob<Dtype>*>& top);
    +
    64 
    +
    82  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    +
    83  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    84  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    85  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    86 };
    +
    87 
    +
    105 template <typename Dtype>
    +
    106 class BNLLLayer : public NeuronLayer<Dtype> {
    +
    107  public:
    +
    108  explicit BNLLLayer(const LayerParameter& param)
    +
    109  : NeuronLayer<Dtype>(param) {}
    +
    110 
    +
    111  virtual inline const char* type() const { return "BNLL"; }
    +
    112 
    +
    113  protected:
    +
    115  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    +
    116  const vector<Blob<Dtype>*>& top);
    +
    117  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    118  const vector<Blob<Dtype>*>& top);
    +
    119 
    +
    136  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    +
    137  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    138  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    139  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    140 };
    +
    141 
    +
    153 template <typename Dtype>
    +
    154 class DropoutLayer : public NeuronLayer<Dtype> {
    +
    155  public:
    +
    162  explicit DropoutLayer(const LayerParameter& param)
    +
    163  : NeuronLayer<Dtype>(param) {}
    +
    164  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    +
    165  const vector<Blob<Dtype>*>& top);
    +
    166  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    +
    167  const vector<Blob<Dtype>*>& top);
    +
    168 
    +
    169  virtual inline const char* type() const { return "Dropout"; }
    +
    170 
    +
    171  protected:
    +
    188  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    +
    189  const vector<Blob<Dtype>*>& top);
    +
    190  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    191  const vector<Blob<Dtype>*>& top);
    +
    192  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    +
    193  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    194  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    195  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    196 
    +
    198  Blob<unsigned int> rand_vec_;
    +
    200  Dtype threshold_;
    +
    202  Dtype scale_;
    +
    203  unsigned int uint_thres_;
    +
    204 };
    +
    205 
    +
    211 template <typename Dtype>
    +
    212 class ExpLayer : public NeuronLayer<Dtype> {
    +
    213  public:
    +
    222  explicit ExpLayer(const LayerParameter& param)
    +
    223  : NeuronLayer<Dtype>(param) {}
    +
    224  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    +
    225  const vector<Blob<Dtype>*>& top);
    +
    226 
    +
    227  virtual inline const char* type() const { return "Exp"; }
    +
    228 
    +
    229  protected:
    +
    240  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    +
    241  const vector<Blob<Dtype>*>& top);
    +
    242  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    243  const vector<Blob<Dtype>*>& top);
    +
    244 
    +
    262  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    +
    263  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    264  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    265  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    266 
    +
    267  Dtype inner_scale_, outer_scale_;
    +
    268 };
    +
    269 
    +
    275 template <typename Dtype>
    +
    276 class LogLayer : public NeuronLayer<Dtype> {
    +
    277  public:
    +
    286  explicit LogLayer(const LayerParameter& param)
    +
    287  : NeuronLayer<Dtype>(param) {}
    +
    288  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    +
    289  const vector<Blob<Dtype>*>& top);
    +
    290 
    +
    291  virtual inline const char* type() const { return "Log"; }
    +
    292 
    +
    293  protected:
    +
    304  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    +
    305  const vector<Blob<Dtype>*>& top);
    +
    306  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    307  const vector<Blob<Dtype>*>& top);
    +
    308 
    +
    326  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    +
    327  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    328  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    329  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    330 
    +
    331  Dtype base_scale_;
    +
    332  Dtype input_scale_, input_shift_;
    +
    333  Dtype backward_num_scale_;
    +
    334 };
    +
    335 
    +
    341 template <typename Dtype>
    +
    342 class PowerLayer : public NeuronLayer<Dtype> {
    +
    343  public:
    +
    351  explicit PowerLayer(const LayerParameter& param)
    +
    352  : NeuronLayer<Dtype>(param) {}
    +
    353  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    +
    354  const vector<Blob<Dtype>*>& top);
    +
    355 
    +
    356  virtual inline const char* type() const { return "Power"; }
    +
    357 
    +
    358  protected:
    +
    369  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    +
    370  const vector<Blob<Dtype>*>& top);
    +
    371  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    372  const vector<Blob<Dtype>*>& top);
    +
    373 
    +
    394  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    +
    395  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    396  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    397  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    398 
    +
    400  Dtype power_;
    +
    402  Dtype scale_;
    +
    404  Dtype shift_;
    +
    406  Dtype diff_scale_;
    +
    407 };
    +
    408 
    +
    413 template <typename Dtype>
    +
    414 class ReLULayer : public NeuronLayer<Dtype> {
    +
    415  public:
    +
    422  explicit ReLULayer(const LayerParameter& param)
    +
    423  : NeuronLayer<Dtype>(param) {}
    +
    424 
    +
    425  virtual inline const char* type() const { return "ReLU"; }
    +
    426 
    +
    427  protected:
    +
    439  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    +
    440  const vector<Blob<Dtype>*>& top);
    +
    441  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    442  const vector<Blob<Dtype>*>& top);
    +
    443 
    +
    472  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    +
    473  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    474  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    475  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    476 };
    +
    477 
    +
    478 #ifdef USE_CUDNN
    +
    479 
    +
    482 template <typename Dtype>
    +
    483 class CuDNNReLULayer : public ReLULayer<Dtype> {
    +
    484  public:
    +
    485  explicit CuDNNReLULayer(const LayerParameter& param)
    +
    486  : ReLULayer<Dtype>(param), handles_setup_(false) {}
    +
    487  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    +
    488  const vector<Blob<Dtype>*>& top);
    +
    489  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    +
    490  const vector<Blob<Dtype>*>& top);
    +
    491  virtual ~CuDNNReLULayer();
    +
    492 
    +
    493  protected:
    +
    494  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    495  const vector<Blob<Dtype>*>& top);
    +
    496  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    497  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    498 
    +
    499  bool handles_setup_;
    +
    500  cudnnHandle_t handle_;
    +
    501  cudnnTensorDescriptor_t bottom_desc_;
    +
    502  cudnnTensorDescriptor_t top_desc_;
    +
    503 };
    +
    504 #endif
    +
    505 
    +
    514 template <typename Dtype>
    +
    515 class SigmoidLayer : public NeuronLayer<Dtype> {
    +
    516  public:
    +
    517  explicit SigmoidLayer(const LayerParameter& param)
    +
    518  : NeuronLayer<Dtype>(param) {}
    +
    519 
    +
    520  virtual inline const char* type() const { return "Sigmoid"; }
    +
    521 
    +
    522  protected:
    +
    533  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    +
    534  const vector<Blob<Dtype>*>& top);
    +
    535  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    536  const vector<Blob<Dtype>*>& top);
    +
    537 
    +
    555  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    +
    556  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    557  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    558  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    559 };
    +
    560 
    +
    561 #ifdef USE_CUDNN
    +
    562 
    +
    565 template <typename Dtype>
    +
    566 class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
    +
    567  public:
    +
    568  explicit CuDNNSigmoidLayer(const LayerParameter& param)
    +
    569  : SigmoidLayer<Dtype>(param), handles_setup_(false) {}
    +
    570  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    +
    571  const vector<Blob<Dtype>*>& top);
    +
    572  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    +
    573  const vector<Blob<Dtype>*>& top);
    +
    574  virtual ~CuDNNSigmoidLayer();
    +
    575 
    +
    576  protected:
    +
    577  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    578  const vector<Blob<Dtype>*>& top);
    +
    579  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    580  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    581 
    +
    582  bool handles_setup_;
    +
    583  cudnnHandle_t handle_;
    +
    584  cudnnTensorDescriptor_t bottom_desc_;
    +
    585  cudnnTensorDescriptor_t top_desc_;
    +
    586 };
    +
    587 #endif
    +
    588 
    +
    597 template <typename Dtype>
    +
    598 class TanHLayer : public NeuronLayer<Dtype> {
    +
    599  public:
    +
    600  explicit TanHLayer(const LayerParameter& param)
    +
    601  : NeuronLayer<Dtype>(param) {}
    +
    602 
    +
    603  virtual inline const char* type() const { return "TanH"; }
    +
    604 
    +
    605  protected:
    +
    616  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    +
    617  const vector<Blob<Dtype>*>& top);
    +
    618  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    619  const vector<Blob<Dtype>*>& top);
    +
    620 
    +
    640  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    +
    641  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    642  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    643  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    644 };
    +
    645 
    +
    646 #ifdef USE_CUDNN
    +
    647 
    +
    650 template <typename Dtype>
    +
    651 class CuDNNTanHLayer : public TanHLayer<Dtype> {
    +
    652  public:
    +
    653  explicit CuDNNTanHLayer(const LayerParameter& param)
    +
    654  : TanHLayer<Dtype>(param), handles_setup_(false) {}
    +
    655  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    +
    656  const vector<Blob<Dtype>*>& top);
    +
    657  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    +
    658  const vector<Blob<Dtype>*>& top);
    +
    659  virtual ~CuDNNTanHLayer();
    +
    660 
    +
    661  protected:
    +
    662  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    663  const vector<Blob<Dtype>*>& top);
    +
    664  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    665  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    666 
    +
    667  bool handles_setup_;
    +
    668  cudnnHandle_t handle_;
    +
    669  cudnnTensorDescriptor_t bottom_desc_;
    +
    670  cudnnTensorDescriptor_t top_desc_;
    +
    671 };
    +
    672 #endif
    +
    673 
    +
    678 template <typename Dtype>
    +
    679 class ThresholdLayer : public NeuronLayer<Dtype> {
    +
    680  public:
    +
    687  explicit ThresholdLayer(const LayerParameter& param)
    +
    688  : NeuronLayer<Dtype>(param) {}
    +
    689  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    +
    690  const vector<Blob<Dtype>*>& top);
    +
    691 
    +
    692  virtual inline const char* type() const { return "Threshold"; }
    +
    693 
    +
    694  protected:
    +
    709  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    +
    710  const vector<Blob<Dtype>*>& top);
    +
    711  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    712  const vector<Blob<Dtype>*>& top);
    +
    714  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    +
    715  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
    +
    716  NOT_IMPLEMENTED;
    +
    717  }
    +
    718 
    +
    719  Dtype threshold_;
    +
    720 };
    +
    721 
    +
    730 template <typename Dtype>
    +
    731 class PReLULayer : public NeuronLayer<Dtype> {
    +
    732  public:
    +
    741  explicit PReLULayer(const LayerParameter& param)
    +
    742  : NeuronLayer<Dtype>(param) {}
    +
    743 
    +
    744  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    +
    745  const vector<Blob<Dtype>*>& top);
    +
    746 
    +
    747  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    +
    748  const vector<Blob<Dtype>*>& top);
    +
    749 
    +
    750  virtual inline const char* type() const { return "PReLU"; }
    +
    751 
    +
    752  protected:
    +
    763  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    +
    764  const vector<Blob<Dtype>*>& top);
    +
    765  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    766  const vector<Blob<Dtype>*>& top);
    +
    767 
    +
    796  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    +
    797  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    798  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    799  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    800 
    +
    801  bool channel_shared_;
    +
    802  Blob<Dtype> multiplier_; // dot multiplier for backward computation of params
    +
    803  Blob<Dtype> backward_buff_; // temporary buffer for backward computation
    +
    804  Blob<Dtype> bottom_memory_; // memory for in-place computation
    +
    805 };
    +
    806 
    +
    807 } // namespace caffe
    +
    808 
    +
    809 #endif // CAFFE_NEURON_LAYERS_HPP_
    +
    caffe::ThresholdLayer
    Tests whether the input exceeds a threshold: outputs 1 for inputs above threshold; 0 otherwise...
    Definition: neuron_layers.hpp:679
    +
    caffe::AbsValLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: neuron_layers.hpp:56
    +
    caffe::DropoutLayer::rand_vec_
    Blob< unsigned int > rand_vec_
    when divided by UINT_MAX, the randomly generated values
    Definition: neuron_layers.hpp:198
    +
    caffe::SigmoidLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Definition: sigmoid_layer.cpp:16
    +
    caffe::ThresholdLayer::ThresholdLayer
    ThresholdLayer(const LayerParameter &param)
    Definition: neuron_layers.hpp:687
    +
    caffe::NeuronLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: neuron_layers.hpp:32
    +
    caffe::TanHLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    +
    caffe::DropoutLayer::scale_
    Dtype scale_
    the scale for undropped inputs at train time
    Definition: neuron_layers.hpp:202
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers could be called...
    Definition: blob.hpp:15
    +
    caffe::ExpLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Definition: exp_layer.cpp:32
    +
    caffe::SigmoidLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: neuron_layers.hpp:520
    +
    caffe::SigmoidLayer
    Sigmoid function non-linearity , a classic choice in neural networks.
    Definition: neuron_layers.hpp:515
    +
    caffe::DropoutLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::ThresholdLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Definition: threshold_layer.cpp:17
    +
    caffe::BNLLLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Computes the error gradient w.r.t. the BNLL inputs.
    Definition: bnll_layer.cpp:25
    +
    caffe::PowerLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: power_layer.cpp:11
    +
    caffe::LogLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::PowerLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::NeuronLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: neuron_layer.cpp:9
    +
    caffe::TanHLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Definition: tanh_layer.cpp:13
    +
    caffe::AbsValLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Computes the error gradient w.r.t. the absolute value inputs.
    Definition: absval_layer.cpp:26
    +
    caffe::ExpLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::PowerLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::AbsValLayer
    Computes .
    Definition: neuron_layers.hpp:47
    +
    caffe::ExpLayer
    Computes , as specified by the scale , shift , and base .
    Definition: neuron_layers.hpp:212
    +
    caffe::TanHLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: neuron_layers.hpp:603
    +
    caffe::PowerLayer::power_
    Dtype power_
    from layer_param_.power_param()
    Definition: neuron_layers.hpp:400
    +
    caffe::PowerLayer::diff_scale_
    Dtype diff_scale_
    Result of .
    Definition: neuron_layers.hpp:406
    +
    caffe::LogLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: neuron_layers.hpp:291
    +
    caffe::PowerLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: neuron_layers.hpp:356
    +
    caffe::DropoutLayer::DropoutLayer
    DropoutLayer(const LayerParameter &param)
    Definition: neuron_layers.hpp:162
    +
    caffe::ThresholdLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::LogLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: log_layer.cpp:11
    +
    caffe::PReLULayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: prelu_layer.cpp:54
    +
    caffe::ReLULayer
    Rectified Linear Unit non-linearity . The simple max is fast to compute, and the function does not sa...
    Definition: neuron_layers.hpp:414
    +
    caffe::ThresholdLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Not implemented (non-differentiable function)
    Definition: neuron_layers.hpp:714
    +
    caffe::SigmoidLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::PowerLayer
    Computes , as specified by the scale , shift , and power .
    Definition: neuron_layers.hpp:342
    +
    caffe::ExpLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::ReLULayer::ReLULayer
    ReLULayer(const LayerParameter &param)
    Definition: neuron_layers.hpp:422
    +
    caffe::DropoutLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: dropout_layer.cpp:14
    +
    caffe::SigmoidLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Computes the error gradient w.r.t. the sigmoid inputs.
    Definition: sigmoid_layer.cpp:27
    +
    caffe::DropoutLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::PReLULayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: prelu_layer.cpp:11
    +
    caffe::NeuronLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: neuron_layers.hpp:33
    +
    caffe::PReLULayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: neuron_layers.hpp:750
    +
    caffe::ReLULayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Computes the error gradient w.r.t. the ReLU inputs.
    Definition: relu_layer.cpp:23
    +
    caffe::PowerLayer::shift_
    Dtype shift_
    from layer_param_.power_param()
    Definition: neuron_layers.hpp:404
    +
    caffe::LogLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::PowerLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Computes the error gradient w.r.t. the power inputs.
    Definition: power_layer.cpp:46
    +
    caffe::BNLLLayer
    Computes if ; otherwise.
    Definition: neuron_layers.hpp:106
    +
    caffe::LogLayer
    Computes , as specified by the scale , shift , and base .
    Definition: neuron_layers.hpp:276
    +
    caffe::DropoutLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Definition: dropout_layer.cpp:34
    +
    caffe::SigmoidLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::ReLULayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: neuron_layers.hpp:425
    +
    caffe::DropoutLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: neuron_layers.hpp:169
    +
    caffe::PReLULayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Computes the error gradient w.r.t. the PReLU inputs.
    Definition: prelu_layer.cpp:91
    +
    caffe::ExpLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Computes the error gradient w.r.t. the exp inputs.
    Definition: exp_layer.cpp:49
    +
    caffe::NeuronLayer
    An interface for layers that take one blob as input ( ) and produce one equally-sized blob as output ...
    Definition: neuron_layers.hpp:25
    +
    caffe::PowerLayer::scale_
    Dtype scale_
    from layer_param_.power_param()
    Definition: neuron_layers.hpp:402
    +
    caffe::DropoutLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: dropout_layer.cpp:52
    +
    caffe::AbsValLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::PReLULayer
    Parameterized Rectified Linear Unit non-linearity . The differences from ReLULayer are 1) negative sl...
    Definition: neuron_layers.hpp:731
    +
    caffe::PReLULayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::PowerLayer::PowerLayer
    PowerLayer(const LayerParameter &param)
    Definition: neuron_layers.hpp:351
    +
    caffe::ReLULayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::LogLayer::LogLayer
    LogLayer(const LayerParameter &param)
    Definition: neuron_layers.hpp:286
    +
    caffe::ReLULayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::BNLLLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::AbsValLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: absval_layer.cpp:10
    +
    caffe::ThresholdLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: neuron_layers.hpp:692
    +
    caffe::ExpLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: neuron_layers.hpp:227
    +
    caffe::PReLULayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Definition: prelu_layer.cpp:66
    +
    caffe::AbsValLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Computes .
    Definition: absval_layer.cpp:18
    +
    caffe::BNLLLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::PowerLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Definition: power_layer.cpp:22
    +
    caffe::PReLULayer::PReLULayer
    PReLULayer(const LayerParameter &param)
    Definition: neuron_layers.hpp:741
    +
    caffe::ReLULayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Definition: relu_layer.cpp:10
    +
    caffe::TanHLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::LogLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Definition: log_layer.cpp:36
    +
    caffe::DropoutLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: dropout_layer.cpp:25
    +
    caffe::BNLLLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Computes if ; otherwise.
    Definition: bnll_layer.cpp:12
    +
    caffe::PReLULayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::AbsValLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: neuron_layers.hpp:54
    +
    caffe::AbsValLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: neuron_layers.hpp:55
    +
    caffe::AbsValLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::ExpLayer::ExpLayer
    ExpLayer(const LayerParameter &param)
    Definition: neuron_layers.hpp:222
    +
    caffe::DropoutLayer
    During training only, sets a random portion of to 0, adjusting the rest of the vector magnitude acco...
    Definition: neuron_layers.hpp:154
    +
    caffe::ThresholdLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: threshold_layer.cpp:10
    +
    caffe::DropoutLayer::threshold_
    Dtype threshold_
    the probability of dropping any input
    Definition: neuron_layers.hpp:200
    +
    caffe::TanHLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Computes the error gradient w.r.t. the sigmoid inputs.
    Definition: tanh_layer.cpp:24
    +
    caffe::ExpLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: exp_layer.cpp:11
    +
    caffe::LogLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Computes the error gradient w.r.t. the exp inputs.
    Definition: log_layer.cpp:59
    +
    caffe::BNLLLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: neuron_layers.hpp:111
    +
    caffe::TanHLayer
    TanH hyperbolic tangent non-linearity , popular in auto-encoders.
    Definition: neuron_layers.hpp:598
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:25
    +
    + +
    +Generated on Mon Oct 19 2015 15:25:08 for Caffe by   +doxygen + 1.8.9.1 +
    + + diff --git a/doxygen/open.png b/doxygen/open.png new file mode 100644 index 0000000000000000000000000000000000000000..30f75c7efe2dd0c9e956e35b69777a02751f048b GIT binary patch literal 123 zcmeAS@N?(olHy`uVBq!ia0vp^oFL4>1|%O$WD@{VPM$7~Ar*{o?;hlAFyLXmaDC0y znK1_#cQqJWPES%4Uujug^TE?jMft$}Eq^WaR~)%f)vSNs&gek&x%A9X9sM + + + + + + +Caffe: include/caffe/parallel.hpp Source File + + + + + + + + + +
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    parallel.hpp
    +
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    1 #ifndef CAFFE_PARALLEL_HPP_
    2 #define CAFFE_PARALLEL_HPP_
    3 
    4 #include <boost/date_time/posix_time/posix_time.hpp>
    5 
    6 #include <vector>
    7 
    8 #include "caffe/blob.hpp"
    9 #include "caffe/common.hpp"
    10 #include "caffe/internal_thread.hpp"
    11 #include "caffe/layer.hpp"
    12 #include "caffe/proto/caffe.pb.h"
    13 #include "caffe/solver.hpp"
    14 #include "caffe/syncedmem.hpp"
    15 #include "caffe/util/blocking_queue.hpp"
    16 
    17 namespace caffe {
    18 
    19 // Represents a net parameters. Once a net is created, its parameter buffers can
    20 // be replaced by ones from Params, to allow parallelization. Params ensures
    21 // parameters are allocated in one consecutive array.
    22 template<typename Dtype>
    23 class Params {
    24  public:
    25  explicit Params(shared_ptr<Solver<Dtype> > root_solver);
    26  virtual ~Params() {
    27  }
    28 
    29  inline size_t size() const {
    30  return size_;
    31  }
    32  inline Dtype* data() const {
    33  return data_;
    34  }
    35  inline Dtype* diff() const {
    36  return diff_;
    37  }
    38 
    39  protected:
    40  const size_t size_; // Size of buffers
    41  Dtype* data_; // Network parameters
    42  Dtype* diff_; // Gradient
    43 
    44 DISABLE_COPY_AND_ASSIGN(Params);
    45 };
    46 
    47 // Params stored in GPU memory.
    48 template<typename Dtype>
    49 class GPUParams : public Params<Dtype> {
    50  public:
    51  GPUParams(shared_ptr<Solver<Dtype> > root_solver, int device);
    52  virtual ~GPUParams();
    53 
    54  void configure(Solver<Dtype>* solver) const;
    55 
    56  protected:
    57  using Params<Dtype>::size_;
    58  using Params<Dtype>::data_;
    59  using Params<Dtype>::diff_;
    60 };
    61 
    62 class DevicePair {
    63  public:
    64  DevicePair(int parent, int device)
    65  : parent_(parent),
    66  device_(device) {
    67  }
    68  inline int parent() {
    69  return parent_;
    70  }
    71  inline int device() {
    72  return device_;
    73  }
    74 
    75  // Group GPUs in pairs, by proximity depending on machine's topology
    76  static void compute(const vector<int> devices, vector<DevicePair>* pairs);
    77 
    78  protected:
    79  int parent_;
    80  int device_;
    81 };
    82 
    83 // Synchronous data parallelism using map-reduce between local GPUs.
    84 template<typename Dtype>
    85 class P2PSync : public GPUParams<Dtype>, public Solver<Dtype>::Callback,
    86  public InternalThread {
    87  public:
    88  explicit P2PSync(shared_ptr<Solver<Dtype> > root_solver,
    89  P2PSync<Dtype>* parent, const SolverParameter& param);
    90  virtual ~P2PSync();
    91 
    92  inline const shared_ptr<Solver<Dtype> >& solver() const {
    93  return solver_;
    94  }
    95 
    96  void Run(const vector<int>& gpus);
    97  void Prepare(const vector<int>& gpus,
    98  vector<shared_ptr<P2PSync<Dtype> > >* syncs);
    99  inline const int initial_iter() const { return initial_iter_; }
    100 
    101  protected:
    102  void on_start();
    103  void on_gradients_ready();
    104 
    105  void InternalThreadEntry();
    106 
    107  P2PSync<Dtype>* parent_;
    108  vector<P2PSync<Dtype>*> children_;
    109  BlockingQueue<P2PSync<Dtype>*> queue_;
    110  const int initial_iter_;
    111  Dtype* parent_grads_;
    112  shared_ptr<Solver<Dtype> > solver_;
    113 
    114  using Params<Dtype>::size_;
    115  using Params<Dtype>::data_;
    116  using Params<Dtype>::diff_;
    117 };
    118 
    119 } // namespace caffe
    120 
    121 #endif
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::Params
    Definition: parallel.hpp:23
    +
    caffe::Solver
    An interface for classes that perform optimization on Nets.
    Definition: solver.hpp:41
    +
    caffe::GPUParams
    Definition: parallel.hpp:49
    +
    caffe::P2PSync
    Definition: parallel.hpp:85
    +
    caffe::DevicePair
    Definition: parallel.hpp:62
    +
    caffe::InternalThread
    Definition: internal_thread.hpp:19
    +
    caffe::BlockingQueue
    Definition: blocking_queue.hpp:10
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/parameter__layer_8hpp_source.html b/doxygen/parameter__layer_8hpp_source.html new file mode 100644 index 00000000000..4148684bb81 --- /dev/null +++ b/doxygen/parameter__layer_8hpp_source.html @@ -0,0 +1,90 @@ + + + + + + + +Caffe: include/caffe/layers/parameter_layer.hpp Source File + + + + + + + + + +
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    parameter_layer.hpp
    +
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    1 #ifndef CAFFE_PARAMETER_LAYER_HPP_
    2 #define CAFFE_PARAMETER_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/layer.hpp"
    7 
    8 namespace caffe {
    9 
    10 template <typename Dtype>
    11 class ParameterLayer : public Layer<Dtype> {
    12  public:
    13  explicit ParameterLayer(const LayerParameter& param)
    14  : Layer<Dtype>(param) {}
    15  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    16  const vector<Blob<Dtype>*>& top) {
    17  if (this->blobs_.size() > 0) {
    18  LOG(INFO) << "Skipping parameter initialization";
    19  } else {
    20  this->blobs_.resize(1);
    21  this->blobs_[0].reset(new Blob<Dtype>());
    22  this->blobs_[0]->Reshape(this->layer_param_.parameter_param().shape());
    23  }
    24  top[0]->Reshape(this->layer_param_.parameter_param().shape());
    25  }
    26  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    27  const vector<Blob<Dtype>*>& top) { }
    28  virtual inline const char* type() const { return "Parameter"; }
    29  virtual inline int ExactNumBottomBlobs() const { return 0; }
    30  virtual inline int ExactNumTopBlobs() const { return 1; }
    31 
    32  protected:
    33  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    34  const vector<Blob<Dtype>*>& top) {
    35  top[0]->ShareData(*(this->blobs_[0]));
    36  top[0]->ShareDiff(*(this->blobs_[0]));
    37  }
    38  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    39  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom)
    40  { }
    41 };
    42 
    43 } // namespace caffe
    44 
    45 #endif
    caffe::ParameterLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: parameter_layer.hpp:29
    +
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::Layer::blobs_
    vector< shared_ptr< Blob< Dtype > > > blobs_
    Definition: layer.hpp:326
    +
    caffe::ParameterLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: parameter_layer.hpp:28
    +
    caffe::ParameterLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: parameter_layer.hpp:15
    +
    caffe::ParameterLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the CPU device, compute the layer output.
    Definition: parameter_layer.hpp:33
    +
    caffe::ParameterLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: parameter_layer.hpp:38
    +
    caffe::ParameterLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: parameter_layer.hpp:26
    +
    caffe::Layer::layer_param_
    LayerParameter layer_param_
    Definition: layer.hpp:322
    +
    caffe::ParameterLayer
    Definition: parameter_layer.hpp:11
    +
    caffe::ParameterLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: parameter_layer.hpp:30
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/pooling__layer_8hpp_source.html b/doxygen/pooling__layer_8hpp_source.html new file mode 100644 index 00000000000..96baf126806 --- /dev/null +++ b/doxygen/pooling__layer_8hpp_source.html @@ -0,0 +1,92 @@ + + + + + + + +Caffe: include/caffe/layers/pooling_layer.hpp Source File + + + + + + + + + +
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    pooling_layer.hpp
    +
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    1 #ifndef CAFFE_POOLING_LAYER_HPP_
    2 #define CAFFE_POOLING_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 namespace caffe {
    11 
    17 template <typename Dtype>
    18 class PoolingLayer : public Layer<Dtype> {
    19  public:
    20  explicit PoolingLayer(const LayerParameter& param)
    21  : Layer<Dtype>(param) {}
    22  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    23  const vector<Blob<Dtype>*>& top);
    24  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    25  const vector<Blob<Dtype>*>& top);
    26 
    27  virtual inline const char* type() const { return "Pooling"; }
    28  virtual inline int ExactNumBottomBlobs() const { return 1; }
    29  virtual inline int MinTopBlobs() const { return 1; }
    30  // MAX POOL layers can output an extra top blob for the mask;
    31  // others can only output the pooled inputs.
    32  virtual inline int MaxTopBlobs() const {
    33  return (this->layer_param_.pooling_param().pool() ==
    34  PoolingParameter_PoolMethod_MAX) ? 2 : 1;
    35  }
    36 
    37  protected:
    38  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    39  const vector<Blob<Dtype>*>& top);
    40  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    41  const vector<Blob<Dtype>*>& top);
    42  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    43  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    44  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    45  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    46 
    47  int kernel_h_, kernel_w_;
    48  int stride_h_, stride_w_;
    49  int pad_h_, pad_w_;
    50  int channels_;
    51  int height_, width_;
    52  int pooled_height_, pooled_width_;
    53  bool global_pooling_;
    54  Blob<Dtype> rand_idx_;
    55  Blob<int> max_idx_;
    56 };
    57 
    58 } // namespace caffe
    59 
    60 #endif // CAFFE_POOLING_LAYER_HPP_
    caffe::PoolingLayer::MaxTopBlobs
    virtual int MaxTopBlobs() const
    Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required...
    Definition: pooling_layer.hpp:32
    +
    caffe::PoolingLayer::MinTopBlobs
    virtual int MinTopBlobs() const
    Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
    Definition: pooling_layer.hpp:29
    +
    caffe::PoolingLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the CPU device, compute the layer output.
    Definition: pooling_layer.cpp:128
    +
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::PoolingLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: pooling_layer.cpp:79
    +
    caffe::PoolingLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: pooling_layer.cpp:230
    +
    caffe::PoolingLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: pooling_layer.hpp:27
    +
    caffe::PoolingLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: pooling_layer.hpp:28
    +
    caffe::PoolingLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: pooling_layer.cpp:14
    +
    caffe::PoolingLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::Layer::layer_param_
    LayerParameter layer_param_
    Definition: layer.hpp:322
    +
    caffe::PoolingLayer
    Pools the input image by taking the max, average, etc. within regions.
    Definition: pooling_layer.hpp:18
    +
    caffe::PoolingLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/power__layer_8hpp_source.html b/doxygen/power__layer_8hpp_source.html new file mode 100644 index 00000000000..20522248440 --- /dev/null +++ b/doxygen/power__layer_8hpp_source.html @@ -0,0 +1,92 @@ + + + + + + + +Caffe: include/caffe/layers/power_layer.hpp Source File + + + + + + + + + +
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    power_layer.hpp
    +
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    1 #ifndef CAFFE_POWER_LAYER_HPP_
    2 #define CAFFE_POWER_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 #include "caffe/layers/neuron_layer.hpp"
    11 
    12 namespace caffe {
    13 
    19 template <typename Dtype>
    20 class PowerLayer : public NeuronLayer<Dtype> {
    21  public:
    29  explicit PowerLayer(const LayerParameter& param)
    30  : NeuronLayer<Dtype>(param) {}
    31  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    32  const vector<Blob<Dtype>*>& top);
    33 
    34  virtual inline const char* type() const { return "Power"; }
    35 
    36  protected:
    47  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    48  const vector<Blob<Dtype>*>& top);
    49  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    50  const vector<Blob<Dtype>*>& top);
    51 
    72  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    73  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    74  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    75  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    76 
    78  Dtype power_;
    80  Dtype scale_;
    82  Dtype shift_;
    84  Dtype diff_scale_;
    85 };
    86 
    87 } // namespace caffe
    88 
    89 #endif // CAFFE_POWER_LAYER_HPP_
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::PowerLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::PowerLayer::power_
    Dtype power_
    from layer_param_.power_param()
    Definition: power_layer.hpp:78
    +
    caffe::PowerLayer::diff_scale_
    Dtype diff_scale_
    Result of .
    Definition: power_layer.hpp:84
    +
    caffe::PowerLayer
    Computes , as specified by the scale , shift , and power .
    Definition: power_layer.hpp:20
    +
    caffe::PowerLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: power_layer.hpp:34
    +
    caffe::PowerLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: power_layer.cpp:9
    +
    caffe::PowerLayer::shift_
    Dtype shift_
    from layer_param_.power_param()
    Definition: power_layer.hpp:82
    +
    caffe::NeuronLayer
    An interface for layers that take one blob as input ( ) and produce one equally-sized blob as output ...
    Definition: neuron_layer.hpp:19
    +
    caffe::PowerLayer::scale_
    Dtype scale_
    from layer_param_.power_param()
    Definition: power_layer.hpp:80
    +
    caffe::PowerLayer::PowerLayer
    PowerLayer(const LayerParameter &param)
    Definition: power_layer.hpp:29
    +
    caffe::PowerLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::PowerLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Definition: power_layer.cpp:20
    +
    caffe::PowerLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Computes the error gradient w.r.t. the power inputs.
    Definition: power_layer.cpp:44
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/prelu__layer_8hpp_source.html b/doxygen/prelu__layer_8hpp_source.html new file mode 100644 index 00000000000..21fbd6efc46 --- /dev/null +++ b/doxygen/prelu__layer_8hpp_source.html @@ -0,0 +1,89 @@ + + + + + + + +Caffe: include/caffe/layers/prelu_layer.hpp Source File + + + + + + + + + +
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    Caffe +
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    prelu_layer.hpp
    +
    +
    +
    1 #ifndef CAFFE_PRELU_LAYER_HPP_
    2 #define CAFFE_PRELU_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 #include "caffe/layers/neuron_layer.hpp"
    11 
    12 namespace caffe {
    13 
    22 template <typename Dtype>
    23 class PReLULayer : public NeuronLayer<Dtype> {
    24  public:
    33  explicit PReLULayer(const LayerParameter& param)
    34  : NeuronLayer<Dtype>(param) {}
    35 
    36  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    37  const vector<Blob<Dtype>*>& top);
    38 
    39  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    40  const vector<Blob<Dtype>*>& top);
    41 
    42  virtual inline const char* type() const { return "PReLU"; }
    43 
    44  protected:
    55  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    56  const vector<Blob<Dtype>*>& top);
    57  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    58  const vector<Blob<Dtype>*>& top);
    59 
    88  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    89  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    90  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    91  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    92 
    93  bool channel_shared_;
    94  Blob<Dtype> multiplier_; // dot multiplier for backward computation of params
    95  Blob<Dtype> backward_buff_; // temporary buffer for backward computation
    96  Blob<Dtype> bottom_memory_; // memory for in-place computation
    97 };
    98 
    99 } // namespace caffe
    100 
    101 #endif // CAFFE_PRELU_LAYER_HPP_
    caffe::PReLULayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Definition: prelu_layer.cpp:67
    +
    caffe::PReLULayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::PReLULayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: prelu_layer.cpp:55
    +
    caffe::PReLULayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Computes the error gradient w.r.t. the PReLU inputs.
    Definition: prelu_layer.cpp:92
    +
    caffe::NeuronLayer
    An interface for layers that take one blob as input ( ) and produce one equally-sized blob as output ...
    Definition: neuron_layer.hpp:19
    +
    caffe::PReLULayer
    Parameterized Rectified Linear Unit non-linearity . The differences from ReLULayer are 1) negative sl...
    Definition: prelu_layer.hpp:23
    +
    caffe::PReLULayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: prelu_layer.cpp:12
    +
    caffe::PReLULayer::PReLULayer
    PReLULayer(const LayerParameter &param)
    Definition: prelu_layer.hpp:33
    +
    caffe::PReLULayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    caffe::PReLULayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: prelu_layer.hpp:42
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/python__layer_8hpp_source.html b/doxygen/python__layer_8hpp_source.html new file mode 100644 index 00000000000..f511d8cf130 --- /dev/null +++ b/doxygen/python__layer_8hpp_source.html @@ -0,0 +1,89 @@ + + + + + + + +Caffe: include/caffe/layers/python_layer.hpp Source File + + + + + + + + + +
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    Caffe +
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    python_layer.hpp
    +
    +
    +
    1 #ifndef CAFFE_PYTHON_LAYER_HPP_
    2 #define CAFFE_PYTHON_LAYER_HPP_
    3 
    4 #include <boost/python.hpp>
    5 #include <vector>
    6 
    7 #include "caffe/layer.hpp"
    8 
    9 namespace bp = boost::python;
    10 
    11 namespace caffe {
    12 
    13 template <typename Dtype>
    14 class PythonLayer : public Layer<Dtype> {
    15  public:
    16  PythonLayer(PyObject* self, const LayerParameter& param)
    17  : Layer<Dtype>(param), self_(bp::handle<>(bp::borrowed(self))) { }
    18 
    19  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    20  const vector<Blob<Dtype>*>& top) {
    21  // Disallow PythonLayer in MultiGPU training stage, due to GIL issues
    22  // Details: https://github.com/BVLC/caffe/issues/2936
    23  if (this->phase_ == TRAIN && Caffe::solver_count() > 1
    24  && !ShareInParallel()) {
    25  LOG(FATAL) << "PythonLayer is not implemented in Multi-GPU training";
    26  }
    27  self_.attr("param_str") = bp::str(
    28  this->layer_param_.python_param().param_str());
    29  self_.attr("phase") = static_cast<int>(this->phase_);
    30  self_.attr("setup")(bottom, top);
    31  }
    32  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    33  const vector<Blob<Dtype>*>& top) {
    34  self_.attr("reshape")(bottom, top);
    35  }
    36 
    37  virtual inline bool ShareInParallel() const {
    38  return this->layer_param_.python_param().share_in_parallel();
    39  }
    40 
    41  virtual inline const char* type() const { return "Python"; }
    42 
    43  protected:
    44  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    45  const vector<Blob<Dtype>*>& top) {
    46  self_.attr("forward")(bottom, top);
    47  }
    48  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    49  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
    50  self_.attr("backward")(top, propagate_down, bottom);
    51  }
    52 
    53  private:
    54  bp::object self_;
    55 };
    56 
    57 } // namespace caffe
    58 
    59 #endif
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::PythonLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: python_layer.hpp:48
    +
    caffe::PythonLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: python_layer.hpp:19
    +
    caffe::PythonLayer::ShareInParallel
    virtual bool ShareInParallel() const
    Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
    Definition: python_layer.hpp:37
    +
    caffe::PythonLayer
    Definition: python_layer.hpp:14
    +
    caffe::PythonLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the CPU device, compute the layer output.
    Definition: python_layer.hpp:44
    +
    caffe::Layer::phase_
    Phase phase_
    Definition: layer.hpp:324
    +
    caffe::Layer::layer_param_
    LayerParameter layer_param_
    Definition: layer.hpp:322
    +
    caffe::PythonLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: python_layer.hpp:41
    +
    caffe::PythonLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: python_layer.hpp:32
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/recurrent__layer_8hpp_source.html b/doxygen/recurrent__layer_8hpp_source.html new file mode 100644 index 00000000000..28f2e45fe71 --- /dev/null +++ b/doxygen/recurrent__layer_8hpp_source.html @@ -0,0 +1,102 @@ + + + + + + + +Caffe: include/caffe/layers/recurrent_layer.hpp Source File + + + + + + + + + +
    +
    + + + + + + +
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    Caffe +
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    recurrent_layer.hpp
    +
    +
    +
    1 #ifndef CAFFE_RECURRENT_LAYER_HPP_
    2 #define CAFFE_RECURRENT_LAYER_HPP_
    3 
    4 #include <string>
    5 #include <utility>
    6 #include <vector>
    7 
    8 #include "caffe/blob.hpp"
    9 #include "caffe/common.hpp"
    10 #include "caffe/layer.hpp"
    11 #include "caffe/net.hpp"
    12 #include "caffe/proto/caffe.pb.h"
    13 #include "caffe/util/format.hpp"
    14 
    15 namespace caffe {
    16 
    17 template <typename Dtype> class RecurrentLayer;
    18 
    25 template <typename Dtype>
    26 class RecurrentLayer : public Layer<Dtype> {
    27  public:
    28  explicit RecurrentLayer(const LayerParameter& param)
    29  : Layer<Dtype>(param) {}
    30  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    31  const vector<Blob<Dtype>*>& top);
    32  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    33  const vector<Blob<Dtype>*>& top);
    34  virtual void Reset();
    35 
    36  virtual inline const char* type() const { return "Recurrent"; }
    37  virtual inline int MinBottomBlobs() const {
    38  int min_bottoms = 2;
    39  if (this->layer_param_.recurrent_param().expose_hidden()) {
    40  vector<string> inputs;
    41  this->RecurrentInputBlobNames(&inputs);
    42  min_bottoms += inputs.size();
    43  }
    44  return min_bottoms;
    45  }
    46  virtual inline int MaxBottomBlobs() const { return MinBottomBlobs() + 1; }
    47  virtual inline int ExactNumTopBlobs() const {
    48  int num_tops = 1;
    49  if (this->layer_param_.recurrent_param().expose_hidden()) {
    50  vector<string> outputs;
    51  this->RecurrentOutputBlobNames(&outputs);
    52  num_tops += outputs.size();
    53  }
    54  return num_tops;
    55  }
    56 
    57  virtual inline bool AllowForceBackward(const int bottom_index) const {
    58  // Can't propagate to sequence continuation indicators.
    59  return bottom_index != 1;
    60  }
    61 
    62  protected:
    67  virtual void FillUnrolledNet(NetParameter* net_param) const = 0;
    68 
    74  virtual void RecurrentInputBlobNames(vector<string>* names) const = 0;
    75 
    81  virtual void RecurrentInputShapes(vector<BlobShape>* shapes) const = 0;
    82 
    88  virtual void RecurrentOutputBlobNames(vector<string>* names) const = 0;
    89 
    96  virtual void OutputBlobNames(vector<string>* names) const = 0;
    97 
    143  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    144  const vector<Blob<Dtype>*>& top);
    145  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    146  const vector<Blob<Dtype>*>& top);
    147  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    148  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    149 
    151  shared_ptr<Net<Dtype> > unrolled_net_;
    152 
    154  int N_;
    155 
    160  int T_;
    161 
    163  bool static_input_;
    164 
    169  int last_layer_index_;
    170 
    175  bool expose_hidden_;
    176 
    177  vector<Blob<Dtype>* > recur_input_blobs_;
    178  vector<Blob<Dtype>* > recur_output_blobs_;
    179  vector<Blob<Dtype>* > output_blobs_;
    180  Blob<Dtype>* x_input_blob_;
    181  Blob<Dtype>* x_static_input_blob_;
    182  Blob<Dtype>* cont_input_blob_;
    183 };
    184 
    185 } // namespace caffe
    186 
    187 #endif // CAFFE_RECURRENT_LAYER_HPP_
    caffe::RecurrentLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: recurrent_layer.cpp:14
    +
    caffe::RecurrentLayer::static_input_
    bool static_input_
    Whether the layer has a "static" input copied across all timesteps.
    Definition: recurrent_layer.hpp:163
    +
    caffe::RecurrentLayer::MinBottomBlobs
    virtual int MinBottomBlobs() const
    Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
    Definition: recurrent_layer.hpp:37
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::RecurrentLayer::AllowForceBackward
    virtual bool AllowForceBackward(const int bottom_index) const
    Return whether to allow force_backward for a given bottom blob index.
    Definition: recurrent_layer.hpp:57
    +
    caffe::RecurrentLayer::last_layer_index_
    int last_layer_index_
    The last layer to run in the network. (Any later layers are losses added to force the recurrent net t...
    Definition: recurrent_layer.hpp:169
    +
    caffe::RecurrentLayer::RecurrentInputBlobNames
    virtual void RecurrentInputBlobNames(vector< string > *names) const =0
    Fills names with the names of the 0th timestep recurrent input Blob&s. Subclasses should define this ...
    +
    caffe::RecurrentLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Definition: recurrent_layer.cpp:245
    +
    caffe::RecurrentLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::RecurrentLayer::T_
    int T_
    The number of timesteps in the layer&#39;s input, and the number of timesteps over which to backpropagate...
    Definition: recurrent_layer.hpp:160
    +
    caffe::RecurrentLayer::RecurrentOutputBlobNames
    virtual void RecurrentOutputBlobNames(vector< string > *names) const =0
    Fills names with the names of the Tth timestep recurrent output Blob&s. Subclasses should define this...
    +
    caffe::RecurrentLayer::RecurrentInputShapes
    virtual void RecurrentInputShapes(vector< BlobShape > *shapes) const =0
    Fills shapes with the shapes of the recurrent input Blob&s. Subclasses should define this – see RNNL...
    +
    caffe::RecurrentLayer::unrolled_net_
    shared_ptr< Net< Dtype > > unrolled_net_
    A Net to implement the Recurrent functionality.
    Definition: recurrent_layer.hpp:151
    +
    caffe::RecurrentLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: recurrent_layer.cpp:183
    +
    caffe::RecurrentLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: recurrent_layer.cpp:277
    +
    caffe::RecurrentLayer::OutputBlobNames
    virtual void OutputBlobNames(vector< string > *names) const =0
    Fills names with the names of the output blobs, concatenated across all timesteps. Should return a name for each top Blob. Subclasses should define this – see RNNLayer and LSTMLayer for examples.
    +
    caffe::RecurrentLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: recurrent_layer.hpp:36
    +
    caffe::Layer::Layer
    Layer(const LayerParameter &param)
    Definition: layer.hpp:40
    +
    caffe::RecurrentLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: recurrent_layer.hpp:47
    +
    caffe::RecurrentLayer::expose_hidden_
    bool expose_hidden_
    Whether the layer&#39;s hidden state at the first and last timesteps are layer inputs and outputs...
    Definition: recurrent_layer.hpp:175
    +
    caffe::Layer::layer_param_
    LayerParameter layer_param_
    Definition: layer.hpp:322
    +
    caffe::RecurrentLayer::MaxBottomBlobs
    virtual int MaxBottomBlobs() const
    Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is requi...
    Definition: recurrent_layer.hpp:46
    +
    caffe::RecurrentLayer::N_
    int N_
    The number of independent streams to process simultaneously.
    Definition: recurrent_layer.hpp:154
    +
    caffe::RecurrentLayer::FillUnrolledNet
    virtual void FillUnrolledNet(NetParameter *net_param) const =0
    Fills net_param with the recurrent network architecture. Subclasses should define this – see RNNLaye...
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/reduction__layer_8hpp_source.html b/doxygen/reduction__layer_8hpp_source.html new file mode 100644 index 00000000000..1d9b927346a --- /dev/null +++ b/doxygen/reduction__layer_8hpp_source.html @@ -0,0 +1,96 @@ + + + + + + + +Caffe: include/caffe/layers/reduction_layer.hpp Source File + + + + + + + + + +
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    reduction_layer.hpp
    +
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    1 #ifndef CAFFE_REDUCTION_LAYER_HPP_
    2 #define CAFFE_REDUCTION_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 namespace caffe {
    11 
    19 template <typename Dtype>
    20 class ReductionLayer : public Layer<Dtype> {
    21  public:
    22  explicit ReductionLayer(const LayerParameter& param)
    23  : Layer<Dtype>(param) {}
    24  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    25  const vector<Blob<Dtype>*>& top);
    26  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    27  const vector<Blob<Dtype>*>& top);
    28 
    29  virtual inline const char* type() const { return "Reduction"; }
    30  virtual inline int ExactNumBottomBlobs() const { return 1; }
    31  virtual inline int ExactNumTopBlobs() const { return 1; }
    32 
    33  protected:
    34  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    35  const vector<Blob<Dtype>*>& top);
    36  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    37  const vector<Blob<Dtype>*>& top);
    38  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    39  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    40  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    41  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    42 
    44  ReductionParameter_ReductionOp op_;
    46  Dtype coeff_;
    48  int axis_;
    50  int num_;
    52  int dim_;
    54  Blob<Dtype> sum_multiplier_;
    55 };
    56 
    57 } // namespace caffe
    58 
    59 #endif // CAFFE_REDUCTION_LAYER_HPP_
    caffe::ReductionLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: reduction_layer.cpp:77
    +
    caffe::ReductionLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: reduction_layer.cpp:9
    +
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::ReductionLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the CPU device, compute the layer output.
    Definition: reduction_layer.cpp:42
    +
    caffe::ReductionLayer::dim_
    int dim_
    the input size of each reduction
    Definition: reduction_layer.hpp:52
    +
    caffe::ReductionLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: reduction_layer.cpp:15
    +
    caffe::ReductionLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: reduction_layer.hpp:31
    +
    caffe::ReductionLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: reduction_layer.hpp:30
    +
    caffe::ReductionLayer::sum_multiplier_
    Blob< Dtype > sum_multiplier_
    a helper Blob used for summation (op_ == SUM)
    Definition: reduction_layer.hpp:54
    +
    caffe::ReductionLayer::op_
    ReductionParameter_ReductionOp op_
    the reduction operation performed by the layer
    Definition: reduction_layer.hpp:44
    +
    caffe::ReductionLayer
    Compute "reductions" – operations that return a scalar output Blob for an input Blob of arbitrary si...
    Definition: reduction_layer.hpp:20
    +
    caffe::ReductionLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::ReductionLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: reduction_layer.hpp:29
    +
    caffe::ReductionLayer::coeff_
    Dtype coeff_
    a scalar coefficient applied to all outputs
    Definition: reduction_layer.hpp:46
    +
    caffe::ReductionLayer::num_
    int num_
    the number of reductions performed
    Definition: reduction_layer.hpp:50
    +
    caffe::ReductionLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::ReductionLayer::axis_
    int axis_
    the index of the first input axis to reduce
    Definition: reduction_layer.hpp:48
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/relu__layer_8hpp_source.html b/doxygen/relu__layer_8hpp_source.html new file mode 100644 index 00000000000..637722a2590 --- /dev/null +++ b/doxygen/relu__layer_8hpp_source.html @@ -0,0 +1,87 @@ + + + + + + + +Caffe: include/caffe/layers/relu_layer.hpp Source File + + + + + + + + + +
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    Caffe +
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    relu_layer.hpp
    +
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    +
    1 #ifndef CAFFE_RELU_LAYER_HPP_
    2 #define CAFFE_RELU_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 #include "caffe/layers/neuron_layer.hpp"
    11 
    12 namespace caffe {
    13 
    18 template <typename Dtype>
    19 class ReLULayer : public NeuronLayer<Dtype> {
    20  public:
    27  explicit ReLULayer(const LayerParameter& param)
    28  : NeuronLayer<Dtype>(param) {}
    29 
    30  virtual inline const char* type() const { return "ReLU"; }
    31 
    32  protected:
    44  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    45  const vector<Blob<Dtype>*>& top);
    46  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    47  const vector<Blob<Dtype>*>& top);
    48 
    77  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    78  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    79  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    80  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    81 };
    82 
    83 } // namespace caffe
    84 
    85 #endif // CAFFE_RELU_LAYER_HPP_
    caffe::ReLULayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::ReLULayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Definition: relu_layer.cpp:9
    +
    caffe::ReLULayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Computes the error gradient w.r.t. the ReLU inputs.
    Definition: relu_layer.cpp:22
    +
    caffe::ReLULayer
    Rectified Linear Unit non-linearity . The simple max is fast to compute, and the function does not sa...
    Definition: relu_layer.hpp:19
    +
    caffe::ReLULayer::ReLULayer
    ReLULayer(const LayerParameter &param)
    Definition: relu_layer.hpp:27
    +
    caffe::NeuronLayer
    An interface for layers that take one blob as input ( ) and produce one equally-sized blob as output ...
    Definition: neuron_layer.hpp:19
    +
    caffe::ReLULayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::ReLULayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: relu_layer.hpp:30
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/reshape__layer_8hpp_source.html b/doxygen/reshape__layer_8hpp_source.html new file mode 100644 index 00000000000..911d553f0d6 --- /dev/null +++ b/doxygen/reshape__layer_8hpp_source.html @@ -0,0 +1,93 @@ + + + + + + + +Caffe: include/caffe/layers/reshape_layer.hpp Source File + + + + + + + + + +
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    reshape_layer.hpp
    +
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    +
    1 #ifndef CAFFE_XXX_LAYER_HPP_
    2 #define CAFFE_XXX_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 namespace caffe {
    11 
    12 /*
    13  * @brief Reshapes the input Blob into an arbitrary-sized output Blob.
    14  *
    15  * Note: similarly to FlattenLayer, this layer does not change the input values
    16  * (see FlattenLayer, Blob::ShareData and Blob::ShareDiff).
    17  */
    18 template <typename Dtype>
    19 class ReshapeLayer : public Layer<Dtype> {
    20  public:
    21  explicit ReshapeLayer(const LayerParameter& param)
    22  : Layer<Dtype>(param) {}
    23  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    24  const vector<Blob<Dtype>*>& top);
    25  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    26  const vector<Blob<Dtype>*>& top);
    27 
    28  virtual inline const char* type() const { return "Reshape"; }
    29  virtual inline int ExactNumBottomBlobs() const { return 1; }
    30  virtual inline int ExactNumTopBlobs() const { return 1; }
    31 
    32  protected:
    33  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    34  const vector<Blob<Dtype>*>& top) {}
    35  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    36  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
    37  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    38  const vector<Blob<Dtype>*>& top) {}
    39  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    40  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
    41 
    43  vector<int> copy_axes_;
    45  int inferred_axis_;
    47  int constant_count_;
    48 };
    49 
    50 } // namespace caffe
    51 
    52 #endif // CAFFE_XXX_LAYER_HPP_
    caffe::ReshapeLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: reshape_layer.hpp:28
    +
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::ReshapeLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the CPU device, compute the layer output.
    Definition: reshape_layer.hpp:33
    +
    caffe::ReshapeLayer::constant_count_
    int constant_count_
    the product of the "constant" output dimensions
    Definition: reshape_layer.hpp:47
    +
    caffe::ReshapeLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: reshape_layer.hpp:30
    +
    caffe::ReshapeLayer::inferred_axis_
    int inferred_axis_
    the index of the axis whose dimension we infer, or -1 if none
    Definition: reshape_layer.hpp:45
    +
    caffe::ReshapeLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: reshape_layer.hpp:39
    +
    caffe::ReshapeLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: reshape_layer.hpp:35
    +
    caffe::ReshapeLayer
    Definition: reshape_layer.hpp:19
    +
    caffe::ReshapeLayer::copy_axes_
    vector< int > copy_axes_
    vector of axes indices whose dimensions we&#39;ll copy from the bottom
    Definition: reshape_layer.hpp:43
    +
    caffe::ReshapeLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: reshape_layer.cpp:32
    +
    caffe::ReshapeLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    Definition: reshape_layer.hpp:37
    +
    caffe::ReshapeLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: reshape_layer.hpp:29
    +
    caffe::ReshapeLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: reshape_layer.cpp:8
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/rng_8hpp_source.html b/doxygen/rng_8hpp_source.html new file mode 100644 index 00000000000..9339f7f68eb --- /dev/null +++ b/doxygen/rng_8hpp_source.html @@ -0,0 +1,78 @@ + + + + + + + +Caffe: include/caffe/util/rng.hpp Source File + + + + + + + + + +
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    Caffe +
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    rng.hpp
    +
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    +
    1 #ifndef CAFFE_RNG_CPP_HPP_
    2 #define CAFFE_RNG_CPP_HPP_
    3 
    4 #include <algorithm>
    5 #include <iterator>
    6 
    7 #include "boost/random/mersenne_twister.hpp"
    8 #include "boost/random/uniform_int.hpp"
    9 
    10 #include "caffe/common.hpp"
    11 
    12 namespace caffe {
    13 
    14 typedef boost::mt19937 rng_t;
    15 
    16 inline rng_t* caffe_rng() {
    17  return static_cast<caffe::rng_t*>(Caffe::rng_stream().generator());
    18 }
    19 
    20 // Fisher–Yates algorithm
    21 template <class RandomAccessIterator, class RandomGenerator>
    22 inline void shuffle(RandomAccessIterator begin, RandomAccessIterator end,
    23  RandomGenerator* gen) {
    24  typedef typename std::iterator_traits<RandomAccessIterator>::difference_type
    25  difference_type;
    26  typedef typename boost::uniform_int<difference_type> dist_type;
    27 
    28  difference_type length = std::distance(begin, end);
    29  if (length <= 0) return;
    30 
    31  for (difference_type i = length - 1; i > 0; --i) {
    32  dist_type dist(0, i);
    33  std::iter_swap(begin + i, begin + dist(*gen));
    34  }
    35 }
    36 
    37 template <class RandomAccessIterator>
    38 inline void shuffle(RandomAccessIterator begin, RandomAccessIterator end) {
    39  shuffle(begin, end, caffe_rng());
    40 }
    41 } // namespace caffe
    42 
    43 #endif // CAFFE_RNG_HPP_
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/rnn__layer_8hpp_source.html b/doxygen/rnn__layer_8hpp_source.html new file mode 100644 index 00000000000..4ebd705a733 --- /dev/null +++ b/doxygen/rnn__layer_8hpp_source.html @@ -0,0 +1,86 @@ + + + + + + + +Caffe: include/caffe/layers/rnn_layer.hpp Source File + + + + + + + + + +
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    rnn_layer.hpp
    +
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    +
    1 #ifndef CAFFE_RNN_LAYER_HPP_
    2 #define CAFFE_RNN_LAYER_HPP_
    3 
    4 #include <string>
    5 #include <utility>
    6 #include <vector>
    7 
    8 #include "caffe/blob.hpp"
    9 #include "caffe/common.hpp"
    10 #include "caffe/layer.hpp"
    11 #include "caffe/layers/recurrent_layer.hpp"
    12 #include "caffe/net.hpp"
    13 #include "caffe/proto/caffe.pb.h"
    14 
    15 namespace caffe {
    16 
    17 template <typename Dtype> class RecurrentLayer;
    18 
    29 template <typename Dtype>
    30 class RNNLayer : public RecurrentLayer<Dtype> {
    31  public:
    32  explicit RNNLayer(const LayerParameter& param)
    33  : RecurrentLayer<Dtype>(param) {}
    34 
    35  virtual inline const char* type() const { return "RNN"; }
    36 
    37  protected:
    38  virtual void FillUnrolledNet(NetParameter* net_param) const;
    39  virtual void RecurrentInputBlobNames(vector<string>* names) const;
    40  virtual void RecurrentOutputBlobNames(vector<string>* names) const;
    41  virtual void RecurrentInputShapes(vector<BlobShape>* shapes) const;
    42  virtual void OutputBlobNames(vector<string>* names) const;
    43 };
    44 
    45 } // namespace caffe
    46 
    47 #endif // CAFFE_RNN_LAYER_HPP_
    caffe::RNNLayer::OutputBlobNames
    virtual void OutputBlobNames(vector< string > *names) const
    Fills names with the names of the output blobs, concatenated across all timesteps. Should return a name for each top Blob. Subclasses should define this – see RNNLayer and LSTMLayer for examples.
    Definition: rnn_layer.cpp:36
    +
    caffe::RNNLayer::RecurrentInputShapes
    virtual void RecurrentInputShapes(vector< BlobShape > *shapes) const
    Fills shapes with the shapes of the recurrent input Blob&s. Subclasses should define this – see RNNL...
    Definition: rnn_layer.cpp:26
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::RecurrentLayer
    An abstract class for implementing recurrent behavior inside of an unrolled network. This Layer type cannot be instantiated – instead, you should use one of its implementations which defines the recurrent architecture, such as RNNLayer or LSTMLayer.
    Definition: lstm_layer.hpp:17
    +
    caffe::RNNLayer::FillUnrolledNet
    virtual void FillUnrolledNet(NetParameter *net_param) const
    Fills net_param with the recurrent network architecture. Subclasses should define this – see RNNLaye...
    Definition: rnn_layer.cpp:42
    +
    caffe::RNNLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: rnn_layer.hpp:35
    +
    caffe::RNNLayer::RecurrentOutputBlobNames
    virtual void RecurrentOutputBlobNames(vector< string > *names) const
    Fills names with the names of the Tth timestep recurrent output Blob&s. Subclasses should define this...
    Definition: rnn_layer.cpp:20
    +
    caffe::RNNLayer
    Processes time-varying inputs using a simple recurrent neural network (RNN). Implemented as a network...
    Definition: rnn_layer.hpp:30
    +
    caffe::RNNLayer::RecurrentInputBlobNames
    virtual void RecurrentInputBlobNames(vector< string > *names) const
    Fills names with the names of the 0th timestep recurrent input Blob&s. Subclasses should define this ...
    Definition: rnn_layer.cpp:14
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/scale__layer_8hpp_source.html b/doxygen/scale__layer_8hpp_source.html new file mode 100644 index 00000000000..4127a24345f --- /dev/null +++ b/doxygen/scale__layer_8hpp_source.html @@ -0,0 +1,91 @@ + + + + + + + +Caffe: include/caffe/layers/scale_layer.hpp Source File + + + + + + + + + +
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    scale_layer.hpp
    +
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    1 #ifndef CAFFE_SCALE_LAYER_HPP_
    2 #define CAFFE_SCALE_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 #include "caffe/layers/bias_layer.hpp"
    11 
    12 namespace caffe {
    13 
    25 template <typename Dtype>
    26 class ScaleLayer: public Layer<Dtype> {
    27  public:
    28  explicit ScaleLayer(const LayerParameter& param)
    29  : Layer<Dtype>(param) {}
    30  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    31  const vector<Blob<Dtype>*>& top);
    32  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    33  const vector<Blob<Dtype>*>& top);
    34 
    35  virtual inline const char* type() const { return "Scale"; }
    36  // Scale
    37  virtual inline int MinBottomBlobs() const { return 1; }
    38  virtual inline int MaxBottomBlobs() const { return 2; }
    39  virtual inline int ExactNumTopBlobs() const { return 1; }
    40 
    41  protected:
    61  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    62  const vector<Blob<Dtype>*>& top);
    63  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    64  const vector<Blob<Dtype>*>& top);
    65  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    66  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    67  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    68  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    69 
    70  shared_ptr<Layer<Dtype> > bias_layer_;
    71  vector<Blob<Dtype>*> bias_bottom_vec_;
    72  vector<bool> bias_propagate_down_;
    73  int bias_param_id_;
    74 
    75  Blob<Dtype> sum_multiplier_;
    76  Blob<Dtype> sum_result_;
    77  Blob<Dtype> temp_;
    78  int axis_;
    79  int outer_dim_, scale_dim_, inner_dim_;
    80 };
    81 
    82 
    83 } // namespace caffe
    84 
    85 #endif // CAFFE_SCALE_LAYER_HPP_
    caffe::ScaleLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    +
    caffe::ScaleLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::ScaleLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: scale_layer.hpp:39
    +
    caffe::ScaleLayer::MinBottomBlobs
    virtual int MinBottomBlobs() const
    Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
    Definition: scale_layer.hpp:37
    +
    caffe::ScaleLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: scale_layer.cpp:12
    +
    caffe::ScaleLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: scale_layer.cpp:145
    +
    caffe::ScaleLayer
    Computes the elementwise product of two input Blobs, with the shape of the latter Blob "broadcast" to...
    Definition: scale_layer.hpp:26
    +
    caffe::ScaleLayer::MaxBottomBlobs
    virtual int MaxBottomBlobs() const
    Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is requi...
    Definition: scale_layer.hpp:38
    +
    caffe::ScaleLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: scale_layer.hpp:35
    +
    caffe::ScaleLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Definition: scale_layer.cpp:117
    +
    caffe::ScaleLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: scale_layer.cpp:76
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
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    +
    + + + + + + +
    +
    Caffe +
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    sgd_solvers.hpp
    +
    +
    +
    1 #ifndef CAFFE_SGD_SOLVERS_HPP_
    2 #define CAFFE_SGD_SOLVERS_HPP_
    3 
    4 #include <string>
    5 #include <vector>
    6 
    7 #include "caffe/solver.hpp"
    8 
    9 namespace caffe {
    10 
    15 template <typename Dtype>
    16 class SGDSolver : public Solver<Dtype> {
    17  public:
    18  explicit SGDSolver(const SolverParameter& param)
    19  : Solver<Dtype>(param) { PreSolve(); }
    20  explicit SGDSolver(const string& param_file)
    21  : Solver<Dtype>(param_file) { PreSolve(); }
    22  virtual inline const char* type() const { return "SGD"; }
    23 
    24  const vector<shared_ptr<Blob<Dtype> > >& history() { return history_; }
    25 
    26  protected:
    27  void PreSolve();
    28  Dtype GetLearningRate();
    29  virtual void ApplyUpdate();
    30  virtual void Normalize(int param_id);
    31  virtual void Regularize(int param_id);
    32  virtual void ComputeUpdateValue(int param_id, Dtype rate);
    33  virtual void ClipGradients();
    34  virtual void SnapshotSolverState(const string& model_filename);
    35  virtual void SnapshotSolverStateToBinaryProto(const string& model_filename);
    36  virtual void SnapshotSolverStateToHDF5(const string& model_filename);
    37  virtual void RestoreSolverStateFromHDF5(const string& state_file);
    38  virtual void RestoreSolverStateFromBinaryProto(const string& state_file);
    39  // history maintains the historical momentum data.
    40  // update maintains update related data and is not needed in snapshots.
    41  // temp maintains other information that might be needed in computation
    42  // of gradients/updates and is not needed in snapshots
    43  vector<shared_ptr<Blob<Dtype> > > history_, update_, temp_;
    44 
    45  DISABLE_COPY_AND_ASSIGN(SGDSolver);
    46 };
    47 
    48 template <typename Dtype>
    49 class NesterovSolver : public SGDSolver<Dtype> {
    50  public:
    51  explicit NesterovSolver(const SolverParameter& param)
    52  : SGDSolver<Dtype>(param) {}
    53  explicit NesterovSolver(const string& param_file)
    54  : SGDSolver<Dtype>(param_file) {}
    55  virtual inline const char* type() const { return "Nesterov"; }
    56 
    57  protected:
    58  virtual void ComputeUpdateValue(int param_id, Dtype rate);
    59 
    60  DISABLE_COPY_AND_ASSIGN(NesterovSolver);
    61 };
    62 
    63 template <typename Dtype>
    64 class AdaGradSolver : public SGDSolver<Dtype> {
    65  public:
    66  explicit AdaGradSolver(const SolverParameter& param)
    67  : SGDSolver<Dtype>(param) { constructor_sanity_check(); }
    68  explicit AdaGradSolver(const string& param_file)
    69  : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
    70  virtual inline const char* type() const { return "AdaGrad"; }
    71 
    72  protected:
    73  virtual void ComputeUpdateValue(int param_id, Dtype rate);
    74  void constructor_sanity_check() {
    75  CHECK_EQ(0, this->param_.momentum())
    76  << "Momentum cannot be used with AdaGrad.";
    77  }
    78 
    79  DISABLE_COPY_AND_ASSIGN(AdaGradSolver);
    80 };
    81 
    82 
    83 template <typename Dtype>
    84 class RMSPropSolver : public SGDSolver<Dtype> {
    85  public:
    86  explicit RMSPropSolver(const SolverParameter& param)
    87  : SGDSolver<Dtype>(param) { constructor_sanity_check(); }
    88  explicit RMSPropSolver(const string& param_file)
    89  : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
    90  virtual inline const char* type() const { return "RMSProp"; }
    91 
    92  protected:
    93  virtual void ComputeUpdateValue(int param_id, Dtype rate);
    94  void constructor_sanity_check() {
    95  CHECK_EQ(0, this->param_.momentum())
    96  << "Momentum cannot be used with RMSProp.";
    97  CHECK_GE(this->param_.rms_decay(), 0)
    98  << "rms_decay should lie between 0 and 1.";
    99  CHECK_LT(this->param_.rms_decay(), 1)
    100  << "rms_decay should lie between 0 and 1.";
    101  }
    102 
    103  DISABLE_COPY_AND_ASSIGN(RMSPropSolver);
    104 };
    105 
    106 template <typename Dtype>
    107 class AdaDeltaSolver : public SGDSolver<Dtype> {
    108  public:
    109  explicit AdaDeltaSolver(const SolverParameter& param)
    110  : SGDSolver<Dtype>(param) { AdaDeltaPreSolve(); }
    111  explicit AdaDeltaSolver(const string& param_file)
    112  : SGDSolver<Dtype>(param_file) { AdaDeltaPreSolve(); }
    113  virtual inline const char* type() const { return "AdaDelta"; }
    114 
    115  protected:
    116  void AdaDeltaPreSolve();
    117  virtual void ComputeUpdateValue(int param_id, Dtype rate);
    118 
    119  DISABLE_COPY_AND_ASSIGN(AdaDeltaSolver);
    120 };
    121 
    130 template <typename Dtype>
    131 class AdamSolver : public SGDSolver<Dtype> {
    132  public:
    133  explicit AdamSolver(const SolverParameter& param)
    134  : SGDSolver<Dtype>(param) { AdamPreSolve();}
    135  explicit AdamSolver(const string& param_file)
    136  : SGDSolver<Dtype>(param_file) { AdamPreSolve(); }
    137  virtual inline const char* type() const { return "Adam"; }
    138 
    139  protected:
    140  void AdamPreSolve();
    141  virtual void ComputeUpdateValue(int param_id, Dtype rate);
    142 
    143  DISABLE_COPY_AND_ASSIGN(AdamSolver);
    144 };
    145 
    146 } // namespace caffe
    147 
    148 #endif // CAFFE_SGD_SOLVERS_HPP_
    caffe::NesterovSolver::type
    virtual const char * type() const
    Returns the solver type.
    Definition: sgd_solvers.hpp:55
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::SGDSolver::type
    virtual const char * type() const
    Returns the solver type.
    Definition: sgd_solvers.hpp:22
    +
    caffe::SGDSolver
    Optimizes the parameters of a Net using stochastic gradient descent (SGD) with momentum.
    Definition: sgd_solvers.hpp:16
    +
    caffe::Solver
    An interface for classes that perform optimization on Nets.
    Definition: solver.hpp:41
    +
    caffe::AdamSolver
    AdamSolver, an algorithm for first-order gradient-based optimization of stochastic objective function...
    Definition: sgd_solvers.hpp:131
    +
    caffe::AdaDeltaSolver
    Definition: sgd_solvers.hpp:107
    +
    caffe::AdaDeltaSolver::type
    virtual const char * type() const
    Returns the solver type.
    Definition: sgd_solvers.hpp:113
    +
    caffe::NesterovSolver
    Definition: sgd_solvers.hpp:49
    +
    caffe::RMSPropSolver
    Definition: sgd_solvers.hpp:84
    +
    caffe::RMSPropSolver::type
    virtual const char * type() const
    Returns the solver type.
    Definition: sgd_solvers.hpp:90
    +
    caffe::AdaGradSolver::type
    virtual const char * type() const
    Returns the solver type.
    Definition: sgd_solvers.hpp:70
    +
    caffe::AdaGradSolver
    Definition: sgd_solvers.hpp:64
    +
    caffe::AdamSolver::type
    virtual const char * type() const
    Returns the solver type.
    Definition: sgd_solvers.hpp:137
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/sigmoid__cross__entropy__loss__layer_8hpp_source.html b/doxygen/sigmoid__cross__entropy__loss__layer_8hpp_source.html new file mode 100644 index 00000000000..e7f2ef8165f --- /dev/null +++ b/doxygen/sigmoid__cross__entropy__loss__layer_8hpp_source.html @@ -0,0 +1,97 @@ + + + + + + + +Caffe: include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp Source File + + + + + + + + + +
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    sigmoid_cross_entropy_loss_layer.hpp
    +
    +
    +
    1 #ifndef CAFFE_SIGMOID_CROSS_ENTROPY_LOSS_LAYER_HPP_
    2 #define CAFFE_SIGMOID_CROSS_ENTROPY_LOSS_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 #include "caffe/layers/loss_layer.hpp"
    11 #include "caffe/layers/sigmoid_layer.hpp"
    12 
    13 namespace caffe {
    14 
    44 template <typename Dtype>
    45 class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
    46  public:
    47  explicit SigmoidCrossEntropyLossLayer(const LayerParameter& param)
    48  : LossLayer<Dtype>(param),
    49  sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
    50  sigmoid_output_(new Blob<Dtype>()) {}
    51  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    52  const vector<Blob<Dtype>*>& top);
    53  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    54  const vector<Blob<Dtype>*>& top);
    55 
    56  virtual inline const char* type() const { return "SigmoidCrossEntropyLoss"; }
    57 
    58  protected:
    60  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    61  const vector<Blob<Dtype>*>& top);
    62  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    63  const vector<Blob<Dtype>*>& top);
    64 
    95  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    96  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    97  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    98  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    99 
    104  virtual Dtype get_normalizer(
    105  LossParameter_NormalizationMode normalization_mode, int valid_count);
    106 
    108  shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
    110  shared_ptr<Blob<Dtype> > sigmoid_output_;
    112  vector<Blob<Dtype>*> sigmoid_bottom_vec_;
    114  vector<Blob<Dtype>*> sigmoid_top_vec_;
    115 
    117  bool has_ignore_label_;
    119  int ignore_label_;
    121  LossParameter_NormalizationMode normalization_;
    122  Dtype normalizer_;
    123  int outer_num_, inner_num_;
    124 };
    125 
    126 } // namespace caffe
    127 
    128 #endif // CAFFE_SIGMOID_CROSS_ENTROPY_LOSS_LAYER_HPP_
    caffe::SigmoidCrossEntropyLossLayer::sigmoid_bottom_vec_
    vector< Blob< Dtype > * > sigmoid_bottom_vec_
    bottom vector holder to call the underlying SigmoidLayer::Forward
    Definition: sigmoid_cross_entropy_loss_layer.hpp:112
    +
    caffe::SigmoidCrossEntropyLossLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Computes the cross-entropy (logistic) loss , often used for predicting targets interpreted as probabi...
    Definition: sigmoid_cross_entropy_loss_layer.cpp:79
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::SigmoidLayer
    Sigmoid function non-linearity , a classic choice in neural networks.
    Definition: sigmoid_layer.hpp:23
    +
    caffe::SigmoidCrossEntropyLossLayer::sigmoid_output_
    shared_ptr< Blob< Dtype > > sigmoid_output_
    sigmoid_output stores the output of the SigmoidLayer.
    Definition: sigmoid_cross_entropy_loss_layer.hpp:110
    +
    caffe::SigmoidCrossEntropyLossLayer::sigmoid_top_vec_
    vector< Blob< Dtype > * > sigmoid_top_vec_
    top vector holder to call the underlying SigmoidLayer::Forward
    Definition: sigmoid_cross_entropy_loss_layer.hpp:114
    +
    caffe::SigmoidCrossEntropyLossLayer::normalization_
    LossParameter_NormalizationMode normalization_
    How to normalize the loss.
    Definition: sigmoid_cross_entropy_loss_layer.hpp:121
    +
    caffe::SigmoidCrossEntropyLossLayer::get_normalizer
    virtual Dtype get_normalizer(LossParameter_NormalizationMode normalization_mode, int valid_count)
    Definition: sigmoid_cross_entropy_loss_layer.cpp:49
    +
    caffe::SigmoidCrossEntropyLossLayer::sigmoid_layer_
    shared_ptr< SigmoidLayer< Dtype > > sigmoid_layer_
    The internal SigmoidLayer used to map predictions to probabilities.
    Definition: sigmoid_cross_entropy_loss_layer.hpp:108
    +
    caffe::SigmoidCrossEntropyLossLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: sigmoid_cross_entropy_loss_layer.cpp:10
    +
    caffe::SigmoidCrossEntropyLossLayer::has_ignore_label_
    bool has_ignore_label_
    Whether to ignore instances with a certain label.
    Definition: sigmoid_cross_entropy_loss_layer.hpp:117
    +
    caffe::SigmoidCrossEntropyLossLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: sigmoid_cross_entropy_loss_layer.hpp:56
    +
    caffe::SigmoidCrossEntropyLossLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::SigmoidCrossEntropyLossLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: sigmoid_cross_entropy_loss_layer.cpp:36
    +
    caffe::SigmoidCrossEntropyLossLayer
    Computes the cross-entropy (logistic) loss , often used for predicting targets interpreted as probabi...
    Definition: sigmoid_cross_entropy_loss_layer.hpp:45
    +
    caffe::LossLayer
    An interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth ...
    Definition: loss_layer.hpp:23
    +
    caffe::SigmoidCrossEntropyLossLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::SigmoidCrossEntropyLossLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Computes the sigmoid cross-entropy loss error gradient w.r.t. the predictions.
    Definition: sigmoid_cross_entropy_loss_layer.cpp:104
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    caffe::SigmoidCrossEntropyLossLayer::ignore_label_
    int ignore_label_
    The label indicating that an instance should be ignored.
    Definition: sigmoid_cross_entropy_loss_layer.hpp:119
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/sigmoid__layer_8hpp_source.html b/doxygen/sigmoid__layer_8hpp_source.html new file mode 100644 index 00000000000..a38f856c5a2 --- /dev/null +++ b/doxygen/sigmoid__layer_8hpp_source.html @@ -0,0 +1,86 @@ + + + + + + + +Caffe: include/caffe/layers/sigmoid_layer.hpp Source File + + + + + + + + + +
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    sigmoid_layer.hpp
    +
    +
    +
    1 #ifndef CAFFE_SIGMOID_LAYER_HPP_
    2 #define CAFFE_SIGMOID_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 #include "caffe/layers/neuron_layer.hpp"
    11 
    12 namespace caffe {
    13 
    22 template <typename Dtype>
    23 class SigmoidLayer : public NeuronLayer<Dtype> {
    24  public:
    25  explicit SigmoidLayer(const LayerParameter& param)
    26  : NeuronLayer<Dtype>(param) {}
    27 
    28  virtual inline const char* type() const { return "Sigmoid"; }
    29 
    30  protected:
    41  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    42  const vector<Blob<Dtype>*>& top);
    43  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    44  const vector<Blob<Dtype>*>& top);
    45 
    63  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    64  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    65  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    66  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    67 };
    68 
    69 } // namespace caffe
    70 
    71 #endif // CAFFE_SIGMOID_LAYER_HPP_
    caffe::SigmoidLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::SigmoidLayer
    Sigmoid function non-linearity , a classic choice in neural networks.
    Definition: sigmoid_layer.hpp:23
    +
    caffe::SigmoidLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Computes the error gradient w.r.t. the sigmoid inputs.
    Definition: sigmoid_layer.cpp:25
    +
    caffe::SigmoidLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: sigmoid_layer.hpp:28
    +
    caffe::NeuronLayer
    An interface for layers that take one blob as input ( ) and produce one equally-sized blob as output ...
    Definition: neuron_layer.hpp:19
    +
    caffe::SigmoidLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Definition: sigmoid_layer.cpp:14
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    caffe::SigmoidLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/signal__handler_8h_source.html b/doxygen/signal__handler_8h_source.html new file mode 100644 index 00000000000..60078ff3608 --- /dev/null +++ b/doxygen/signal__handler_8h_source.html @@ -0,0 +1,80 @@ + + + + + + + +Caffe: include/caffe/util/signal_handler.h Source File + + + + + + + + + +
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    Caffe +
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    signal_handler.h
    +
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    +
    1 #ifndef INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_
    2 #define INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_
    3 
    4 #include "caffe/proto/caffe.pb.h"
    5 #include "caffe/solver.hpp"
    6 
    7 namespace caffe {
    8 
    9 class SignalHandler {
    10  public:
    11  // Contructor. Specify what action to take when a signal is received.
    12  SignalHandler(SolverAction::Enum SIGINT_action,
    13  SolverAction::Enum SIGHUP_action);
    14  ~SignalHandler();
    15  ActionCallback GetActionFunction();
    16  private:
    17  SolverAction::Enum CheckForSignals() const;
    18  SolverAction::Enum SIGINT_action_;
    19  SolverAction::Enum SIGHUP_action_;
    20 };
    21 
    22 } // namespace caffe
    23 
    24 #endif // INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::SignalHandler
    Definition: signal_handler.h:9
    +
    caffe::ActionCallback
    boost::function< SolverAction::Enum()> ActionCallback
    Type of a function that returns a Solver Action enumeration.
    Definition: solver.hpp:32
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/silence__layer_8hpp_source.html b/doxygen/silence__layer_8hpp_source.html new file mode 100644 index 00000000000..8a1d4773fd4 --- /dev/null +++ b/doxygen/silence__layer_8hpp_source.html @@ -0,0 +1,89 @@ + + + + + + + +Caffe: include/caffe/layers/silence_layer.hpp Source File + + + + + + + + + +
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    Caffe +
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    silence_layer.hpp
    +
    +
    +
    1 #ifndef CAFFE_SILENCE_LAYER_HPP_
    2 #define CAFFE_SILENCE_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 namespace caffe {
    11 
    16 template <typename Dtype>
    17 class SilenceLayer : public Layer<Dtype> {
    18  public:
    19  explicit SilenceLayer(const LayerParameter& param)
    20  : Layer<Dtype>(param) {}
    21  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    22  const vector<Blob<Dtype>*>& top) {}
    23 
    24  virtual inline const char* type() const { return "Silence"; }
    25  virtual inline int MinBottomBlobs() const { return 1; }
    26  virtual inline int ExactNumTopBlobs() const { return 0; }
    27 
    28  protected:
    29  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    30  const vector<Blob<Dtype>*>& top) {}
    31  // We can't define Forward_gpu here, since STUB_GPU will provide
    32  // its own definition for CPU_ONLY mode.
    33  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    34  const vector<Blob<Dtype>*>& top);
    35  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    36  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    37  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    38  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    39 };
    40 
    41 } // namespace caffe
    42 
    43 #endif // CAFFE_SILENCE_LAYER_HPP_
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::SilenceLayer::MinBottomBlobs
    virtual int MinBottomBlobs() const
    Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
    Definition: silence_layer.hpp:25
    +
    caffe::SilenceLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::SilenceLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: silence_layer.cpp:9
    +
    caffe::SilenceLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: silence_layer.hpp:21
    +
    caffe::SilenceLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the CPU device, compute the layer output.
    Definition: silence_layer.hpp:29
    +
    caffe::SilenceLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: silence_layer.hpp:24
    +
    caffe::SilenceLayer
    Ignores bottom blobs while producing no top blobs. (This is useful to suppress outputs during testing...
    Definition: silence_layer.hpp:17
    +
    caffe::SilenceLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: silence_layer.hpp:26
    +
    caffe::SilenceLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/slice__layer_8hpp_source.html b/doxygen/slice__layer_8hpp_source.html new file mode 100644 index 00000000000..407f0aa74f9 --- /dev/null +++ b/doxygen/slice__layer_8hpp_source.html @@ -0,0 +1,90 @@ + + + + + + + +Caffe: include/caffe/layers/slice_layer.hpp Source File + + + + + + + + + +
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    slice_layer.hpp
    +
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    +
    1 #ifndef CAFFE_SLICE_LAYER_HPP_
    2 #define CAFFE_SLICE_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 namespace caffe {
    11 
    18 template <typename Dtype>
    19 class SliceLayer : public Layer<Dtype> {
    20  public:
    21  explicit SliceLayer(const LayerParameter& param)
    22  : Layer<Dtype>(param) {}
    23  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    24  const vector<Blob<Dtype>*>& top);
    25  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    26  const vector<Blob<Dtype>*>& top);
    27 
    28  virtual inline const char* type() const { return "Slice"; }
    29  virtual inline int ExactNumBottomBlobs() const { return 1; }
    30  virtual inline int MinTopBlobs() const { return 1; }
    31 
    32  protected:
    33  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    34  const vector<Blob<Dtype>*>& top);
    35  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    36  const vector<Blob<Dtype>*>& top);
    37  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    38  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    39  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    40  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    41 
    42  int count_;
    43  int num_slices_;
    44  int slice_size_;
    45  int slice_axis_;
    46  vector<int> slice_point_;
    47 };
    48 
    49 } // namespace caffe
    50 
    51 #endif // CAFFE_SLICE_LAYER_HPP_
    caffe::SliceLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::SliceLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: slice_layer.cpp:10
    +
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::SliceLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: slice_layer.hpp:28
    +
    caffe::SliceLayer::MinTopBlobs
    virtual int MinTopBlobs() const
    Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
    Definition: slice_layer.hpp:30
    +
    caffe::SliceLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the CPU device, compute the layer output.
    Definition: slice_layer.cpp:76
    +
    caffe::SliceLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: slice_layer.hpp:29
    +
    caffe::SliceLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::SliceLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: slice_layer.cpp:97
    +
    caffe::SliceLayer
    Takes a Blob and slices it along either the num or channel dimension, outputting multiple sliced Blob...
    Definition: slice_layer.hpp:19
    +
    caffe::SliceLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: slice_layer.cpp:22
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/softmax__layer_8hpp_source.html b/doxygen/softmax__layer_8hpp_source.html new file mode 100644 index 00000000000..9359b027304 --- /dev/null +++ b/doxygen/softmax__layer_8hpp_source.html @@ -0,0 +1,91 @@ + + + + + + + +Caffe: include/caffe/layers/softmax_layer.hpp Source File + + + + + + + + + +
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    softmax_layer.hpp
    +
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    1 #ifndef CAFFE_SOFTMAX_LAYER_HPP_
    2 #define CAFFE_SOFTMAX_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 namespace caffe {
    11 
    17 template <typename Dtype>
    18 class SoftmaxLayer : public Layer<Dtype> {
    19  public:
    20  explicit SoftmaxLayer(const LayerParameter& param)
    21  : Layer<Dtype>(param) {}
    22  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    23  const vector<Blob<Dtype>*>& top);
    24 
    25  virtual inline const char* type() const { return "Softmax"; }
    26  virtual inline int ExactNumBottomBlobs() const { return 1; }
    27  virtual inline int ExactNumTopBlobs() const { return 1; }
    28 
    29  protected:
    30  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    31  const vector<Blob<Dtype>*>& top);
    32  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    33  const vector<Blob<Dtype>*>& top);
    34  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    35  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    36  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    37  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    38 
    39  int outer_num_;
    40  int inner_num_;
    41  int softmax_axis_;
    43  Blob<Dtype> sum_multiplier_;
    45  Blob<Dtype> scale_;
    46 };
    47 
    48 } // namespace caffe
    49 
    50 #endif // CAFFE_SOFTMAX_LAYER_HPP_
    caffe::SoftmaxLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: softmax_layer.hpp:25
    +
    caffe::SoftmaxLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::SoftmaxLayer::sum_multiplier_
    Blob< Dtype > sum_multiplier_
    sum_multiplier is used to carry out sum using BLAS
    Definition: softmax_layer.hpp:43
    +
    caffe::SoftmaxLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the CPU device, compute the layer output.
    Definition: softmax_layer.cpp:27
    +
    caffe::SoftmaxLayer
    Computes the softmax function.
    Definition: softmax_layer.hpp:18
    +
    caffe::SoftmaxLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::SoftmaxLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: softmax_layer.cpp:10
    +
    caffe::SoftmaxLayer::scale_
    Blob< Dtype > scale_
    scale is an intermediate Blob to hold temporary results.
    Definition: softmax_layer.hpp:45
    +
    caffe::SoftmaxLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: softmax_layer.cpp:63
    +
    caffe::SoftmaxLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: softmax_layer.hpp:26
    +
    caffe::SoftmaxLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: softmax_layer.hpp:27
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/softmax__loss__layer_8hpp_source.html b/doxygen/softmax__loss__layer_8hpp_source.html new file mode 100644 index 00000000000..00e7c640429 --- /dev/null +++ b/doxygen/softmax__loss__layer_8hpp_source.html @@ -0,0 +1,100 @@ + + + + + + + +Caffe: include/caffe/layers/softmax_loss_layer.hpp Source File + + + + + + + + + +
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    softmax_loss_layer.hpp
    +
    +
    +
    1 #ifndef CAFFE_SOFTMAX_WITH_LOSS_LAYER_HPP_
    2 #define CAFFE_SOFTMAX_WITH_LOSS_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 #include "caffe/layers/loss_layer.hpp"
    11 #include "caffe/layers/softmax_layer.hpp"
    12 
    13 namespace caffe {
    14 
    43 template <typename Dtype>
    44 class SoftmaxWithLossLayer : public LossLayer<Dtype> {
    45  public:
    54  explicit SoftmaxWithLossLayer(const LayerParameter& param)
    55  : LossLayer<Dtype>(param) {}
    56  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    57  const vector<Blob<Dtype>*>& top);
    58  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    59  const vector<Blob<Dtype>*>& top);
    60 
    61  virtual inline const char* type() const { return "SoftmaxWithLoss"; }
    62  virtual inline int ExactNumTopBlobs() const { return -1; }
    63  virtual inline int MinTopBlobs() const { return 1; }
    64  virtual inline int MaxTopBlobs() const { return 2; }
    65 
    66  protected:
    67  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    68  const vector<Blob<Dtype>*>& top);
    69  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    70  const vector<Blob<Dtype>*>& top);
    98  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    99  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    100  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    101  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    102 
    107  virtual Dtype get_normalizer(
    108  LossParameter_NormalizationMode normalization_mode, int valid_count);
    109 
    111  shared_ptr<Layer<Dtype> > softmax_layer_;
    113  Blob<Dtype> prob_;
    115  vector<Blob<Dtype>*> softmax_bottom_vec_;
    117  vector<Blob<Dtype>*> softmax_top_vec_;
    119  bool has_ignore_label_;
    121  int ignore_label_;
    123  LossParameter_NormalizationMode normalization_;
    124 
    125  int softmax_axis_, outer_num_, inner_num_;
    126 };
    127 
    128 } // namespace caffe
    129 
    130 #endif // CAFFE_SOFTMAX_WITH_LOSS_LAYER_HPP_
    caffe::SoftmaxWithLossLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::SoftmaxWithLossLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the CPU device, compute the layer output.
    Definition: softmax_loss_layer.cpp:89
    +
    caffe::SoftmaxWithLossLayer::softmax_layer_
    shared_ptr< Layer< Dtype > > softmax_layer_
    The internal SoftmaxLayer used to map predictions to a distribution.
    Definition: softmax_loss_layer.hpp:111
    +
    caffe::SoftmaxWithLossLayer::MinTopBlobs
    virtual int MinTopBlobs() const
    Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
    Definition: softmax_loss_layer.hpp:63
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::SoftmaxWithLossLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: softmax_loss_layer.cpp:39
    +
    caffe::SoftmaxWithLossLayer::MaxTopBlobs
    virtual int MaxTopBlobs() const
    Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required...
    Definition: softmax_loss_layer.hpp:64
    +
    caffe::SoftmaxWithLossLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: softmax_loss_layer.hpp:61
    +
    caffe::SoftmaxWithLossLayer::SoftmaxWithLossLayer
    SoftmaxWithLossLayer(const LayerParameter &param)
    Definition: softmax_loss_layer.hpp:54
    +
    caffe::SoftmaxWithLossLayer::softmax_top_vec_
    vector< Blob< Dtype > * > softmax_top_vec_
    top vector holder used in call to the underlying SoftmaxLayer::Forward
    Definition: softmax_loss_layer.hpp:117
    +
    caffe::SoftmaxWithLossLayer::normalization_
    LossParameter_NormalizationMode normalization_
    How to normalize the output loss.
    Definition: softmax_loss_layer.hpp:123
    +
    caffe::SoftmaxWithLossLayer
    Computes the multinomial logistic loss for a one-of-many classification task, passing real-valued pre...
    Definition: softmax_loss_layer.hpp:44
    +
    caffe::SoftmaxWithLossLayer::softmax_bottom_vec_
    vector< Blob< Dtype > * > softmax_bottom_vec_
    bottom vector holder used in call to the underlying SoftmaxLayer::Forward
    Definition: softmax_loss_layer.hpp:115
    +
    caffe::SoftmaxWithLossLayer::ignore_label_
    int ignore_label_
    The label indicating that an instance should be ignored.
    Definition: softmax_loss_layer.hpp:121
    +
    caffe::LossLayer
    An interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth ...
    Definition: loss_layer.hpp:23
    +
    caffe::SoftmaxWithLossLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Computes the softmax loss error gradient w.r.t. the predictions.
    Definition: softmax_loss_layer.cpp:118
    +
    caffe::SoftmaxWithLossLayer::get_normalizer
    virtual Dtype get_normalizer(LossParameter_NormalizationMode normalization_mode, int valid_count)
    Definition: softmax_loss_layer.cpp:59
    +
    caffe::SoftmaxWithLossLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: softmax_loss_layer.hpp:62
    +
    caffe::SoftmaxWithLossLayer::prob_
    Blob< Dtype > prob_
    prob stores the output probability predictions from the SoftmaxLayer.
    Definition: softmax_loss_layer.hpp:113
    +
    caffe::SoftmaxWithLossLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: softmax_loss_layer.cpp:11
    +
    caffe::SoftmaxWithLossLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    caffe::SoftmaxWithLossLayer::has_ignore_label_
    bool has_ignore_label_
    Whether to ignore instances with a certain label.
    Definition: softmax_loss_layer.hpp:119
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/solver_8hpp_source.html b/doxygen/solver_8hpp_source.html new file mode 100644 index 00000000000..d562fada51a --- /dev/null +++ b/doxygen/solver_8hpp_source.html @@ -0,0 +1,83 @@ + + + + + + + +Caffe: include/caffe/solver.hpp Source File + + + + + + + + + +
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    solver.hpp
    +
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    1 #ifndef CAFFE_SOLVER_HPP_
    2 #define CAFFE_SOLVER_HPP_
    3 #include <boost/function.hpp>
    4 #include <string>
    5 #include <vector>
    6 
    7 #include "caffe/net.hpp"
    8 #include "caffe/solver_factory.hpp"
    9 
    10 namespace caffe {
    11 
    20  namespace SolverAction {
    21  enum Enum {
    22  NONE = 0, // Take no special action.
    23  STOP = 1, // Stop training. snapshot_after_train controls whether a
    24  // snapshot is created.
    25  SNAPSHOT = 2 // Take a snapshot, and keep training.
    26  };
    27  }
    28 
    32 typedef boost::function<SolverAction::Enum()> ActionCallback;
    33 
    40 template <typename Dtype>
    41 class Solver {
    42  public:
    43  explicit Solver(const SolverParameter& param,
    44  const Solver* root_solver = NULL);
    45  explicit Solver(const string& param_file, const Solver* root_solver = NULL);
    46  void Init(const SolverParameter& param);
    47  void InitTrainNet();
    48  void InitTestNets();
    49 
    50  // Client of the Solver optionally may call this in order to set the function
    51  // that the solver uses to see what action it should take (e.g. snapshot or
    52  // exit training early).
    53  void SetActionFunction(ActionCallback func);
    54  SolverAction::Enum GetRequestedAction();
    55  // The main entry of the solver function. In default, iter will be zero. Pass
    56  // in a non-zero iter number to resume training for a pre-trained net.
    57  virtual void Solve(const char* resume_file = NULL);
    58  inline void Solve(const string resume_file) { Solve(resume_file.c_str()); }
    59  void Step(int iters);
    60  // The Restore method simply dispatches to one of the
    61  // RestoreSolverStateFrom___ protected methods. You should implement these
    62  // methods to restore the state from the appropriate snapshot type.
    63  void Restore(const char* resume_file);
    64  // The Solver::Snapshot function implements the basic snapshotting utility
    65  // that stores the learned net. You should implement the SnapshotSolverState()
    66  // function that produces a SolverState protocol buffer that needs to be
    67  // written to disk together with the learned net.
    68  void Snapshot();
    69  virtual ~Solver() {}
    70  inline const SolverParameter& param() const { return param_; }
    71  inline shared_ptr<Net<Dtype> > net() { return net_; }
    72  inline const vector<shared_ptr<Net<Dtype> > >& test_nets() {
    73  return test_nets_;
    74  }
    75  int iter() { return iter_; }
    76 
    77  // Invoked at specific points during an iteration
    78  class Callback {
    79  protected:
    80  virtual void on_start() = 0;
    81  virtual void on_gradients_ready() = 0;
    82 
    83  template <typename T>
    84  friend class Solver;
    85  };
    86  const vector<Callback*>& callbacks() const { return callbacks_; }
    87  void add_callback(Callback* value) {
    88  callbacks_.push_back(value);
    89  }
    90 
    91  void CheckSnapshotWritePermissions();
    95  virtual inline const char* type() const { return ""; }
    96 
    97  protected:
    98  // Make and apply the update value for the current iteration.
    99  virtual void ApplyUpdate() = 0;
    100  string SnapshotFilename(const string extension);
    101  string SnapshotToBinaryProto();
    102  string SnapshotToHDF5();
    103  // The test routine
    104  void TestAll();
    105  void Test(const int test_net_id = 0);
    106  virtual void SnapshotSolverState(const string& model_filename) = 0;
    107  virtual void RestoreSolverStateFromHDF5(const string& state_file) = 0;
    108  virtual void RestoreSolverStateFromBinaryProto(const string& state_file) = 0;
    109  void DisplayOutputBlobs(const int net_id);
    110  void UpdateSmoothedLoss(Dtype loss, int start_iter, int average_loss);
    111 
    112  SolverParameter param_;
    113  int iter_;
    114  int current_step_;
    115  shared_ptr<Net<Dtype> > net_;
    116  vector<shared_ptr<Net<Dtype> > > test_nets_;
    117  vector<Callback*> callbacks_;
    118  vector<Dtype> losses_;
    119  Dtype smoothed_loss_;
    120 
    121  // The root solver that holds root nets (actually containing shared layers)
    122  // in data parallelism
    123  const Solver* const root_solver_;
    124 
    125  // A function that can be set by a client of the Solver to provide indication
    126  // that it wants a snapshot saved and/or to exit early.
    127  ActionCallback action_request_function_;
    128 
    129  // True iff a request to stop early was received.
    130  bool requested_early_exit_;
    131 
    132  DISABLE_COPY_AND_ASSIGN(Solver);
    133 };
    134 
    139 template <typename Dtype>
    140 class WorkerSolver : public Solver<Dtype> {
    141  public:
    142  explicit WorkerSolver(const SolverParameter& param,
    143  const Solver<Dtype>* root_solver = NULL)
    144  : Solver<Dtype>(param, root_solver) {}
    145 
    146  protected:
    147  void ApplyUpdate() {}
    148  void SnapshotSolverState(const string& model_filename) {
    149  LOG(FATAL) << "Should not be called on worker solver.";
    150  }
    151  void RestoreSolverStateFromBinaryProto(const string& state_file) {
    152  LOG(FATAL) << "Should not be called on worker solver.";
    153  }
    154  void RestoreSolverStateFromHDF5(const string& state_file) {
    155  LOG(FATAL) << "Should not be called on worker solver.";
    156  }
    157 };
    158 
    159 } // namespace caffe
    160 
    161 #endif // CAFFE_SOLVER_HPP_
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::WorkerSolver
    Solver that only computes gradients, used as worker for multi-GPU training.
    Definition: solver.hpp:140
    +
    caffe::Solver::Callback
    Definition: solver.hpp:78
    +
    caffe::Solver
    An interface for classes that perform optimization on Nets.
    Definition: solver.hpp:41
    +
    caffe::Solver::type
    virtual const char * type() const
    Returns the solver type.
    Definition: solver.hpp:95
    +
    caffe::ActionCallback
    boost::function< SolverAction::Enum()> ActionCallback
    Type of a function that returns a Solver Action enumeration.
    Definition: solver.hpp:32
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/solver__factory_8hpp_source.html b/doxygen/solver__factory_8hpp_source.html new file mode 100644 index 00000000000..c269fe78f7f --- /dev/null +++ b/doxygen/solver__factory_8hpp_source.html @@ -0,0 +1,81 @@ + + + + + + + +Caffe: include/caffe/solver_factory.hpp Source File + + + + + + + + + +
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    + + + + + + +
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    Caffe +
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    solver_factory.hpp
    +
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    1 
    38 #ifndef CAFFE_SOLVER_FACTORY_H_
    39 #define CAFFE_SOLVER_FACTORY_H_
    40 
    41 #include <map>
    42 #include <string>
    43 #include <vector>
    44 
    45 #include "caffe/common.hpp"
    46 #include "caffe/proto/caffe.pb.h"
    47 
    48 namespace caffe {
    49 
    50 template <typename Dtype>
    51 class Solver;
    52 
    53 template <typename Dtype>
    54 class SolverRegistry {
    55  public:
    56  typedef Solver<Dtype>* (*Creator)(const SolverParameter&);
    57  typedef std::map<string, Creator> CreatorRegistry;
    58 
    59  static CreatorRegistry& Registry() {
    60  static CreatorRegistry* g_registry_ = new CreatorRegistry();
    61  return *g_registry_;
    62  }
    63 
    64  // Adds a creator.
    65  static void AddCreator(const string& type, Creator creator) {
    66  CreatorRegistry& registry = Registry();
    67  CHECK_EQ(registry.count(type), 0)
    68  << "Solver type " << type << " already registered.";
    69  registry[type] = creator;
    70  }
    71 
    72  // Get a solver using a SolverParameter.
    73  static Solver<Dtype>* CreateSolver(const SolverParameter& param) {
    74  const string& type = param.type();
    75  CreatorRegistry& registry = Registry();
    76  CHECK_EQ(registry.count(type), 1) << "Unknown solver type: " << type
    77  << " (known types: " << SolverTypeListString() << ")";
    78  return registry[type](param);
    79  }
    80 
    81  static vector<string> SolverTypeList() {
    82  CreatorRegistry& registry = Registry();
    83  vector<string> solver_types;
    84  for (typename CreatorRegistry::iterator iter = registry.begin();
    85  iter != registry.end(); ++iter) {
    86  solver_types.push_back(iter->first);
    87  }
    88  return solver_types;
    89  }
    90 
    91  private:
    92  // Solver registry should never be instantiated - everything is done with its
    93  // static variables.
    94  SolverRegistry() {}
    95 
    96  static string SolverTypeListString() {
    97  vector<string> solver_types = SolverTypeList();
    98  string solver_types_str;
    99  for (vector<string>::iterator iter = solver_types.begin();
    100  iter != solver_types.end(); ++iter) {
    101  if (iter != solver_types.begin()) {
    102  solver_types_str += ", ";
    103  }
    104  solver_types_str += *iter;
    105  }
    106  return solver_types_str;
    107  }
    108 };
    109 
    110 
    111 template <typename Dtype>
    112 class SolverRegisterer {
    113  public:
    114  SolverRegisterer(const string& type,
    115  Solver<Dtype>* (*creator)(const SolverParameter&)) {
    116  // LOG(INFO) << "Registering solver type: " << type;
    117  SolverRegistry<Dtype>::AddCreator(type, creator);
    118  }
    119 };
    120 
    121 
    122 #define REGISTER_SOLVER_CREATOR(type, creator) \
    123  static SolverRegisterer<float> g_creator_f_##type(#type, creator<float>); \
    124  static SolverRegisterer<double> g_creator_d_##type(#type, creator<double>) \
    125 
    126 #define REGISTER_SOLVER_CLASS(type) \
    127  template <typename Dtype> \
    128  Solver<Dtype>* Creator_##type##Solver( \
    129  const SolverParameter& param) \
    130  { \
    131  return new type##Solver<Dtype>(param); \
    132  } \
    133  REGISTER_SOLVER_CREATOR(type, Creator_##type##Solver)
    134 
    135 } // namespace caffe
    136 
    137 #endif // CAFFE_SOLVER_FACTORY_H_
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::Solver
    An interface for classes that perform optimization on Nets.
    Definition: solver.hpp:41
    +
    caffe::SolverRegistry
    Definition: solver_factory.hpp:54
    +
    caffe::SolverRegisterer
    Definition: solver_factory.hpp:112
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/split__layer_8hpp_source.html b/doxygen/split__layer_8hpp_source.html new file mode 100644 index 00000000000..cc224c744b7 --- /dev/null +++ b/doxygen/split__layer_8hpp_source.html @@ -0,0 +1,89 @@ + + + + + + + +Caffe: include/caffe/layers/split_layer.hpp Source File + + + + + + + + + +
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    Caffe +
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    split_layer.hpp
    +
    +
    +
    1 #ifndef CAFFE_SPLIT_LAYER_HPP_
    2 #define CAFFE_SPLIT_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 namespace caffe {
    11 
    18 template <typename Dtype>
    19 class SplitLayer : public Layer<Dtype> {
    20  public:
    21  explicit SplitLayer(const LayerParameter& param)
    22  : Layer<Dtype>(param) {}
    23  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    24  const vector<Blob<Dtype>*>& top);
    25 
    26  virtual inline const char* type() const { return "Split"; }
    27  virtual inline int ExactNumBottomBlobs() const { return 1; }
    28  virtual inline int MinTopBlobs() const { return 1; }
    29 
    30  protected:
    31  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    32  const vector<Blob<Dtype>*>& top);
    33  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    34  const vector<Blob<Dtype>*>& top);
    35  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    36  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    37  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    38  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    39 
    40  int count_;
    41 };
    42 
    43 } // namespace caffe
    44 
    45 #endif // CAFFE_SPLIT_LAYER_HPP_
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::SplitLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::SplitLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: split_layer.hpp:26
    +
    caffe::SplitLayer::MinTopBlobs
    virtual int MinTopBlobs() const
    Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
    Definition: split_layer.hpp:28
    +
    caffe::SplitLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: split_layer.cpp:34
    +
    caffe::SplitLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the CPU device, compute the layer output.
    Definition: split_layer.cpp:26
    +
    caffe::SplitLayer
    Creates a "split" path in the network by copying the bottom Blob into multiple top Blobs to be used b...
    Definition: split_layer.hpp:19
    +
    caffe::SplitLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: split_layer.hpp:27
    +
    caffe::SplitLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::SplitLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: split_layer.cpp:9
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/splitbar.png b/doxygen/splitbar.png new file mode 100644 index 0000000000000000000000000000000000000000..fe895f2c58179b471a22d8320b39a4bd7312ec8e GIT binary patch literal 314 zcmeAS@N?(olHy`uVBq!ia0vp^Yzz!63>-{AmhX=Jf(#6djGiuzAr*{o?=JLmPLyc> z_*`QK&+BH@jWrYJ7>r6%keRM@)Qyv8R=enp0jiI>aWlGyB58O zFVR20d+y`K7vDw(hJF3;>dD*3-?v=<8M)@x|EEGLnJsniYK!2U1 Y!`|5biEc?d1`HDhPgg&ebxsLQ02F6;9RL6T literal 0 HcmV?d00001 diff --git a/doxygen/spp__layer_8hpp_source.html b/doxygen/spp__layer_8hpp_source.html new file mode 100644 index 00000000000..68a4207abd9 --- /dev/null +++ b/doxygen/spp__layer_8hpp_source.html @@ -0,0 +1,99 @@ + + + + + + + +Caffe: include/caffe/layers/spp_layer.hpp Source File + + + + + + + + + +
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    spp_layer.hpp
    +
    +
    +
    1 #ifndef CAFFE_SPP_LAYER_HPP_
    2 #define CAFFE_SPP_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 namespace caffe {
    11 
    18 template <typename Dtype>
    19 class SPPLayer : public Layer<Dtype> {
    20  public:
    21  explicit SPPLayer(const LayerParameter& param)
    22  : Layer<Dtype>(param) {}
    23  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    24  const vector<Blob<Dtype>*>& top);
    25  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    26  const vector<Blob<Dtype>*>& top);
    27 
    28  virtual inline const char* type() const { return "SPP"; }
    29  virtual inline int ExactNumBottomBlobs() const { return 1; }
    30  virtual inline int ExactNumTopBlobs() const { return 1; }
    31 
    32  protected:
    33  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    34  const vector<Blob<Dtype>*>& top);
    35  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    36  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    37  // calculates the kernel and stride dimensions for the pooling layer,
    38  // returns a correctly configured LayerParameter for a PoolingLayer
    39  virtual LayerParameter GetPoolingParam(const int pyramid_level,
    40  const int bottom_h, const int bottom_w, const SPPParameter spp_param);
    41 
    42  int pyramid_height_;
    43  int bottom_h_, bottom_w_;
    44  int num_;
    45  int channels_;
    46  int kernel_h_, kernel_w_;
    47  int pad_h_, pad_w_;
    48  bool reshaped_first_time_;
    49 
    51  shared_ptr<SplitLayer<Dtype> > split_layer_;
    53  vector<Blob<Dtype>*> split_top_vec_;
    55  vector<vector<Blob<Dtype>*>*> pooling_bottom_vecs_;
    57  vector<shared_ptr<PoolingLayer<Dtype> > > pooling_layers_;
    59  vector<vector<Blob<Dtype>*>*> pooling_top_vecs_;
    61  vector<Blob<Dtype>*> pooling_outputs_;
    63  vector<FlattenLayer<Dtype>*> flatten_layers_;
    65  vector<vector<Blob<Dtype>*>*> flatten_top_vecs_;
    67  vector<Blob<Dtype>*> flatten_outputs_;
    69  vector<Blob<Dtype>*> concat_bottom_vec_;
    71  shared_ptr<ConcatLayer<Dtype> > concat_layer_;
    72 };
    73 
    74 } // namespace caffe
    75 
    76 #endif // CAFFE_SPP_LAYER_HPP_
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::SPPLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: spp_layer.cpp:205
    +
    caffe::SPPLayer::pooling_layers_
    vector< shared_ptr< PoolingLayer< Dtype > > > pooling_layers_
    the internal Pooling layers of different kernel sizes
    Definition: spp_layer.hpp:57
    +
    caffe::SPPLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the CPU device, compute the layer output.
    Definition: spp_layer.cpp:188
    +
    caffe::SPPLayer::flatten_top_vecs_
    vector< vector< Blob< Dtype > * > * > flatten_top_vecs_
    top vector holders used in call to the underlying FlattenLayer::Forward
    Definition: spp_layer.hpp:65
    +
    caffe::SPPLayer::pooling_top_vecs_
    vector< vector< Blob< Dtype > * > * > pooling_top_vecs_
    top vector holders used in call to the underlying PoolingLayer::Forward
    Definition: spp_layer.hpp:59
    +
    caffe::SPPLayer::split_top_vec_
    vector< Blob< Dtype > * > split_top_vec_
    top vector holder used in call to the underlying SplitLayer::Forward
    Definition: spp_layer.hpp:53
    +
    caffe::SPPLayer
    Does spatial pyramid pooling on the input image by taking the max, average, etc. within regions so th...
    Definition: spp_layer.hpp:19
    +
    caffe::SPPLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: spp_layer.hpp:28
    +
    caffe::SPPLayer::pooling_bottom_vecs_
    vector< vector< Blob< Dtype > * > * > pooling_bottom_vecs_
    bottom vector holder used in call to the underlying PoolingLayer::Forward
    Definition: spp_layer.hpp:55
    +
    caffe::SPPLayer::concat_bottom_vec_
    vector< Blob< Dtype > * > concat_bottom_vec_
    bottom vector holder used in call to the underlying ConcatLayer::Forward
    Definition: spp_layer.hpp:69
    +
    caffe::SPPLayer::flatten_layers_
    vector< FlattenLayer< Dtype > * > flatten_layers_
    the internal Flatten layers that the Pooling layers feed into
    Definition: spp_layer.hpp:63
    +
    caffe::SPPLayer::flatten_outputs_
    vector< Blob< Dtype > * > flatten_outputs_
    flatten_outputs stores the outputs of the FlattenLayers
    Definition: spp_layer.hpp:67
    +
    caffe::SPPLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: spp_layer.cpp:65
    +
    caffe::SPPLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: spp_layer.cpp:146
    +
    caffe::SPPLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: spp_layer.hpp:30
    +
    caffe::SPPLayer::concat_layer_
    shared_ptr< ConcatLayer< Dtype > > concat_layer_
    the internal Concat layers that the Flatten layers feed into
    Definition: spp_layer.hpp:71
    +
    caffe::SPPLayer::split_layer_
    shared_ptr< SplitLayer< Dtype > > split_layer_
    the internal Split layer that feeds the pooling layers
    Definition: spp_layer.hpp:51
    +
    caffe::SPPLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: spp_layer.hpp:29
    +
    caffe::SPPLayer::pooling_outputs_
    vector< Blob< Dtype > * > pooling_outputs_
    pooling_outputs stores the outputs of the PoolingLayers
    Definition: spp_layer.hpp:61
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
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    Caffe +
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    syncedmem.hpp
    +
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    1 #ifndef CAFFE_SYNCEDMEM_HPP_
    2 #define CAFFE_SYNCEDMEM_HPP_
    3 
    4 #include <cstdlib>
    5 
    6 #include "caffe/common.hpp"
    7 
    8 namespace caffe {
    9 
    10 // If CUDA is available and in GPU mode, host memory will be allocated pinned,
    11 // using cudaMallocHost. It avoids dynamic pinning for transfers (DMA).
    12 // The improvement in performance seems negligible in the single GPU case,
    13 // but might be more significant for parallel training. Most importantly,
    14 // it improved stability for large models on many GPUs.
    15 inline void CaffeMallocHost(void** ptr, size_t size, bool* use_cuda) {
    16 #ifndef CPU_ONLY
    17  if (Caffe::mode() == Caffe::GPU) {
    18  CUDA_CHECK(cudaMallocHost(ptr, size));
    19  *use_cuda = true;
    20  return;
    21  }
    22 #endif
    23  *ptr = malloc(size);
    24  *use_cuda = false;
    25  CHECK(*ptr) << "host allocation of size " << size << " failed";
    26 }
    27 
    28 inline void CaffeFreeHost(void* ptr, bool use_cuda) {
    29 #ifndef CPU_ONLY
    30  if (use_cuda) {
    31  CUDA_CHECK(cudaFreeHost(ptr));
    32  return;
    33  }
    34 #endif
    35  free(ptr);
    36 }
    37 
    38 
    45 class SyncedMemory {
    46  public:
    47  SyncedMemory()
    48  : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(0), head_(UNINITIALIZED),
    49  own_cpu_data_(false), cpu_malloc_use_cuda_(false), own_gpu_data_(false),
    50  gpu_device_(-1) {}
    51  explicit SyncedMemory(size_t size)
    52  : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(size), head_(UNINITIALIZED),
    53  own_cpu_data_(false), cpu_malloc_use_cuda_(false), own_gpu_data_(false),
    54  gpu_device_(-1) {}
    55  ~SyncedMemory();
    56  const void* cpu_data();
    57  void set_cpu_data(void* data);
    58  const void* gpu_data();
    59  void set_gpu_data(void* data);
    60  void* mutable_cpu_data();
    61  void* mutable_gpu_data();
    62  enum SyncedHead { UNINITIALIZED, HEAD_AT_CPU, HEAD_AT_GPU, SYNCED };
    63  SyncedHead head() { return head_; }
    64  size_t size() { return size_; }
    65 
    66 #ifndef CPU_ONLY
    67  void async_gpu_push(const cudaStream_t& stream);
    68 #endif
    69 
    70  private:
    71  void to_cpu();
    72  void to_gpu();
    73  void* cpu_ptr_;
    74  void* gpu_ptr_;
    75  size_t size_;
    76  SyncedHead head_;
    77  bool own_cpu_data_;
    78  bool cpu_malloc_use_cuda_;
    79  bool own_gpu_data_;
    80  int gpu_device_;
    81 
    82  DISABLE_COPY_AND_ASSIGN(SyncedMemory);
    83 }; // class SyncedMemory
    84 
    85 } // namespace caffe
    86 
    87 #endif // CAFFE_SYNCEDMEM_HPP_
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::SyncedMemory
    Manages memory allocation and synchronization between the host (CPU) and device (GPU).
    Definition: syncedmem.hpp:45
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
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    +
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    Caffe +
    +
    +
    + + + + + + + + +
    +
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    + +
    + + +
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    +
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    tanh_layer.hpp
    +
    +
    +
    1 #ifndef CAFFE_TANH_LAYER_HPP_
    2 #define CAFFE_TANH_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 #include "caffe/layers/neuron_layer.hpp"
    11 
    12 namespace caffe {
    13 
    22 template <typename Dtype>
    23 class TanHLayer : public NeuronLayer<Dtype> {
    24  public:
    25  explicit TanHLayer(const LayerParameter& param)
    26  : NeuronLayer<Dtype>(param) {}
    27 
    28  virtual inline const char* type() const { return "TanH"; }
    29 
    30  protected:
    41  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    42  const vector<Blob<Dtype>*>& top);
    43  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    44  const vector<Blob<Dtype>*>& top);
    45 
    65  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    66  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    67  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    68  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    69 };
    70 
    71 } // namespace caffe
    72 
    73 #endif // CAFFE_TANH_LAYER_HPP_
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::TanHLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::TanHLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: tanh_layer.hpp:28
    +
    caffe::NeuronLayer
    An interface for layers that take one blob as input ( ) and produce one equally-sized blob as output ...
    Definition: neuron_layer.hpp:19
    +
    caffe::TanHLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Definition: tanh_layer.cpp:11
    +
    caffe::TanHLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::TanHLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Computes the error gradient w.r.t. the sigmoid inputs.
    Definition: tanh_layer.cpp:22
    +
    caffe::TanHLayer
    TanH hyperbolic tangent non-linearity , popular in auto-encoders.
    Definition: tanh_layer.hpp:23
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/threshold__layer_8hpp_source.html b/doxygen/threshold__layer_8hpp_source.html new file mode 100644 index 00000000000..2a87602d118 --- /dev/null +++ b/doxygen/threshold__layer_8hpp_source.html @@ -0,0 +1,87 @@ + + + + + + + +Caffe: include/caffe/layers/threshold_layer.hpp Source File + + + + + + + + + +
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    threshold_layer.hpp
    +
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    1 #ifndef CAFFE_THRESHOLD_LAYER_HPP_
    2 #define CAFFE_THRESHOLD_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 #include "caffe/layers/neuron_layer.hpp"
    11 
    12 namespace caffe {
    13 
    18 template <typename Dtype>
    19 class ThresholdLayer : public NeuronLayer<Dtype> {
    20  public:
    27  explicit ThresholdLayer(const LayerParameter& param)
    28  : NeuronLayer<Dtype>(param) {}
    29  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    30  const vector<Blob<Dtype>*>& top);
    31 
    32  virtual inline const char* type() const { return "Threshold"; }
    33 
    34  protected:
    49  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    50  const vector<Blob<Dtype>*>& top);
    51  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    52  const vector<Blob<Dtype>*>& top);
    54  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    55  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
    56  NOT_IMPLEMENTED;
    57  }
    58 
    59  Dtype threshold_;
    60 };
    61 
    62 } // namespace caffe
    63 
    64 #endif // CAFFE_THRESHOLD_LAYER_HPP_
    caffe::ThresholdLayer
    Tests whether the input exceeds a threshold: outputs 1 for inputs above threshold; 0 otherwise...
    Definition: threshold_layer.hpp:19
    +
    caffe::ThresholdLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::ThresholdLayer::ThresholdLayer
    ThresholdLayer(const LayerParameter &param)
    Definition: threshold_layer.hpp:27
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::ThresholdLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Not implemented (non-differentiable function)
    Definition: threshold_layer.hpp:54
    +
    caffe::ThresholdLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: threshold_layer.cpp:8
    +
    caffe::NeuronLayer
    An interface for layers that take one blob as input ( ) and produce one equally-sized blob as output ...
    Definition: neuron_layer.hpp:19
    +
    caffe::ThresholdLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Definition: threshold_layer.cpp:15
    +
    caffe::ThresholdLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: threshold_layer.hpp:32
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/tile__layer_8hpp_source.html b/doxygen/tile__layer_8hpp_source.html new file mode 100644 index 00000000000..fa21e51803c --- /dev/null +++ b/doxygen/tile__layer_8hpp_source.html @@ -0,0 +1,89 @@ + + + + + + + +Caffe: include/caffe/layers/tile_layer.hpp Source File + + + + + + + + + +
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    tile_layer.hpp
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    1 #ifndef CAFFE_TILE_LAYER_HPP_
    2 #define CAFFE_TILE_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 namespace caffe {
    11 
    15 template <typename Dtype>
    16 class TileLayer : public Layer<Dtype> {
    17  public:
    18  explicit TileLayer(const LayerParameter& param)
    19  : Layer<Dtype>(param) {}
    20  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    21  const vector<Blob<Dtype>*>& top);
    22 
    23  virtual inline const char* type() const { return "Tile"; }
    24  virtual inline int ExactNumBottomBlobs() const { return 1; }
    25  virtual inline int ExactNumTopBlobs() const { return 1; }
    26 
    27  protected:
    28  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    29  const vector<Blob<Dtype>*>& top);
    30  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    31  const vector<Blob<Dtype>*>& top);
    32 
    33  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    34  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    35  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    36  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    37 
    38  unsigned int axis_, tiles_, outer_dim_, inner_dim_;
    39 };
    40 
    41 } // namespace caffe
    42 
    43 #endif // CAFFE_TILE_LAYER_HPP_
    caffe::TileLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: tile_layer.cpp:38
    +
    caffe::TileLayer
    Copy a Blob along specified dimensions.
    Definition: tile_layer.hpp:16
    +
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::TileLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: tile_layer.hpp:25
    +
    caffe::TileLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: tile_layer.hpp:24
    +
    caffe::TileLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::TileLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the CPU device, compute the layer output.
    Definition: tile_layer.cpp:24
    +
    caffe::TileLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: tile_layer.hpp:23
    +
    caffe::TileLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: tile_layer.cpp:9
    +
    caffe::TileLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/upgrade__proto_8hpp_source.html b/doxygen/upgrade__proto_8hpp_source.html new file mode 100644 index 00000000000..d963fc31598 --- /dev/null +++ b/doxygen/upgrade__proto_8hpp_source.html @@ -0,0 +1,78 @@ + + + + + + + +Caffe: include/caffe/util/upgrade_proto.hpp Source File + + + + + + + + + +
    +
    + + + + + + +
    +
    Caffe +
    +
    +
    + + + + + + + + +
    +
    + + +
    + +
    + + +
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    +
    +
    upgrade_proto.hpp
    +
    +
    +
    1 #ifndef CAFFE_UTIL_UPGRADE_PROTO_H_
    2 #define CAFFE_UTIL_UPGRADE_PROTO_H_
    3 
    4 #include <string>
    5 
    6 #include "caffe/proto/caffe.pb.h"
    7 
    8 namespace caffe {
    9 
    10 // Return true iff the net is not the current version.
    11 bool NetNeedsUpgrade(const NetParameter& net_param);
    12 
    13 // Check for deprecations and upgrade the NetParameter as needed.
    14 bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param);
    15 
    16 // Read parameters from a file into a NetParameter proto message.
    17 void ReadNetParamsFromTextFileOrDie(const string& param_file,
    18  NetParameter* param);
    19 void ReadNetParamsFromBinaryFileOrDie(const string& param_file,
    20  NetParameter* param);
    21 
    22 // Return true iff any layer contains parameters specified using
    23 // deprecated V0LayerParameter.
    24 bool NetNeedsV0ToV1Upgrade(const NetParameter& net_param);
    25 
    26 // Perform all necessary transformations to upgrade a V0NetParameter into a
    27 // NetParameter (including upgrading padding layers and LayerParameters).
    28 bool UpgradeV0Net(const NetParameter& v0_net_param, NetParameter* net_param);
    29 
    30 // Upgrade NetParameter with padding layers to pad-aware conv layers.
    31 // For any padding layer, remove it and put its pad parameter in any layers
    32 // taking its top blob as input.
    33 // Error if any of these above layers are not-conv layers.
    34 void UpgradeV0PaddingLayers(const NetParameter& param,
    35  NetParameter* param_upgraded_pad);
    36 
    37 // Upgrade a single V0LayerConnection to the V1LayerParameter format.
    38 bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection,
    39  V1LayerParameter* layer_param);
    40 
    41 V1LayerParameter_LayerType UpgradeV0LayerType(const string& type);
    42 
    43 // Return true iff any layer contains deprecated data transformation parameters.
    44 bool NetNeedsDataUpgrade(const NetParameter& net_param);
    45 
    46 // Perform all necessary transformations to upgrade old transformation fields
    47 // into a TransformationParameter.
    48 void UpgradeNetDataTransformation(NetParameter* net_param);
    49 
    50 // Return true iff the Net contains any layers specified as V1LayerParameters.
    51 bool NetNeedsV1ToV2Upgrade(const NetParameter& net_param);
    52 
    53 // Perform all necessary transformations to upgrade a NetParameter with
    54 // deprecated V1LayerParameters.
    55 bool UpgradeV1Net(const NetParameter& v1_net_param, NetParameter* net_param);
    56 
    57 bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param,
    58  LayerParameter* layer_param);
    59 
    60 const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type);
    61 
    62 // Return true iff the Net contains input fields.
    63 bool NetNeedsInputUpgrade(const NetParameter& net_param);
    64 
    65 // Perform all necessary transformations to upgrade input fields into layers.
    66 void UpgradeNetInput(NetParameter* net_param);
    67 
    68 // Return true iff the Net contains batch norm layers with manual local LRs.
    69 bool NetNeedsBatchNormUpgrade(const NetParameter& net_param);
    70 
    71 // Perform all necessary transformations to upgrade batch norm layers.
    72 void UpgradeNetBatchNorm(NetParameter* net_param);
    73 
    74 // Return true iff the solver contains any old solver_type specified as enums
    75 bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param);
    76 
    77 bool UpgradeSolverType(SolverParameter* solver_param);
    78 
    79 // Check for deprecations and upgrade the SolverParameter as needed.
    80 bool UpgradeSolverAsNeeded(const string& param_file, SolverParameter* param);
    81 
    82 // Read parameters from a file into a SolverParameter proto message.
    83 void ReadSolverParamsFromTextFileOrDie(const string& param_file,
    84  SolverParameter* param);
    85 
    86 } // namespace caffe
    87 
    88 #endif // CAFFE_UTIL_UPGRADE_PROTO_H_
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/doxygen/vision__layers_8hpp_source.html b/doxygen/vision__layers_8hpp_source.html new file mode 100644 index 00000000000..872230f8bf0 --- /dev/null +++ b/doxygen/vision__layers_8hpp_source.html @@ -0,0 +1,737 @@ + + + + + + +Caffe: include/caffe/vision_layers.hpp Source File + + + + + + + + + + +
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    Caffe +
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    + + +
    + +
    + + +
    +
    +
    +
    vision_layers.hpp
    +
    +
    +
    1 #ifndef CAFFE_VISION_LAYERS_HPP_
    +
    2 #define CAFFE_VISION_LAYERS_HPP_
    +
    3 
    +
    4 #include <string>
    +
    5 #include <utility>
    +
    6 #include <vector>
    +
    7 
    +
    8 #include "caffe/blob.hpp"
    +
    9 #include "caffe/common.hpp"
    +
    10 #include "caffe/common_layers.hpp"
    +
    11 #include "caffe/data_layers.hpp"
    +
    12 #include "caffe/layer.hpp"
    +
    13 #include "caffe/loss_layers.hpp"
    +
    14 #include "caffe/neuron_layers.hpp"
    +
    15 #include "caffe/proto/caffe.pb.h"
    +
    16 
    +
    17 namespace caffe {
    +
    18 
    +
    23 template <typename Dtype>
    +
    24 class BaseConvolutionLayer : public Layer<Dtype> {
    +
    25  public:
    +
    26  explicit BaseConvolutionLayer(const LayerParameter& param)
    +
    27  : Layer<Dtype>(param) {}
    +
    28  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    +
    29  const vector<Blob<Dtype>*>& top);
    +
    30  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    +
    31  const vector<Blob<Dtype>*>& top);
    +
    32 
    +
    33  virtual inline int MinBottomBlobs() const { return 1; }
    +
    34  virtual inline int MinTopBlobs() const { return 1; }
    +
    35  virtual inline bool EqualNumBottomTopBlobs() const { return true; }
    +
    36 
    +
    37  protected:
    +
    38  // Helper functions that abstract away the column buffer and gemm arguments.
    +
    39  // The last argument in forward_cpu_gemm is so that we can skip the im2col if
    +
    40  // we just called weight_cpu_gemm with the same input.
    +
    41  void forward_cpu_gemm(const Dtype* input, const Dtype* weights,
    +
    42  Dtype* output, bool skip_im2col = false);
    +
    43  void forward_cpu_bias(Dtype* output, const Dtype* bias);
    +
    44  void backward_cpu_gemm(const Dtype* input, const Dtype* weights,
    +
    45  Dtype* output);
    +
    46  void weight_cpu_gemm(const Dtype* input, const Dtype* output, Dtype*
    +
    47  weights);
    +
    48  void backward_cpu_bias(Dtype* bias, const Dtype* input);
    +
    49 
    +
    50 #ifndef CPU_ONLY
    +
    51  void forward_gpu_gemm(const Dtype* col_input, const Dtype* weights,
    +
    52  Dtype* output, bool skip_im2col = false);
    +
    53  void forward_gpu_bias(Dtype* output, const Dtype* bias);
    +
    54  void backward_gpu_gemm(const Dtype* input, const Dtype* weights,
    +
    55  Dtype* col_output);
    +
    56  void weight_gpu_gemm(const Dtype* col_input, const Dtype* output, Dtype*
    +
    57  weights);
    +
    58  void backward_gpu_bias(Dtype* bias, const Dtype* input);
    +
    59 #endif
    +
    60 
    +
    62  inline int input_shape(int i) {
    +
    63  return (*bottom_shape_)[channel_axis_ + i];
    +
    64  }
    +
    65  // reverse_dimensions should return true iff we are implementing deconv, so
    +
    66  // that conv helpers know which dimensions are which.
    +
    67  virtual bool reverse_dimensions() = 0;
    +
    68  // Compute height_out_ and width_out_ from other parameters.
    +
    69  virtual void compute_output_shape() = 0;
    +
    70 
    +
    72  Blob<int> kernel_shape_;
    +
    74  Blob<int> stride_;
    +
    76  Blob<int> pad_;
    +
    78  Blob<int> conv_input_shape_;
    +
    80  vector<int> col_buffer_shape_;
    +
    82  vector<int> output_shape_;
    +
    83  const vector<int>* bottom_shape_;
    +
    84 
    +
    85  int num_spatial_axes_;
    +
    86  int bottom_dim_;
    +
    87  int top_dim_;
    +
    88 
    +
    89  int channel_axis_;
    +
    90  int num_;
    +
    91  int channels_;
    +
    92  int group_;
    +
    93  int out_spatial_dim_;
    +
    94  int weight_offset_;
    +
    95  int num_output_;
    +
    96  bool bias_term_;
    +
    97  bool is_1x1_;
    +
    98  bool force_nd_im2col_;
    +
    99 
    +
    100  private:
    +
    101  // wrap im2col/col2im so we don't have to remember the (long) argument lists
    +
    102  inline void conv_im2col_cpu(const Dtype* data, Dtype* col_buff) {
    +
    103  if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
    +
    104  im2col_cpu(data, conv_in_channels_,
    +
    105  conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
    +
    106  kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
    +
    107  pad_.cpu_data()[0], pad_.cpu_data()[1],
    +
    108  stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff);
    +
    109  } else {
    +
    110  im2col_nd_cpu(data, num_spatial_axes_, conv_input_shape_.cpu_data(),
    +
    111  col_buffer_shape_.data(), kernel_shape_.cpu_data(),
    +
    112  pad_.cpu_data(), stride_.cpu_data(), col_buff);
    +
    113  }
    +
    114  }
    +
    115  inline void conv_col2im_cpu(const Dtype* col_buff, Dtype* data) {
    +
    116  if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
    +
    117  col2im_cpu(col_buff, conv_in_channels_,
    +
    118  conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
    +
    119  kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
    +
    120  pad_.cpu_data()[0], pad_.cpu_data()[1],
    +
    121  stride_.cpu_data()[0], stride_.cpu_data()[1], data);
    +
    122  } else {
    +
    123  col2im_nd_cpu(col_buff, num_spatial_axes_, conv_input_shape_.cpu_data(),
    +
    124  col_buffer_shape_.data(), kernel_shape_.cpu_data(),
    +
    125  pad_.cpu_data(), stride_.cpu_data(), data);
    +
    126  }
    +
    127  }
    +
    128 #ifndef CPU_ONLY
    +
    129  inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) {
    +
    130  if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
    +
    131  im2col_gpu(data, conv_in_channels_,
    +
    132  conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
    +
    133  kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
    +
    134  pad_.cpu_data()[0], pad_.cpu_data()[1],
    +
    135  stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff);
    +
    136  } else {
    +
    137  im2col_nd_gpu(data, num_spatial_axes_, num_kernels_im2col_,
    +
    138  conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
    +
    139  kernel_shape_.gpu_data(), pad_.gpu_data(),
    +
    140  stride_.gpu_data(), col_buff);
    +
    141  }
    +
    142  }
    +
    143  inline void conv_col2im_gpu(const Dtype* col_buff, Dtype* data) {
    +
    144  if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
    +
    145  col2im_gpu(col_buff, conv_in_channels_,
    +
    146  conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
    +
    147  kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
    +
    148  pad_.cpu_data()[0], pad_.cpu_data()[1],
    +
    149  stride_.cpu_data()[0], stride_.cpu_data()[1], data);
    +
    150  } else {
    +
    151  col2im_nd_gpu(col_buff, num_spatial_axes_, num_kernels_col2im_,
    +
    152  conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
    +
    153  kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(),
    +
    154  data);
    +
    155  }
    +
    156  }
    +
    157 #endif
    +
    158 
    +
    159  int num_kernels_im2col_;
    +
    160  int num_kernels_col2im_;
    +
    161  int conv_out_channels_;
    +
    162  int conv_in_channels_;
    +
    163  int conv_out_spatial_dim_;
    +
    164  int kernel_dim_;
    +
    165  int col_offset_;
    +
    166  int output_offset_;
    +
    167 
    +
    168  Blob<Dtype> col_buffer_;
    +
    169  Blob<Dtype> bias_multiplier_;
    +
    170 };
    +
    171 
    +
    188 template <typename Dtype>
    +
    189 class ConvolutionLayer : public BaseConvolutionLayer<Dtype> {
    +
    190  public:
    +
    219  explicit ConvolutionLayer(const LayerParameter& param)
    +
    220  : BaseConvolutionLayer<Dtype>(param) {}
    +
    221 
    +
    222  virtual inline const char* type() const { return "Convolution"; }
    +
    223 
    +
    224  protected:
    +
    225  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    +
    226  const vector<Blob<Dtype>*>& top);
    +
    227  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    228  const vector<Blob<Dtype>*>& top);
    +
    229  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    +
    230  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    231  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    232  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    233  virtual inline bool reverse_dimensions() { return false; }
    +
    234  virtual void compute_output_shape();
    +
    235 };
    +
    236 
    +
    251 template <typename Dtype>
    +
    252 class DeconvolutionLayer : public BaseConvolutionLayer<Dtype> {
    +
    253  public:
    +
    254  explicit DeconvolutionLayer(const LayerParameter& param)
    +
    255  : BaseConvolutionLayer<Dtype>(param) {}
    +
    256 
    +
    257  virtual inline const char* type() const { return "Deconvolution"; }
    +
    258 
    +
    259  protected:
    +
    260  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    +
    261  const vector<Blob<Dtype>*>& top);
    +
    262  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    263  const vector<Blob<Dtype>*>& top);
    +
    264  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    +
    265  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    266  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    267  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    268  virtual inline bool reverse_dimensions() { return true; }
    +
    269  virtual void compute_output_shape();
    +
    270 };
    +
    271 
    +
    272 #ifdef USE_CUDNN
    +
    273 /*
    +
    274  * @brief cuDNN implementation of ConvolutionLayer.
    +
    275  * Fallback to ConvolutionLayer for CPU mode.
    +
    276  *
    +
    277  * cuDNN accelerates convolution through forward kernels for filtering and bias
    +
    278  * plus backward kernels for the gradient w.r.t. the filters, biases, and
    +
    279  * inputs. Caffe + cuDNN further speeds up the computation through forward
    +
    280  * parallelism across groups and backward parallelism across gradients.
    +
    281  *
    +
    282  * The CUDNN engine does not have memory overhead for matrix buffers. For many
    +
    283  * input and filter regimes the CUDNN engine is faster than the CAFFE engine,
    +
    284  * but for fully-convolutional models and large inputs the CAFFE engine can be
    +
    285  * faster as long as it fits in memory.
    +
    286 */
    +
    287 template <typename Dtype>
    +
    288 class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
    +
    289  public:
    +
    290  explicit CuDNNConvolutionLayer(const LayerParameter& param)
    +
    291  : ConvolutionLayer<Dtype>(param), handles_setup_(false) {}
    +
    292  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    +
    293  const vector<Blob<Dtype>*>& top);
    +
    294  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    +
    295  const vector<Blob<Dtype>*>& top);
    +
    296  virtual ~CuDNNConvolutionLayer();
    +
    297 
    +
    298  protected:
    +
    299  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    300  const vector<Blob<Dtype>*>& top);
    +
    301  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    302  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    303 
    +
    304  bool handles_setup_;
    +
    305  cudnnHandle_t* handle_;
    +
    306  cudaStream_t* stream_;
    +
    307 
    +
    308  // algorithms for forward and backwards convolutions
    +
    309  cudnnConvolutionFwdAlgo_t *fwd_algo_;
    +
    310  cudnnConvolutionBwdFilterAlgo_t *bwd_filter_algo_;
    +
    311  cudnnConvolutionBwdDataAlgo_t *bwd_data_algo_;
    +
    312 
    +
    313  vector<cudnnTensorDescriptor_t> bottom_descs_, top_descs_;
    +
    314  cudnnTensorDescriptor_t bias_desc_;
    +
    315  cudnnFilterDescriptor_t filter_desc_;
    +
    316  vector<cudnnConvolutionDescriptor_t> conv_descs_;
    +
    317  int bottom_offset_, top_offset_, bias_offset_;
    +
    318 
    +
    319  size_t *workspace_fwd_sizes_;
    +
    320  size_t *workspace_bwd_data_sizes_;
    +
    321  size_t *workspace_bwd_filter_sizes_;
    +
    322  size_t workspaceSizeInBytes; // size of underlying storage
    +
    323  void *workspaceData; // underlying storage
    +
    324  void **workspace; // aliases into workspaceData
    +
    325 };
    +
    326 #endif
    +
    327 
    +
    335 template <typename Dtype>
    +
    336 class Im2colLayer : public Layer<Dtype> {
    +
    337  public:
    +
    338  explicit Im2colLayer(const LayerParameter& param)
    +
    339  : Layer<Dtype>(param) {}
    +
    340  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    +
    341  const vector<Blob<Dtype>*>& top);
    +
    342  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    +
    343  const vector<Blob<Dtype>*>& top);
    +
    344 
    +
    345  virtual inline const char* type() const { return "Im2col"; }
    +
    346  virtual inline int ExactNumBottomBlobs() const { return 1; }
    +
    347  virtual inline int ExactNumTopBlobs() const { return 1; }
    +
    348 
    +
    349  protected:
    +
    350  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    +
    351  const vector<Blob<Dtype>*>& top);
    +
    352  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    353  const vector<Blob<Dtype>*>& top);
    +
    354  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    +
    355  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    356  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    357  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    358 
    +
    360  Blob<int> kernel_shape_;
    +
    362  Blob<int> stride_;
    +
    364  Blob<int> pad_;
    +
    365 
    +
    366  int num_spatial_axes_;
    +
    367  int bottom_dim_;
    +
    368  int top_dim_;
    +
    369 
    +
    370  int channel_axis_;
    +
    371  int num_;
    +
    372  int channels_;
    +
    373 
    +
    374  bool force_nd_im2col_;
    +
    375 };
    +
    376 
    +
    377 // Forward declare PoolingLayer and SplitLayer for use in LRNLayer.
    +
    378 template <typename Dtype> class PoolingLayer;
    +
    379 template <typename Dtype> class SplitLayer;
    +
    380 
    +
    386 template <typename Dtype>
    +
    387 class LRNLayer : public Layer<Dtype> {
    +
    388  public:
    +
    389  explicit LRNLayer(const LayerParameter& param)
    +
    390  : Layer<Dtype>(param) {}
    +
    391  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    +
    392  const vector<Blob<Dtype>*>& top);
    +
    393  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    +
    394  const vector<Blob<Dtype>*>& top);
    +
    395 
    +
    396  virtual inline const char* type() const { return "LRN"; }
    +
    397  virtual inline int ExactNumBottomBlobs() const { return 1; }
    +
    398  virtual inline int ExactNumTopBlobs() const { return 1; }
    +
    399 
    +
    400  protected:
    +
    401  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    +
    402  const vector<Blob<Dtype>*>& top);
    +
    403  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    404  const vector<Blob<Dtype>*>& top);
    +
    405  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    +
    406  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    407  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    408  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    409 
    +
    410  virtual void CrossChannelForward_cpu(const vector<Blob<Dtype>*>& bottom,
    +
    411  const vector<Blob<Dtype>*>& top);
    +
    412  virtual void CrossChannelForward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    413  const vector<Blob<Dtype>*>& top);
    +
    414  virtual void WithinChannelForward(const vector<Blob<Dtype>*>& bottom,
    +
    415  const vector<Blob<Dtype>*>& top);
    +
    416  virtual void CrossChannelBackward_cpu(const vector<Blob<Dtype>*>& top,
    +
    417  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    418  virtual void CrossChannelBackward_gpu(const vector<Blob<Dtype>*>& top,
    +
    419  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    420  virtual void WithinChannelBackward(const vector<Blob<Dtype>*>& top,
    +
    421  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    422 
    +
    423  int size_;
    +
    424  int pre_pad_;
    +
    425  Dtype alpha_;
    +
    426  Dtype beta_;
    +
    427  Dtype k_;
    +
    428  int num_;
    +
    429  int channels_;
    +
    430  int height_;
    +
    431  int width_;
    +
    432 
    +
    433  // Fields used for normalization ACROSS_CHANNELS
    +
    434  // scale_ stores the intermediate summing results
    +
    435  Blob<Dtype> scale_;
    +
    436 
    +
    437  // Fields used for normalization WITHIN_CHANNEL
    +
    438  shared_ptr<SplitLayer<Dtype> > split_layer_;
    +
    439  vector<Blob<Dtype>*> split_top_vec_;
    +
    440  shared_ptr<PowerLayer<Dtype> > square_layer_;
    +
    441  Blob<Dtype> square_input_;
    +
    442  Blob<Dtype> square_output_;
    +
    443  vector<Blob<Dtype>*> square_bottom_vec_;
    +
    444  vector<Blob<Dtype>*> square_top_vec_;
    +
    445  shared_ptr<PoolingLayer<Dtype> > pool_layer_;
    +
    446  Blob<Dtype> pool_output_;
    +
    447  vector<Blob<Dtype>*> pool_top_vec_;
    +
    448  shared_ptr<PowerLayer<Dtype> > power_layer_;
    +
    449  Blob<Dtype> power_output_;
    +
    450  vector<Blob<Dtype>*> power_top_vec_;
    +
    451  shared_ptr<EltwiseLayer<Dtype> > product_layer_;
    +
    452  Blob<Dtype> product_input_;
    +
    453  vector<Blob<Dtype>*> product_bottom_vec_;
    +
    454 };
    +
    455 
    +
    456 #ifdef USE_CUDNN
    +
    457 
    +
    458 template <typename Dtype>
    +
    459 class CuDNNLRNLayer : public LRNLayer<Dtype> {
    +
    460  public:
    +
    461  explicit CuDNNLRNLayer(const LayerParameter& param)
    +
    462  : LRNLayer<Dtype>(param), handles_setup_(false) {}
    +
    463  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    +
    464  const vector<Blob<Dtype>*>& top);
    +
    465  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    +
    466  const vector<Blob<Dtype>*>& top);
    +
    467  virtual ~CuDNNLRNLayer();
    +
    468 
    +
    469  protected:
    +
    470  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    471  const vector<Blob<Dtype>*>& top);
    +
    472  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    473  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    474 
    +
    475  bool handles_setup_;
    +
    476  cudnnHandle_t handle_;
    +
    477  cudnnLRNDescriptor_t norm_desc_;
    +
    478  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
    +
    479 
    +
    480  int size_;
    +
    481  Dtype alpha_, beta_, k_;
    +
    482 };
    +
    483 
    +
    484 template <typename Dtype>
    +
    485 class CuDNNLCNLayer : public LRNLayer<Dtype> {
    +
    486  public:
    +
    487  explicit CuDNNLCNLayer(const LayerParameter& param)
    +
    488  : LRNLayer<Dtype>(param), handles_setup_(false), tempDataSize(0),
    +
    489  tempData1(NULL), tempData2(NULL) {}
    +
    490  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    +
    491  const vector<Blob<Dtype>*>& top);
    +
    492  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    +
    493  const vector<Blob<Dtype>*>& top);
    +
    494  virtual ~CuDNNLCNLayer();
    +
    495 
    +
    496  protected:
    +
    497  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    498  const vector<Blob<Dtype>*>& top);
    +
    499  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    500  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    501 
    +
    502  bool handles_setup_;
    +
    503  cudnnHandle_t handle_;
    +
    504  cudnnLRNDescriptor_t norm_desc_;
    +
    505  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
    +
    506 
    +
    507  int size_, pre_pad_;
    +
    508  Dtype alpha_, beta_, k_;
    +
    509 
    +
    510  size_t tempDataSize;
    +
    511  void *tempData1, *tempData2;
    +
    512 };
    +
    513 
    +
    514 #endif
    +
    515 
    +
    521 template <typename Dtype>
    +
    522 class PoolingLayer : public Layer<Dtype> {
    +
    523  public:
    +
    524  explicit PoolingLayer(const LayerParameter& param)
    +
    525  : Layer<Dtype>(param) {}
    +
    526  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    +
    527  const vector<Blob<Dtype>*>& top);
    +
    528  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    +
    529  const vector<Blob<Dtype>*>& top);
    +
    530 
    +
    531  virtual inline const char* type() const { return "Pooling"; }
    +
    532  virtual inline int ExactNumBottomBlobs() const { return 1; }
    +
    533  virtual inline int MinTopBlobs() const { return 1; }
    +
    534  // MAX POOL layers can output an extra top blob for the mask;
    +
    535  // others can only output the pooled inputs.
    +
    536  virtual inline int MaxTopBlobs() const {
    +
    537  return (this->layer_param_.pooling_param().pool() ==
    +
    538  PoolingParameter_PoolMethod_MAX) ? 2 : 1;
    +
    539  }
    +
    540 
    +
    541  protected:
    +
    542  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    +
    543  const vector<Blob<Dtype>*>& top);
    +
    544  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    545  const vector<Blob<Dtype>*>& top);
    +
    546  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    +
    547  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    548  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    549  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    550 
    +
    551  int kernel_h_, kernel_w_;
    +
    552  int stride_h_, stride_w_;
    +
    553  int pad_h_, pad_w_;
    +
    554  int channels_;
    +
    555  int height_, width_;
    +
    556  int pooled_height_, pooled_width_;
    +
    557  bool global_pooling_;
    +
    558  Blob<Dtype> rand_idx_;
    +
    559  Blob<int> max_idx_;
    +
    560 };
    +
    561 
    +
    562 #ifdef USE_CUDNN
    +
    563 /*
    +
    564  * @brief cuDNN implementation of PoolingLayer.
    +
    565  * Fallback to PoolingLayer for CPU mode.
    +
    566 */
    +
    567 template <typename Dtype>
    +
    568 class CuDNNPoolingLayer : public PoolingLayer<Dtype> {
    +
    569  public:
    +
    570  explicit CuDNNPoolingLayer(const LayerParameter& param)
    +
    571  : PoolingLayer<Dtype>(param), handles_setup_(false) {}
    +
    572  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    +
    573  const vector<Blob<Dtype>*>& top);
    +
    574  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    +
    575  const vector<Blob<Dtype>*>& top);
    +
    576  virtual ~CuDNNPoolingLayer();
    +
    577  // Currently, cuDNN does not support the extra top blob.
    +
    578  virtual inline int MinTopBlobs() const { return -1; }
    +
    579  virtual inline int ExactNumTopBlobs() const { return 1; }
    +
    580 
    +
    581  protected:
    +
    582  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    +
    583  const vector<Blob<Dtype>*>& top);
    +
    584  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    +
    585  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    586 
    +
    587  bool handles_setup_;
    +
    588  cudnnHandle_t handle_;
    +
    589  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
    +
    590  cudnnPoolingDescriptor_t pooling_desc_;
    +
    591  cudnnPoolingMode_t mode_;
    +
    592 };
    +
    593 #endif
    +
    594 
    +
    601 template <typename Dtype>
    +
    602 class SPPLayer : public Layer<Dtype> {
    +
    603  public:
    +
    604  explicit SPPLayer(const LayerParameter& param)
    +
    605  : Layer<Dtype>(param) {}
    +
    606  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    +
    607  const vector<Blob<Dtype>*>& top);
    +
    608  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    +
    609  const vector<Blob<Dtype>*>& top);
    +
    610 
    +
    611  virtual inline const char* type() const { return "SPP"; }
    +
    612  virtual inline int ExactNumBottomBlobs() const { return 1; }
    +
    613  virtual inline int ExactNumTopBlobs() const { return 1; }
    +
    614 
    +
    615  protected:
    +
    616  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    +
    617  const vector<Blob<Dtype>*>& top);
    +
    618  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    +
    619  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    +
    620  // calculates the kernel and stride dimensions for the pooling layer,
    +
    621  // returns a correctly configured LayerParameter for a PoolingLayer
    +
    622  virtual LayerParameter GetPoolingParam(const int pyramid_level,
    +
    623  const int bottom_h, const int bottom_w, const SPPParameter spp_param);
    +
    624 
    +
    625  int pyramid_height_;
    +
    626  int bottom_h_, bottom_w_;
    +
    627  int num_;
    +
    628  int channels_;
    +
    629  int kernel_h_, kernel_w_;
    +
    630  int pad_h_, pad_w_;
    +
    631  bool reshaped_first_time_;
    +
    632 
    +
    634  shared_ptr<SplitLayer<Dtype> > split_layer_;
    +
    636  vector<Blob<Dtype>*> split_top_vec_;
    +
    638  vector<vector<Blob<Dtype>*>*> pooling_bottom_vecs_;
    +
    640  vector<shared_ptr<PoolingLayer<Dtype> > > pooling_layers_;
    +
    642  vector<vector<Blob<Dtype>*>*> pooling_top_vecs_;
    +
    644  vector<Blob<Dtype>*> pooling_outputs_;
    +
    646  vector<FlattenLayer<Dtype>*> flatten_layers_;
    +
    648  vector<vector<Blob<Dtype>*>*> flatten_top_vecs_;
    +
    650  vector<Blob<Dtype>*> flatten_outputs_;
    +
    652  vector<Blob<Dtype>*> concat_bottom_vec_;
    +
    654  shared_ptr<ConcatLayer<Dtype> > concat_layer_;
    +
    655 };
    +
    656 
    +
    657 } // namespace caffe
    +
    658 
    +
    659 #endif // CAFFE_VISION_LAYERS_HPP_
    +
    caffe::BaseConvolutionLayer::kernel_shape_
    Blob< int > kernel_shape_
    The spatial dimensions of a filter kernel.
    Definition: vision_layers.hpp:72
    +
    caffe::LRNLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: lrn_layer.cpp:10
    +
    caffe::Im2colLayer
    A helper for image operations that rearranges image regions into column vectors. Used by ConvolutionL...
    Definition: vision_layers.hpp:336
    +
    caffe::PoolingLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::LRNLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: vision_layers.hpp:398
    +
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers could be called...
    Definition: blob.hpp:15
    +
    caffe::ConvolutionLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: vision_layers.hpp:222
    +
    caffe::PoolingLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::LRNLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::BaseConvolutionLayer::EqualNumBottomTopBlobs
    virtual bool EqualNumBottomTopBlobs() const
    Returns true if the layer requires an equal number of bottom and top blobs.
    Definition: vision_layers.hpp:35
    +
    caffe::Im2colLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: im2col_layer.cpp:95
    +
    caffe::PoolingLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: vision_layers.hpp:532
    +
    caffe::SPPLayer::pooling_layers_
    vector< shared_ptr< PoolingLayer< Dtype > > > pooling_layers_
    the internal Pooling layers of different kernel sizes
    Definition: vision_layers.hpp:640
    +
    caffe::PoolingLayer::MaxTopBlobs
    virtual int MaxTopBlobs() const
    Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required...
    Definition: vision_layers.hpp:536
    +
    caffe::ConvolutionLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: conv_layer.cpp:45
    +
    caffe::SPPLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: spp_layer.cpp:65
    +
    caffe::SPPLayer::flatten_top_vecs_
    vector< vector< Blob< Dtype > * > * > flatten_top_vecs_
    top vector holders used in call to the underlying FlattenLayer::Forward
    Definition: vision_layers.hpp:648
    +
    caffe::SPPLayer::pooling_top_vecs_
    vector< vector< Blob< Dtype > * > * > pooling_top_vecs_
    top vector holders used in call to the underlying PoolingLayer::Forward
    Definition: vision_layers.hpp:642
    +
    caffe::LRNLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: vision_layers.hpp:397
    +
    caffe::SPPLayer::split_top_vec_
    vector< Blob< Dtype > * > split_top_vec_
    top vector holder used in call to the underlying SplitLayer::Forward
    Definition: vision_layers.hpp:636
    +
    caffe::DeconvolutionLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: deconv_layer.cpp:27
    +
    caffe::SPPLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: vision_layers.hpp:612
    +
    caffe::BaseConvolutionLayer::input_shape
    int input_shape(int i)
    The spatial dimensions of the input.
    Definition: vision_layers.hpp:62
    +
    caffe::BaseConvolutionLayer::MinBottomBlobs
    virtual int MinBottomBlobs() const
    Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
    Definition: vision_layers.hpp:33
    +
    caffe::SPPLayer
    Does spatial pyramid pooling on the input image by taking the max, average, etc. within regions so th...
    Definition: vision_layers.hpp:602
    +
    caffe::Im2colLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::PoolingLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: vision_layers.hpp:531
    +
    caffe::Im2colLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::Im2colLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: vision_layers.hpp:345
    +
    caffe::SPPLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: spp_layer.cpp:188
    +
    caffe::Im2colLayer::pad_
    Blob< int > pad_
    The spatial dimensions of the padding.
    Definition: vision_layers.hpp:364
    +
    caffe::DeconvolutionLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: vision_layers.hpp:257
    +
    caffe::SPPLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: vision_layers.hpp:611
    +
    caffe::BaseConvolutionLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: base_conv_layer.cpp:173
    +
    caffe::PoolingLayer::MinTopBlobs
    virtual int MinTopBlobs() const
    Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
    Definition: vision_layers.hpp:533
    +
    caffe::ConvolutionLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::BaseConvolutionLayer
    Abstract base class that factors out the BLAS code common to ConvolutionLayer and DeconvolutionLayer...
    Definition: vision_layers.hpp:24
    +
    caffe::LRNLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: lrn_layer.cpp:166
    +
    caffe::Im2colLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: im2col_layer.cpp:11
    +
    caffe::SPPLayer::pooling_bottom_vecs_
    vector< vector< Blob< Dtype > * > * > pooling_bottom_vecs_
    bottom vector holder used in call to the underlying PoolingLayer::Forward
    Definition: vision_layers.hpp:638
    +
    caffe::SPPLayer::concat_bottom_vec_
    vector< Blob< Dtype > * > concat_bottom_vec_
    bottom vector holder used in call to the underlying ConcatLayer::Forward
    Definition: vision_layers.hpp:652
    +
    caffe::PoolingLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: pooling_layer.cpp:82
    +
    caffe::BaseConvolutionLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: base_conv_layer.cpp:13
    +
    caffe::ConvolutionLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: conv_layer.cpp:27
    +
    caffe::BaseConvolutionLayer::pad_
    Blob< int > pad_
    The spatial dimensions of the padding.
    Definition: vision_layers.hpp:76
    +
    caffe::ConvolutionLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::SPPLayer::flatten_layers_
    vector< FlattenLayer< Dtype > * > flatten_layers_
    the internal Flatten layers that the Pooling layers feed into
    Definition: vision_layers.hpp:646
    +
    caffe::PoolingLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: pooling_layer.cpp:17
    +
    caffe::SPPLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: vision_layers.hpp:613
    +
    caffe::LRNLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: lrn_layer.cpp:70
    +
    caffe::Im2colLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: im2col_layer.cpp:146
    +
    caffe::Layer::Layer
    Layer(const LayerParameter &param)
    Definition: layer.hpp:40
    +
    caffe::SPPLayer::flatten_outputs_
    vector< Blob< Dtype > * > flatten_outputs_
    flatten_outputs stores the outputs of the FlattenLayers
    Definition: vision_layers.hpp:650
    +
    caffe::LRNLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: vision_layers.hpp:396
    +
    caffe::BaseConvolutionLayer::col_buffer_shape_
    vector< int > col_buffer_shape_
    The spatial dimensions of the col_buffer.
    Definition: vision_layers.hpp:80
    +
    caffe::SPPLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: spp_layer.cpp:146
    +
    caffe::Im2colLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: vision_layers.hpp:346
    +
    caffe::LRNLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::ConvolutionLayer::ConvolutionLayer
    ConvolutionLayer(const LayerParameter &param)
    Definition: vision_layers.hpp:219
    +
    caffe::DeconvolutionLayer
    Convolve the input with a bank of learned filters, and (optionally) add biases, treating filters and ...
    Definition: vision_layers.hpp:252
    +
    caffe::Im2colLayer::kernel_shape_
    Blob< int > kernel_shape_
    The spatial dimensions of a filter kernel.
    Definition: vision_layers.hpp:360
    +
    caffe::Layer::layer_param_
    LayerParameter layer_param_
    Definition: layer.hpp:322
    +
    caffe::PoolingLayer
    Pools the input image by taking the max, average, etc. within regions.
    Definition: vision_layers.hpp:378
    +
    caffe::SplitLayer
    Creates a "split" path in the network by copying the bottom Blob into multiple top Blobs to be used b...
    Definition: common_layers.hpp:663
    +
    caffe::DeconvolutionLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    +
    caffe::DeconvolutionLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    +
    caffe::SPPLayer::concat_layer_
    shared_ptr< ConcatLayer< Dtype > > concat_layer_
    the internal Concat layers that the Flatten layers feed into
    Definition: vision_layers.hpp:654
    +
    caffe::BaseConvolutionLayer::output_shape_
    vector< int > output_shape_
    The spatial dimensions of the output.
    Definition: vision_layers.hpp:82
    +
    caffe::SPPLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: spp_layer.cpp:205
    +
    caffe::Im2colLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: im2col_layer.cpp:117
    +
    caffe::SPPLayer::split_layer_
    shared_ptr< SplitLayer< Dtype > > split_layer_
    the internal Split layer that feeds the pooling layers
    Definition: vision_layers.hpp:634
    +
    caffe::ConvolutionLayer
    Convolves the input image with a bank of learned filters, and (optionally) adds biases.
    Definition: vision_layers.hpp:189
    +
    caffe::BaseConvolutionLayer::conv_input_shape_
    Blob< int > conv_input_shape_
    The spatial dimensions of the convolution input.
    Definition: vision_layers.hpp:78
    +
    caffe::DeconvolutionLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: deconv_layer.cpp:45
    +
    caffe::PoolingLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: pooling_layer.cpp:233
    +
    caffe::PoolingLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: pooling_layer.cpp:131
    +
    caffe::Im2colLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: vision_layers.hpp:347
    +
    caffe::LRNLayer
    Normalize the input in a local region across or within feature maps.
    Definition: vision_layers.hpp:387
    +
    caffe::SPPLayer::pooling_outputs_
    vector< Blob< Dtype > * > pooling_outputs_
    pooling_outputs stores the outputs of the PoolingLayers
    Definition: vision_layers.hpp:644
    +
    caffe::Im2colLayer::stride_
    Blob< int > stride_
    The spatial dimensions of the stride.
    Definition: vision_layers.hpp:362
    +
    caffe::BaseConvolutionLayer::stride_
    Blob< int > stride_
    The spatial dimensions of the stride.
    Definition: vision_layers.hpp:74
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:25
    +
    caffe::BaseConvolutionLayer::MinTopBlobs
    virtual int MinTopBlobs() const
    Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
    Definition: vision_layers.hpp:34
    +
    caffe::LRNLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: lrn_layer.cpp:94
    +
    + +
    +Generated on Mon Oct 19 2015 15:25:08 for Caffe by   +doxygen + 1.8.9.1 +
    + + diff --git a/doxygen/window__data__layer_8hpp_source.html b/doxygen/window__data__layer_8hpp_source.html new file mode 100644 index 00000000000..915a7062295 --- /dev/null +++ b/doxygen/window__data__layer_8hpp_source.html @@ -0,0 +1,85 @@ + + + + + + + +Caffe: include/caffe/layers/window_data_layer.hpp Source File + + + + + + + + + +
    +
    + + + + + + +
    +
    Caffe +
    +
    +
    + + + + + + + + +
    +
    + + +
    + +
    + + +
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    +
    +
    window_data_layer.hpp
    +
    +
    +
    1 #ifndef CAFFE_WINDOW_DATA_LAYER_HPP_
    2 #define CAFFE_WINDOW_DATA_LAYER_HPP_
    3 
    4 #include <string>
    5 #include <utility>
    6 #include <vector>
    7 
    8 #include "caffe/blob.hpp"
    9 #include "caffe/data_transformer.hpp"
    10 #include "caffe/internal_thread.hpp"
    11 #include "caffe/layer.hpp"
    12 #include "caffe/layers/base_data_layer.hpp"
    13 #include "caffe/proto/caffe.pb.h"
    14 
    15 namespace caffe {
    16 
    23 template <typename Dtype>
    24 class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
    25  public:
    26  explicit WindowDataLayer(const LayerParameter& param)
    27  : BasePrefetchingDataLayer<Dtype>(param) {}
    28  virtual ~WindowDataLayer();
    29  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
    30  const vector<Blob<Dtype>*>& top);
    31 
    32  virtual inline const char* type() const { return "WindowData"; }
    33  virtual inline int ExactNumBottomBlobs() const { return 0; }
    34  virtual inline int ExactNumTopBlobs() const { return 2; }
    35 
    36  protected:
    37  virtual unsigned int PrefetchRand();
    38  virtual void load_batch(Batch<Dtype>* batch);
    39 
    40  shared_ptr<Caffe::RNG> prefetch_rng_;
    41  vector<std::pair<std::string, vector<int> > > image_database_;
    42  enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM };
    43  vector<vector<float> > fg_windows_;
    44  vector<vector<float> > bg_windows_;
    45  Blob<Dtype> data_mean_;
    46  vector<Dtype> mean_values_;
    47  bool has_mean_file_;
    48  bool has_mean_values_;
    49  bool cache_images_;
    50  vector<std::pair<std::string, Datum > > image_database_cache_;
    51 };
    52 
    53 } // namespace caffe
    54 
    55 #endif // CAFFE_WINDOW_DATA_LAYER_HPP_
    caffe::Batch
    Definition: base_data_layer.hpp:49
    +
    caffe::BasePrefetchingDataLayer
    Definition: base_data_layer.hpp:55
    +
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    caffe::WindowDataLayer
    Provides data to the Net from windows of images files, specified by a window data file...
    Definition: window_data_layer.hpp:24
    +
    caffe::WindowDataLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: window_data_layer.hpp:33
    +
    caffe::WindowDataLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: window_data_layer.hpp:32
    +
    caffe::WindowDataLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: window_data_layer.hpp:34
    +
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    +
    + +
    +Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +doxygen + 1.8.12 +
    + + diff --git a/gathered/examples/00-classification.ipynb b/gathered/examples/00-classification.ipynb new file mode 100644 index 00000000000..6cce2dc7d5d --- /dev/null +++ b/gathered/examples/00-classification.ipynb @@ -0,0 +1,842 @@ + + + + + + + + + Caffe | Image Classification and Filter Visualization + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Classification: Instant Recognition with Caffe\n", + "\n", + "In this example we'll classify an image with the bundled CaffeNet model (which is based on the network architecture of Krizhevsky et al. for ImageNet).\n", + "\n", + "We'll compare CPU and GPU modes and then dig into the model to inspect features and the output." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Setup\n", + "\n", + "* First, set up Python, `numpy`, and `matplotlib`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# set up Python environment: numpy for numerical routines, and matplotlib for plotting\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "# display plots in this notebook\n", + "%matplotlib inline\n", + "\n", + "# set display defaults\n", + "plt.rcParams['figure.figsize'] = (10, 10) # large images\n", + "plt.rcParams['image.interpolation'] = 'nearest' # don't interpolate: show square pixels\n", + "plt.rcParams['image.cmap'] = 'gray' # use grayscale output rather than a (potentially misleading) color heatmap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Load `caffe`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# The caffe module needs to be on the Python path;\n", + "# we'll add it here explicitly.\n", + "import sys\n", + "caffe_root = '../' # this file should be run from {caffe_root}/examples (otherwise change this line)\n", + "sys.path.insert(0, caffe_root + 'python')\n", + "\n", + "import caffe\n", + "# If you get \"No module named _caffe\", either you have not built pycaffe or you have the wrong path." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* If needed, download the reference model (\"CaffeNet\", a variant of AlexNet)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CaffeNet found.\n" + ] + } + ], + "source": [ + "import os\n", + "if os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n", + " print 'CaffeNet found.'\n", + "else:\n", + " print 'Downloading pre-trained CaffeNet model...'\n", + " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Load net and set up input preprocessing\n", + "\n", + "* Set Caffe to CPU mode and load the net from disk." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "caffe.set_mode_cpu()\n", + "\n", + "model_def = caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt'\n", + "model_weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n", + "\n", + "net = caffe.Net(model_def, # defines the structure of the model\n", + " model_weights, # contains the trained weights\n", + " caffe.TEST) # use test mode (e.g., don't perform dropout)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Set up input preprocessing. (We'll use Caffe's `caffe.io.Transformer` to do this, but this step is independent of other parts of Caffe, so any custom preprocessing code may be used).\n", + "\n", + " Our default CaffeNet is configured to take images in BGR format. Values are expected to start in the range [0, 255] and then have the mean ImageNet pixel value subtracted from them. In addition, the channel dimension is expected as the first (_outermost_) dimension.\n", + " \n", + " As matplotlib will load images with values in the range [0, 1] in RGB format with the channel as the _innermost_ dimension, we are arranging for the needed transformations here." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean-subtracted values: [('B', 104.0069879317889), ('G', 116.66876761696767), ('R', 122.6789143406786)]\n" + ] + } + ], + "source": [ + "# load the mean ImageNet image (as distributed with Caffe) for subtraction\n", + "mu = np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')\n", + "mu = mu.mean(1).mean(1) # average over pixels to obtain the mean (BGR) pixel values\n", + "print 'mean-subtracted values:', zip('BGR', mu)\n", + "\n", + "# create transformer for the input called 'data'\n", + "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n", + "\n", + "transformer.set_transpose('data', (2,0,1)) # move image channels to outermost dimension\n", + "transformer.set_mean('data', mu) # subtract the dataset-mean value in each channel\n", + "transformer.set_raw_scale('data', 255) # rescale from [0, 1] to [0, 255]\n", + "transformer.set_channel_swap('data', (2,1,0)) # swap channels from RGB to BGR" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. CPU classification\n", + "\n", + "* Now we're ready to perform classification. Even though we'll only classify one image, we'll set a batch size of 50 to demonstrate batching." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# set the size of the input (we can skip this if we're happy\n", + "# with the default; we can also change it later, e.g., for different batch sizes)\n", + "net.blobs['data'].reshape(50, # batch size\n", + " 3, # 3-channel (BGR) images\n", + " 227, 227) # image size is 227x227" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Load an image (that comes with Caffe) and perform the preprocessing we've set up." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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vAqVQbxvPBEJwHEd8Z1W22xNju7F/2Pn4crC/TAKKBefM4juKyeIIujtFkh+lMaFlclzN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w6H4LmXKSzzup33Bz5+8wlhecbNmlhypNnYsffcQww19/4aGX0xC9tmSOwIiMA6PXHcbHOgTy5Z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tOZ2GOkEfd/eMIXJ5/VRdu52rHkQSIIUR61t9XmNahA8ixFnJxmQfsrLSb3DM2bPJk4068z08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oxBRRmbs00dNMLX3vo6437HNKpj8/n9Gp9DwfQbQjhi24YwjEt763B9y7t/8SOeP7vGyj0C\nE3/2Y0XWLg+3ZBHaZk3jHZC4PihC9NPnNzzYrFh3Pa8/eEwjtzz75Ll+lmkifqPw1jca+s2G4TAs\naGQeI2NR7mDfQ26b5f7VOFbbU4fKn5rdrVNKxKxS7xBGxulIV5HDtvGqssoF46wqm2YjXlEUIyed\n82fb9cIPy6XgW0fbtLTrNWcPH0GjiHIaRg4MvLi+5P2PP+Ddzz/lo0+fMMydtrZnu+oYDnv82rMP\ne2JNJ+i6RqkUVnTO2lMsS0oRFxRVy7VFt/QPivJDBcEa4W4oOiWRxokkyq00UNGkiiwZR0FRYV/K\ngsjZnDBo18PWgPT5dXOb3xiDK/qOd/eSjMI1xlhKCYtrgHO6z0i2SzDzbEXhKsfSuoKUrIkWZua5\naeyJE4+ImjzPqItBsKLUCTWAiMzxxk4MwRgKOgZmNJ8vUAksFK8B0rGcVItGuUez8j3neCL3k1TI\nE/W7MNgaFwXH40TjtpQspGwUfa/3mLI+XxGNPzNilpgfiiC+ocuZnFssPWnOn80TqmTMkAOm5MUt\n3hRorJBNofGOxkVC5elKfS7WJpxvmPKAnYOJi9I9ConjkGk7Q9c/BmrrOhgoTtf/HKhySNq+IYZM\nTJFWGmy3osaBksvEKKf64hddX1ohZZ3qGE7Fi8PYluNxJCZqPt5JCgnaijJwF1P9QgH185lwJ4Uf\nULT95etCZKtVgEipbQmYHWcxuUa/6P/Fgq0bVNupt4dvhBxzfe089UtdIGxt750+3yK3r+20w17j\nO/Rn+jyMxNoCcsu9Y4062pYq82zcElkSlVG4cMlKSQsD0jmPbwTvQWyqk62wGJ43thYJogOTiLcz\nR0x9VxpR76cSDcXM8lJbiee6KExjXFyaSRkTjUqBo7C/Hmk7XaR9ZziOgWaIGBlonF9UTTOPJ+ZQ\n5d6nNq2RhLMW71bYDKZ4tWRA4eeSDY1vcFZVNu2s3ugsCW0V51Yo4k+ZgJXHFoIWs17SQmQsUcCk\nhRwunJSOJRfEqAeYI2Ead8q/kqSKRoMW17Z6kDEvxJlsClmiktTRBREglRrEXCw5Vg6V7ZYxrK8v\nuLYhDRPH47xBdVjnaJqGYzxQ0hfzzZzzeGtxtuF6d8ubb7xVv9/M1dUlb775OuvtCudbrl4qofz1\n9T1WfcPzZ5+wXnWIF65fKEfIuoYQC4fdSBwDwzHT+M3ybJytwzZr/tb5fV3A/sO//0N+8Oc/4tf+\n1i/xG9/523zz69/gg4+UbH6cEiCc33vENE3q4r7I0S1nmy2fP/kQbw2vPnrIeNDnMYTIze01F+f3\nwWSO+wPb7TmutmL61YbDMNJ3K8Kww5kGZ7VYmsaIXXlSPhCzusOfb7S1+cP/+H1eXu6w/j4f/vQJ\nf/aj91g/UF+nb/2Nb+Nbx6rXeJkQbvG1NbB7eeD5k8+5OUaevP+Utx/2vPP6O3qPh1s+/OQFsTS8\n9vordE1LW6XjQ0xYGhW1SCantNAVmq5lDBMU5Wq2XYOvY2aaDjX6RzgMe9q2p+1OG2lKGdd4tSJw\njjIrs0omxUxKkRjV2TuEumOI1TzM1rHZbLSFUy0zjrc7fvT+X/Luh3/J5/sX3Nxc4oBNTSmICL3v\n8X1hsAORQKxqQLteY+SOIKOKagAa42icJdMx5UTO1RUdKCVgsgp4CgWLX7iapiSMi5rPqvThpS3k\nKiVDrMVECDkvzy3HxO1wYExZxTqEhRhus9IacsnKZRSZ/dcxdS5qRzBRsl3iU8hGf5coF1JjVmZx\njqqpdb+uVgjz60pRbrBJGLGUdArHNSZjTaFpwErGpLJ41qmrO+Q7ysAYyklIZNyyReas/oxLW8zo\nfFMOr1kO4KDFnjHUsHvBGLdYHKRJ2I+Jbddq67QIJc2FjQqTklFOVOZ0H7p3K0fO4/B2Q1qI21mL\nQ5uwFPXgmqNlRJ36rTE0Tlj1DaHuF1MAKYa+FVpvaVpZ7GsQMAQwmUxkmOyi2tuuzmjbnpISrhis\nbVVVCBgR2s5ipkSKCWPcEleT7erneHZ/9fryCilvcWKWIsla1NfGOQ77iRDLMvm/SJQziw3C/DP9\nq7L89/PZfNZINdhyS/Zb21nER90UnW7KJwL7F1EvYwptqw91LIo2TWPBWp2wS7FkbfVOSvX01Z0+\ng7XEGmxZDGykqxlxLL4eEEEszvkT30v0/lKIFFODbr7ABctY2xJCoBSDrwO/bT1tK7Sd9tmZCfP1\nhCFZiz5r1YDSSF4I8kksJYtyrAwkA1IXN+N0UchFk8ULqcYU1MUmZlI0GGM1vX6ciyXh9vaWbtUr\nilQM9k7KfQgHUomavp7DMhGdc0j2UEolhMui7PCuq3l6tmYm3l0UlM+UKEgS9dCajUzFElOVE5dI\nSdPyOpMdxRg0OgGsuGUBm0btzTfOgylIY+fHqXw6Y5EkFZYyFJPvROgoYdaKpUjBpMhcZI1S6Jyj\nxyFBcMUtWccmZFwxqnIqBePcUjgfhwljAm3rWa1W5DzRtjWPK3fK0yuFfrMmZnj5UvlM9+6vcTlT\niHURcmwqabxpGjKJddczJsP5vQ0ffPQxoFlVm9WWTb/iuRU6Z7mtnB5nWyIQY8KWyhmbV5/pyB/8\nr/+SuPsnhOOe1gpTzb86HEf244RvNPutr+o1gG3fM+Uj675j5bfEeMAYLdyGca8IpFvx4uVTDIm2\nOcNVn6nDqAa7bee5SpHeX7DkRXY9tB4fPdMhcrG94Pnnarfx47/8CbZZ82d//D0+vrzhb/7Wr3M0\nWhBcDyObsuHq5oZnL56y2z3nbK3P++23vsHXfvkdRDyffPgJP/zRu4x1cHznV94hSOB2N9K+uGKz\n7lmv++V5l5KYpkkpI/YU1yNOaKWt3kc/l0hvVRMXQlAUoGRCLVycFbxXcnspGUmFWXKfq7q36VpS\niMQUFkWS73uMOFb9hq7fYqRRtR7w9JOf8ad/+qc8iVfsSmBVA7kn0UJy3W0AoVs7FeSg4cgAaRqh\na0hxUrVytqTZz2+2dzGFNJmamFo5p2I1LigqYmExC+JeTKNGnDW7bn5G82WNq7YxonNxXjMsZFmz\nCwP7KTBlg52VeSkvYd8Y9WCaaVcp6qEKowiMMafxnZNGXOHBVfTMLgdazQoUk7HOkdKkYiVU8GOK\nqsJLDJRosHOhiKlWOVH5wJWXNV/GGKx3NMXSTXIqlAAkk/KoBaE1QGQxtyaTkmJc8/ssN4kCA/os\nHSV7pAqJUs6M+0BDopSgHYN57UPD68VASoYkgaaOKTGGkDKUpHxC65efYUF8IJkjhsqjqvwpNWPV\n71v9tDTDFaCLDlMs3jm6xuI7S92e8I1aOsyh1eO4J5dn9T0TFxtH21nyNCk6eycCyFhL4zzZRExK\ni2ludi05nLiLv+j68sjm3mDFL0TmVJSgLcZireF4HAnTDP/OH7bKTwucJBTzVep/p1aetfbkCWK1\n7TMnT7ed0LQeW1txSpg7DSgkUwpMIdN4i3W1+rYB36hKLCcVaS6bvld0Ky7IgCwTw4rQ91tSyuz3\nRy0WZkuB1iLS07SNynwl68BCWzum6CKhC0xGZh8lW8BEirRkHMUEpKmoUmfxnal5Wloc2NIs72uL\nxcai1XdjcC2LU21p1IG95EjKGeM9d1uU1gklaojuFBK52goQhSkajiRaV9TbZarF2a5l8Inx7ICI\nQtGrPHtl6Z/HOKkdA5P6HIAmk7sOW/TEKDYt9g/ZCGJafTZZFLKfi1o82KA5dAaEQuRQx5MlJwdG\nsLIG02CkLvpmwCarzvCm0BRDqUTkTKE4Pd056XDekypcZbP+TmPVf6QQiPjFEd8QEbG6QEgluc5m\nno2jLR2r0tG1gomFUlFH3zgyIz4LicwhRXUcBrrW6XM73LAbR4oxuIoQbDZnmEZRP/FCW+JSoJjR\ncP+110kh4Ypl2/esVrohDlMgDyOrzrC/OfK1197kT76rnk8//ehD/tYv/w1e/cZbvJx2bC+fs3ui\naFXJIM7inTrkxxgWpPJbb7/B//EHf8D3/92f8OjBA2JOvLjUdlmYDsThiDeFpu2InIxxnS9cXb2k\nbVuKcQxBKEVVYq4Y2m7NEPcM44G+70nW09YyM6TCenNGTJFcYHKG1UYnTjQejhHvGiJHxDiefvB5\nnaf3+JPvf5fv/ewjfuv3f5+u73n9XJGs3/nNf4DnIf/b//5/8t4HH/Gz5y/YPdfi9P33X/Cd3/y7\nvPLogrfffMAvfeMf8h/+8N8C8L2Pn/L3/6tvYcXQthf4VbMIO0yBOB0pYcCJrW0gWZ6pFUcRtEgy\nGb9kN/r6fZ8QccOsWhOmacKmRNetQO5691i8ZMRGCpbUNAthXopgamC37TroevbPtT357sc/4Nn+\nKYe4w9qGjd9gtgU3C8lK5jiNdL5j3Z0jItwEfe1uumTTrglZGJKuE3PCQpYRZzMkr47gjIsdQUeP\nTXoIRmBMh+XAU4zH09JnS2tU6LCs0QLaMip417D1blEhYhKP3Dnn8ZzdMBBT4FAL0MPxSEm6AhUi\n0cTl0CaoP6Fki7derVzqQpyLQ0S9sowpiEt6Tygi1dgGjPrYtdYTk87DGAWKJxVLmGYjzpOKT0xC\nTnG8SzHsXU+MGTGBtl0BFn9UtZ1+x1ktIwRMnMCxUDrIhVLGSoh3pOiZ2xRivYZLZ1vXpzUi85jS\nwvh2OLIyG8Rm2vo602Tm/NfcDIqoz0h91oSIUjLZZ5CC2NmzTxMwWrsi/n/svcvPLWmW3vVb671E\n7L2/68mTJ2+Vt8rqrqpudTdWg7sB01wli7axLCFGFgz5U5CYMUJCHiAmiBkTBAgh2RhjsCzRxi7b\nZXd3XTNPnjx5rt9l7x3x3hisN2KfQq4eeFIMMqRUKvXlt7+9d0S8sd61nuf3yEyWtBjnIWeqQvEm\nUWhZDVUDjCF3tJAiG0WCX/++YIDd1eFH4XhcpCeJOSeud2+zjRtqLifhvyRKmaF3yFQcroNhg3jm\nf76PbT1+dR0p5/DOr26p0Y2UWDjosVfFgf2+s11mgxDWPg+G0whP5Bc1UfbvNyylvdDx3hGiY+is\npDiIUc1j7L9XqUtsQX89H5SSGzlPhNhdXYM59kJQcrMLZNnRWXfKHn6uak+m7o6Izk4ScWy3AzHG\nVSOUq4E6N5sNw1AQdyKQ12q7E7CK2nldXTbTlGhr78J0Z4uNf4HROb9Yd7UDULuOwAk1d5KtBEKQ\n1b3RmlCLUIu1gI0b1y9+NX1Aa5lG63bpZWempGQXuQu6AhD7JyGlxu3d0Ypnlzj2vxe9MqXJWtSt\nGN+oO/paa4Z3UG/wvDdbhT0ME4zhQlWkk5ipEXURJwecNEQSrRcuVEdrHcrag6ddRxHUYs7QWqo9\nrKrg+o0vNQG2IEYX+ki2j4pxlNmcOHZKKtLy6QZXs123ZfyVjPUCUMQx94eMEaD9iT9VjaulU6aK\nBRHX7trMdOREt/POqXJzb5qd+/sDF9dXIEIpiRA9V1vTCLXc2B/uGOOGn/3sZ3zoPBePrCNVWyWl\nwuXV2+wPz/nsww+56kHAX/z8MZ99+B0uNme89/YD5ukD5m4vfPrVK8JmxKlB/Gqt67jsvfev+Oij\nb/PtT7/Lq5c3/Pinn/PlF7ZL9AzstuekuZLmxmG/562H5/08JegxS61UUs1cdg1UKwlKwTvhretr\nbu/vjevUyeaP3n+PUgwXcX39FsPVW7hOL89Twonj/n7PELY8/fIpT15aR+rpq9f80Q9+yHc++w28\nKC+f3/DX/tp/DBh09Mc/fs2PHj/h86dPKcjaJfjpT38KdeZf+p3f4ONPPkRC5nf/jb8AwN//O3+H\nH3155Le//xmb7Y7ryx3z3v5eSXuoiX1RpFUrGstp9OOcZz4aimIYxlUfGUIgpcScEs6NjOO4jnVT\nOhJCIMaIakO9Y0ljOhytUyHVAsxFWWGGIQRcHNBxhxu34DxPn30FwM3d6y5r2JKaMae4q6Sb3gVT\nIWwGqtrGa8ax7d2quTZKmVAHpQqto16W+7vVjDqH94Krb0glajH2GgLe0VDTtQDRVXxzxDAyeCW4\nut7D3gfQgtOAd4JoWEd7qsLh6PCDI25Gck2Mk6378f6eaZqYponcJuY8o30dEnG2vxYLld4M41pk\nlWKTAOdtnRdfcN2N57VCnfvabSHCdL1tbeZSVBFaVXI+TQVMaVKRYMBMFwS/PK6bpSZoUkSdaWqD\ne+OZURAvNI6mAfZYnBig0f5GztXWONcoKy1/YIgjTjxCoJWBcdj191oQtc2RtMw8s3Z4nXSOV63m\nnsuJ0iuiEB1ahSSFoB60rCPY2mxzINJswzi9kbzh1YCazb772pS8wEEDQGMYwIWMuMLQZ3siNg1a\nTKrOOQ79vbw6vCTMFtGl52+zGYdT7dAapWQrkJ3VDOvzS4RY/+xS6VdWSA1DwPuwis0FxzhGYoxM\n00zw+1UguXcH5rlSi+N4nKn1JEZebZdvxMYsC4oVUAGnDR/Ah7LuWgysaREqqoq6RipLgrR0enZd\nsZNLazQOlm0UvFBLp+D2LJ/aiuHpxdqyEk64BcMbnNACo4/4jirI5VRwSBfgL63oVs0eumpfcluZ\nNzQrdlSXnZiuuYPDEBg39jkXAbKqXyGgIla55+IQCVaoLO3frrtS6R09ZS0InCu0YlTgUvpY8v/D\n7lL15AThbDzRlmvBFc90SOQtOBpzzymTYaCVRpomUs3gK7mlN64WRdVZW78Yq8Re0/4dw0jWGRWH\nVLuhajLlgxKA2Vg065i175462M1JIyyCcnHklrrF2NNUIJ/eS2sGjvXOIxrWvEDFkRuUfMp99L6u\n3QXDTDSqFAozooZRAAg+4LLRjwWF2pi7bsXjiEANjjRNfafaNXIpMU+JXDMhOM4urzizeojDfuJw\nnG3k1wSn8XQdIBwPM2e7C46HiR/8oz/iy+c2vvvo0++xHd+mycw4jlww8Zf/3X8bgL/+X/+3/OQn\n/5Tvfv/Xubi44OzsJd/+7GPAHqxfPntCbTMX51vOz8959513APj0o4d8+tlnvH655/HjJ9zd7bl9\ntdDLr3nnnXcYYsxKtBcAACAASURBVCTPiagBt9KNj+w2G+qcSCX/wmh+3ttIKx8mpvnIg6trNIRl\nlcXHwHR7z/Fw4PLiAue3pLSsCwEnlU3cIE356Y9+uhKV/+4/+CdcPfqQb3/71/j69gt+9OPH/Gf/\n+X9hn+PT7/PDP/lT/uE//gGPHz/D+8xZ76gfjjP/4Af/mIvzHc4Lw9k53/7sN+z3fuPP8Y9/8nN+\n/fu/xVvDNfNcCc4KQpcT1Stj80i2EcjyoJHWICdamhm8Jzi/IgX2+zvu9rcEP+C82GiwFxmLoHua\njkxpAf0uXSvBu0DtuABpkLq1228iw8UVbfsAzs6tiO0PqKvLh0hQHj/7nCevnuM2Hg2O8z7aHJyJ\n2vdlsvtzN+BmK9BchcIR56tFVum8rqdOiomTa0UGYZ4dQwcdt1KsQHNicVpV1k3y4FxHsxjxPDiI\ny8+CJ45LTJNpXNtiXvEODQO1eXLOzEWJnbG13W457I/c7Q/M84HD8ZZpWhTHtoH1YiP86Dyud3+D\n39Ba14/6hoaGX/SmUkjJ9EgiFSkZ16NsYhNSF3sHP1KzkcPt+o14H5E2QW8W+P7sqsX0mINzpJw7\nu07X+1ucZ8oH03Sqx2tbCzuRZq+nxmoSLatOmWbaJMHTqgOJqxh7HAacmFSg5kzTtmrrRArbMFKo\nHPYzmyGYman/PfHG2aI/oxfkg8J6fpxTELd+3y1n69CqUlPGB9YmiPNAM0yQ87YudIIPrSebLNE6\ntVZ8z3gaRTnOB17vnxJ9xfm3VilMmytIopHM9FDbeo2qa8ThzwZyfoM/+Ob45vjm+Ob45vjm+Ob4\n5vgXPH5lHSkf/QpnAxvRhWA7zhijuXeWrZmzdtt0zHTN7Sq4rrXRWul6KPvZm2JzyzurxCiEWFfy\nt/MWWOy8Rbc4MT0QmJAaogUnVgMMLsJgyPjgyZ41rmEVODuDZooz9EHJjbZ2VhrqbReYU6XW42qd\njy7igyV1a3futbqQxFk/S5rVIJZrIKa+4fLoAki3aCh618k1age3IWXVZakzHVSM0XaoybRagOX0\nqfWgTIR/ctG1ZtlHrVVyrj3Go/8MsffWxX5TqYxnvUPUGiqOQSM1FYqqkWvtRaHvcFLNXY/Qu03e\nmXFI1RyXLa2ZgKkVUpkZNxdE9air/Vx1TZJNzI08LzP0zqFKxau5SwTBiVst0KqN5mrXDyzGhUWz\n4pAW8SjqRusGvIFpUBGaeiiyjmd0Uaprse9OE04aTdNKVBYRwjgwlgGdCmlOlGUMiUAtpMNEKplW\n6imuqGMiWmvkOXPUaXXZnF9edQNCIwTb3bZOTZ4mI7U//vwxj955i0bk/rWNBH/6pz/ivW8JkxwY\nQ0PKzO98x9x+/+Ff/Uv8d//T/8DsE9/7+CO++2vf4csnBvk8H7c8eueaVI5cnF2y2W159NBCgj/6\n5FPmeebx45/x5VdPefLkicVBAB9/9AmffvwBXguKOUAP9z37TROH/XHtNp+fn6+OxU2MaM3cvLgn\nBiU6z+78iqnvoOfjzO7sinQ4sJ8yu0tHmk9jVh8q3g88+fHPSKXywx/+CIB/9Cef8wf/3r9D3Dqm\np/e8ePWcv/l3/77di/5/Zhw8rSR2waOy5fMn1snbjJ6rB2/xxeMnfPjh+4Sx8dWXTwD44L2PePns\nOS/v7/jkk094+ewrLhYtYzqQj3fmcKuVuZVVOJySdSA3mw0hnEGdmTsRvbXGZtyZOaBWUrbxERjc\nMyXLaQtug9OIW+KvuoaT7kATV9dxIQ5kd467ehtipM2mPQMYd1t+8uWP2O/3DE3xKfPg8ppjdwre\nHl/hknAmgewc6kamdt8v/UxrAXGFJZy9dXCqqKEZBGcdCn0DtdJp6MGZgUNbIPQOYPSDjW9aQrQx\nxsjQ9a9OLWtOnOJUDK2gC+LAo96E1LV4Ys6kpbPtPEPcstkkjsc7jtOW/f3U75kJihHGo3OGoen3\nthHZnQFJPagvK97BxrNd7C8JkqOUxel4RFqi5kKuhnkoXRtas9KcTTREWjf3LCgZgVaJbmMqgVkY\nhpGh54XmbCPieY609oJWCiH2bpUqqaauI6q02tiMvYueHanZvVFyNQB0/xjBFYbR41tAUeZaOMx2\nftO05zAdkGAi9ikVwiKpdc3Go652hyVr1SFqUWzOAVLMcNbZz847anO02UanMbp1ra1lMRxpd4Ke\nDF8aHNoczg206qhSyXUJFrf3Li1wf39Ayysue6JD7d056myjb05TLzPi//90tDcOAcERl9GILJZY\nMceFbMHbDdV07m49oTYxCnpa1F9iVsWi60LyZiFVazJLb1zGe/ZbfomHcc0KLFgDQQs2YxXtbXHX\nTuK1OlvrMDpy0l9wCaoKlYyXXgT60xhuyUGyMaIJB6WexmwL0Vu9aSVOUXtqI6sGk6oFZOopgFIV\ngnOkOa24B7BWqRWKFv5ZF8G6nP6mLTTemCCyiPU5WV6zaaAWxglgIsw2U5vFatTKqj0qmf7/2bw8\nTQcaNorYbHZsh8joBiSDRFmz2NLcUG+RD6gVoMPYk8U9ZjdeXJlGuervpVBKskJaI4IJ/O3zFVRK\ndwEZhyv65TxVasoI/lRErZ+v9tl/Wb/PlezeGoozG7ZEoh8sagdMzKqK9HZ4mQvD4NZgzyZ9ZOss\nyqcKa4t7no+UWtFWcPRrZ5kJt0ZKBcXce7meiOgxRmorNhIfz2kia3xOKYlxNP2fItSU1/GlGwe8\nOI7zzItnT/n004953kXTh5s7bl495/rRQE4HNiEg1RbM3/tz3yXrnv/1b/xt/vjuwHd+8zM++JbF\noKh3XL/3AKVytt3x8O131kzAJ1/d8vzZCz7/2dd88fOfc79/xW99zyJifu9f+V2ury6Y5j25jLRa\nOB6WBfqOWg+WQBD1lJMJHKYjMSibsx1aKzlnog/IkmSfK34E5zypNsLZOSwOpGOlzZWSjjz98ivu\nDjN/6+/9PfscccNhTqQ2WfhpmYh9HZpbYk7OEB9aefHqS37vz/8eAH/9v/ovubl5xV/5D/59Hj9+\nwreHcw7d3HB3fsPFgy0/evwzPvv2p5ydXVD3VmS9fvWMdHjGvL+npGTC8CUotz888nzobibB9wig\n8/NztuOOw/Ge4+G+b/iWyCEHorZJCp4ly61fUITooZnGSrQydF3Z9uISxpEavLmLObGZBpSLuGH3\n7vvMeeJ+f0RdQbuT6pA93gVCCOxzJuWJIdjrplJwUWnNAqaHYcC5Bccw0xhxatpMQwt0Zp/amG4I\nHpFC1LaYCJFm5gsbY3ZTjCzJBRYHNgweJ57gA7WLpoMO5OypxUxGOTh8l1aE2igZvM8ggguRGHps\n1pzI6WCZbq3inBVvdm8LITiid4QITfMqt7D1aDAuVHBoaByO/frOrW9UWTerpW+S82GmZmHcCkil\nSiV1jdBuO/RnpDPMgwq1lPX8j+NI8BF1b3HMI6W+NCQA9GdORTThvJLnuo4EhziiOOZJaK7gXF35\nek6FMQ4ELNotSsP1jckUG3eHe5sCOsjJiPR2LmxjHqRZ6oOTdVyo4nq6Rpfo6NojoNZGTsmcm9GT\ncqCsCRMjKsMaM/amW1PVo9IRDeqpqVJ0QcJAiBt8c5SUmI43HLy95u7sgpyLaXCbYWsWbpf0z/Fn\nHb86jZT6nmy9PPisu1JtFI4TKNLx7Zwj7CkVct+t5XXXlhEGSu3ASWEV7IkIueyp1aFqF+wi51ly\n9oYghNCoRVe9lmLOwQZrsbUmbysgNkP1g9lkVwslDSeNaZp6lXyy5Jb+0F/el/Zkd1hE5NXS0Z03\n3cCik8Ctbo1x8KRZV/cV0qNcRHoUxAkEZ8HHi3bKQ7NFeck5sotZqaUSmu0+fH/QBB/ItfYFoX+2\nLrrMNZm1mQSiqIZup+3FYuvxP/VALZnaH/oP332fYRM7KFSIzUP//BVz5aAZcreuLl235glIF3/W\nHqWwdHIMrtfqhPhg31N7w3lJRaVZsRnCqZj1AZTeoai9aOnnKXfwXvM413r21WkC7kTx6vBqWXJx\nkV5gBaBzirhIqtkgoXHR8p10CKKN4svqanMefBZqzsylmJtksWtLgGI6mZISDocPi7bKkWtlmiYO\naWaMp9t5HLfUNndeUbFqui8MZU6UdrRImeT4+vETLi+vAUg397z46nNuX3zJJIHt+SUfvP9x/4yJ\nv/j7v8sHl2/xN//2/8kPf/AP0dGE6OPFFdvths0wMt0c+cmf/Jj7e3tgfPnkFc+f3TDtD3z46JLf\n/70/4Ld/83ftXFSY719Sa+Xl8xdcnZ+x3S1dzBkRGKNHVDgcDiuMdL+/43A8sh0iQYRxe87d4cjW\ndbGEa9w+f87ZxSVThpoqbtN3mBtPeX3g+Zcvmavwg3/yz/jTL81F+P3f+n12uw33r++5e33EI7iu\nPQoyWJEfPOoi98cbPvvsU/u9732H1hq/8zt/js9/8hM+ev9Ttg97+HKPtnr96objceL9d9/lWE1s\n/qoUW/Ny5u7115RW2ewWga8lzKl6bu+PXF09IPaugza1h8HeYlNynqk9/skNZ93MkpjSEZFTruNm\nu2UMW0QdKpW5qTlcgVYa0swpVlvrAvB+3d5PXEtkL43kZqY6Mb++4Xz3EIB3theUJiQtHCt4cZxF\nMw3kUpjZ26IuFevOv/E3e7adtGI29+5YLc1E7F6dOYXbSmPANbAGayNEQV22HFaguUbzQhy8hfS6\ngHaGWHODdXubI+eZqSixu4drU/bzgdYjbPwU8a4HNMZEq9bVbZ13tZiAvPOMcejrjNikYY346tpU\nDKSc5Yj2h/cwBO5nRy4zuSpzztRe1JWUoNpGOI7GVFo284JtnqSpoQdKo6lnM9r3fba7RprxyIbh\nW/b8dNYBbHIklBuO+ZZMtuidctKAbrcXtDrjfccV9PU0umiTE2/nitbY9hw+H6BK43Z/SwqFUU+s\nw6RCiFbYVinUUNeiw3ntut1iUW3ltIGcpoxoI4YAweEnpSz4ms45VAmgfVrSFoyBsbxas+umeoeI\nnd+cqy33dcYP1uWekq1RbnKMw45SErWJRcIsCIsAS67gLzt+ZYWUCczEHBYstk8BDzlVynwa72w2\nI7VWjvNExhhF0rsStZqt0TlHbUsrdHHtZdTZg1EdhCgr/sB7GCKECM4XC6hdSOrVMAqyPmBPnR4A\npNgTQAtNlNJHJq2pjcyadJdheZMdSi6LaND3i+gNkbi3Tomq/gK9XGRYnXVttMUzL8G0aSYXE4Q7\ntwQeLy18KyAsL9Ch4q3g6sWiC0qau7W2s7DaSpwVHA4EihRKTaSycDRsDOeCIr7ifCV30WFr9IWy\nmMCQxrbTu88vdrjgzMkoYh3IRTyJmMujFmrJBKdrmrc2q34FR66dFN8/o3oHtVDqhGsRxa8dQFlH\nmBVXrUhcBOwijeDEGFC19vd9Kuhrbp3fJTapXMM57fOHTmj2zhH9UkQruYG2aN2xpkQvp1Gr68aE\nZuDVxX0KIM1GyIfUyE2QUpFeZG2kEZrSSiFNRgJeBO7HacLAPA7fidhLcS6t0kpjajYqpmSOPRQx\nTRmphXEI1Oq4vb1lc24Pmo8+fpc//fHPef16Igk8f/2c+6N1Vj768FOmPbz37hl/6S/+eR5/+Zyf\nfWnuu9v7PXdfv+Z+ThxvJ0qpnJ9ZQfD9D67QDx/y6Ucf8r1f+4QQhNe3dj3d3808vLjkfg83t895\n/vWXBG8jwXEIHO4mDlS8s1b7fr/v58Ko/LUWK8idUlXXc5z3me3mDFFlsxupx+NKW1YJ3L14xcuX\nr7m5n/nx508pHR0g0dICBh+twGjK2N1+BSE16Xl0iRAf8kd/9A/X+1tE+IO/8G/y3/zxn6BB2U89\nu/JO2YVtf4A2pnTP+aV9N+nwkGdf3oMLNGe5ckvn7O72HlTY7c6t8M+ZFy9slHp3t2cYjMlWa+G4\nP6yd5OBmsnpaS31Mr4Rx6fzLyswZtxtcbeu4TFWpc6bOGfWO11895vEXf2zvM9/TXCXlPXWe2TS1\njTBL4oOyz4lD3nOoM9U5Wt/Gx00kz3cEP9I0UepESvadOh1tzZKKozCIMna+XCoZJxh+xjVKLevf\nC2HDODobdfZClze67aUU5txwY8QPkdC7Y6UpWsH5ASTijpVSF55dZlg34YHkoaTebT86Sk340QTM\nraYTy7DZiMkHmzY471cDUi7deSi2kWklr2t7xiYPToRDStR6SiaoxXI1pykThtbdh/Yxa5ttkiID\ntSibTUDayHZz3b+bAcVSCUo54v0AK75HGFUZZs/d/pbiHLm79ub0ms244+xswzwbDNUv5O/gaHU2\nY4AaM2rhlkEhBCEGIWPut7IYYiq0uXR3aHfRL/iDzthCLGNVyfgFmyA2zWmlUhNI8LRerdSSKCwy\nDt+ff8vz2SOq1NQ1QFJwaoWU+oAUm3o00kk6BMzprnPHggUrt0Zti4tf1+7ULzt+hRExrndN7L8X\n7ROYSl68EnW5oWZUhWEIzMV0A2Hb27haORwmcxk0uqPvjYeo2hddKXgvhGg/C772QsOcVUrB90Jq\nPiRS7rZzFQTtQbdQymQzby2oM6hhY2ljKqVkREaLO3lDP2SRL4mmDW3WoVoqZXV93LfMe51f3V4q\nETCmk3OBcTwRwWvLzMdKKq0zptppJNjn64aXqOTUdWSrm8Ie8o0eHi1+XVBdh4aKCKkWUp5OHTm3\ndMmW4rSR5m4RdjY2aEVpVbh+cM31Ww8A6yGNowFKS8q0dmJ4OOcpKRO90rLVSSuU0OYSNHKH3J1G\ngopQexxBrRV8pvaHpZSGVhvZnkav9pq5Tvb5vaflxXn4hnPSqnFytUWp9u9b1fhgipGmnXOEXiil\nZmNTh40CY/SouB4lAuoSpSbTOgiQwfWfueKAyDzZ+w9eif1c+BqJKKXMhA4fXNxnx+ORViubjT1Q\nJavBRAEE1NtGwwUrRJcde8E6bQXh7m5Pzkfu58N6nq4utpxtztDBUSXy6s6o5z/4f/4B737rY64v\nBi7GyubjR3z8LXPmpWPhbn/LfEz2gPduhdieeQFXaSUxH54z7QvHvS1SQXc4CQzecb4buLy8YDpY\nAZJmCOrIcwEPYxzWaz+lhNaMG2wMVSuMF+cc9saZqimzu7rmcJio9wfO33qELhqxY2K/3zOVzOGY\n0XBO8FZI3t1PzPPM1fU5b797xU+fjusooqZCUGeQ16qcX2x5/sr+3v/2f/xfPLg45+c/+Zy3H76D\n3wTEL8402N8e2bnIEJWZPWdv9Sib/Ii716+Z04HLq4c0gdevTK92OMycn58ZRb01Xrx+tVLfYzzy\n6NGIRfkI29352lEvtcI0o6GQSiaGzQm2m03igDRUAoM7TZFrtcDtWiuUxlgSY7++r6+vcBthvjlS\naYQmHOuRu2Ln6lCEGTikI41mY9jeIdtEh6uRuTu4cDOlLN/NQIgLSboxH+raxfU4QkcXWDh5xveR\nUctK02JwxQoxOrT/vZRmxAWmoGx8RGNYalOiehBPbYEKbFXX+2miQe/qqhaC9yy9moJSlyd5qdDc\n2iHyztFaxgUHreM6+rNEpSANtBns0yCeC5sqoSREM9oU13SNXDKndsW3Yg7fcOrEQyGlA5uzK6QO\nbIYLgt9S8yKHsOdZrcqggblktP/NEAZDJuCYjhlKoro+vix3HOfn7MYPcG5DSXV9ljpnm5kMFnJf\n8omR5wpki82qJdOWERnQtJKYCc0cyUFPrj2kUuqM8+aK90FMvwdoj5xpKqTWMCDp4qo3EEStM0Kf\nyPRTo2JdNO3a1jYvyCRWaGtrNkXyetro1mrTq3FzZmo9VeN80XES7o1Gyj/n+NUBOQdnVl+/5HhB\na7MxI1AcjbhoaHLCB9MQuclTU6L2qnbcDDhVjoeZnCu5yLpoOFVEZlqzblfl1LGRsIiKbbwjuq6X\nqKu9o1QRCTZ2bMsMZ6a1hPeB4mtv+9mP1niTakVU6VZM+1kmF8sHKvVIbJGwfXPU5Ggd67AZwtpu\n9+o7P0VQiYTmVgieUrnXxt3B4h6MtH668J2zi9drAK92w7IUKJWmHmUgOk/wYm1UbE6dqnU6LI/N\n03o3o2KdjlJyTyT3axckOMiqlFq5uBx59Oghu+1ZP99WXKSUaOqQpqeOlJjOzEugxZEmeS1+nNq8\nXsppAWqdidKkmbC9VNslFU/rO9baY3FyBXGOlk/tdm1C7REM86p3X4BupmsqxQjmFVbbcYxvMFfE\n9AatL66hWpfNKtlqGip3tu6+fARSNriqC9Ra2R9sbPAiN1w1wF1wxqnxdelkeXz1di42Z6exLjAO\nF+Sc8eMOUW+awCXfrgqxL/bpOJtSY7TCZlOUY4OSZ3x0DGHk7saKpX/6T/6Y7//Gb3NxNkIpPHjw\ngNc7K7K++Po5X/zkj3m9u+LRw3fYDbKCB892jQ/feRfvI6UKt/cGygS7f/P+yDwfqVmIYcf1uGwi\nAsfjDfu712wvt7x4+YRNz4x7/533mQ577vd3jBu7Vul6vJcvb/EK1+fvsjmLZPEc0kyn9RKG0bpX\n1REGjzJz/+KlfTclUvHMc0Zz5d2rh1zurJB69fIJLb9NOkauzt/mwfUjdl9b1+3rpy+7lbzR6sRu\niBwm05b94R/+IaMXPvnwXb7//e/iwsmgcX1xzQ9//iMuHz5k4yNXjzbopuNUdjveffc9tE2Uw8hh\nvl2FrZeXl2y3G47HzDEdccOW3cbuJ/U2mnLOMgpbZeXuqVZEjzgJaE8vWDMvnTOwbM7MLhGdp3aa\ntB4cjBk2dlMM3nHdxeY3t0KuykYuqB5yu6GUzHSwom8vExojIo4glaYnlKSqYRRqTkboD56wcpYS\nzg1d7ycU7k+Fa614UUJ0VDdb/MqiIarWcXPNOsRRtRtKgCK4BlFGEEf1SugWeG2KjzuqOkqxLpQu\no9sxcJSOhZgnUi20vvFWX2ilGrnfCa0WE6AD6Ma0plot6kt03Vy7ahv/VroOVcXyZDHTi3ZN8BA8\naSprR12rR3yy52GplJIYxlNMFzpR64HtcIGXHY1hZWxJS6hTNsPIITe8Ktq7MpYzuAeM7K3a8B0Z\nk0XJc2PSPWe7SyaE1gubVmcSzda90pMwenFKtTFqYwYnVO9tNAbUmg3zEwS0nopsoNQjziUQi/cR\n53A9GktVyUkoNeCc0tpxfT4byb7ZSLPN1BzX5A11lskoGm0T6dvaHaRWvLe1VyVbo0Xs+m5lpLZM\nmowZ6YQ1HghO9/IvO77BH3xzfHN8c3xzfHN8c3xzfHP8Cx6/so5Uq0oIwxvWeUvktv/O1DeE2uod\nvnpCKIzR0cpMXXRJ4pHRRnDTXJA5UxenXK14F03536waX+ylvpk1Fik0hCpQZUEOGEW3NvDSba59\n/u6d7+OFhA9KKMIq4k2O1pTWSv+nrdlBZvk1wZsqPcuu75K82mzYynJKa2ziUu4LDd+zywa0ulXr\nEeMINBPU55kQWWms3lckGAlcarY4k07tBhDZoGK0dSMAn2JgSi32HfZOkVPPONhOOJWZYz6amzB4\nCoUORiYmT+pBzm89vOL8Yrvuok7vywjllhLeu1w1U3CkWvEu0N6gyjYarZpeSTD0w5uuzFYyCXs/\nlsPVz33r2rhmWjuvsgJX7XQJNMu7QhZdXO+A9eR4EWHwfhX7l9518t4RnAUWly7CN2qzna3oPa1G\nVG10Yq9bcaGRq6NgcNGhv9dj80gVxrhjg4dUmLr+IITGVBVaMVuuNvJkf/OYM+fn54y7HYc84xnW\n8Y7tQm38SZ2Z5gPHw9zvma4vE4umH0fPWRd4j+OWp18/5sMPPiKMgZe3L1ft3FvnO966uOLZq1sO\ndy/I90Ja8OxSObt9aWPGMFJwnJ2b+NXHMw73rznsb5kPR6RZtAnA8bhnf39Da5kvf/4TLi7Oef/X\nfg2Ap0+/JOfMdjMQJTDtZzZbu9jOL7Yc726Z05GhRHbnZ+xz4dg7dqVkYv88Z2db5umwtvi/fvIV\n98miaLZnjqvrDdvuPnvy+oZXz29599HbbM4KH35wzssX5ky8f3Xk7nBjbjQRM14sAEWZ+PCTT/ng\nw09pOqAaiIPtdg8lMeaZT997h1YSl1cf4LEOIFm5vnoLJzMvXzwnTpHcw1Lz8Y67u9e4uOPq6gEq\nntQzvxxCLZ5cGmm2hIKxO12RHloeIuIs6HcZmcx5wlMY4xZNFfGCdsinGzaUGAzP8eoFd88+J/fP\nF9S6Wn4z4KqnJaVV7RElkGfr0m/GHakkUqus5G/JNJ9x+0RqDe8cm01fa5vH68gQzsxxFTy+f8Zp\nOlBSQrwlKoDrGZVmppmnjLRCiMqUZlwHHJvguyGuGuJEfG8Jg7SABu3mE5gmQbb9uzkc8SNsAiCK\nm4UpnNb2XBvBNUSKmY0W2Uw1lIaI0bp9qCiLy1uRUik1IV5xNaxZFFU8qnuc2HfipSF5AQMLInOX\nj/Rc0HWaYM+Oyg2FAecGFF0d6a2HXJfS9bgygPRulVbrXkvXGIlb81elBUouTMcbhmFg3Ow47rv+\nVayblNIRLxkfTsHMqsrgHaKRUMxlvESjzSUjbtFtm+a0LiMc6e7QlsjFjAWLpGUcI9k1kkL1ii9u\nNeeYVgu8RmoRmzytDjvpE5xq64zXNfNRfFvd8irSgaoLEmWCLEx1osxm2lreZyf6/JnHr6yQWkTX\nOZ9EzEpjLsWEXXKySboaUK04TXivxEHJ/cIouYCACzB07c7yhdfSoDm8WgtVG+sFHpwiUoBCbQUn\n8Y307ABzXRdeKz4Wu66AYvNxtXEaC0q+LbEqiZoztSeX22ta8VRKw4sn18LcZ/OhJssvV4d6uwna\noh9ST6veRnUa8BLXYqgkE2Y3iajeoa4sRjhCgBgtjqAUQVsxqnrXZXnddfeg4Q68Gymt5831c9AK\niFpcz9Ial1Qtq66LpV2U9bvxpbFpwma44urqzPg3b0TWmBZLUW/p3MvYs2ahlYbzAT94apnXGbt4\nQbNhJ1orr53ThgAAIABJREFU/Ubosu/SyCVRy7zGCtWFL5Zsdi9utswprStuwhalxdUo4NxKSa8U\nGs4Eou4X756T1spibRaGk/1eRb232I3uynQOFhe0b97GqVLJuUERAtbGHqODHJF9JU2ThZz2h9Bh\nqr2V7ympQlVS18mIH5gKtHlis9sS/GbVnmhvR5daiHFg5/1quza92kA6TkzHPa9e3bPb2XsZBqGW\nmS+//DnvfutDDlNeHZRn2w1nw8hZaLgw4Mctt0f7e5vtGdP9LWeXI9fvvs+wueL6yhxdt6/v+Pzn\nP2Ke9uR0oNXKWR9RvTw853C4YZoSY/R89zufcX9nuqNnX31FHAcudluLraiyRko5sXiYw34ixEzc\nFgYR9n09SQczZQTnKHVGnayjmP3dPbk5tpdvIRcbfue3z/jiqy8A+O//x7/Jy6/vuPsoM+XXXF9f\n8+2Pe8GbJ37y8wP3d0dUAilXdjt7P5/8+ve5enDJbrfj4cN3GeLAe+9ZAfan//THvHu15dc++Ra7\nsxEftycjzfUDXn/1IyYyFxdnHCbPzdHWr2m+4erqghA3pJQ4lsMpQkMid3c3cAchjmy3Z+v16YNF\nG1W8rSsi6/gdZ1kNU5oRGYjeIb34ruMGt42k21fkV89pxwMp9fM77LgEXtzNxOi54oxN2XLXC9ez\nzRHcSHORY6nMdXHVwdwKSWG7CyRVJMIQrMgMElAZ8G4khJEgkZBsE3EIgeP+jqYVtMfx6BLJVBF6\nDJcrVC2n+7s1Ui3kVhkJwEjtDnCcEp3du+KUQSrS1++KQkpIruy2nllMtwqQnRJzD4evhSqnPL3W\nJnI2A0orUMuM7wWYa4WqlUYfA+JXU0BKCafKMHhKanjvGIZT7Iy6RvAFldmift4wPJWSaCFQ5Z65\nPEWZ2Axm0iizZy6mQfUaKdXo8WCOYdFE8ANt08iZHmxso71Yba29u3vG+VklxL5LLglU0dbIJVNq\n6/FcdERPZXAKITJJYu7ZgaUuxiFdTTuNRYvru4i+WqOjVVpZxtMzvmfqVcHW065pMbF/RtqIiu9u\n7K7TBaQ5KtmSOVojdAJ9KYXSTH5TCbjm1/ckWlaZT8U25ctaM8+pu7d/+fErK6Smec84jmtXJuXZ\nuETNuiHNtdOHlMJqZ+/QxQX5L6ImXhb6gnCKOjGZSUM0Y8mNb1yMUgFzzqkzN1joQsMGuIyJzFVA\nTnAuC6tUajPHgcRTLlpOZeVKqQLuFxkXiLmManeKLUiBWm2xq7XrqpyuQkY0oDjEOTwOxNFax0LU\nSm2RcSPsxnPmdGPxC4CXTFDL6iqqdtM6Magl4MQCYp0zmyicOCSpzWi3Xpso3OJ1wPRJpQZqtdgX\nqdBrM0Yy2+GM3XZgt7kmxgHvl6iEjLoMLeK96w6VaflazG2piyDw5KD0PtKqkMvBduHUXyheck85\nL2VLKstiBTWJccXUirbaJpouyeLd5Vmw8OJmwkR7zQZSUUv8pJS8ittbNVCbOmgld2hcL2pbMRGk\nU7woUgTnPLJoU7wJUVs+UNtELceVXaV6RWkG9Bu8MATTVwFEGfBzoRwtlyrnzOV6Lfag7+4wrDR8\n72TOx8n0H1h3ptV52RYiKPOUqKWw2Qxsrq6ZD7YxefFiz+4sMPjM4f6W8wdvc3PTuTelcn88ME0T\nPid2TvnsE0MjbHeXHI9HLs6vmIrZ2+eukbp9+Zz716/Is6UrOrGgXoA6H7h//YJhGPj2J5/QSuLZ\nU2MsbceBYYjUPOO3G2qauX3ZrxlpTMfEZrR7Z39za53TpZNbC/N04Lgf8IPDh4Gha4+0Nuqk1E1k\ntxnYCvxHf/Wv9F+b+V/+xv/O5mLLxx9/ytXb53z2O5a7c9deMm4cX331FXf7ezbDyPvvvw/AWw+v\nefDgiqurB5xfXLO7eMDXHchZX9/y+3/wb+GYuP7gW+jlxs4nUHbQPEQUjaCzdU8BzjZbapvIrXB/\nPCDqCb39uz9YJul2u2W3O2Mct6sjt7VmWhgXCDH+ArCQJssui3E8M9F0t6o3oB325Fcvubt9TSiN\n2B8mguk2yzTjUya3wOwTm26YyGXHIR+Z8t54aA3SIrZ3SqlKbqYpDXFk8HZ9B92hXnDOuq8+7ohd\nND3MnoMfuN3fUmU2ltrCkVKFGDgeC/OU8bGgXXflaqPlZp1XNSfvsmmN0TGXTFPBB9NmLew1BiM6\n1zr3rg9s+3qZXSEn8K4wz0rJHte7mCVN1HpvExWO1Hyk6XKvFRyZpoY7UdfWSB513a3mKiEq6vLa\nkRFxqNugklCdTVcki+FnwrtoWtw8o36Glmh9I6zOMuR8cNSaQTltTF1DPGipaKmdKdaF6G7LEMxx\nnrKSp7zqZksW1M19WjNTayb3QHrLePXWAGkOiXri8rVMkWQ6VgqzQFzMWaF1t7m3Z5w0WncFCEcq\nh/7sN9PVWki1mZKbubSzAnXFE6hCST3XVMx8tgZ9O3PNtmZFuKiunLRaLZqpIVAN0hzjicu2xOH8\nsuNXVkiJFOZ0IMqSLl3IpTLnRK4F3/SEI2gF1YS6hA+d1L1QXrO5uNRZ+9H3zg7Yl1haWaFdIrzR\nZTjZGbUDNJdEcu8a2UlngFixtVrgO0PKCqa27izBblrvhVIW4nRdg4BrNVusuVNyf71FiJ4MLqZC\nzkZ3jj3jimq7SfFAtvZ2XOBFCLAlzRDdllxGKvf9zdzjvbWGtVQC5jZaFmKqEnRDCLHnBc643pFr\nOjLPM1UsZ7CRVyuoBTo24iC44qjl5IrwDkYX2URHXICgYfl+EjCwZCtJ7z4CzGlmZjIgah0QF3HL\nCLI6XPC4atRdKx77udMjjZlcMnOezJ0my/fdaKnBlEBAXGY69puhIwHSrNAMb+D7guFdIOfSbeV1\nLT76Lxqri9a/l7J2a7wArSI9xLqJuSjXawMF2RLVU8VAf74X9Vs9w+uOWAZcUaSWdbcXVcjt2Gnm\nmVQyx3kpJqxVnlKiVsf27ES6j8OGOFZaKRz2N8zTkVaXvMZKcI5x8AR/ovADXJxfc3m1pWnj+esb\ninN8+IGRzW9e3RLDwNnFOc45ppy4fWlCbCkJ3Miz50+5u7sjxkjp5zDt70mHF9y8fMHl+Q5wfN0F\n3HM6cn6xY7fbUmriyZPHa5c6esc4bHHdGr2f99Q+aoox0kQ5zBPXqhwO96SUVkpzoxoZvCQOt3fc\n3nzFqPbwrvPEvD9ycXmNVHj29Vc8eses4//pf/KHfPj+OX/r7/6Qr34kDMNnXL9r9+Jvfvd7fPzu\nzJOnjzmmPdtx5PLSiqyry0e8/dZDQhS+evGSn/7JHzM9NXH7v/5bv00YEsO7I5fvPaIipD6ene+e\nsRsd0z4yHV4zH/andchHE+OnShxgSmUFNqqPzClT90frDsbGrlv81TlymgxM2de+uMIjLSTdy0AV\nE+UOcdcv0kg97Il55iwqJfm1oypYp3+jDtEAUZiKo0/nSU7J5UgiM/SO+mIKqU4IxQqKGpU4boi6\n6/fbSIhiG4DicOoYtvazzbhjMyZojvv9K1I5rLKGQB/P5cKUE/OcVgyNusgQA60GgwU3z+KdV4k0\nDNjaNDOnslruTfA+2yaxF4OtjxJHddSgpJlewCToMhHRbF1vbOxXq9DKIu43R1rwSquNkvZ4v2Bm\nGiV0l6QUwmBdToB0rIj2Qqiz8E7PrEVq0igloTIhbs80L7zBS7x3lNonDHWidnelZeXd0iRbUHCt\nS80DCN7Z8yCGQJrt/wcYopoEph0Rijmsy4JqmHtIckOl4URoqzTDsBEtp164WCcIsGc1HsfQwccN\nWex3sjPJCkcqCe2OdrBuXdNMqwnnI1p17cRDNpB2H/u1dhLMO20d0i143/D+DTPYrEDBR985huYO\ntHN4crT/suNXVkg557oD6aQxoalhA9psY7HFyS2YtdyDq+Ar3Ylnl5Zraq3ftFjQeyFllRBRAqoF\n5ywSBuhdFmufGlW1rdWvw9mDrBrSXqlrCKM9eG2mjcgKeVxes1XpxZdYobUUbtKnlV6gCDZaOsEz\nrYv1RkCxX4pIw32qM4hcrW7Vl+gQybnPi0OAwa2t79Ic6D1IomoDo0OsbWVqRNpI8BtSTebk6Z9/\nO46ICHO6R+hxMEsNolhCPXv7ULDuooIbGZwStBJcxYeyzu1p1XhPGimlUgWOvYV/d7g1XICOWAv4\n5DLy3rpT2m+w2rIFmPaT0cSAh3M+EotfeSLW8avkZOniLirLBZWL2cBrkc7VyYbPAEQjTXvh65wV\nwn0xCa0YNNA5VBspTbhFV9cahQQydldI6zTgxZKcjdybG4VgxXjvoLU8g3jyZPylmhK5f6e3s42J\nKUorFnuz2fTX7Iue7zEh3nsWMJ0Ldk1lJvvuNEJnLEVv48IY7YFzd3fHfraFFl8Ik+dwnMg18/LV\nT3n9yorzj7/1Ic+fP2OeDlye7XA68PKLzwG4unzAuDvj2csXlHQg+lNy/HgR2UThnbevOBwm9tNx\nfSTcHQ9cX1/z9YtnFmwt7o3IksbL1y+5vDhD0tydagtqw7PZ7sg58/XXXxGDQ9/kTHnTWN7f3nHl\nrnj59TPmO+sQ5arszs8Z/C25OjbxQJn6mL0E/vIf/Gt8+sF7/N8/+BOePfkxXz+1MdSn3/mQ9z8a\n2Z6NIJlBPak/FDbbK1JyHI5Hnj7+irBP/KvfNa3XB1dbLh80vvVbH0IMtNZ6IAqEeeLw+jXzfOTu\n5objdN8DV0HiBuccZToyJeM91TUo1sC8x6N1uF3wq6ZDVaHWPrb3NupbrPoxWMczF4vI3uxoHXIq\nrSB5Yp7ukGliLoVj71TWnEhpwtcjW2c8pqoDvsdkTfM9zXmESHGO5lmLvtaOBN8gFJqOwMnpOwwe\n1BHdDnUbaptWZ3EYBgv/vTJyd5rqGp+Ty2TPBefQqtTmSHnh65lbN6fGPFfcxtZbgNIaTgNQ6RP+\nNSWjttQ3vAK1kEpZ0y5EKy57qtoIL3plWrAviyu8axgbnlr6ho4exdKTGdQ16uL+X5IpghAHoSRH\nGbpGqmXQYhretiQ5LBto08aJHHDamOZK0aW3YhurBS0gKD4ISO/Wp5lc7/GxF44VSumOPh+RGmhi\nCQrOybqPVDFGXUFso6NvdPGrhdg775FarZHQ38vog63ZmKQl6EBc9Go0avI0NeCuSMP3gsVkIBta\nOUDb0zis+INWDTFTmgGZbYLDekirxqjqk50l+q3VijpvlPk3or3sWov4UrvT3jrei0Pb+3FNYPll\nx6+skEIqrdZ1ji5VkIq1sstxLaIAnI9WlUvBu0ZzrFTZQgXx+CxkyUaFXgoUHXEx9PZmpSlr67CJ\nGtPJe+vEtGKvBSDFbMPFFmMVUL8IsWeQmcbc07NPxZm1Uo2eqtotmstL9ggArw5RZ+PD3sIPzqJK\nqNAo5DRR++7SqWdOe2MTSSTGYd1dpZRIybLUVD1IRDttNhfPnJXS7kAz3tvoznfCr7QNrfS2P47S\n4lowiGu4mmn5aPbSVli6K606cIp3FsnSmPrCBDFEojcyu+oNLm6h7zxLFhsxyb6/vyNzf3jneWIc\nbETqNJiOaokRUICI02APldrWc2/xJ2rFzCIWX24QOXWTagXJsu4wUuq5is3yld7MEkQKQUzb5tX1\nxa8/hACK9FiKSi1q11a/DlUCIXbKsQxIG9aCwSjNDSRxnF4xTTfreOsOZatXnMUHuOqseOzrXm3K\ndnNGq5njfTZaWL85Uk1dXG6bgTTNv2BzPx4nbm5umOeJIURc70ZGVzjc33B/e8d0PKK+MQx2Dqe0\n5+YOdsNIyYnL86t1t/fF469499E77PcHfvrFE0opuD6evt2bbT+lwvXFOa9ef804mtYppHPSXN7Q\nb7nV5r09O+d+P1Oa8vjpM/KceOvaujyPNo/sutdIazCnwu7MBOz7yR64FxcXzNOelDO+lbWQrjmT\n54TzDWnGoHr68kt7P8NoGquHZ8af8raoA2xC4Djd8v7Dkbf/wvd4+mzin/3MEAef//hPucl3vP3w\nPYZh4OvDK+auERucjfpu757z6GLLd7/zAbu+iXjw4SW/+S9/H0SYnj3m5vY50wsr6s7Ta473e26O\ne2rNFrm0dkGbAR39gAuFlhOs3XZ4+PARITimdOD29vWawxdj5Gy3Y7fb2XUtpiiya7/HboQRDRF3\ndg6dpP7/svcmy5JsWZrWt3ajjZmdxpvrt4suMzIyozIkK5EqeAUmPABzhDFzGPEENWFeiPASiCDM\ngAGIUFKJQJUUWZkR3LgRt3X305mZqu5mMVhb1U5ARoLkJGrgGnJFQtzdzjFTU9269lr///357nvS\n0wNOlFIrmg3VAhhqwJlO04XIUhayVlwbz9v65Cidb53shLR7w1dPjDuScxQHiSN11TrFHU5HvIvs\ne0fWfiuWg+8bU22gLDPLeeI8GW4hBI9o09asHZs2wkkpkZcJTTNzPhPrnrgx1CqLJrxmSqlEH1at\n9bpQoGWBOqF1oZYGK60Dy3KkZpCqdH5Aor1wmk5G4XeZqsm+s1X8rBlxF72tk2A6W+x9d72QnXW6\n9od+A02LzJb44E2OkkvG1WYW0bZRxJOymauqHnFtgzmXE1o9pVS6LuJid8EYkHG+Q1QRjXQB8orT\ncQXNgHZ4H/DFUTat00JKgX7XMfQ7cn4msdCFWpO9KTF96YojEPUseSaGZmwSh1unFG2yAxXnIiJ1\nQxc559tGt6eLdjbXjFCb5mhbtwuow/sV7+Ao0uxMIvjoqWvebTG+lWOFp10MbUhFfMDp5Vmw9UA0\nIfXvL5U+4A8+HB+OD8eH48Px4fhwfDj+gccfrCMVgidp2gCDRoM13ZSKkutyCTAsC6KuVdOV2EFo\nrdO5AuqoIkQf0MiWGefE7L/Fn0EiXcdlLCaJkie6bh2xuY1grYD3PTUoNUNVj1vBi3Gg6olcrL1s\n77vtNrzNU1f8gnPhmUUUajbtVIwRJLE2T7QKWtfQ5trcae11XsglUZZE7A503bB1uQzI5+ldpIsj\nuZqGDEBl4LR0zGkV5S/4OOCdpV172aHV0rq996QSjOcPZH0EmQnBUUo0hIOsxF0bN3rfN42S28jI\n1r0znZgPZwge0XF7Xa2ZabHsr+P5YRsXVk1Q2ijNBUC22bxzmPgvCAHHknXrnK0iWu8sykP1GXTT\nt121FgNwlkTZkP/FRpla0To0SNtK950JocMH3yj18uzzKaHzjcJfrGvWvou+GxAXTbCrypIWSxFq\ngsUaEqUWimYL8nSFc+vOzbUwzYn3xyNRrtnHPTfNZdRFbyLdaoHJJVfm1slbR8jWhVpJ9qvgGNO5\n1UqMHd0wbGG4czqjEug6aZ2riYcH+5kffTRaVqIIIXTsdrsNIHh6euR8deAf/3v/hP/zr/8GZeH6\n2q6nu7s7bq9vKUWZz0eGwy3zbB03lxURx+nRbNXnZSa1vwvDSMoLaEfsPCJn5iYMrgXGbiClyu3h\nGomeU9OHxc4+zzQtDN1IyhOlPLOdG22Rx6NlNFaEYddMGprR9MT56S05BuDSye3HAy/f7PDvvuLr\nr77ixx9f8ZPPjd7+1Xfv+eWvvuB4PjM/Fh7u7/js1SsAdv1AWRI//PglL18ceHHV86Of/hCAH//8\nxxAH9Pt39G7BPXxLvf8WgO9Pj5zvHzktZ3rfgYucWwRUHzy73d5uvTFQZaI2ga9Z9xOJwnQyiOAK\nMh2GweJ0zjOx6xnHfhsXqlOIHTIO+GGkituiPgKFsYukUqjjgegyY7uflumJnBNeLKZKq0Ct25hG\nXU9KJ7xTA/NWxW0aqY7sHFV6MjPKifN01+6ba14cPqKLESkzIoG+OcVsLDmQQkJefYbD8823vwbg\nPL9rgvAAzn6frCFONVOLCbBtzZkJYwPAOkE1oySiSuumNMF8SgRpUWMlE+rCtK6JGBZGq6NW11IL\nVp1XJOUTeZnIVGo5bWDg4JTOVZwzp5vWHjZ4ZKVqopMOHSIpKaV1TyqKJUJ5xPVorZTmaEv1TJAd\nJQWLmREhp4nY1hpHtOZQ69DVIjbVYTUiKM587ECxsav9ZKqHkhM+ekLsSGtmYGkgS/X0cUd0shHh\nSxUyBW3dU+cDMbT8wjJT6olaJ8QLKnXrRnsfwGmLUoug/Yah0Tb2zCW39zzgV0EeZ6omnB8aAqfb\nANbibJRdsge1jN01BNua1cVo60vCu3GLDnLOUXMECTb1aJ3EdtJ+Z3T4dx1/wNFee4A3x0jOGXGF\nSkY1oa5sD6maaS2/2rJ5hLpa0T2AQDb2g5du09PYw9XEvyE6Ylfpw6WV50NvwRkpEfzKlQBhMNy+\nmitLVDdrrWg0Zof3oMa9WgWQPjhysmwlaqXWi0vQiMJ2I4fg8Z3g3RqE7LbwYdSjVHK7aTo6RBy5\nJs7zO1twaOM58VswZ9VCP3T4sAogO4jXdMlxnoVcn3BuR2hRGOgIauJ7FeNwLCvLNTk0N3eanSrq\niu53QBuFeQc81624hPpoZGYXURJajZejEsiNvOs8lHliWSsplOwmCrPxrIib0UBw+FCJ7JiXB0Nm\n1LXgLcQYgExNGSRt8u5atbHDAhVbxNYCN6ngNEJxiDOMxKodK0WJIRKjBWOmUrZRYnTmKHWxo5c9\nhLI5L0s9QRWqLAgVLwfUs+kbXDW3i5LaSFLo2oJagc4NjIdrOg7UuTDRRireI7lF3QSlpsq5Rag4\nb3o11CJ+Ui6cS3PDoQy7gWE3ME0TT6enbfRT8sRut0OGjkDPro+M2RaUaVqY5/fsD9d4B998/dvN\nzXl7e8uXX37J09OJTz/7EV988QVLI3v3/cB5KYzjaDZoLcSh5cnNJ3JO3NzccDzPFC1bLMX7998T\n3IBIQEvm5uqW2MZXZQLpI8Nuh/RG8PatkJinhXEcSaXgi2VNOgePLYtumo+ICNM5caWVlx+9wLfA\n3zIt+F3HPJ95tXtNHgZSe4At6Z6r/sDnf/ILMp63X/+GsRXgP32z5yev/hFv707Ms9CP/5gsdr6P\n55kYI1fXI4frPW8+/YT9jWmPjk/vSI8PpDQjeeL88J56stel+0fy6RHVmdlnXL/n6qoVZ+3BqM7G\nDssiPGU731oFH4y1tOSZSthCoruuA/F4pziKEaybu67rOrrYE1+8gJuP0BBxU7tmpgWHMdJIM4mK\na7qUmpWSTLMj3qMN7dE3FEmumb46igaiE2L0tJqXY53xnafDc5CeOfc8NNfi3CV44RGiWdylXjAO\nXaTrPKqeXfcpb17uuDrcAvD23be8ff8lKb3HS6AoeFbEQ8L5R5Y60OsVUQVdmrbKZVwQxPeod6Yx\nXWO1QiTNBaona2SqGc2rRkrpXE+WloiR2XSVnhEfeoo7k3LAl57jZN9T8Uc7UeqMgycXfY6IifWr\nKt1gD+uhxSqpKs5lck1m9qlCSW2NEnC9oybL+wvBgSp5PeFaETeYY7vYxDJuIm6l5IlSM74Gywdc\no7OSxQZJsJQNUehaQaSuNySOtpgs57ZnTZARl2wdtoeGM6kKTe7CgZw7kKfGtWpru2ozY0mLJyps\nu1Zn3CytNHzDskXLiHos4cDhXWiFZotHys5kFeKADvcMQZPzTNXFOFfeo7VszvFh7JpkQgjiUYSy\nxqLhWXNxf9/xhyuk1G6Y9aJalsTmM/9/HOIKWm0mKy7gn802Q7WwWN+bldFcddsrzfHmsE6NpC0O\noR8C3oOwoNV0yG6Fg2rBEVrEQpvDtgVDqsOHnlQzzlnY7CbLwdhBGnPTUjljUkHLAHTN6WcuGv/s\n7IvzxkYriQqksoZeLqCBWjNLfeTh0XEYzXLddTtUoyXbOxORrsJvdRk8ZJ/JNaLLniDdJki0my2Y\nM1E8OEfvnnGzJFNrQpxZlsvaHazLtmsQB85n3OaiU3wQ1F2QE5mlfY6ZVBZbICqonMllFfNFwwm4\nSgx2Aa8z/VoLwfU4gS6OFH1A62ofdgTMLaIBJOTNhOAEK7hVoHrUh63oSWkBVVxxqPf40G1hoV1n\noE0rbgPO+Q1KWLQj9p6h7wgus6QTaQOuWkfNBwdhotaAqmeNkLFku9yKqdmKzK1b6c39t+WoDZSp\nQQlzonOesmSkVOAi4I+xZwwdqsJ5XkyDtl3DysP7Ew9P9+ScWxhnK/hjgNNEFzy7oePFy1fb654e\nH5hPZ6ZpJqeJkg1pALAsO5xz/Pbr3zDsev705z/lr//aQm1zzuxl5Objj+i6wLvv37K0BzRa6PuI\nq846haVYbBEGjs2pMAw7tHrrSrXzsLu+Zjy8IPQ9Uyq4eWHNROxCR86FrotUhIByfHy0+wWYi8Wg\nSBPdvrt7fwlI78CLJx3PlMPM9csbTi0rSMvC6fTE1YuX/OQ/+Ce8/M3HfPG//RUAx/JAdcLV64E3\n4w1LLlRvHdcf33xOPwz0o2M/7piPC6f3poMq9czp/j0P777j/puvqdO0ZZ+pwHg9sOsGvDPd0qpH\nfJoeLcC9WPFSSmHfIlv6fsf5fOY8Pdhmynf0rSPVdR37/YhQyTWxLBNd60gNvmM83JKHPWV/sId5\newBrsQ2Ja0aLUQLntqEbu4jUYs40pImb3SYOdrnQ9R6XJibNFky+Coc9zGpRJXiPiKPhqTifH7m7\n/4Y3t58hridQN5u7byC2YdchWqg54HvrgO4+vuZ2f8tvv/lXPB3fMsTh0sUPPbUeKfnIku9xacA3\nIZAkpbgBvKJ5xlG278IIzNEeBvNCLcvWiWhzDATBq9HmtKz3momyQ+xxAaLPm5ZtyTCfT8TOAM9a\nC64t/LUKQkCkIi7TDeZwA3v54bBDRVlSYF4cKVuhXJPH9wNZlxZLptSSN62qk4qWuWnAPDkLsa5u\nVmM65bS0rnOETbMFIYh13UpAc7y44711/JdlIlXDNGxEjerwsmumLRPHuxV8HT0aRpvoxIJ30HXr\nlKJs+XdV0zPNJxsmqNSFeT7b80TWqUHBuWAmLY0IlwzZGEzvLKzA0LJhDBDTFHtvEWG5LiyrecP1\njMM4G731AAAgAElEQVRLaq7WOBG3PUu8+H93OVLQOEgbJLFSmkOsNlDixYFlGT/q7eEtwkUQFrB/\n6wJBrDJffyYKzhl7xAuIE8JqaQQLAC2Ccx6nzoBsQElC9GJckOqsFbsliwMEpHUBpOXy2fs0iyeS\nLeNH2ZxiK9FbBDrviMGzGgFWEbpzNChZpja1ccoR1FtBJjPTfM/YrynfA0tSvNi4UOQy2mpnmBAC\nfT8QXSBr2UZYqNuSvbUJ/8WvN3HBBzYSunAZNWpL1Fab1eG8bCwwJVOx7KyKtZAvLDAQKWYkUEV9\nJnTtwe6i3SSSQBYc3RYGbFRfKxS7bkfGb0VdycV+P0LJ66jrIvvTdi1UUZx4iq6tf99YXhWKXUOy\nOj3Ls+tSzN12ETBboZvzgosz4i5p5aCozhStOEt8RjRsThMpDqcLSGot40rNK43XCpHzfKaqsG/5\ndwC+qJl3xFMlIZ3fEB7LaeJ892gjzi4YlmEd+6bE+XxE88JhHO3zrZT1cU9ZZnrfcbW/Ji+JqXUI\nciqEYML+w/4adL8R0Z+enlA1Ds6/+Kv/hZ8+vufnP/8FAF//9hscytdffckQA1eHA0ODLtZk7J5l\nqQxjRwgveGruOiVy/3DidH7k+nrHsOvpBuvkXL14RfQjEq1cLtOyFe19b07FPvbt2kzMy7K5pTxC\n0oJ4YU6JgN9oyy5aV2AYBu4f3rF/fcvrj6wL9HD/ZJ2A8yMaRq7/+Gf8tI39vvrV3yAeltPMN4/f\ncnDCyxsL5R6cMh/vSefCXc5Q4K5l+3VdJT3e8/T9d6SnO7ouEJvzMt5cMy0z6WzZZ+n+/Ua1ly7g\nfDCXXfTk4G2NA1KaqbVye3tLpTQXql1rx+MjHqEbA1UquyFyaEiBMOyp3Yg/3JLjgC8Vt26MVCDY\nw3yZZ1KqlGf8HO89YQ01TgviHP0a8ovHVUWd4MrEJBfmk8+OUAvVBaN5y0JovLuaTjydvuPm6soM\nPmrwZbDutxCJweOHiquRZW5mmlS5Gjt++Eng7d0veZr+htxo8ZZ1N6J6Ipcj8/KAa8Lwfe9BI6lU\nXKjgKqVtkmtpWJqc7c+025zceUn0IRIlstjz+TLyV7VJSmkwXokM0a7h4ISiPdT5mXRkHT8rXjqU\nZGJ1GbZszhh6As25rj1OlL4hcWrNLLPawoExAoWwic0rhnVxOHJubsE1mSNYoLL4tjZeTOd436GN\naSjONYnL6m675L2WOpvpZW0eIcRutE4UtoavSAUtyRofXkg1NIPAagazsyHNOY2ojflpI0hnkhJl\naQkh67PEJCRaPSFERN2WdOLDSp63gtD+/cWItCzmohSB2Dmm5tY9np4QdvTdjpyMhr++z5Iu2a+/\n7/jDRcSsJqn2kBLvkCrkZknwrJohc0lUDANQtTR6tL3eokrERmbi0VK313lvWg8v9vAPIWwBtHaz\nNv8rjioO6lq5RnD2M9NinaTqVqtrcxZosFbtNvgBJwXvCzmL2TOL227Erjf7vcfTdULvAn6FtrlC\nxZwBQRwiAdcumnl5ZIxXeBdRiRQqj+fvt3MW/A3gGrcF0mYTrK3SV1QFUwWVzdlSSkIxCGnRhSLL\npmnIuuA7j19WcnfcClfnoGhunTXT7KwXuPd24YsoKReCiIEogeAPVPUUzgg2ypJGp681mVszz6g2\nrZSu9mEr0Lx3SPVEf9iKs3N5AinW0ldzxHi5LDalps39WUqhlpVD0m7IYKR1XbEUNNdHC5xeAYcr\nKwr11FJwJGrIuOi2vzMOmmkyqi7mvqnLRrZ3Thq3o6AlEwnQxqx9HemHK0Lcc4i3SGurA2gq6JJN\nm9K+t02bUBKxcb2KJs7zzDyto5g2dlTl8f6BnCtdK872EhGt3L39nvu339L3HcNwgbyWUk3LRiW4\nSyTR+fwtwzDQdR278cD7u7cbS+YvfvHn/Jt//b+TljPqd6gmrm9aRIxccf94ovLEMI4sS+buqTk2\nE+yvbnh4uKOox3V7Xr02TdJ+/xJtLslOFA0jtA5gnhe6GHDeNa2JMlxfMz9sJ46cClk7cCO7/oqS\n7EG7pCOejiKFEITj27fsd63TsR94/PYb3FLw5yN3X0/biO3Hn7wEhPfv31GnO4KDNJkz8fHxLQDT\ncqZmu36WNoKdlwmH8vLlK15/9jlaZh4fTCP0/ddfMjea/csXr7nZH8idXRdJbNPgvWe337No4uG9\nudaeHs+kZcF703wFF7cRBjnxMBfCPrK/ubLuX+sshP0Bd3ON9hFXM7KcqVNjDJWEFANHqgqUsk0J\n0rKQ0mIj8prxnTl0U9Htd7paCAp7P6AsnNdNm+vsTlYhKWQ8rmkAx+jIJXH39BXiFjp3jdYWWaOR\nwQWiQo4Rv++39ft8eiTXjA89V1cf0e+UY1sX5/QWQkLCgsyZIAsE25jWYbZIFXV2L4ewOblRh5aE\n5orS47ynJjs3dTkxzU90boevI1UvzKeqCe8roo2nVB1B1s6pJ1dPKvfUspBMRWXnGxrc0gph67rv\n2nphBV1WJRRwriOvSRCloE6NoZcncs6mN2pNAsEKcNQ1V3PcOu7e+8bhS0AmZ2Ecrch20lMLW6qG\nyRRaL672BOcoYoT59MwdP4wdMTrrqGeL11p5b6WY1EWwjTeF7T50HSzzQtf1OF+aC37d7Cp1RQFZ\nqXbBQKhDQjAHqbMx4lbnqEOqQ50596zLvzomO7zLlGLnzFRCl5Hd8fQ9bv8S73f2uhWJ84zK/vuO\nP2hHSuR3OwioawLiiEjZWCuGHNWtKCp6qer9mhGngFoHZn3oh+AaFsAe+s5X015B2+4YWFGcCe/y\n1rEwumvwpr0o1K3yW0reRoulYJAx//yEmxW35FWUvd5sC8GNBGcQxPBMUG3Fhl2gqtJGZ60YlMq8\nPNDFhJPR2EDV5u+PT47DLhLDgZQnG2O14qTr7SaeF5vPp2LFzwplzLlSvIOq5GqjprVblWtqor2x\nVfpuEy56V1jyRNWMk4hi7VcwjVQtE1kD4hX1smXm+bYwxd4I9mURVK1VvSwT+IlUHlGuUZ6P6ITq\nMuK0CUsPm5AzFRuT1WJ2VicrLgFsP2a7L9WWTbZp50JrQStF1pz2dRds+qWSKkG0zclXXopxuCzD\nyxHW686+fKMNY6gIoTZ+zDqidJCqkftTRYu7IBsQ+hCNC1UyeV62HZBpgx0uO9BKfbY7ct4SAXQu\npFpIS2WZn3V4S7Fislb2h2u6wRbph7s7as100eKTdKqbvkbV6OilFFJKLNOydVy7oSNE65ANfuDH\nP/oj7u7swd6Fnp///Od8/c2v6caRVy8/Yj7baG9eEndPR6Y5473y9HDaugBJhb4b+OSzzynZITpy\nf9ciQp7ecuivePV6oOuF85QosoJTO3w0HlBWy3Bzuxviqtl5KLiwIOIZYrcxYQDO0yNBR2LYMXYH\nKBnfvsdSldh3nKcj4xCZv/uKhyf7jONhTy5wfbjhR599zLv375kb6PD+/TtevHhBL/BwPrEsCy9f\nWud4N36EC8J5ynz73Vse7t6ynOxnXg2em2EgO4drbKP1ikzzbJRbhLu7R0qtlwdiMHZSrRXBo1I3\niG/XdQRnTL68JATP7soKxew8QQp1ekQk4pbZrOvY5kNaPmkcAqe8sGwjz/VBUokx2satVsoax2Wu\nHLrQ9HEquNWSXky2MaWMemnjnDbeKQGYydPESb9h7p/oRyuknQT2Pm5FgkjY8ABaiwE18wlVoQs3\n+INdG6fkWOp7amlC4zLB0jpZ0wz+jISOWh25rqkZ9jNrVkPx4JrkwT51CI7H+0emuhB9xfnrbU1E\ns3VyYmyonLBFspQ6k7PgtCP4kVzmbZ21TXabvrjeNtBb3qtSpOIKlOhJqaDTOtqyDNWiGDTThbbJ\na8+MKFYAiQE2nQRqW6NTKm3zbCkiuczMs33I4DpC6MklU0tCJJKS3cOSLwYq7yI1V3L7Lhaf6YPB\nlktlwzush0kO2ji/5gumoY3+01ItJ1KV0thc6oSibdqj3ob3W9yax+mezo2mjQozMa6dLEdeTD8F\n1jVb342I5ftuEyvqmkJHzgWVmePpHeNQcYy4dVQalbpc1o+/6/iAP/hwfDg+HB+OD8eH48Px4fgH\nHn/A0V7T37SdYGi0WUeHSMV5vwnPAOZ5bo63gJa6jcUuAbSCVGkz0hXqZcG0tbK54ta/E3FGsm2t\nPxF3Gac0i2bNFe9MsCxuzQCiAeB8005dxpQqgtaCiOXx9bHfumMCDTjpW9ftQusWadlAuqDkpida\n9WELqUz4uCAIubB18eblCa1vubl2m5NhxR+IWAejiX5aBy1T286iVAEKLM5auE43IX7RiiDE2JMl\nU6ay7b5itHFpSslGad6hraWsMlEyhM4RQ2+gzLXLJ9YKHodbVJXcXbqDVSaQmVQdKT+Bd3h3saWq\n2HjSvnuP5DamcJ4qEVnBhRK2KBtFsYw9QKWFL9tn70OkqrS/M6jmpQNWzN7s1OCFodt2Qii40BAN\nYu7KdStiIz6LFcpptqBRyqYVKMVGryYtq6SUWVpMCGEhFaErCZ2gd90W6eGqa51Ku2dSmrcuwDJn\nzk9POLXve1kyaRVFqpGvc1nIRVnKI/pgYygtSuwjYexJota1a9u2OS/U88zY91zfXpNS4v7eXudj\nh0ogDge63RXv7idev7SxwLdv31GkEHe33N3fk/R+07kddlf85I9f83g6c3/3SJUB19v3dPz+HV9/\n85YfffYp59Mdd29/u92HXq0j+9GrV3zy2Y843Lzern2RHhctBNy7jj7sOE0z09pd6V5Q5gVXT3Ru\nIRe/QV6HbuR4mhgPtya9jY5j6xDtXr4ifvQx7mrH/DBzffWapzZK/e7LL/FdRNNMmhfm5czc7pmb\nwx7NiafHB3JOfPrpZ9u44d37d+TpzPuHe8N8uEp/aKNU1CzjeKalkMr52RhdCSHSjzuEjnkqjIN1\neby3rvnxeLZILX8Zp+RS6Pqe613P/nDFfr8njNaN1LFHtFLPJ4qqmTw204dFhqRlbmBj/l/aENMd\nZot3Uodfu2C+J0U4a8YXpTO/rZ3v4HiazqaazFBEtvXNeTPhS5mZH8+QnraEhT4Kc44E6S3MPAnS\nui4xDMzzQteN9KVnyU+0Wwb1N7gkzHKPSiaVE77aeSvpTF06arFMTHKLd8I0T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h6kLU2FmzlEsB8fNYTiQhRjENYmNFVkwA1AxYSvG6UBvZmxdENBNq6mK5MI8hjHGUMmj3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\nmqAYfsg3sKhv50hcYl4QBQuCKPZGII9KmRWZQdvzQqRSHcb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P2RhGixNUQEtBnCfPplcM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6nVoYasNuXKPTAT83XmLl2g4sxfaMNRiD45o7Fyjrz942Zr4G1iVhJSqMmfOe6xnm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\nuVwu3OdlH5m9PP/E8/MLzjn+7d/+jZTrTjAOIfDy8sLnz59JqfD6+mdaH4e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caPfQS7zDQ01LG6qKyqDo+sa6G0xJdvfsKLu0wqAqIFQfAJ2qxso9ZoJj53D5hBHE4malsx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WLbkfnv6erUbMyx+5vW7kMlCo7Jd4zZUpZEaj3UxrLJPXQmPJEapFWiOnim35wB+kIogN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YM6067IvdJFhtW9uvmYWv/NIUiHMymdixMd8zLavNZkNZlnbzMnYGAIN4YF/gO3KDj+crXSej\nT3K7ViDXxeKYhw8fWkBlpZkoYMD9gcXRwU5HzPiECSEZDFTrYbs15W/Tdm3s/DbX18mGy4trojDk\nyZMnCg+nd9Mg8PCEtgTJM8JejGMsW2RH29Q0ZYGvx9h6cXkuoR/h+y6+9m806y/0d89Yfd3dsYEG\nA0UIkC1tKxkOd/IleZ5ra6WUui6tmjUozGG62bJer63dUFmWXF1p7aY45rXXXsP3fUtUcPTPGtJJ\n3OuRavxJqBNX4zrgeR7DwQDZdUw0FuTs7Eyp0JcZ682tIoY4ob63Sq01x6NqG4TrEGr2cFk0dI1g\nOl9wcnJfM7SEHacgCKxNSq/Xu0OIAbXJO45qqw514o7rsrldqfalbqUkGs80mc/4c49+msALieM+\n8XCAE/h4wx7Qkt4u8SKHfn9IUVa46KR27JJtN9Rtg0Tc2S+MCbmv9dLyoiDoxXiaLFRlLV6spEtq\nGqbzGUGk5mKWFZR5qRmRNWW5OyQb/ThDbrk6f8F9DShv25qqVgfMIs85Oblnn5vsHNbrFd/5zru8\n+dY36I9H/Pm/8DMA/NCX3+BXf+XXSX/v9/hS13FxcWE9Px8/fkzbVAx6CpOocETq+V1dfcDt7S2D\ngWJCHh0dsbxRL/bVZk3VtNyuN1qvL7d6hbPZgkcPX2U0P6YtUkazKdOJWuN+FFKnOY8ePybPU95v\nO6aanHNycgp+QLHZcnV1xWp5i9BuD/dPHygcWtNyev8BD84e7UgI/T7z+a/xxhtv2HdFEAQ7q5/Z\nlM1mw7e+9Tbvvfc+0+mMkxPVhlscHSGl5Obmhv/7V36Z8/NzXn3y2M43IVwEPsurJbOjBcfHKnH1\nfdWe9DyHZJ3QtCUPH6jEbjqZcXJ6nyDwWC6XlOUOx1uVLUKod4Lvu7z22qt88IHSSXv33Xd59OiR\n1shzGAx6ZLodHvVCoqDPhbbBSpKM0VjtQ5vNhm2SKlajcHjv/Q8sXisMQ46OjpjNFqxWa7KswNHJ\n0nxxbHW04t5APV9zKO8r5u5sNrNsfbMO9yEaHxWfWiIVRr7FNgE4tCBbmlqQJhXD8ZQvnD0B4JXZ\nPeZaY6V2BbKVhK4ZqErfeGcrGeYlXFYVks7ImuA4jrUJaZoaPwqt1ck+rb7rttR1ZR+m7++qIALw\nPQ9H02jDMLSaGb6vHMKV/16rN57dI67qAt93NYW+sxpLxm7ADGCh/aBAvWjMS9F4IZkBvri4wHVd\nJl6EI5TGirnOJEkoioQ8S3AcT8vfB9ZzyvMCVT0QHts0I9mmzGdKKK7tGrbbLV33XbYv7LSLzCkn\njmO6usERCrQdBTFdrZzjvcCzi3u5XBJ7IcfH95AozJlJpIwQnBf4FpNlOtGdNnydTqe8fPmSsix3\nDMKqvoNt2vfv2263VsTOcYXFdABaEkCSZZJ+P76TnBoQvnnu5tmDMZ4WOI45ERt/qR3D03E8Tf9W\nAEpzPaPBUCdhFf2BMp+ezncAxtVqZQ2GPc9jrROpi4sLm1y3nRLhMx5mnhtQlDmg9IYkjcUVVlWh\ncRemClpZfSZJyWAY8eDBIx49ekhWVBQa++c5LqJTFdzJZKJeBAbc3lYI6SA6XwPkozvVE8/zcH21\nnvZ1VzzPrB+FPRP49oTXNjVV3VKVuX0h7AP0q6riZqXm/MAbUGmKP66D7Dpubm4YjUZUdW0NYEEl\nRK67o1d3UtJWu+qwEfEsSwWa3Z9HRhxxeXPDeDy2bL+6LnGEZLNZ2Yqj8TIti5q2a4j6vgL9xyOG\nhlrtqgTw6N4xw/GQdLu19whqXzo6PkYaL789DF6apoTaF6woCius2bYdl9dXxHpvuk0SsqWq/pbD\nIb4XUmUlm2TNjAVNXVKkazopcAKIYhe6AtcTeFqWg7ahHwc0nVT+Zm1jx3Gf9SWlpEXNdz/SSZgr\nEAiqSrJarXG1+TOoA0ygDwPGvLyud1gp31e+j1JKHtyfI/Q8rVupdOwkdqzM/pZlOQKfKJzyhVf+\nBL1Bn4tzbeXDmtffeI2TkxOEEGy3WybjmV2HReny7NmHmnY/pN83GmJHIB1O7h9zenqKEILpTDG+\nZKdwbUVVEvciPEdYoomDsqbqSlXFHvZHrDXOz8s2JOsN/bhHVSvsmXmmq9tbtuvMVrbUPav1ezya\nEEYRsmmp69L6eAJ8+OGHeJ7Lq6++YklUURTZZzOZTOk6yWAwpiolTd3Z+79//4wkSbhaqnUTRCFx\nT+1jZVOTaOmd2XzE8vKcWLM9p9OpNb5OM+XvOdfSJ6+++irDwdgeLkejHY63KkqkcGiaiqZs+c6H\n77Fdq0P5bLZgNlvguj7b7QZka1nnXQcX5+r99pWvfIVn5y8sDiqvG8aeZ42aPc+zBwxjxmzwil3X\nEQ93wppKLLhmMOhpI3t9gC5z4kgdmnONAzTv2aOjI5v8fVR8amDzT/xDD3GIQxziEIc4xCE+ZnwU\n2PxTSaQOcYhDHOIQhzjEIf5FiANr7xCHOMQhDnGIQxziY8YhkTrEIQ5xiEMc4hCH+JjxiSdSQoif\nFUK8LYT4thDiv/ikP/8Qf/QQQvxvQogLIcQ39r42E0L8qhDiW0KIXxFCTPa+97f0+L4thPgLn85V\nH+KjQgjxUAjxz4QQbwohvimE+E/11w9j+jkMIUQkhPhNIcTXhBBvCSH+e/31w3h+jkMI4QohviqE\n+Ef634fx/IzGJ5pICSFc4H8Bfhb4YeDfFUL80Cd5DYf4WPG/o8ZsP/5L4FellF8Efk3/GyHEDwP/\nDmp8fxb4X4UQh8rnZytq4G9KKb8M/GngP9Lr8DCmn8OQUhbAT0spfwz4UeCnhRB/lsN4ft7jbwDK\n8VfFYTw/o/FJP+yfAt6RUr4vpayBvw/83Cd8DYf4I4aU8v8Bbr/ry38R+Hn9958H/i39958DflFK\nWUsp3wfeQY37IT4jIaV8KaX8mv57AvwecMZhTD+3IaU03ikByuTjlsN4fm5DCPEA+DeAv4sR8zqM\n52c2PulE6gx4uvfvZ/prh/j8xT0p5YX++wVwT//9FDWuJg5j/BkOIcQT4MeB3+Qwpp/bEEI4Qoiv\nocbtn0kp3+Qwnp/n+J+A/xyrgggcxvMzG590InXQWvgXMOS+U/RH/JdP6loO8YOHEGIA/F/A35BS\nbve/dxjTz1dIKTvd2nsA/CtCiJ/+ru8fxvNzEkKIfxO4lFJ+lV016k4cxvOzFZ90IvUceLj374fc\nzaQP8fmJCyHECYAQ4j5wqb/+3WP8QH/tEJ+hEEL4qCTqF6SU/0B/+TCmn/OQUq6Bfwx8hcN4fl7j\nXwb+ohDiO8AvAv+aEOIXOIznZzY+6UTqt4HXhRBPhBABCiD3Dz/hazjEH0/8Q+Cv6r//VeAf7H39\nLwshAiHEK8DrwP/3KVzfIT4ihPJR+XvAW1LK/3nvW4cx/RyGEGJhGFxCiBj4GeCrHMbzcxlSyv9K\nSvlQSvkK8JeBfyql/A84jOdnNj5Rrz0pZSOE+I+BX0YBIv+elPL3PslrOMQfPYQQvwj8OWAhhHgK\n/NfA/wD8khDirwHvA38JQEr5lhDil1Bskwb46/Ign/9Ziz8D/PvA14UQX9Vf+1scxvTzGveBn9dM\nLQdVZfw1PbaH8fz8hxmbw/r8jMbBIuYQhzjEIQ5xiEMc4mPGQWviEIc4xCEOcYhDHOJjxiGROsQh\nDnGIQxziEIf4mHFIpA5xiEMc4hCHOMQhPmYcEqlDHOIQhzjEIQ5xiI8Zh0TqEIc4xCEOcYhDHOJj\nxiGROsQhDnGIQxziEIf4mHFIpA5xiEMc4hCHOMQhPmYcEqlDHOIQhzjEIQ5xiI8Z/z8idWnfzj2L\n3gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "image = caffe.io.load_image(caffe_root + 'examples/images/cat.jpg')\n", + "transformed_image = transformer.preprocess('data', image)\n", + "plt.imshow(image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Adorable! Let's classify it!" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "predicted class is: 281\n" + ] + } + ], + "source": [ + "# copy the image data into the memory allocated for the net\n", + "net.blobs['data'].data[...] = transformed_image\n", + "\n", + "### perform classification\n", + "output = net.forward()\n", + "\n", + "output_prob = output['prob'][0] # the output probability vector for the first image in the batch\n", + "\n", + "print 'predicted class is:', output_prob.argmax()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* The net gives us a vector of probabilities; the most probable class was the 281st one. But is that correct? Let's check the ImageNet labels..." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "output label: n02123045 tabby, tabby cat\n" + ] + } + ], + "source": [ + "# load ImageNet labels\n", + "labels_file = caffe_root + 'data/ilsvrc12/synset_words.txt'\n", + "if not os.path.exists(labels_file):\n", + " !../data/ilsvrc12/get_ilsvrc_aux.sh\n", + " \n", + "labels = np.loadtxt(labels_file, str, delimiter='\\t')\n", + "\n", + "print 'output label:', labels[output_prob.argmax()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* \"Tabby cat\" is correct! But let's also look at other top (but less confident predictions)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "probabilities and labels:\n" + ] + }, + { + "data": { + "text/plain": [ + "[(0.31243637, 'n02123045 tabby, tabby cat'),\n", + " (0.2379719, 'n02123159 tiger cat'),\n", + " (0.12387239, 'n02124075 Egyptian cat'),\n", + " (0.10075711, 'n02119022 red fox, Vulpes vulpes'),\n", + " (0.070957087, 'n02127052 lynx, catamount')]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# sort top five predictions from softmax output\n", + "top_inds = output_prob.argsort()[::-1][:5] # reverse sort and take five largest items\n", + "\n", + "print 'probabilities and labels:'\n", + "zip(output_prob[top_inds], labels[top_inds])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* We see that less confident predictions are sensible." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Switching to GPU mode\n", + "\n", + "* Let's see how long classification took, and compare it to GPU mode." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 loop, best of 3: 1.42 s per loop\n" + ] + } + ], + "source": [ + "%timeit net.forward()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* That's a while, even for a batch of 50 images. Let's switch to GPU mode." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 loops, best of 3: 70.2 ms per loop\n" + ] + } + ], + "source": [ + "caffe.set_device(0) # if we have multiple GPUs, pick the first one\n", + "caffe.set_mode_gpu()\n", + "net.forward() # run once before timing to set up memory\n", + "%timeit net.forward()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* That should be much faster!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Examining intermediate output\n", + "\n", + "* A net is not just a black box; let's take a look at some of the parameters and intermediate activations.\n", + "\n", + "First we'll see how to read out the structure of the net in terms of activation and parameter shapes.\n", + "\n", + "* For each layer, let's look at the activation shapes, which typically have the form `(batch_size, channel_dim, height, width)`.\n", + "\n", + " The activations are exposed as an `OrderedDict`, `net.blobs`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data\t(50, 3, 227, 227)\n", + "conv1\t(50, 96, 55, 55)\n", + "pool1\t(50, 96, 27, 27)\n", + "norm1\t(50, 96, 27, 27)\n", + "conv2\t(50, 256, 27, 27)\n", + "pool2\t(50, 256, 13, 13)\n", + "norm2\t(50, 256, 13, 13)\n", + "conv3\t(50, 384, 13, 13)\n", + "conv4\t(50, 384, 13, 13)\n", + "conv5\t(50, 256, 13, 13)\n", + "pool5\t(50, 256, 6, 6)\n", + "fc6\t(50, 4096)\n", + "fc7\t(50, 4096)\n", + "fc8\t(50, 1000)\n", + "prob\t(50, 1000)\n" + ] + } + ], + "source": [ + "# for each layer, show the output shape\n", + "for layer_name, blob in net.blobs.iteritems():\n", + " print layer_name + '\\t' + str(blob.data.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Now look at the parameter shapes. The parameters are exposed as another `OrderedDict`, `net.params`. We need to index the resulting values with either `[0]` for weights or `[1]` for biases.\n", + "\n", + " The param shapes typically have the form `(output_channels, input_channels, filter_height, filter_width)` (for the weights) and the 1-dimensional shape `(output_channels,)` (for the biases)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "conv1\t(96, 3, 11, 11) (96,)\n", + "conv2\t(256, 48, 5, 5) (256,)\n", + "conv3\t(384, 256, 3, 3) (384,)\n", + "conv4\t(384, 192, 3, 3) (384,)\n", + "conv5\t(256, 192, 3, 3) (256,)\n", + "fc6\t(4096, 9216) (4096,)\n", + "fc7\t(4096, 4096) (4096,)\n", + "fc8\t(1000, 4096) (1000,)\n" + ] + } + ], + "source": [ + "for layer_name, param in net.params.iteritems():\n", + " print layer_name + '\\t' + str(param[0].data.shape), str(param[1].data.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Since we're dealing with four-dimensional data here, we'll define a helper function for visualizing sets of rectangular heatmaps." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def vis_square(data):\n", + " \"\"\"Take an array of shape (n, height, width) or (n, height, width, 3)\n", + " and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\"\"\"\n", + " \n", + " # normalize data for display\n", + " data = (data - data.min()) / (data.max() - data.min())\n", + " \n", + " # force the number of filters to be square\n", + " n = int(np.ceil(np.sqrt(data.shape[0])))\n", + " padding = (((0, n ** 2 - data.shape[0]),\n", + " (0, 1), (0, 1)) # add some space between filters\n", + " + ((0, 0),) * (data.ndim - 3)) # don't pad the last dimension (if there is one)\n", + " data = np.pad(data, padding, mode='constant', constant_values=1) # pad with ones (white)\n", + " \n", + " # tile the filters into an image\n", + " data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n", + " data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n", + " \n", + " plt.imshow(data); plt.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* First we'll look at the first layer filters, `conv1`" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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hhBAN0EuUEEIIIUQDjt6JokGafol49vvy4e971FhiHpOvYm1oSOehuxD6Pfy9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\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# the parameters are a list of [weights, biases]\n", + "filters = net.params['conv1'][0].data\n", + "vis_square(filters.transpose(0, 2, 3, 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* The first layer output, `conv1` (rectified responses of the filters above, first 36 only)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Z7nrH+wULyFbd0NdYl1dXV2PnrbVGW1lAGC4Zk0qlPEE6SUT1oNoLCAgICAgI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KfAolWVToe9N9VCm8XyZqv0slo0qQGED99oNGr0xfl8vjIBmJ2lTMH7slcUoExdwGg0arSrioNj\n1hSWM3CPO3fuhDLzYhfjAYvE6XSaNH8os0fOI009F2DTPTY27ADx85//3MzM3n///UZZ8CwW+vME\njo0R3i2W8igF5UmlTGzKm5ATLPNHTpkKVSqnlBnaT/T8G5vB2bypPjKpuE9qIc+iX1XnJXNZu91u\nnKf6WuwDrRZB/je12FALrph5S+GyBOe4l19g8ObJl4mfnxN/p57JYFO2evdSEbxa5KTqlSPbq7lc\nRXpX/Yrn1JJvSOwd/Hvy/ZSAXzkTrfP8atqrqKioqKioqNgQV8JI5XbHcO9Xu3GsFhULMpvNVqJh\nXwZg9oDo88aNG6EMSMjK5sOYScwDK++tra1wv1xE7RTYbVyZ35SrKZshsMNn9s+Lqnd3dwMTwat7\nMCQsjFZQ9KlnmlQcJBWNXSXn5fsBKp4PtwdMRVz3bH7Bu6D+mFFi85cSGfv2vH379kpEa7PVvIRc\nf55pVKzL9evXQ2wqvJMKxcBMA/cDnyyZgf6uBOtM46sdrnLPZ/z4xz82M81IMavgd7bMDuYcENTc\notqIy+8TK8dcyf0OlU0YOScHfz8OnZDaqXOZ2VyeMlcAOXNqaQwlZoB9f2QTIJdBmd0Um6FiT6XY\nJz6n1JR3meyTAjPs7MiDv9lM79kzZQJk6Qv+X1KGVCYF1UYpKLNbjt3j+ZXDrZjFQ8H4b2Uu9IhC\nauyp9zBrssCxkCI5VEaqoqKioqKiomJDXAkjBaZpe3s7CLaxWj88PCxiZkrdmmPA7lS5AwOtVivs\ngsFctFqt4M4Odia2yk6tZCESPzo6CoLcBw8emJneqTNy76hYBNYOKR2UX60Ph8MGu3d8fNxY9c9m\ns1CXzKKgbXinoX7Du6PNx+Nx4/1iAduUnsO3BYvXlWiQn5FiEFHOZ8+eNbRly+VyJWxEDKzB4r6L\n+9y9e9fMzL7++uuGeF319SdPnjRYDO4TKlgm5wnE+7KOUNW572e5scXsjGKOfvOb35iZ2V/8xV+Y\nmdkf/vCHxjn9fr8xfiaTSZgvWHCtkNOhqF2nb/eYm7d/RkzQXrqTB5QDQspRgpFqE85SwPWiNH7+\nmHJbV+/LOjEuk2JZPEul8tHlXPv9+TG8ahbKP0tp0XzbdTqdtbW0nU6nKFAkz4sM74SRY1lKy8X3\nwb3x7pPJpEij3Ol0GmNvkxAEXKaSMR/TjnlnpxIN5pUspNh0g4mW40RhIYBJc3t7O0ycPk0KY2dn\nJ1yDhvnmm29ChaQ+Dvv7+417cwPnIkan3lOBBwW88F577TUzM/vrv/5r+5//+R8zs5Ck1ez8Y45G\n5w8zzBLtdjvUn/KUwHuZrdYhm9HMzkTVGARs8vDJVFVsHBYoM3iAmZ21KxbVnIgXdZ1a2Kj3UILX\nra2tFVONB98bixb1PBXjC2VXFLqZNTzWnj592ngnHvRYULEoPTXhPX/+vJFAWXmicFJq7gdoa4xB\nnkzYfASUiofZlKrGAN4d/f7NN98MJk+APTXZ/IpNTMzk6ReWHP1dJV1mc59KxKq8Jtksb3ZW92pz\no0yJ/n7KecKs2TYKuY+NMhWizGbaEcd7O6qo7b6seIZf/K0jqlbmOyUYXndR/31CjVWek7gefR0t\nl0u52FUR63NQC9USZxO16MgtSrisqQXHZUQQj8WWAnJeiv7dWfahkPu+M6ppr6KioqKioqJiQ1xp\n0mKzZjylXq9nBwcHZnYeE+rk5CQI0HPRrrH7w87/4OCg4RJ/dHQUzuNdu99RdLvdhjno+Pg4mWgX\niO3kAJU4+N133zWzM7d2jl6N8734nnfbLIJWjJOKycRAPeB5X331VUN8y8fxbr1eL5SBd8Co/1Qi\nVi4fi7qVeQlg4abfAakd4XQ6lfUBcF2oXHsAlx0Cb7XjU3ntuN18GZfLZejvYGgePHgQ6pzL59tj\nuVw2wilwPYMR477GJkhmXnE/FYXZu/vHzAcA2n46nYa/gZ2dnTB+kGT6zp07jTpgsxCYYmbqYrtz\nZQ5U8FGYYztvZYJBvfE8oESrvr9z27CDgU+qrmKuxaBYJz/ftVqtcB5YMuWso6DmMDW3cXtxrCdv\nnjNrslOLxaIhhuaQJyrHX4lL/vcNFSqg2+023OiV80cMF3mnUvOhErer8BjcDql7KjaY5RWKHVMy\nDd93YrGlVBgc/92OhfvwzDaXb53EzJWRqqioqKioqKjYEFfKSKnI29PpNOxK8a/ZuU4CK8jBYNCw\n5x8eHiaF1hxkDLt7rGxVqIXT09NwLe57fHwsmRDsvDkf3qZBQR89ehQYqTt37oRngFlQQm/e/aoV\nNBg9jswOdLvdldxVAHRLn376qZmdMRyoJ2ZHsCNIsXtbW1uNHbrKDs67FLWLUCEPgFhUWg4o6qEE\nvjExbew3jjrNDAj6bypIqHqXzz//PPzNjBMYLvw2m80agmHWJfHOSrnsK/G/escSlorB40wFkfV4\n9OhRYM8YPvzB8fFxKKsPxgsoDY3fxQ4Gg0ZbcCR1Pp9z9pnpkCL83JTmQoVdOD09bfTL2D38GOj1\nekntJo9L/KbqX2ly1HOVzk6Vm9kWFcLAa3jUM9W45OCgzAAyixW73/cF3w/YnT4VGNOHOgCUED8F\npaVilp/7tqqvVH47ZpVUTkt8i/C9K2VVY7nxUnNuKtgo9xOA64Wf5ecn1vqtwwJeyUIKH+GDg4Mw\nuWCREBOseU++drsdJlOO4KzgE7GORqNQ0bEULQAajD9e/li32w1lwcdua2urKGlxv9+369evrxzj\nRJzojJzclhMkl0Z1fvz4sZmtLoaAmKkG1wA8MLgzoj25jfykwIsm7qBYIPMHUS08PH0bi8KsJigV\naRfg+FVoY7QhA/XMdDXXGSeXjT1jOByGSQYL25cvX4Z3V5HmGWqTgH6H8vGiHfe5ceNGw5SsJqCY\nJyw7FgDeC8isaVZrt9uNTcRsNrNr166Z2bl5qdfryXGItubzUb/37t0zs2adoD5SkzgvaLgd/Htw\nW3N9pSh/9B3lERuLfcYeoTjPT/p8LWcuSH0w1MIntliOgb1eS5IXc5n5Wn6WX/jk0tDkBNJ8H/z7\nfSymUuZtNkcpYbkS1/s50wuk1fhSmyC/QFVxuubzuWybEqh+H/sW4blsSldmt3VNmCo+FHsLp9pG\n9R1fnnVRTXsVFRUVFRUVFRviShipkjxyZuer2P39/UbcmMViEXZ9avfJ4kovUOXIvGrngl3lcDgM\n9wHbwrsJpgA9nb5YLJJ5BHHecDgMu2Ksjr/66quwO00J1jnSuP/dTIthvfgXUCJD1BtYI2YXeOfg\nTWfz+bwh5uc2Ynd2lIsjfad2CSmqW+1mYqEYPGLRrj17cvv2bfvss89Wztvf319hEwCYRr/66isz\n07tGs1UnCLNVQTazcyqHlh9Lw+FQsrceXC/sOKDgr2exsRJ9os05Jlgqfx3H/2JTNfob6pEF0rGd\nqwq34M/l8qvxo8SwPC5SLFDKjD8ajUK74vx+vx9CnDBb4HfUzBp78XLsXRXblnIRjzFIqXhZfA/l\nSKHOU4LxXFwjfy2g5gMlSv8+kAvj4McCg8sMsEPIcrlsjE8l8C+VD3Q6nQZzxRH6lRksFwrFn8cJ\nvtmpI9dvUygRgCvpQYx98u/BJlbfbilURqqioqKioqKiYkNcqdg8p0/C6joXxRhgUS0LbaFf4eCF\nancF3RL+bbfbYUWtxNAsqvSrWBXegAF377t374b3hCbpyZMnjRV6p9MJ7AiCjj548CDUDXRPd+/e\nDc/+8MMPk2UA+v1+Y4XPdanenXf+KD/YLt5Rqxx+YExYG8OBAr2Og7U7XC8lmg0WPKu8egwfoV3l\nFOOdJs7nXSDXEXIVgpHisBAc3R/lYQG1DzOB6xlK03R0dLQS3BT381oK3s1yu6k69WwrR8fn8/zu\nLpZ/zdc/16kKR8B9B2MYrOD169dXgtb6HTq71vuAm2bnLFxMM1ISSFDtpnu9XqPPqqj9k8mk2M3a\nB7TNgZko9QyvS4mxAtBwYl7hd2P4HTxrfJQAna8rFVKrMnv9T2nwSsWCravR8c/1UbMVM6UcIFQZ\nfDgF3z+vX78e3l19b1LPiDnZKKapNMq+Z8yYnVWOD+oZqf6pGNMYUqJ5QLGZqi+WaKauZCGFgckT\nICqt3+83zELsnYRJf7lcNoSgw+EwfJCxwHj58mX4sOBD+uLFi1A5+M3svCPg4z6ZTIpEkMoLiCNq\nK2AhdefOHXvvvffM7DwJstl5x8LH+M6dO6G+MGhevHgRyo/F4unpaZjklGj6yZMnDU+/09PTUL9s\n4lMLj1RiYpWskidzb2rgjo37KTH3bDYLf/Nix0+EPIGyByHaGn0ntpDy97tx40ZYjOBa1UYqdY7/\n218D8EJKxdoBOIYSwMJt9lz17X56eppMDYG6Oj4+TqbRyVHc3tul3+83PHlYcA+Mx+NGFHj2omOT\nHsYo7hFrS/6g+QlUmXFjEzQLu81W062o8zAPqAjyarFm1mwT1Y9zjiWpxZgyp8QSHqt+7D/SHCkf\niEW9V4u0lPmrRMAd+13dV6HEjJhDqTlSee2peSG2wORr/XVKaqE29cpsyMgtXvyxGPw38Pr1641s\nIRzXj+UaJea+3CKX0775ROCMlIdtjSNVUVFRUVFRUfE940oYKcRIGgwGwUUbO/5utxvYGs435WNT\nLJfLwHCk4kj1er3AKrA7Pxgcjl/DoQbwjBRwba/XC2wRdpAx0SmitePdvvzyy1BmsD0cTgGr4k8/\n/TScx/GE8BvqVFG2ZtqFnFfcysU9F9cE91ArdrBOzH556leZkmKJlFMmJ8WcoUw7OzuhLEpozzke\nObq62Wp/QdswC8jPU2EcELmb2QpfV9xP8PyDg4OVGGr8PmarMbzA1rz55pvh+TAlMrwYlQXXKRNA\nv99v5JtU7Bhfj/N3d3cbO0J+X/TZw8PD4hx0zFiZNRmplMmR20EJqD3rOZvNGuJ1rje+X8opJBdj\nSpkU2fnCbDUvmAr3wWMZ85LKv6ieqUIspHbjMVYtZ6YCPDuqwh+oEAExQbZ/ljIBqjJdBjPF92u3\n26HemJ31zB+XLxcnKhUeQVkFFotmPsIYfB2piPXL5TKMB47bmDLj4t3Z4sTwfWqxWMh4iKUhETAf\ncvw85ZCh7qfiEyrHqxwqI1VRUVFRUVFRsSGuhJHCbptXsfwvdmPY8SGytoePzKwQ2yli14wd7mAw\nWNFp4f6pAHpgztrtdthdg2GLaTegefrRj35kZmdRrLHrZJYMu0noE5gBUO+Ee7DmRrnYo7xmmr3i\nXY53DffCXsDrQ3hnw4wE3gXl6vV6DXaMI5EzwECwXobd7P1vwPHx8UpeMw8VXBP3mEwmoV05iCmu\n4ff2OdTMzvsCs314N6UhA7A7Y4zH4xUtk9lZ2AU8g+ulRMPDehjFPHL4EN8e8/m8SCvX7XYlOwbw\ntZ7hYgaTmTPPenkoHZES7vsdNbMdXC6f35DHDO9sUValS2G9ngo269ur3++H8ax26rgHvyuPM6/r\nUi7dymkmF+E6B880sYu9EvMqjRT/35+nzo+xFSVsxkVDI/jyKR0Y/87MVEro7jVfuCZlDci9byrq\nu+8vHimtLz/fs5gxa4U/j3NQcn9PMXR8LMWOr/vbus5M4dzsGa8AmAR3dnYaUacPDw9DpXLD4jws\nXg4PD8OCQcWOSYGpZExY3W43fKxhfut2u2FS5SjMr7322kqZX7x40ZiYY4sOpHxh+h3voUxF6Dgn\nJyfJgY/GPjo6kg3PMYhU5/YLEDOzt956y8zMPv74YzPTVO18Pg8mVtQRi+/Ze8+bzvhjjjKpWEYc\nC8w/20zHB2PTCMrHH3U2K5mdmaGwWOI+ifvxB9V7hrInCkep98JonlhUbC7g9PS0Ib7m9wRu3rwZ\nFlK5FEEpTxReAPuPg9qkKDMtrjezpLMDn8eLCfQN9i7EWPniiy8a7xAz/fkYb8rUoRYWvGBMTdJM\n/SszswJ7IiqzoP+w8L14gc6LYJTTi/nNrGEWXi6baX7Mmh9XZUbieGN8nu9PKrR36k0AACAASURB\nVD6UMrHFNncliyVuN2VSUs/9vqE8CH35YotJgOtMeYYDsUU9p9Qxi5v7fBR+PkdFTE85TfB3hfuO\nH+vD4TDckxPb4xoeK36Bz32MzfClqWi4rPg35iRhdj6+1bfRo5r2KioqKioqKio2xJUwUoiD1O12\n7euvvzaz89XfYDBYCVNgdpZX64033gjXmJn98Y9/lLnHwAikEieqVejz58/D7hM74YODg3A9WI39\n/f3AFnzyySdmtuoSnwPYLlz7+PHjwHaw+yZMPlih7+7uBnMKflM52XiXzCYiFtAralXdCyEkmG5F\n26AMs9mssRtnc5rKv8fhCLjdzbRZZTabNUw5zO4oChZtdHJyYvfv3zczC2EmuD4Afi6bJTksQwyD\nwSA8m+sx5SLOdesZ1W+//baRf1ExiY8fPy5O1JpKUMzviPNUdO8cfJiJmPkNY0nFvuFr0A4pM6KH\n302aNVkCDokBcIgVZQZnoT3mllSMKWaueEedig+WYlHU+bGkxblQCGY6LAT31xjbof7vz1eiahUP\nKSVAjwnGU3Gk2DTmGa6LmvFKoRi4EuE7g820yrEA6Pf7Yazh26XYJ64jZrhwDR/zTimdTmclT56/\nH0tflIkY9/EZIvy7++TrLGhP1R+HweF6U4nW/d+lfaLEtFcZqYqKioqKioqKDXGlGikOjIkV7mg0\nCqtYnPfw4cOwC2RxLhgL7Fg5TxvvXkt3JXguNBkPHz4MzACCeu7s7IR7s36i5Bm3b98O5cP7cBgH\nsDeKaTs5OVkRxpeAmQ4lJEW9xfQdvAMB/I4/5va8v79vZqtR6b2eg9kHtfNCnU4mk8YOiHVTaseA\nftLtdhts0k9+8pMQ9T0VLbrb7crdM8A6rJTuh495bRbfl3VR0KNBj/fVV181Mqmzfur11183M7OP\nPvpIMkIoF9pF1f3JyUlgHDmkiAdrfbjf4xroGDl8BEPlDASYTXn06JGZnbPMk8kklC+WPw73ZHYp\npZFivZvKoeev5TGvoqfzPfy1/H/MXTxnKVdt1qX48nEYB0YqF2CK3YmJg0vhGYmYWDglQFflY8ZJ\nBRb15/PfKUH7JkjN86y9VYxUSsun7snWg2632zg+mUzCOOZneL0u/4a2Yf0SM06+7Tj4Kj/f6+q4\nDbm9vJNQLNtBKlo/31eFNVAsNICycPgQFe6Dz1+HhQeuZCGFSY7NZCj8y5cvGwLca9euBW83jgmD\nToYJ16wp3r1161aY9JlWLMFisQiLm7ffftvMzj6e//u//2tmq6aJkkF68+ZN+9d//VczO48xtAnW\nFdebrQ5cFbPJn8fiW9T5G2+8EVJzAIqeHw6HMq0P2oY/9LxYApSJxXduFjvzRwyLJnzwHjx4YL/7\n3e9Wrv3www/D9Tww/QKk0+k0PAO3t7cbC0GmtRW4jrAwwX35OiyMOHYTR95HHYAm537Pf+Na5fGl\n+ilHnEd/Z2cIv4jlPoT7cWwpLDBjqZ187DNeFKkPOTs5+A9HzOyUEn/HxNf80TLT46zT6YRncF9L\nLV4UuP6USdR/HJTjzXQ6bTjr+DLE3lFFJ+f4O2rzlIOKMO7LkBJXowxcTn+eMh/5+8QWhP7Zmyys\nUov2fr/fiO7P53HdetO5SgjMYnM2dfF91SKHvUT9eaoMPFa8AJ3LoGJjKUcArnu/EVNmcF68pBY5\nbC7H3MbfEG+C5PvlYqmp39YxC1fTXkVFRUVFRUXFhrjSpMXj8bghKL1x40b4jXcnEHTz7tW7g45G\no7Bqxs663W6vzUQxsDKH6/xsNkvGxknhiy++WDHHfJ9Q9CeLeP1uTbFV165dC4wUh2fArgNMCe+O\n+RloL44z5MWSZs2wAQzsRJ4/fy53DGBSUH6YUMzOTU5Pnz5tmOLa7XbDXNVutxtmqJjpI5WPDvdg\nF3ZOoOvNQrwrUoJsPu6TQqvI28xwqP6Hd+MYZNjV9fv9Rl9Ihdfga3N1lTIjxSh23HudPFh8T7NV\nMwQfQ9/ivuhZSrXb5jZXjg/cTzlGGaDMy5554cTi7GSBa5ih9ZkZ2JySi/RcytKk2B1lsuPrPEMz\nn89XzKSxZ6nwByq8QKx8pQxDKXPl63Q6ncqxkXqnnAA9VZexcvkyxNpXlcvPubE8eEpq4VlRZtYV\nFFuUYhz5d56f1DUpRxCuU98OyjxbgspIVVRUVFRUVFRsiCthpL755pvwt99hdDqdECIA2gjWfzBS\nQlAgdm0pUC6EaUhpL3K4rCBx2L3du3evoYNhdomDKfLKW63WldbC3/vNN9+0P/zhDyu/3bhxI+zM\nWeCN54HNULqFbrfbaDMO3MlAn1DiZ34Pv4vgcAQIVMiuvwCLEdkF2GuZuGxgg1IC4xwmk0loJwjB\nmYVSfYbLjvJ4ETv/rXJoMXCPO3fuNBip3d3dZNgBjoqM/qIYW9ZtoI04QCtrH83O3jsV8oL7eGrX\nq7Qn/Dvvin2f4DLkcs+xlgXXeq1fq9VqsAUc1kIJo3lc+t2zcjNXiPXJTcXXSv+lyh5jUVD3qB/W\nQylGl+/nWVul1/HH/X1yiInkY+fgWYpRTIn7LwpuB+6fnvGL9XvMXzg+nU6LWS/fp9jioHSv/Fwf\n6qDT6cjMGvyesbLExr5yMEnNzRxMeJP2uVLTntn5C+OD9/z587Bo+b6hFhgwDbFIGB91NHqpua7V\natm9e/fMbDVBMToDPvRsvmQBLK7BZPz5558Xv1tuklGdjBP/mpn9+te/btTR8fFxQ2SohI/8wQDU\nIBgOh2HgMM2sBqcaYByp3MxW+lLqYzifzxvpQMzO+wSeyx99jsbtP7hm5wsj9G31keA0OVjEdLvd\nhkmR/1aTDS+gfMLb6XQqRaSA+hD4Z/rfcD0WT9PpNJhVY956Zmf93jsxsHckFgS7u7srJmAznYhY\n1SmXW3kTcawdtQBQIv2c2UB9gPziJrcAUfdSaW24H/s+zX0nhW632/CYXiyaSYHXQUpYzn/7/saL\nK4aqU2924YUUP+uyFiu5Mq1zXqlJMbbhLjGn8t8sBPdjgNP3pOKYLZfLRhaDmPBdOQRh3uFvpP9O\nsHyAx7pfcLEXIM9j6tvlF+s8jjjyO8aKirLvx1sK1bRXUVFRUVFRUbEhrpyRumx4t/ZWq1UcLkCt\nbL1w9/79+2GVq8xMDC9UffbsWRC+I5wD2B4+/4033miwJrwr2ETsjpU8RxpP7XaHw2FgNPg9cRwr\n/sPDw8ZORpkMmV3CO6vEzs+fP2/EfRoMBo38YcoE2Ov1ApugHAxUMk3enYCJ4l2RZ2nUrpJNgMwG\n+Gt5l6UYJ9VPFeuBf2Psg2LcUgJ+L2JmnJ6eNhgargMWu6OPob+o+Frcj7nPefZmMpk0coYxmNVg\nNgNscU6k78vA4HxfSniqYugAPO+k4khxP2CWwOxsbPm+oNqaWQVgNpvJmGaeuWITay4CumfgVJ3F\n6ta73fP1m0TPT+GyTWeMV8VwlT4r9nxlvksxV9yHUqJ0vkeJnEWVbz6fS2uNP/f09FRmL1Bzm/9m\n8Njn+QJ9S7HsqdAniuEq6Z+VkaqoqKioqKio2BCt73OlDXQ6naXZZjsR6E729/cbgcfm83nQF6nc\ncan7LZdLuXrGSpUDh5beG6zTuloqRm6X5YORbW1thTAEo9HI/vjHP5rZqtiXwwAAnnVQjE+/32+4\nVqsyKps8t/Vbb71lZmYff/xx+I1t36lo4ypnE/Daa6812Dp26VdQ9nJmEj2TVxrsjwXI0Alw5GB1\nrWpjrhf/3MFg0NAWxQD9F+p0Nps1njscDuVOjsuPdwOQA/Ozzz6zu3fvmtl5G73//vuNcrAeCvWs\nNF+sReNypvL4sTszoII4KnYk1rc92AUfKA3BoFyrY6yiYpU8OAjqZUDVSy7SM9cfkGKuNhF/XwZi\nTgceKjyD0mZdJdgRoUQPZ9bUXynNaqyOlLbUByPNBQxl+G8Nj4GUjpXnXiVez9WFyrlZcr5jqaSA\n7UpMexehclmUXgosHNCAs9ksLHIQsfxf/uVf5EIHlY1j68StgeAZzzo8PAzJihXQwNevXw8dCguG\n4XDYEAy/ePEidCL8e3x8nHyPfr/fmHz7/f6KOYOfwb+ZrS6W/G8AL8JUWysxMibio6OjUFYv1jZb\nHSxeHI46iIHpY08lc8Jejp7t62o+nxdHDPeIfZRULBOchwXc9vZ2KAvOG4/HRZ6g7KWIOtvZ2Wmk\ngZnP53KB4s2CXD6uAzwDCymF2WwW+jZfy9HVAbQnC/79+/o6TXksAdxW3pmAn7tcLhuC19ls1kiW\nzeC29H2MF9eoAzbtKS88jAv2qOKI6vibPflK4nT5svp6UfXHHzm/mFTxnHILqZTQ/lUIxkvuF5vr\nSr5ZOceBywZLBZSjj4L3TFbHGOzNrBaWvNBPSQCUXAJ9dzweZ+Ov4R19iquTk5NkahhvNufy+ffE\nM1Ibrxiqaa+ioqKioqKiYkP8nxKbt9vtsIPDzrvb7YYVKCeKxSoT+cA4tpUC7tvr9YpMdNeuXVuJ\nsYN7pKhLFt+iXLzy988dDodhNY5/OVq4Wqlvb2/L/Ed+d83XMpWMnQBMnYoZVILmTqcT6oNZB4SD\nUKY67Fj4GcqNWrnY4lls1sNvp6enK0yU2Wp7cP35XQlHOy+llFXuNrW755hGvp/EzJPKlOVDA/A5\nzN7BFIcEzteuXWuMg93d3cYOjt2VWTyOfoX4b71eTwqKFWOB3Sz6BjMr/G7KvMn9Td3bs6jL5bJR\nv8xscXumWJ0UO2rWNFm2Wq1GLjbe8ftzzbQJk+/rQwns7e0lI8srETnGMuc0VOxoik1iU7GCYkpS\nJu3LZnJKc6gpgfxFQh7wfdT/L2oyVHOPEpv7MAAx05kvj5qf2MlBOdekhOM8BnBcjWsuA7eDCiXj\n5S2tVmtFwuDLyVD5AVWYkRwqI1VRUVFRUVFRsSF+MIwUVrFbW1thBcjMhQK0RyxAxSoXu7Lnz5/L\nFaWKeA6dBlakh4eHduvWLTMz+9nPfmZmWkCr0G635bklwb1SAQ3NzlfgzEhhR3/9+vVwXkzE6oWs\nfB6v9L3Q+vT0NPy2jkYNZVbhCiB45za6efOmmen2h/7m5cuXjXdX7uVbW1vhPkp/o4TOsMM/fvxY\naloA9L8cm4nynZ6eJhku3vn5IHkcGE+FreCdld9Bcp42QEUxjumJlFAVZcG78fXoL7EowT6Exmw2\nKxJN884adTYej1fGVIolTAm8F4tFI3TKZDIJTBMHYVXtgPIr1ot376XiYO4zZqvMEMbHkydPGuxD\njC33QVq5DpgNRpmZvQW4fyihcum7qfttChaHlwrZS4+VskSl2iilMboIRqNRaAfUfUzjo5wWVH/3\nUFkH1L2YWVVzKr5L3333XSgrviXj8XglHynKjvthbdBqtUL/5XdDXaYyHLAwX4UywXhjdmydLCZX\n4rXX7/eXZmeD28cMMluNuwRAMI4YRK1WK0zYmMRS3lklQMP+8pe/NDOzH/3oR8H88Y//+I9mZvZf\n//Vf4fxf/OIX4TpMVGisJ0+e2H//939fqDxm54u73d3d8J6YLBeLRfgYoX64A85ms3AuD3Yv4lbC\nTk4rkqO4vfBUxZGaTqeSKkW9oR+8fPkyLJZg3uQys+kH7cUiaNQ/zBW8eFLmCqaFU9Q0I0X58iSG\nv3lhyNGcca8UdczJXD0NHbt2XZMjNiLD4XBl0QygDdm7DxsVeGDypiH1/JwXGHvKqMWr8trjelEf\ntdL6AFTMtRxSSVL5N8xfnBnAJ+ztdrth7LGDjFq8pKI649j+/v7K4taj1ITFZkkWt+PYZcWDipXv\nIufFyubrfpPF0/f9DVUOP4BKmdTpdBrygk3Kz/0kFUONf/NzJfcTZdpH3z05OUl+Vxg+jRePi9xi\nKFU+nsuprLKTVdNeRUVFRUVFRcWGuBJGqtVqNR7Kq0/sOrHSHI1GYZUIhiVF410WOIlrKop5t9sN\n1CVMAV9++eXaVLcC5zJDfeC35XLZEHb7fFl+93L37t2Qf453rn5lzpHNFdQuX+3A2a3dmxfMznfo\nuM9isWi0La7z13qXed6NsUku5R7LZVVsB+6DY8p1VjEDbIZiurokVhGbXZjRU1HCcR/1jup9UlGv\nt7a2ilgYLt+DBw/MLJ73UQlfU0JvhndhVlG7mTnlnbKKHM6mAi+Wj0WnV04YGH/erOKfu65YWfVJ\nPuadCJgZ4ms9g8hl5HAOOC/mBOF/U0wCm0vXZXUUcuxTKTvlz1exoFRMMPVuMTPduu950fAI3mQ3\nGAyS+ShVWXl8eNZzPB7LWEvoJ+w44hNOj8fj8O3D2BoMBjK3pwc7iXEezpQpUUkelGMLW4q8haU0\nrIUrf2WkKioqKioqKiouE1fCSJnZ1YeHraioqKioqKgoxw8nsjlTnKk4OCl0u92G+JY9lkAzzmaz\naAoKfj6Lg1NxXcw0TQmKM5XehO9TKuxUYfnXWfymhImqXP662HnrxoVhqLQslwFFt5dS8DnR97pg\nM5NCLgWIN28rrzaOGZbyeoulISkF2kvFwwLY4QJlUWWKtZESlgNsylbjSo3h1Hssl83EpBdFqu/D\nfJ1Lcr4uXnvttWCKRZtMJpOi/r69vR36DpxOVJ3s7e01HFZS0oFNUDpuc44KbN5aN+J3CheZGzaZ\nk2LOLP57x2bJ3PulxOFwruB7s4RC1SULu/kcs9U5bd1xxnKeVLzGlBmc73NZ35dc/VbTXkVFRUVF\nRUXFhvjBiM0j5zX+ZhGch3L9VPdjF0fsyth1mgXLJSxVjr0pjWSrVtGbMFMq+nfpjpGf58sT20n5\n8pSyQG+++WbYdXz77bdF5XtVWEc8WuL6m2OkgHa73WCnuK/BiWE2m8m4Wj7XGuffw2/9fj/EvPri\niy+K3lGB4z8pJg2MlMrDx/C7We8gETufx3csKrWqc/wGZmsymVyIkeI4bmZpNtBsNe6Tupd/lxwD\nglAgi8Ui9An0oW63m3QSAXZ2dsL4VlkFcL+9vb3wDJRTxTGL1XtJlPDc2MuxD77M0+m0+JpUWVL3\nuCjLy88zy4cR4HJxnZbOvan4YMDW1lY4b5NQQujnniVlxPJi+jrudruN+IDT6bQ4krsHs3f8d6oN\n2ckil7S4MlIVFRUVFRUVFRviBxPZnFennoWJ7Vj9alyxUVtbW418VLwKVSvzUi1FahegMsPz/Xx0\nZwZrAThQGJCz1/sd1TrgiNq5XF3+ebH/x/DFF18ExiW3gyzdYfryKV3FOsFG1f3VjrVUM+Z/WywW\nYeeGcA6TyST8hvxn9+/fD/2Xd3oc+Tr2vpPJJDBRf/d3f2dmZr/5zW+S5VTvm2NFmQHz5WQXa38/\n1juq8BBcVxfRCXIuQ/8e7A7OwLtwLsDUfKOAeyimiQPB+vJ6cF5DlMVja2uriJFSYUYYKgfZugxM\nao6IgVkA325cLjUPoP36/X5oV84agOOlOdSY6fDu+VwXJYEoY4gxqx4cSkCdy9YW9BPU1dHR0YqO\n2Ewzanx/sK3tdjuMY7ZuqPcC46qihDNSrDG3ude3cXlZN4nzoItutVoNFlWN+U6nk2yndXRxV27a\nUx/7i9DuqajEKokrD7iSj3Wv12sMUo5RwlApIkrQbrfD/XgBpT4EsesBn2qkFDlhJ1BqEtsk7gqj\nVDzIMXb431S5UueVxjIpMe1xUlAup/q44jgmsW63G/o2ynJyciIFuSo+EB83M/vJT35iH3zwQeN9\nfBym2MfWRxPm+sGCsN/vhxRAwK1bt2QKJHyoUnG21FjwH+tSc6rf0PT7fbkAQYoo1MO6qZHMzuvq\n5s2bIYYbm1r8B3ITsxEyG5idL74vMo+yyThmosVxs8sXm5utmmLxLD9H55yAgFLB+EXMjPyM0phg\npRuDTeQa6He7u7uhb6uMBano37G+iPfjNDMqBVjJ5oTnMR7XWMzhvOfPnxd/Q9B/OUmzn7PMVr/h\nsXeskc0rKioqKioqKl4hrty0V+LO2u/3GyvH+Xy+YoIDUiK5XI4vFTnYMxwxZsK7gfLOZt0o7FzO\n3M4Uq2vOWaiSEa+LizI5qWvUM3K7Ix+ZOfZunmlQSXxLy7lcLovrsKQeuM9ydGKUD6JlVQez2azh\nPs9mIXaKwPUqZ2CK1WRmFTR5r9eTYwo7b4jYnz59GuqKI83fuHHDzM6dCY6Pj2U4AFzL/VhF7Ue5\nSpIcx6CS7+ZMylwWP+/k+pV6N44w7sHzXSlgIv/mm29CnbO4PZc/0uzsXf27xdgoTlYNlMwJihla\nLpeSkVSsssoggD6IOlOOK4qN4gj3KRMPszLI4fns2bOGuY+vTfWJmONSSuS+iUgfZf7uu+/CmHvj\njTfM7Izd8fPEyclJo9/FTJjeoUSxY9PpVM75qC+f2xTXmJ3VJcYc/t3b2wtzUY5dVP3cv1u/32+M\ni+Vy2ZDllHw3KiNVUVFRUVFRUbEhrpyRSgEryJiNNrWbVKt6rI57vd5KbrfYeSx4U/fGypV3T/6+\nHiUixNxOg3cGOK9EYLoOlB4hVobS+3k2iXd6uRALQGqnrnZ18/m8wTTmdhhKJJsT2ZfWh2/3k5OT\n0Cew210ulzIYnb9Wib/Nzt8vxT58/vnnQVcDHROzsnCJv3fvXrg3doPc76B3un79ejiO504mk4Yg\n+ujoKGi9eCzguejH/X6/oXdcLs9zSzKrdRk6z9FoJNkXH/6Ehcw5Nsvj6dOngS1CP1bjtpTBbrVa\nDWHx8fHxSm5K/x4pLBaLUOe5EBboqxdBSmNopuvBa2oXi0VgNpmdVX3Cs94qjIMCM4R4FutYuWwp\nbWZuDklpeGPlU/WhgHJz+cEqYnzl+gg7k/jvXex74cePYiRPT08DM8T5U/15L168COehnx4eHjbq\njdsV35ytra1GsNHJZLLyDQc2seT8oBdSKeToUT7uFy+5iSrnrea9ABV6vV4jVtV4PG6YntRA4oSN\naFSOfaUSipZ6R6yDEo/FGNQE4M1zF7mfEgxzLDD20El98HIiw9IF0rofc74vygch8/b29oqXmyqX\nR8xEZLbaTznJKOK+8D18f3r48GHoi1h4TSaTsNDjhReu4clQmSn9xN1ut8M1/FHkxMRmZ22K52GB\ndtF4Pqij7e3tINIGhsNh48PIgvzUc1W5Xr58aW+//baZnZufVLvlEs8C7HWE+8xmM7n4SQnGFVIb\nvW63u9EYNlt9X9xjOp02ypfbUHF7wMyb855jDz5/nmpfP7+YpRNaczJf1Yaxud4fZzE5v1PKC5sX\nEyWi+sViIRfx7D3vn5v77pXMr2qOMTvffOHf0Wi0kqwc//rzeAzwvzjOC2C8ExZhx8fHocyIzTYc\nDsPYxHuWeL9X015FRUVFRUVFxYb4k2GkSilMgFfWMQGhWXpX2ev1ZAyLlDBasQApoSrT2vwMv+qP\nMXDKFdbvqDZBzkX3IgyN2tUp016KDeLypUxxzFKpsqlnsClwXTflUihmDWA2hlESf8Xfx2zVDZnH\ngrpGjRVcC/Hy7du3Q3uBDWAhfS7Gjn8+mzKZzfK74263m4wtcxEoxkZFCVfhKhRibDB2yrn+BBMg\nTKwx5spHfzbT81JpKJMSts1Fel4LpXMISzdy9wF47mWnJL4n/6Zy1anx+PLly8a8vr29HcybX331\nVTgXbAeHyVAMjH9Gu91umIz9eUpor1hKfz5/x3h+9AwYs0V8D5zHITu8wD4XLgdOIjFmzTu05JxJ\nmCVDf2fTLsfVMzvrz3gPfhZ+Q3s9f/48tKFnxJLlyZ5RUVFRUVFRUVEh8YNkpDj/HaDYB+9CrMIV\n8G9YUU+n00agSrXDmU6n4RqI3FRE4G63G46nAoHmWA8OcpbSNJQyNRfBOuzTuuDdBHYqLK4uYX9S\nOzV/bYqRKhWMX3YdAIvFouGC2+l0JKuwabli/eHhw4dmZvbOO++Ymdm7774bjjFT6HfU33zzTWPn\nymDtiHLp5sjCeIZnEDiSc04UvEkEfwBj+eTkJLA72IEeHR01WLH5fF7ESClHgFarZV9++aWZrebL\nU8g5rfjnYL4YDAYbB57kXGuYg9vtdmO+y81PseeZrc7HqZ1+7B1S9cHsiApyC0sC2A51r8FgEFhI\n7rO+DiaTidQK+fm/NBAol38T8Lso8bWyoqCN0Q/UuFXM6nw+Dw4NzAax5hHn+XHd7/fD36wDRvgO\nzv+YChTK5fT9WH1n18EmeQZ/kAupUq8VFY8EYPOWpzBjEVcBfNgmk8nKxG529rHD3+iIo9HIHj16\nFL2filujjuU8ZZRA0X/QYp5cOajI3Oui1PzFAtlUGXNePaXwC9WY+VAJI/2H9DIWqR4+mvh4PE6a\nj3Peh37hw30CC1eOJvz555+bmdmdO3dWFhZ4lhqPqZQKeJ/BYNBIL2HWFNWyMBuT2MHBQcOjU3lK\npeqhBNz/UhHhcaz0YzcajYJwXqXMyX0klNemR7fbbYhvt7e3k2YR9LHRaCSfgQ8k2lyVs9PprP2x\nucjHbV1vW+WMwzGNlCMHcHJyEj7qcD5Q84XZucNDKu5cLK1Sar6NvW9q8wLw9y7nAcmOTGarbZ3b\nrPu/+Tw1RlDXynlmZ2en0Y9z87zyDExdU+qUwnWsHNdiqKa9ioqKioqKiooN8YNkpIBUXAqmb5k1\nSDEqvPL2O8zFYhF2Y8otlHd5fnWtkofG3qckzkir1VphwHC+Eigqk+cm7M1lmK5KQxjwe/jcZTH2\nqaR8ObOmMimUiuZflWnP7Ly/sfnYxyozOy83+m6pwwWLQ9GPeYeGNnjx4kVDpHnz5s3ArGCs5ISg\nOL63txd2uamQDerY06dP7dq1ayvlizlcXCTMB+qXY1kpKJFuCnt7e6HeFPORukdpHDm1y+aYRyqO\nGIvJPXPJZrdUzsPZbFZkdrkslI69XOJZz8oMBoOVEAw4B0xUKkTBzs5OI4Exf5OUMxOLtlOx+S7K\neuM++J4tl0v5TfPjjy01OVmFz6iwtbXVcGjp9/tJ6xKeG2NfU5IMhp+zMvw0vgAAIABJREFUOp1O\nQ7jPuUpT+TI3Na9WRqqioqKioqKiYkO0XuVOO/rQVmvth162+zmAXVmr1cpGzfbPz5WptMwpHQ7r\nevz92B4eQ0o0+H1D6X5SAUXVjpBt8urdfH0oMX8sKJxHaSgGfw2esWmdK1fonZ2dRq49zhUWuw/K\noupe/aZ20l7DpdpPjZ+tra2wC0T0dMW2xNqPGTWzuHaSxa1Ks5EC72a9xqbdbgdNGeo5phnzeOut\nt+zjjz9e+U3psSaTSaOfc7uqumE3dLTJvXv3zOw80rzZeeiEmM7Fs62dTieEn8CuPcacK3f6Ulwk\n9+WmephSJlExwKosb7/9djgOjWEpm9HpdBr1p9ojFgKitM7xLhwcel0nAZTXbDVQaIpRUw5hPLY4\nH61ZXi8I9Hq9ZHBlZa0C2u12YOhg7Xnx4oXsJ0DEIiIr/Qdt2kt9IEvBnUiZwVScETQ0T5gpjyke\nBDxJqGvQiHi3yWTSKBcvmlRsES6vfy7jVQijLwLlKck0MM7xsYLM9IJLDVz+sJutToY8EZR4beZM\ni5exuFcTvFqEqWS/4/G4KLJ9zLlC/Yb78OSJ8qlo8dx+PtbO6elpg3aPLepS5txc6o91+zmbVlLm\nRTb3ezNODjlPUh6v3osx1heV9zGi02MB9MEHH4T69fKAXPlarVYws6T69EU3s+rZfnFV6hDC4MjW\nfv7kdCDchuydaHZWV17UzQsf1O2HH34YFtmp/qf6bKlH4jpmJjV+ODYf3p1N2Wrjjj6jTPK8APKb\nidFo1Bgb3F6qXFx2vwHi7zan9MEciPNUTCiOaccLL0gOcE273W58N1VssZJ2qKa9ioqKioqKiooN\n8YNmpGI7ODO9a18ul41dXY425J0Q/la5yZQrpGID1OoV53GMEmXC4P+nqEsVQVqZv0rcZS8TOYam\nRGivGAlmkNT9uB1Kdg/rithL7rPp/UrNFSr+knpfxRbt7u4mTTW5iNCqLJ5VmkwmK/3c7IwxY+G5\nmRaWMtuSMvvwHBArX2n9l7KJPj9kaWiWXPiCFBsYKxMYEJhnmFVgMTHu7eOTeaANAeXQ8n1Bmas8\nY6LYHTbdq3AfDJiZ0YaTySS07507d8xsNUq5si6waUw5XTDjsw4uEkOKoaw4ykzv/wbwfhyj0dcr\nMzmo++Pj4zDuAXbgQF/j7ASc8Ni/P9ezSm6O4zzf8ZxU8r1Q/X2xWDRMjyqvpEdlpCoqKioqKioq\nNsQPmpECmKHJ6aZKVvacvZx3a1gh847aa2lYCMp6Er+jarfbDXdQ3sHwDtzvRHMCT94le8Zs04Cc\nl4GYqNVMa8ZYFMo2chWd3oPrI5epnrUCsfutg8t2fMjp78zO6g9hNjgfncd8Pg/MBfrb4eFhUi+D\nHboSr6uQEtxG/tlm527Xsd07a61wnddmKczn8waLYrZ+Py9x0gDQd8Da5UI/AN4xwIN30Z6dWiwW\n4XlK2M9AO6gAmUrPxTrQEp3Y94FSpw7FSCmrRew9WMyPf/EdYCbKzyetViu0B66NMY4lcw3XfUoD\nuw6UhYPHhXe44e+Osqxw3/H1yqwN1zXGu3p3zDGLxaLRL9vtdiOkCzuOcLn8+ON5CGOF5yzFPvHc\n6p+xXDaDqpbgShZSKQ84/p0rocRLhDsHTxioYKaK/QeDB2kq7svp6Wmj/KrR5/N5+Hh5KhNl4H/V\nu3AdzGazhgfEbDa7tKStlw1Py6uYIvzBgGkiF3UeUB9zNs/iuevEjPJlZ7Mb30MtckqSYOe801JC\n6tFoFCYRtYDie+A8lbBTASYPVX/L5VK+m48js1wuQ1tiEaHinPE4UyYbjvui6vIy4hetswBOeQml\noLxsOa6WiuvD16pNhBK8Iwo3PMcYqcTXpWZws6aDTG4hum7kfX5fJVvg+/p77u/vh0VkziMV40Yt\nHFSyeWC5XIbfYQIcjUb29ddfy/fDNTHMZrNkHXG/KfEu9teqBYg3f3a73cb8ube310g51G63w/jH\nIvL4+DjpLZx7dw/2ouY5klOmcZn8tUDKLMye0K9i41BNexUVFRUVFRUVG+JKGClFx/IOTK2oAWap\nVFwlv3tid9bUqn65XNobb7xhZue7xcePHzdiaCyXyyCc5YjPXlS3WCwaK+herxfux3FpFF3tV825\nOFf8DNyPxX+XbY4qFZYDaqe3WCwaAtAY4+BZkdFoFOqQdxYlu4xcTJmUGF0xKlyuFJbLpdwp+/6u\nyjcej+3+/ftmZiHxLbN3HG9MJbzFvZUAGTt65Ta+XC5DG+Eeh4eHDXdwJfBUEaGVKVuBj6XYvnWS\nwm4CPLsk952/DuMf88WtW7fso48+WjlPhYC4ceNGiAHFUCwWzLiIxs1Q/Wnduup0OtKxIGVO9XOm\n2apcQplTgFSfUGXnsnA5UVYwduz4ELuP2Rk7i/Gg4n7h3fb390PMrk36n5+nNkk2HzN1poTW/nqz\n8/K/ePGiIRVptVqh3vDvzs6OdHxAu0J6sLe3F1hA1Z8ZaC+Mt1jmEoCtPSoau7IQpQBG0mw1TpvZ\n2XjMoTJSFRUVFRUVFRUb4ko1UmarjIuZziytdtYxKDtqKmo2hLHHx8f22WefmZkFZip2LVzJOUt4\nSZRW3ikB7NbOK2qVB8uDXUkBZupUsLJNdj4Km+iNVHuqnbSvD6Wb2tvbk8Jff22MffJlKc0Ozlog\nFSYjFRiTmUZmWbyuj5lV3vGBkcCO7/T0VLI1KQYn5crLDhIM9HfkvuNdO7MAXu82HA4bLErp7l0J\nX/m5fB6CUr4KbDpWuB5R/ty7g3Hc398PkeBTGI1GoX+wFkTp0jYF64NyAYoB1YfU3MYshHfMiZXF\ngx2C+P74Tc0vKag50+z83T/55BMzO9NK+XHL+kmGmt/9HBJrI58TltHv95MC75TVgDW3yrkKUP3/\n8PBQfq+9FeXrr79u6JO5LGC6u91umGNKdZEc+d9rqZiV43kZf7PgnllWlAXA3FXCRl9pipjSj3rO\nU487m0oHUgpv9rh7925SUHjr1i0zO0t/oZ6DhsN9eZByg/mPa6vVaiwIOTotX+cHM99PeTvmzFr+\nmWblcZ9y8G2iYoCUtuHt27fDJJmKkB2LPePFvMq7L/Z+Je+uhOpqsba1tRXqQE0Yqg7YeyslUFfA\ntX/2Z39m77777sqxWN9Q5kg/Vra3txsCVDX537p1ayWNiUfKI9HsfOODsnhnAhxPiWFzyDkOrJvW\nCFALS8Zf/dVfmZnZkydPwgcbUJuJ69ev28HBgZmdRdpeB4PBYKN0IR68QUul8uGPNT6gytMQ6PV6\nK3GGgFRiefYk83HJVJn29vaSH8ncAkmd79u9dEzx3MD9L5U4WQmoY0j1WbRHp9MpWjSoeUy9+8HB\nQWgvyBF4XuTYUhxZ3qw8g4ACe9GreuEFl990qAV/p9PhsSJXvNW0V1FRUVFRUVGxIX7QSYtV3huO\n7ozj6+6stra2GoJcvh92L6puFIPw+uuv26NHj1Z+U8lymVEqjZCc2x2Xir43ofdLWacUC6TuoaLJ\nc5gEJR70O9FYmAS1g/NlUDsWxUKV7jBjsZbAFnz77bfRspid7wgBxVzG2t+bCDg2SmpXt7+/H9if\nL774onH89ddfN7Mzet7H+opFHYeoGsJS1UY7Ozvh3fGeiqnZ398PdL/qLwCzBcvlMpgfcS2jJERF\nDJftrKHw9ttvm5nZxx9/3HjO7u5ug6Xb29sLfYfjICl4BiTHxpSilJHiNuS8mvwvY3t7O/QJ7m+Y\nB3CNehYzJjh/a2srMNgQSs/nc/vlL39pZucM6Oeffx4kA8zip8YU96tN+0kJC+5ZExUqgEMJAFy/\nnEsvF3ohBR+ep9Vq5t/LAXnzbt68aQ8fPjSz87rsdDpJ5wDgspjVHKg+KiNVUVFRUVFRUXGZuBJG\nqtPpLM3iOdQ2XdUrpmET92h2wcSKG7ujwWBQLB4t0WnFdGLKpp3KNxgTGV6G4DS1W4qVP8dEAesG\nnGNWLxW5WZWJ82CpsAZATrBZwsBxkE4WYXtXXi4n2JSjo6NGIEh+ho9czu8W01Wo9yhhaN555x17\n7733Vq7d2dmRkbtRLg4cee/ePTOzsOM0O98V436TyUQK+NV7Ajh/Nput6BxURHAgNx5TQRlTx8ya\nUccHg0F4Duu1lAYs1Q5geYbDYYNB2t/fD+3K7uVKg4TncvlTzHspuN+vO2+zBgmaO2YmlUOI17Sw\nUJ2v8+FoZrNZeAZHg8dx/La/vx8YDmY1wWyhjabTaVEGhhxU8E3+hqn5sXROvwiLyjqidRlc1hap\nNizNDlCqc07l8WR4/eQ69ZJjpK5kIdVut5dmeQpTgb0F8Pe6H9QYMOlDDJuLooznM+2OCfey6EZ+\n35SADvALx8tYSHFZmMrPlcXDf4yuXbvWiC/CJp1YGczOqHo285np2Excxlw0YbW4Snn8+fubrUZm\nVjGZgNTHczAYNOKq8AeQRbWpeDTe888D1DovitgTtaSsAG9iMGEdHR2F+sCH/NmzZ404Q2wWSonE\nFY3P/V0J/EsxGo1k5HAgt5BS90t9MFQ9A2zq9V5FjBs3boRnoC4nk0m4niPcY1GqYq+lNjFqo8SL\nRnbQWNdzjJ+hFiOq3/nFy2AwKHIsUO/BJkCeQ0o3dxy7LXZ+bg7h9y5dmK07p3N7rUtc5BzCMNaH\nw2EjKj0nMsZcPZ1OQ13judy3faqYWJkVSr9JahOWQzXtVVRUVFRUVFS8Ilyp2FzlBVJMQg45s4ZH\nzg0ZYNMedu+lcUlicYl8/A0VsZx3AbndgqeD+V4q/MEmyO00U6au3O7ORwzO7S5VmIQU86JMuznW\ns3RHqpgwYLlcrpgV/HOVODQWK8bsbNfmTV29Xq+RQwvPQRnMVpkcNT7eeecdM7OVcAgoU7/fl2Ml\n5YaOZ/T7/dCeqo3YRb1E0B67D3ARRmp7e7uRo3A+nzfGq3JDV1AJoBkQlvtI52ZnbuO4NzsqeHCe\nOQ6dgh0++g7XC7dXSpScArNPHK/HC7JzbugMVb/qN89ScQ7P3LjlfmkWj8PG72l2xpyn5n2ey1Mx\nrRRUmWPzbUpszmVR3w6WNfh758rjWSLv4FECmFXNmnM8Z+MotS7x/OTfib8NQMzigPkEz1VrCP6O\nWmWkKioqKioqKiouFz/o8AcMz7yoIGkxd3WsOnkljBUydrYczC/FVvV6vbC6x/NPT0831gfgnmZp\nTRa7tavs1XgWr9Bns1mDASnVpa0TuNOXgZ/DOqESlo3LivNz7vFqF5VzSU4JsnNB7nJaKxzzjBT3\nT5XzLqdpwr25P3O7+2s58J3Kv+ehWJTt7e3AhKUCaeI5/hl+N650Tt1uN5Rf6YoUa8BMnWKQ+Fq/\ny1WBYJlNQH9i7Qa77JcwN4PBQIq5UZfQSCFQIWM4HIb+gfrgOkP793q9oG/D+b1eLzwX1+zs7IS/\neY7BO6G9YmyAYh98f9uECWStHO7NzgJqfihhi7vdbqijVNDPUqgwA2quWS6bee6Yvcuxcql5kdln\nFTi49B2YGeLvhC9fbk5V5VP593I6Y39v1lRtmlVge3tb5m5lETyOoa55HlB6xB+k2Ly1ZmTz2Hkp\nwd66MTLUM27cuBE6BSd2VXS1j6TrTWx4rjcVqAmBvVPWEZard/KJH0uRa5vUwpF/V+fxIPX1piYj\nBSXmjcWWUmVed3EVW4AC/jg/Qy2QcGw4HDbeI9euOW82PxG0Wi3pFQVwnLNUDCBewKUW3pyKwVP2\n/X4//KbaCuWcTCaN/qe8BTlNEnuxcsJu/7HnxQYL9335eQwrkzKXIbVYY5Rs1trtdlhoqdQZyKjw\n7NmzUIdYgGxtbYX3xLV7e3uh3rjspfGc/EeJExnjPVS6KkZqAaRE37wJVOCxivdFWyqnEzWXqbGS\n2+ykEDPnls4rqbmm0+k0vgk8/6T6p0K73Q71xXXuNzkxZ4NUW6OPc1nx22AwCH0Rz+K+zdlAfKww\nRZ5wVHRuy9JNsYJqhyo2r6ioqKioqKh4RfiTNe1xlGO16lTxTZgtWjd+EZvffKTyVquZG68UKVZj\nHcRcXFPCxHXjjFykrMwWcVum3PdTUCaiWHyj3E7PbNUstIl51oMZKd7Z+vvxTpPNsJ6xVGyrqoOc\nSTZlPmSTEotrfVyinDMBjzdlJlPXlIpuU6EuuA0RW+bk5KTBfHU6HRk6JcVs8Dnr9gUlqs9BMVd4\n99u3b5vZqlkQ9TccDkP5wVh2u91wH54PvKmGRcn4bTAYhGs4ej7OQz/ibBEx1gnHfP0xw7Fu9PnY\nnOTjQ3FZS++pTI+ptu/3+w2zm4oqrsqhWM2YwxLPE748vV6v2HEH4PdF/fO1vj74PBV9HogxV8wC\nx65dBz7K+nQ6LbY4+DItl+fxvHCP8XhcGamKioqKioqKileFHwwjlbLxql174t7hGrN8/qMcC7FJ\n4EkzzazEhJsKqZ0ZBxQDYqJpH1Yip9PalIEpga9LDqqqdi653G5AaRT73Lut209Sx5Ur/jp5oVLh\nArj+VB8DVF9j1iWlJ2OGiDVP+K2kHzNLqpipdZ0OcmxQjAVUUOOL83yZXSyormLtFAMbu1Y5D9y5\ncyfcx8zsm2++aVzLzgF499lsJjVZigVUgUeVYw7A7Ihn8nJBMJUjSul8y2VHmXFtjolJaQxj4O9T\nSfnW1VualQd9xfXD4bDxrlzn3imK/y6tX2bjFHLv5JnB7yMv3mAwaGiDVciG2HeFvydmZ+1CfUUy\nUl314/cFNvekJvXYR9Z3lOXyPJw9T5C5yRdQE0uJ4NrFmWjct1Twxl5Piub1FHusTH6h5MuT6kgX\nWUDlBpUXSbJQVIHNEPg7tcDkjz4PJO+ZEau/0n4CqEHK8JHDWVzPda/MVZhw2MSDa1QKEGCxWDQW\nYSoGTc6kzceVGDnnYWh2Np7QTqhzjtek6ozvq8ZUyuSFZ/Lz+N2VswH/xiauGNhcye2lTLalqaQ8\n2OzGiwPUR6rOW61Wo45i3njKw0zNCSWLv+Vy2ZjDc/NLaiPHf3OdqjhNbHL09+DneS9FRm7uwruV\nOu2o83KSAV9+/23y767iYPG7lZpHefzgPXkDXjIvxuI/8qIaz/Lfw1IHI4VWqxmLcjweN+JmqflW\nmdqVPKgo5uRGpa+oqKioqKioqLh60x67JJuV57JaB9iJAOoZaudq1hS5t1rNSORm65shQJdPp9Ns\nCAO+ry8Tfo+5Dft3ipWrNLKw3y3FxOEpk6lipBT7xFDOBgqpnSWzHZ7NXEdsXmL+ZNEi2mY8Hq/t\nlsumKr8z4phmKdNNjJ5XZvBS+JgssbAJyLGXyp+osL29nYwBpBix5XK5srs2i79Tak5g8apnWa5d\nuxaemdqpbiJKBwaDQSPB6unpaahLPF/V+WAwsNdee83MzB49etQoJ5tp/bup8CE5xwI1Z6rQBNwX\nlft+CeOcq1NmdktCF3Aom5TE4FXAz63KGSdWFlXnKv8m1703na7DAqFe2fSo+kmsnPxOXAZmgFNy\nk1y2Ex+xntnR1Pe43W43EqjH+ksVm1dUVFRUVFRUvCJcOSPlwQG2UDYWjKsVqRKq847OvyNrENiG\nWyoEF+9TdE1sVexX1GqnyyLN0vKx5qE0/EFK94XymuW1Y6rOlVZFoTT3XIqtY3dqL25VbtLsMpti\nzNR9WJfGdn9f5ypCeyzvo9I5pcaAYmjQx7k+lN4k51yR0p2l8oyZNfVc/X7fbty4YWZmX3/9deN8\nRkp8y+/G44IjY6eA+UHpjlKC/IODA3v69Gny3psCLMpoNAp1CSZPZTZQdd7pdOzBgwdmZvbixQsz\nO8vXh2uZqVnXgUbNs4odAZitZlYBUE4iuYDKm7CnJWDLiNdccaidXJ35/lc6V8dCHSh2j+vU3z/G\nKvr6XywWDael+XzeiDC+XC7XEuVfFDn2qRT7+/tmZithP1IZSZgRU7lvaZxJRupKFlKdTmf5//9t\niF9zH1ee9EuoSUVN83M2oXRZOGeW90RQg6HUtMNImcFiHTA10aUWNOt4SgLqPVNljS3U1ILWp1tZ\nLpdFgmc8m+/Hk9a6cWuUV4fqO0rQzFHMeZGAQY9jPBEiHhI+ioy9vb3G76XJsnnSVEmV142UzOCN\nEKdeMDtrq/v375vZ+eLq2bNnjXuo9xiNRuEjF4uv4zdhZvlYV2Y6srkyifb7/bU/kqXAAnOxWAQH\nBdU2gIqk3W637d69e2Z23rc/+eSTcBwR02NJeFPjQcWq4zG17nxRYiJn5GLMcfng6IH3mE6nSdlI\nKs5VSbnMVtNCqdQkvFhMEQJqE8DXoHzcF0uBOXMymcjvoprLeDzgucBleOGxI9UmplUf17HUMzjm\nvACvTsxPLKGxatqrqKioqKioqLhc/OBMe2bnK17eJaiVr8pvVyqaBpjuU0JmRYmK91mbYVIrZRbL\nqbxKKZOOSpCqcu3FzJDeXdTfK1XulFAwxdqsI1RXx1L3TolNeSefY8dK2lPtbJRpz+ycdfj222/N\nbFVUffPmTTMze/LkSeO6mAkQsYUgLDZLswrr7rxLzS4KPC7AhLBZ7Fe/+pWZmf3ud7+T5fT9YDab\nBaYBdeH7q2cuzc4E4maa+cKY6/V6K+ZHs7jjS4olBEr7DodTwE54Mplkc/GZnfVjb3Zpt9t29+5d\nMzs3X3700UfhOCeW9dHJzdLvrtgMZpxT4xHt0ul0kmMvZWbu9XqSYUdbl5r9VDYDlnqk+nkpe8PH\nUkwTj1Ufry3GNjELiHunIpEr602v12uEBmi326H9S5Op5+YE75SiQvso5GQw6h5wxpjP541xsbW1\nJeOg+bH38uXLxjEW8FtlpCoqKioqKioqLhdXwkh1u92l2argDeWYTqfFTMimKN0tliInNk/t1ErZ\nLOXqWhIFXu3gShmfkkjauWjiwDo7dF/mWI6yFKsXC3GB8/216j1iQnXfnjFGCkzIuq7/169fDzuq\nVGRzBnZjvKMCYm3k6yqmGfBM7Sau/eizu7u7DX3Om2++GZgqVX6OXI2dcowd8WEDzHQAU79r39ra\nKs7T6AOtKuT6O+ssvf7z9PQ0OaY4sKQSxkKDBkbj2bNnDWZ2a2sr1CH3z5SzhooInmKkYmLzi4Qa\nKNEb9fv90KfxjrPZbGNmtUQIzs82W51/PNufC6VycHBgZmc6tlRQ01brPPgqsydo69LQKqij0tx8\nCrksFSXXm6W/NYxUtoCdnR3JWJdEWVdt7axBPxyxea/XCwspXwlqkt7f3w8TLM6PCS0V9adMgJ5u\nvWh6kU0RW0hh8sc7xsqWit8R89orEYLHvLDWRcrUynWpFnxKbM50dGqCYqwrKFeTYYn3ntmqecGn\nrhiPx7I+fPlu3rwZzHt4b048m8JoNAofOo5wnfK8K03gzWXPbQ7MzurFLzqUV5HZubkM56lJdH9/\nXy5Kuc6ViVqZVnz075jpVPUFb+qITb4lm6t+v98wz8RMiqhX9gxT9QTTHu53cnISysL3xkIf/WUy\nmTT6NKc14uf6jyab3VQfY8C5AqZRjgK/LnjRBMT6WAkua5Ot5rHSuVWlZ+K//ebO/+3nV7URVXXU\n7XaDJILPx3NV0nJ+vv8W8XNjZTUrX/DFxpOPHbe9vR2+n1g3sBmeNy7+2xHzjqX6qKa9ioqKioqK\niorLxA9GbM47SU91x3aSfuXNZhfFduRcoql8jWvV8VTdbRLxm5kJteJPicRjwmIfLTcm7My5iZrF\n6ypVRzmzpVr9A6WmRyAX74WZBNV3gFKGhsvky8p1yqwGfsPOT0Wdns/nwYX94cOH4TiE6thlMTPg\n46Ix1K6dse77KuSewczUrVu3zMzs8ePHjfN4fCuRK+6D3/h9efzz7j/FTqh4VP69+BgzoSkWpdfr\nJeO+cUgM3C8lXjc7byfUCzNNfA6E/WjP7777TprlwAyBuVKmDu7bauzhGRzxHWOP+6diYNHWh4eH\nyXlHOQExlMXBz2cx0xOLx3HsMq0PMbbS1wtbREojm3e73eSYxVg5PT0NbQ08ffo0KYJn9pHZSbOz\nvu1NZ8qqwdISgKUn/n1KsK5ZNhVPEuUxM+l4wfVD11ZGqqKioqKioqLiMtHNn/L9gFftqZ0eVsLM\n+LBoLhUgjFezJcLDmB6mZAUdi4qtXDp5B+fBDE0q1xWHjMCuk+sxJ1pUAm/vHhtjXnxZ1Y5VMU2x\nkA6pHTCLEVEfXG9e3KqCk/q/PXIiRyUsV0D5wdTs7u4GNinlvjudTgMTxWEcEDIBu6zpdBravdSF\nXYF1LrFggGZpd3CVq47HCteZck1mPQ+Ad+LnQ0OF3bYvr9cWKu1IKryJh2onJfD2dZMLL8G795L5\nhLUbSmOoygIW6uTkJNQX2mmxWCSDm/py8t+DwaDxvs+fP29oX5iV5bkLz+Oo/eq5KnSLCnHgQ8Uw\nWD+jNEGxwK4lKNFNlmpbh8Nh6NuqPlRZcwwyO0P4aPwcdgXzSa/XC6woj2cfSibGXHodq6rHXH3E\n+jSOratf4/kE5VMOEMxMK7Y7hysx7Q2Hw6WZ9tBjupIHkF/4cNyN0rhFADf6ZSdJ5ufn4qSocpmd\nTVRoRH4PUPYYUMrDyQPPUfF1+Lkp01AKOa+uFM2roDx9YiZSDHBvUkC5zOKxYkqgFn+xydcvXpbL\nZaN8vlwxxBL2przFsICYzWZyoiuJAt/pdMI7pz6upZ6aOJehkpFyu3kRuNl5fK2XL1+G8isR/nK5\nXIkl4+/Dz/XR6dvt8yS+m5h2NomQb3bWbnhOyguQU2fhWSp1R7fbDSZgNp2hHriuUlBeoHju9evX\ng+cl15mqN1zDY29dE7IyQSlTJc5bLpehHdbdVGwCHtObPk+NKb9A95vTbrcb2gnPG4/HjWTp/X5f\nblRUveIZvEHzfWV7eztccxn1qjboHEeON/RKkoHzvFce/3Z6ehrKXJLQ2qOa9ioqKioqKv4fe2/W\nI1lWXY/vmCPHyqy5qpvupoHGZmiwAYFsS8gPf/0+gR/9CS352baRqecLAAAgAElEQVSEkJENRgKE\noBszQ8/V1TVX5RCZERn/h/Q6uWLfdYYbGdXZwFkvlRV3OtM995y19167ouI54UKdzUsVphWYEmdH\ndb+K7XQ60vyxLEvBKHXSVbm7eKeWWhmnktFubW2FHQartvKK25vJcs7hOWdzdZ4PP42ZKJXJzj8D\nz4lB7fy5fRX173dZh4eHUk7B4zyh0PP5PDAD6KPDw8PGOGdzH+8Q/a5JOaOyOYCBpLXvvPPOQl1i\n9WSUMKbz+TzpqMrne9PzeDwO5gP81ul0GkzZ9vZ2aBcui1cpv3btWnBa53KBvVNq5ix1wHUqZZUU\ny1aids5g9hn1i5lMzBZN2ejDWIDJSy+9tPDbZDKx9957L3pvPJ+ZUFbg9ybF7e3tYGZWsiUMZg7N\nFhmp0m9Pbuz6fithd3CdMgsqWZUcO47zMD5xD5WlQCUMNztjSvi5aCPua25zHh98bRugrEpLbTgc\nBvaP3yWV+9QfY53D3Lzt66YwGo1kBo9l0e12g2YX6nZ8fCzntspIVVRUVFRUVFQ8J3xi5A8YfkXI\nodWl/gtqB8G2Y8UCeftrqVCc2nnP5/NilgusE4d2w/aP8q+trYXysf9MihUbDocNh13eXTF7V8pE\nlDAbMXFDVVZWeEbdSxAThUtJRKBvlG9J6jlm7WUw2HYPP4bJZNLwKSgVFFxfXw/txm3E/kP+mPIj\nAWJsW0nIeanSc7fbbWRkZ58gLldb3xJmalmZGW2uchDys9CW+Hc4HIZyoe25nil/rslk0nr8ArEx\n6+ci9mNMMVLj8di+9KUvmdlZPse7d+9K5tI77LMjuJKZYX8yn7csxkgpKKV51I3npNQcw/6Hylcy\npfRfipy/K8DzAMYizo+F3as5WPnuoF2YxfK+fB5KbJqzXeAc9X1C3+A95HM4GAZlZGaq7RzJzt8q\no0IpuwbfYRbrBaOKMcbjWEmecM5d71/n/MQ+OcrmnCLGa1TE1J8j9zEzHSHBdPSyqrlmZQu3mPYR\np3IwK3dEVVAfvlgKBnb69sdjEXAqGs/TscpJdzabNcyPw+Gw8ZLm7ncelFL/PE6UflXbiSAFXkip\npLkpB1U296m6gY7mSBw84+nTp63p/VRQBEdv8vkl0Zs8JvE3jwtMgD5ljIei2mP0O56t2ggYDodh\nzKKdlYN/bnxioRpLqr4s1tfXQz24TCVOsltbW/blL3/ZzMzeffddMzN7++23G/fgBSjqeHR0lAwO\nUGk51EJKvTMqITub2nEu11FpeJU8gxXucwtzv3HY2NiQ755/HpvVgDbuActot3nzHb+HJaZ2j5RL\nBhZUo9EoLMiZiOAx48t3nlRSDPQh3rPpdNpqE8zo9XqN9uN+wbuA5/DxXq/HbVNNexUVFRUVFRUV\nq8Qn0rQHKAc17GJHo1Gg7bGy5hUwr5RTq3WlI7MMS+JX422S+XrKkTVeVH4zgHdFMadpNlP64zm2\nxVPwMTpYhayncrulEHMALSlzLIwWwDiK1aOtGS+1Y2V2BIiZqH347tHRkWxTz/xdv37d7t69u/CM\n9fX1UM9lQpM5KAHIqcTj/JRZFWYBxT5xf6h68zM988PXcltyzjB2QsY16Du1w1VSIcxE+ICRXq+3\nNCOl2u3GjRvh2WDUSiVbbt26ZZ/5zGfMzOyXv/ylmZ06PCv1b4WSpMCMnGnPs5Nc19R9u91uMFvD\nZMOJh1NJaxXW1tYac6RSQFeIqY5785xyGYklPk+x6Oq7xyw1s1AYE3hf+Dc2L+KefAzPxrWcHzT3\n/WSpCZTZmx6fB3KZPMxOTdaoG2sqek0zzDVmZ8w0M1eoh2PlKyNVUVFRUVFRUbFKXAgjNRqN5maL\nq16WI1BqxUqgMOdo7Z/BzE9qV9dWioGxjNhXSmixLXinMRgMGsxWrL9L2lIJqDJivmI45n9r41CY\nQqrsyneMy3qe8Z9zNvcsIO9O2T/Fj/ft7e0wflToMcC+aIqtUA63qTIz48O7t5JwdRXSzeMPWF9f\nD2Xmeis5EuUwnhLhZd8Ydoz27+La2lr4jesECQOUhZ1XAcUwsC+i2jGnGHEVqHL79u3QZ+zjVeJX\n85WvfCXMJz/4wQ/M7HSMlTpBe6ZB+UMx8Nv29naYo2O+QjjGTssoiwLal7M2+O9ALMdjqs1Tx5Q0\nBtcz1Qfb29tJKQu0wdHRUcNPTNUhVre2Dv6l86xySvfHzfLitW3Fac0W+9jstP1Kyry1tRW+mzFr\nQQmYYfftyjIU9kl0NleTofrwzWYze+GFF8zsrOM4mSuf7zt2e3s7dKiKElLKvMpZO2fa8WCHPI7o\nwfVMC/sPEOvhcAoOUJH47dmzZ41n+5fGv3SlEVdsMklR0rGXVOlf+QksVpYUUjpW6sOsnKCXGfNt\n+5+d67l9/H3W19fDuOQoO39vPo+BZKT46I/H44YqPk/IbZ1cVeSdqkcMKnKQU9yYxWl6/M7RrCmz\nAUdNoZ7KRLi9vS31nnAtnlGqucXmeWViL41IxAJjZ2cnOPiqDWQK3/72t+3OnTtmZvarX/0q/I4N\nHsqS07vKlVnNlUBqjPF8kTIZxaLAFJQ5GOXBsaOjI5k8WG3uUhGr3gyPe6fgg11Go1HjXeZ2YTOs\nN93xfdjcy+VRSbBxHN/Rvb29sABR7ZZre7wrvFH3mxgeE5wWqGSRMxgMwtzBi3/cp9Q1A+Vk0yOb\ndpW2GMAuJlVHqqKioqKioqLiOeFCnc1jTr90nplpc18bsxCcprFSLlUfLnV8NlveRJRjZVI7hG63\nG5zvsavwbIZnpNpIDrRlYRhK6TlF+eZ2Qm0ZJt7t+ufGHKNL2JpShWR2fOY2KzXj4hrsqPf29hq6\nP8z23bp1y8zMPvjgg8A+qBBl3kmWtkGqXZQmELOtqd0991FqXGGMHx0dhb5UZjc2p6qE1inncGbt\nVKLb1LuwtrbWYBj4vNy48mrs3W43mHJL5zgwk9/4xjfspz/9qZmdmQW5LCltsWWgzEzLZKnImQM9\nUkxnLr8iM8W+/My682++DIPBIPQrK5KDyUNQRakkQuw7wHOXZz03NzdDXVPtx6YpNQenAlt4noDz\nv1Jrj7F7PkBqGVY7B38f5WbAcwMn81ZtjneJg7/AEFtlpCoqKioqKioqVotPjPyB8uHxdnj+Te34\n2FchFY7c7XYbsgF8n2UcoM9zLXYEKPt4PA7lwwqeQzqxA3r69GlgfCBAeHx8HMpy9epVe/PNN81M\nC+YBKlQ/FqKLsrKzbsp5s1QdmMu2bN6oTqcjc+2VOj+WnKd2tnw+j1nvLMs2+VRQwuXLl8MOCIyP\n2Rnrk2JCX3/9dfvZz35mZukxye+Z8ofhMvt3Re0+lWPsaDRq+BvxbjHVBkrqgPHiiy+a2SkzxSHO\nXmTw5OSkiBnZ3d0NLAI7Zit2A+DccyoYoATD4bDhW6IU8HN45ZVXzOx0nnjjjTeWKssyYL8flqGI\nQbEx7P+nxuIyATzqO+GZl5gzdyq/qZobOChCzaPqPSx10mZ2DNcoZXP8vbGx0VCd73a7jXoqP8Ht\n7W053pXfH/sS4Vmp+Rrz2MnJSeMZ7AuGssfyJfo1wXg8DgwTyyD5jBl8b2BrayvUAxaCDz74QH7H\nPpHO5oPBIDwUjYCOjr0sXiWcFWgBlQ5mOByGjstpd3gzRMykiBctRY/3er2FCASUBdL1uO+jR49W\nErkWQyrCo602UuyaUoq2NNFt6QK6dDIq1Yfy5Ss1OaiFhVqcqrIMh8PwoeCJ7ebNm2ZmwXGYwUlB\n8XFgTRiMfXZy9RMplxll2t3dZQrbzOLOwSkTETt4YgJF3fj9hilrPB6H+7DjvV8Uz2azRmqX0WgU\nyn9wcCCdYAGOREObsxM++l1pUKVSTp3n/b1y5UqYH84TtQsn4gcPHiQXf6sGjyGlip6ac3H+7du3\nQxtgnIzH48biNBZwkUJpH6nFE4PTAZktF9EN8Lu3TIQ4rt3a2gr1wyYrtglk5W6z03ogGwLK4N99\nDyRDf/fddxvz3draWiPAIxbZ6B3GOR0Qz4+YY3CszUJ61ajO5hUVFRUVFRUVzwmfGNMeh3H63WRO\nv4ju2wi93NzcDCtfrJSZfQKWUYE+j95UDmATsBNaNvmm3/nkGB3ewaWo5pwqMZA7T+1EfFmZ3WnL\nhCltpBgD5+vLzpI5J0n/m9KRykE56IPBfPDggXTs9s9nbSnWqkHdUjv64XDY2FVy2LBy5kyZPLjt\n8X5funRJOqsyg8zPZ+TMqvP5vGF65nsy+5RiKFQ/rALqudeuXQu/MSNQyvLiPAQbKFmY2HX+3jx/\nMvvoAymGw2F4LtpZ5V9UwUSKCb1+/Xp4HvIDbm9vBxblo48+ajwXfZlrH9SHzeVKKykl8cJlZuaq\nJHCk0zlLNt1W8dsrqqMMKtceyj8ajcJ4R5s+ffo0tBebwXzAxvb2dphjWAEf7wFbdlIK+bmxW+ri\nUeI+EAPeYTb7twXrThJTWRmpioqKioqKiopV4kIZqbW1tbAqVbZprGxv3LgRVtLvvfee4VocZ8HN\ntv4KHBLpWR8lzpbbZfG1is3I2eJjuHHjRtjJc5mYeTNrMleekVJ+X74uuK4k75ZXUjdrt/NSuxPl\nsKmuU2UBcIyVigG1s1H92u/3Gw6eqswqh97JyUkI22dfjxKhzatXr4bdonou2lmponMZFNOYExvE\nbtazeP5+HsPhsOG/xGXFtewQziHZ3v9hY2NjQcQvBS6zF9r9uKGYMg6P9+W6dOlSuAZyBTlZGAbu\nDZ+6t956S85FnoVR7NhoNFoQQTbLs6kpH8w28GOW2ZaUTAeH9rf9lsVkUFLgOdHLm8xms0aeOx67\nOL9DQs+ACoCKSSIoQU5/L7Ozb83a2lqYg7g8q/DxAxTTyHVahhGKPcesfV+Px2MpBwMGDuuL6XQa\n2oiZfSr/J8fZnE17mPigUbG/vx8WA6UpP1ImKH7RcihJNXJyciKpX0A9C+VjR2qg1GzJz8G/6+vr\ngVZG2be2tsJL0+v1GhGQsft6B281mY/H4/DB47biCC++h3+GcrBM0eilL3ouiICTFZulF0Uog1m5\nppV6xtHRUTiPzXOs0o26QdGaqXbUHQ6hjx8/biTf5cgZtdjg+pZqZPnoGfxulv+ooh6YlDi1Sw5o\nK5jm9vf3G/UcDodh0cnjhSfr837QY+CIWr/5y2lGQZfm2bNnjfbf2NgIYwImrGVcBV599VUzO11I\nAal5jMcOPizLZBrgoAm/+FILFZ5DclDZLlIbB3wUWQE79S5fvXo1zJUcVZpaxKYiCNX8rr4/59Gx\nwz1xn48TKlH0Mu4tPuKvNHk0AxsHTveGPjk+Pg6/pcy4akEbQ3U2r6ioqKioqKh4TrhQRiqWjBir\nXKVO3HblurW1Fc6FiUflOmIo80DMvGSWVz1P3cOsyaj1+/2wq/cMENdDKTD7MnjWTDFDXAemZf3O\nSOWDUqwSm9243EoXhE1cZotMhNpNKChGUunRsBOxN5Nxsko8T41PpZrL7cwh+34MKOaK24TNs2on\n73dZZukxmNIBOz4+luyD2nGn3rkUA/P48eMFMyTOQ99wDi3PFsbMWynn9pjkRApqfLLDNcqNsjDL\ny1BtxNo5ZtqBfjgcBg04sCOl8gU8FsFI3b17N4zZUkfsHAPic8Wxkj8HBuEdRlspx2BlAsqZ7jEm\nu93uApMbw6VLl4KDND9Lsd9+Hjg4OGjMHZy/Eu/o48eP5RxSAq6bkptR8i8MZuzbspd43s7OThgn\nXG70IQeYeMfyHGuJccXah+xy4plmnit5TDDbaVb+XjDQN6PRKJg6oRfH7Zf71lRGqqKioqKioqLi\nOeFCGKl+vz83W3TOY2dUOs/MTlfR3v9mfX09rJqx89vf35e7dg8O882trrEa5120Z7Nu3rwZVvfs\nw6Fs4z7D/HA4DLt/zgHly98mtyBw5cqV4KCuduop/yBmWTjsvq3gJaCckWN1gq8NdjExnwLl84Sy\ncP8qoU3vVK2c67e2thrO+5ubmzLc2bcLsyM5PwJ/PObknkKprxfqrbKcMwvFAoR+95zzO0T/jUYj\nKXXgoWQSVI7EHNowUuy3iOe1Bd5lZqlYLR4M6N27d5PluHbtmpmdqiqXAH5zx8fHYd6Bz8jBwUEY\ns2q8sTyEfwdiArSp95tZA9+Gg8GgEbK/qu8Ny9yoeytGuvS+PnhGKZLzmGTWFc/ld8kzK1wmNcZz\nTCHut76+3nhvptOpZP7xHH42rgEL1el0wv3asKKpsgJg8g4ODhrngnFEWc1O3y2wijnFdFyDeXmZ\nMYZ7dLvdUCe8K7PZjKVQPjnO5sPhcG522lilar4qUgp/pybazc3NIvXXK1euhKiZHNpO8Ao8OfGC\n0Sz+8sNpGQP/6dOnMnkrwztuxz7mqcmSIw0xiWNglSblVGbEmGm3FOol9kr50+lUqnCnFoR8fxzn\nxV1qgcIq3Eq7yz+LP/68+FOTLqdA8GVXKVhSYNVxjKGdnZ2GmrRK1dLv9xdMITGsr6+H89D2sQWL\nXzzHyuzfOY56LF1IxT4sCugHYDabNRIjczQe+prTPHEb+TQl6+vr9sr/pXf5xS9+kS27mdlnPvMZ\nMzvtNyxUOZVQKpFtadJiZXIC2NzDEWYp80gqopc/pMuoVyvzMYB+5ojE0kWCes/RV3/84x9bl7MU\nMX0qr0d1XmfzktQ7/K7wYsOb4tj8yd+kZYJ5/HlwFdjb25N9DGAc9Hq9UDf+9mPs49/Hjx8X6zNW\n015FRUVFRUVFxXPChcsfqISdHjFzCt3PzE5XjctSunwf/MuhlbHnxY6x6RGr8tI8URsbG8W7p5zD\nu5eIyLEVvEvxStqK7VC/KV0lZt44NFmZWJU+i2/zUj0v5VzPeRrBEBwdHcm2UeOJnam5HfjfyWQS\n/mZzrmdUmGVRLBkzCKWhxn73nzMVshO+YoZSz+VdXsqMx3XD3/j35ORkwbHX7HT8qfdMMSpsolK7\ndM/4dTqdpCmPg018jjWG0onjZ6bmVlx769Yte+mll8zM7Pvf/370fAYYqTt37jQSWcec4VPgdlaB\nPqyxg3856wTu4bNKzGaz5BzJ775il5fNHMGMON7V/f39Rp8rDbfRaNRwQVCBN1wPdsL3ZsbRaNR4\nv81MmuTU3AXwseclf9Dr9RYsB7Fy5VDab211rAaDgXTsP48Olp+3j4+Pw5hBPQ4ODnicVEaqoqKi\noqKiomKVuHBGymN3dzesMOGHw7t2rMLZdyEVdsr+HLxT9zv0k5OTopXtcDhsOL6XSh2Mx+MFZ1SU\nXe1s1aoe18CGPp/PAyuSC5Ut3b2oXYLaGaVYwJjongoX9v4AsXxKJcrcMaTkNBieEVK+XqxYzwKZ\n/p4xfx2/i43tOv15OVV5roO/djqdJhX1cWw2mwX2AUwH+wR5eQCzxXBwLhfO80EkPn8Y39eXiZ2g\nzRb7HMzVZDJZULb2be532DjPv59ra2sNwVMWVV2F+jMDAsRXr14N70rK74bzm0Eu4aOPPjpXnk+v\nws159Rhg5dA+ShaG210xXCmh3xg8c3VyciIDH1QuOxyHX+nDhw8bfbi7uxtC4XPlSPmOsl+kgnr3\nFCuXY3J8PdfX1xdYE9xX+VCm7pcKHOL+UtYFFiBGW+O8WJCFlz8ozVwwn8/l2Gkrl8MBZOgzXl+o\n9s/5SF3IQqrb7QYdKTQCT8QqokY5K7KTotkilZxz3FaDCAMex7a3t8PggBMuUtTweaPRKHx44Iw9\nHA6DqWOZyc472nkn4BQw0T59+lSm+vDPUBFrZtpBtMQxvtPphL7hRJdeF+bw8DA5yaTMeLxYK40m\nwwvJ2lcoP3+48ZGLmapYqRzwk+B8Pl+IJm2DmM6MStmjooR8fTkaB2Y8Hp8M9Dk+nqWJe3MfJe5T\n73AdAy/YzOKJtIE2UXtsvgfUB+N5AeN+MBgUB9ykwKZ7pUOkPtIqSbe/lts09YFhs1au/UpMP7EI\nwlViOBw2vhdqHmI9sZwLQglU6jH+qLNuk1okcpnxLnHkrQqGUNemEo+31U1UePnll8P3C3PDhx9+\nGObcGzdumNmpeRvPxpxwcHAQ1gTLBCT5hd5sNity9+n1emHe5AUalaGa9ioqKioqKioqVokLYaTM\n7EIeWlFRUVFRUVGxJCojVVFRUVFRUVGxSvTzp6weqwrbLBHG/Dhs7f55ZnGhSu+8qhwZWV23lDGM\ntUXK2bytGvZ0OpX18rm4Yo7qnEsO8KJwSq4g138cEu+Vu3OyAUregB3blYO68h3zod8c0PBJgMoz\ntko2+rzvWUlf58q8TK69jwOr8Ln6uOcxICfwmgtmUZInJbIQ53Xub9vmsaAPAD5tmCdKxSR5DlFO\n4MuMjZyDP7CsfEQOq547FDjATNWj1C821a+5d4qfm6vvhSykVgW1gPIvYqnmznA4bO3UpgZv6kNV\nqu6am0SUSvgyKutcHj9RMJSzKS9AfOJHs+YCiTVAAKUPptpSlQX3NFt05oYOEvelj66MRfoA7ISp\nor78R6PT6RRHoKwCuUnfQ0VCtvkQlEz255lYY5EySlF7Ffg4PgT8jNJ0VCnE9JhUFG2sPKnjsfNj\nc1HpgjU1lyn19JxeU2mKKo4mKwFv3tR8p9T91cbR36/T6WTrhOemUjFxNF6q/LEy+DpxFGjbccnP\nOs/CNxUNrrQDGakk7dxu3L6p7zHfw3/PSsZ6Ne1VVFRUVFRUVCyJTzQjlVqRcsJG1kEpWf2bNc0z\ns9nM/u7v/s7MyhWGUxo/vJNXFOwyNG8qDFmBQ7sZahfhc4UxVG4n3un5EPzRaNQIx+92u0EuACG7\nz549kxpPnsqNta/fbe7s7IQwWzwD5eH6solS1ZflNxTDpNo/pdO0KqhdbAmtHWMSY/dN/R1DSgMr\nV75VjfdStGGjlO5X2/aIsUklZSllBp9H+6dQco1i5WazWdacjvP8s0rNm1/84hftzTffjJbF/87P\nKB1rrGmVwnA4bMwJylzK91JMSM6EBeR06fid8uVnnUM+5tkdbkvUQ7Heuf7KjWO+T6w+6j3rdDqN\nhNzMeqbasfQ8jwsX5Pw4NFtWCdVJMZTQnqy/wmhLwccmek+Zdjqd1ronqYSjN2/etDt37iz8ppLL\nckJcn3rGbNEU6LVRuJ2VvZwF3tA37IcFHyos7tSkxPR3LqGtT5LK16Csz8Nfp+RdaWO2WtavIvdR\nAmITn3ruKuaBVbc5Z4LnzUTpB20VJr3ShdQybbqK++V8pBS8jhhfWzo3pcqnFi+5j3ppInqUj012\n50lvo9w0cte2bXO1cMuNz7bjbmtrK8yLLKTtteLU+6NMjwrquxK7X8ofKje21TxG59aovYqKioqK\nioqKVeLCGakSxCJH4FjMyTRL0w8ofP3rXzczs09/+tNmZvYv//IvyfNLIwewUler+06nmUA1Vt9l\nHEb9Kjy2M8NxmKjYNJfa6XHiT7WrQxuNRqMFlXOz+O4Dz8POdX9/X6pcA1CTf/ToUSPp7sbGRqgL\n7yBV8uXSSEn/DLVTOg87smpnaK4vyjQej7PK4qtAyW5WHec2KN3dl7Z5rH1TaZlSTsvqnRoMBmFM\nlPZlaRRtW9Nd7NxVsYD+WalnqHdPvT+5cnJbpRzQVVn5Hr7fYm2Wsi7w3OTVyWMm3tS4itVHmdja\nQj1XJQJXYPNr6l28du2amZ2mMFJ9V8r+lTLwPlCq1DxbOufzd9QqI1VRUVFRUVFRsVpcOCPVVusi\np2mUggq3Rf1zuyJgZ2enwXDkHKRzvlJgVCAFkCsH5wDK9V/p7kWxQIBilVL90OmcyQHkZCj87oT7\ngXdPyNnEORkBZoh8v3IOPX6mb+N+v98Ie1U7Fk4KnGI/P24fKXVM7T5T/m65Z+TO9wmolylz2/nA\n79TPw0gt6+vCuSD5/NS156k7X4trUqHsOS2otliGHVFMD49F3/b8DPbvLG1Lxfz755udvSN4rur7\n+XzeYMRVO+ckDLhsqVyk/N6qebYtI9XGL8nP2/w8f33sGPDaa6/Zb37zm8Z5vs3575QP7MnJSfCv\nzeWnLGVyUxqIjJyP1IUvpM4Dr/HTxuOekxniWtzv1q1bZnY6kD/66KNWZeJB4jvlxo0bIaosFSVn\nlhYhUwOfk/DyS9xGC8Ns8WOCDyOcwrms3rzFiGV4LxGFHA6H4Tw2L/qoOL72+vXrZnaabVx9DP1v\nnPCY718SXbO2ttYwia3atPc8kBIRVQKqqQ98zJTl+7yNY/YqHKifx0KqBFx3DppQSYFTDsqqfKXm\nvk996lNmZvbOO+/Iei27kMo58y5jZsqZvUrKjOf1ej25qPHzhXI25udyX5WYc80sOT8ySt4fdR7r\n2J1nIaXuzdfnNLdKTWxeJzCmSVhiYi19Lp/H6wHlXM/mZfzry6jGHSd4tmraq6ioqKioqKhYLT7R\nOlIAryZ556BWsaUMG3aQ2H2Yna2gEb4ZW6HD/IWd0Gg0ClRjahfz5MkTufpXzJqniJnWZn0otRtb\nRndH6SD5XSdDMU4psxE7padMrGZWxBYNBgPb3t42s1MmymwxJJnP8+VZX19fSEmDsgDKGVL99nFo\nRym0DWvngAYus9rx+/DznOYNMJ1OG89dhklqG26/DKMeuyZ171z5gVh6JLPFditJu2J21l/Hx8dJ\n5/Xd3V0zO2WkPPuwSrNe7Pk5cPv58ZNj6lKh+NPptMFwsZmZdYRU+dX7j28CzxuqzikmStVXsUI8\nZ/r7nEc2wyP1rcS3SJlTlWnS7Gw+RPkPDg4a377RaGRf+tKXzMzsJz/5ycIzzfKmdPXc1Hcq9e6x\nGZzNuJ7Nms1moW4qY0cMlZGqqKioqKioqFgSn0gfKb/rUKqp6rzYsdLdKxzZvDN5DsPhMKyUsUvJ\n5fHJOZYu43Rr1qxjiT2902mKjMbaPCVxgGfEdhgppgcYDoeBuVLOy+ijk5OTcJ+UWKLyh2J2TImD\nljI+3mGU63ORSYt9+dsk5yxFyTWlIniM87AdzHCeB8s43E46lnsAACAASURBVKeY5mXETX1ZcnIV\nn//8583M7Fe/+tXKEv+WoNRfJ1emVUgxcHv7XJ+xMikZlLbSM219eebzecOpO/adU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GFX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PwW3wODycw/2KBOpxOMWaFQ0PXI8Bguj5nGfoE102g0tC+xvuv1uvab1y82TlXkSFqGa5D+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\nQeA3mUxG9ybGqlgsmv4mP/rRj0RE5A9/+IN+B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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "feat = net.blobs['conv1'].data[0, :36]\n", + "vis_square(feat)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* The fifth layer after pooling, `pool5`" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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d6fwfzz33XNa6H/3oR7UeN7r2jh49msQmJiaS2LVr12rNZXNzs3jbfjUZN+Hi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\nos+lqLE+V9S4/fTTT2dte+7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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "feat = net.blobs['pool5'].data[0]\n", + "vis_square(feat)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* The first fully connected layer, `fc6` (rectified)\n", + "\n", + " We show the output values and the histogram of the positive values" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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NZ03jXHv2o7Vr/aW1cqW/tCTplFOyLZ+nBaGp27mp6xWDY49Nd2e9j3Mmk5RW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\nFLIAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "feat = net.blobs['fc6'].data[0]\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(feat.flat)\n", + "plt.subplot(2, 1, 2)\n", + "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* The final probability output, `prob`" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "feat = net.blobs['prob'].data[0]\n", + "plt.figure(figsize=(15, 3))\n", + "plt.plot(feat.flat)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note the cluster of strong predictions; the labels are sorted semantically. The top peaks correspond to the top predicted labels, as shown above." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6. Try your own image\n", + "\n", + "Now we'll grab an image from the web and classify it using the steps above.\n", + "\n", + "* Try setting `my_image_url` to any JPEG image URL." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# download an image\n", + "my_image_url = \"...\" # paste your URL here\n", + "# for example:\n", + "# my_image_url = \"https://upload.wikimedia.org/wikipedia/commons/b/be/Orang_Utan%2C_Semenggok_Forest_Reserve%2C_Sarawak%2C_Borneo%2C_Malaysia.JPG\"\n", + "!wget -O image.jpg $my_image_url\n", + "\n", + "# transform it and copy it into the net\n", + "image = caffe.io.load_image('image.jpg')\n", + "net.blobs['data'].data[...] = transformer.preprocess('data', image)\n", + "\n", + "# perform classification\n", + "net.forward()\n", + "\n", + "# obtain the output probabilities\n", + "output_prob = net.blobs['prob'].data[0]\n", + "\n", + "# sort top five predictions from softmax output\n", + "top_inds = output_prob.argsort()[::-1][:5]\n", + "\n", + "plt.imshow(image)\n", + "\n", + "print 'probabilities and labels:'\n", + "zip(output_prob[top_inds], labels[top_inds])" + ] + } + ], + "metadata": { + "description": "Instant recognition with a pre-trained model and a tour of the net interface for visualizing features and parameters layer-by-layer.", + "example_name": "Image Classification and Filter Visualization", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + }, + "priority": 1 + }, + "nbformat": 4, + "nbformat_minor": 0 +} + + +
    +
    + + diff --git a/gathered/examples/01-learning-lenet.ipynb b/gathered/examples/01-learning-lenet.ipynb new file mode 100644 index 00000000000..15fcd2448d4 --- /dev/null +++ b/gathered/examples/01-learning-lenet.ipynb @@ -0,0 +1,1350 @@ + + + + + + + + + Caffe | Learning LeNet + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Solving in Python with LeNet\n", + "\n", + "In this example, we'll explore learning with Caffe in Python, using the fully-exposed `Solver` interface." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Set up the Python environment: we'll use the `pylab` import for numpy and plot inline." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pylab import *\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Import `caffe`, adding it to `sys.path` if needed. Make sure you've built pycaffe." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "caffe_root = '../' # this file should be run from {caffe_root}/examples (otherwise change this line)\n", + "\n", + "import sys\n", + "sys.path.insert(0, caffe_root + 'python')\n", + "import caffe" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* We'll be using the provided LeNet example data and networks (make sure you've downloaded the data and created the databases, as below)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading...\n", + "Creating lmdb...\n", + "Done.\n" + ] + } + ], + "source": [ + "# run scripts from caffe root\n", + "import os\n", + "os.chdir(caffe_root)\n", + "# Download data\n", + "!data/mnist/get_mnist.sh\n", + "# Prepare data\n", + "!examples/mnist/create_mnist.sh\n", + "# back to examples\n", + "os.chdir('examples')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Creating the net \n", + "\n", + "Now let's make a variant of LeNet, the classic 1989 convnet architecture.\n", + "\n", + "We'll need two external files to help out:\n", + "* the net `prototxt`, defining the architecture and pointing to the train/test data\n", + "* the solver `prototxt`, defining the learning parameters\n", + "\n", + "We start by creating the net. We'll write the net in a succinct and natural way as Python code that serializes to Caffe's protobuf model format.\n", + "\n", + "This network expects to read from pregenerated LMDBs, but reading directly from `ndarray`s is also possible using `MemoryDataLayer`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from caffe import layers as L, params as P\n", + "\n", + "def lenet(lmdb, batch_size):\n", + " # our version of LeNet: a series of linear and simple nonlinear transformations\n", + " n = caffe.NetSpec()\n", + " \n", + " n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n", + " transform_param=dict(scale=1./255), ntop=2)\n", + " \n", + " n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n", + " n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n", + " n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n", + " n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n", + " n.fc1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n", + " n.relu1 = L.ReLU(n.fc1, in_place=True)\n", + " n.score = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))\n", + " n.loss = L.SoftmaxWithLoss(n.score, n.label)\n", + " \n", + " return n.to_proto()\n", + " \n", + "with open('mnist/lenet_auto_train.prototxt', 'w') as f:\n", + " f.write(str(lenet('mnist/mnist_train_lmdb', 64)))\n", + " \n", + "with open('mnist/lenet_auto_test.prototxt', 'w') as f:\n", + " f.write(str(lenet('mnist/mnist_test_lmdb', 100)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The net has been written to disk in a more verbose but human-readable serialization format using Google's protobuf library. You can read, write, and modify this description directly. Let's take a look at the train net." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "layer {\r\n", + " name: \"data\"\r\n", + " type: \"Data\"\r\n", + " top: \"data\"\r\n", + " top: \"label\"\r\n", + " transform_param {\r\n", + " scale: 0.00392156862745\r\n", + " }\r\n", + " data_param {\r\n", + " source: \"mnist/mnist_train_lmdb\"\r\n", + " batch_size: 64\r\n", + " backend: LMDB\r\n", + " }\r\n", + "}\r\n", + "layer {\r\n", + " name: \"conv1\"\r\n", + " type: \"Convolution\"\r\n", + " bottom: \"data\"\r\n", + " top: \"conv1\"\r\n", + " convolution_param {\r\n", + " num_output: 20\r\n", + " kernel_size: 5\r\n", + " weight_filler {\r\n", + " type: \"xavier\"\r\n", + " }\r\n", + " }\r\n", + "}\r\n", + "layer {\r\n", + " name: \"pool1\"\r\n", + " type: \"Pooling\"\r\n", + " bottom: \"conv1\"\r\n", + " top: \"pool1\"\r\n", + " pooling_param {\r\n", + " pool: MAX\r\n", + " kernel_size: 2\r\n", + " stride: 2\r\n", + " }\r\n", + "}\r\n", + "layer {\r\n", + " name: \"conv2\"\r\n", + " type: \"Convolution\"\r\n", + " bottom: \"pool1\"\r\n", + " top: \"conv2\"\r\n", + " convolution_param {\r\n", + " num_output: 50\r\n", + " kernel_size: 5\r\n", + " weight_filler {\r\n", + " type: \"xavier\"\r\n", + " }\r\n", + " }\r\n", + "}\r\n", + "layer {\r\n", + " name: \"pool2\"\r\n", + " type: \"Pooling\"\r\n", + " bottom: \"conv2\"\r\n", + " top: \"pool2\"\r\n", + " pooling_param {\r\n", + " pool: MAX\r\n", + " kernel_size: 2\r\n", + " stride: 2\r\n", + " }\r\n", + "}\r\n", + "layer {\r\n", + " name: \"fc1\"\r\n", + " type: \"InnerProduct\"\r\n", + " bottom: \"pool2\"\r\n", + " top: \"fc1\"\r\n", + " inner_product_param {\r\n", + " num_output: 500\r\n", + " weight_filler {\r\n", + " type: \"xavier\"\r\n", + " }\r\n", + " }\r\n", + "}\r\n", + "layer {\r\n", + " name: \"relu1\"\r\n", + " type: \"ReLU\"\r\n", + " bottom: \"fc1\"\r\n", + " top: \"fc1\"\r\n", + "}\r\n", + "layer {\r\n", + " name: \"score\"\r\n", + " type: \"InnerProduct\"\r\n", + " bottom: \"fc1\"\r\n", + " top: \"score\"\r\n", + " inner_product_param {\r\n", + " num_output: 10\r\n", + " weight_filler {\r\n", + " type: \"xavier\"\r\n", + " }\r\n", + " }\r\n", + "}\r\n", + "layer {\r\n", + " name: \"loss\"\r\n", + " type: \"SoftmaxWithLoss\"\r\n", + " bottom: \"score\"\r\n", + " bottom: \"label\"\r\n", + " top: \"loss\"\r\n", + "}\r\n" + ] + } + ], + "source": [ + "!cat mnist/lenet_auto_train.prototxt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's see the learning parameters, which are also written as a `prototxt` file (already provided on disk). We're using SGD with momentum, weight decay, and a specific learning rate schedule." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# The train/test net protocol buffer definition\r\n", + "train_net: \"mnist/lenet_auto_train.prototxt\"\r\n", + "test_net: \"mnist/lenet_auto_test.prototxt\"\r\n", + "# test_iter specifies how many forward passes the test should carry out.\r\n", + "# In the case of MNIST, we have test batch size 100 and 100 test iterations,\r\n", + "# covering the full 10,000 testing images.\r\n", + "test_iter: 100\r\n", + "# Carry out testing every 500 training iterations.\r\n", + "test_interval: 500\r\n", + "# The base learning rate, momentum and the weight decay of the network.\r\n", + "base_lr: 0.01\r\n", + "momentum: 0.9\r\n", + "weight_decay: 0.0005\r\n", + "# The learning rate policy\r\n", + "lr_policy: \"inv\"\r\n", + "gamma: 0.0001\r\n", + "power: 0.75\r\n", + "# Display every 100 iterations\r\n", + "display: 100\r\n", + "# The maximum number of iterations\r\n", + "max_iter: 10000\r\n", + "# snapshot intermediate results\r\n", + "snapshot: 5000\r\n", + "snapshot_prefix: \"mnist/lenet\"\r\n" + ] + } + ], + "source": [ + "!cat mnist/lenet_auto_solver.prototxt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Loading and checking the solver\n", + "\n", + "* Let's pick a device and load the solver. We'll use SGD (with momentum), but other methods (such as Adagrad and Nesterov's accelerated gradient) are also available." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "caffe.set_device(0)\n", + "caffe.set_mode_gpu()\n", + "\n", + "### load the solver and create train and test nets\n", + "solver = None # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)\n", + "solver = caffe.SGDSolver('mnist/lenet_auto_solver.prototxt')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* To get an idea of the architecture of our net, we can check the dimensions of the intermediate features (blobs) and parameters (these will also be useful to refer to when manipulating data later)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('data', (64, 1, 28, 28)),\n", + " ('label', (64,)),\n", + " ('conv1', (64, 20, 24, 24)),\n", + " ('pool1', (64, 20, 12, 12)),\n", + " ('conv2', (64, 50, 8, 8)),\n", + " ('pool2', (64, 50, 4, 4)),\n", + " ('fc1', (64, 500)),\n", + " ('score', (64, 10)),\n", + " ('loss', ())]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# each output is (batch size, feature dim, spatial dim)\n", + "[(k, v.data.shape) for k, v in solver.net.blobs.items()]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('conv1', (20, 1, 5, 5)),\n", + " ('conv2', (50, 20, 5, 5)),\n", + " ('fc1', (500, 800)),\n", + " ('score', (10, 500))]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# just print the weight sizes (we'll omit the biases)\n", + "[(k, v[0].data.shape) for k, v in solver.net.params.items()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Before taking off, let's check that everything is loaded as we expect. We'll run a forward pass on the train and test nets and check that they contain our data." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'loss': array(2.365971088409424, dtype=float32)}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver.net.forward() # train net\n", + "solver.test_nets[0].forward() # test net (there can be more than one)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train labels: [ 5. 0. 4. 1. 9. 2. 1. 3.]\n" + ] + }, + { + "data": { + "image/png": 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QPKRCoKCggObmZgYHB5PeBEroW/1z4/fC4qy2tpacnJykjWtfC7ZSqcRsNlNT\nU0NTU5PYDEo4tkpIQmxtbREOh8XY9W4/4YQJKWzNfihevJ2ysjKOHTvGuXPnqKioIDMzk6dPn/LZ\nZ5/x6aef7kl5/X5GeChvt5pNTEzQ1dXF8vLyvj5Ud2lpidHR0aQflvFdiouLefXVV7l06RIlJSW4\n3W4+//xzOjs7efLkCVarlba2Nqqrq6mrq6O0tBSDwUBnZyePHj2ip6eHoaEhPB6PWPcwPz/P2toa\nIyMj9Pb2cvToUWpra9Hr9SwvL/PFF19w+/btpH6uQCCA2+3eEx+4EGLKycnBZDIxMzNDKBT6yfBn\nYWEhra2tvPfeexw+fDhp49t3gi1Y0EwmEw6Hg+rqalpaWjh8+DAlJSVkZmaKK7BwOIzT6RQ9ug8e\nPEhqVl6pVIpjqq+vx+VyiSEDo9FIVlaWeC5lXV0dR44cob6+HrVajdvt5sGDB9y7d4/h4eGkjXG3\nUKvV6PX6Xctu/xh5eXk0NjaKjf4F0XM6nQwNDREMBvfEXvZzUSgU3ztlKBWUlZVx9uxZKisrMRgM\nuFwuNjc3sVqtNDU1UVRUJJ6IXlhYSCAQYGRkhKtXr/Lw4UMmJyd3JOi3H74hHGG1tLTE06dPRVfT\n119/nfRkqhCOO3fuHJmZmeJDPCsrS7ScLi8v77oXX6/Xi/23hYMnvtt2YjsKhQKNRoPJZKKtrY2L\nFy9y9OhRsrKyxBDt5ubmrnrw951gC9VF9fX1nDx5ktdff11sIyogTGifz8fAwAB//vOf6erqSlr5\nr+BWELY9bW1tBINBvvrqKzGGXFZWRltbG0eOHKGwsBCr1YpGo8Hr9TI5OUl/fz83b95kcnIyKWPc\nTWQymfgASkWP5CNHjvCrX/0Ks9m8w861vLzMzMzMvulp8UNkZ2dTUVHxvZLvZFNVVcWZM2fExmHp\n6em89tprnDp1Cr1ej91uFws9EokEDx8+5MMPP+Qvf/kLbrf7J3cDc3NzzM3N0dHRkfTPsp2JiQnk\ncjnvvvsu2dnZYliktLQUgA8++EDs5b2bZGVl0dzczNtvv01rayubm5s8fvwYj8fzzA6LarUaq9XK\n4cOHuXz5Mm+88QZarVYsTY/FYkSj0R0Om5dlXwi2UJklVOydP3+e+vp6SkpKsNvtO07EECbxo0eP\n6OvrY3h4mPn5+aQneARkMhklJSXodDrq6upYW1sjkUiQn58v2qV0Oh2hUIipqSnGx8fFJM3i4mLK\nD1t4EbYiJw0vAAAGlUlEQVT7S5MpQEJZb1lZmehO2dzcxO/3i72jA4HAvl5dw99WZtt3B6kgEokQ\nCAQwGAwolUqxuyF8mwAbGxvD6XQyOzvLxMQET548YWRkJOV95F+EeDwuxsu3l4In8/o2Njby61//\nmoaGBmw2G8FgkPb2dgoLC59Z9JKbm0tpaSkVFRUUFxeLD2yPx8PU1BS9vb18+eWXu3r4x54JtuBv\nttvt5Ofnk5WVRU5ODmVlZZw7d45Dhw6JK4dwOIzP52NpaYnx8XGGhoa4f/++ePp0Mr9EoTx+ZWUF\nv9+PXq/HYrGQmZlJQUGBmJQTuqBFo1Fx5d/T07OjSdVBQqvVfq+f724jnMqem5tLbm4uaWlpYnXl\nl19+yeDgIJFIZN8LttDvXPANp4rJyUlu3bpFWVkZJpMJlUollpgLcfXp6WlmZmYYGxsTWxfs9+sJ\n3z6Mnj59SkVFBQUFBeLryZzrubm51NXVkZeXJ67qT5w4QW1t7TPj6Xa7nUOHDpGdnY1cLicSibC6\nuiq2XO7p6WF4eHhXTRB7JthyuRytVsvJkyd5++23xeog4dTx7bFTQQD/+te/0t3dzdjYGOFwOCUG\n+lgsxszMDOPj48zPz4vFE0J4RDivTSaTEQ6Hcbvd3Lt3j/fff59r166xtbV1ICbId7FYLBw6dCip\nJ2cL94BwSrdcLicQCDA7O8vHH3/M6Ojonhya8Lx4vV6mpqaIx+NJ35Vs59atW4yMjNDS0iJa8Px+\nP48fP6anp4dAICDaXAW3w34s+34WoVCI+/fvU1dXx9GjR/dkDHq9nldeeeUHr5tcLhd/QqEQLpeL\n3t5ePvjgA65evUokEtn1+zelgq1SqcRzDKurq3E4HNTV1VFZWYnRaESr1YoCIWyNBwcHuX37Nvfv\n32diYgK3200oFEpZuezm5iarq6vcunWL9fV12traaGlpoby8XJyYoVCIgYEBcRs/OjrK2NjYnp6B\n+DIIopOKhON3BU5I1sTj8QMh1vDtSSnz8/MsLS1htVpRKBSYzWb0ev2u9I/4IYSTdx48eMDIyAga\njWZHL+ztQn3QEGoMFhYW8Hg83ztuKxn09PTwv//7v7S1tVFTU0N+fv4zd5gbGxv4/X6i0SjhcFjs\nLbR97q+vryfl2qdUsIUS0/b2dtrb23E4HKLrIxqN4vf7xfPOfD6feEZiV1cXAwMD4rYzlQiWQcH+\ntLS0hMfjYX5+XizY8fv9dHd38/DhQ4aGhva9De2HCIfDrK2tpUwot9sxg8Hgvji9+0UQdlZPnjzB\nZrOJfbSdTifBYDBphz8IBzrMzMzs+nvvNUJZeF9fH9nZ2WKnPKfT+cKVwT+F0AXS6/WKTeSys7PR\naDRsbW2JDpqVlRVmZmbw+/2sra0xOzvL4OAgQ0NDSbcjplSwBR/zkSNHaGhoEEMfQoLhyZMnTExM\nsLm5SXd3N/fu3WN1dZVwOJz0CqKfIhKJiE/7mzdv7ggVCBnhWCwmrmoOIktLS4yMjCStrPa7bGxs\n4Ha7mZ6eZnp6mrKyspT83WQQCATo6OjAbrdz4sQJLl68KDZRcjqd+97pst8QYvF/+ctfuHbtmljI\nIlgPkyGKq6urYnWtcELM22+/jcPhYGNjg2+++Ub86enpEXv5CBWOqZj7KRXsYDBIV1cXLpeLTz75\nRHxd+BJ8Ph8rKyskEgkWFxfFY6/2w5ZO6CMi9BL5v8jMzAyff/45c3NzosslmRVlQtjr5s2bLC4u\nYjKZxJOGvnu81X5nfX2dvr4+ampqKCkpoaioiJMnTxIKhbh69Soej+fAhHj2C1tbW6yvr6dsvgkL\nL0Gc/X4/Q0NDGI3GHfelx+PB5XIRCoVSXtwjS5YYymSyvVdZCYkUIRx0cf78ed555x1aW1uJRCL0\n9vby7//+72KiXELi55BIJJ6Zud4XPmwJiYOO0HhM6Gzn9Xo5ceIEjY2N5OTksLCwIAm2xEsjrbAl\nJHYR4Si7mpoaSktL0Wg0dHR0sLCw8H82lCax+/zQClsSbAkJCYl9RsoFW0JCQkJid0l+ZYSEhISE\nxK4gCbaEhITEAUESbAkJCYkDgiTYEhISEgcESbAlJCQkDgiSYEtISEgcECTBlpCQkDggSIItISEh\ncUCQBFtCQkLigCAJtoSEhMQBQRJsCQkJiQOCJNgSEhISBwRJsCUkJCQOCJJgS0hISBwQJMGWkJCQ\nOCBIgi0hISFxQJAEW0JCQuKAIAm2hISExAFBEmwJCQmJA4Ik2BISEhIHhP8H8pS7yD5yyasAAAAA\nSUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# we use a little trick to tile the first eight images\n", + "imshow(solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray'); axis('off')\n", + "print 'train labels:', solver.net.blobs['label'].data[:8]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test labels: [ 7. 2. 1. 0. 4. 1. 4. 9.]\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "imshow(solver.test_nets[0].blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray'); axis('off')\n", + "print 'test labels:', solver.test_nets[0].blobs['label'].data[:8]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Stepping the solver\n", + "\n", + "Both train and test nets seem to be loading data, and to have correct labels.\n", + "\n", + "* Let's take one step of (minibatch) SGD and see what happens." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "solver.step(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do we have gradients propagating through our filters? Let's see the updates to the first layer, shown here as a $4 \\times 5$ grid of $5 \\times 5$ filters." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.5, 24.5, 19.5, -0.5)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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jFTwwn93j7J28IyBzl56sTpaurot9FallO3DApo6nS85qMkS9O7qfyl2WnLeCHYvQYu8o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\nDppK7vaW09lj2ulwVUYVfUUegxuDGe4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "imshow(solver.net.params['conv1'][0].diff[:, 0].reshape(4, 5, 5, 5)\n", + " .transpose(0, 2, 1, 3).reshape(4*5, 5*5), cmap='gray'); axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Writing a custom training loop\n", + "\n", + "Something is happening. Let's run the net for a while, keeping track of a few things as it goes.\n", + "Note that this process will be the same as if training through the `caffe` binary. In particular:\n", + "* logging will continue to happen as normal\n", + "* snapshots will be taken at the interval specified in the solver prototxt (here, every 5000 iterations)\n", + "* testing will happen at the interval specified (here, every 500 iterations)\n", + "\n", + "Since we have control of the loop in Python, we're free to compute additional things as we go, as we show below. We can do many other things as well, for example:\n", + "* write a custom stopping criterion\n", + "* change the solving process by updating the net in the loop" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0 testing...\n", + "Iteration 25 testing...\n", + "Iteration 50 testing...\n", + "Iteration 75 testing...\n", + "Iteration 100 testing...\n", + "Iteration 125 testing...\n", + "Iteration 150 testing...\n", + "Iteration 175 testing...\n", + "CPU times: user 12.6 s, sys: 2.4 s, total: 15 s\n", + "Wall time: 14.4 s\n" + ] + } + ], + "source": [ + "%%time\n", + "niter = 200\n", + "test_interval = 25\n", + "# losses will also be stored in the log\n", + "train_loss = zeros(niter)\n", + "test_acc = zeros(int(np.ceil(niter / test_interval)))\n", + "output = zeros((niter, 8, 10))\n", + "\n", + "# the main solver loop\n", + "for it in range(niter):\n", + " solver.step(1) # SGD by Caffe\n", + " \n", + " # store the train loss\n", + " train_loss[it] = solver.net.blobs['loss'].data\n", + " \n", + " # store the output on the first test batch\n", + " # (start the forward pass at conv1 to avoid loading new data)\n", + " solver.test_nets[0].forward(start='conv1')\n", + " output[it] = solver.test_nets[0].blobs['score'].data[:8]\n", + " \n", + " # run a full test every so often\n", + " # (Caffe can also do this for us and write to a log, but we show here\n", + " # how to do it directly in Python, where more complicated things are easier.)\n", + " if it % test_interval == 0:\n", + " print 'Iteration', it, 'testing...'\n", + " correct = 0\n", + " for test_it in range(100):\n", + " solver.test_nets[0].forward()\n", + " correct += sum(solver.test_nets[0].blobs['score'].data.argmax(1)\n", + " == solver.test_nets[0].blobs['label'].data)\n", + " test_acc[it // test_interval] = correct / 1e4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Let's plot the train loss and test accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tVTHRhi302f4ujBpV6KY4juMUE41VdXe4oKofBgVME5HEAnpNRIbWqmlFwmHv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\nJGkQBpAkaRA3ABGGQ9Z+SfjXAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_, ax1 = subplots()\n", + "ax2 = ax1.twinx()\n", + "ax1.plot(arange(niter), train_loss)\n", + "ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\n", + "ax1.set_xlabel('iteration')\n", + "ax1.set_ylabel('train loss')\n", + "ax2.set_ylabel('test accuracy')\n", + "ax2.set_title('Test Accuracy: {:.2f}'.format(test_acc[-1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The loss seems to have dropped quickly and coverged (except for stochasticity), while the accuracy rose correspondingly. Hooray!\n", + "\n", + "* Since we saved the results on the first test batch, we can watch how our prediction scores evolved. We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(8):\n", + " figure(figsize=(2, 2))\n", + " imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')\n", + " figure(figsize=(10, 2))\n", + " imshow(output[:50, i].T, interpolation='nearest', cmap='gray')\n", + " xlabel('iteration')\n", + " ylabel('label')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We started with little idea about any of these digits, and ended up with correct classifications for each. If you've been following along, you'll see the last digit is the most difficult, a slanted \"9\" that's (understandably) most confused with \"4\".\n", + "\n", + "* Note that these are the \"raw\" output scores rather than the softmax-computed probability vectors. The latter, shown below, make it easier to see the confidence of our net (but harder to see the scores for less likely digits)." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(8):\n", + " figure(figsize=(2, 2))\n", + " imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')\n", + " figure(figsize=(10, 2))\n", + " imshow(exp(output[:50, i].T) / exp(output[:50, i].T).sum(0), interpolation='nearest', cmap='gray')\n", + " xlabel('iteration')\n", + " ylabel('label')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6. Experiment with architecture and optimization\n", + "\n", + "Now that we've defined, trained, and tested LeNet there are many possible next steps:\n", + "\n", + "- Define new architectures for comparison\n", + "- Tune optimization by setting `base_lr` and the like or simply training longer\n", + "- Switching the solver type from `SGD` to an adaptive method like `AdaDelta` or `Adam`\n", + "\n", + "Feel free to explore these directions by editing the all-in-one example that follows.\n", + "Look for \"`EDIT HERE`\" comments for suggested choice points.\n", + "\n", + "By default this defines a simple linear classifier as a baseline.\n", + "\n", + "In case your coffee hasn't kicked in and you'd like inspiration, try out\n", + "\n", + "1. Switch the nonlinearity from `ReLU` to `ELU` or a saturing nonlinearity like `Sigmoid`\n", + "2. Stack more fully connected and nonlinear layers\n", + "3. Search over learning rate 10x at a time (trying `0.1` and `0.001`)\n", + "4. Switch the solver type to `Adam` (this adaptive solver type should be less sensitive to hyperparameters, but no guarantees...)\n", + "5. Solve for longer by setting `niter` higher (to 500 or 1,000 for instance) to better show training differences" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0 testing...\n", + "Iteration 25 testing...\n", + "Iteration 50 testing...\n", + "Iteration 75 testing...\n", + "Iteration 100 testing...\n", + "Iteration 125 testing...\n", + "Iteration 150 testing...\n", + "Iteration 175 testing...\n", + "Iteration 200 testing...\n", + "Iteration 225 testing...\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ObSIiqKpElpsBC4ETgFXAVOA8VZ0f2Se1Qs3fVXVgMdoXxwV3LzAXKzInWALC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\n8Zh0YhISptHOm5dbgKL06mVWSZs2iWrMlZXJAgR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train_net_path = 'mnist/custom_auto_train.prototxt'\n", + "test_net_path = 'mnist/custom_auto_test.prototxt'\n", + "solver_config_path = 'mnist/custom_auto_solver.prototxt'\n", + "\n", + "### define net\n", + "def custom_net(lmdb, batch_size):\n", + " # define your own net!\n", + " n = caffe.NetSpec()\n", + " \n", + " # keep this data layer for all networks\n", + " n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n", + " transform_param=dict(scale=1./255), ntop=2)\n", + " \n", + " # EDIT HERE to try different networks\n", + " # this single layer defines a simple linear classifier\n", + " # (in particular this defines a multiway logistic regression)\n", + " n.score = L.InnerProduct(n.data, num_output=10, weight_filler=dict(type='xavier'))\n", + " \n", + " # EDIT HERE this is the LeNet variant we have already tried\n", + " # n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n", + " # n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n", + " # n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n", + " # n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n", + " # n.fc1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n", + " # EDIT HERE consider L.ELU or L.Sigmoid for the nonlinearity\n", + " # n.relu1 = L.ReLU(n.fc1, in_place=True)\n", + " # n.score = L.InnerProduct(n.fc1, num_output=10, weight_filler=dict(type='xavier'))\n", + " \n", + " # keep this loss layer for all networks\n", + " n.loss = L.SoftmaxWithLoss(n.score, n.label)\n", + " \n", + " return n.to_proto()\n", + "\n", + "with open(train_net_path, 'w') as f:\n", + " f.write(str(custom_net('mnist/mnist_train_lmdb', 64))) \n", + "with open(test_net_path, 'w') as f:\n", + " f.write(str(custom_net('mnist/mnist_test_lmdb', 100)))\n", + "\n", + "### define solver\n", + "from caffe.proto import caffe_pb2\n", + "s = caffe_pb2.SolverParameter()\n", + "\n", + "# Set a seed for reproducible experiments:\n", + "# this controls for randomization in training.\n", + "s.random_seed = 0xCAFFE\n", + "\n", + "# Specify locations of the train and (maybe) test networks.\n", + "s.train_net = train_net_path\n", + "s.test_net.append(test_net_path)\n", + "s.test_interval = 500 # Test after every 500 training iterations.\n", + "s.test_iter.append(100) # Test on 100 batches each time we test.\n", + "\n", + "s.max_iter = 10000 # no. of times to update the net (training iterations)\n", + " \n", + "# EDIT HERE to try different solvers\n", + "# solver types include \"SGD\", \"Adam\", and \"Nesterov\" among others.\n", + "s.type = \"SGD\"\n", + "\n", + "# Set the initial learning rate for SGD.\n", + "s.base_lr = 0.01 # EDIT HERE to try different learning rates\n", + "# Set momentum to accelerate learning by\n", + "# taking weighted average of current and previous updates.\n", + "s.momentum = 0.9\n", + "# Set weight decay to regularize and prevent overfitting\n", + "s.weight_decay = 5e-4\n", + "\n", + "# Set `lr_policy` to define how the learning rate changes during training.\n", + "# This is the same policy as our default LeNet.\n", + "s.lr_policy = 'inv'\n", + "s.gamma = 0.0001\n", + "s.power = 0.75\n", + "# EDIT HERE to try the fixed rate (and compare with adaptive solvers)\n", + "# `fixed` is the simplest policy that keeps the learning rate constant.\n", + "# s.lr_policy = 'fixed'\n", + "\n", + "# Display the current training loss and accuracy every 1000 iterations.\n", + "s.display = 1000\n", + "\n", + "# Snapshots are files used to store networks we've trained.\n", + "# We'll snapshot every 5K iterations -- twice during training.\n", + "s.snapshot = 5000\n", + "s.snapshot_prefix = 'mnist/custom_net'\n", + "\n", + "# Train on the GPU\n", + "s.solver_mode = caffe_pb2.SolverParameter.GPU\n", + "\n", + "# Write the solver to a temporary file and return its filename.\n", + "with open(solver_config_path, 'w') as f:\n", + " f.write(str(s))\n", + "\n", + "### load the solver and create train and test nets\n", + "solver = None # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)\n", + "solver = caffe.get_solver(solver_config_path)\n", + "\n", + "### solve\n", + "niter = 250 # EDIT HERE increase to train for longer\n", + "test_interval = niter / 10\n", + "# losses will also be stored in the log\n", + "train_loss = zeros(niter)\n", + "test_acc = zeros(int(np.ceil(niter / test_interval)))\n", + "\n", + "# the main solver loop\n", + "for it in range(niter):\n", + " solver.step(1) # SGD by Caffe\n", + " \n", + " # store the train loss\n", + " train_loss[it] = solver.net.blobs['loss'].data\n", + " \n", + " # run a full test every so often\n", + " # (Caffe can also do this for us and write to a log, but we show here\n", + " # how to do it directly in Python, where more complicated things are easier.)\n", + " if it % test_interval == 0:\n", + " print 'Iteration', it, 'testing...'\n", + " correct = 0\n", + " for test_it in range(100):\n", + " solver.test_nets[0].forward()\n", + " correct += sum(solver.test_nets[0].blobs['score'].data.argmax(1)\n", + " == solver.test_nets[0].blobs['label'].data)\n", + " test_acc[it // test_interval] = correct / 1e4\n", + "\n", + "_, ax1 = subplots()\n", + "ax2 = ax1.twinx()\n", + "ax1.plot(arange(niter), train_loss)\n", + "ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\n", + "ax1.set_xlabel('iteration')\n", + "ax1.set_ylabel('train loss')\n", + "ax2.set_ylabel('test accuracy')\n", + "ax2.set_title('Custom Test Accuracy: {:.2f}'.format(test_acc[-1]))" + ] + } + ], + "metadata": { + "description": "Define, train, and test the classic LeNet with the Python interface.", + "example_name": "Learning LeNet", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + }, + "priority": 2 + }, + "nbformat": 4, + "nbformat_minor": 0 +} + + +
    +
    + + diff --git a/gathered/examples/02-fine-tuning.ipynb b/gathered/examples/02-fine-tuning.ipynb new file mode 100644 index 00000000000..e3654d675cf --- /dev/null +++ b/gathered/examples/02-fine-tuning.ipynb @@ -0,0 +1,1237 @@ + + + + + + + + + Caffe | Fine-tuning for Style Recognition + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fine-tuning a Pretrained Network for Style Recognition\n", + "\n", + "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\n", + "\n", + "The advantage of this approach is that, since pre-trained networks are learned on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful generic visual feature that you can treat as a black box. On top of that, only a relatively small amount of data is needed for good performance on the target task." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we will need to prepare the data. This involves the following parts:\n", + "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n", + "(2) Download a subset of the overall Flickr style dataset for this demo.\n", + "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "caffe_root = '../' # this file should be run from {caffe_root}/examples (otherwise change this line)\n", + "\n", + "import sys\n", + "sys.path.insert(0, caffe_root + 'python')\n", + "import caffe\n", + "\n", + "caffe.set_device(0)\n", + "caffe.set_mode_gpu()\n", + "\n", + "import numpy as np\n", + "from pylab import *\n", + "%matplotlib inline\n", + "import tempfile\n", + "\n", + "# Helper function for deprocessing preprocessed images, e.g., for display.\n", + "def deprocess_net_image(image):\n", + " image = image.copy() # don't modify destructively\n", + " image = image[::-1] # BGR -> RGB\n", + " image = image.transpose(1, 2, 0) # CHW -> HWC\n", + " image += [123, 117, 104] # (approximately) undo mean subtraction\n", + "\n", + " # clamp values in [0, 255]\n", + " image[image < 0], image[image > 255] = 0, 255\n", + "\n", + " # round and cast from float32 to uint8\n", + " image = np.round(image)\n", + " image = np.require(image, dtype=np.uint8)\n", + "\n", + " return image" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Setup and dataset download\n", + "\n", + "Download data required for this exercise.\n", + "\n", + "- `get_ilsvrc_aux.sh` to download the ImageNet data mean, labels, etc.\n", + "- `download_model_binary.py` to download the pretrained reference model\n", + "- `finetune_flickr_style/assemble_data.py` downloadsd the style training and testing data\n", + "\n", + "We'll download just a small subset of the full dataset for this exercise: just 2000 of the 80K images, from 5 of the 20 style categories. (To download the full dataset, set `full_dataset = True` in the cell below.)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading...\n", + "--2016-02-24 00:28:36-- http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz\n", + "Resolving dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)... 169.229.222.251\n", + "Connecting to dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)|169.229.222.251|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 17858008 (17M) [application/octet-stream]\n", + "Saving to: ‘caffe_ilsvrc12.tar.gz’\n", + "\n", + "100%[======================================>] 17,858,008 112MB/s in 0.2s \n", + "\n", + "2016-02-24 00:28:36 (112 MB/s) - ‘caffe_ilsvrc12.tar.gz’ saved [17858008/17858008]\n", + "\n", + "Unzipping...\n", + "Done.\n", + "Model already exists.\n", + "Downloading 2000 images with 7 workers...\n", + "Writing train/val for 1996 successfully downloaded images.\n" + ] + } + ], + "source": [ + "# Download just a small subset of the data for this exercise.\n", + "# (2000 of 80K images, 5 of 20 labels.)\n", + "# To download the entire dataset, set `full_dataset = True`.\n", + "full_dataset = False\n", + "if full_dataset:\n", + " NUM_STYLE_IMAGES = NUM_STYLE_LABELS = -1\n", + "else:\n", + " NUM_STYLE_IMAGES = 2000\n", + " NUM_STYLE_LABELS = 5\n", + "\n", + "# This downloads the ilsvrc auxiliary data (mean file, etc),\n", + "# and a subset of 2000 images for the style recognition task.\n", + "import os\n", + "os.chdir(caffe_root) # run scripts from caffe root\n", + "!data/ilsvrc12/get_ilsvrc_aux.sh\n", + "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n", + "!python examples/finetune_flickr_style/assemble_data.py \\\n", + " --workers=-1 --seed=1701 \\\n", + " --images=$NUM_STYLE_IMAGES --label=$NUM_STYLE_LABELS\n", + "# back to examples\n", + "os.chdir('examples')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define `weights`, the path to the ImageNet pretrained weights we just downloaded, and make sure it exists." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\n", + "weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n", + "assert os.path.exists(weights)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the 1000 ImageNet labels from `ilsvrc12/synset_words.txt`, and the 5 style labels from `finetune_flickr_style/style_names.txt`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded ImageNet labels:\n", + "n01440764 tench, Tinca tinca\n", + "n01443537 goldfish, Carassius auratus\n", + "n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias\n", + "n01491361 tiger shark, Galeocerdo cuvieri\n", + "n01494475 hammerhead, hammerhead shark\n", + "n01496331 electric ray, crampfish, numbfish, torpedo\n", + "n01498041 stingray\n", + "n01514668 cock\n", + "n01514859 hen\n", + "n01518878 ostrich, Struthio camelus\n", + "...\n", + "\n", + "Loaded style labels:\n", + "Detailed, Pastel, Melancholy, Noir, HDR\n" + ] + } + ], + "source": [ + "# Load ImageNet labels to imagenet_labels\n", + "imagenet_label_file = caffe_root + 'data/ilsvrc12/synset_words.txt'\n", + "imagenet_labels = list(np.loadtxt(imagenet_label_file, str, delimiter='\\t'))\n", + "assert len(imagenet_labels) == 1000\n", + "print 'Loaded ImageNet labels:\\n', '\\n'.join(imagenet_labels[:10] + ['...'])\n", + "\n", + "# Load style labels to style_labels\n", + "style_label_file = caffe_root + 'examples/finetune_flickr_style/style_names.txt'\n", + "style_labels = list(np.loadtxt(style_label_file, str, delimiter='\\n'))\n", + "if NUM_STYLE_LABELS > 0:\n", + " style_labels = style_labels[:NUM_STYLE_LABELS]\n", + "print '\\nLoaded style labels:\\n', ', '.join(style_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Defining and running the nets\n", + "\n", + "We'll start by defining `caffenet`, a function which initializes the *CaffeNet* architecture (a minor variant on *AlexNet*), taking arguments specifying the data and number of output classes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [ + "from caffe import layers as L\n", + "from caffe import params as P\n", + "\n", + "weight_param = dict(lr_mult=1, decay_mult=1)\n", + "bias_param = dict(lr_mult=2, decay_mult=0)\n", + "learned_param = [weight_param, bias_param]\n", + "\n", + "frozen_param = [dict(lr_mult=0)] * 2\n", + "\n", + "def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1,\n", + " param=learned_param,\n", + " weight_filler=dict(type='gaussian', std=0.01),\n", + " bias_filler=dict(type='constant', value=0.1)):\n", + " conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n", + " num_output=nout, pad=pad, group=group,\n", + " param=param, weight_filler=weight_filler,\n", + " bias_filler=bias_filler)\n", + " return conv, L.ReLU(conv, in_place=True)\n", + "\n", + "def fc_relu(bottom, nout, param=learned_param,\n", + " weight_filler=dict(type='gaussian', std=0.005),\n", + " bias_filler=dict(type='constant', value=0.1)):\n", + " fc = L.InnerProduct(bottom, num_output=nout, param=param,\n", + " weight_filler=weight_filler,\n", + " bias_filler=bias_filler)\n", + " return fc, L.ReLU(fc, in_place=True)\n", + "\n", + "def max_pool(bottom, ks, stride=1):\n", + " return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n", + "\n", + "def caffenet(data, label=None, train=True, num_classes=1000,\n", + " classifier_name='fc8', learn_all=False):\n", + " \"\"\"Returns a NetSpec specifying CaffeNet, following the original proto text\n", + " specification (./models/bvlc_reference_caffenet/train_val.prototxt).\"\"\"\n", + " n = caffe.NetSpec()\n", + " n.data = data\n", + " param = learned_param if learn_all else frozen_param\n", + " n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4, param=param)\n", + " n.pool1 = max_pool(n.relu1, 3, stride=2)\n", + " n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\n", + " n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2, param=param)\n", + " n.pool2 = max_pool(n.relu2, 3, stride=2)\n", + " n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\n", + " n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1, param=param)\n", + " n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2, param=param)\n", + " n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2, param=param)\n", + " n.pool5 = max_pool(n.relu5, 3, stride=2)\n", + " n.fc6, n.relu6 = fc_relu(n.pool5, 4096, param=param)\n", + " if train:\n", + " n.drop6 = fc7input = L.Dropout(n.relu6, in_place=True)\n", + " else:\n", + " fc7input = n.relu6\n", + " n.fc7, n.relu7 = fc_relu(fc7input, 4096, param=param)\n", + " if train:\n", + " n.drop7 = fc8input = L.Dropout(n.relu7, in_place=True)\n", + " else:\n", + " fc8input = n.relu7\n", + " # always learn fc8 (param=learned_param)\n", + " fc8 = L.InnerProduct(fc8input, num_output=num_classes, param=learned_param)\n", + " # give fc8 the name specified by argument `classifier_name`\n", + " n.__setattr__(classifier_name, fc8)\n", + " if not train:\n", + " n.probs = L.Softmax(fc8)\n", + " if label is not None:\n", + " n.label = label\n", + " n.loss = L.SoftmaxWithLoss(fc8, n.label)\n", + " n.acc = L.Accuracy(fc8, n.label)\n", + " # write the net to a temporary file and return its filename\n", + " with tempfile.NamedTemporaryFile(delete=False) as f:\n", + " f.write(str(n.to_proto()))\n", + " return f.name" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's create a *CaffeNet* that takes unlabeled \"dummy data\" as input, allowing us to set its input images externally and see what ImageNet classes it predicts." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dummy_data = L.DummyData(shape=dict(dim=[1, 3, 227, 227]))\n", + "imagenet_net_filename = caffenet(data=dummy_data, train=False)\n", + "imagenet_net = caffe.Net(imagenet_net_filename, weights, caffe.TEST)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define a function `style_net` which calls `caffenet` on data from the Flickr style dataset.\n", + "\n", + "The new network will also have the *CaffeNet* architecture, with differences in the input and output:\n", + "\n", + "- the input is the Flickr style data we downloaded, provided by an `ImageData` layer\n", + "- the output is a distribution over 20 classes rather than the original 1000 ImageNet classes\n", + "- the classification layer is renamed from `fc8` to `fc8_flickr` to tell Caffe not to load the original classifier (`fc8`) weights from the ImageNet-pretrained model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def style_net(train=True, learn_all=False, subset=None):\n", + " if subset is None:\n", + " subset = 'train' if train else 'test'\n", + " source = caffe_root + 'data/flickr_style/%s.txt' % subset\n", + " transform_param = dict(mirror=train, crop_size=227,\n", + " mean_file=caffe_root + 'data/ilsvrc12/imagenet_mean.binaryproto')\n", + " style_data, style_label = L.ImageData(\n", + " transform_param=transform_param, source=source,\n", + " batch_size=50, new_height=256, new_width=256, ntop=2)\n", + " return caffenet(data=style_data, label=style_label, train=train,\n", + " num_classes=NUM_STYLE_LABELS,\n", + " classifier_name='fc8_flickr',\n", + " learn_all=learn_all)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the `style_net` function defined above to initialize `untrained_style_net`, a *CaffeNet* with input images from the style dataset and weights from the pretrained ImageNet model.\n", + "\n", + "\n", + "Call `forward` on `untrained_style_net` to get a batch of style training data." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "untrained_style_net = caffe.Net(style_net(train=False, subset='train'),\n", + " weights, caffe.TEST)\n", + "untrained_style_net.forward()\n", + "style_data_batch = untrained_style_net.blobs['data'].data.copy()\n", + "style_label_batch = np.array(untrained_style_net.blobs['label'].data, dtype=np.int32)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pick one of the style net training images from the batch of 50 (we'll arbitrarily choose #8 here). Display it, then run it through `imagenet_net`, the ImageNet-pretrained network to view its top 5 predicted classes from the 1000 ImageNet classes.\n", + "\n", + "Below we chose an image where the network's predictions happen to be reasonable, as the image is of a beach, and \"sandbar\" and \"seashore\" both happen to be ImageNet-1000 categories. For other images, the predictions won't be this good, sometimes due to the network actually failing to recognize the object(s) present in the image, but perhaps even more often due to the fact that not all images contain an object from the (somewhat arbitrarily chosen) 1000 ImageNet categories. Modify the `batch_index` variable by changing its default setting of 8 to another value from 0-49 (since the batch size is 50) to see predictions for other images in the batch. (To go beyond this batch of 50 images, first rerun the *above* cell to load a fresh batch of data into `style_net`.)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def disp_preds(net, image, labels, k=5, name='ImageNet'):\n", + " input_blob = net.blobs['data']\n", + " net.blobs['data'].data[0, ...] = image\n", + " probs = net.forward(start='conv1')['probs'][0]\n", + " top_k = (-probs).argsort()[:k]\n", + " print 'top %d predicted %s labels =' % (k, name)\n", + " print '\\n'.join('\\t(%d) %5.2f%% %s' % (i+1, 100*probs[p], labels[p])\n", + " for i, p in enumerate(top_k))\n", + "\n", + "def disp_imagenet_preds(net, image):\n", + " disp_preds(net, image, imagenet_labels, name='ImageNet')\n", + "\n", + "def disp_style_preds(net, image):\n", + " disp_preds(net, image, style_labels, name='style')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "actual label = Melancholy\n" + ] + }, + { + "data": { + "image/png": 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yvUJS3DfmLUsURECnW6oGgUxGBSVh7180Q52jz0UjoWnYDZxRmdmA1bqxNeau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6EmVpSTd7osozItUDOtN5ksBa2bdacbpJGyRKLhrAxEttNG0O8AWK1TShCtYaElalB19S3dVz6K\nXgeZAe33aKYvR2IdTeigmbP1JOo9DWnhRCfq9gN9t7T7M2Wwry9fyqgTAAxWi5KN+oL8uSkq32g2\n2qdUlFa0ZdBgXq1RPe7aviIYv6/FivJHwgyxUgkMa1OJqh/Y1xsUHuQrUfF9fOzd4+WEgcaLlyRk\nCw8DLOjE2R9G0F8Vc0ATjEsrRGvq5wRHIqxy5sr826+xaeUiuGdCAZLg/R0JlPzEBh+5SS44i3bE\n5alb04mllqPutSMTC12jwWsYoEIqyVJ16DWgPr+6bVDruq2+wC3KLyAkU8qN59VIt3Q4otfKUMlH\ne7p2ayHDUHg3AA2GbeWZam8innnD65bLb5Ol5NsK33nztYK0ne+0oN1dcb+GBEKrkyj00d/F3Sfy\nzwb8IS0fCmPzsyUINn3zoaNuLwrdAOgci9j6aEZsjZPEI1YC9y4pa/v+P8nxosIgx0pliaavbg5i\n7L1voIZj+KrhE5l7KUdBVYASAmLOKEKUeKx4vgFy8qHg+Q6rokNiG3EnAW0aWlCtuAf1HZFu5Tds\n6KFFA0ohmCnMyutL66+6iWQq3+W8Aetdma/tnxrrZRIyjKlvjDDLG95j1dTf36A5DPaOQ9LRWr0E\n491NZKuuEjKhceLaA2Ubosy1d+gEPLFs9T41NCMRcLoQtj0roZzY4l449H62Pd2vtyzp7wgRKOG4\nfFilKOwe3ErY2LoTBoVRTJ+jUOPlSrlE74/pPV5jKZ+iPIR51+/4Bug/UrOdrztetrnJ6nRjxzYn\n0VNAGAIaaFkf5KbISQRPiKiOt0ZCvvd2S0N3/BuS4lZb2fdWQgWFXADQ/t0+p/r5ENtTuwYFkdOM\nWQ1LA6jpuolEOukKupS0pOVmKgRZjrylZ3tmEoUQDZeKgqrkIdFECyej8JGjEJDQKuIzVTF2D8FQ\ndiS6xuC5BhbprZ1Ao+1f3T+x84YseA9WRkofsTFPiJmVqbu2Nba73IZd277/sLqmbG1d/Lm2z4Ku\nrnywXRhYz0oINeixjij0tfqcdV9EQHo/3+3ag0Y8cl3vK2N4nkQQ9xIIeO62ec/xYsJgrYlhCxYO\n85GETyZ0RBKyZofJJufDOOi4cjqzdm1qu1ZGES1CGr1RwHNdcScoVOPATRAziUAkrVOza/wZCcKc\neQPMON/uqZAKIV7VN1gSsZqVlJlBalcoMamtCagiKxB7NxE4kQCsQ4/pPO2EfEPnHOi6fQ6hkzQH\nxnA6tEqsFRwu7bOtazJFvq9OwxAy2JxntQ7FsPfnKYm932MJj43pdc+1dNGvbszxrmDYBYGYPbbL\nag93kU1TdbUAK6LQ+4ESLvdxpB26B7Lzks6zCqXUagg2FHkyc9IkJHg+kdYmvOrHZj586HjBTkcL\ny9kdObJJRBbLeNXPI7y65YStEqphnibGys8Xk5cUbwIpgQL91Dn4azQKILd0iah5E4QEQQGVThqp\njk3Q+jc03M0S4ys6ny2kR7wx45aVuRHvHWzs5iYB5jmYWpA3GqxOyfViMM07W5XwUfNY2MqZ7S4X\noDU8gLlqYG5Er1lB/xIOfBzo9S0fkQRsIR2aTKjU51jU9NvF28nY64RNIMQqjkQFCTcNX4xWt9Y0\nsh/vs+9Dm77QVordf8L6121B785cX7o/fQurfjNKqd2v6bbG2MwbtGKT41RLJCGhYTI/xaHFiYiR\nsHWt7GsnFQoyucnO3kJzYYB39VuhAhg2lhTL9TYIVQgii9BatBNG0A4MAJioEmkAq31xefDaHt7J\nUAVfrO4vbMteDjnbUmuvkINRSE5+iPxb+QROoeHqxOStTetZTOgHbVNCNBbsAXAUgzbH8R7MMLGy\nyxFSQzrPn2VeGcHYaJsJYrp2kWpeN2R+yOzhPpGoK1EG2QtgWML7BH1e12hZ2WW8u0DYzToxRa+x\nTEadqKNGJUyKKoWkorRF0dn2uTbq4pnw2P94V7tLmBepI4Wq4oIrtkjKzvASbPy9GF+v9oMXAgiL\n7qzMtVeG4oeOFxMGt8cF9wVn1Z2PjCbUrEIW4gCBOBdweAkDPfvwJj+EYblhxNZAY7PB0rkQRawB\nAQZdkHqbi9ouBRG0VexXW17mgL5OyA/6EoKtcNw7tGRkc+M92SYc5Gu39IYRdaRvpLSfdc6FnEYd\nkbk3E6xc/yCRrFJ/XSlH5AGjf2Yj9ogaoa6AmFKtBeIrmQkSTsoyBNREMV+PQlZabqEWCQqxZpQW\nEwPUO3cpYvux/yXYLiHotr/XG1jUsPkh8n2i0BIIfYJdO+u6YvQSSM8QSHn5674MBSsR9CHl9bSn\nuxbfTQqlKevZYsVGI0IU2zkCuJ03jFFZDh88XkwY/Lk/NvDlZ2+x3n6OHz0NfHn9GOenP8Enn7/B\nd37++/j8Rz/GH/oDP4PP5sAnXz7C5xVxGTjxEQ4E1jJMm/AL4HEAkSPBzQYcJyKcjTCdzHJDUpyx\nWKiltOgje4SsdJAttr8mweZXcxMV407UwtCmtGksRHhCcpAw1qJ3PorhcjOzKnNVXoXDyfSyB9uO\ntSLEIh7IMRlAoacc+BqRcWnlY8damE8nHo5rNluNZGxlWsoPEJWnQIYvc6CbjlS1ZXBK8Za45ZFm\nXjk32TzR2O1DFpvsaScSsAhMBIankJu7T8ENsRKvhBLVINJecKVUwxBuvIdVgmBVaTsbohjXBlaC\no0al8X7uGBAtqNCgodBJVoOimLs0ic7BH2vpDyEToZMKit6JuYr4tNxIG8qy0UxCx7zeiknJnApz\nzYXwwOeffobzPPH28RG3m4bfvf94MWHwx78NfPLqAb/zj38b9ru/gz/75/5F4Hc+xyfnd/Dqo0fE\nH/0GXn/0Cj/+wY/x9P3XePvmht//c9/C3/1/foLf/vFneP1wwF59D//4d38XX/z4d/FLv/hH8Y8+\nd+B4wMUdH10W/PBkTDMsGxjHwDDHOSfWPGGxMM0RbrA4U/MMB2wgYmG6HIUE7NpMo0BIRweWsAY3\ny86FOLKEOAl/YoUnobOTzjCHhSM8m2qZGWK2XShveAr3CcwMs4apbFXMwoYg7O8gxiyEsUFHm4GY\n2UhDxk9ezjfB6FhrYgI4RqMpnT8VZ0dVAsQ5kTA/rIuLsj4CaQ4VSusQa1hgLpoEnnsVU4I3qJFV\ny5Hm5AKwhuHAwBk5in5Glve6EGIA7oMMTnZekzkcAzmZMg8NiI1YOMYF5o7sl8KZCGNQCAXLpY0d\nlqxQIGxlyjSlPLMVEGtx7qLX58X26mGY58r7nGsi1sI6W7OvWHi63bhWC/N2w/n4BIfhnHndc07M\neSIW8MPf+QHmWljnic8//wKffvIJvvXt7+DX/t7f/1qefDFhcLwaOP/+38Uf+tlv4ydvP8XH1wPz\nYeDy3W/gkx9+gu///PcQBnz56sDP/v7v4PqN78Ke3uBPnobx5kf403/mz+C7Dx/jGL+AH/7W38ff\n/bXfwL/yz/8Z/No/+CF+4zPg6bO3+OTpAW/OwHH7DNcx8Plnn8Jvn+LbP/MHML/5s8DlFV49DPgx\ncJ7AeTOcYTjjEcMmcA4ACwNRQzzNDZOowN0Ra2JMMsXhiHXLOQS3gRWOsCxDdkw8+SoBcVr2DjiW\npaaXoGc4Mk5qzaUpTI7TTjZ4sjSdLAWFD0Ow1gMAECfDq8ncHobb7YZ5OzEuEzbsLt8/sDDGwLAk\nsDEOXM1xu03YccB8YK18SBG2jlKgljkNFpM9RhIFLGOINYKzMZ0a2xJtRGD5wjonLscB6ca1FswH\njuOKwdbLcS6c54SNQf+JENPE4QdGQrTU1hZwPxIxeZ7rqoS1i2POBbPAeTtxuz3hAsOnP/5dwAwf\nPbzG4/mEx8dHfPLpp7hcrzjnicenRzw+PuKb3/wYWIE5J87bDWsmOrleLni4PlTG5xeff4HzPBED\nePP0iMfHJzy+fYtzLpy3ZODb7Ybb+YTb7YZzTUTkWlikcGCFNmKeNDDp+4GlA3qlOaqR7BMpILIN\nfa7vb/yDf4jLOHDOu6bq7xz2T5qd9P/lYWbxf/5XfxHrt34Lf+8f/xi/9Au/D69/8ZfxxQ9+A5eP\nvptE/vYN5u0Njm99G1/85m/i+s3v4dW3v4llwNPMmcOXAC4ffRtPP/4JPn/7Br/v+7+AmDdcXr/C\n0wp8/sUNuHyMH/34x/jBb/5D/Ny3PsKPzoEf/ujH+PxHn+Pt6fgMA5/dvsQVjp/71nfg14/w9vgI\n9vHPAjZgthDjgCNRRTCHIb33KfXLmZUrij17cU76FoZlqS2TYBQhXGHsCsRMMxsZ2pIT0Tg7YEuA\nGrQRxxh00qkyDrg93XC73fDxxx8lIQI4jgNPT09Ya+HVwwWPb9/kRizg+nDB649e49PPPsPT0yO+\n9Y1v4ic/+QRffvklvve97+GLLz7HmideXa84joFzZcvueU6M4YgznazH9cBxOXB5uMDHYLJY7TeA\nYLdjCQPQPPL6zJsvv8QYDh+eCCUmPvv0c2AFnp6e8M3vfBsP1wfMpyc8rRMeOXfRYZjnxFoLxziy\nKen5NsfULwBzYs6JH/zwh3Az3M5bJU/M88Tt6cTrywVffvkGYcAxRkZI1kxGHwOxggNwJnV7miVr\nzuyGZMBtnjhXdtMqB7ZlFyqcE09zIdgSXk7DtC+yacuiYE6nZ4ZU1lrlezmuSYM59DYAIx0cF1wv\nF8CAy/VaFaduhlevHtKUPQ6c5w3/2V/9q4jdCbIdL4YMhj3g4Q/9Ev6X//5v40/+yi/i+tG38dnT\nb+L1L/4M3K64nW9g5xMiHJ/77+A7v/AHgZiAH3g4Jl5h4OnNp3jz9gt88vQGv/D9n8Htzad4G1mo\ndFxe47vf+Q7cA09fOn7XA9+6DvzyH/9l+PUV/sb/8bfx9/7O/40/+8/+Cr77/T+JWAPnm0/w9u3E\nr/+jH2M6EOf5/zL3JrG2rul91+9tv241uz/dPbetulW3XI4dYzsJ2HLkAJEIAcEEMUEIIiEkmgES\nEp4AQbKY4AEEDAGMjBCZMQiKIqMYhGKiBNlO7LKd8vWtus1p99ndar/mbRm86xxbcbksYUXlNdp7\n7aW9dvO9z/c8/+6h309EMyN5z24c8UB0EwLY+4gPgeg9MZa7QRICg0QaSY6etm0Y3ISWGp/TwUsA\nWisiGS0llTFvxpEAZd7LRWvhXrfkh7EgxgxKEWMsi1xDKIItpchCMB06AGsMKQRiBiElwQekKnoO\nn0KZmbNAa41RGu88kNFKFmWnFDzVhhQ8gkPhIRNeKwoPF3vMiXRoh18PuK/NOeX1h7vRoUC+no3l\nQTvxeoyRooxbSmtS8uVnU2WkE5SlKiE6jDHU0uCCRyvF5CNKSkIIVHWFEZJ+6mmrCh8Sg480Sh3G\ntbKoNAJKSVI6RJ8JyTYFUkhA2V0olX7T/8R8WHiaMzEGUiy7HVM86GEOVS+lgtdoY0hSEl8zNFEQ\nsiQU1xVGGbQyGKuZtS1tVb+50RijqawphdXYgv8cxpWqMuVuf8B0Qopoqd6E0SIOCt7DeKhU+bsI\nMlmVsee7Pb5nxWB49ozt9S25v+Fbnz3nq0eP2PmR4wC2rcmDp799iT4+5+jRBXRzpNGoLEn9HWG/\nIynF6YN3OX0rM7meKLa0yzP8bo9VCWESQlVoLZA5sNlfc9SfcPdkzdOPP+b87Iy3Ls7IMvHxt36H\nrDt+4Af/BG/fWzGGAd/OWV/dwDTSXTzA9yOdUqjs6Y6W0B3x2RcvMHXNZrOmlpqsBUJZnj+9xMVE\nf/WSxcNTnlzegFT0biInWG32+JC4dv4wb4PVhpAKbqGkQGtFrSxScbiYBTlEtDEHhFgXY1FKRDcS\nMkw+IIUkpnSQFicUUBtTZveYaIREGnXIEcxIEVCyHCprFS5kQgj4yZW8gwzSB4Q6aBQSKKnegJdG\nCaxSKMThzsbvdgEcItikJPpSwF6zHLW2JCDkxDA4tpPn1e0NTVVRac3QO2TO1N2c1c0Nb791n3Hq\nebVe0diKm82W2lY4PzGfzfntT7/N6WyJPb3g5vaOKo58/Uvv8ve++W20Mex2Oxe91DAAACAASURB\nVGazlpwy4zQWwBaBpeBJUoBVhpQSe7cnA7aqCTnRtQ1t02KMBV0xuohSitOTE5RSSG3wEYRSaFMd\nAMXCs8SU0FVVsIdUnKRSgTbqDRgqpCQecIIYI947pnhgf1JGxkydC7gdYiKkUK6LlFCHoj668Q3o\nmmIAEbG2wg0TWhuE/O5TwB9aDIQQPwf8BeBVzvn7D8/9J8BfAq4OL/upnPPfPHztPwL+DQot/e/l\nnP+P7/R9vQGn4K3332M5q9mNG87PH3H77Anze/cQMSGswTYGMXtIGPdoNcOPG/xqzae/9ssslkse\nfigYtaGuOupuVgI5KsV4d4dbeUTO2AxvXRwxaxtUO4NXN3ztQcujr7xfLlwluWLGTFXMK8EqNlTU\n2PUldQOirkA4wvGCtL/lsyfP0M8+453332OR9gzXE48vzlicnBNC5uXzp/ypr5+jguAfPI388A9+\nP7/8q7/JWWOZHx1RK43panY+4dYropDskuLF50+xdcV2c8cew7TdstlPVPMljRH0Y+TzTz4h1zOO\nqo4+Dmy2I4vlEqkybj+Uu2sWhChRShIFhCgY8ghkFImYwfmElqq8Zkokmcq6cRfIKRJjKu0ooIQq\nzEzwCAExCWIqnYRRBZvoEQeZNIQUSDm/0UTEDDHB5D2zRUelLMWhFHHeM2XJbpre9BOTi8zaFjeN\npGSJ2xv+0r/8J/hv/sbHfG1RI6Pn1Try7vuPWV/d0hhLZwxfffw2Q0osGktQxwQEKyf46ocfYYzG\n2gptLFLpMopIRQiZJAVaKJTSUBlETLgMQ8wg1Rt8KBww45ihPbAF4wEfEIhD2KxkjBEEVNoUufZh\nxUwZBws2o9VhneCBOQoxlk3UUiGVwhpLjpEQC77io8fnREgRqSVWVKWj0kCIxJypbIPWihTDAXtK\nGKVo6obsSuf0RyoGwP8E/FfA//x7nsvAz+Scf+YfKRxfA/4V4GvAI+BvCSE+zL93/cvhYZtztnef\n80989AhlGqRpiPstzcmC/e013ekFup4z3K6Rbc32xSvmbz1k3K1oZyfc//LXqKsKUVuW7QVJC6bN\nHT71KKmxixPSfkdKmfmspT7s6Ms5M79/ga0lTTMvCLDIfHR/zqxp0ELQdR05TThvqdtTwjCR8Gyu\nntF2Le998D6by5f4/Y7b2zsUAltbNs+/hR/2jJNk+eF7vPjN3+JPf/0D6nmH8Y6TL7+HdD03zz/l\n/OKck9PH+NwjbMMH5w/4QK2YffD9XH3+CUeP3mZ69hmcnDGfXzDisVnwy7+Q+PDHfpRn3/wmTdvw\nySfP+Yl/9ifZ3N7xzW89Z9KW66sVn1yueP7ihhgcCy3Z+0TICiUjUgis1vgM292ASB6XBY2psGog\nSE0UGuEjj09aksyMUyYkDWQ6W9iHWVMzs6UFv96P7ENBwk0UtFZz3FScdDPuhom7wWGE4GTRIY3G\nasMQAxKFy+Wg1UajtUVri38NL5gaKxTPneQnfuKC1gi+LCsygtYYvAFQzGxVaE4pCKEc1IhAS8EU\nEolIipnJx0OUYpnzFQc6kQxaE2KiMpJTW2OlQMjyWill2aIUf1e0JIXA6AIyp5QIh7EjhlykClIQ\nU8THRIjpDdMQU8EevPO8VgjGA64AkGJEaoUPnkpbXAgFq0gF3I2h0OQxlf+l874UE6VIORK9P5Be\nBRgmRuqu5na1+qMVg5zz3xZCvPsdvvSdBpB/EfhrOWcPfCaE+AT4UeDv/r5isL+ld7e8U11g5jVh\nvCYJGFc7ZkcPGTcrop/oFh0oxfLslBglFZpZTmRtcVkxRonf3mJqhd/dopf34VD9VZzo2gXbm5cM\nw552PmdmKrLUKN3gNyvu7m4wWnFqE7OjJbvbl+R2gakM+9sbbDJUdc243nNydoQ0muQF9x484PrF\nF7z1/rtUdc2w2aN1jTltaI3ld37r1zBuS57eZnu15cN3HqC1REbopMXoDrKjao7wRqK05rNXK77v\nKxXKVEhR8ennX/B9b3+NHDzn58esVzf88J/7p9lvnoMQvP2lr/Lgg6/gx8i8a/jxH/9TDDfPUF9/\nj8FvaWb3eHLb89knn/KtLy759OUdc6V5uOw4nneMIbNOGaU1C62pBHSNobKaoe8xKjOzmkYXDGu2\naLFVw27nud1NDAFQFUMSvJUSY/C4UDKs9j6wi/CF1qTZ64xGyQshEbpgJEooKqvQRmGUxihByJmY\nE1praqmo2gorE0lrHqlIdBmXCx3Xx0DCEKYRFwWjC1TWghQEH0BJYoosqopaV1SNxWgDIqG1REuB\nd46mqgjRE3xACYXziU0/ECX03qFQjH5k8h6fElpXSKkYXzMELkAGHxxZFCGdPBwtgcRUmhg93rs3\nxyb4wDQ5hBR0bUs8FAIlJSGmN4t4D1AAQkBwnrZpGN3IOEzMFnOmaaSpa/zk3ojb2rah3+2BiDWW\nfnKYAJth+qMVg+/y+HeFEP8a8MvAf5BzXgEP/5GD/5TSIfy+h3nwgA/DD6AXHWG/wSzvo0jo5QXT\n+hlaWezRKcN2RfYJKTTGOGgtdzfPC4UXJciAPLmPHwI+CdL6CjNboExLXS1JUTE/vsdicQRoJrdD\n6oq6m2OOj3nwtR9i3G+Z+lumzTXCdixPH5CGFRfvfYWgGoTzLL/8FjFl/LAnKYX0E2dKYpoapgnP\nSF03CN2iz464/Px3OH38IaK7oIobxHGHjBF5fIJJmuxX6PoRcbih7S5QUmH9xM03f5nZ8hwpI2eP\nHkLec/fiM+h+gMl58s4xjYlHb71Ne3QfkeCOK3JWbHZrpK2R7QwGRZCSr3z4Pl//+kdM+x37/Z6b\n2y1Pnz4Dn8jRE4aRYYyMAXYBXqwmYGJImdvBsRknhixxUjEFR5I7ktGkrCg34B0iOkIsCcXSGIyx\ndKZi2VRUHkKOxJzwIRKcQ4Y9ulKkULQdPhwCWI0ixsTMViitSFMELXBR4X1PazTaGIwwaC1R2mNp\nySqQtcZPA3VlwSekKqBpyBkRCw6TFYzDSFYKoyTTNGKsZbV3dLYqLIBW+Dwx9RNWWWpTEQ7AoxIS\npRRaj7zWP1RKE0JkdA6jLTkopFGYxhTwL2V2ux0CMMYSQkRrxWzesFhKnHMopRjTWChBpYhuKl0v\niRACCE0/7NFaMXiHQTFvWoZ+wBiDzNB2DcF5vHO4yTGOA/NZxzRNpBC4vb3FKPOPpRj8LPCXDx//\nZ8B/Afybf8BrvyNqoRP0+1tsu6R3GXv7kuN3voxWFhcW3N3doaZrbK0gJCqbyCkT/ETo1+ijc6bL\nZ0yra47PHiCbChUU/u6OanFKTBMxAyGSDnE+4+6GED1aTYg6kL3EeU/KmpwlQlfkBP12dbArK/y0\nJ417YizUnHcjzbxjdfkc2S6QMTO9ekGWjnR8gYged3vFxfk9js7uMa6fs98PLOcLgl+RfGRmBauX\nV+TmFKk17sUX5PMHBCIxG67u9rD6hPb8bb74xjdoTu4T+gli5jd+9Zf4YgN/8Z/7SbbrnuB36GpB\n9BOiNlhlCWGP3+7wfiJs95jZcUlKFpKj5RGL2YxpHPGTY70b+LWna/6vT9d8sRqKCEiWO5ShRmmB\n0BktDaYyGAFaS4zR5BhJUZLRRdiVEjJltBJERpLzOCGIMaGUoZYC0VaEIIghYKxB5kxnK+KhBVay\noPwiS2RtkEZDv+do3jBkydjvmS0NPkV8PzJqSaUl0zRgpWIYyjo0ETIyJ3yCTKJShqkfaOqOEDNW\naoQpY8+9hSVOHh89Rham6/h4SXATk3d01pY7c4yknImuMApKSQZZPAVaV0BCKUjesx1HEhllVNEe\nxMgwDOSc8V4wTQUf8aEAt1VVE2LEuYnK1rgYidFjlUGksgtDC0UIkXQoSlVVIcgE78kH9ZQypci2\nTYN3vjAQTYMfJ3z8x6BAzDm/ev2xEOJ/AP73w6fPgMe/56VvHZ77fY+/8r/9n7jtHVl8xp9874wf\n/6GvYm3LfhpI45b54hjZdbjNFTNVE+Yz8JJ5u2Q/DNjjR+R+IHvPTErM8T18pdgOW9qze4Tdhqnf\nE9xEymCqDi0qhFL4aWKSkdzVGKAyhojF0hBcRPqelCbu9j3t7AjTtqQQqdoW3bWEzS2m6jBKIaqa\n5uE7BNdjFyeEcSBnx0Ja+rtLQr+Dowe8Wm1prKVrZuhasDx/RJKJrCVSHzFcfsa7H/0Q1XzGr/zq\nb/DDP/aTXH7y69T3H/Pqs2+zuP+Qqmr54Gs/xAdVizIV+75nMVviQ4AckFnjh4FMwClBWO/ZhsD0\n/CnUHaenF/gYiCGjtCIq6BYt/+T3tfyZL9/jk6st33ix4oubntvtSEQRpCZkiQsedVAABgdeHWzV\nQqK0whz8BVOOxCRoTV3EMQc9hDaiAJZCEQREqVDaEoIjx8h8NkdLXdKelEBnQR9GRIaumpFF5KLS\n+KqmaVuGzQY5P2G72zImOOo6Yk5YZdiOA5WxpCnSj45Z10BMWK0xShLTBFIwjnuqumXcT4wxkIXA\nGEVwE+uDEKi11UEHJfCTx4WINhqtS0sec0BkWYRCKuGCx5oKrTXBe5SA3WZNiKFQmlJT1xbnXQH7\nUiJ4z9D3CK1pmxofPWGaigiMTMiRLDIpeKy1RUsRAuvtlspaurah3/ekULAGJSTWGILzfPE7H3Pz\n6mVRWEr5nY7iH60YCCEe5JxfHD79l4BvHD7+68D/KoT4Gcp48GXg//1O3+Nf/ws/BnlkuLqlO54x\n7CZGe0d49RmLd76OnM8J6yuk1Hz867/KV/78X0TZIpVFGowIrMeRyjS8/O3fQJ2/QueyqmvzrX8I\n7RHDsMVWNXFcIbJDSI2pLJaIX71kch1icQEuI5sF0Qu8v0UYQ7QVMmpStvR9T//5bzO79xh9eoau\nO9rjhmnYoQEnZxiZsF2HNoa7J9/kF3/pV/iRH/6T2PP30EDPxOe//vf46p/750l1R7//Fk13is2J\naBv2k8TYiBCaH/mRP02/XXH0/vdhnWd5dsF0tWI7a5ifv4WNkudPPqNta9Y+gZLsxh4pepLWCJ/R\nzMnNDBkCQg74ybG9vsPH8h7JjaArVExMU8/W73lcz/mhjy7o3Zb1zTXXz5/z5HbNRte4kw94Js9J\nFNVk8B4hy13ptW5fxMjZfIH3/uA7MAclYREd6YNZppKWrEvrHg9uzM1uizKKxlRM2x4hJUZrqspA\njviccX5iHAdIAVTR4z++uIcUkil6Xlxe0rVtGTelYEyZR+cnbPsB5ya6rqVSkka37PxIN+uotEbM\nGupxoNEGpGKhZ4XiGyei5A0IlypF3VUooVjvduyHka5tsVogtGDyCY0huYiPnrquUVKzH1YoKVge\nLTFS4p3n+OQM7x3JJJwfSaSi+DwwF04JBu9wo+d4vqCrK+5ubjk9PmG/21NXFQ+6Bt+PhGlECxhz\nQmeB845+7Lk4O+WxfIfz+w+Yz1ta3fCbv/b3/+Bz/YcpEIUQfw34CeAMuAT+Y+DPAj9IGQE+Bf6t\nnPPl4fU/RaEWA/Dv55x/4Tt8z/x3/pefLuosLUnrNYgJKzQ+SSY3UlcS7wMSSYwZWVforiM6X/IM\npgE7riAXTrx68CWS74kIwt017YNH1O0RQlucC/hpj65m5f38HvyItg2BTEoWVXWoSiEwODchlMXY\niuBGpNS4YYPAYaUAoYp4jITUhphgGPaoYU/IgbadYapjbi+fYboFdVORmgYx9Wz3a2bNCcpWhDAw\nxcTqs084e/fLiDixvr6iO7+HzBLTdnhhkEKxffo5upJMLrIdd7R2UdSKRjANI4qDas4HRu/wo0MZ\nXQpbCNRNS/C+cGOiiGf6YU/TLbBNDUIy7nq6pgHhCW7i+tU1w901bQo4qdi2D7gUp7h6Tnd8VNRw\nKeFCZHKecSxy3RgjMYTSEodQlIq5RKAZpVBSkTJYa0ghFmGRKnRbdL54E4pEsXQb0whCMOtaRCxK\nwJgiWQpmtmKcJrQ1KKWwxiByZhwn5rMZq826qBaJ/O6yalmKitYYqYu/wXvG5FFaE0I6KPgyRll8\n8lilisRbKrQ2bIce76YS3ydKmz9rakZf2nGNJMSAEKIUshDQB/FXCqFoKaqqFEElWc7mjOPIfhiY\n1w1KSAbvSDkTfAEa67rBOQchMowjpjKknJl1NUob8JHJTaWQ5YyWgtoabF3hd3uigJ/72f/y/78C\nMef8r36Hp3/uu7z+p4Gf/sO+b7EiR7JLpG6OcBW77BFTaadjBq0txImmm5Palv00UnWnKCuRURL7\nBWwukWksM32MyCTANhgMzg0EF9D1Emu7N9t+le7IYsXm5jnYOd3RMTFFokukFNDKInzExxFZKXLM\npf1Sc7JSZd6Vimm3RcZE9hPS1IRaY7s5qoLt1XOahw9YPX+GtBfUIdMniVUz+mmHEbC92yHjjou3\nv8Tm1Qtsu8BnxeZ2hR48k7UkP6J1i7Fw/WqPUBIlDDebG7TShCnjgmPyjkpKkpJv/A0mRVolDwYt\nqGqL0gqXE4RAUzdUShODYxq2iO2W7fWID57r9RpNBhQ7IcmyIaLQIjFOE7u7NbW1hT4LkTAWVWZK\nRXOvtGKaJqAUgbZu2A97fIwEH2jrhrEfUFpijcYFX+bxGBnGHqEt0XtSitw/O+PlzWWRVkvD8XLG\nfr9nco6Nc0ityYeW3Jqal5dXzNuWcRwYhv3rBcZIUYrQFEZkgqN2zujL4RGVRrtSBAIJnzxVZdm7\nkXnb4J0jhERMESVkYTtmM2IICCEJwTP05fdDCoTVxV8CaGsKgDo4jNV0XemewkFFmcn0ff8mpXk9\n7pnGCWMM87ZDqRqpBHNbM1nDbhioJIgEi1mL9yNjHJjpitt+X7qjg5x6sxtht+HR2Tku/TF1Laa0\nw+gF0WosgpASlWlQZoEjY8M1UtSEKRPCRNqNWBex7UWpgELA/IgpeCq1pQLEyQWTy1Qi4Y1GJQCD\nVGWmQyqkLXZa2Z3x8W98zuUnf5s/80/9KEcP3yfkUOS4cURIjZ9GZARV18hsmMaAriRWG1KC5cV9\npv0GF0eEn6hrQ86eGCzV8hHbJ58xu3iLfb/m+nIF0uFHj9IVkT0aRZ0bnq0ukabl5e0NCkcMpTUd\nxi1WVNxsv4U1NZW0xf1XGwiOoDVNY1mIiihqfI7Yqi2z6cGE1FQV3m3ZrG/Y70ZscFTWkIQDbbma\nHNc3N5xXhrvJY3TDrJ7T1ccM44hPiZgFG33CZSiYQ9tarKwYnWe33x8cdpGqrqmMOfDdiRgjxhi8\nc7x48QJtDYv5jO2wZz8OkBNdXTP2iX4cQUBTV7RtSz8WwLapG65vb6iahnEcOTmZk4Xk6uaW09Nj\n+mGg3+x459FDpFJ88q1PaeYzhnGk6zrqpmO922OULgEuUmIqi+gq1rsNImcm52mbiuN2htCaM21Y\n7TdYJF4blNJv3KV105Fi5vbmFqM1Vkq6ukFkwbPVLVppRMqYxnK6WKKFYL3ZkHLiZLkge0/sR5qm\n4bbfFmqxqkkhYCrLcTuDlFi7gf1+D4BViv008PTyBY/uP+SsmzGlSBKw3q05r2ccLxZ8+uo5tbZ0\nbcsUJoZ9z4OLe+zGPa+ur7j3+DsSe28e3zOj0i/+zL9DbWrkfEnWVfHPuwEXB2bH98locp7QyjD1\ne4TUUFm0c0RdIUVCSYGUlu31c1Qa8Bi680eluoeIqevifhevjSUSoRQ5R6RUPLu84W/8wi/y7v0j\n/oU//2fpXfEokg6KPG2I7pA5EF2hxbJAWkvKkX6z5frlM9K4ozk+JwiJyoKUxBv76ugmNqsVi8WC\nKShOFhXD6PAUO9roHI0UuBDJylDlIjZpFzXBWOK45dkXK770/e/TKksWmuxdUZ4hGNwOIxQWhe5m\nJFWC1Xw/kP3Efr+jkkUYlHJktdlxvV7zcF6hyYzDSN3MmELGx8g0FXdeyJ4pKaKd0esT5PE9bveO\n7RQ5PjkpLX2KxHTwPqRAP/Q4H5icJ6XEOI5UVVV4cOcIKRUWImec86VYSFnGHQXGaKZxOJh0JFVV\nsdttefzgPvtpZOxHZIYpBeZdR4oJYTTZebTRDONQZLk+0NlCo603O3RVMex3NFVdvBLJU7cNMsKY\nAvNujgiBKfgS5nLoZJTRaCFRRpHDa7twIuaMTxGrDU1VMUV/+D9W+BCYKCNBLSQagbIGoRXrzQZE\npq5qQs40xiIz3G5XGK2p64bdbosbJo7m8+La7QdsVVEpxeAmVIIxFL1CVVtmbUsms7q7oWkaRM5s\n9z2trRjcBBLun5ySg2c39vzVv/IHjwnfs2Lwd37+PyUMAyomxGKJMC1+e8fkBTM1YI4elDuOlChj\nkd7hRURISw4Rpcvz2XkwhhRGCIewCa0Rpi7pLkIf/Oqh+OMP3nlx2PrbbzbEfk3V1dh2werVK/zQ\nU3UzXMpIWQ6+G8Yyz+byzwVZLME5krICXebn6+sbTHJFFZYVs7piN0WsFbgoSg7DgbpbHJ+grEEb\nw+b2FiUVs1rzbLXmwfwYM6+5ub3i1Ys1P/ijP4CfUqGHkifHVJxoLhC8IyZPnkaSdzx58pT58REP\nLs548uQ5fnKcnB+zXq2Z+pHeeR4sagIaFxPD6CAHYpQMkyMg8aplL1rG6gjRHaGMoWpbjLWUvIXi\nyFSiOBl7N+FCEd/EEOiHAe89Qhb7dHIBoRVNXZFiwoVAXVVIBLvdjqap6ZoKrSTb/UCIEXkw5+iY\nGGNivV7z3jvvsN1tCg4QPGOO+PWO2fES5x1KKryPkCP7vqft5mit2O12dG2ND5GT2Yzb3Raryzy9\n3e4wlSnzf0okWQrqvOkYvQMpcG6CXPwjRmucL5kCVhXX5+RdSet6vcsCwIWiDYiewbvyfkoVD0LM\nDMNAEJFF0zKrG6YQSGQ0RX9QjF+ZedfRuwmjNavVmu1uy3K+AJGZNS3TMKIrjRElH6EfJza7HXVV\n4f1UfCmqGLV+/r/7b//4uRa9j8h2RtjeIUbHb/3Kb/LwSPLgw+/HpZphv6dZFq9BjoGkFDhIsUc1\nLT4ndAJhatLkUNIQRCxS1uCRwiGoSxKMTAhpYHIoW1DocRy4/OLb5CQ5efCIT58+x/hn7PqhvN/w\nBDtf8ODRY9JhzZA+OOvcVJx8hsx+mvCDwxqN0hI/7cjS8I1PX3H/bMny7JSLi5aKTHaBJDIx+eLd\nj55WanJbcXH8LnnwxOi5X9WIWByRJ92Sxbsd42ZFXdWgJC4JJJE8jMiUif0ekzOjc4zO8+R6y0dn\np/gYePDoLabdmqoS7JXg4Vv3efHiFde7CauLacYfcgJ6F7nR99H3v4SsG3zM1FqiVSYCPmfi5A6m\nqUNyj3DEEBiGkZAKs6EFNNZCTiQfWDQNsSnW89V2w/2TU1pq+n4oklqZcW7C9SPKSEzT0NkGkRMv\nb684Pznh4dkZOpWgj7NZx+QC3351TdM2iMpyvdnwzvkF+31Pu2ixQrJabxE5MvYelyKdUsy05Wa1\nYecnjBy4P7/g4qjjdj+y6ydOlh1WmCJd9wHnPV3bkm3GTwUMjSEgkOzGkZh6zDRQGUWlNEoqQozk\nLBiChyFS1zWNKerHkGKJz8uZ05MThnHkbrPiZrPm/Oik5G6MIzInZm2LD4EQE37yTPuBRdMSD+E8\ns3lH21UMuy0xCa6ur2m7GT5Ejo+PyJNnuWh58fwFX3rnXfpp+K5n8nvWGfzSz/2HaHNK6FdEVfPp\n0xcc71ecvvcWzeIeDkEKPUrXyKZBZknWsYB5gMTgSUiliXFCiUxWDUoKjIyk0ZOlKtZbqUlIYgyM\nQ19CMqYJHycEhbeVVUWYJoQfkboh5sjt1SXTbkvbNJj5EVFIpPc0VQE4P3v+nMYabjd7KiFZnHR0\n9QKpy6gws5mnL16x6BpOLi5wGUIIxXaq1CHUNKIx2LYhWl3irGIkxYiPkRwDwXvqqmIcehpjqWZF\nhiriwXrkekIYQVq0FWyurlltdhzNNSTNq6tLVncbqkZSyQrbzAnJweTYTIkJTS/nPBHnNPfeKilR\nosyqAknMiZBK8lCIxXRT/PdF0JNiQArFME2InNFGI3Jmtb4jKl24cVmUdVVdc3e3RpGp6gptFDkm\nalvjc2az3WAErPdbjo5PqLUpnV2MuGlikzwn1Zzj0yM+/uxbfPDgLW53W46bOVklru5WTLuRXGuM\nMpzPZ+ymMl4c3N8YbRiGQKMU3cwQlGImJT56pjEyBYeqK0SW3GzW1FbTGkuIgvWwpbU1QmmUApkj\nyWd88EWgVDUsZjPWfiq5BCkTXjs5DwlXvRuYnEMjqeqmZA7Ict9Kk+N6u2UaRx6enyFipqotQcB+\nt+Wm39FVLfePjrm8fMnyeIlIUBuLrS23N7d0s4679R3Hx0dUWfL06gWtrfn82RP+1t/463/8xoT/\n+2d/CmMbpBX4fkKISJoiIeyhnbO63vD43fdIUiF0QdCVEARVIspwHlG1+GFEaI1UB024qggpEaeR\nOI7E6EFpgpsI3hNCQCmDEpqMJGaPDxMkweg9Wig6WzGEgRgC1tZMfdGgx0NackiJtq6xxrIeJuZt\nQ9NUTDFgDpui6qph12/4+//wUxazY776wUOak6NDjs8hzJRyey1xfcVko7Q5GGcOacmi5PdFXxyY\nOQVAoYXAk1A5lKwCXYC74Abcbs8nn3zCk6eXfPXxMTe9RuKxKrF3mU4V49I2amJ1xIoZG9lgZwu6\ntsPoQqsVvQAIqfAh4EPk9U4Bqw1Sq4ORJ7Hb7okpUlt7kB4f2B1hCsJOIsaArSrqqmEc9mWLs9SM\nzhULcSpApK1r+n5fJNraYIxmt+vLKBYiMkbqecPddkNKkmXXIoSkaSyruw1ZFCu2VaocpJC4W21w\nk6OuNMIohLD0buT+fIbUhn0/kmKkthWTm0hKMG/b8rcPge0wsJh3dEpxu93TdS2jLxSfVYCQJCmY\nvKNtmiKb9gnvAhOR5By1LvjClMLBICVLboEy7HYbFm1HSol+cgglCqUafPbAAwAAIABJREFUEnVl\nsW2DGyeqLPCi5FdM+z1H8yW3uwJQ1pVGVyX2T6XEfuipjQUJcQp4Ij//V3/2j9+YEKZA1Si8kEgd\niAHkbEZFh9vfcHR2xN3LJ8yPz9Ay45uOJA1WtWSpiJVERkddWaYwgaogBHbbK4IL3F1fsTw5Ztis\nCINjPzpubzegFPN5w/lihg8BFyLtbE5VV9gsWN3c8rIfOD894WY/0q9ecr7oqNqG2/WEi5mjWbn4\nkszMaoMk4cOEshZjiqPOC0G9POWf+Ylznj99yXa7w1qFsDVojVK6pP1IWaStWZKiJ/kSviGkQhlT\nsAGrD9SmJHmNzAmUQMcSEuJHx269IYSeqR/5/Pk1DxcNz9oFxliOW0HOkv3oGULilWu5MyeMsyWm\n6uhmDSciFbo0Ftedz5nB54LNxIQ/eOtjTggpQUsiiXHfI4Sg7ZpyIe93pFx+n5wy22GF1gZ5UB82\ntiITUUYz7HsyxSBUa42sSpvthhFzUN9pZQoWIQptqlNmihn2E+eLU7bbHT4nZrZmtdqwGQZEhnfm\n91n1W6aQWHY1690WgcAc7vILW3N+NEfHxF0/spzPuNusiN5ztFyABD8NLGczYq6YzTo22y3Pbu6I\n+XdThLrljBQjzgVmdYPq5kyxYCZJahCw6BqkqBhGz7GdM04O5zyKgm9ZrUr3lRP73RZlDEezI+Ry\nyeQcRpQciT7siAmskqScMNpws12z73vmdUNXtQzTQNaawXtm7Zy77Ro/TXzw+DEvX736rmfye1YM\n6sWcab3GzBYEHxGNJfuRMCVErNhtrtl6xfFsj7Iz0mZNOjlnN2zQpsYoQU+iTpmYAvurO7Z3O1ZX\nG45OOo6Oj9heXTP2A7ayxfhhW+p2xtuP76EUrFcbXl0+Q4+RRw/v0aKZRk/ddkRpuH9keSUU18PI\nxULxta+8h06C3TTivcNNI01tcJNjGjJ1neFIYXVFTIGYBfsR5mfn/IOPP+fmdsVHX/vKwUpdvBZK\nlsBNpEAqi0i/a7FNB4/769iuqjJ4BLhAHCeGuzXXn3+GmLV8+9kNj+YtoqkwWnG1WvHle2ds+g2D\nKxfa1im+4U6wp+/SLZccGUUWobACATy+vLcp6r9a2SJfztA0TQnlyhyWuiRi8IfQ0dLeK6CtLTEm\nVv0ehOT+vXtkKbi+uUXXNXs3ldyimOhmc3w/oEwiUopRDIFNPxSBTwhsdztOT0+pjOBsPudus+Ko\nm7PNjjxMRCLHtuPJ8xe8+/Yj2tmMfthQtRVy3BFSZH1zy9nJkoBkXrWcLma8urtB9Bt2EY7rGX4M\nKFNjDOw2K6KUTD5wvd1jKSEnQhsu7t8jTp62NkyTY7PZII3GHbIMjxcL6qamsjU7N7Lerah2jrkt\nxeD6do3WCnlYtGKjYchwMl8ijaIfB2ZNQw6e9bana1rOlg2r/Q6rBE4KImC1YsiOeddyerQkkpnG\nge1+i4iZetaihYQQ+ej9L/HZiyfYxn7XM/k9GxP+n5//y8TdSFKZqpkRw2FBRmXw2x3by+c0bcvH\n3/yCD750n37Vc+9rH9Hailw1jJPj7vaO8a7Qdq8uX/DFk0vaSnN2dszJyTHX6xUvbrYsq4qL8xOk\n0ex3PVopQvTc3K7I0nJ0fELX1eWHyxkjwGpJEqoYfEIJ+8hSEGM5PFJprK3RB7NLOCjs6sqA0OQ0\nISIIo7F1S10ZblcbyIl2Nn+94b143N+Ek5ZEIKnkIRIbNOLNQtHgigMt9nuic7x6fsmLzZpWSbQy\nhKnHikREshsnKq24GwIOyzY1fBFnqOU5praoLA7bnCRWSUxd471H5Yw0urj/KOh0Ofup6OtTKpLw\nFMtaPAQpRbSWWKOw1jCMjugjSmp2+x1CFeqxqWvcFHB+orYWYiIIaKoKLRX77RZU6ZaIgQen59zu\nVogk0E3Fq6fPObr3gO3mhkcPHjLuekxd0W93CGuRMTJrG/bbHUkJTpqWz66uUcrQVZausvgcmUIA\nIQjDyOnZGf20Zxh6lvMllam526yxooSu9jEgUsZaxWbX09R1sT7nSKs1o4tMIVHpomzStSX6QJgC\nTVOj6jL2qZjwIeN9KB1FY1nttoyjw40Tnsx81tFoU2LPfenAeu9Ytg3jNKCMZbPdvukkGlvR78s4\npY0uEe4HZaZGsNntyWQabUucf8z8j//9f/3Hb0yYXMAoiwgFLwjjhNYaWdVIlTh6/D7D5ROWRw11\nZbllw9XTJ8yUJDYLxnEgDB5qxTT1GCk5PVuQk2A7RuY+0JiG+yeCy5s7pqeBB+enzGcdV6sV3/z0\nOeMUeXx+gpagZWmByYkYMwOxZCS4kaeXd/SDx0jBvFaMKbDajYisePvBBSenR9RGkw4yXKFASosw\nRVfvnMPHhK0PphcpyC4QCEhpDtr9giCV4pxQouwCiCmiXMC7id31KxCZ0YEWib13XBzNGfqeFCYG\n53G2InjHmARjsqxzxSdDgzh6hKws2lZkipJOH3YrBMD3e9xU7LTKF1uuVOUCl7KkCZdHQT1e71GQ\nBxdjZTVZSIb9hEue2WzGsB9p2hpHJg4R7yaMUhhTQ8hko2i0JMfIfnRgFLXWTN7hBbjkEcGxrJe8\n6ndUXcPNesNbJ6domZlEZNxsmFWGMSTaWcfcaj55vuVkseBmGLg4WeJc4Hq1wpycFwejsaScODpe\n0iDpk2E/OirjWa/WLOYzJleA3tZI2qpBypJdOHjPfhjIIqMp+QtCakJOxBAIUhC8ozY1CAjjhGnq\nkh15ULIPfiDGCbLAVsVQJEJgHAdMnUGL4jWRAmsrVvs9+jBanZ/MAcE0OpTSLJcLcoZpHHDDxOQ9\np6dHWKOprGXqRwY34Xykberveia/d1HpOZPzDlk19NseqRomdnS5RndLsvfsXGSfYXO9IibJs+tb\n3LDjq2+/RVUvkc7jXWKYdkx9j3OBi7MjVN3gvMNqyxQromh4tdugW4Odevr1ng/u3+M3n97y/HrN\n8VFL11qUVaSQEUGWCKwpY0TFh2+/xWac+Lvffs5vfONjPjqb82M/8nW2rnBsKUSm5EBIfEyoGKnb\njrq2+MkVQ4r3GG3ItiD0vt/hcsaIRDUvij6nR2Qs8VlhGIh+Tw7gtyu8d2x3IwEB2bGcn3BWa/os\nIUkGF5B2Xi7aqiJpGLzkk41j186pYyL3Ayl4pIZKK8axjAW6qjDWMF8uD8nLpUvT+rDj6ZDUHA9x\nXmXRiaSyBS8hxrLHICUqDfud4269RslIq2uWTcvdIYZdizIC9cFhlUZZCXju/IaH846cSieUZUWa\nIttdJqYBKyW91Hx0cUROin7vmRyczmrwAlNnjuY1u7sbvnr/Ids4setXbEfF+WzBg+UcR6DTFTf7\nDceLOUTB5XZNN7M8XJ4zO2m4fO64vL7luK2xtqUfI1s8UxgQQjMNIyEKKmt4cH6GlJLNfsveOXxS\nnDY1wWk+v77CCs29o2PCNKKsBUroyqKy7HY7Rim5tzxidkhYatqGfr1FomA5hzGwXq/QRiPbilev\nNqg0MV+0nJ0cMw6eq9Ud52cn7FJkuZzTdE2JU58cg5+wTUWlDTtfYu++2+N7Nib8wn/+b/9/zL1Z\nrx1Zduf323NEnHPuzEsyh6qsUlWqSlBbliw03DZgP/rbGQb8kWzYMhpCN9BuqdAaqnImk+QdzhDD\nHv2w4t70g5V+sBtZ8ZJMMi/JvCf22mv913+g213SSuVUpQqPhxPeC3318f0j2q222U2hPVjdMZ4W\n+sHRiuJuv+f26prHcSanyuV2Q7PCy7cGVKvsj0eWrLg822C8MB0Px8OqXdd8eBhp2nBxseV8e/Z8\nAHxw6Jb45rt3vHn/yO3NJZ9//nM+7Cf+7b//Z37/7Vv+5PUZ//ov/4xuGEipQS1yUwa/Wn0/BWGs\nycxKU2pCK4VVsBQlTj99hzZaisa0kE570unAcZrpXEfKCasNi2qMx5nHD3ecXWz54v0DZ33AuY5p\nTDQDCkWKhbFU/ukR3rsrQn9BRjYExmhKSdQcSfPMdndGGHpBo71nGPo1D6KK7VitWCWod2ti2bXE\nRMlJSEJWc384ACL/3XS9WH2lgtWO42lcU50SORWC9zSl6JwAjBZHQXEYRwZjGLYd45Jwqq3mKWJj\n/uFxj26a892Ad4b9tNCrjrvxATs4XnQDd6eJszBQqMQcsdrjOo+zcgj3y8iLzZaqDQHF3WEEVdDO\noWvhcT/y6c0VX98/cBhHdrsd58MGZ8Epy5gXjuOMM1ayLGrDWYfxms46UizMKZNKxXpNZwwawxRn\nFApjDTknGhprDMYo6URTIuUqwGJrdN7RW8PhtFBao/eWzhvuT0dqKvTBshkG9scTXejQtXCcIsYo\nrIEQOkrJxGVBvMKln+s7x//8P/2Pf3xjwv44MsdMTYrDdCQM5xwe78E4XFPobiBYxZwWam6UYoCF\nzgeoGqUym16xLHvIhcMpchxPnO96nHMoZwleWtoPDweWVLi5OsM5gw+Bmgy1KV68GISRqBVYjeu8\nUFPHiQxsLi657c+I88y3X37HMAQ+uQ6odslnH92gQeysmhIMQD211HVdra1dhhJatPjxNpQVq2ya\nmIe2cSTnhenhnu+//obL6wtiVnhTqNow5gI50nDcz5VN0dycX7LERQhc1lKrrP/GpfF+KexrwLoe\nay0ti4RbWw3Ko7XB+4FaRTjkg9xcp3HCeSeOvEZMNHJjtdUS/626ZjammmnRMPiA9+JrmHNlmhPL\nNNJ1HednHeO8sMxtVakqvLEcT5PoGKy4EllT2fU9hcrlVpSgeV5oJpBzZrc9Y+M13z8eUbPGB1A2\nczPccFhmCopXVxccY+bth3tuthcsJAblOU4LF51HtZ6HceFsCCwVVMsMoed+OhCsxwVLNYrbm3Ou\n0haaYi6JeaoMthJrxaDJVdKXUlmYlwUzGVLfrZkGEILDG+mAjFHUnHg4jnRrB1afUpxKxWCpVXNK\nAkbfhsD94cSw61Etc5wSpTb240Swgd2ZZ38c+e7NO/q+g1pZUqLvLHNKaOuxRgGG0HVYbTgcDjjv\nmZb5R8/kT1YMChsm10EcOSZI40K/2Yh7LJqxRc5MIC6NsTackiSZaVFk2/A+sNld8ocv3vLt3cRn\nr8+5PutpqnEYj9wfNb33dJ3js5+/ImhH33tiXvjq99/x/nHk5atLPnr9mq4fULpS50iZTuSSePdw\n4s39iWWc+bM/+YTPP/8Z+9PE4/fv+LCfRDjUe4xeI1Ga5AOqVtArb0CtLXepGd30iglqrFVUa3Et\nsWAwx5G6/54vvn3P9eUAKlCmkcuLC/aPD2S/4zguLA/37K6u+ejFObkoWnN43RhjpKjKvCSW3Hic\nImPx7MKWbw970rSnKAfOCiZg3EoOEqPu1lhlxRLhpZKkG0mcmn6WxIpjkRC5yvp1yhmCtmhViElm\n0xQXfNejjGKeZs43W9T5BWOciPNMipWhG8itMeeK8YY+eBbg0gfmceawLKRauTkfiDnjvSXpxicv\nXvAP33wFfkPeH7i8CHxyseOwjHTWEGvj9dUV284zFYMzjXF/wtOYUgWVmRfNYUlcasNxWTjlzGa7\npSiN8gaTGme7Had5oSwS/HtaJs42A8dS2fYdNVcmGrbzGDTWGkwTU9JcxUNRK4MOoocZ+o5pWWhK\nsRl6TFX0PjDFkaIa55sNAGMu9JsBtOLm8pJhG8mlYFVPrywPy8Rxmbm4uqSVwpwl+GY7dDweTozT\nyOVuw/3dA9Z7Hu7v2Qw9Z+dn/OHLL3/0TP5kxWCeRpb5hG2F4/FAKne021eEGrFWwXGkXe747t0d\n3+4bF1tprc9DYDJwfr6l5cBu2/HaaIZNhwsWrSr/8Q/3/O//+I5f3pzz159/wquzHSVnlhxx3YbN\n7Q1f3P+BN2/v2HqPf6Xp+gE6xeF45P4wsdtt+cWvf8X7/Uh6+MDbb77FWc/28opf+A0mzgTvKVhM\nlai052CRklBqRdqVJB51wUOTuLJyfOBv/vbf8SeXHa9//VsO+/e8efM9X331lqtwzfbVS7754hs+\n9VsI59TjI8F4vhwjfngks6HmyHGZCEiqUqYJdjBHjilQt7f0w5abIfHuzVdoN6BUj1KG1oRSPJ4k\noKQ1sN6KB6CSiDJtFMF7Ce2cZ5x34re3gpo8BXcUIUEZ73Cmgc4E5+idZYqR704juTbO+0AHbIeB\njOKs6ziMB05L5tx7HkpG5cyHY8Roy7DdcowT4zKiFXSdp6+ZTd+zsY6PhisYKl8dHum9R6WGPneo\nOOOsYj8d2TjPtDRudmdsho5truQCTYs126YfUNOJm4tbvn8YaSlSTg7lHTkvBNuwyjNrxd0o/gxX\n51uO00ROCe8dm6Gn5EpcElrDduNZTiOD93TGsOSEBrqu4/ryAmsapjbG48IyF1CG4EGv8m3rHLlE\nHh8m3sTE2aanc4ZxmfjycWRz3vPR9SVWa+aYMM1jtZItmVIEYzidJhmvaZyf7xj6gbu7e7bb7Y+e\nyZ+sGNzf7Tn/+AV5nLk8v2ReImed4e5YOMwKawPHOXOxC/z+/Vv+17878epsy+3ljvPO41zF68h2\nt+HV7QUxTeRl4m4/kavlv/3Nz3h1teX1yxuR0WqF9x4XDL+8PacvH/GH7x95tx/ZbCeC78hVMezO\nGHZn8qKnxIvOwEevmZfI4/0DNiXONxvqxj/Hprc1UIQVI9Ba8gnVyjIzGLHsjgtff/8ONU28vjxj\n1ytMiUynEw3Hrz67pvgt7XDkNFYOpxPVdnz3bk8/dHz28prHKaOIGKvogqD+MVVSaUxLZc5gwoAd\nOjbX59jamHLl8O5rdM2S4uR7chWLcOeEhx9jlJVrbYKIlyQviJH8hZoLdQ1WbU2s4mpN5Fye49KK\nqqSUcDawtIa2lp+9uKSkQltza2sptAbH8QQozrtAZwxXfQ9F0VQhxcJ2cMxxpNTKNnSk08Sw3bKP\nM0PvGdOJXR/YmkpuCbQoC3MSTr9pYIKji41lHlmKpFBt1hRqHzyKRj+IM/LN1vMhwjhP6Jox/YDV\n4v9Iaby83MnquxZenG0ZY8ZrwWFmDVqJiUytmavNwFIK+2leRyGhXD8ej5ScsFrTFFKMamWeI85Y\nSmn0QdPZDl1FcdhbR3CWTOPlTcemC8zzSKmFZV4oteKTpu8Has5Yo+j6jsNjxDhhiKZlYbsZKP85\nPBD//3j+8HDiM2+4PN/Qq4pV4nIz7vfUCr/59AVff/+Of3qzZ4yNV9cXfP7xDSpYuibzrA8WqzWp\naEoNKDfQ73r+/FwRgke1QsuF6izaWVIqLEukKcXu/Iq/uLxak/4aaRnlNm8SU4ZzuD4QjyP779+R\nUashZWaaJvquRzv3bEiBkmRgCRrVwtJrkhgMmlQU42nki2/e8WmofPSrX1If7rh7+5ZWE+PpxJvl\nxKssstdjyjzcPRIuDduzc+K8MNdEF/yaD5iebPZRSvQZUy6MzWL6Ldp3tNborOXF7Q1LaaSHbyGO\nAia6Dq1k/hUcppDygjIW64IAoKXQtMSFrUny5CxZgw7JAFBacAWtAWVoJQNZ5lXVJIuxc3gvBiZO\nWZRRjDFiFKjUmFPGGY2y8mOlRANxe7GhzpX9PHNzcU5qkBexa9PBMcaMMo5NsBxypqZIyolCwhjF\nxls2O8/dA6SUhQOxzLy6vuL+/oBykny9CR0lZn5+e0WLmSUlgjfkmPDOkrMmp4p3jkYhp4JRsJQM\ntdEFh6NxTGKOEnMkNrjcbdHryjovkpJsvJfDXSUJOtaMs4paM8dplvd6DZb1WlFa4WGMKK1ZxomU\nIlZLyCta3JmtE9r13fsjSim2T2vfNcmq7zumaRRz2R95frJi8Ltv3vDdm/dcbzcMXnN7s2HOhQ+n\nkV9f7JiWyPup8u++eOR86/irX77guvdULeGa2mhOS+aUCs5ObJ0j6YwNMg8brUBZSmvyAinQJmCM\nw61ZhRJFAUo3MQNZwy7MmgMYx0bJmaI1b9/fUYswFbdnG9EO1IqpSg6/sc+sQQEN1+jNqpjjI3Wu\nbLdn/Hd/fcO7f/wnmA7MceLNmzu+HyNv7xbuHva8+OszHg7w17/+lFwrD/uJbfAklBiJloJqSmjP\nrUh6dI3iEVAqWVnxHmwNqiDIKMX+5oqRhjp8L9ZYtdB8QNuBnCPOOnTn0dYwzzNDCOQomw9tzco+\nbEBbHXkzQ99hgJQztWmcNWy3mt6Ls9LpMOJ6J8GoMTN4xyllOqXZWfkMTW9EM1IbccmMMXExDExp\nYVAdkxJDEor8efuo+fTqnLePHxi6M7RuoCxVG5o13F5fcFomLjcDUTumeUbrxnYzYJykRqV5xjqR\nwtcCmcrSMn6RhOTzzUBTmeACqTVM0yxK9vxkzfvDgcFafLAob0k5CzFLK5aY2O4CGxNQpTGnRcAV\nKsNgMBp6a4lZMedCU5ouOHZ94GwTeTxNQBPnqhBoDXZDj7WKxzLz0etbPuwfCdryyfCCcZ5IKXM6\nHtltt0hyE+x2G1JKHA5HDoe9dKnmj7QY/A9//im/e3/CWUffB2znKIeJf/PbT/nd1/f8zd/+A6e5\n8OtPLkQvnjNjkpjsh3mmFkm6dZ2j946LbU/oeoatYggdKIWyhhQX9vsRZwxnOwdV0bRC60a3puc2\nZdBVGHn5GSirpCUS50jKUTgDJZNipEaPXVOJa63UnCTU0uo1Gtw8Z+ZpCt98fYeOE+Fnt3TK8oc3\n79h8aLy+vSJ0PVdhy+0u8s9B8d33D/zVn/6KxyWxrGuqnEayVpQiQRq1NWoV4LI2SeTNtZGLQq+m\nHhZQq/qxt5rbjefNvOXu8EBNCyqLUYvJlawU2SdMslgr5KlkDE0ZWs44hUR/KYUyFlRDq0bOkao1\nymgxRMkaHzytVMaSMT5IvqJrEmxSGt5ZvDaM0yRS4ZpWpSkEJ5Rnrw1h2NCoXIeA2TimnChxZnvW\nsZxOvNheyEqvbdjPM59e79Zo8owOg0S3t0JVYk5zHEdccNKFZcN2EKC2axUXCy5LDF0sheO4UAFq\nZugCoXf4omkUGgXnDEtKLGnm/GwLaLRVbJxhmSI1FvzQOCbxhDRKLga7JlOXKmY7eUkYgCIp02Hw\nvOgsrVackc0ENBF00Xj98gXj6USJhX1ZCNZhtRJlbhXLOesk0i44K2PRZsNmECelp6CWf+n5yYrB\nl3dHvnhz4OdXZ1wGS140fej44s2Jje/47OUFnenYeSu22kbYcrvdlvOLC1mTaAkYza1xWBKnXHHe\nUJWWUMqi8drw6vqCWhLzPDKOiX7Tc7bbgJbOoT638wq9EmtcCEI9LpmWC2d9x7AdcN7JQU9JgjvX\nVgwqqkgvXde8TKgUGp98+hFlHlF5ZjrMvHp5xnya2Z8SeUl8fLPhH789EothazqOcSInKDSC90xx\nXteVckvXLBZcpck/cxVJMTx1KQpltJAEm3QqF8FRzrccl1uW/SNMD9S5ULXH+IB1imXKFGNw3pJS\npGHF3RjAGJEsr+sylCLlinXgnQTFeqOFDl0k4dk5oXOnJDwJg2RRqJX8kkvGK0VpCmU1WlXONh3B\niX9EbhqrGvuxQKmoTaCmwmnO+BC5MI7HOYLR7E8TtVZ2XUfQjVMstFrEtoyC9opN8JxKgtzQrbGU\nmZQrXec4H3qm3HDFYBUUFOMyizdjrcQCnVMCBCuDGbTIoTU8HGeC9RKe6qTFrw1ZI7bCtIjpikMw\nk9wKcrwrMVeM1ZL/WGGal3X9bUhJgMkxJmrJ1LplXiI5ZgoF0wVx8qoLu7OeeZIOsVVIWYxplYLD\n8Ygzlpb/SDED6wb+q1/uuD7rhHQREyo4bMvMh0TzllIbUwbvFMooNkFi0ZrSOG/RrdAH2XE7I07A\nWmvm44mSEqdxQinP7e0NJS3cv3vHP765B+V4fXvD2fmWfhPY+l6Sc3UDVUhLYv/hDt91aBuINTJP\nEW3s6qEn8WC1SHy3xGDV1eZ6ze1bAUWymGt2WhFPld9/+Q3KNK5fv2AeE3038OFx5p/ffuDrD5mP\nLjsOU0Yhh2xpwhuLpRCUeS48GihVRgatNVo3tJacAo3cFiDEmForVsPlxhNvLvi2KQ5xxi2PFAtN\nazgVyfMzjla8tMRAzRWnxaefVvHeQ9Pk0qhFaNIteHa7LbZJt7AkuYEU0FkjNmiq4JyFlME68Yss\nikai0lhywzVYYkFtDSVmtLakWhnniAuOc2uJTXF9ueXweOI+ZXTnuBk2/P7tA5fbjrOznpoTbx9P\nBK8pKXKxGdg4CFURrWMsgiVFVThOC7pkri8cvW0o51mKfP8HFdDGiBfkLDZ7zoBWUuyC0pKCrCqq\nVXrnQFtUgxQjU5b/N+89tTZKruxnAX97b7HekskSvFIrD/sjTYlBn/eG02EmN7nsaIqHxxPGGbpt\nTy0Jo1ZeiJMV9na7gQpxNTHZbjfM88J3377h5vqK3v24UOknKwa9KRhgWma07znOC3963vFwPPK/\nfPuBw1J4ddbz0c0FV/3A1nnR+nuD1+BdT8yJogy4wGk84ZLEm2tdyaWxNJnZhtMRHwLnNy95UTx/\n//UH/v4//AFN4U9uL/irz3/Gi49eEvqOEjUlJqY4s8RI3/e8enVNNYpaiqRDp0rViookONNAP20W\naqU1EfI0pam5cn93j4l73u0jqSi2veXCG/6PL77nn756SzCev/r8Y16/qpxZzeAsS4YURRRkMWw6\nQ10ysZRV+qpXrX0lZSlE3sCSIy0nWkpioFIbrSgqDWcqt1tHK1t0uuTh+5GaI75BsxbrPZVGjDOm\nOEppdKuASRuhIZcqUWOS7htEZKUkQqzVhvVWQLSaafNMtQ6rLamJtl+XRmcUeW50XaBVBQWUklFC\nGUUtjZwVwSvmVDnfBnKuHMcJasVWxXY7MObE7aaDVri9DtxsNnx390AXLB9dDQTf8WG/x3sNRXGI\nE0tpbHqH0vB6d8XeHoixMMZMapGaNaVCsJppWuiCp/MOrTQhWDTn//iXAAAgAElEQVRwnDPTMpGS\nFFxjxVrvcBjR2tANDqOFu7FUsYK3TmOV5oWXX6utYZQm6PgsUrNB0drqh9kqofNsVlGddH+Vzrk1\nCdoKk7PzYsEeC/O4x7u1cCMaCaPh5csbTuOJPvzniVf7//z83Vd3fHxzxYtgoQpN9u3DSNOW6/Md\n57ny6iwQnMKYtnoYOjpTqTExxhnvPFZDGUeccWLRrTS1itFnTZWLvocVzOuc5Revr7h9ccZxadwf\nFlpJxNbI8yx2VMYQhh7j/eqfL7e+URajDM1q2hNaS5WvoUkUupE8hVYLtSTSnJhOMzFFpsc9Iez4\n2fWOXAtffH3H/ePM7cUFn//sBl8zn78QgcxhFL88tKIV6QxaEdMU0+Rgtyo3FUbhmqYUhWoNVRMt\nzaTFrCAq2LWjUFXh0bzoLerqjFIix+NRchJTpuQsmwNjKbrRWiWnSGqVqsBYh3dIF+LFwQeaaBWU\nGLM4vRq6ek/NWV5CF4Sz0ArKCpjYNJiUsFavAGTBGo00JA5nGxgwTosVe64Ya+icZ5lGeqXZesdx\nkRXfaU5sXSIYi66KJSdKkjh4o0AHA7rD5cqyJA41Y+xI0LJ5mnMWG32tRH1pBRtppaDQeKvXDI+C\n7zxh6KhVCrNCgmmdc+h1hqc2dMuUSTwUnXWILQwSq45mXu3hZZqTxCMJXFW0Cl0w1LqOHCvZK3SO\n2hSH0whZMY8T7qkAz5HgHG31pXNGk3Ki7xyKnv83bcJPVgx+/upC2ta0oCUwl8O4sB06/uJnLyhZ\nJLBKg7UGZRRUcDTeHvd8c3/ixeacm6sBa8XyqSaZY6syZBSHWUwsQxB6cmuy9z8Lnsut45cvL3DG\nkLUhlcoyjRitsH1PHzxjntdDWFG1iOuQlsOIEnaejAt5lSKvhppKUY3lcX/PssxshkDe7Kg4UmmU\nVNie7fgvhg6DYtsZxkkxzwWlGzFlilICalVp0ylFTE+UCJlKyc9uSXWVT7cnf6SSKTGSrUcZIwVN\nO4np1pXeaS43nlQusc6LAel0QhVxgnL9BqMLJotNfXNhpbkWqHJz1VrxPuCtkMFUE1LOFBOlKWqO\nOCuGtEsuaDIoSLXA2ll440mtyZqtPYGMkkmhlWAj3llaKhSv2fSWoDydURzGE6YPlCZS796Jw1LN\niaoNWmnuT0eMG6gVGsKHcM7QhyDEqZqw1omxjpZb2RnNkgp+jY+LpWKd8EeMlgAYqNLJADlV4iLM\nw74LaKNYYmScxRT3eBoZhgEQizujFXmpWGflsDfhAcSYZJNlDTGLx6ZeI9V100zjTAieukg+RB8s\nORUpBNqwzPE5tKaUpwAbfjBv1ZLT+GPPT1YMfvPRJd5U/vaf7/ji7gODNbzYDuyXymAnXl3tUKqQ\nMsS4sNsMLHlkP3YoG6g2EmsjlkYzoFOjFTHkBNheXHB9dSnCnZyZY8R6h9MeqzRNFeI0UrQV7n0q\nmFaJteBKw/c9MWeW04i1lrAZcE4ouis6CDI6op/ZCrJ7b+uLTefo2owpigvX8/Y4MloBkrSptFyZ\n08Lbh8KrXY+zlsMilOASI6UqMOLNR2OdJzVP4rJaZWPR2uoKraQ7qFX23a01pLWQWDqtRPQFUFLG\nlsqLoZdAF60o80hdJlpZaC2Rm6JajyoTzizUqLChx/nw3AHFCHMtUDIG0NrgnCgzlbFi/ArMacJb\ns2Isa85iFeWiWvUOpVSMMpgqDEiy8PqVblwECRJNJpFaQzuPyogEula8Aaca3x0XNr2jd46u6+i8\nfc7XfPq7xJxwVopHafL97byjNk1ME9YYVKtsOrFcq7WRShImpjPQKkZbQKGNwoU1WSotmKLW8JeM\ndYab60u6dTSYZzGOlELTxGBXV5o14gRlxafTpIazoqA9nWZygSnOYuM+VwzgnFjVB++oZc2ztFYK\nvhLT3nmOdMHRqPT9IDZqP/L8ZMXg3WFhYzVFKebcGHPhEPfsfMfPrrcEp9E1M+XMY0x0zoKzLEX0\n6L/56DVBaaaaoSlSSeimUblSWqY2hXaWs8sdZll4eLhjPIoZZgg93juJ6qqJ2OAwzTijxGUGWJaI\nVRq321IV63xeQBeUkeawgdB39dohIGhfXSKVgl8SuQn4V1rl6qzndIr4zrI/jNAqX7655+1YabcT\nL69v8U5zPFaW2hi8R62rTkHXC1pLztFTh5BLES6A1mhVnpF6rVeuw1okaJVWmhCrnrz3XMIZiw+e\nYdhw9+g5NkWjoK2AZ01bAQrnhahkJFEoMUGpBbse/IzYslm3zstKoaomprg20oqMxgVLSZlUIikV\nWhNUXistzMQpMisoTbPdBrwxeG8YT7Os5FSkVrDGoLUSb8pcMJ2nNs3t1RmlSqd2semYU2JehPyk\nQKzbckWhoVWWPGOdFQBbGfF4rI2KJlcl0vBWRc3a1PpZtB/YpkZRqiKlijZSeL0LeB+kUFeho9cG\nXRcouTDPkVzLc7QcyFZKUfGr36dsa2SNqXSm77cIx8PSirg0LylCFF2MtXq9GBTOWsxgOByP1FVw\nltJCa/X/+TCuz09WDP63v/uGs2D47NML/uLjARPEOmoTOoIWim/Tmm7oYKhrxbNYbYXVphrJ1tUL\nzjDGiFdyG7WqKDmJcjF4jK5M30z87ov3PCyZYej5s198wi8/fkWqhdAaoe9FpNPaevE3uWmt6AxB\ng2pUKm0NFm2rE7BZbxxdkVVlzuRl4e50QNOIOQlYlhPTnDnvDF++feBuTry7P/GbT294mAsvdaNm\nuNhumGohxyKAJA1lNC2tpCP1dOSVzLGtiixaa6yqa4yWUIStVmiz6giQnEVBvi3RO2pt9M5irRMv\ngVKoywlTkoTNmEJTBuU85smleJqoMTFr8M6sB9OA8qiqybWhlcMYhQ8ddV7IrRHTCoBU2YZYIzTd\nphsxCVHMWMF8mhI1aCuaZY4sS2S324oBTZX9+TKdRMeCZZkXeucYpwnrDJ3VjPPE6ZSwvadzFr2G\n1RglRCetJPUpeEdZRWVaGZoWkFShcEaxTAlvAsaotYAB69caBNOQYowUiXW3rJpYwpWmoDZyy9Qm\nQbTzaiSjAe8t1qgVtxCgFqU4pokQDMF1kupcpJCoVRmrjZXYdy3ejr0Nz+E03nuM1szLQugCyzIL\n9fxHnp9OqFQrr4cei+Z864llYesNV5c7lhihSUJvZxpnNjDFJO61vScYg+nVKgPV7Pd7/sMXH+iC\n51efvmQz9OhgmFPi/Xdv6IaA7bZEvWcfG9hKagXbWXSzTKeZkhtaG7nhW4aSqEpBFmN2te74rbVg\nhN5aa6amjKpScdO8MJdCyYm4P9A7xRwh5oUNjnFqWFU4xoWzzYb3aeSzlx2/ejFwKob9aSS4QC7i\nMaibrOFkQyH2Y0rJGqq2Rm36h5Fk1Rp4C7HM1DjRgqc1Jy+28IVXbKNgjWE3BNlbpwxVceY1+mLL\n/gTx4QMsBe0DzUimokHArFqKkHqsIS4zrVaCE429ftowJANOwmZySfjgaLVgqmZZCkVXZg2KhtWN\noetRNAZvOZ4iZzvL3Yc9BcN4ilzvLMsU2Qwe3Qsx5xQT3hpSakL5LZHzIVBLE0fghBCiloRXYIeO\nBnhjyTmhkK6o1oo2hrg6UGvVyKWJR6U1+ODW7/sTA1NGLa3lVn7SeDQk3xCFWMrXirUyRLamyDmB\nVnKhtCoxen41xq2NWqr8vWt59n4I1sifqSQRLHSenMRFurOWlBPOGnLJq/O3pImldMJ5i3WW4/FA\nCJLb8GPPT1YM/s2fvmTnZW9/fT5w2FfuDgt//9X3bIJl8IElZ24vN8SiCNbifY8PWpDmWknzAkbz\n4eHE29PMRa2olum8wuie0sEyzYyHjLOWv/rtZ3hr6KzCOsdhfxTgaNXZa2OopdKKWttIwQMK8gHV\nWKjFYKx0A0rLnvmp+aqtMi7iTvPuw4E/+2jL9esb3t8f+Or7Bw5LJE4zqWr+1W8+5b++6Km1cZwn\n3p8maHC103IzABiZV2utayFQz8xG1A9rvtJEPKS1FASVEiVOpKXHOIexioqYq7QV9BRIQxGMZ1rE\nHKNRuOgCXhseSmM63FHijPcV5wdS1ehS0E1yKy1G9upGE+PEPj4yu5VmbIWG7IKl67x4UKA5TjMN\n2Pie2sT8NcaMIVKbJEZ1fcfxKBb3tVS0riwJ9o+PXHPGtg/MywmDhPPuOv1c1BJFkphrYdcHnJEQ\nks77dbyREVBbsW5T+ofuqlVkhWqMxM2XioU1Ek5RaxM8QSmx5Vf/NyC3rSPB0zZBSYFurdIQIFAp\n8YnAmOc1X0pF+CJKEZxZ/3sLVVKpa2nUlokx0ZoiBEcfxGJOjFFkm6OAGKP8fWpjmRMuOLw3Ipm2\n5hnq+peen45n4Nxq+qlYcmHjHHub+Ju/+4YeCMFQsXxyMaCM5tc3L+iHSIuW/uqcZT+JOKharl+8\n4L+/OsehCM6xzDNKaZwPErjiO1paxPizwTJFMaOzmjwnSdU1llYTTclt0Fqj5PJ861stFTrXQo2i\nX1Ba/AD0GorinEVtCncf3uJNE9ORaaKrcD0EYml8tyz8/t0D/+VvPxVsImcG77GxMc6ZJYr9e61P\nWgC1ZiesuMX6cqwLrRUPaGgNuiqMUhglgFcpSTgCVqOl7pKzaAtqa4LoW41WFmMU0xKJMdO1zPX5\njr3VHPd7VInk6UB1PaqWHwpTibQmRdSs684lVVRWuCLjzTw3ltmRhkHYiKXSeUNUBVXVeugaJfQo\nBbswoLRmniOpZealshs6dDCEGmjA/vHAsPFk1ZjHmcUo3ny456PrK4JX5JpxxpKzHF5RE1ZiLDhj\naFHWtIWM1ZZc8zp+GZYl8hhlzTn0nRSYnFYlqhbFYRMuSV2LcF5Ht1JEN6OoLMtCa+KYVVZyljXy\nXkkn8gTwsorCsowNyshEqmS0gIqzVjCg1Um7pAxGANyyhssYLY7W2mh0p/HO4byjlERzTkDa8ke6\nTXg8LhQUu8ETc8UrxWaz49OXl7x9dySrRqrwhw8P7DrPiy5wjI1dd03fO7796j1jMXzYJ3Z94KJ3\n9KFfJblCM25LRI6r6PZLSqinub6UdWbTOC0VuZQqNF8tu95aC/WJ4queEHtBxOs6eyu1dhPIreBp\n3L64RtX3vD1EHu6/w2jH1fUObUQleGiZt28+cHW+o+s8NTUuh8bFumosrYpmohQBC586BWS1ae16\nU5UmLwkCMj7ZqTkNuURqWqglkJOs2qxCcI/Vt7HVhoBXhs3gCcEyjTNjK9jScLstm37H6bgnjnus\ngjGnFb3OVK2wvl9ZFgKgVsQKrBRxIG6lUessSsSc0NrQEhyPlRrBdZbt2RnONmpOlAIkiCUxp8Jm\nM7DZWGxwTCdh+33z4YHrckYIPQ+He9ywwYWew5w4G7Z8OB4YwpZaRb24pEoqy6oOrCJOqgllLUuT\nzzmlKBwLrSSyc10V55QppVJUou970QE0nkHKVqEUwQG0MZTVeWiOQvqyDSgyWojrfV5j6WQ00EpL\n9mQsGCNr8LquBlVj/Z6J3Lo1AQhTTNggmy3nZYSx+qljESDRGFl511V411pbA3D+5ecnKwaKwuO4\nsHWGyxfntDU1+V//yUt+13dsLcypsR0sF33HThfG0tA+YKsYefztP7/lm33EtcbLXc+ffnrLn19s\n6EJYD2tD2UCaJnqnacUIOUgprHUYZSg6ktZWX+knYkldIUMl/bRaqzFqZRzKS69WBNgajVMiWiq5\n8urlJ3w4Ft58+Q1b30hxYdgZdtsdH6dKyonffxh5PC389ue3+OBJ4xNQaLBNY1RlQV7KUquUtLU4\nSCegV8KLwrQqcIBSWKXonagMY5yoi6NoRV6t16UFVrQqbXBtDWsqQYHTGrvpGIIkRceU2ClNb3fE\nPqC0Yj8tHI8nao7r90iYcamIZ4Fh7VC8R6sGJTHnwmman1dpta24uBHfhzRPfJjnNaDF4ENH8AZn\nLa5mHh8mWi4UJTTm3XZDxqBipCqDb4rOd0zLxP1+BCT9WgGpCIo/zTO2OmJZZewozs8cOSWsFatx\nGlijuDkfsNYIGAdY58S5W8vqD5lSyTk/d21tBZ4bgt6fn21lzVsSzon1Xa3iIUmTG5218CsF1uu1\nYysSnKNFhGZXiv1TB0LjeQVpjVmVjQKwC5aTn7vIeZ5lZWm0kM7+WMeEV1dbbi42nA+Wq60FPzCP\nJ768P3J3v+eg4C9/9SmbjWe3GYDMpXLEceLhMPHi5Sf8Onf8siZQhm0f2BmxGKcVVBVzUNXAaIvk\nFMkazCh5aeVD8EIUWok0rSFpumrlDawyA7SCJjeL3AgraEjD9IGHD3fUnHg/FT57ccavPnvNV999\nSwZC55lTxVfF2TDwmVOU7x6JqfAPX77n9npg2+9ITbAJp8zz7SQfouAapWahshahHxstjiFGK4y2\nNAoOI/yHVihxJp+knWy10mpHNUbGBq3XF66R8mrXZtvzyx26Jwv3ypnuWKx0ELvdltP5ltMcOT18\ngLxQmqZog7diMFKauC+nFKm54rVbuQ8SCw/iGj2cdQxd4Hg6CkBKW92UMqc1/CZPFT9smGNm02la\nAWMN6PYsAX6cIttBMfQB5S0DIvjJTTPFhe7arizHxtY5HkujDx6MpqaGD5acJVW6Vemy+uBRXlr4\nXCopRVJs6+ZEQLpaZTxzzv7A7Vs3OC1npmWRz4+GIsncrhWqCbMUJCZNa4VuYpxSq1w6dX2/Si1A\nW1WJ8nPGCLeiNhHHpZTXjmXlqGTpZmKKgp+oJ7s786Nn8icrBscp8tHlDuchz5P45cfEf/rujvtT\n5GbXsekNF9ue3DRnl5cEE7hr70hN2qK//PgKFyy4jpojJc1iUDpHOcnWQp2kBCixBqtVoslSK5im\nsNrRqszedUXLn4g5uQlAZ7TQjLVuYvSr1DPeoVuh1MoX7x6Zpom//+7A5X/zC/7so5/zr377Genh\nAds0S4HDGLk623A3HiWkpSoyiilWdl2TYJPayEpuBtdENCt9wRPwp4TnsFqP1/YkdOW5HWxNE6z4\nDIxpJDZhRYKiOodTYk0iDkVidSYj0g8gJE2AsFLBWVCdl1sNuBw826HjXY0c9w9Y3aG1xdZENpqc\nEiVZqaRGiDs5JVIRLCh0Fm+MrOeqdAM5LczjiRACxSdybmjbMMoRvMXbLVqx5jkYUe4ZQ+cd1mi6\nYCBlTG08jpF5mcHKry+p0HlZOQu5x6B1hSK3/sODjKV+pVPr1liSoPPWOjlgpVJyoZr2rAh1xq4e\nl5WGWn9cMFU4AlOKOG1FZqyVZEmyEsKUfG7SeK6fTWXdaPBcrGlQcnnmlrRVb6O1xqz/3lpjnuXn\nnHMrKU3hcM+mtrVW2Wb8yPOTFYOvHyZA0weHt4o+T8zVcnW+4dXVBbcvzjDK4rXj8LjnfLdhXCau\nbq5IS+TNt9/jrGWjBoKDtLrMCNFDaMe1ZEwzktBTV7S3AishCSo5RiHjrL2fUisBRGl0W5nn61qv\nVjBtvT1XPr5Tcjvf3t6ynyc+73Zsdpd8ePuOXlU23cA4zuy85ZAaj9PEu/uTAD/Ar2+vxLVGSaHR\nWq8GK4JIxyWirV07AbXyXSqgSSk/6xfqEzLe5PexKDovU/xUI3WR7D+jFbWu61INGLO6MjVJSmoK\njRSbp/ayloL3ApCllGi5YlXh5cU5mxCITbwXx/1IP/RMpbDMwjikZZaqscajjRhsqFzonGFOmVMU\nWW0rYIzHWU9uDaMUy5LYnfXokuSmq5BapmTHVDJeK1on/IjTLBbuWiswclNvjKZ3jnmO7HrRH+yX\nGdMqqirmeQGl+e77R7rOsj0bCBsnKH3O5JiF60HFOyOhOCvPVCkZH1OCXLPYwynDE0HVWYOz4iYF\n0s2llNdtA+uq8EnYtt7yNLyVceLp4Mt6UoxzlHjrsSwFZcUXgfVzqqWuBDMIq8x+WWSUySqvAOcf\nqYT59sUNOc1MKTIlxRSlJfvF7SXBWYbtjqoMsw5kJ+EkJWW03jIvCfqO7W5HHWdO48L9YWRDZrPd\nYLuOJWXIUVKClRh4KiWoukLR1ltGNTCrkKdVUeC1VmVkUGZFkWHNp5I8gSa74lplHIHEpYefvbrF\ntMJpXPiH//QtcZk4O7+iUonjidD1fPnuAaUaoTX2sWBVYxxP7IathCohbaR4EAqZx1tHLlnmhidh\nVJXuITcxyqirdPnpUVpWU50DYmGOI0utKFUlYVlbiSQzrC8irMOm3EJIF4IGY+zKtitYIwy+aZ6w\nWnF1tiGXytFqagoYDNp6Wk7UnIQcliu6kyTs1Bx57amVNXjn1x293I5NNXovugRrDdM0s388kGrF\nhh5tNc5WLoYt8zJzGkdybqAMMUUuzza0lMTN2TtSzmw7xzjOnGZJeFqWTMyOvnd8eP9AtkLYGoyY\n2E5jJJayhsWCs5pGppYmlni1rgY2K9NzTaZuNBHMZdEu+LV41FaFNr+a0kCRcUFpnlK2Wdt9wYNW\nNaR5kqwjWwyg5EgfepT+gYBGA2Okk8sproC3vAtPnYNSihDCj57Jn06bcHvGNGf2h5nvH2e+OD0w\nRqHcvtjtuL1odEHmoLPNObk2alXc3z2yjBPBe2xKTDlTWmNwCqqm0kilMi+FYBSxijzWIKu0kgUw\nahVY27uiKwpD02uuYW3rfjdRlKx0jNEYq0E5qioYZbEKYqwr5z/yf/7uGw6nI04X4pj47ONLLm86\nDmXg/tv3uNp4d3/AecUfvp94eb3FeYe2fl0UNlHnrR550LDO0Kq49dT1cGqlqRWc9M2UJpoFRaOu\nNGVQtOdPt0HKzHkinSpagfEbod2u66Yn2rJaxx+ZlFbmJ0rMQY2luSYpzamQ4ogqiW030J0POAOp\nVNxS2O9PBKs4LieUka6md510QM5IulQt9F0QokzJlNIwxsGSOU4nri8uuD8dSEum6zd0AYJ11JYp\nc+QwLywFttbig2E7nK+0Y0PfWXKuOF14dzey2fYUNHEW09DDsogHY7B8fN7RsmJaCjvfGOeZOVdC\n8ATdsMYDhjlHQLQuuSZxijIKtwbVOiOKxPp0aainAlDJua7bqB+o4rU+GcEIGB28X8lschlJdya+\nm0+MwqKEgo1qq0OzFyKc0iwxys8FL9aAWq9eCnXd5vyRdgZnHQxuoMbMf9xP/O7diWlJWGN4OCRq\nbrw891xuxBbsEDNqs6WcHqElllPk5DTWWpwzOO1BF5Ylk2ax9LLGkZ9ooeuH8kQN1euOV9hgjWY0\nylooldYUSokcuJZCLAnrjOz4owRbtqZ53B+5vH3Bru9pSTEud9xNhY/OAx+/PuPqPFDnhdBtODs/\nZ//+ga0PZFU5KcvFJqCMZwiOJY7ULNVeW0HSYxbEeT2RqJUW/4xgV2lHFYJbCBou/Ifa6vPNw7oW\nNaUx5Yl4AldWALWJ2SmrzZZa8ZDnZWb7QZelV6WmDQqaZzoJ7bqUTHCWy01PqQ1vE1Y1pnlexUcy\nXihlUKtyriD6hTRPRAQJR2tMK885j+M0oprm/2LuzX5tW9Pzrt/XjW52q9vN2ft01bkq5ThuCHGE\nYzuJEgUkBBcocBEkBNzlApQrkn8gAi4Q4hKJCxqBiEAKSIQoRgRIh6PYcYJdSZXLPnXavffae3Vz\nztF9LRfvWOscOWVbihNVTalU56y11zp7rTnGN97meX7Pquto6xYdZ0osjCkx6EABNt0ap2WI6qzB\n54zWirZ2hHnm0M+SDFUkK9FHz65paLXGaMmsDH1gP3rhY+SIMoZGWdT9liRnmqbBOpkvzUHaFnTB\noBeV90KILpkYZTUYfVzcpIsN2eoH0VJKWViURb5WLTbnez+JvMrDweB9wBhD8JEpiGZBZhlp+dwi\niTfir4gxLk5MlkoXpumH1Kj08eUBhSCtVNPx6NRSaejqisY5LmrHycmaqnJQaZTXuJywuzUvXr9B\n1xWhaByy580RqlqCKpX9XBpqVEFl2b/GLLhvreTpl1MBK7p9o83SH8sBgVYoKpQuKJVAK+aY+cff\ne4Elslu3fOfDN/zckwu2j065vXzDl9864f1nZ9S1w1lL9gNvjjOH62s2XcNxHvnKW1vu5ky3OaEz\nEvs1jqKTt4t7LflA7SoKEZ3BOMs8z1I+ZhYN/WKlRm7StGjpRTUJKGH2aQpOG5TTaJ2gJCY/MudM\nKpmmlfAOg0ixixJoixwIy2BLqYe9uxYBHcWC3XSM1jH5CaZRRC9KCbxEZ2xtUHrHNE2ERbasgBI0\nVVVjlWw6rJWZSUkyHK3qGpMzwzDgnGQTGKW4uhrZ7hyrtqOqHZVZVqipME6eaZ6pKvGAFBJNXVFr\ny6qxlJKoGoNPjtmL0OzoZ+6OE4OLC7INxjlIIpKzpKSYQ6QfR2l1rMxxTBFXpbUitjJaUPNzCJRU\nFoWjRWswyAbiXqKccqLMcsCmIvoXoxXRB+KyFbi/iUH+nLGG4APjOC6rYGFVmmXrlaM4brVaKFvl\n85YkhAAsmQz2hxSIepsM8dhDu+a9x1vO1nd0tmGaEyena3ScWa1brm/uKCkz5oLqoaoc7XaDtQpX\njHAKYkYZiMiNYKKgpIoqoOVCU0laAW0tZJkA13WFWowxFiVVgawTBLahxUueFQ/RYk8uzgEZgL33\nXo0uienuBlJk3bTyBDQQssYUS9cVqs2a2I/ouuOTyzdYEzndPcHawsvLG3bbDuNqTMms24oYAlf7\nnpPTDkJBIFaaULKYamIRQ80yzMoRrFl23/cmGSMDUF1EYisE1YyqLEolpuRJ44FkNMYagZcsFYG6\nl1d8YS9dltYhF1C5YJVBWWg2lpBrjseBq7sDXVvTNQ2alhA87cowTo7rK1FX+piouxpnEDWjsXKY\noFh1a+I8kuYBlGwCphiYZ5mGr09P5caOnn4/Mhe49jOrakVQCuMsWhlqqygGUAVbMuMQMRU45Xj5\nek8xmdY2zKlgNPgwctKc0FUNGI1dnuAhydS/Hzw3tyN1ZyymaiwAACAASURBVOgqR06Jumuwi8ch\nzAEfxIeQYiSGBFHcpF3rMFYz9SNlGe6FlEjLYBIt750zRtrXUphmL+rUyVO5GleJuAjAh8RwnIGJ\nqja0XUvXiEPyXroeQpRWErkWrNWS7vQgXvv+r9/xMFBKvQP8N8BjZIz6X5ZS/gul1BnwPwLvAd8D\n/s1Syu3yNX8B+PeQSvA/KKX8te/3vV9eXZPnxHvnZ2ys5eSko787orSmNY4pB6IvjLNH3Rxx24ba\nWfbXN7htR2cMJi+R2EXYAApDUeXz6K8l5AKWIZmx4rxTkFGkUrDlc6lxzgmMqAxV+jx5OC/jLV0K\nbz/aoRcdwMUplBw43NxBkVRilEZlDSWQlGJVtyinGVThvNL8+kefcb6pqFPAKi3Bq2lmP83kUjjb\ndEsa0QT7ImYWa1g3FT5HoRw9rAUhRkFjUzToJX8il4en8P26UavF9ouImKqcmZInjj3aWCytbCQU\naJZ0JWRivggXRfWI/LvV4sk3WuLYndGkXLCqUFlNtemYRsscAtpZ7PkZx3FmGCdKKfhBiNXKGtn+\nGHHlBT+jUdjKiLxXm8WLodi2NdFP+FgYfZQEoUrmELW1WA3rSvSQVlthDvYj1jps0gx5FBFXVNxN\nE87CZrNFYOmZum7o+55xyAwhibJ0YQ8klSlFYuXmGJn3g1QGWt6DlCFEQe1bY2mcVFpDP3I8DuIJ\n0ZDC0jbkjNEy9C0FQhR3aEpL6T9HfMjENFNljdGS36k0NO3CUtBKVIYF/ByXDYS0uEXFxdloZODr\np9+zziAAf66U8itKqTXwS0qpXwD+XeAXSin/qVLqPwL+PPDnlVLfBP4t4JvAc+D/UEr9SPk+Rupf\n/eAzSlTczJ7z1YanT3Y8Oj9nOO7Zbtb4mxkRYBdM7aiqijjPGKsI80wwIhIJIcmE3FmUL7jKUIyl\naBkmykRbL7bZQCnxYbMQQ4S06MSRE+R+gluW/XKpBLZxv16MMaONVAoSLGLIcocxhiBkoQI2JWzT\nkAKMwwyVZldvePLkjHVdsT8cUG2LdRVjGJinSMjyVHhxCPRjz09/9SmXSWGnmcmPWCpcnYUgpEWE\norQihbg47PTn68koT4eEyK810k/GBa9VLKRY8H7AD0aEWfczhhLFKmwXrfx9qVCWtgGIFIwSNR0x\n02jDk7Md0zwTF9GLlNqFrBKrTcembdn3I8M0M5SlqknCYLDWivJTSRbkPQyVHDHO0rUNx+NBDuiY\nqa0l5czK1szRk2ZP0YVeQVaFumiOkycqWBnD3M8ko2mWliPmRF1rVlVNiULF+uTlIH/fXPBKyndb\nQKvCZt1w6KfFf6CYfMRE+RmVYgnVlXZTJM3yfWISVaqWLHqpMo0GXdBOC2K/wDCMhOMkQBgjLVld\nu2XQmIgxYI2S1mPhRApkdYHP3b83KS9shMIwjChtF7FXfIC7/FMdBqWUl8DL5Z+PSql/tNzk/xrw\n88sf+6+B/2s5EP514H8opQTge0qp7wJ/CPh/f+v3NtrByvHq5oDVlvVe0aiC1WIB1VpTrxvU3uFD\nokoRazXjMdKoihBE5x3mQNU6nDXSIeeM0YKgVlFYB8qAWRJocoiys7eyj04+LhwDmZprbSheACZW\nGbQVMZJFU1IipoipHMQkE/+QKCpT1RX1uqVtNR9991M+vDziY+IP/vhXWa02DMcBlTPffP9dPv7k\nU4wxnHYdQWnCZMlmYrOqeHMXOF5dsk+Gfph5drpjHhrGfESXwpurW05Pt7SVqCjnGHCuIqdESgVt\nlbypRlaElTb4IE/zlGWT4IxZ9t+JEhJ+2DOhaTdb0ALQSMu6URnxQ0hHYpbGRNoG7odfSp6tWina\nxpGyIYTIPE4oIk0lPMaq0bTVlikkrocGPw3oDL7A6L2Eh6eZbDUGzdgfKBicVxADOUaqpsPVNUpZ\njv0Nh8PIatUsMFdNvz+iUIxGUa9rNtYxz4X9PNDWLXNWXGxbMo5+HOlvLsX1ubzPOss+frvphF05\n9Mwh0041SlVLCnOirRxNU5GCJ+VMa50cCAWmKeJZDufFEZmCKBCTCqIWzXCcZvwyjJzmQEgZlTO7\n9Zq2NczTgDKOymm6rkErJZFqujBOEg+vlcJPQURPWTYMdVNTOS1tsFZ0XYNdthf/1IfBF19KqfeB\nnwR+EXhSSnm1fOoV8GT552e/5cb/BDk8/onXH/jyY5ny25pGK7qVY/KR7aZmngI313syFft+ZlfD\nza1ns+5IORFSwSmHKgmsIWmN9rLSUXVFCUmsxUURl70wOHmyKYNB5LqqKKpGNPcyvLGi6oLPS7n8\neVCJMVJ+qgJFWwyKYg1xnqi6lpILf+cf/Cbf++iSm2Ogtpofu7nl5MvPOH38Hr/4t36JogT+Wdct\n/TBRrRva1qGKILtTSTxZW06i4dcv71Avrvk3/vhP8cFn0i60i0dgUcEzTwOpkojwQial8rBFKKmA\nEu6e9IwKnTNaF6l2lnaDkojTHq8LVVmhqxptpboS7KnYfCn33/te0SAleVGyRjNLkpSrqgUAqmjq\nuEhpgZJw9aIWVIXgDFpBKIpjP9FPI7nIYbZuagbvKUkAps4K3ksZI6zDcZL2zjlQmdo6jNaMJFTR\nKJUoPnB9cyQUTcgRWzxdq/DzyOAjGUvvM00rQbHaLg7HZBiHkZQKKSqaRkJ5YpihSF5GTImieCAg\nzT4xzzNzjOQkkmEtc0ORixuF0QJ4Oc6y4pvnwBgi2mratqXOhZjEOTuMQWY2KZCjXIvWmgXPn4he\nAK6gFyBMIiNehlKEvjSMnrp2HI89lbPyd/29HgZLi/A/A/9hKeWgvjBZKqUUdU+H/P6v7/u5XW2X\nfrNhPwV0Fsnu9WGmqxKnFycQxGwSVUQXw9DPrNqGfpgBS9OIRj3HRMgyZS1R1FxGZAcLsUZ24woN\nC/Xn3okopZcQmpXWy2GzrPQWOTJ6majnz3+gBYwuEtCqws+elDIOxztPH/Ejdc1uVaOUIUyRqpp4\n8tZjPnv1hnajeX17EJJQcMw5cnk3s6k03/rkmst9z7snKz7ae8I48Xf/8Ye8te1AGdrGPRiMQsw0\nbUtRi9HK6AcXnTBP1BKCIiWkuf+5S5HhqrJLCIyAZOPcC+K8XWGrZvFkGEw2FFNQmgdBi6wc9VI1\nyPQ6yWABlZdZQluhklv27HKTaKNxKtNZTVIVVSWzhnXj2I81d4cenSLOGp6enXLbT+zzkaI0UcE8\nDeIRKYrKaVzliMFzPBwWHmAgZhn+Xt/NoMWerWLG5xFnFIckLaQxkNKM044SI7OPZCWpT/PYY7SV\nmwtZS4I4MzWCOj8cehGJaSE8s+hUYFkIp4I1eoHmFPwU8THjk2hIlNa0bU1dWQzgF42EtjKvUUrI\nSuMsNHDFcu0Whc+Sp5BjWOzNUFWGylYP18fspcoOORArR9c1v+N9/rseBkophxwE/20p5S8vH36l\nlHpaSnmplHoLuFw+/inwzhe+/O3lY//E6xd+6Vti8dSGZ08u+NLpqfwAVhOnkbpZ4Yc9ZycnhDBT\nWcvYz7SuQ9eW/TTKOkyLEywZizZFfAtGDDgqCprbWENC0mV0ljdyTgpbOUxerMjL+jGViJ8mnJML\ngSi6Al1ZEpmSlpZC3QuUROte0KgceO98y11/lB4vKMasaSP4yxs2uvD2+U7ozdrCPHE9BDatw1nH\nZtvxjbcyfYz8xnWPz5CL4Rd+9VN+9FHHH//Jb9DHIulESoOSGYoqDXf9rezineQ+5CKbBV1kC6HU\nYoa5P82QTB+jFLU1OF2YQ8L7gTl6QtVR0hpXV1Cc7BINYgJbKgAxF/EFi7UMakky5NSLU1KZhReR\nZCePKkL3Xay2jXHYEDHKYdWK28OeYTjSVR2P1h1tZajblus3b8i5cBhHNusVWluG21smH2hWK3RO\nqMoSE0zek4rMV1L0YhLKhakfUa6icmpxByqGQ8/sZ1JRhBQ52WykQrDieBTOgIBUjLWi8kOhrCP4\ngJ88caFJKaVomloi02dPKjD7mXGaUBmscTijpTJd0OjT6DFaL1F6ATVmNtv1sioMFAXHYcTY6nOZ\nuDLE2UvLaxS1q8RMlRcbfhbX7ovPPuPq9cvlwfd7qAyUlAD/FfCtUsp//oVP/a/AvwP8J8v//+Uv\nfPy/V0r9Z0h78DXg736/7/3zP/Z16q4jzx7bVAzjJOPKGHGrNbc3N5jacWYtWkd0KCQSh0NP0xim\nOBHrlrubnrPHT0S0YQyuqTnc3lLXFXbxhicvNlHtxE+A0qQUiJNALe5BJdkPYr1tK6kIjEJVNbaI\nv56U5IDI9w6zIk+WqDjZtWgdOYxH/tavfo+Prg802jAMI3/uz/4Zzp6d0PcH1PUdfuh5dtrhx4bf\neP0h4yFwuupoXM1752tWmzV/4zuvuN6PBBJznLHrNdsObKmYvUSv2awlIKVkQoRNIxF0GOm5SxQx\nUmVExZgQqeu9TkHESwW5QxOVlTZijp5pCJQUKXkFdU1xFaZoilELCAZRKmaFhH3q+xUOwIOuviDD\nVq0Mwn5Ii66joJNUbtZqjK6ojKGpKrrGcTz0sGDmqhQIk0flhLGG85NTxjBwfZxxxtBtOtbriv5m\nYO4Hdl3NPXtwHjxKZ7rK4nOA5aYZhpmmqrCVZb1pUb1mXhD1V/s9zmi0l7i9tm1wrcSn5RTFmlyE\nbRlCRmvLqnGELHmGOQSmJEE+SsvDyWGYkqwMQ1wYi8pIhZkzXVdjKo1ztcwdxkkAKUoGhW3ToJRh\nHid8zDgnswSBuETBni1KR2sNfT+gUJycnvL8+Vs4K+K7v/dLv/xPdxgAPwP828A/VEr9/eVjfwH4\nj4G/pJT691lWi8sF8C2l1F8CvoWs/f9sued6/9b/sDEScRUjxYMuEJfy0enCphOzydgfRcseEofk\nebTdcH04sm0qxjniVh0USbMJzlGXJcEmxAWdDk1doZMMz/QyqdVIbNY8xcVcZ7BOUy0AUYVgriii\nI9dKfSGCXSbqRmtUEYnzNEaUdfy9D674jTcjt1NBxYBBuHfWKXTwtJsOt16hb2+xKvLNd5/w2cs3\nbDpHVSmOUfOltUN99Smv746YtiGGnpOq5fKuxxTLNAe2509IRqjLkNmuO9RyW99j0ypnJMMgy7TZ\nKiUHgmxSpW1Yun9rNFGL487oggkZ73vGOJPajrpdUaoK6yxpcejJk1/w62Vxcyq9SKGWUl6rzwU0\nANqaz9uXpJmmcVmzabq2IsVEXRkqbZjGkXYjIqL9HGjaljlETlct6phRaSYC4zQxHvdoY5nGiRQm\nVps1ldGCSS+Km9sDTVNhEMBNCDJ41Siur/fkxZVYVRVV06GUWICHeSYrJdqAlIll8Qwo6dOtUiiV\nCX6iKDEPhWUlapzFTxMpyLjqHr1GuR/DytA6KQGW3NOnFAJuZRHDWZUgRiJRthBFoXKmaWq8T3jv\niWNaDHbqAZ8vsmfhMpRsFl/Lb//63bYJfxPQv82n/8Rv8zV/EfiLv+N/FdAWKqNIUdJoq7bBhkAM\nmVxntHU0VcXN1Z7NbsPoE7Vp+PTyBrda8fxkR4yZeS74fkTnIGvHYyCXRDSaMHusUVTLE8ZVkpEY\nk1QK917wfhKC7Nnq5POet67lQPDSC5q8KNAWiahSGmUNKidc3RFCpOA4fesJf3R1wpQSk5+5qBU6\n3NFfz5RxoN3uaE4e8SYk1NDz9qMntHXHcR759ZdXnDeaT/qe06bi6fuPSRH2R0M0hdvbmevjLSdr\nR7l+zYRi29XYDBMwF4VTanlqyzbQKkOgILxkuYisXiLWl1KyqOUAVGCywuqC1QqXJEQkDj3JB6qu\no27bB0qUsZaiy+diFoV4JJRMtu+BMSprmXXdm3IWzb0xC8gzRExtmBcFoVWRVSeBs45M6wy5JPTm\nhHEKVEre87ffOuezN9d0qxUvXie6RhQ8tqrIIRJjoChFLFq0KCFyO3iwjq5bkeYZoxJhlu0RSrIL\nu/WKHNNicRY1l58mCpqQs2RTLq5CZfTStkmbWVISrqEpECTYpHZucbwLxjwvPX0Ms0BQlViVnRZa\ntE9JoMDI76xrKzElaUmHWtViGY8xMg4DSktrEmKQNbP+fMU5jAPBe9ZduxinfvvXD0yBeNiP7HaO\npxcX9NFze7Nnt90wzSPTMFDVDXM/4XYbqk2DbWvurgcePXnOv/TH/iif/Mav8vrDT9nsTonB8/ik\ng6S4vrri7Okjhqu7xf0nSSdGI8GgJaOLIgeZjDvEZLLvj7S1rG+oNdtVK8QhW4iTJ2iFTpk8zsIT\nsGax+iLDRyDNM29vtqgTGR4pa6jSjO9n1HEiB5jiAXuYpe3ImWzgfLdhOzWsVmuuXr3gwxd3jP4V\nbb1lszY825xgE3w0TVzOM6TI7GfuRsPHFN57csHJifTQzJ6YRRE3x0ywoklvq0Z0F4oFjPE5bVkp\n2ffLk0USr40t6JhxWbIcRz8yHSTHsV2txc5bCiz2aoBFDY36AusvL9Ld++qg5Pwgb1Qo2rbF2yiE\n4CRPzMpZrMpsu4bXH13yzpef0QaFyZ5r76mtY3NW8Wjb0fcRbSJfe/8xt1OinmfUfHywmGc0u92G\nq9s98+R5cnqOUp7jNFO05mbf44zDFmiXsBNHIhlHU3f0/YH94UBjLLZyZGBeTELaGFarFYpCiIlQ\nCj4lQojUxuIqK5uqGBZsOvhhIC4Mh65rsY1hHmeGaRKpcsokMqtuJahz70kJ6qaFLOa7QqHve+Y5\nLvqPJO7KuiFqkUJPS54EqdC2LSkn/PxDmptwuZ85+hv2/cjFds3Z+Snr3Y5tily/foMpmkChsxaX\nhD6zO9sQjea7v/z3mKaJujEM/YHVdkeOiv04YrqOtt3Smx5XMsoppphIKeJsouoEvJmLiDWS0RhV\nYRW8ur5l03W4Yuiv9qw2nXgY7P1VLqvEUgoqL6pHq8lEqRSMXcwvMp/I1sCcKCOEJQZLa8s4jNjK\nCeE2JopOWFtYhYA9P+Wkbvjw9RU+SEhKVhndreDqijdXM6dPtyTv+fjlG14M8Cuf3vL7n++42DZc\n33l88rxzvqNqa7wxFLesFReFWix5GTBK+K08ocV/QZLtg1ZKjDXLQaoVzDERhgPRz7i2pW46qrpC\nIU8cWTlm8uJuuh8s3ttoRR0nLYvAZOVp2lRi9TVKLXJokRN3taM72/Dy8jWbrqbd7NgpxTB5HrUb\nDne3PL3YcbO/o1KFtVNkU3PrR2yBVdOinGEcJDrOOIvPnujFXGWMrGpJWViJ0S8Qk0zXtpQUZI1J\nIZSIVlaUqBTUwh2Yp/nzNkjL79ktBCmZ2UT8MvG3S4Va40R1OAU8Hh9FcJai4NCUlVbQKGn1coyM\nvcy2oir4WTgMxt6DS2TVmedAzolqsVQ7a+W9jomcIvGHNUTlo+s7VrWjPQ5c7QeenO4YRomD2mxX\npDHS1i1V01GGo8AgsazamuQ9+5s3tLZCdy3NdoPzkfn4itXJKVcvX0sqTS1yYTFpGIpxxFCW3q+I\ncrAoWuuoKstt7nHrBmcNJRX8OFNKEf2B/nzPXrQMzYQwltEFSkqYJfwjFTCVI4491ekpXfIcbw+8\nuOtpuo5xnLHKsN7UdNtT8tCTlOJuf0dlatq24vn5KeM041Ydqt8TkuftTpOfXfB4VaGc5c2UedR4\nirF8ernngxfXDHPmy892dJXicLzjLmQer1uUatBKcigVRVyZS08qCb8ylU7IDStGSREUucXnr43C\nx0xInrkP+HHEVQ1121LVok0wWqOtQWmDNuVBr6G1kQpkUeClh5Xn522GWQw9MlyzOKd4+viMYZwZ\njgdu727IWXF2dsZwHOmPkXVraKpTjkOgaJns79ZbGpMFKFtVTONIt9kyThPDPNHWDj9F8v28yhoh\nJVMoKXL0kXkepOxXEvaaYniAyaS44PUAVFqGeY5UitCotSLlTIiyQtSIOKtEmTuEIq1qyllaNHX/\n81ucc6LZ0IvPIcmcZxpHGleJld5Y6qoSDqJ1hGwYBhkY5pRI92TmRb9QSpZ8i9/lnvyBHQb7OZBN\nxZt+QNHzyXVPW8k0+UefP6IA9ph48qRm9fwZ8yRwCn8YmFLkOPfEGFmtGkKObHZr6tcV4+Ud7cWG\ndrVlONwIkCKkpbdTTPPIXArWijFI5JwZZxXnTSfinZSX2BSDKYkQI6WpqIxM6EvKC4xUyj0Qim02\nmrlktE+8vr7l6fNHnK5XfPrtz7jte37t9Z5vfXTLv/oT79A+esrZScX2S8+ZvGL/7W9j0ByPbyAZ\n7OaMMN3w9Okj9iGyOtkyuYbu9oZud0GcJ/7A07e43B/47PbIp1nx8U3g7bOKd09XfOnxBamt+NbH\nr/nOBx/x/rvQ6BqjFxSWEmGOyjJgnQiSwmwkDNRaQy76AdRh7mEcFJwRtZ6Pkel4x9gfqOqGdr3G\n1TUmCeqsJNk8pFJAC/G5quyihFOLD0KeoveVhOzXpSc3taaQWSuJYAcYDj2qZMIs69/Lq2usrtE5\ncnqyQZ9umObIp1fXmDnQKItVGlUCtnIcvWceZnyI7HZbamO4e/2Kfp5Jy8OhqzoKmaigRKEdGVPR\nugrnDGGaGfoBV1WQFFonxiAyz7qqiV5gsiJOWjDp1tI1HUpByPHBfai1XtjSEpJaSqIfD6hiRIKf\nRMyUl1lV3/fUTkJkrLHs9weUFSxfTCI5jilSVbVoDELAGujalkPf/4735A/sMDDO4MPEnAupFMbj\n4s3ThVf7Pe9fnPNTX37OOE+4/R270zUu9BzP1uRxptFPGHIiDBMvP/iY+v13cV2Dzz02a26vb9BW\nUoB0bRl8wrZaQCIpEKNHLzzEjMbnTG1Z8OKeZC3Ji47fhyhOR6MWG6ic/KoylCyns7E1kLHZgk4y\nVR9neu/5n37xu8zDyKw1N70n7/e8/5ULqqLYf/YCNU9UF2dwd8Mv/spL/oX3n7AykYuzM/avbnEn\njnhzxebRU9TdLd/+4CO+/u4j5lxhXcWLm1t+/vc/59XdlrdP1nTrFSHO6FnxaNXy4vSUYQ64rmby\nkxiNlKgpy/Ikrp0hW7Xo3ZMEiigFRiGoARErqQeL7BI4UilizoS55xBmTNVQtx11XWOsJZsFme7M\n4sf3AgFdBm4RaQvuh29l+Xf1haGkteKsjCmz2q6YfZRINu9JITJMd5yfPyHPgTkeUdpx1tSMypDj\nyPZ0h0USk3fbLfM04efA/nAgVY4eMF3Lpqpom4bbw5GuXVFQ7A93y4q1QHTMs1COlIJpHMQs5CrM\nEkXvp/mBQWCspXIObUV3EVNaDHVi9JJZQ1gGriL6SjGKqE0rhkmyQdq2FsNZEuFciJnUz1Dk88UH\nNHKYUIrY30uW1i9LSGxYEpp+p9cP7DBwCy/OmYTTS549GtfVVEuAxe3tHc/feUIYe+6SwCxU6Emz\n8OnW644ZD0px8/o109TLcM1UTN5zdrHl2I+EMbNabYilkGJm2PfUbUO37TApk33Gi9dR1oQhkPqJ\nbr2iGPGSZx/QbS2iIy2qMmZ5GhhToGR0Lg8JRudWYriny2v+4Nff4XrK9NPMj1ewO1kRhkSpWq4P\ne/S4Z7ub2HQNp9s1btVilfD99seJF59c4/zE0xz59gef8MmV53uXV/RppiQF2VKmzI+caLLJVGSu\nx4L1AxWGH336CJUSytXEAil4KVtLkUjwsKxXl/bH6PvyXS1hrTIjEcXhEvcFoOTPplxwJhNixI9H\n0jwR6oa6afDG4uparOR8TvYt+gta+WUyr9UXMiGLDMrutw8y4BSYjbWa2j2mHycO/cD1VeLm9g2b\nbsPFxSm+FMrguesH0jBz4hxt1RCMxlQVoy3YdUc1OIZh5KRZ4XMkFmmTdquOEDNTTHRtS20lWHUa\nB0nfMk7qRmNQLORkBJ2WksyPSikMw4A15iHjMHj/gGWXwyPK9bowB1gMW9ZaQioPnI04eaZ7FoTc\nBdhqYTUmSdcW9Fp+IFeJWUki7qcpisbjn5U34Z/1K8XE6cmO959e0PcDTWXYtAaVHcrC+49X+AJd\n1fDp9WvWXU1QhmmY6bZbtNPMQ88cPF23pnKKcVJYt6b3IyXP1KM80V6OI68PPWdn52x2OzbG4srI\nziZoO6Z+oswBpzRjKqSwRIMrQzby9CImpnGmXbWQC6VEVExYLRisqm4EChI8KE2eZ0pVoUrm+XnL\n06RJ5ZR61WFLT4gQ8kTjGsp6y/XrNzQKHq9qGlW4eXPLkyePsF2D8xN5VXEzZvoQiNry6RgocySU\nTFPgb3/3NX/6p7/Gm5sbdo3mzZQ53ezQztAlR7RBNBNLfDeA0kr4C9qJTbgUKuMwRRGjp6S4pExL\n3JyCB+aiUnJjK0TBqY3cDC5JalKceuapFy7BakWXVzhtqaoKV9kHgMr9ijGrZQ6zAGnL4sUvy0DT\nqAUVnsEqCbHZrFqaxnF+smUMnk8/ecV3/tG3uTg5YX2y40tvnXD058z9gbvDHY8eP2YOgUpbamcZ\n+szZ8yccj4M4LUOkqu5zGCNjPxKMwmA52W1QZc314cg8e5y1KNMsa8GCIjJNI+LqlEqn6zruMw1k\n0CehLKUUsk4LVblI7HvKAjEJnpyLHAJJmJMaTQpykKRwX1nJA885J6j0SuLrYgjMcxDNSZHcz6qq\nKCUSgv8d70n122iC/rm+lFLlJ7/5DXRRnHQttauoneZ81wopZvT8xFcfsaocaIvbbDje3lFCxJeM\nsoZH52dcv3rDPdy06RqSj8wpMOREjSOFSL06oW4dRUV8dhBmamc42Z2xdtC2sB+O5OpEVjPXL/Dz\nvAytPMkYplyIs7ATu65h1dZSZitFIlM5h60ESmIVaFMR80z0kvBbKo3VFn8caeqaaETZm5jRtsVh\nlr7acvfqJUUrbqaJNI7oqub2+shXv/5lxjny7Nzy3/31f8z3Lm8wxRJSJHhPVUV++u0zfu4bz7m+\nPrJ58pgpSFtjKkOcItpYfBC1WilFdALLTVaKIkS5Vkq0mwAAIABJREFUkAosfL60KDbNookXKXfM\n+QGbLuavhcK7cAdilG2Fj4lUoChpz7rVmqqu5X9V9SCKuecuKs3DQSVDNdniiMFGbhyLpdiCTrKq\ny8sgLyZJHDoeJi6vr7l+9YoUCienO1brjhfXN2yM5p2vfJn99YHRjwx9j6kqfBKepFEwDQMnZ+cE\nBdfDREmRFGf640RT1RhXoa1m7AfZJKApRrgUKkt1lBdq8X3smig+5eeJMZFJD/gz2c7K4DCn9CB4\nizFxeiqUqHGSTUgMcvMrYJjG5VDWyzYjU9lFsBTC8j0CZhnWtnWN1pr/7a/8L5RS1G+9J+EHWBmU\nZeJ6OY4000QGXt0NOAOPt2tujp7ZJTablkeVo318ztyP7K/vOAwDVwtTPqdE1TUC2ExS2m6UJnqP\nbizOBMbDLD1hq5lCpsweM9+xvXjC7BP76wP1qeP09AQ/C1NvniI+K+aQOHoPpbBSmhzlokQbYhHl\nXC4SAhtzISgFOXC+3dDsDOP1FTkaboZbvvPhNT5l+hg4HDNff7rhZ/7YzzIdb4hHz/HmEqzGBU8X\nAnfAm8sbUol88J3v8uhkQ2rO+bHHG0oxXO735LkwR0PUhbpbsd6tqbsVN0MgZ4FrlGNhu90QU8BV\nBpUE/JFSJGvBbKcQF/CyBHuwcPtTknzJotSDTVYrsTYv1fziATBLzkKWC3B5Lx6Sg3JkPNwxHi11\n21A3LXXTPFQG95iwHKNUB1+YK6R0305AVpniJfj1nhAJmWoZVJ6fbdjuVoxvPyGMkdHPeO85OT3l\ncHXLr/3ar3J+eo5pG6rUCfsizBRtCTmgq5p+HOlWHRebNX6amftMt61Q2nCcPWA53e3wzcRhHBln\nT4ywblrII/08Yp2jciIWksMzYY3FVQtLMyWMsUvmYiZ40QXEGB7CYK/fBFnJKg0LXDUtTEZnHTFn\ncXIum2/v/eJZkTmQMYtYS2t8DFTmhxR7pjMUJbhpn4RNOIdRtOb1RMkb2q6j261JJJpVh/aR1DZy\nAcaM2rYonyhaY7qWME2CB68rpsVfj4JSaQ6HA/0hs21btqsV/e0tt2miOjvj5PSM1in0/prQT3z7\no1umIqKd232PMopdW9N1Neu2pqkWfsAcqNpK/PwhMvkgJh7ruLu6JjqFnSIff/wZv/z6yEfXRzSW\nbdfwYn/k5uaGP/UvF9Znz5iOgV/7vz9ivLtiheLZl97mWcpsdjspG1PGti2v7wZaFfjD75yS7WP2\nhwMZTWHm2arm1asb9v3EgOLZo3NK1PTjzOvhirq1bDanaGXJOi4iF1BZnlzWWupKLrKQJdcPCyZb\n6eFLIZlFgVkWtr/WqCJrMF0kWiwj2QA6RqISRFtaoJwheaajZ+qP2LqhaVuaboVddv16ySzUi4lH\nlJQaskIZRcrCdUwpUhbWm4KH2DaVM04rqqYlV5lSVhImkyPzbsfl1Rs+fvmSXduxWW3QNeh1Ja4/\nt3qIYj/OHh8S0YvIKxZom5ZVU0GKZB9o6wqtFZ115GVAqKqa1shTPOck1YyRv2cMHr24OzOQYqAE\nyVS8NxHJNiCBysxzRGuRyYdpXmzpWfQFWj9UQyBELoGnLE5bpXDG4qzFWJkv/J7gJv88XwWxC5uS\n6ZYQkccXO5racNE4NpsKVxL9zR2ubWjnSD9N5NpJvzcMVMYSVMaExHx1gypZYCOLndnHmco6YR34\nSHN+weat53D7hlINfHg70qUjJycn3Lx8hWs7PvjwFW/6gVFXtK3h/OIMpwq7tSOHgE8BEzQ6ZbCK\nHD06O4x2tK3GNDVx39P7yHeuRsLNnvOvfo189f9xvtqQyWxax81YcRMn/sHf/RX+yJ/6k6zfO+Nn\nfjby8uMXXF++4c3LF3z9a7+fNx99wPvPz7mdIPuR7tEjfJy43nt+9KLhzeYxZu6BlrxAW9O6oQkz\nJUOpG1pruZlmPnt9yzdcIbgVVmlWzjKnjHU1xjrmmFApLrTfjF9KdKUSKQNFY0omLxdVyhKprhF4\nTM6ZKQhZymhNbQ162RZV1jB5T2U0VsvXhunAYR4IwVPVLc5VNF0LZGKWkjj7yFwSVdUQfVxw4cgT\nMGdUFnNUznGpJJbwEZZBmlJUlabOFfPOcbJZ8aV33ubDzz7hw48+IfYzTdexPrkgzqOE+J5u6FqL\n3m2YQyTMEasEmqOrVgC1KXHzei9P/MbiR8/UDw/hN6pkXCUuw8l7sIbaye/NpEQOkovoFyVjXJ76\ntbMPGyuQgFg/eLpuJUDUnFmvV8ScsYi8OeUkmZVJKglrjQS2OrvI6zMxRb5vb/CF1w9sZvCn/8TP\no2Ok6ypUTjS142zbibY+BlZNTSyZWok02FnHMXh0Nox+plhN3bWkaWaeZzbdikAm9BOulotq6Aec\nsWgDx16gm2jN9vSMx2894tWLS8ZpYt8nzp8+ZV0p+v6KGOTiq6wl9JO4qJSCEJZ1GeRxoRU7iyky\n0HJK42OiOzul+MDc94RZ886Xz/jrv/RtPnxxxbqx/OQ7J/ztD/bc9J7f9/YZ/8of+gZtU7GfZTft\nk2IcBubbW17cjEzDHY8fXbA7q7h++YaTzRm+abj77BNAs+vW1LXo5l+8uuVi1/LRzYFHXcN2tyOE\nGdtUoK1YuClM0yQrMjSmqilEgWKERFb3T3/NvaUml/vocVEQij9DPp6WNJ/7PjklGZTpRe+vlLSE\n9zh3wbXJ78rHRCxQMFhb0a5W0j4sfbRZvofWaknYXgaX98aoxYmZl778i9DP+9QjrSVJqyB/RxCZ\n+hwi+77nxWevuby+wVYOV9U4Y/FzpHGWzboRF6efUFGe/s5WdHWFrh0ZMWqlZdsVk2DN/TJDua9w\n7unH2miKAh+8XD9KE4Jf2ilkQ/AFQVZMUQ4VBG5jlJFkJK0XtLqROcRiTIopLi5ceY8q6wTuGsQK\n/dd+4X//bWcGP7DD4M/8yZ+jVnC+ackl47SmbhzdqsPEZdgVPco63ILWSjFTGccQg4RbImATs4RF\nRCMXWt22OGNEH1AURhX640jKssO1ribFhC6Frl3RY8kl0OTIsO+5GUaevPWEt56c4ZpOiL3jyDB5\nEgLvTNETtZR4+8NMPw48Wze8/ZW3WbeGcH3FdDhQcsVNv2efHL3W1Bkery17U1E5y844VO1gOFDv\nziRCbDywahtss+bu5o6XN3c0pbBu4FsfXfL07ISmqyWkNWXGJa16ngq//L0XtK7w2UEm4rumZldV\nXPczz09XdF3D65s9e594tGt47/GZ6OpVom5rjtcHulVL16wAAcCkJKEpqRS0tQ9pv3khMd9vrO41\nAiUXhnGGZR8PoLSAOZF7EYCQBDoqT0Wx/CY02spN2bQtdS1PYmU+D4u9H5QpJbMnozRFF4G9L05J\nreUg00o94MjKItOVDUBexE4CvrkbJm7vDrx6fUXJmfVqRQaCD0xTIOXCtu0oKokBzDnJajD3DETQ\nxkqLFSIhRmEKKKFv38NPYgiyucji2E2L+vM+YTultHAh5Tfqg+c+del+6BiisDYBmqpZ+BJyIJbl\noBXX4gLkUUJaUgr+6i/81R++AeJvfvqGde2ougo3z6x3p4Tome722NpyumqYb4444coQKstMll5z\ntYJSaE5XHG/3dK4Rs1CKGBRmzkSVKDkSpkDRltW6Ed22MdR4nLP4WPjsxQu+96pn9eiUrYo03ZaL\n3ZZtpXBlhvqMfHVFbQxhvSX1NygfICRCSQyHSTgHDWgbOX/2lEfvPOf13/k/yaPman/HXR/41Y9f\ncDPDu2eav33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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "batch_index = 8\n", + "image = style_data_batch[batch_index]\n", + "plt.imshow(deprocess_net_image(image))\n", + "print 'actual label =', style_labels[style_label_batch[batch_index]]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "top 5 predicted ImageNet labels =\n", + "\t(1) 69.89% n09421951 sandbar, sand bar\n", + "\t(2) 21.76% n09428293 seashore, coast, seacoast, sea-coast\n", + "\t(3) 3.22% n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty\n", + "\t(4) 1.89% n04592741 wing\n", + "\t(5) 1.23% n09332890 lakeside, lakeshore\n" + ] + } + ], + "source": [ + "disp_imagenet_preds(imagenet_net, image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also look at `untrained_style_net`'s predictions, but we won't see anything interesting as its classifier hasn't been trained yet.\n", + "\n", + "In fact, since we zero-initialized the classifier (see `caffenet` definition -- no `weight_filler` is passed to the final `InnerProduct` layer), the softmax inputs should be all zero and we should therefore see a predicted probability of 1/N for each label (for N labels). Since we set N = 5, we get a predicted probability of 20% for each class." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "top 5 predicted style labels =\n", + "\t(1) 20.00% Detailed\n", + "\t(2) 20.00% Pastel\n", + "\t(3) 20.00% Melancholy\n", + "\t(4) 20.00% Noir\n", + "\t(5) 20.00% HDR\n" + ] + } + ], + "source": [ + "disp_style_preds(untrained_style_net, image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also verify that the activations in layer `fc7` immediately before the classification layer are the same as (or very close to) those in the ImageNet-pretrained model, since both models are using the same pretrained weights in the `conv1` through `fc7` layers." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "diff = untrained_style_net.blobs['fc7'].data[0] - imagenet_net.blobs['fc7'].data[0]\n", + "error = (diff ** 2).sum()\n", + "assert error < 1e-8" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Delete `untrained_style_net` to save memory. (Hang on to `imagenet_net` as we'll use it again later.)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "del untrained_style_net" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Training the style classifier\n", + "\n", + "Now, we'll define a function `solver` to create our Caffe solvers, which are used to train the network (learn its weights). In this function we'll set values for various parameters used for learning, display, and \"snapshotting\" -- see the inline comments for explanations of what they mean. You may want to play with some of the learning parameters to see if you can improve on the results here!" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from caffe.proto import caffe_pb2\n", + "\n", + "def solver(train_net_path, test_net_path=None, base_lr=0.001):\n", + " s = caffe_pb2.SolverParameter()\n", + "\n", + " # Specify locations of the train and (maybe) test networks.\n", + " s.train_net = train_net_path\n", + " if test_net_path is not None:\n", + " s.test_net.append(test_net_path)\n", + " s.test_interval = 1000 # Test after every 1000 training iterations.\n", + " s.test_iter.append(100) # Test on 100 batches each time we test.\n", + "\n", + " # The number of iterations over which to average the gradient.\n", + " # Effectively boosts the training batch size by the given factor, without\n", + " # affecting memory utilization.\n", + " s.iter_size = 1\n", + " \n", + " s.max_iter = 100000 # # of times to update the net (training iterations)\n", + " \n", + " # Solve using the stochastic gradient descent (SGD) algorithm.\n", + " # Other choices include 'Adam' and 'RMSProp'.\n", + " s.type = 'SGD'\n", + "\n", + " # Set the initial learning rate for SGD.\n", + " s.base_lr = base_lr\n", + "\n", + " # Set `lr_policy` to define how the learning rate changes during training.\n", + " # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\n", + " # every `stepsize` iterations.\n", + " s.lr_policy = 'step'\n", + " s.gamma = 0.1\n", + " s.stepsize = 20000\n", + "\n", + " # Set other SGD hyperparameters. Setting a non-zero `momentum` takes a\n", + " # weighted average of the current gradient and previous gradients to make\n", + " # learning more stable. L2 weight decay regularizes learning, to help prevent\n", + " # the model from overfitting.\n", + " s.momentum = 0.9\n", + " s.weight_decay = 5e-4\n", + "\n", + " # Display the current training loss and accuracy every 1000 iterations.\n", + " s.display = 1000\n", + "\n", + " # Snapshots are files used to store networks we've trained. Here, we'll\n", + " # snapshot every 10K iterations -- ten times during training.\n", + " s.snapshot = 10000\n", + " s.snapshot_prefix = caffe_root + 'models/finetune_flickr_style/finetune_flickr_style'\n", + " \n", + " # Train on the GPU. Using the CPU to train large networks is very slow.\n", + " s.solver_mode = caffe_pb2.SolverParameter.GPU\n", + " \n", + " # Write the solver to a temporary file and return its filename.\n", + " with tempfile.NamedTemporaryFile(delete=False) as f:\n", + " f.write(str(s))\n", + " return f.name" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we'll invoke the solver to train the style net's classification layer.\n", + "\n", + "For the record, if you want to train the network using only the command line tool, this is the command:\n", + "\n", + "\n", + "build/tools/caffe train \\\n", + " -solver models/finetune_flickr_style/solver.prototxt \\\n", + " -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n", + " -gpu 0\n", + "\n", + "\n", + "However, we will train using Python in this example.\n", + "\n", + "We'll first define `run_solvers`, a function that takes a list of solvers and steps each one in a round robin manner, recording the accuracy and loss values each iteration. At the end, the learned weights are saved to a file." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def run_solvers(niter, solvers, disp_interval=10):\n", + " \"\"\"Run solvers for niter iterations,\n", + " returning the loss and accuracy recorded each iteration.\n", + " `solvers` is a list of (name, solver) tuples.\"\"\"\n", + " blobs = ('loss', 'acc')\n", + " loss, acc = ({name: np.zeros(niter) for name, _ in solvers}\n", + " for _ in blobs)\n", + " for it in range(niter):\n", + " for name, s in solvers:\n", + " s.step(1) # run a single SGD step in Caffe\n", + " loss[name][it], acc[name][it] = (s.net.blobs[b].data.copy()\n", + " for b in blobs)\n", + " if it % disp_interval == 0 or it + 1 == niter:\n", + " loss_disp = '; '.join('%s: loss=%.3f, acc=%2d%%' %\n", + " (n, loss[n][it], np.round(100*acc[n][it]))\n", + " for n, _ in solvers)\n", + " print '%3d) %s' % (it, loss_disp) \n", + " # Save the learned weights from both nets.\n", + " weight_dir = tempfile.mkdtemp()\n", + " weights = {}\n", + " for name, s in solvers:\n", + " filename = 'weights.%s.caffemodel' % name\n", + " weights[name] = os.path.join(weight_dir, filename)\n", + " s.net.save(weights[name])\n", + " return loss, acc, weights" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's create and run solvers to train nets for the style recognition task. We'll create two solvers -- one (`style_solver`) will have its train net initialized to the ImageNet-pretrained weights (this is done by the call to the `copy_from` method), and the other (`scratch_style_solver`) will start from a *randomly* initialized net.\n", + "\n", + "During training, we should see that the ImageNet pretrained net is learning faster and attaining better accuracies than the scratch net." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running solvers for 200 iterations...\n", + " 0) pretrained: loss=1.609, acc=28%; scratch: loss=1.609, acc=28%\n", + " 10) pretrained: loss=1.293, acc=52%; scratch: loss=1.626, acc=14%\n", + " 20) pretrained: loss=1.110, acc=56%; scratch: loss=1.646, acc=10%\n", + " 30) pretrained: loss=1.084, acc=60%; scratch: loss=1.616, acc=20%\n", + " 40) pretrained: loss=0.898, acc=64%; scratch: loss=1.588, acc=26%\n", + " 50) pretrained: loss=1.024, acc=54%; scratch: loss=1.607, acc=32%\n", + " 60) pretrained: loss=0.925, acc=66%; scratch: loss=1.616, acc=20%\n", + " 70) pretrained: loss=0.861, acc=74%; scratch: loss=1.598, acc=24%\n", + " 80) pretrained: loss=0.967, acc=60%; scratch: loss=1.588, acc=30%\n", + " 90) pretrained: loss=1.274, acc=52%; scratch: loss=1.608, acc=20%\n", + "100) pretrained: loss=1.113, acc=62%; scratch: loss=1.588, acc=30%\n", + "110) pretrained: loss=0.922, acc=62%; scratch: loss=1.578, acc=36%\n", + "120) pretrained: loss=0.918, acc=62%; scratch: loss=1.599, acc=20%\n", + "130) pretrained: loss=0.959, acc=58%; scratch: loss=1.594, acc=22%\n", + "140) pretrained: loss=1.228, acc=50%; scratch: loss=1.608, acc=14%\n", + "150) pretrained: loss=0.727, acc=76%; scratch: loss=1.623, acc=16%\n", + "160) pretrained: loss=1.074, acc=66%; scratch: loss=1.607, acc=20%\n", + "170) pretrained: loss=0.887, acc=60%; scratch: loss=1.614, acc=20%\n", + "180) pretrained: loss=0.961, acc=62%; scratch: loss=1.614, acc=18%\n", + "190) pretrained: loss=0.737, acc=76%; scratch: loss=1.613, acc=18%\n", + "199) pretrained: loss=0.836, acc=70%; scratch: loss=1.614, acc=16%\n", + "Done.\n" + ] + } + ], + "source": [ + "niter = 200 # number of iterations to train\n", + "\n", + "# Reset style_solver as before.\n", + "style_solver_filename = solver(style_net(train=True))\n", + "style_solver = caffe.get_solver(style_solver_filename)\n", + "style_solver.net.copy_from(weights)\n", + "\n", + "# For reference, we also create a solver that isn't initialized from\n", + "# the pretrained ImageNet weights.\n", + "scratch_style_solver_filename = solver(style_net(train=True))\n", + "scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\n", + "\n", + "print 'Running solvers for %d iterations...' % niter\n", + "solvers = [('pretrained', style_solver),\n", + " ('scratch', scratch_style_solver)]\n", + "loss, acc, weights = run_solvers(niter, solvers)\n", + "print 'Done.'\n", + "\n", + "train_loss, scratch_train_loss = loss['pretrained'], loss['scratch']\n", + "train_acc, scratch_train_acc = acc['pretrained'], acc['scratch']\n", + "style_weights, scratch_style_weights = weights['pretrained'], weights['scratch']\n", + "\n", + "# Delete solvers to save memory.\n", + "del style_solver, scratch_style_solver, solvers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the training loss and accuracy produced by the two training procedures. Notice how quickly the ImageNet pretrained model's loss value (blue) drops, and that the randomly initialized model's loss value (green) barely (if at all) improves from training only the classifier layer." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ROQeFwjsdShwuuADYvx84etT1dX2eYOtWYP16632UlgI9e/LPRnFISFCzsnYE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\nI6HE4ZdDpOZFkpPNp20JB0Gt2BZCjBNC7BVCHBBCPGGxTaY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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(np.vstack([train_loss, scratch_train_loss]).T)\n", + "xlabel('Iteration #')\n", + "ylabel('Loss')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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rQuOqcvDiVuL2qW1z41YqRg4clLQiB7U2kAoeIaZShjGxcyu5UQ1WKEdAmg1O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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(np.vstack([train_acc, scratch_train_acc]).T)\n", + "xlabel('Iteration #')\n", + "ylabel('Accuracy')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the testing accuracy after running 200 iterations of training. Note that we're classifying among 5 classes, giving chance accuracy of 20%. We expect both results to be better than chance accuracy (20%), and we further expect the result from training using the ImageNet pretraining initialization to be much better than the one from training from scratch. Let's see." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def eval_style_net(weights, test_iters=10):\n", + " test_net = caffe.Net(style_net(train=False), weights, caffe.TEST)\n", + " accuracy = 0\n", + " for it in xrange(test_iters):\n", + " accuracy += test_net.forward()['acc']\n", + " accuracy /= test_iters\n", + " return test_net, accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy, trained from ImageNet initialization: 50.0%\n", + "Accuracy, trained from random initialization: 23.6%\n" + ] + } + ], + "source": [ + "test_net, accuracy = eval_style_net(style_weights)\n", + "print 'Accuracy, trained from ImageNet initialization: %3.1f%%' % (100*accuracy, )\n", + "scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights)\n", + "print 'Accuracy, trained from random initialization: %3.1f%%' % (100*scratch_accuracy, )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. End-to-end finetuning for style\n", + "\n", + "Finally, we'll train both nets again, starting from the weights we just learned. The only difference this time is that we'll be learning the weights \"end-to-end\" by turning on learning in *all* layers of the network, starting from the RGB `conv1` filters directly applied to the input image. We pass the argument `learn_all=True` to the `style_net` function defined earlier in this notebook, which tells the function to apply a positive (non-zero) `lr_mult` value for all parameters. Under the default, `learn_all=False`, all parameters in the pretrained layers (`conv1` through `fc7`) are frozen (`lr_mult = 0`), and we learn only the classifier layer `fc8_flickr`.\n", + "\n", + "Note that both networks start at roughly the accuracy achieved at the end of the previous training session, and improve significantly with end-to-end training. To be more scientific, we'd also want to follow the same additional training procedure *without* the end-to-end training, to ensure that our results aren't better simply because we trained for twice as long. Feel free to try this yourself!" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running solvers for 200 iterations...\n", + " 0) pretrained, end-to-end: loss=0.781, acc=64%; scratch, end-to-end: loss=1.585, acc=28%\n", + " 10) pretrained, end-to-end: loss=1.178, acc=62%; scratch, end-to-end: loss=1.638, acc=14%\n", + " 20) pretrained, end-to-end: loss=1.084, acc=60%; scratch, end-to-end: loss=1.637, acc= 8%\n", + " 30) pretrained, end-to-end: loss=0.902, acc=76%; scratch, end-to-end: loss=1.600, acc=20%\n", + " 40) pretrained, end-to-end: loss=0.865, acc=64%; scratch, end-to-end: loss=1.574, acc=26%\n", + " 50) pretrained, end-to-end: loss=0.888, acc=60%; scratch, end-to-end: loss=1.604, acc=26%\n", + " 60) pretrained, end-to-end: loss=0.538, acc=78%; scratch, end-to-end: loss=1.555, acc=34%\n", + " 70) pretrained, end-to-end: loss=0.717, acc=72%; scratch, end-to-end: loss=1.563, acc=30%\n", + " 80) pretrained, end-to-end: loss=0.695, acc=74%; scratch, end-to-end: loss=1.502, acc=42%\n", + " 90) pretrained, end-to-end: loss=0.708, acc=68%; scratch, end-to-end: loss=1.523, acc=26%\n", + "100) pretrained, end-to-end: loss=0.432, acc=78%; scratch, end-to-end: loss=1.500, acc=38%\n", + "110) pretrained, end-to-end: loss=0.611, acc=78%; scratch, end-to-end: loss=1.618, acc=18%\n", + "120) pretrained, end-to-end: loss=0.610, acc=76%; scratch, end-to-end: loss=1.473, acc=30%\n", + "130) pretrained, end-to-end: loss=0.471, acc=78%; scratch, end-to-end: loss=1.488, acc=26%\n", + "140) pretrained, end-to-end: loss=0.500, acc=76%; scratch, end-to-end: loss=1.514, acc=38%\n", + "150) pretrained, end-to-end: loss=0.476, acc=80%; scratch, end-to-end: loss=1.452, acc=46%\n", + "160) pretrained, end-to-end: loss=0.368, acc=82%; scratch, end-to-end: loss=1.419, acc=34%\n", + "170) pretrained, end-to-end: loss=0.556, acc=76%; scratch, end-to-end: loss=1.583, acc=36%\n", + "180) pretrained, end-to-end: loss=0.574, acc=72%; scratch, end-to-end: loss=1.556, acc=22%\n", + "190) pretrained, end-to-end: loss=0.360, acc=88%; scratch, end-to-end: loss=1.429, acc=44%\n", + "199) pretrained, end-to-end: loss=0.458, acc=78%; scratch, end-to-end: loss=1.370, acc=44%\n", + "Done.\n" + ] + } + ], + "source": [ + "end_to_end_net = style_net(train=True, learn_all=True)\n", + "\n", + "# Set base_lr to 1e-3, the same as last time when learning only the classifier.\n", + "# You may want to play around with different values of this or other\n", + "# optimization parameters when fine-tuning. For example, if learning diverges\n", + "# (e.g., the loss gets very large or goes to infinity/NaN), you should try\n", + "# decreasing base_lr (e.g., to 1e-4, then 1e-5, etc., until you find a value\n", + "# for which learning does not diverge).\n", + "base_lr = 0.001\n", + "\n", + "style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\n", + "style_solver = caffe.get_solver(style_solver_filename)\n", + "style_solver.net.copy_from(style_weights)\n", + "\n", + "scratch_style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\n", + "scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\n", + "scratch_style_solver.net.copy_from(scratch_style_weights)\n", + "\n", + "print 'Running solvers for %d iterations...' % niter\n", + "solvers = [('pretrained, end-to-end', style_solver),\n", + " ('scratch, end-to-end', scratch_style_solver)]\n", + "_, _, finetuned_weights = run_solvers(niter, solvers)\n", + "print 'Done.'\n", + "\n", + "style_weights_ft = finetuned_weights['pretrained, end-to-end']\n", + "scratch_style_weights_ft = finetuned_weights['scratch, end-to-end']\n", + "\n", + "# Delete solvers to save memory.\n", + "del style_solver, scratch_style_solver, solvers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now test the end-to-end finetuned models. Since all layers have been optimized for the style recognition task at hand, we expect both nets to get better results than the ones above, which were achieved by nets with only their classifier layers trained for the style task (on top of either ImageNet pretrained or randomly initialized weights)." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy, finetuned from ImageNet initialization: 53.6%\n", + "Accuracy, finetuned from random initialization: 39.2%\n" + ] + } + ], + "source": [ + "test_net, accuracy = eval_style_net(style_weights_ft)\n", + "print 'Accuracy, finetuned from ImageNet initialization: %3.1f%%' % (100*accuracy, )\n", + "scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights_ft)\n", + "print 'Accuracy, finetuned from random initialization: %3.1f%%' % (100*scratch_accuracy, )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll first look back at the image we started with and check our end-to-end trained model's predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "top 5 predicted style labels =\n", + "\t(1) 55.67% Melancholy\n", + "\t(2) 27.21% HDR\n", + "\t(3) 16.46% Pastel\n", + "\t(4) 0.63% Detailed\n", + "\t(5) 0.03% Noir\n" + ] + }, + { + "data": { + "image/png": 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yvUJS3DfmLUsURECnW6oGgUxGBSVh7180Q52jz0UjoWnYDZxRmdmA1bqxNeau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6EmVpSTd7osozItUDOtN5ksBa2bdacbpJGyRKLhrAxEttNG0O8AWK1TShCtYaElalB19S3dVz6K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\ncDPDu2eav33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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(deprocess_net_image(image))\n", + "disp_style_preds(test_net, image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Whew, that looks a lot better than before! But note that this image was from the training set, so the net got to see its label at training time.\n", + "\n", + "Finally, we'll pick an image from the test set (an image the model hasn't seen) and look at our end-to-end finetuned style model's predictions for it." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "actual label = Pastel\n" + ] + }, + { + "data": { + "image/png": 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WKs9hFZYitTN2cR87zMqTyhlYiOUvdRVeXdJREPOPBUQLuaLGrEgo3mWYO8JX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9p9Ybt4Of19W7OXfbsAgKNmoNTwI0vwBGcQKvGY5rN9HPF6L//O5aJjNCQtQWBMX/OJhfm4G1Ge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\nKJVy+/ZPkPUZu9dfpDdYJ8gc51qO7ryOOHqPk7d+SLY8oaEk6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EIa92mO1lg/t8jkyCSB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\nv+3j8qpvhwWF6TsmyabsE1+C94x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FzzNvLUUeNPY7UYZSIpOA1VfGkiSSHlslWq/o8cor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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "batch_index = 1\n", + "image = test_net.blobs['data'].data[batch_index]\n", + "plt.imshow(deprocess_net_image(image))\n", + "print 'actual label =', style_labels[int(test_net.blobs['label'].data[batch_index])]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "top 5 predicted style labels =\n", + "\t(1) 99.76% Pastel\n", + "\t(2) 0.13% HDR\n", + "\t(3) 0.11% Detailed\n", + "\t(4) 0.00% Melancholy\n", + "\t(5) 0.00% Noir\n" + ] + } + ], + "source": [ + "disp_style_preds(test_net, image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also look at the predictions of the network trained from scratch. We see that in this case, the scratch network also predicts the correct label for the image (*Pastel*), but is much less confident in its prediction than the pretrained net." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "top 5 predicted style labels =\n", + "\t(1) 49.81% Pastel\n", + "\t(2) 19.76% Detailed\n", + "\t(3) 17.06% Melancholy\n", + "\t(4) 11.66% HDR\n", + "\t(5) 1.72% Noir\n" + ] + } + ], + "source": [ + "disp_style_preds(scratch_test_net, image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of course, we can again look at the ImageNet model's predictions for the above image:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "top 5 predicted ImageNet labels =\n", + "\t(1) 34.90% n07579787 plate\n", + "\t(2) 21.63% n04263257 soup bowl\n", + "\t(3) 17.75% n07875152 potpie\n", + "\t(4) 5.72% n07711569 mashed potato\n", + "\t(5) 5.27% n07584110 consomme\n" + ] + } + ], + "source": [ + "disp_imagenet_preds(imagenet_net, image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n", + "\n", + "http://demo.vislab.berkeleyvision.org/" + ] + } + ], + "metadata": { + "description": "Fine-tune the ImageNet-trained CaffeNet on new data.", + "example_name": "Fine-tuning for Style Recognition", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + }, + "priority": 3 + }, + "nbformat": 4, + "nbformat_minor": 0 +} + + +
    +
    + + diff --git a/gathered/examples/brewing-logreg.ipynb b/gathered/examples/brewing-logreg.ipynb new file mode 100644 index 00000000000..a75bd257be7 --- /dev/null +++ b/gathered/examples/brewing-logreg.ipynb @@ -0,0 +1,1226 @@ + + + + + + + + + Caffe | Off-the-shelf SGD for classification + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Brewing Logistic Regression then Going Deeper\n", + "\n", + "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "import os\n", + "os.chdir('..')\n", + "\n", + "import sys\n", + "sys.path.insert(0, './python')\n", + "import caffe\n", + "\n", + "\n", + "import os\n", + "import h5py\n", + "import shutil\n", + "import tempfile\n", + "\n", + "import sklearn\n", + "import sklearn.datasets\n", + "import sklearn.linear_model\n", + "\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ZsmQhkpQhEGjir//6Ty6pBHu1mI5x9/v9yLKMy+W67OvV7Xbz29/uJBBQOHLk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\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luroFURRRlDwTE+1cdlkFb7xxknRKxGi0YTIbaGx0cPfdX/pY1lH5pP9RUhSFn/zk/5BO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BaDQabrr9dqp7euizWvEFArT399PgdGLKyOCDrVs5sH8/DodjzHaSJNHW1sbJkyfp7e4e9yJQ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S5dN9iyZT8PP/wEx47d8IYo2UgJ/+2/we///pXek++HEIk6cvw4vOMdV3pvXjtc0UrZK43z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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X, y = sklearn.datasets.make_classification(\n", + " n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n", + " n_clusters_per_class=2, hypercube=False, random_state=0\n", + ")\n", + "\n", + "# Split into train and test\n", + "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n", + "\n", + "# Visualize sample of the data\n", + "ind = np.random.permutation(X.shape[0])[:1000]\n", + "df = pd.DataFrame(X[ind])\n", + "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.781\n", + "Accuracy: 0.781\n", + "Accuracy: 0.781\n", + "Accuracy: 0.781\n", + "1 loop, best of 3: 372 ms per loop\n" + ] + } + ], + "source": [ + "%%timeit\n", + "# Train and test the scikit-learn SGD logistic regression.\n", + "clf = sklearn.linear_model.SGDClassifier(\n", + " loss='log', n_iter=1000, penalty='l2', alpha=5e-4, class_weight='auto')\n", + "\n", + "clf.fit(X, y)\n", + "yt_pred = clf.predict(Xt)\n", + "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Save the dataset to HDF5 for loading in Caffe." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Write out the data to HDF5 files in a temp directory.\n", + "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n", + "dirname = os.path.abspath('./examples/hdf5_classification/data')\n", + "if not os.path.exists(dirname):\n", + " os.makedirs(dirname)\n", + "\n", + "train_filename = os.path.join(dirname, 'train.h5')\n", + "test_filename = os.path.join(dirname, 'test.h5')\n", + "\n", + "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n", + "# To show this off, we'll list the same data file twice.\n", + "with h5py.File(train_filename, 'w') as f:\n", + " f['data'] = X\n", + " f['label'] = y.astype(np.float32)\n", + "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n", + " f.write(train_filename + '\\n')\n", + " f.write(train_filename + '\\n')\n", + " \n", + "# HDF5 is pretty efficient, but can be further compressed.\n", + "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n", + "with h5py.File(test_filename, 'w') as f:\n", + " f.create_dataset('data', data=Xt, **comp_kwargs)\n", + " f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n", + "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n", + " f.write(test_filename + '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define logistic regression in Caffe through Python net specification. This is a quick and natural way to define nets that sidesteps manually editing the protobuf model." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from caffe import layers as L\n", + "from caffe import params as P\n", + "\n", + "def logreg(hdf5, batch_size):\n", + " # logistic regression: data, matrix multiplication, and 2-class softmax loss\n", + " n = caffe.NetSpec()\n", + " n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n", + " n.ip1 = L.InnerProduct(n.data, num_output=2, weight_filler=dict(type='xavier'))\n", + " n.accuracy = L.Accuracy(n.ip1, n.label)\n", + " n.loss = L.SoftmaxWithLoss(n.ip1, n.label)\n", + " return n.to_proto()\n", + "\n", + "train_net_path = 'examples/hdf5_classification/logreg_auto_train.prototxt'\n", + "with open(train_net_path, 'w') as f:\n", + " f.write(str(logreg('examples/hdf5_classification/data/train.txt', 10)))\n", + "\n", + "test_net_path = 'examples/hdf5_classification/logreg_auto_test.prototxt'\n", + "with open(test_net_path, 'w') as f:\n", + " f.write(str(logreg('examples/hdf5_classification/data/test.txt', 10)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we'll define our \"solver\" which trains the network by specifying the locations of the train and test nets we defined above, as well as setting values for various parameters used for learning, display, and \"snapshotting\"." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from caffe.proto import caffe_pb2\n", + "\n", + "def solver(train_net_path, test_net_path):\n", + " s = caffe_pb2.SolverParameter()\n", + "\n", + " # Specify locations of the train and test networks.\n", + " s.train_net = train_net_path\n", + " s.test_net.append(test_net_path)\n", + "\n", + " s.test_interval = 1000 # Test after every 1000 training iterations.\n", + " s.test_iter.append(250) # Test 250 \"batches\" each time we test.\n", + "\n", + " s.max_iter = 10000 # # of times to update the net (training iterations)\n", + "\n", + " # Set the initial learning rate for stochastic gradient descent (SGD).\n", + " s.base_lr = 0.01 \n", + "\n", + " # Set `lr_policy` to define how the learning rate changes during training.\n", + " # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\n", + " # every `stepsize` iterations.\n", + " s.lr_policy = 'step'\n", + " s.gamma = 0.1\n", + " s.stepsize = 5000\n", + "\n", + " # Set other optimization parameters. Setting a non-zero `momentum` takes a\n", + " # weighted average of the current gradient and previous gradients to make\n", + " # learning more stable. L2 weight decay regularizes learning, to help prevent\n", + " # the model from overfitting.\n", + " s.momentum = 0.9\n", + " s.weight_decay = 5e-4\n", + "\n", + " # Display the current training loss and accuracy every 1000 iterations.\n", + " s.display = 1000\n", + "\n", + " # Snapshots are files used to store networks we've trained. Here, we'll\n", + " # snapshot every 10K iterations -- just once at the end of training.\n", + " # For larger networks that take longer to train, you may want to set\n", + " # snapshot < max_iter to save the network and training state to disk during\n", + " # optimization, preventing disaster in case of machine crashes, etc.\n", + " s.snapshot = 10000\n", + " s.snapshot_prefix = 'examples/hdf5_classification/data/train'\n", + "\n", + " # We'll train on the CPU for fair benchmarking against scikit-learn.\n", + " # Changing to GPU should result in much faster training!\n", + " s.solver_mode = caffe_pb2.SolverParameter.CPU\n", + " \n", + " return s\n", + "\n", + "solver_path = 'examples/hdf5_classification/logreg_solver.prototxt'\n", + "with open(solver_path, 'w') as f:\n", + " f.write(str(solver(train_net_path, test_net_path)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Time to learn and evaluate our Caffeinated logistic regression in Python." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.770\n", + "Accuracy: 0.770\n", + "Accuracy: 0.770\n", + "Accuracy: 0.770\n", + "1 loop, best of 3: 195 ms per loop\n" + ] + } + ], + "source": [ + "%%timeit\n", + "caffe.set_mode_cpu()\n", + "solver = caffe.get_solver(solver_path)\n", + "solver.solve()\n", + "\n", + "accuracy = 0\n", + "batch_size = solver.test_nets[0].blobs['data'].num\n", + "test_iters = int(len(Xt) / batch_size)\n", + "for i in range(test_iters):\n", + " solver.test_nets[0].forward()\n", + " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", + "accuracy /= test_iters\n", + "\n", + "print(\"Accuracy: {:.3f}\".format(accuracy))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do the same through the command line interface for detailed output on the model and solving." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I0224 00:32:03.232779 655 caffe.cpp:178] Use CPU.\n", + "I0224 00:32:03.391911 655 solver.cpp:48] Initializing solver from parameters: \n", + "train_net: \"examples/hdf5_classification/logreg_auto_train.prototxt\"\n", + "test_net: \"examples/hdf5_classification/logreg_auto_test.prototxt\"\n", + "test_iter: 250\n", + "test_interval: 1000\n", + "base_lr: 0.01\n", + "display: 1000\n", + "max_iter: 10000\n", + "lr_policy: \"step\"\n", + "gamma: 0.1\n", + "momentum: 0.9\n", + "weight_decay: 0.0005\n", + "stepsize: 5000\n", + "snapshot: 10000\n", + "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n", + "solver_mode: CPU\n", + "I0224 00:32:03.392065 655 solver.cpp:81] Creating training net from train_net file: examples/hdf5_classification/logreg_auto_train.prototxt\n", + "I0224 00:32:03.392215 655 net.cpp:49] Initializing net from parameters: \n", + "state {\n", + " phase: TRAIN\n", + "}\n", + "layer {\n", + " name: \"data\"\n", + " type: \"HDF5Data\"\n", + " top: \"data\"\n", + " top: \"label\"\n", + " hdf5_data_param {\n", + " source: \"examples/hdf5_classification/data/train.txt\"\n", + " batch_size: 10\n", + " }\n", + "}\n", + "layer {\n", + " name: \"ip1\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"data\"\n", + " top: \"ip1\"\n", + " inner_product_param {\n", + " num_output: 2\n", + " weight_filler {\n", + " type: \"xavier\"\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"accuracy\"\n", + " type: \"Accuracy\"\n", + " bottom: \"ip1\"\n", + " bottom: \"label\"\n", + " top: \"accuracy\"\n", + "}\n", + "layer {\n", + " name: \"loss\"\n", + " type: \"SoftmaxWithLoss\"\n", + " bottom: \"ip1\"\n", + " bottom: \"label\"\n", + " top: \"loss\"\n", + "}\n", + "I0224 00:32:03.392365 655 layer_factory.hpp:77] Creating layer data\n", + "I0224 00:32:03.392382 655 net.cpp:106] Creating Layer data\n", + "I0224 00:32:03.392395 655 net.cpp:411] data -> data\n", + "I0224 00:32:03.392423 655 net.cpp:411] data -> label\n", + "I0224 00:32:03.392442 655 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n", + "I0224 00:32:03.392473 655 hdf5_data_layer.cpp:93] Number of HDF5 files: 2\n", + "I0224 00:32:03.393473 655 hdf5.cpp:32] Datatype class: H5T_FLOAT\n", + "I0224 00:32:03.393862 655 net.cpp:150] Setting up data\n", + "I0224 00:32:03.393884 655 net.cpp:157] Top shape: 10 4 (40)\n", + "I0224 00:32:03.393894 655 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:03.393901 655 net.cpp:165] Memory required for data: 200\n", + "I0224 00:32:03.393911 655 layer_factory.hpp:77] Creating layer label_data_1_split\n", + "I0224 00:32:03.393924 655 net.cpp:106] Creating Layer label_data_1_split\n", + "I0224 00:32:03.393934 655 net.cpp:454] label_data_1_split <- label\n", + "I0224 00:32:03.393945 655 net.cpp:411] label_data_1_split -> label_data_1_split_0\n", + "I0224 00:32:03.393956 655 net.cpp:411] label_data_1_split -> label_data_1_split_1\n", + "I0224 00:32:03.393970 655 net.cpp:150] Setting up label_data_1_split\n", + "I0224 00:32:03.393978 655 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:03.393986 655 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:03.393995 655 net.cpp:165] Memory required for data: 280\n", + "I0224 00:32:03.394001 655 layer_factory.hpp:77] Creating layer ip1\n", + "I0224 00:32:03.394012 655 net.cpp:106] Creating Layer ip1\n", + "I0224 00:32:03.394021 655 net.cpp:454] ip1 <- data\n", + "I0224 00:32:03.394029 655 net.cpp:411] ip1 -> ip1\n", + "I0224 00:32:03.394311 655 net.cpp:150] Setting up ip1\n", + "I0224 00:32:03.394323 655 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:03.394331 655 net.cpp:165] Memory required for data: 360\n", + "I0224 00:32:03.394348 655 layer_factory.hpp:77] Creating layer ip1_ip1_0_split\n", + "I0224 00:32:03.394358 655 net.cpp:106] Creating Layer ip1_ip1_0_split\n", + "I0224 00:32:03.394366 655 net.cpp:454] ip1_ip1_0_split <- ip1\n", + "I0224 00:32:03.394374 655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_0\n", + "I0224 00:32:03.394386 655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_1\n", + "I0224 00:32:03.394395 655 net.cpp:150] Setting up ip1_ip1_0_split\n", + "I0224 00:32:03.394404 655 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:03.394424 655 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:03.394443 655 net.cpp:165] Memory required for data: 520\n", + "I0224 00:32:03.394450 655 layer_factory.hpp:77] Creating layer accuracy\n", + "I0224 00:32:03.394462 655 net.cpp:106] Creating Layer accuracy\n", + "I0224 00:32:03.394479 655 net.cpp:454] accuracy <- ip1_ip1_0_split_0\n", + "I0224 00:32:03.394489 655 net.cpp:454] accuracy <- label_data_1_split_0\n", + "I0224 00:32:03.394497 655 net.cpp:411] accuracy -> accuracy\n", + "I0224 00:32:03.394510 655 net.cpp:150] Setting up accuracy\n", + "I0224 00:32:03.394536 655 net.cpp:157] Top shape: (1)\n", + "I0224 00:32:03.394543 655 net.cpp:165] Memory required for data: 524\n", + "I0224 00:32:03.394551 655 layer_factory.hpp:77] Creating layer loss\n", + "I0224 00:32:03.394562 655 net.cpp:106] Creating Layer loss\n", + "I0224 00:32:03.394569 655 net.cpp:454] loss <- ip1_ip1_0_split_1\n", + "I0224 00:32:03.394577 655 net.cpp:454] loss <- label_data_1_split_1\n", + "I0224 00:32:03.394587 655 net.cpp:411] loss -> loss\n", + "I0224 00:32:03.394603 655 layer_factory.hpp:77] Creating layer loss\n", + "I0224 00:32:03.394624 655 net.cpp:150] Setting up loss\n", + "I0224 00:32:03.394634 655 net.cpp:157] Top shape: (1)\n", + "I0224 00:32:03.394641 655 net.cpp:160] with loss weight 1\n", + "I0224 00:32:03.394659 655 net.cpp:165] Memory required for data: 528\n", + "I0224 00:32:03.394665 655 net.cpp:226] loss needs backward computation.\n", + "I0224 00:32:03.394673 655 net.cpp:228] accuracy does not need backward computation.\n", + "I0224 00:32:03.394682 655 net.cpp:226] ip1_ip1_0_split needs backward computation.\n", + "I0224 00:32:03.394690 655 net.cpp:226] ip1 needs backward computation.\n", + "I0224 00:32:03.394697 655 net.cpp:228] label_data_1_split does not need backward computation.\n", + "I0224 00:32:03.394706 655 net.cpp:228] data does not need backward computation.\n", + "I0224 00:32:03.394712 655 net.cpp:270] This network produces output accuracy\n", + "I0224 00:32:03.394721 655 net.cpp:270] This network produces output loss\n", + "I0224 00:32:03.394731 655 net.cpp:283] Network initialization done.\n", + "I0224 00:32:03.394804 655 solver.cpp:181] Creating test net (#0) specified by test_net file: examples/hdf5_classification/logreg_auto_test.prototxt\n", + "I0224 00:32:03.394836 655 net.cpp:49] Initializing net from parameters: \n", + "state {\n", + " phase: TEST\n", + "}\n", + "layer {\n", + " name: \"data\"\n", + " type: \"HDF5Data\"\n", + " top: \"data\"\n", + " top: \"label\"\n", + " hdf5_data_param {\n", + " source: \"examples/hdf5_classification/data/test.txt\"\n", + " batch_size: 10\n", + " }\n", + "}\n", + "layer {\n", + " name: \"ip1\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"data\"\n", + " top: \"ip1\"\n", + " inner_product_param {\n", + " num_output: 2\n", + " weight_filler {\n", + " type: \"xavier\"\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"accuracy\"\n", + " type: \"Accuracy\"\n", + " bottom: \"ip1\"\n", + " bottom: \"label\"\n", + " top: \"accuracy\"\n", + "}\n", + "layer {\n", + " name: \"loss\"\n", + " type: \"SoftmaxWithLoss\"\n", + " bottom: \"ip1\"\n", + " bottom: \"label\"\n", + " top: \"loss\"\n", + "}\n", + "I0224 00:32:03.394953 655 layer_factory.hpp:77] Creating layer data\n", + "I0224 00:32:03.394964 655 net.cpp:106] Creating Layer data\n", + "I0224 00:32:03.394973 655 net.cpp:411] data -> data\n", + "I0224 00:32:03.394984 655 net.cpp:411] data -> label\n", + "I0224 00:32:03.394994 655 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n", + "I0224 00:32:03.395009 655 hdf5_data_layer.cpp:93] Number of HDF5 files: 1\n", + "I0224 00:32:03.395937 655 net.cpp:150] Setting up data\n", + "I0224 00:32:03.395953 655 net.cpp:157] Top shape: 10 4 (40)\n", + "I0224 00:32:03.395963 655 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:03.395970 655 net.cpp:165] Memory required for data: 200\n", + "I0224 00:32:03.395978 655 layer_factory.hpp:77] Creating layer label_data_1_split\n", + "I0224 00:32:03.395989 655 net.cpp:106] Creating Layer label_data_1_split\n", + "I0224 00:32:03.395997 655 net.cpp:454] label_data_1_split <- label\n", + "I0224 00:32:03.396005 655 net.cpp:411] label_data_1_split -> label_data_1_split_0\n", + "I0224 00:32:03.396016 655 net.cpp:411] label_data_1_split -> label_data_1_split_1\n", + "I0224 00:32:03.396028 655 net.cpp:150] Setting up label_data_1_split\n", + "I0224 00:32:03.396036 655 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:03.396044 655 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:03.396051 655 net.cpp:165] Memory required for data: 280\n", + "I0224 00:32:03.396059 655 layer_factory.hpp:77] Creating layer ip1\n", + "I0224 00:32:03.396069 655 net.cpp:106] Creating Layer ip1\n", + "I0224 00:32:03.396075 655 net.cpp:454] ip1 <- data\n", + "I0224 00:32:03.396085 655 net.cpp:411] ip1 -> ip1\n", + "I0224 00:32:03.396100 655 net.cpp:150] Setting up ip1\n", + "I0224 00:32:03.396109 655 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:03.396116 655 net.cpp:165] Memory required for data: 360\n", + "I0224 00:32:03.396138 655 layer_factory.hpp:77] Creating layer ip1_ip1_0_split\n", + "I0224 00:32:03.396148 655 net.cpp:106] Creating Layer ip1_ip1_0_split\n", + "I0224 00:32:03.396157 655 net.cpp:454] ip1_ip1_0_split <- ip1\n", + "I0224 00:32:03.396164 655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_0\n", + "I0224 00:32:03.396174 655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_1\n", + "I0224 00:32:03.396185 655 net.cpp:150] Setting up ip1_ip1_0_split\n", + "I0224 00:32:03.396194 655 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:03.396203 655 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:03.396209 655 net.cpp:165] Memory required for data: 520\n", + "I0224 00:32:03.396216 655 layer_factory.hpp:77] Creating layer accuracy\n", + "I0224 00:32:03.396225 655 net.cpp:106] Creating Layer accuracy\n", + "I0224 00:32:03.396234 655 net.cpp:454] accuracy <- ip1_ip1_0_split_0\n", + "I0224 00:32:03.396241 655 net.cpp:454] accuracy <- label_data_1_split_0\n", + "I0224 00:32:03.396250 655 net.cpp:411] accuracy -> accuracy\n", + "I0224 00:32:03.396260 655 net.cpp:150] Setting up accuracy\n", + "I0224 00:32:03.396270 655 net.cpp:157] Top shape: (1)\n", + "I0224 00:32:03.396276 655 net.cpp:165] Memory required for data: 524\n", + "I0224 00:32:03.396283 655 layer_factory.hpp:77] Creating layer loss\n", + "I0224 00:32:03.396291 655 net.cpp:106] Creating Layer loss\n", + "I0224 00:32:03.396299 655 net.cpp:454] loss <- ip1_ip1_0_split_1\n", + "I0224 00:32:03.396307 655 net.cpp:454] loss <- label_data_1_split_1\n", + "I0224 00:32:03.396317 655 net.cpp:411] loss -> loss\n", + "I0224 00:32:03.396327 655 layer_factory.hpp:77] Creating layer loss\n", + "I0224 00:32:03.396339 655 net.cpp:150] Setting up loss\n", + "I0224 00:32:03.396349 655 net.cpp:157] Top shape: (1)\n", + "I0224 00:32:03.396356 655 net.cpp:160] with loss weight 1\n", + "I0224 00:32:03.396365 655 net.cpp:165] Memory required for data: 528\n", + "I0224 00:32:03.396373 655 net.cpp:226] loss needs backward computation.\n", + "I0224 00:32:03.396381 655 net.cpp:228] accuracy does not need backward computation.\n", + "I0224 00:32:03.396389 655 net.cpp:226] ip1_ip1_0_split needs backward computation.\n", + "I0224 00:32:03.396396 655 net.cpp:226] ip1 needs backward computation.\n", + "I0224 00:32:03.396404 655 net.cpp:228] label_data_1_split does not need backward computation.\n", + "I0224 00:32:03.396412 655 net.cpp:228] data does not need backward computation.\n", + "I0224 00:32:03.396420 655 net.cpp:270] This network produces output accuracy\n", + "I0224 00:32:03.396427 655 net.cpp:270] This network produces output loss\n", + "I0224 00:32:03.396437 655 net.cpp:283] Network initialization done.\n", + "I0224 00:32:03.396455 655 solver.cpp:60] Solver scaffolding done.\n", + "I0224 00:32:03.396473 655 caffe.cpp:219] Starting Optimization\n", + "I0224 00:32:03.396482 655 solver.cpp:280] Solving \n", + "I0224 00:32:03.396489 655 solver.cpp:281] Learning Rate Policy: step\n", + "I0224 00:32:03.396499 655 solver.cpp:338] Iteration 0, Testing net (#0)\n", + "I0224 00:32:03.932615 655 solver.cpp:406] Test net output #0: accuracy = 0.4268\n", + "I0224 00:32:03.932656 655 solver.cpp:406] Test net output #1: loss = 1.33093 (* 1 = 1.33093 loss)\n", + "I0224 00:32:03.932723 655 solver.cpp:229] Iteration 0, loss = 1.06081\n", + "I0224 00:32:03.932737 655 solver.cpp:245] Train net output #0: accuracy = 0.4\n", + "I0224 00:32:03.932749 655 solver.cpp:245] Train net output #1: loss = 1.06081 (* 1 = 1.06081 loss)\n", + "I0224 00:32:03.932765 655 sgd_solver.cpp:106] Iteration 0, lr = 0.01\n", + "I0224 00:32:03.945551 655 solver.cpp:338] Iteration 1000, Testing net (#0)\n", + "I0224 00:32:03.948048 655 solver.cpp:406] Test net output #0: accuracy = 0.694\n", + "I0224 00:32:03.948065 655 solver.cpp:406] Test net output #1: loss = 0.60406 (* 1 = 0.60406 loss)\n", + "I0224 00:32:03.948091 655 solver.cpp:229] Iteration 1000, loss = 0.505853\n", + "I0224 00:32:03.948102 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:03.948113 655 solver.cpp:245] Train net output #1: loss = 0.505853 (* 1 = 0.505853 loss)\n", + "I0224 00:32:03.948122 655 sgd_solver.cpp:106] Iteration 1000, lr = 0.01\n", + "I0224 00:32:03.960741 655 solver.cpp:338] Iteration 2000, Testing net (#0)\n", + "I0224 00:32:03.963214 655 solver.cpp:406] Test net output #0: accuracy = 0.7372\n", + "I0224 00:32:03.963249 655 solver.cpp:406] Test net output #1: loss = 0.595267 (* 1 = 0.595267 loss)\n", + "I0224 00:32:03.963276 655 solver.cpp:229] Iteration 2000, loss = 0.549211\n", + "I0224 00:32:03.963289 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:03.963299 655 solver.cpp:245] Train net output #1: loss = 0.549211 (* 1 = 0.549211 loss)\n", + "I0224 00:32:03.963309 655 sgd_solver.cpp:106] Iteration 2000, lr = 0.01\n", + "I0224 00:32:03.975945 655 solver.cpp:338] Iteration 3000, Testing net (#0)\n", + "I0224 00:32:03.978435 655 solver.cpp:406] Test net output #0: accuracy = 0.7732\n", + "I0224 00:32:03.978451 655 solver.cpp:406] Test net output #1: loss = 0.594998 (* 1 = 0.594998 loss)\n", + "I0224 00:32:03.978884 655 solver.cpp:229] Iteration 3000, loss = 0.66133\n", + "I0224 00:32:03.978911 655 solver.cpp:245] Train net output #0: accuracy = 0.8\n", + "I0224 00:32:03.978932 655 solver.cpp:245] Train net output #1: loss = 0.66133 (* 1 = 0.66133 loss)\n", + "I0224 00:32:03.978950 655 sgd_solver.cpp:106] Iteration 3000, lr = 0.01\n", + "I0224 00:32:03.992017 655 solver.cpp:338] Iteration 4000, Testing net (#0)\n", + "I0224 00:32:03.994509 655 solver.cpp:406] Test net output #0: accuracy = 0.694\n", + "I0224 00:32:03.994525 655 solver.cpp:406] Test net output #1: loss = 0.60406 (* 1 = 0.60406 loss)\n", + "I0224 00:32:03.994551 655 solver.cpp:229] Iteration 4000, loss = 0.505853\n", + "I0224 00:32:03.994562 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:03.994573 655 solver.cpp:245] Train net output #1: loss = 0.505853 (* 1 = 0.505853 loss)\n", + "I0224 00:32:03.994583 655 sgd_solver.cpp:106] Iteration 4000, lr = 0.01\n", + "I0224 00:32:04.007200 655 solver.cpp:338] Iteration 5000, Testing net (#0)\n", + "I0224 00:32:04.009686 655 solver.cpp:406] Test net output #0: accuracy = 0.7372\n", + "I0224 00:32:04.009702 655 solver.cpp:406] Test net output #1: loss = 0.595267 (* 1 = 0.595267 loss)\n", + "I0224 00:32:04.009727 655 solver.cpp:229] Iteration 5000, loss = 0.549211\n", + "I0224 00:32:04.009738 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:04.009749 655 solver.cpp:245] Train net output #1: loss = 0.549211 (* 1 = 0.549211 loss)\n", + "I0224 00:32:04.009758 655 sgd_solver.cpp:106] Iteration 5000, lr = 0.001\n", + "I0224 00:32:04.022734 655 solver.cpp:338] Iteration 6000, Testing net (#0)\n", + "I0224 00:32:04.025177 655 solver.cpp:406] Test net output #0: accuracy = 0.7824\n", + "I0224 00:32:04.025193 655 solver.cpp:406] Test net output #1: loss = 0.593367 (* 1 = 0.593367 loss)\n", + "I0224 00:32:04.025545 655 solver.cpp:229] Iteration 6000, loss = 0.654873\n", + "I0224 00:32:04.025562 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:04.025573 655 solver.cpp:245] Train net output #1: loss = 0.654873 (* 1 = 0.654873 loss)\n", + "I0224 00:32:04.025583 655 sgd_solver.cpp:106] Iteration 6000, lr = 0.001\n", + "I0224 00:32:04.038586 655 solver.cpp:338] Iteration 7000, Testing net (#0)\n", + "I0224 00:32:04.041016 655 solver.cpp:406] Test net output #0: accuracy = 0.7704\n", + "I0224 00:32:04.041033 655 solver.cpp:406] Test net output #1: loss = 0.593842 (* 1 = 0.593842 loss)\n", + "I0224 00:32:04.041059 655 solver.cpp:229] Iteration 7000, loss = 0.46611\n", + "I0224 00:32:04.041071 655 solver.cpp:245] Train net output #0: accuracy = 0.6\n", + "I0224 00:32:04.041082 655 solver.cpp:245] Train net output #1: loss = 0.46611 (* 1 = 0.46611 loss)\n", + "I0224 00:32:04.041091 655 sgd_solver.cpp:106] Iteration 7000, lr = 0.001\n", + "I0224 00:32:04.053722 655 solver.cpp:338] Iteration 8000, Testing net (#0)\n", + "I0224 00:32:04.056171 655 solver.cpp:406] Test net output #0: accuracy = 0.7788\n", + "I0224 00:32:04.056187 655 solver.cpp:406] Test net output #1: loss = 0.592847 (* 1 = 0.592847 loss)\n", + "I0224 00:32:04.056213 655 solver.cpp:229] Iteration 8000, loss = 0.615126\n", + "I0224 00:32:04.056224 655 solver.cpp:245] Train net output #0: accuracy = 0.8\n", + "I0224 00:32:04.056236 655 solver.cpp:245] Train net output #1: loss = 0.615126 (* 1 = 0.615126 loss)\n", + "I0224 00:32:04.056244 655 sgd_solver.cpp:106] Iteration 8000, lr = 0.001\n", + "I0224 00:32:04.068853 655 solver.cpp:338] Iteration 9000, Testing net (#0)\n", + "I0224 00:32:04.071291 655 solver.cpp:406] Test net output #0: accuracy = 0.7808\n", + "I0224 00:32:04.071307 655 solver.cpp:406] Test net output #1: loss = 0.593293 (* 1 = 0.593293 loss)\n", + "I0224 00:32:04.071650 655 solver.cpp:229] Iteration 9000, loss = 0.654997\n", + "I0224 00:32:04.071666 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:04.071677 655 solver.cpp:245] Train net output #1: loss = 0.654998 (* 1 = 0.654998 loss)\n", + "I0224 00:32:04.071687 655 sgd_solver.cpp:106] Iteration 9000, lr = 0.001\n", + "I0224 00:32:04.084717 655 solver.cpp:456] Snapshotting to binary proto file examples/hdf5_classification/data/train_iter_10000.caffemodel\n", + "I0224 00:32:04.084885 655 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/hdf5_classification/data/train_iter_10000.solverstate\n", + "I0224 00:32:04.084960 655 solver.cpp:318] Iteration 10000, loss = 0.466505\n", + "I0224 00:32:04.084977 655 solver.cpp:338] Iteration 10000, Testing net (#0)\n", + "I0224 00:32:04.087514 655 solver.cpp:406] Test net output #0: accuracy = 0.77\n", + "I0224 00:32:04.087532 655 solver.cpp:406] Test net output #1: loss = 0.593815 (* 1 = 0.593815 loss)\n", + "I0224 00:32:04.087541 655 solver.cpp:323] Optimization Done.\n", + "I0224 00:32:04.087548 655 caffe.cpp:222] Optimization Done.\n" + ] + } + ], + "source": [ + "!./build/tools/caffe train -solver examples/hdf5_classification/logreg_solver.prototxt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you look at output or the `logreg_auto_train.prototxt`, you'll see that the model is simple logistic regression.\n", + "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n", + "That network is given in `nonlinear_auto_train.prototxt`, and that's the only change made in `nonlinear_logreg_solver.prototxt` which we will now use.\n", + "\n", + "The final accuracy of the new network should be higher than logistic regression!" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from caffe import layers as L\n", + "from caffe import params as P\n", + "\n", + "def nonlinear_net(hdf5, batch_size):\n", + " # one small nonlinearity, one leap for model kind\n", + " n = caffe.NetSpec()\n", + " n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n", + " # define a hidden layer of dimension 40\n", + " n.ip1 = L.InnerProduct(n.data, num_output=40, weight_filler=dict(type='xavier'))\n", + " # transform the output through the ReLU (rectified linear) non-linearity\n", + " n.relu1 = L.ReLU(n.ip1, in_place=True)\n", + " # score the (now non-linear) features\n", + " n.ip2 = L.InnerProduct(n.ip1, num_output=2, weight_filler=dict(type='xavier'))\n", + " # same accuracy and loss as before\n", + " n.accuracy = L.Accuracy(n.ip2, n.label)\n", + " n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n", + " return n.to_proto()\n", + "\n", + "train_net_path = 'examples/hdf5_classification/nonlinear_auto_train.prototxt'\n", + "with open(train_net_path, 'w') as f:\n", + " f.write(str(nonlinear_net('examples/hdf5_classification/data/train.txt', 10)))\n", + "\n", + "test_net_path = 'examples/hdf5_classification/nonlinear_auto_test.prototxt'\n", + "with open(test_net_path, 'w') as f:\n", + " f.write(str(nonlinear_net('examples/hdf5_classification/data/test.txt', 10)))\n", + "\n", + "solver_path = 'examples/hdf5_classification/nonlinear_logreg_solver.prototxt'\n", + "with open(solver_path, 'w') as f:\n", + " f.write(str(solver(train_net_path, test_net_path)))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.838\n", + "Accuracy: 0.837\n", + "Accuracy: 0.838\n", + "Accuracy: 0.834\n", + "1 loop, best of 3: 277 ms per loop\n" + ] + } + ], + "source": [ + "%%timeit\n", + "caffe.set_mode_cpu()\n", + "solver = caffe.get_solver(solver_path)\n", + "solver.solve()\n", + "\n", + "accuracy = 0\n", + "batch_size = solver.test_nets[0].blobs['data'].num\n", + "test_iters = int(len(Xt) / batch_size)\n", + "for i in range(test_iters):\n", + " solver.test_nets[0].forward()\n", + " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", + "accuracy /= test_iters\n", + "\n", + "print(\"Accuracy: {:.3f}\".format(accuracy))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do the same through the command line interface for detailed output on the model and solving." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I0224 00:32:05.654265 658 caffe.cpp:178] Use CPU.\n", + "I0224 00:32:05.810444 658 solver.cpp:48] Initializing solver from parameters: \n", + "train_net: \"examples/hdf5_classification/nonlinear_auto_train.prototxt\"\n", + "test_net: \"examples/hdf5_classification/nonlinear_auto_test.prototxt\"\n", + "test_iter: 250\n", + "test_interval: 1000\n", + "base_lr: 0.01\n", + "display: 1000\n", + "max_iter: 10000\n", + "lr_policy: \"step\"\n", + "gamma: 0.1\n", + "momentum: 0.9\n", + "weight_decay: 0.0005\n", + "stepsize: 5000\n", + "snapshot: 10000\n", + "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n", + "solver_mode: CPU\n", + "I0224 00:32:05.810634 658 solver.cpp:81] Creating training net from train_net file: examples/hdf5_classification/nonlinear_auto_train.prototxt\n", + "I0224 00:32:05.810835 658 net.cpp:49] Initializing net from parameters: \n", + "state {\n", + " phase: TRAIN\n", + "}\n", + "layer {\n", + " name: \"data\"\n", + " type: \"HDF5Data\"\n", + " top: \"data\"\n", + " top: \"label\"\n", + " hdf5_data_param {\n", + " source: \"examples/hdf5_classification/data/train.txt\"\n", + " batch_size: 10\n", + " }\n", + "}\n", + "layer {\n", + " name: \"ip1\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"data\"\n", + " top: \"ip1\"\n", + " inner_product_param {\n", + " num_output: 40\n", + " weight_filler {\n", + " type: \"xavier\"\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu1\"\n", + " type: \"ReLU\"\n", + " bottom: \"ip1\"\n", + " top: \"ip1\"\n", + "}\n", + "layer {\n", + " name: \"ip2\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"ip1\"\n", + " top: \"ip2\"\n", + " inner_product_param {\n", + " num_output: 2\n", + " weight_filler {\n", + " type: \"xavier\"\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"accuracy\"\n", + " type: \"Accuracy\"\n", + " bottom: \"ip2\"\n", + " bottom: \"label\"\n", + " top: \"accuracy\"\n", + "}\n", + "layer {\n", + " name: \"loss\"\n", + " type: \"SoftmaxWithLoss\"\n", + " bottom: \"ip2\"\n", + " bottom: \"label\"\n", + " top: \"loss\"\n", + "}\n", + "I0224 00:32:05.811061 658 layer_factory.hpp:77] Creating layer data\n", + "I0224 00:32:05.811079 658 net.cpp:106] Creating Layer data\n", + "I0224 00:32:05.811092 658 net.cpp:411] data -> data\n", + "I0224 00:32:05.811121 658 net.cpp:411] data -> label\n", + "I0224 00:32:05.811143 658 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n", + "I0224 00:32:05.811189 658 hdf5_data_layer.cpp:93] Number of HDF5 files: 2\n", + "I0224 00:32:05.812254 658 hdf5.cpp:32] Datatype class: H5T_FLOAT\n", + "I0224 00:32:05.812677 658 net.cpp:150] Setting up data\n", + "I0224 00:32:05.812705 658 net.cpp:157] Top shape: 10 4 (40)\n", + "I0224 00:32:05.812721 658 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:05.812729 658 net.cpp:165] Memory required for data: 200\n", + "I0224 00:32:05.812739 658 layer_factory.hpp:77] Creating layer label_data_1_split\n", + "I0224 00:32:05.812752 658 net.cpp:106] Creating Layer label_data_1_split\n", + "I0224 00:32:05.812762 658 net.cpp:454] label_data_1_split <- label\n", + "I0224 00:32:05.812774 658 net.cpp:411] label_data_1_split -> label_data_1_split_0\n", + "I0224 00:32:05.812785 658 net.cpp:411] label_data_1_split -> label_data_1_split_1\n", + "I0224 00:32:05.812798 658 net.cpp:150] Setting up label_data_1_split\n", + "I0224 00:32:05.812808 658 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:05.812816 658 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:05.812824 658 net.cpp:165] Memory required for data: 280\n", + "I0224 00:32:05.812831 658 layer_factory.hpp:77] Creating layer ip1\n", + "I0224 00:32:05.812841 658 net.cpp:106] Creating Layer ip1\n", + "I0224 00:32:05.812849 658 net.cpp:454] ip1 <- data\n", + "I0224 00:32:05.812860 658 net.cpp:411] ip1 -> ip1\n", + "I0224 00:32:05.813179 658 net.cpp:150] Setting up ip1\n", + "I0224 00:32:05.813196 658 net.cpp:157] Top shape: 10 40 (400)\n", + "I0224 00:32:05.813210 658 net.cpp:165] Memory required for data: 1880\n", + "I0224 00:32:05.813230 658 layer_factory.hpp:77] Creating layer relu1\n", + "I0224 00:32:05.813241 658 net.cpp:106] Creating Layer relu1\n", + "I0224 00:32:05.813251 658 net.cpp:454] relu1 <- ip1\n", + "I0224 00:32:05.813258 658 net.cpp:397] relu1 -> ip1 (in-place)\n", + "I0224 00:32:05.813271 658 net.cpp:150] Setting up relu1\n", + "I0224 00:32:05.813279 658 net.cpp:157] Top shape: 10 40 (400)\n", + "I0224 00:32:05.813287 658 net.cpp:165] Memory required for data: 3480\n", + "I0224 00:32:05.813294 658 layer_factory.hpp:77] Creating layer ip2\n", + "I0224 00:32:05.813304 658 net.cpp:106] Creating Layer ip2\n", + "I0224 00:32:05.813313 658 net.cpp:454] ip2 <- ip1\n", + "I0224 00:32:05.813321 658 net.cpp:411] ip2 -> ip2\n", + "I0224 00:32:05.813336 658 net.cpp:150] Setting up ip2\n", + "I0224 00:32:05.813345 658 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:05.813379 658 net.cpp:165] Memory required for data: 3560\n", + "I0224 00:32:05.813401 658 layer_factory.hpp:77] Creating layer ip2_ip2_0_split\n", + "I0224 00:32:05.813417 658 net.cpp:106] Creating Layer ip2_ip2_0_split\n", + "I0224 00:32:05.813426 658 net.cpp:454] ip2_ip2_0_split <- ip2\n", + "I0224 00:32:05.813434 658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_0\n", + "I0224 00:32:05.813446 658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_1\n", + "I0224 00:32:05.813457 658 net.cpp:150] Setting up ip2_ip2_0_split\n", + "I0224 00:32:05.813465 658 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:05.813473 658 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:05.813480 658 net.cpp:165] Memory required for data: 3720\n", + "I0224 00:32:05.813488 658 layer_factory.hpp:77] Creating layer accuracy\n", + "I0224 00:32:05.813499 658 net.cpp:106] Creating Layer accuracy\n", + "I0224 00:32:05.813508 658 net.cpp:454] accuracy <- ip2_ip2_0_split_0\n", + "I0224 00:32:05.813515 658 net.cpp:454] accuracy <- label_data_1_split_0\n", + "I0224 00:32:05.813524 658 net.cpp:411] accuracy -> accuracy\n", + "I0224 00:32:05.813539 658 net.cpp:150] Setting up accuracy\n", + "I0224 00:32:05.813547 658 net.cpp:157] Top shape: (1)\n", + "I0224 00:32:05.813555 658 net.cpp:165] Memory required for data: 3724\n", + "I0224 00:32:05.813565 658 layer_factory.hpp:77] Creating layer loss\n", + "I0224 00:32:05.813585 658 net.cpp:106] Creating Layer loss\n", + "I0224 00:32:05.813599 658 net.cpp:454] loss <- ip2_ip2_0_split_1\n", + "I0224 00:32:05.813616 658 net.cpp:454] loss <- label_data_1_split_1\n", + "I0224 00:32:05.813627 658 net.cpp:411] loss -> loss\n", + "I0224 00:32:05.813642 658 layer_factory.hpp:77] Creating layer loss\n", + "I0224 00:32:05.813663 658 net.cpp:150] Setting up loss\n", + "I0224 00:32:05.813671 658 net.cpp:157] Top shape: (1)\n", + "I0224 00:32:05.813679 658 net.cpp:160] with loss weight 1\n", + "I0224 00:32:05.813695 658 net.cpp:165] Memory required for data: 3728\n", + "I0224 00:32:05.813704 658 net.cpp:226] loss needs backward computation.\n", + "I0224 00:32:05.813712 658 net.cpp:228] accuracy does not need backward computation.\n", + "I0224 00:32:05.813720 658 net.cpp:226] ip2_ip2_0_split needs backward computation.\n", + "I0224 00:32:05.813729 658 net.cpp:226] ip2 needs backward computation.\n", + "I0224 00:32:05.813735 658 net.cpp:226] relu1 needs backward computation.\n", + "I0224 00:32:05.813743 658 net.cpp:226] ip1 needs backward computation.\n", + "I0224 00:32:05.813751 658 net.cpp:228] label_data_1_split does not need backward computation.\n", + "I0224 00:32:05.813760 658 net.cpp:228] data does not need backward computation.\n", + "I0224 00:32:05.813772 658 net.cpp:270] This network produces output accuracy\n", + "I0224 00:32:05.813787 658 net.cpp:270] This network produces output loss\n", + "I0224 00:32:05.813809 658 net.cpp:283] Network initialization done.\n", + "I0224 00:32:05.813905 658 solver.cpp:181] Creating test net (#0) specified by test_net file: examples/hdf5_classification/nonlinear_auto_test.prototxt\n", + "I0224 00:32:05.813944 658 net.cpp:49] Initializing net from parameters: \n", + "state {\n", + " phase: TEST\n", + "}\n", + "layer {\n", + " name: \"data\"\n", + " type: \"HDF5Data\"\n", + " top: \"data\"\n", + " top: \"label\"\n", + " hdf5_data_param {\n", + " source: \"examples/hdf5_classification/data/test.txt\"\n", + " batch_size: 10\n", + " }\n", + "}\n", + "layer {\n", + " name: \"ip1\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"data\"\n", + " top: \"ip1\"\n", + " inner_product_param {\n", + " num_output: 40\n", + " weight_filler {\n", + " type: \"xavier\"\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu1\"\n", + " type: \"ReLU\"\n", + " bottom: \"ip1\"\n", + " top: \"ip1\"\n", + "}\n", + "layer {\n", + " name: \"ip2\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"ip1\"\n", + " top: \"ip2\"\n", + " inner_product_param {\n", + " num_output: 2\n", + " weight_filler {\n", + " type: \"xavier\"\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"accuracy\"\n", + " type: \"Accuracy\"\n", + " bottom: \"ip2\"\n", + " bottom: \"label\"\n", + " top: \"accuracy\"\n", + "}\n", + "layer {\n", + " name: \"loss\"\n", + " type: \"SoftmaxWithLoss\"\n", + " bottom: \"ip2\"\n", + " bottom: \"label\"\n", + " top: \"loss\"\n", + "}\n", + "I0224 00:32:05.814131 658 layer_factory.hpp:77] Creating layer data\n", + "I0224 00:32:05.814142 658 net.cpp:106] Creating Layer data\n", + "I0224 00:32:05.814152 658 net.cpp:411] data -> data\n", + "I0224 00:32:05.814162 658 net.cpp:411] data -> label\n", + "I0224 00:32:05.814180 658 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n", + "I0224 00:32:05.814220 658 hdf5_data_layer.cpp:93] Number of HDF5 files: 1\n", + "I0224 00:32:05.815207 658 net.cpp:150] Setting up data\n", + "I0224 00:32:05.815227 658 net.cpp:157] Top shape: 10 4 (40)\n", + "I0224 00:32:05.815243 658 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:05.815253 658 net.cpp:165] Memory required for data: 200\n", + "I0224 00:32:05.815260 658 layer_factory.hpp:77] Creating layer label_data_1_split\n", + "I0224 00:32:05.815270 658 net.cpp:106] Creating Layer label_data_1_split\n", + "I0224 00:32:05.815279 658 net.cpp:454] label_data_1_split <- label\n", + "I0224 00:32:05.815287 658 net.cpp:411] label_data_1_split -> label_data_1_split_0\n", + "I0224 00:32:05.815299 658 net.cpp:411] label_data_1_split -> label_data_1_split_1\n", + "I0224 00:32:05.815310 658 net.cpp:150] Setting up label_data_1_split\n", + "I0224 00:32:05.815318 658 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:05.815326 658 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:05.815335 658 net.cpp:165] Memory required for data: 280\n", + "I0224 00:32:05.815341 658 layer_factory.hpp:77] Creating layer ip1\n", + "I0224 00:32:05.815351 658 net.cpp:106] Creating Layer ip1\n", + "I0224 00:32:05.815358 658 net.cpp:454] ip1 <- data\n", + "I0224 00:32:05.815367 658 net.cpp:411] ip1 -> ip1\n", + "I0224 00:32:05.815383 658 net.cpp:150] Setting up ip1\n", + "I0224 00:32:05.815398 658 net.cpp:157] Top shape: 10 40 (400)\n", + "I0224 00:32:05.815413 658 net.cpp:165] Memory required for data: 1880\n", + "I0224 00:32:05.815435 658 layer_factory.hpp:77] Creating layer relu1\n", + "I0224 00:32:05.815450 658 net.cpp:106] Creating Layer relu1\n", + "I0224 00:32:05.815459 658 net.cpp:454] relu1 <- ip1\n", + "I0224 00:32:05.815469 658 net.cpp:397] relu1 -> ip1 (in-place)\n", + "I0224 00:32:05.815479 658 net.cpp:150] Setting up relu1\n", + "I0224 00:32:05.815486 658 net.cpp:157] Top shape: 10 40 (400)\n", + "I0224 00:32:05.815495 658 net.cpp:165] Memory required for data: 3480\n", + "I0224 00:32:05.815501 658 layer_factory.hpp:77] Creating layer ip2\n", + "I0224 00:32:05.815510 658 net.cpp:106] Creating Layer ip2\n", + "I0224 00:32:05.815518 658 net.cpp:454] ip2 <- ip1\n", + "I0224 00:32:05.815527 658 net.cpp:411] ip2 -> ip2\n", + "I0224 00:32:05.815542 658 net.cpp:150] Setting up ip2\n", + "I0224 00:32:05.815551 658 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:05.815559 658 net.cpp:165] Memory required for data: 3560\n", + "I0224 00:32:05.815570 658 layer_factory.hpp:77] Creating layer ip2_ip2_0_split\n", + "I0224 00:32:05.815579 658 net.cpp:106] Creating Layer ip2_ip2_0_split\n", + "I0224 00:32:05.815587 658 net.cpp:454] ip2_ip2_0_split <- ip2\n", + "I0224 00:32:05.815600 658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_0\n", + "I0224 00:32:05.815619 658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_1\n", + "I0224 00:32:05.815640 658 net.cpp:150] Setting up ip2_ip2_0_split\n", + "I0224 00:32:05.815654 658 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:05.815662 658 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:05.815670 658 net.cpp:165] Memory required for data: 3720\n", + "I0224 00:32:05.815677 658 layer_factory.hpp:77] Creating layer accuracy\n", + "I0224 00:32:05.815685 658 net.cpp:106] Creating Layer accuracy\n", + "I0224 00:32:05.815693 658 net.cpp:454] accuracy <- ip2_ip2_0_split_0\n", + "I0224 00:32:05.815702 658 net.cpp:454] accuracy <- label_data_1_split_0\n", + "I0224 00:32:05.815711 658 net.cpp:411] accuracy -> accuracy\n", + "I0224 00:32:05.815722 658 net.cpp:150] Setting up accuracy\n", + "I0224 00:32:05.815732 658 net.cpp:157] Top shape: (1)\n", + "I0224 00:32:05.815738 658 net.cpp:165] Memory required for data: 3724\n", + "I0224 00:32:05.815747 658 layer_factory.hpp:77] Creating layer loss\n", + "I0224 00:32:05.815754 658 net.cpp:106] Creating Layer loss\n", + "I0224 00:32:05.815762 658 net.cpp:454] loss <- ip2_ip2_0_split_1\n", + "I0224 00:32:05.815770 658 net.cpp:454] loss <- label_data_1_split_1\n", + "I0224 00:32:05.815779 658 net.cpp:411] loss -> loss\n", + "I0224 00:32:05.815790 658 layer_factory.hpp:77] Creating layer loss\n", + "I0224 00:32:05.815811 658 net.cpp:150] Setting up loss\n", + "I0224 00:32:05.815829 658 net.cpp:157] Top shape: (1)\n", + "I0224 00:32:05.815843 658 net.cpp:160] with loss weight 1\n", + "I0224 00:32:05.815867 658 net.cpp:165] Memory required for data: 3728\n", + "I0224 00:32:05.815876 658 net.cpp:226] loss needs backward computation.\n", + "I0224 00:32:05.815884 658 net.cpp:228] accuracy does not need backward computation.\n", + "I0224 00:32:05.815892 658 net.cpp:226] ip2_ip2_0_split needs backward computation.\n", + "I0224 00:32:05.815901 658 net.cpp:226] ip2 needs backward computation.\n", + "I0224 00:32:05.815908 658 net.cpp:226] relu1 needs backward computation.\n", + "I0224 00:32:05.815915 658 net.cpp:226] ip1 needs backward computation.\n", + "I0224 00:32:05.815923 658 net.cpp:228] label_data_1_split does not need backward computation.\n", + "I0224 00:32:05.815932 658 net.cpp:228] data does not need backward computation.\n", + "I0224 00:32:05.815938 658 net.cpp:270] This network produces output accuracy\n", + "I0224 00:32:05.815946 658 net.cpp:270] This network produces output loss\n", + "I0224 00:32:05.815958 658 net.cpp:283] Network initialization done.\n", + "I0224 00:32:05.815978 658 solver.cpp:60] Solver scaffolding done.\n", + "I0224 00:32:05.816000 658 caffe.cpp:219] Starting Optimization\n", + "I0224 00:32:05.816016 658 solver.cpp:280] Solving \n", + "I0224 00:32:05.816030 658 solver.cpp:281] Learning Rate Policy: step\n", + "I0224 00:32:05.816048 658 solver.cpp:338] Iteration 0, Testing net (#0)\n", + "I0224 00:32:05.831967 658 solver.cpp:406] Test net output #0: accuracy = 0.4464\n", + "I0224 00:32:05.832033 658 solver.cpp:406] Test net output #1: loss = 0.909841 (* 1 = 0.909841 loss)\n", + "I0224 00:32:05.832186 658 solver.cpp:229] Iteration 0, loss = 0.798509\n", + "I0224 00:32:05.832218 658 solver.cpp:245] Train net output #0: accuracy = 0.6\n", + "I0224 00:32:05.832247 658 solver.cpp:245] Train net output #1: loss = 0.798509 (* 1 = 0.798509 loss)\n", + "I0224 00:32:05.832281 658 sgd_solver.cpp:106] Iteration 0, lr = 0.01\n", + "I0224 00:32:05.859506 658 solver.cpp:338] Iteration 1000, Testing net (#0)\n", + "I0224 00:32:05.862799 658 solver.cpp:406] Test net output #0: accuracy = 0.8156\n", + "I0224 00:32:05.862818 658 solver.cpp:406] Test net output #1: loss = 0.44259 (* 1 = 0.44259 loss)\n", + "I0224 00:32:05.862853 658 solver.cpp:229] Iteration 1000, loss = 0.537015\n", + "I0224 00:32:05.862864 658 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:05.862875 658 solver.cpp:245] Train net output #1: loss = 0.537015 (* 1 = 0.537015 loss)\n", + "I0224 00:32:05.862885 658 sgd_solver.cpp:106] Iteration 1000, lr = 0.01\n", + "I0224 00:32:05.883155 658 solver.cpp:338] Iteration 2000, Testing net (#0)\n", + "I0224 00:32:05.886435 658 solver.cpp:406] Test net output #0: accuracy = 0.8116\n", + "I0224 00:32:05.886451 658 solver.cpp:406] Test net output #1: loss = 0.434079 (* 1 = 0.434079 loss)\n", + "I0224 00:32:05.886484 658 solver.cpp:229] Iteration 2000, loss = 0.43109\n", + "I0224 00:32:05.886497 658 solver.cpp:245] Train net output #0: accuracy = 0.9\n", + "I0224 00:32:05.886508 658 solver.cpp:245] Train net output #1: loss = 0.43109 (* 1 = 0.43109 loss)\n", + "I0224 00:32:05.886518 658 sgd_solver.cpp:106] Iteration 2000, lr = 0.01\n", + "I0224 00:32:05.907243 658 solver.cpp:338] Iteration 3000, Testing net (#0)\n", + "I0224 00:32:05.910521 658 solver.cpp:406] Test net output #0: accuracy = 0.8168\n", + "I0224 00:32:05.910537 658 solver.cpp:406] Test net output #1: loss = 0.425661 (* 1 = 0.425661 loss)\n", + "I0224 00:32:05.910905 658 solver.cpp:229] Iteration 3000, loss = 0.430245\n", + "I0224 00:32:05.910922 658 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:05.910933 658 solver.cpp:245] Train net output #1: loss = 0.430245 (* 1 = 0.430245 loss)\n", + "I0224 00:32:05.910943 658 sgd_solver.cpp:106] Iteration 3000, lr = 0.01\n", + "I0224 00:32:05.931205 658 solver.cpp:338] Iteration 4000, Testing net (#0)\n", + "I0224 00:32:05.934479 658 solver.cpp:406] Test net output #0: accuracy = 0.8324\n", + "I0224 00:32:05.934496 658 solver.cpp:406] Test net output #1: loss = 0.404891 (* 1 = 0.404891 loss)\n", + "I0224 00:32:05.934530 658 solver.cpp:229] Iteration 4000, loss = 0.628955\n", + "I0224 00:32:05.934542 658 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:05.934553 658 solver.cpp:245] Train net output #1: loss = 0.628955 (* 1 = 0.628955 loss)\n", + "I0224 00:32:05.934583 658 sgd_solver.cpp:106] Iteration 4000, lr = 0.01\n", + "I0224 00:32:05.955108 658 solver.cpp:338] Iteration 5000, Testing net (#0)\n", + "I0224 00:32:05.958377 658 solver.cpp:406] Test net output #0: accuracy = 0.8364\n", + "I0224 00:32:05.958395 658 solver.cpp:406] Test net output #1: loss = 0.404235 (* 1 = 0.404235 loss)\n", + "I0224 00:32:05.958432 658 solver.cpp:229] Iteration 5000, loss = 0.394939\n", + "I0224 00:32:05.958444 658 solver.cpp:245] Train net output #0: accuracy = 0.9\n", + "I0224 00:32:05.958456 658 solver.cpp:245] Train net output #1: loss = 0.39494 (* 1 = 0.39494 loss)\n", + "I0224 00:32:05.958466 658 sgd_solver.cpp:106] Iteration 5000, lr = 0.001\n", + "I0224 00:32:05.978703 658 solver.cpp:338] Iteration 6000, Testing net (#0)\n", + "I0224 00:32:05.981973 658 solver.cpp:406] Test net output #0: accuracy = 0.838\n", + "I0224 00:32:05.981991 658 solver.cpp:406] Test net output #1: loss = 0.385743 (* 1 = 0.385743 loss)\n", + "I0224 00:32:05.982347 658 solver.cpp:229] Iteration 6000, loss = 0.411537\n", + "I0224 00:32:05.982362 658 solver.cpp:245] Train net output #0: accuracy = 0.8\n", + "I0224 00:32:05.982373 658 solver.cpp:245] Train net output #1: loss = 0.411537 (* 1 = 0.411537 loss)\n", + "I0224 00:32:05.982383 658 sgd_solver.cpp:106] Iteration 6000, lr = 0.001\n", + "I0224 00:32:06.003015 658 solver.cpp:338] Iteration 7000, Testing net (#0)\n", + "I0224 00:32:06.006283 658 solver.cpp:406] Test net output #0: accuracy = 0.8388\n", + "I0224 00:32:06.006301 658 solver.cpp:406] Test net output #1: loss = 0.384648 (* 1 = 0.384648 loss)\n", + "I0224 00:32:06.006335 658 solver.cpp:229] Iteration 7000, loss = 0.521072\n", + "I0224 00:32:06.006347 658 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:06.006358 658 solver.cpp:245] Train net output #1: loss = 0.521073 (* 1 = 0.521073 loss)\n", + "I0224 00:32:06.006368 658 sgd_solver.cpp:106] Iteration 7000, lr = 0.001\n", + "I0224 00:32:06.026715 658 solver.cpp:338] Iteration 8000, Testing net (#0)\n", + "I0224 00:32:06.029965 658 solver.cpp:406] Test net output #0: accuracy = 0.8404\n", + "I0224 00:32:06.029983 658 solver.cpp:406] Test net output #1: loss = 0.380889 (* 1 = 0.380889 loss)\n", + "I0224 00:32:06.030015 658 solver.cpp:229] Iteration 8000, loss = 0.329477\n", + "I0224 00:32:06.030028 658 solver.cpp:245] Train net output #0: accuracy = 0.9\n", + "I0224 00:32:06.030040 658 solver.cpp:245] Train net output #1: loss = 0.329477 (* 1 = 0.329477 loss)\n", + "I0224 00:32:06.030048 658 sgd_solver.cpp:106] Iteration 8000, lr = 0.001\n", + "I0224 00:32:06.050626 658 solver.cpp:338] Iteration 9000, Testing net (#0)\n", + "I0224 00:32:06.053889 658 solver.cpp:406] Test net output #0: accuracy = 0.8376\n", + "I0224 00:32:06.053906 658 solver.cpp:406] Test net output #1: loss = 0.382756 (* 1 = 0.382756 loss)\n", + "I0224 00:32:06.054271 658 solver.cpp:229] Iteration 9000, loss = 0.412227\n", + "I0224 00:32:06.054291 658 solver.cpp:245] Train net output #0: accuracy = 0.8\n", + "I0224 00:32:06.054314 658 solver.cpp:245] Train net output #1: loss = 0.412228 (* 1 = 0.412228 loss)\n", + "I0224 00:32:06.054337 658 sgd_solver.cpp:106] Iteration 9000, lr = 0.001\n", + "I0224 00:32:06.074646 658 solver.cpp:456] Snapshotting to binary proto file examples/hdf5_classification/data/train_iter_10000.caffemodel\n", + "I0224 00:32:06.074808 658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/hdf5_classification/data/train_iter_10000.solverstate\n", + "I0224 00:32:06.074889 658 solver.cpp:318] Iteration 10000, loss = 0.532798\n", + "I0224 00:32:06.074906 658 solver.cpp:338] Iteration 10000, Testing net (#0)\n", + "I0224 00:32:06.078208 658 solver.cpp:406] Test net output #0: accuracy = 0.8388\n", + "I0224 00:32:06.078225 658 solver.cpp:406] Test net output #1: loss = 0.382042 (* 1 = 0.382042 loss)\n", + "I0224 00:32:06.078234 658 solver.cpp:323] Optimization Done.\n", + "I0224 00:32:06.078241 658 caffe.cpp:222] Optimization Done.\n" + ] + } + ], + "source": [ + "!./build/tools/caffe train -solver examples/hdf5_classification/nonlinear_logreg_solver.prototxt" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n", + "shutil.rmtree(dirname)" + ] + } + ], + "metadata": { + "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.", + "example_name": "Off-the-shelf SGD for classification", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + }, + "priority": 4 + }, + "nbformat": 4, + "nbformat_minor": 0 +} + + +
    +
    + + diff --git a/gathered/examples/cifar10.html b/gathered/examples/cifar10.html new file mode 100644 index 00000000000..6956d286115 --- /dev/null +++ b/gathered/examples/cifar10.html @@ -0,0 +1,162 @@ + + + + + + + + + Caffe | CIFAR-10 tutorial + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + +

    Alex’s CIFAR-10 tutorial, Caffe style

    + +

    Alex Krizhevsky’s cuda-convnet details the model definitions, parameters, and training procedure for good performance on CIFAR-10. This example reproduces his results in Caffe.

    + +

    We will assume that you have Caffe successfully compiled. If not, please refer to the Installation page. In this tutorial, we will assume that your caffe installation is located at CAFFE_ROOT.

    + +

    We thank @chyojn for the pull request that defined the model schemas and solver configurations.

    + +

    This example is a work-in-progress. It would be nice to further explain details of the network and training choices and benchmark the full training.

    + +

    Prepare the Dataset

    + +

    You will first need to download and convert the data format from the CIFAR-10 website. To do this, simply run the following commands:

    + +
    cd $CAFFE_ROOT
+./data/cifar10/get_cifar10.sh
+./examples/cifar10/create_cifar10.sh
+
    +
    + +

    If it complains that wget or gunzip are not installed, you need to install them respectively. After running the script there should be the dataset, ./cifar10-leveldb, and the data set image mean ./mean.binaryproto.

    + +

    The Model

    + +

    The CIFAR-10 model is a CNN that composes layers of convolution, pooling, rectified linear unit (ReLU) nonlinearities, and local contrast normalization with a linear classifier on top of it all. We have defined the model in the CAFFE_ROOT/examples/cifar10 directory’s cifar10_quick_train_test.prototxt.

    + +

    Training and Testing the “Quick” Model

    + +

    Training the model is simple after you have written the network definition protobuf and solver protobuf files (refer to MNIST Tutorial). Simply run train_quick.sh, or the following command directly:

    + +
    cd $CAFFE_ROOT
+./examples/cifar10/train_quick.sh
+
    +
    + +

    train_quick.sh is a simple script, so have a look inside. The main tool for training is caffe with the train action, and the solver protobuf text file as its argument.

    + +

    When you run the code, you will see a lot of messages flying by like this:

    + +
    I0317 21:52:48.945710 2008298256 net.cpp:74] Creating Layer conv1
+I0317 21:52:48.945716 2008298256 net.cpp:84] conv1 <- data
+I0317 21:52:48.945725 2008298256 net.cpp:110] conv1 -> conv1
+I0317 21:52:49.298691 2008298256 net.cpp:125] Top shape: 100 32 32 32 (3276800)
+I0317 21:52:49.298719 2008298256 net.cpp:151] conv1 needs backward computation.
+
    +
    + +

    These messages tell you the details about each layer, its connections and its output shape, which may be helpful in debugging. After the initialization, the training will start:

    + +
    I0317 21:52:49.309370 2008298256 net.cpp:166] Network initialization done.
+I0317 21:52:49.309376 2008298256 net.cpp:167] Memory required for Data 23790808
+I0317 21:52:49.309422 2008298256 solver.cpp:36] Solver scaffolding done.
+I0317 21:52:49.309447 2008298256 solver.cpp:47] Solving CIFAR10_quick_train
+
    +
    + +

    Based on the solver setting, we will print the training loss function every 100 iterations, and test the network every 500 iterations. You will see messages like this:

    + +
    I0317 21:53:12.179772 2008298256 solver.cpp:208] Iteration 100, lr = 0.001
+I0317 21:53:12.185698 2008298256 solver.cpp:65] Iteration 100, loss = 1.73643
+...
+I0317 21:54:41.150030 2008298256 solver.cpp:87] Iteration 500, Testing net
+I0317 21:54:47.129461 2008298256 solver.cpp:114] Test score #0: 0.5504
+I0317 21:54:47.129500 2008298256 solver.cpp:114] Test score #1: 1.27805
+
    +
    + +

    For each training iteration, lr is the learning rate of that iteration, and loss is the training function. For the output of the testing phase, score 0 is the accuracy, and score 1 is the testing loss function.

    + +

    And after making yourself a cup of coffee, you are done!

    + +
    I0317 22:12:19.666914 2008298256 solver.cpp:87] Iteration 5000, Testing net
+I0317 22:12:25.580330 2008298256 solver.cpp:114] Test score #0: 0.7533
+I0317 22:12:25.580379 2008298256 solver.cpp:114] Test score #1: 0.739837
+I0317 22:12:25.587262 2008298256 solver.cpp:130] Snapshotting to cifar10_quick_iter_5000
+I0317 22:12:25.590215 2008298256 solver.cpp:137] Snapshotting solver state to cifar10_quick_iter_5000.solverstate
+I0317 22:12:25.592813 2008298256 solver.cpp:81] Optimization Done.
+
    +
    + +

    Our model achieved ~75% test accuracy. The model parameters are stored in binary protobuf format in

    + +
    cifar10_quick_iter_5000
+
    +
    + +

    which is ready-to-deploy in CPU or GPU mode! Refer to the CAFFE_ROOT/examples/cifar10/cifar10_quick.prototxt for the deployment model definition that can be called on new data.

    + +

    Why train on a GPU?

    + +

    CIFAR-10, while still small, has enough data to make GPU training attractive.

    + +

    To compare CPU vs. GPU training speed, simply change one line in all the cifar*solver.prototxt:

    + +
    # solver mode: CPU or GPU
+solver_mode: CPU
+
    +
    + +

    and you will be using CPU for training.

    + + +
    +
    + + diff --git a/gathered/examples/cpp_classification.html b/gathered/examples/cpp_classification.html new file mode 100644 index 00000000000..b8ee3e543b7 --- /dev/null +++ b/gathered/examples/cpp_classification.html @@ -0,0 +1,133 @@ + + + + + + + + + Caffe | CaffeNet C++ Classification example + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + +

    Classifying ImageNet: using the C++ API

    + +

    Caffe, at its core, is written in C++. It is possible to use the C++ +API of Caffe to implement an image classification application similar +to the Python code presented in one of the Notebook examples. To look +at a more general-purpose example of the Caffe C++ API, you should +study the source code of the command line tool caffe in tools/caffe.cpp.

    + +

    Presentation

    + +

    A simple C++ code is proposed in +examples/cpp_classification/classification.cpp. For the sake of +simplicity, this example does not support oversampling of a single +sample nor batching of multiple independent samples. This example is +not trying to reach the maximum possible classification throughput on +a system, but special care was given to avoid unnecessary +pessimization while keeping the code readable.

    + +

    Compiling

    + +

    The C++ example is built automatically when compiling Caffe. To +compile Caffe you should follow the documented instructions. The +classification example will be built as examples/classification.bin +in your build directory.

    + +

    Usage

    + +

    To use the pre-trained CaffeNet model with the classification example, +you need to download it from the “Model Zoo” using the following +script: + +./scripts/download_model_binary.py models/bvlc_reference_caffenet + +The ImageNet labels file (also called the synset file) is also +required in order to map a prediction to the name of the class: + +./data/ilsvrc12/get_ilsvrc_aux.sh + +Using the files that were downloaded, we can classify the provided cat +image (examples/images/cat.jpg) using this command: + +./build/examples/cpp_classification/classification.bin \ + models/bvlc_reference_caffenet/deploy.prototxt \ + models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \ + data/ilsvrc12/imagenet_mean.binaryproto \ + data/ilsvrc12/synset_words.txt \ + examples/images/cat.jpg + +The output should look like this: + +---------- Prediction for examples/images/cat.jpg ---------- +0.3134 - "n02123045 tabby, tabby cat" +0.2380 - "n02123159 tiger cat" +0.1235 - "n02124075 Egyptian cat" +0.1003 - "n02119022 red fox, Vulpes vulpes" +0.0715 - "n02127052 lynx, catamount" +

    + +

    Improving Performance

    + +

    To further improve performance, you will need to leverage the GPU +more, here are some guidelines:

    + +
      +
    • Move the data on the GPU early and perform all preprocessing +operations there.
      • +
    • If you have many images to classify simultaneously, you should use +batching (independent images are classified in a single forward pass).
      • +
    • Use multiple classification threads to ensure the GPU is always fully +utilized and not waiting for an I/O blocked CPU thread.
      • +
    + + +
    +
    + + diff --git a/gathered/examples/detection.ipynb b/gathered/examples/detection.ipynb new file mode 100644 index 00000000000..86cb3d0eb56 --- /dev/null +++ b/gathered/examples/detection.ipynb @@ -0,0 +1,8454 @@ + + + + + + + + + Caffe | R-CNN detection + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[R-CNN](https://github.com/rbgirshick/rcnn) is a state-of-the-art detector that classifies region proposals by a finetuned Caffe model. For the full details of the R-CNN system and model, refer to its project site and the paper:\n", + "\n", + "> *Rich feature hierarchies for accurate object detection and semantic segmentation*. Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. CVPR 2014. [Arxiv 2013](http://arxiv.org/abs/1311.2524).\n", + "\n", + "In this example, we do detection by a pure Caffe edition of the R-CNN model for ImageNet. The R-CNN detector outputs class scores for the 200 detection classes of ILSVRC13. Keep in mind that these are raw one vs. all SVM scores, so they are not probabilistically calibrated or exactly comparable across classes. Note that this off-the-shelf model is simply for convenience, and is not the full R-CNN model.\n", + "\n", + "Let's run detection on an image of a bicyclist riding a fish bike in the desert (from the ImageNet challenge—no joke).\n", + "\n", + "First, we'll need region proposals and the Caffe R-CNN ImageNet model:\n", + "\n", + "- [Selective Search](http://koen.me/research/selectivesearch/) is the region proposer used by R-CNN. The [selective_search_ijcv_with_python](https://github.com/sergeyk/selective_search_ijcv_with_python) Python module takes care of extracting proposals through the selective search MATLAB implementation. To install it, download the module and name its directory `selective_search_ijcv_with_python`, run the demo in MATLAB to compile the necessary functions, then add it to your `PYTHONPATH` for importing. (If you have your own region proposals prepared, or would rather not bother with this step, [detect.py](https://github.com/BVLC/caffe/blob/master/python/detect.py) accepts a list of images and bounding boxes as CSV.)\n", + "\n", + "-Run `./scripts/download_model_binary.py models/bvlc_reference_rcnn_ilsvrc13` to get the Caffe R-CNN ImageNet model.\n", + "\n", + "With that done, we'll call the bundled `detect.py` to generate the region proposals and run the network. For an explanation of the arguments, do `./detect.py --help`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: Logging before InitGoogleLogging() is written to STDERR\n", + "I0218 20:43:25.383932 2099749632 net.cpp:42] Initializing net from parameters: \n", + "name: \"R-CNN-ilsvrc13\"\n", + "input: \"data\"\n", + "input_dim: 10\n", + "input_dim: 3\n", + "input_dim: 227\n", + "input_dim: 227\n", + "state {\n", + " phase: TEST\n", + "}\n", + "layer {\n", + " name: \"conv1\"\n", + " type: \"Convolution\"\n", + " bottom: \"data\"\n", + " top: \"conv1\"\n", + " convolution_param {\n", + " num_output: 96\n", + " kernel_size: 11\n", + " stride: 4\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu1\"\n", + " type: \"ReLU\"\n", + " bottom: \"conv1\"\n", + " top: \"conv1\"\n", + "}\n", + "layer {\n", + " name: \"pool1\"\n", + " type: \"Pooling\"\n", + " bottom: \"conv1\"\n", + " top: \"pool1\"\n", + " pooling_param {\n", + " pool: MAX\n", + " kernel_size: 3\n", + " stride: 2\n", + " }\n", + "}\n", + "layer {\n", + " name: \"norm1\"\n", + " type: \"LRN\"\n", + " bottom: \"pool1\"\n", + " top: \"norm1\"\n", + " lrn_param {\n", + " local_size: 5\n", + " alpha: 0.0001\n", + " beta: 0.75\n", + " }\n", + "}\n", + "layer {\n", + " name: \"conv2\"\n", + " type: \"Convolution\"\n", + " bottom: \"norm1\"\n", + " top: \"conv2\"\n", + " convolution_param {\n", + " num_output: 256\n", + " pad: 2\n", + " kernel_size: 5\n", + " group: 2\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu2\"\n", + " type: \"ReLU\"\n", + " bottom: \"conv2\"\n", + " top: \"conv2\"\n", + "}\n", + "layer {\n", + " name: \"pool2\"\n", + " type: \"Pooling\"\n", + " bottom: \"conv2\"\n", + " top: \"pool2\"\n", + " pooling_param {\n", + " pool: MAX\n", + " kernel_size: 3\n", + " stride: 2\n", + " }\n", + "}\n", + "layer {\n", + " name: \"norm2\"\n", + " type: \"LRN\"\n", + " bottom: \"pool2\"\n", + " top: \"norm2\"\n", + " lrn_param {\n", + " local_size: 5\n", + " alpha: 0.0001\n", + " beta: 0.75\n", + " }\n", + "}\n", + "layer {\n", + " name: \"conv3\"\n", + " type: \"Convolution\"\n", + " bottom: \"norm2\"\n", + " top: \"conv3\"\n", + " convolution_param {\n", + " num_output: 384\n", + " pad: 1\n", + " kernel_size: 3\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu3\"\n", + " type: \"ReLU\"\n", + " bottom: \"conv3\"\n", + " top: \"conv3\"\n", + "}\n", + "layer {\n", + " name: \"conv4\"\n", + " type: \"Convolution\"\n", + " bottom: \"conv3\"\n", + " top: \"conv4\"\n", + " convolution_param {\n", + " num_output: 384\n", + " pad: 1\n", + " kernel_size: 3\n", + " group: 2\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu4\"\n", + " type: \"ReLU\"\n", + " bottom: \"conv4\"\n", + " top: \"conv4\"\n", + "}\n", + "layer {\n", + " name: \"conv5\"\n", + " type: \"Convolution\"\n", + " bottom: \"conv4\"\n", + " top: \"conv5\"\n", + " convolution_param {\n", + " num_output: 256\n", + " pad: 1\n", + " kernel_size: 3\n", + " group: 2\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu5\"\n", + " type: \"ReLU\"\n", + " bottom: \"conv5\"\n", + " top: \"conv5\"\n", + "}\n", + "layer {\n", + " name: \"pool5\"\n", + " type: \"Pooling\"\n", + " bottom: \"conv5\"\n", + " top: \"pool5\"\n", + " pooling_param {\n", + " pool: MAX\n", + " kernel_size: 3\n", + " stride: 2\n", + " }\n", + "}\n", + "layer {\n", + " name: \"fc6\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"pool5\"\n", + " top: \"fc6\"\n", + " inner_product_param {\n", + " num_output: 4096\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu6\"\n", + " type: \"ReLU\"\n", + " bottom: \"fc6\"\n", + " top: \"fc6\"\n", + "}\n", + "layer {\n", + " name: \"drop6\"\n", + " type: \"Dropout\"\n", + " bottom: \"fc6\"\n", + " top: \"fc6\"\n", + " dropout_param {\n", + " dropout_ratio: 0.5\n", + " }\n", + "}\n", + "layer {\n", + " name: \"fc7\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"fc6\"\n", + " top: \"fc7\"\n", + " inner_product_param {\n", + " num_output: 4096\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu7\"\n", + " type: \"ReLU\"\n", + " bottom: \"fc7\"\n", + " top: \"fc7\"\n", + "}\n", + "layer {\n", + " name: \"drop7\"\n", + " type: \"Dropout\"\n", + " bottom: \"fc7\"\n", + " top: \"fc7\"\n", + " dropout_param {\n", + " dropout_ratio: 0.5\n", + " }\n", + "}\n", + "layer {\n", + " name: \"fc-rcnn\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"fc7\"\n", + " top: \"fc-rcnn\"\n", + " inner_product_param {\n", + " num_output: 200\n", + " }\n", + "}\n", + "I0218 20:43:25.385720 2099749632 net.cpp:336] Input 0 -> data\n", + "I0218 20:43:25.385769 2099749632 layer_factory.hpp:74] Creating layer conv1\n", + "I0218 20:43:25.385783 2099749632 net.cpp:76] Creating Layer conv1\n", + "I0218 20:43:25.385790 2099749632 net.cpp:372] conv1 <- data\n", + "I0218 20:43:25.385802 2099749632 net.cpp:334] conv1 -> conv1\n", + "I0218 20:43:25.385815 2099749632 net.cpp:105] Setting up conv1\n", + "I0218 20:43:25.386574 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\n", + "I0218 20:43:25.386610 2099749632 layer_factory.hpp:74] Creating layer relu1\n", + "I0218 20:43:25.386625 2099749632 net.cpp:76] Creating Layer relu1\n", + "I0218 20:43:25.386631 2099749632 net.cpp:372] relu1 <- conv1\n", + "I0218 20:43:25.386641 2099749632 net.cpp:323] relu1 -> conv1 (in-place)\n", + "I0218 20:43:25.386649 2099749632 net.cpp:105] Setting up relu1\n", + "I0218 20:43:25.386656 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\n", + "I0218 20:43:25.386663 2099749632 layer_factory.hpp:74] Creating layer pool1\n", + "I0218 20:43:25.386675 2099749632 net.cpp:76] Creating Layer pool1\n", + "I0218 20:43:25.386682 2099749632 net.cpp:372] pool1 <- conv1\n", + "I0218 20:43:25.386690 2099749632 net.cpp:334] pool1 -> pool1\n", + "I0218 20:43:25.386699 2099749632 net.cpp:105] Setting up pool1\n", + "I0218 20:43:25.386716 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\n", + "I0218 20:43:25.386725 2099749632 layer_factory.hpp:74] Creating layer norm1\n", + "I0218 20:43:25.386736 2099749632 net.cpp:76] Creating Layer norm1\n", + "I0218 20:43:25.386744 2099749632 net.cpp:372] norm1 <- pool1\n", + "I0218 20:43:25.386803 2099749632 net.cpp:334] norm1 -> norm1\n", + "I0218 20:43:25.386819 2099749632 net.cpp:105] Setting up norm1\n", + "I0218 20:43:25.386832 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\n", + "I0218 20:43:25.386842 2099749632 layer_factory.hpp:74] Creating layer conv2\n", + "I0218 20:43:25.386852 2099749632 net.cpp:76] Creating Layer conv2\n", + "I0218 20:43:25.386865 2099749632 net.cpp:372] conv2 <- norm1\n", + "I0218 20:43:25.386878 2099749632 net.cpp:334] conv2 -> conv2\n", + "I0218 20:43:25.386899 2099749632 net.cpp:105] Setting up conv2\n", + "I0218 20:43:25.387024 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\n", + "I0218 20:43:25.387042 2099749632 layer_factory.hpp:74] Creating layer relu2\n", + "I0218 20:43:25.387050 2099749632 net.cpp:76] Creating Layer relu2\n", + "I0218 20:43:25.387058 2099749632 net.cpp:372] relu2 <- conv2\n", + "I0218 20:43:25.387066 2099749632 net.cpp:323] relu2 -> conv2 (in-place)\n", + "I0218 20:43:25.387075 2099749632 net.cpp:105] Setting up relu2\n", + "I0218 20:43:25.387081 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\n", + "I0218 20:43:25.387089 2099749632 layer_factory.hpp:74] Creating layer pool2\n", + "I0218 20:43:25.387097 2099749632 net.cpp:76] Creating Layer pool2\n", + "I0218 20:43:25.387104 2099749632 net.cpp:372] pool2 <- conv2\n", + "I0218 20:43:25.387112 2099749632 net.cpp:334] pool2 -> pool2\n", + "I0218 20:43:25.387121 2099749632 net.cpp:105] Setting up pool2\n", + "I0218 20:43:25.387130 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n", + "I0218 20:43:25.387137 2099749632 layer_factory.hpp:74] Creating layer norm2\n", + "I0218 20:43:25.387145 2099749632 net.cpp:76] Creating Layer norm2\n", + "I0218 20:43:25.387152 2099749632 net.cpp:372] norm2 <- pool2\n", + "I0218 20:43:25.387161 2099749632 net.cpp:334] norm2 -> norm2\n", + "I0218 20:43:25.387168 2099749632 net.cpp:105] Setting up norm2\n", + "I0218 20:43:25.387176 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n", + "I0218 20:43:25.387228 2099749632 layer_factory.hpp:74] Creating layer conv3\n", + "I0218 20:43:25.387249 2099749632 net.cpp:76] Creating Layer conv3\n", + "I0218 20:43:25.387258 2099749632 net.cpp:372] conv3 <- norm2\n", + "I0218 20:43:25.387266 2099749632 net.cpp:334] conv3 -> conv3\n", + "I0218 20:43:25.387276 2099749632 net.cpp:105] Setting up conv3\n", + "I0218 20:43:25.389375 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n", + "I0218 20:43:25.389408 2099749632 layer_factory.hpp:74] Creating layer relu3\n", + "I0218 20:43:25.389421 2099749632 net.cpp:76] Creating Layer relu3\n", + "I0218 20:43:25.389430 2099749632 net.cpp:372] relu3 <- conv3\n", + "I0218 20:43:25.389438 2099749632 net.cpp:323] relu3 -> conv3 (in-place)\n", + "I0218 20:43:25.389447 2099749632 net.cpp:105] Setting up relu3\n", + "I0218 20:43:25.389456 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n", + "I0218 20:43:25.389462 2099749632 layer_factory.hpp:74] Creating layer conv4\n", + "I0218 20:43:25.389472 2099749632 net.cpp:76] Creating Layer conv4\n", + "I0218 20:43:25.389478 2099749632 net.cpp:372] conv4 <- conv3\n", + "I0218 20:43:25.389487 2099749632 net.cpp:334] conv4 -> conv4\n", + "I0218 20:43:25.389497 2099749632 net.cpp:105] Setting up conv4\n", + "I0218 20:43:25.391810 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n", + "I0218 20:43:25.391856 2099749632 layer_factory.hpp:74] Creating layer relu4\n", + "I0218 20:43:25.391871 2099749632 net.cpp:76] Creating Layer relu4\n", + "I0218 20:43:25.391880 2099749632 net.cpp:372] relu4 <- conv4\n", + "I0218 20:43:25.391888 2099749632 net.cpp:323] relu4 -> conv4 (in-place)\n", + "I0218 20:43:25.391898 2099749632 net.cpp:105] Setting up relu4\n", + "I0218 20:43:25.391906 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n", + "I0218 20:43:25.391913 2099749632 layer_factory.hpp:74] Creating layer conv5\n", + "I0218 20:43:25.391923 2099749632 net.cpp:76] Creating Layer conv5\n", + "I0218 20:43:25.391929 2099749632 net.cpp:372] conv5 <- conv4\n", + "I0218 20:43:25.391937 2099749632 net.cpp:334] conv5 -> conv5\n", + "I0218 20:43:25.391947 2099749632 net.cpp:105] Setting up conv5\n", + "I0218 20:43:25.393072 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n", + "I0218 20:43:25.393108 2099749632 layer_factory.hpp:74] Creating layer relu5\n", + "I0218 20:43:25.393122 2099749632 net.cpp:76] Creating Layer relu5\n", + "I0218 20:43:25.393129 2099749632 net.cpp:372] relu5 <- conv5\n", + "I0218 20:43:25.393138 2099749632 net.cpp:323] relu5 -> conv5 (in-place)\n", + "I0218 20:43:25.393148 2099749632 net.cpp:105] Setting up relu5\n", + "I0218 20:43:25.393157 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n", + "I0218 20:43:25.393167 2099749632 layer_factory.hpp:74] Creating layer pool5\n", + "I0218 20:43:25.393175 2099749632 net.cpp:76] Creating Layer pool5\n", + "I0218 20:43:25.393182 2099749632 net.cpp:372] pool5 <- conv5\n", + "I0218 20:43:25.393190 2099749632 net.cpp:334] pool5 -> pool5\n", + "I0218 20:43:25.393199 2099749632 net.cpp:105] Setting up pool5\n", + "I0218 20:43:25.393209 2099749632 net.cpp:112] Top shape: 10 256 6 6 (92160)\n", + "I0218 20:43:25.393218 2099749632 layer_factory.hpp:74] Creating layer fc6\n", + "I0218 20:43:25.393226 2099749632 net.cpp:76] Creating Layer fc6\n", + "I0218 20:43:25.393232 2099749632 net.cpp:372] fc6 <- pool5\n", + "I0218 20:43:25.393240 2099749632 net.cpp:334] fc6 -> fc6\n", + "I0218 20:43:25.393249 2099749632 net.cpp:105] Setting up fc6\n", + "I0218 20:43:25.516396 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n", + "I0218 20:43:25.516445 2099749632 layer_factory.hpp:74] Creating layer relu6\n", + "I0218 20:43:25.516463 2099749632 net.cpp:76] Creating Layer relu6\n", + "I0218 20:43:25.516470 2099749632 net.cpp:372] relu6 <- fc6\n", + "I0218 20:43:25.516480 2099749632 net.cpp:323] relu6 -> fc6 (in-place)\n", + "I0218 20:43:25.516490 2099749632 net.cpp:105] Setting up relu6\n", + "I0218 20:43:25.516497 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n", + "I0218 20:43:25.516505 2099749632 layer_factory.hpp:74] Creating layer drop6\n", + "I0218 20:43:25.516515 2099749632 net.cpp:76] Creating Layer drop6\n", + "I0218 20:43:25.516521 2099749632 net.cpp:372] drop6 <- fc6\n", + "I0218 20:43:25.516530 2099749632 net.cpp:323] drop6 -> fc6 (in-place)\n", + "I0218 20:43:25.516538 2099749632 net.cpp:105] Setting up drop6\n", + "I0218 20:43:25.516557 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n", + "I0218 20:43:25.516566 2099749632 layer_factory.hpp:74] Creating layer fc7\n", + "I0218 20:43:25.516576 2099749632 net.cpp:76] Creating Layer fc7\n", + "I0218 20:43:25.516582 2099749632 net.cpp:372] fc7 <- fc6\n", + "I0218 20:43:25.516589 2099749632 net.cpp:334] fc7 -> fc7\n", + "I0218 20:43:25.516599 2099749632 net.cpp:105] Setting up fc7\n", + "I0218 20:43:25.604786 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n", + "I0218 20:43:25.604838 2099749632 layer_factory.hpp:74] Creating layer relu7\n", + "I0218 20:43:25.604852 2099749632 net.cpp:76] Creating Layer relu7\n", + "I0218 20:43:25.604859 2099749632 net.cpp:372] relu7 <- fc7\n", + "I0218 20:43:25.604868 2099749632 net.cpp:323] relu7 -> fc7 (in-place)\n", + "I0218 20:43:25.604878 2099749632 net.cpp:105] Setting up relu7\n", + "I0218 20:43:25.604885 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n", + "I0218 20:43:25.604893 2099749632 layer_factory.hpp:74] Creating layer drop7\n", + "I0218 20:43:25.604902 2099749632 net.cpp:76] Creating Layer drop7\n", + "I0218 20:43:25.604908 2099749632 net.cpp:372] drop7 <- fc7\n", + "I0218 20:43:25.604917 2099749632 net.cpp:323] drop7 -> fc7 (in-place)\n", + "I0218 20:43:25.604924 2099749632 net.cpp:105] Setting up drop7\n", + "I0218 20:43:25.604933 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n", + "I0218 20:43:25.604939 2099749632 layer_factory.hpp:74] Creating layer fc-rcnn\n", + "I0218 20:43:25.604948 2099749632 net.cpp:76] Creating Layer fc-rcnn\n", + "I0218 20:43:25.604954 2099749632 net.cpp:372] fc-rcnn <- fc7\n", + "I0218 20:43:25.604962 2099749632 net.cpp:334] fc-rcnn -> fc-rcnn\n", + "I0218 20:43:25.604971 2099749632 net.cpp:105] Setting up fc-rcnn\n", + "I0218 20:43:25.606878 2099749632 net.cpp:112] Top shape: 10 200 1 1 (2000)\n", + "I0218 20:43:25.606904 2099749632 net.cpp:165] fc-rcnn does not need backward computation.\n", + "I0218 20:43:25.606909 2099749632 net.cpp:165] drop7 does not need backward computation.\n", + "I0218 20:43:25.606916 2099749632 net.cpp:165] relu7 does not need backward computation.\n", + "I0218 20:43:25.606922 2099749632 net.cpp:165] fc7 does not need backward computation.\n", + "I0218 20:43:25.606928 2099749632 net.cpp:165] drop6 does not need backward computation.\n", + "I0218 20:43:25.606935 2099749632 net.cpp:165] relu6 does not need backward computation.\n", + "I0218 20:43:25.606940 2099749632 net.cpp:165] fc6 does not need backward computation.\n", + "I0218 20:43:25.606946 2099749632 net.cpp:165] pool5 does not need backward computation.\n", + "I0218 20:43:25.606952 2099749632 net.cpp:165] relu5 does not need backward computation.\n", + "I0218 20:43:25.606958 2099749632 net.cpp:165] conv5 does not need backward computation.\n", + "I0218 20:43:25.606964 2099749632 net.cpp:165] relu4 does not need backward computation.\n", + "I0218 20:43:25.606971 2099749632 net.cpp:165] conv4 does not need backward computation.\n", + "I0218 20:43:25.606976 2099749632 net.cpp:165] relu3 does not need backward computation.\n", + "I0218 20:43:25.606982 2099749632 net.cpp:165] conv3 does not need backward computation.\n", + "I0218 20:43:25.606988 2099749632 net.cpp:165] norm2 does not need backward computation.\n", + "I0218 20:43:25.606995 2099749632 net.cpp:165] pool2 does not need backward computation.\n", + "I0218 20:43:25.607002 2099749632 net.cpp:165] relu2 does not need backward computation.\n", + "I0218 20:43:25.607007 2099749632 net.cpp:165] conv2 does not need backward computation.\n", + "I0218 20:43:25.607013 2099749632 net.cpp:165] norm1 does not need backward computation.\n", + "I0218 20:43:25.607199 2099749632 net.cpp:165] pool1 does not need backward computation.\n", + "I0218 20:43:25.607213 2099749632 net.cpp:165] relu1 does not need backward computation.\n", + "I0218 20:43:25.607219 2099749632 net.cpp:165] conv1 does not need backward computation.\n", + "I0218 20:43:25.607225 2099749632 net.cpp:201] This network produces output fc-rcnn\n", + "I0218 20:43:25.607239 2099749632 net.cpp:446] Collecting Learning Rate and Weight Decay.\n", + "I0218 20:43:25.607255 2099749632 net.cpp:213] Network initialization done.\n", + "I0218 20:43:25.607262 2099749632 net.cpp:214] Memory required for data: 62425920\n", + "E0218 20:43:26.388214 2099749632 upgrade_proto.cpp:618] Attempting to upgrade input file specified using deprecated V1LayerParameter: ../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel\n", + "I0218 20:43:27.089423 2099749632 upgrade_proto.cpp:626] Successfully upgraded file specified using deprecated V1LayerParameter\n", + "GPU mode\n", + "Loading input...\n", + "selective_search_rcnn({'/Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg'}, '/var/folders/bk/dtkn5qjd11bd17b2j36zplyw0000gp/T/tmpakaRLL.mat')\n", + "Processed 1570 windows in 102.895 s.\n", + "/Users/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2453: PerformanceWarning: \n", + "your performance may suffer as PyTables will pickle object types that it cannot\n", + "map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]\n", + "\n", + " warnings.warn(ws, PerformanceWarning)\n", + "Saved to _temp/det_output.h5 in 0.298 s.\n" + ] + } + ], + "source": [ + "!mkdir -p _temp\n", + "!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\n", + "!../python/detect.py --crop_mode=selective_search --pretrained_model=../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=../models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This run was in GPU mode. For CPU mode detection, call `detect.py` without the `--gpu` argument.\n", + "\n", + "Running this outputs a DataFrame with the filenames, selected windows, and their detection scores to an HDF5 file.\n", + "(We only ran on one image, so the filenames will all be the same.)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1570, 5)\n", + "prediction [-2.62247, -2.84579, -2.85122, -3.20838, -1.94...\n", + "ymin 79.846\n", + "xmin 9.62\n", + "ymax 246.31\n", + "xmax 339.624\n", + "Name: /Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg, dtype: object\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "df = pd.read_hdf('_temp/det_output.h5', 'df')\n", + "print(df.shape)\n", + "print(df.iloc[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1570 regions were proposed with the R-CNN configuration of selective search. The number of proposals will vary from image to image based on its contents and size -- selective search isn't scale invariant.\n", + "\n", + "In general, `detect.py` is most efficient when running on a lot of images: it first extracts window proposals for all of them, batches the windows for efficient GPU processing, and then outputs the results.\n", + "Simply list an image per line in the `images_file`, and it will process all of them.\n", + "\n", + "Although this guide gives an example of R-CNN ImageNet detection, `detect.py` is clever enough to adapt to different Caffe models’ input dimensions, batch size, and output categories. You can switch the model definition and pretrained model as desired. Refer to `python detect.py --help` for the parameters to describe your data set. There's no need for hardcoding.\n", + "\n", + "Anyway, let's now load the ILSVRC13 detection class names and make a DataFrame of the predictions. Note you'll need the auxiliary ilsvrc2012 data fetched by `data/ilsvrc12/get_ilsvrc12_aux.sh`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name\n", + "accordion -2.622471\n", + "airplane -2.845788\n", + "ant -2.851219\n", + "antelope -3.208377\n", + "apple -1.949950\n", + "armadillo -2.472935\n", + "artichoke -2.201684\n", + "axe -2.327404\n", + "baby bed -2.737925\n", + "backpack -2.176763\n", + "bagel -2.681061\n", + "balance beam -2.722538\n", + "banana -2.390628\n", + "band aid -1.598909\n", + "banjo -2.298197\n", + "...\n", + "trombone -2.582361\n", + "trumpet -2.352853\n", + "turtle -2.360859\n", + "tv or monitor -2.761043\n", + "unicycle -2.218467\n", + "vacuum -1.907717\n", + "violin -2.757079\n", + "volleyball -2.723689\n", + "waffle iron -2.418540\n", + "washer -2.408994\n", + "water bottle -2.174899\n", + "watercraft -2.837425\n", + "whale -3.120338\n", + "wine bottle -2.772960\n", + "zebra -2.742913\n", + "Name: 0, Length: 200, dtype: float32\n" + ] + } + ], + "source": [ + "with open('../data/ilsvrc12/det_synset_words.txt') as f:\n", + " labels_df = pd.DataFrame([\n", + " {\n", + " 'synset_id': l.strip().split(' ')[0],\n", + " 'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]\n", + " }\n", + " for l in f.readlines()\n", + " ])\n", + "labels_df.sort('synset_id')\n", + "predictions_df = pd.DataFrame(np.vstack(df.prediction.values), columns=labels_df['name'])\n", + "print(predictions_df.iloc[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the activations." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, 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], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.gray()\n", + "plt.matshow(predictions_df.values)\n", + "plt.xlabel('Classes')\n", + "plt.ylabel('Windows')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's take max across all windows and plot the top classes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name\n", + "person 1.835771\n", + "bicycle 0.866110\n", + "unicycle 0.057080\n", + "motorcycle -0.006122\n", + "banjo -0.028209\n", + "turtle -0.189831\n", + "electric fan -0.206788\n", + "cart -0.214235\n", + "lizard -0.393519\n", + "helmet -0.477942\n", + "dtype: float32\n" + ] + } + ], + "source": [ + "max_s = predictions_df.max(0)\n", + "max_s.sort(ascending=False)\n", + "print(max_s[:10])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The top detections are in fact a person and bicycle.\n", + "Picking good localizations is a work in progress; we pick the top-scoring person and bicycle detections." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top detection:\n", + "name\n", + "person 1.835771\n", + "swimming trunks -1.150371\n", + "rubber eraser -1.231106\n", + "turtle -1.266037\n", + "plastic bag -1.303265\n", + "dtype: float32\n", + "\n", + "Second-best detection:\n", + "name\n", + "bicycle 0.866110\n", + "unicycle -0.359139\n", + "scorpion -0.811621\n", + "lobster -0.982891\n", + "lamp -1.096808\n", + "dtype: float32\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + 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"currentAxis = plt.gca()\n", + "\n", + "det = df.iloc[i]\n", + "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n", + "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\n", + "\n", + "det = df.iloc[j]\n", + "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n", + "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's cool. Let's take all 'bicycle' detections and NMS them to get rid of overlapping windows." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def nms_detections(dets, overlap=0.3):\n", + " \"\"\"\n", + " Non-maximum suppression: Greedily select high-scoring detections and\n", + " skip detections that are significantly covered by a previously\n", + " selected detection.\n", + "\n", + " This version is translated from Matlab code by Tomasz Malisiewicz,\n", + " who sped up Pedro Felzenszwalb's code.\n", + "\n", + " Parameters\n", + " ----------\n", + " dets: ndarray\n", + " each row is ['xmin', 'ymin', 'xmax', 'ymax', 'score']\n", + " overlap: float\n", + " minimum overlap ratio (0.3 default)\n", + "\n", + " Output\n", + " ------\n", + " dets: ndarray\n", + " remaining after suppression.\n", + " \"\"\"\n", + " x1 = dets[:, 0]\n", + " y1 = dets[:, 1]\n", + " x2 = dets[:, 2]\n", + " y2 = dets[:, 3]\n", + " ind = np.argsort(dets[:, 4])\n", + "\n", + " w = x2 - x1\n", + " h = y2 - y1\n", + " area = (w * h).astype(float)\n", + "\n", + " pick = []\n", + " while len(ind) > 0:\n", + " i = ind[-1]\n", + " pick.append(i)\n", + " ind = ind[:-1]\n", + "\n", + " xx1 = np.maximum(x1[i], x1[ind])\n", + " yy1 = np.maximum(y1[i], y1[ind])\n", + " xx2 = np.minimum(x2[i], x2[ind])\n", + " yy2 = np.minimum(y2[i], y2[ind])\n", + "\n", + " w = np.maximum(0., xx2 - xx1)\n", + " h = np.maximum(0., yy2 - yy1)\n", + "\n", + " wh = w * h\n", + " o = wh / (area[i] + area[ind] - wh)\n", + "\n", + " ind = ind[np.nonzero(o <= overlap)[0]]\n", + "\n", + " return dets[pick, :]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "scores = predictions_df['bicycle']\n", + "windows = df[['xmin', 'ymin', 'xmax', 'ymax']].values\n", + "dets = np.hstack((windows, scores[:, np.newaxis]))\n", + "nms_dets = nms_detections(dets)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Show top 3 NMS'd detections for 'bicycle' in the image and note the gap between the top scoring box (red) and the remaining boxes." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scores: [ 0.86610985 -0.70051557 -1.34796357]\n" + ] + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEACAYAAACqOy3+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvUmMZll23/e7wxu+KeaInKuys+aq7ibdraZE0oIgU4Qt\n", + "mrAsGISgrTfaWAa88tYbwzagnQEbhGUv5I1XNiBKIE3SNCi2SDfZbLC72TVmVWVV5RQZ8ze+9+7k\n", + "xb3vfV9ERTYJg8Vim3G6oyLjG95w371n+J9z/leEELiSK7mSK7mSnzyRX/YFXMmVXMmVXMn/N7lS\n", + "4FdyJVdyJT+hcqXAr+RKruRKfkLlSoFfyZVcyZX8hMqVAr+SK7mSK/kJlSsFfiVXciVX8hMqX4gC\n", + "F0L8B0KId4UQHwgh/ssv4hxXciVXciV/3UX8RdeBCyEU8B7w94BHwB8B/ziE8M5f6Imu5Equ5Er+\n", + 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"hHAxhPBQvL0LfAPRUj3o66IEGCIsKefNgV4TY8ydwPuBf4vSeR7wNYFbX0K5A/j90e/PxPsOqp0M\n", + "IVyKty8BJ+PtM8jaqH3Pr5Mx5rVIhvJFDvi6GGOsMeYh5LN/NoTwdQ74mgD/DPg5YDyOedDX5JY7\n", + "8BVm8WUsSO53s/X5nl07Y8wM+O+ICPbO+LGDuC4hBB9COAfcCfxJY8y7r3v8QK2JMeZPA5dDCA8y\n", + "RN977KCtidqtduDPAneNfr+LvTvlQbNLxphTAMaY08DleP/163RnvO97zowxKeK8PxJC+Fi8+8Cv\n", + "C0AIYQv438D9HOw1+WHgg8aYJ4GPAj9mjPkIB3tNgFvvwL8CnDXGvNYYkwF/Afj4LT6GV5N9HPhQ\n", + "vP0h4GOj+3/WGJMZY74fOAt8aR+O77tqRmaJ/x3wWAjhn48eOrDrYow5pmgKY8wE+AngQQ7wmoQQ\n", + "PhxCuCuE8P3AzwKfCSH8ZQ7wmvS2D53kn0LQBk8Av7DfXdxb+Lk/CjwH1Egf4K8Am8BvAb8H/CZw\n", + "ePT8D8c1+ibw3v0+/u/SmrwDqWk+hDipB4H3HeR1Ad4APBDX5BHg5+L9B3ZNrlufH2VAoRz4NVmN\n", + "0q9sZStb2W1qq0nMla1sZSu7TW3lwFe2spWt7Da1lQNf2cpWtrLb1FYOfGUrW9nKblNbOfCVrWxl\n", + "K7tNbeXAV7ayla3sNrWVA1/Zyla2stvUVg58ZStb2cpuU/t/6S2bnP6vZqYAAAAASUVORK5CYII=\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(im)\n", + "currentAxis = plt.gca()\n", + "colors = ['r', 'b', 'y']\n", + "for c, det in zip(colors, nms_dets[:3]):\n", + " currentAxis.add_patch(\n", + " plt.Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],\n", + " fill=False, edgecolor=c, linewidth=5)\n", + " )\n", + "print 'scores:', nms_dets[:3, 4]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was an easy instance for bicycle as it was in the class's training set. However, the person result is a true detection since this was not in the set for that class.\n", + "\n", + "You should try out detection on an image of your own next!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(Remove the temp directory to clean up, and we're done.)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "!rm -rf _temp" + ] + } + ], + "metadata": { + "description": "Run a pretrained model as a detector in Python.", + "example_name": "R-CNN detection", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + }, + "priority": 6 + }, + "nbformat": 4, + "nbformat_minor": 0 +} + + +
    +
    + + diff --git a/gathered/examples/feature_extraction.html b/gathered/examples/feature_extraction.html new file mode 100644 index 00000000000..fe73c5cab3c --- /dev/null +++ b/gathered/examples/feature_extraction.html @@ -0,0 +1,139 @@ + + + + + + + + + Caffe | Feature extraction with Caffe C++ code. + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + +

    Extracting Features

    + +

    In this tutorial, we will extract features using a pre-trained model with the included C++ utility. +Note that we recommend using the Python interface for this task, as for example in the filter visualization example.

    + +

    Follow instructions for installing Caffe and run scripts/download_model_binary.py models/bvlc_reference_caffenet from caffe root directory. +If you need detailed information about the tools below, please consult their source code, in which additional documentation is usually provided.

    + +

    Select data to run on

    + +

    We’ll make a temporary folder to store things into.

    + +
    mkdir examples/_temp
+
    +
    + +

    Generate a list of the files to process. +We’re going to use the images that ship with caffe.

    + +
    find `pwd`/examples/images -type f -exec echo {} \; > examples/_temp/temp.txt
+
    +
    + +

    The ImageDataLayer we’ll use expects labels after each filenames, so let’s add a 0 to the end of each line

    + +
    sed "s/$/ 0/" examples/_temp/temp.txt > examples/_temp/file_list.txt
+
    +
    + +

    Define the Feature Extraction Network Architecture

    + +

    In practice, subtracting the mean image from a dataset significantly improves classification accuracies. +Download the mean image of the ILSVRC dataset.

    + +
    ./data/ilsvrc12/get_ilsvrc_aux.sh
+
    +
    + +

    We will use data/ilsvrc212/imagenet_mean.binaryproto in the network definition prototxt.

    + +

    Let’s copy and modify the network definition. +We’ll be using the ImageDataLayer, which will load and resize images for us.

    + +
    cp examples/feature_extraction/imagenet_val.prototxt examples/_temp
+
    +
    + +

    Extract Features

    + +

    Now everything necessary is in place.

    + +
    ./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10 leveldb
+
    +
    + +

    The name of feature blob that you extract is fc7, which represents the highest level feature of the reference model. +We can use any other layer, as well, such as conv5 or pool3.

    + +

    The last parameter above is the number of data mini-batches.

    + +

    The features are stored to LevelDB examples/_temp/features, ready for access by some other code.

    + +

    If you meet with the error “Check failed: status.ok() Failed to open leveldb examples/_temp/features”, it is because the directory examples/_temp/features has been created the last time you run the command. Remove it and run again.

    + +
    rm -rf examples/_temp/features/
+
    +
    + +

    If you’d like to use the Python wrapper for extracting features, check out the filter visualization notebook.

    + +

    Clean Up

    + +

    Let’s remove the temporary directory now.

    + +
    rm -r examples/_temp
+
    +
    + + +
    +
    + + diff --git a/gathered/examples/finetune_flickr_style.html b/gathered/examples/finetune_flickr_style.html new file mode 100644 index 00000000000..ada1fe19025 --- /dev/null +++ b/gathered/examples/finetune_flickr_style.html @@ -0,0 +1,229 @@ + + + + + + + + + Caffe | Fine-tuning for style recognition + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + +

    Fine-tuning CaffeNet for Style Recognition on “Flickr Style” Data

    + +

    Fine-tuning takes an already learned model, adapts the architecture, and resumes training from the already learned model weights. +Let’s fine-tune the BVLC-distributed CaffeNet model on a different dataset, Flickr Style, to predict image style instead of object category.

    + +

    Explanation

    + +

    The Flickr-sourced images of the Style dataset are visually very similar to the ImageNet dataset, on which the bvlc_reference_caffenet was trained. +Since that model works well for object category classification, we’d like to use this architecture for our style classifier. +We also only have 80,000 images to train on, so we’d like to start with the parameters learned on the 1,000,000 ImageNet images, and fine-tune as needed. +If we provide the weights argument to the caffe train command, the pretrained weights will be loaded into our model, matching layers by name.

    + +

    Because we are predicting 20 classes instead of a 1,000, we do need to change the last layer in the model. +Therefore, we change the name of the last layer from fc8 to fc8_flickr in our prototxt. +Since there is no layer named that in the bvlc_reference_caffenet, that layer will begin training with random weights.

    + +

    We will also decrease the overall learning rate base_lr in the solver prototxt, but boost the lr_mult on the newly introduced layer. +The idea is to have the rest of the model change very slowly with new data, but let the new layer learn fast. +Additionally, we set stepsize in the solver to a lower value than if we were training from scratch, since we’re virtually far along in training and therefore want the learning rate to go down faster. +Note that we could also entirely prevent fine-tuning of all layers other than fc8_flickr by setting their lr_mult to 0.

    + +

    Procedure

    + +

    All steps are to be done from the caffe root directory.

    + +

    The dataset is distributed as a list of URLs with corresponding labels. +Using a script, we will download a small subset of the data and split it into train and val sets.

    + +
    caffe % ./examples/finetune_flickr_style/assemble_data.py -h
+usage: assemble_data.py [-h] [-s SEED] [-i IMAGES] [-w WORKERS]
+
+Download a subset of Flickr Style to a directory
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -s SEED, --seed SEED  random seed
+  -i IMAGES, --images IMAGES
+                        number of images to use (-1 for all)
+  -w WORKERS, --workers WORKERS
+                        num workers used to download images. -x uses (all - x)
+                        cores.
+
+caffe % python examples/finetune_flickr_style/assemble_data.py --workers=-1 --images=2000 --seed 831486
+Downloading 2000 images with 7 workers...
+Writing train/val for 1939 successfully downloaded images.
+
    +
    + +

    This script downloads images and writes train/val file lists into data/flickr_style. +The prototxts in this example assume this, and also assume the presence of the ImageNet mean file (run get_ilsvrc_aux.sh from data/ilsvrc12 to obtain this if you haven’t yet).

    + +

    We’ll also need the ImageNet-trained model, which you can obtain by running ./scripts/download_model_binary.py models/bvlc_reference_caffenet.

    + +

    Now we can train! The key to fine-tuning is the -weights argument in the +command below, which tells Caffe that we want to load weights from a pre-trained +Caffe model.

    + +

    (You can fine-tune in CPU mode by leaving out the -gpu flag.)

    + +
    caffe % ./build/tools/caffe train -solver models/finetune_flickr_style/solver.prototxt -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel -gpu 0
+
+[...]
+
+I0828 22:10:04.025378  9718 solver.cpp:46] Solver scaffolding done.
+I0828 22:10:04.025388  9718 caffe.cpp:95] Use GPU with device ID 0
+I0828 22:10:04.192004  9718 caffe.cpp:107] Finetuning from models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel
+
+[...]
+
+I0828 22:17:48.338963 11510 solver.cpp:165] Solving FlickrStyleCaffeNet
+I0828 22:17:48.339010 11510 solver.cpp:251] Iteration 0, Testing net (#0)
+I0828 22:18:14.313817 11510 solver.cpp:302]     Test net output #0: accuracy = 0.0308
+I0828 22:18:14.476822 11510 solver.cpp:195] Iteration 0, loss = 3.78589
+I0828 22:18:14.476878 11510 solver.cpp:397] Iteration 0, lr = 0.001
+I0828 22:18:19.700408 11510 solver.cpp:195] Iteration 20, loss = 3.25728
+I0828 22:18:19.700461 11510 solver.cpp:397] Iteration 20, lr = 0.001
+I0828 22:18:24.924685 11510 solver.cpp:195] Iteration 40, loss = 2.18531
+I0828 22:18:24.924741 11510 solver.cpp:397] Iteration 40, lr = 0.001
+I0828 22:18:30.114858 11510 solver.cpp:195] Iteration 60, loss = 2.4915
+I0828 22:18:30.114910 11510 solver.cpp:397] Iteration 60, lr = 0.001
+I0828 22:18:35.328071 11510 solver.cpp:195] Iteration 80, loss = 2.04539
+I0828 22:18:35.328127 11510 solver.cpp:397] Iteration 80, lr = 0.001
+I0828 22:18:40.588317 11510 solver.cpp:195] Iteration 100, loss = 2.1924
+I0828 22:18:40.588373 11510 solver.cpp:397] Iteration 100, lr = 0.001
+I0828 22:18:46.171576 11510 solver.cpp:195] Iteration 120, loss = 2.25107
+I0828 22:18:46.171669 11510 solver.cpp:397] Iteration 120, lr = 0.001
+I0828 22:18:51.757809 11510 solver.cpp:195] Iteration 140, loss = 1.355
+I0828 22:18:51.757863 11510 solver.cpp:397] Iteration 140, lr = 0.001
+I0828 22:18:57.345080 11510 solver.cpp:195] Iteration 160, loss = 1.40815
+I0828 22:18:57.345135 11510 solver.cpp:397] Iteration 160, lr = 0.001
+I0828 22:19:02.928794 11510 solver.cpp:195] Iteration 180, loss = 1.6558
+I0828 22:19:02.928850 11510 solver.cpp:397] Iteration 180, lr = 0.001
+I0828 22:19:08.514497 11510 solver.cpp:195] Iteration 200, loss = 0.88126
+I0828 22:19:08.514552 11510 solver.cpp:397] Iteration 200, lr = 0.001
+
+[...]
+
+I0828 22:22:40.789010 11510 solver.cpp:195] Iteration 960, loss = 0.112586
+I0828 22:22:40.789175 11510 solver.cpp:397] Iteration 960, lr = 0.001
+I0828 22:22:46.376626 11510 solver.cpp:195] Iteration 980, loss = 0.0959077
+I0828 22:22:46.376682 11510 solver.cpp:397] Iteration 980, lr = 0.001
+I0828 22:22:51.687258 11510 solver.cpp:251] Iteration 1000, Testing net (#0)
+I0828 22:23:17.438894 11510 solver.cpp:302]     Test net output #0: accuracy = 0.2356
+
    +
    + +

    Note how rapidly the loss went down. Although the 23.5% accuracy is only modest, it was achieved in only 1000, and evidence that the model is starting to learn quickly and well. +Once the model is fully fine-tuned on the whole training set over 100,000 iterations the final validation accuracy is 39.16%. +This takes ~7 hours in Caffe on a K40 GPU.

    + +

    For comparison, here is how the loss goes down when we do not start with a pre-trained model:

    + +
    I0828 22:24:18.624004 12919 solver.cpp:165] Solving FlickrStyleCaffeNet
+I0828 22:24:18.624099 12919 solver.cpp:251] Iteration 0, Testing net (#0)
+I0828 22:24:44.520992 12919 solver.cpp:302]     Test net output #0: accuracy = 0.0366
+I0828 22:24:44.676905 12919 solver.cpp:195] Iteration 0, loss = 3.47942
+I0828 22:24:44.677120 12919 solver.cpp:397] Iteration 0, lr = 0.001
+I0828 22:24:50.152454 12919 solver.cpp:195] Iteration 20, loss = 2.99694
+I0828 22:24:50.152509 12919 solver.cpp:397] Iteration 20, lr = 0.001
+I0828 22:24:55.736256 12919 solver.cpp:195] Iteration 40, loss = 3.0498
+I0828 22:24:55.736311 12919 solver.cpp:397] Iteration 40, lr = 0.001
+I0828 22:25:01.316514 12919 solver.cpp:195] Iteration 60, loss = 2.99549
+I0828 22:25:01.316567 12919 solver.cpp:397] Iteration 60, lr = 0.001
+I0828 22:25:06.899554 12919 solver.cpp:195] Iteration 80, loss = 3.00573
+I0828 22:25:06.899610 12919 solver.cpp:397] Iteration 80, lr = 0.001
+I0828 22:25:12.484624 12919 solver.cpp:195] Iteration 100, loss = 2.99094
+I0828 22:25:12.484678 12919 solver.cpp:397] Iteration 100, lr = 0.001
+I0828 22:25:18.069056 12919 solver.cpp:195] Iteration 120, loss = 3.01616
+I0828 22:25:18.069149 12919 solver.cpp:397] Iteration 120, lr = 0.001
+I0828 22:25:23.650928 12919 solver.cpp:195] Iteration 140, loss = 2.98786
+I0828 22:25:23.650984 12919 solver.cpp:397] Iteration 140, lr = 0.001
+I0828 22:25:29.235535 12919 solver.cpp:195] Iteration 160, loss = 3.00724
+I0828 22:25:29.235589 12919 solver.cpp:397] Iteration 160, lr = 0.001
+I0828 22:25:34.816898 12919 solver.cpp:195] Iteration 180, loss = 3.00099
+I0828 22:25:34.816953 12919 solver.cpp:397] Iteration 180, lr = 0.001
+I0828 22:25:40.396656 12919 solver.cpp:195] Iteration 200, loss = 2.99848
+I0828 22:25:40.396711 12919 solver.cpp:397] Iteration 200, lr = 0.001
+
+[...]
+
+I0828 22:29:12.539094 12919 solver.cpp:195] Iteration 960, loss = 2.99203
+I0828 22:29:12.539258 12919 solver.cpp:397] Iteration 960, lr = 0.001
+I0828 22:29:18.123092 12919 solver.cpp:195] Iteration 980, loss = 2.99345
+I0828 22:29:18.123147 12919 solver.cpp:397] Iteration 980, lr = 0.001
+I0828 22:29:23.432059 12919 solver.cpp:251] Iteration 1000, Testing net (#0)
+I0828 22:29:49.409044 12919 solver.cpp:302]     Test net output #0: accuracy = 0.0572
+
    +
    + +

    This model is only beginning to learn.

    + +

    Fine-tuning can be feasible when training from scratch would not be for lack of time or data. +Even in CPU mode each pass through the training set takes ~100 s. GPU fine-tuning is of course faster still and can learn a useful model in minutes or hours instead of days or weeks. +Furthermore, note that the model has only trained on < 2,000 instances. Transfer learning a new task like style recognition from the ImageNet pretraining can require much less data than training from scratch.

    + +

    Now try fine-tuning to your own tasks and data!

    + +

    Trained model

    + +

    We provide a model trained on all 80K images, with final accuracy of 39%. +Simply do ./scripts/download_model_binary.py models/finetune_flickr_style to obtain it.

    + +

    License

    + +

    The Flickr Style dataset as distributed here contains only URLs to images. +Some of the images may have copyright. +Training a category-recognition model for research/non-commercial use may constitute fair use of this data, but the result should not be used for commercial purposes.

    + + +
    +
    + + diff --git a/gathered/examples/imagenet.html b/gathered/examples/imagenet.html new file mode 100644 index 00000000000..8e9e4575695 --- /dev/null +++ b/gathered/examples/imagenet.html @@ -0,0 +1,168 @@ + + + + + + + + + Caffe | ImageNet tutorial + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + +

    Brewing ImageNet

    + +

    This guide is meant to get you ready to train your own model on your own data. +If you just want an ImageNet-trained network, then note that since training takes a lot of energy and we hate global warming, we provide the CaffeNet model trained as described below in the model zoo.

    + +

    Data Preparation

    + +

    The guide specifies all paths and assumes all commands are executed from the root caffe directory.

    + +

    By “ImageNet” we here mean the ILSVRC12 challenge, but you can easily train on the whole of ImageNet as well, just with more disk space, and a little longer training time.

    + +

    We assume that you already have downloaded the ImageNet training data and validation data, and they are stored on your disk like:

    + +
    /path/to/imagenet/train/n01440764/n01440764_10026.JPEG
+/path/to/imagenet/val/ILSVRC2012_val_00000001.JPEG
+
    +
    + +

    You will first need to prepare some auxiliary data for training. This data can be downloaded by:

    + +
    ./data/ilsvrc12/get_ilsvrc_aux.sh
+
    +
    + +

    The training and validation input are described in train.txt and val.txt as text listing all the files and their labels. Note that we use a different indexing for labels than the ILSVRC devkit: we sort the synset names in their ASCII order, and then label them from 0 to 999. See synset_words.txt for the synset/name mapping.

    + +

    You may want to resize the images to 256x256 in advance. By default, we do not explicitly do this because in a cluster environment, one may benefit from resizing images in a parallel fashion, using mapreduce. For example, Yangqing used his lightweight mincepie package. If you prefer things to be simpler, you can also use shell commands, something like:

    + +
    for name in /path/to/imagenet/val/*.JPEG; do
+    convert -resize 256x256\! $name $name
+done
+
    +
    + +

    Take a look at examples/imagenet/create_imagenet.sh. Set the paths to the train and val dirs as needed, and set “RESIZE=true” to resize all images to 256x256 if you haven’t resized the images in advance. +Now simply create the leveldbs with examples/imagenet/create_imagenet.sh. Note that examples/imagenet/ilsvrc12_train_leveldb and examples/imagenet/ilsvrc12_val_leveldb should not exist before this execution. It will be created by the script. GLOG_logtostderr=1 simply dumps more information for you to inspect, and you can safely ignore it.

    + +

    Compute Image Mean

    + +

    The model requires us to subtract the image mean from each image, so we have to compute the mean. tools/compute_image_mean.cpp implements that - it is also a good example to familiarize yourself on how to manipulate the multiple components, such as protocol buffers, leveldbs, and logging, if you are not familiar with them. Anyway, the mean computation can be carried out as:

    + +
    ./examples/imagenet/make_imagenet_mean.sh
+
    +
    + +

    which will make data/ilsvrc12/imagenet_mean.binaryproto.

    + +

    Model Definition

    + +

    We are going to describe a reference implementation for the approach first proposed by Krizhevsky, Sutskever, and Hinton in their NIPS 2012 paper.

    + +

    The network definition (models/bvlc_reference_caffenet/train_val.prototxt) follows the one in Krizhevsky et al. +Note that if you deviated from file paths suggested in this guide, you’ll need to adjust the relevant paths in the .prototxt files.

    + +

    If you look carefully at models/bvlc_reference_caffenet/train_val.prototxt, you will notice several include sections specifying either phase: TRAIN or phase: TEST. These sections allow us to define two closely related networks in one file: the network used for training and the network used for testing. These two networks are almost identical, sharing all layers except for those marked with include { phase: TRAIN } or include { phase: TEST }. In this case, only the input layers and one output layer are different.

    + +

    Input layer differences: The training network’s data input layer draws its data from examples/imagenet/ilsvrc12_train_leveldb and randomly mirrors the input image. The testing network’s data layer takes data from examples/imagenet/ilsvrc12_val_leveldb and does not perform random mirroring.

    + +

    Output layer differences: Both networks output the softmax_loss layer, which in training is used to compute the loss function and to initialize the backpropagation, while in validation this loss is simply reported. The testing network also has a second output layer, accuracy, which is used to report the accuracy on the test set. In the process of training, the test network will occasionally be instantiated and tested on the test set, producing lines like Test score #0: xxx and Test score #1: xxx. In this case score 0 is the accuracy (which will start around 1/1000 = 0.001 for an untrained network) and score 1 is the loss (which will start around 7 for an untrained network).

    + +

    We will also lay out a protocol buffer for running the solver. Let’s make a few plans:

    + +
      +
    • We will run in batches of 256, and run a total of 450,000 iterations (about 90 epochs).
      • +
    • For every 1,000 iterations, we test the learned net on the validation data.
      • +
    • We set the initial learning rate to 0.01, and decrease it every 100,000 iterations (about 20 epochs).
      • +
    • Information will be displayed every 20 iterations.
      • +
    • The network will be trained with momentum 0.9 and a weight decay of 0.0005.
      • +
    • For every 10,000 iterations, we will take a snapshot of the current status.
      • +
    + +

    Sound good? This is implemented in models/bvlc_reference_caffenet/solver.prototxt.

    + +

    Training ImageNet

    + +

    Ready? Let’s train.

    + +
    ./build/tools/caffe train --solver=models/bvlc_reference_caffenet/solver.prototxt
+
    +
    + +

    Sit back and enjoy!

    + +

    On a K40 machine, every 20 iterations take about 26.5 seconds to run (while a on a K20 this takes 36 seconds), so effectively about 5.2 ms per image for the full forward-backward pass. About 2 ms of this is on forward, and the rest is backward. If you are interested in dissecting the computation time, you can run

    + +
    ./build/tools/caffe time --model=models/bvlc_reference_caffenet/train_val.prototxt
+
    +
    + +

    Resume Training?

    + +

    We all experience times when the power goes out, or we feel like rewarding ourself a little by playing Battlefield (does anyone still remember Quake?). Since we are snapshotting intermediate results during training, we will be able to resume from snapshots. This can be done as easy as:

    + +
    ./build/tools/caffe train --solver=models/bvlc_reference_caffenet/solver.prototxt --snapshot=models/bvlc_reference_caffenet/caffenet_train_iter_10000.solverstate
+
    +
    + +

    where in the script caffenet_train_iter_10000.solverstate is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc).

    + +

    Parting Words

    + +

    Hope you liked this recipe! +Many researchers have gone further since the ILSVRC 2012 challenge, changing the network architecture and/or fine-tuning the various parameters in the network to address new data and tasks. +Caffe lets you explore different network choices more easily by simply writing different prototxt files - isn’t that exciting?

    + +

    And since now you have a trained network, check out how to use it with the Python interface for classifying ImageNet.

    + + +
    +
    + + diff --git a/gathered/examples/mnist.html b/gathered/examples/mnist.html new file mode 100644 index 00000000000..4bbf2e2909e --- /dev/null +++ b/gathered/examples/mnist.html @@ -0,0 +1,377 @@ + + + + + + + + + Caffe | LeNet MNIST Tutorial + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + +

    Training LeNet on MNIST with Caffe

    + +

    We will assume that you have Caffe successfully compiled. If not, please refer to the Installation page. In this tutorial, we will assume that your Caffe installation is located at CAFFE_ROOT.

    + +

    Prepare Datasets

    + +

    You will first need to download and convert the data format from the MNIST website. To do this, simply run the following commands:

    + +
    cd $CAFFE_ROOT
+./data/mnist/get_mnist.sh
+./examples/mnist/create_mnist.sh
+
    +
    + +

    If it complains that wget or gunzip are not installed, you need to install them respectively. After running the script there should be two datasets, mnist_train_lmdb, and mnist_test_lmdb.

    + +

    LeNet: the MNIST Classification Model

    + +

    Before we actually run the training program, let’s explain what will happen. We will use the LeNet network, which is known to work well on digit classification tasks. We will use a slightly different version from the original LeNet implementation, replacing the sigmoid activations with Rectified Linear Unit (ReLU) activations for the neurons.

    + +

    The design of LeNet contains the essence of CNNs that are still used in larger models such as the ones in ImageNet. In general, it consists of a convolutional layer followed by a pooling layer, another convolution layer followed by a pooling layer, and then two fully connected layers similar to the conventional multilayer perceptrons. We have defined the layers in $CAFFE_ROOT/examples/mnist/lenet_train_test.prototxt.

    + +

    Define the MNIST Network

    + +

    This section explains the lenet_train_test.prototxt model definition that specifies the LeNet model for MNIST handwritten digit classification. We assume that you are familiar with Google Protobuf, and assume that you have read the protobuf definitions used by Caffe, which can be found at $CAFFE_ROOT/src/caffe/proto/caffe.proto.

    + +

    Specifically, we will write a caffe::NetParameter (or in python, caffe.proto.caffe_pb2.NetParameter) protobuf. We will start by giving the network a name:

    + +
    name: "LeNet"
+
    +
    + +

    Writing the Data Layer

    + +

    Currently, we will read the MNIST data from the lmdb we created earlier in the demo. This is defined by a data layer:

    + +
    layer {
+  name: "mnist"
+  type: "Data"
+  transform_param {
+    scale: 0.00390625
+  }
+  data_param {
+    source: "mnist_train_lmdb"
+    backend: LMDB
+    batch_size: 64
+  }
+  top: "data"
+  top: "label"
+}
+
    +
    + +

    Specifically, this layer has name mnist, type data, and it reads the data from the given lmdb source. We will use a batch size of 64, and scale the incoming pixels so that they are in the range [0,1). Why 0.00390625? It is 1 divided by 256. And finally, this layer produces two blobs, one is the data blob, and one is the label blob.

    + +

    Writing the Convolution Layer

    + +

    Let’s define the first convolution layer:

    + +
    layer {
+  name: "conv1"
+  type: "Convolution"
+  param { lr_mult: 1 }
+  param { lr_mult: 2 }
+  convolution_param {
+    num_output: 20
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+  bottom: "data"
+  top: "conv1"
+}
+
    +
    + +

    This layer takes the data blob (it is provided by the data layer), and produces the conv1 layer. It produces outputs of 20 channels, with the convolutional kernel size 5 and carried out with stride 1.

    + +

    The fillers allow us to randomly initialize the value of the weights and bias. For the weight filler, we will use the xavier algorithm that automatically determines the scale of initialization based on the number of input and output neurons. For the bias filler, we will simply initialize it as constant, with the default filling value 0.

    + +

    lr_mults are the learning rate adjustments for the layer’s learnable parameters. In this case, we will set the weight learning rate to be the same as the learning rate given by the solver during runtime, and the bias learning rate to be twice as large as that - this usually leads to better convergence rates.

    + +

    Writing the Pooling Layer

    + +

    Phew. Pooling layers are actually much easier to define:

    + +
    layer {
+  name: "pool1"
+  type: "Pooling"
+  pooling_param {
+    kernel_size: 2
+    stride: 2
+    pool: MAX
+  }
+  bottom: "conv1"
+  top: "pool1"
+}
+
    +
    + +

    This says we will perform max pooling with a pool kernel size 2 and a stride of 2 (so no overlapping between neighboring pooling regions).

    + +

    Similarly, you can write up the second convolution and pooling layers. Check $CAFFE_ROOT/examples/mnist/lenet_train_test.prototxt for details.

    + +

    Writing the Fully Connected Layer

    + +

    Writing a fully connected layer is also simple:

    + +
    layer {
+  name: "ip1"
+  type: "InnerProduct"
+  param { lr_mult: 1 }
+  param { lr_mult: 2 }
+  inner_product_param {
+    num_output: 500
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+  bottom: "pool2"
+  top: "ip1"
+}
+
    +
    + +

    This defines a fully connected layer (known in Caffe as an InnerProduct layer) with 500 outputs. All other lines look familiar, right?

    + +

    Writing the ReLU Layer

    + +

    A ReLU Layer is also simple:

    + +
    layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "ip1"
+  top: "ip1"
+}
+
    +
    + +

    Since ReLU is an element-wise operation, we can do in-place operations to save some memory. This is achieved by simply giving the same name to the bottom and top blobs. Of course, do NOT use duplicated blob names for other layer types!

    + +

    After the ReLU layer, we will write another innerproduct layer:

    + +
    layer {
+  name: "ip2"
+  type: "InnerProduct"
+  param { lr_mult: 1 }
+  param { lr_mult: 2 }
+  inner_product_param {
+    num_output: 10
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+  bottom: "ip1"
+  top: "ip2"
+}
+
    +
    + +

    Writing the Loss Layer

    + +

    Finally, we will write the loss!

    + +
    layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip2"
+  bottom: "label"
+}
+
    +
    + +

    The softmax_loss layer implements both the softmax and the multinomial logistic loss (that saves time and improves numerical stability). It takes two blobs, the first one being the prediction and the second one being the label provided by the data layer (remember it?). It does not produce any outputs - all it does is to compute the loss function value, report it when backpropagation starts, and initiates the gradient with respect to ip2. This is where all magic starts.

    + +

    Additional Notes: Writing Layer Rules

    + +

    Layer definitions can include rules for whether and when they are included in the network definition, like the one below:

    + +
    layer {
+  // ...layer definition...
+  include: { phase: TRAIN }
+}
+
    +
    + +

    This is a rule, which controls layer inclusion in the network, based on current network’s state. +You can refer to $CAFFE_ROOT/src/caffe/proto/caffe.proto for more information about layer rules and model schema.

    + +

    In the above example, this layer will be included only in TRAIN phase. +If we change TRAIN with TEST, then this layer will be used only in test phase. +By default, that is without layer rules, a layer is always included in the network. +Thus, lenet_train_test.prototxt has two DATA layers defined (with different batch_size), one for the training phase and one for the testing phase. +Also, there is an Accuracy layer which is included only in TEST phase for reporting the model accuracy every 100 iteration, as defined in lenet_solver.prototxt.

    + +

    Define the MNIST Solver

    + +

    Check out the comments explaining each line in the prototxt $CAFFE_ROOT/examples/mnist/lenet_solver.prototxt:

    + +
    # The train/test net protocol buffer definition
+net: "examples/mnist/lenet_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.9
+weight_decay: 0.0005
+# The learning rate policy
+lr_policy: "inv"
+gamma: 0.0001
+power: 0.75
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet"
+# solver mode: CPU or GPU
+solver_mode: GPU
+
    +
    + +

    Training and Testing the Model

    + +

    Training the model is simple after you have written the network definition protobuf and solver protobuf files. Simply run train_lenet.sh, or the following command directly:

    + +
    cd $CAFFE_ROOT
+./examples/mnist/train_lenet.sh
+
    +
    + +

    train_lenet.sh is a simple script, but here is a quick explanation: the main tool for training is caffe with action train and the solver protobuf text file as its argument.

    + +

    When you run the code, you will see a lot of messages flying by like this:

    + +
    I1203 net.cpp:66] Creating Layer conv1
+I1203 net.cpp:76] conv1 <- data
+I1203 net.cpp:101] conv1 -> conv1
+I1203 net.cpp:116] Top shape: 20 24 24
+I1203 net.cpp:127] conv1 needs backward computation.
+
    +
    + +

    These messages tell you the details about each layer, its connections and its output shape, which may be helpful in debugging. After the initialization, the training will start:

    + +
    I1203 net.cpp:142] Network initialization done.
+I1203 solver.cpp:36] Solver scaffolding done.
+I1203 solver.cpp:44] Solving LeNet
+
    +
    + +

    Based on the solver setting, we will print the training loss function every 100 iterations, and test the network every 500 iterations. You will see messages like this:

    + +
    I1203 solver.cpp:204] Iteration 100, lr = 0.00992565
+I1203 solver.cpp:66] Iteration 100, loss = 0.26044
+...
+I1203 solver.cpp:84] Testing net
+I1203 solver.cpp:111] Test score #0: 0.9785
+I1203 solver.cpp:111] Test score #1: 0.0606671
+
    +
    + +

    For each training iteration, lr is the learning rate of that iteration, and loss is the training function. For the output of the testing phase, score 0 is the accuracy, and score 1 is the testing loss function.

    + +

    And after a few minutes, you are done!

    + +
    I1203 solver.cpp:84] Testing net
+I1203 solver.cpp:111] Test score #0: 0.9897
+I1203 solver.cpp:111] Test score #1: 0.0324599
+I1203 solver.cpp:126] Snapshotting to lenet_iter_10000
+I1203 solver.cpp:133] Snapshotting solver state to lenet_iter_10000.solverstate
+I1203 solver.cpp:78] Optimization Done.
+
    +
    + +

    The final model, stored as a binary protobuf file, is stored at

    + +
    lenet_iter_10000
+
    +
    + +

    which you can deploy as a trained model in your application, if you are training on a real-world application dataset.

    + +

    Um… How about GPU training?

    + +

    You just did! All the training was carried out on the GPU. In fact, if you would like to do training on CPU, you can simply change one line in lenet_solver.prototxt:

    + +
    # solver mode: CPU or GPU
+solver_mode: CPU
+
    +
    + +

    and you will be using CPU for training. Isn’t that easy?

    + +

    MNIST is a small dataset, so training with GPU does not really introduce too much benefit due to communication overheads. On larger datasets with more complex models, such as ImageNet, the computation speed difference will be more significant.

    + +

    How to reduce the learning rate at fixed steps?

    +

    Look at lenet_multistep_solver.prototxt

    + + +
    +
    + + diff --git a/gathered/examples/net_surgery.ipynb b/gathered/examples/net_surgery.ipynb new file mode 100644 index 00000000000..3c1ddaf6511 --- /dev/null +++ b/gathered/examples/net_surgery.ipynb @@ -0,0 +1,6963 @@ + + + + + + + + + Caffe | Editing model parameters + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Net Surgery\n", + "\n", + "Caffe networks can be transformed to your particular needs by editing the model parameters. The data, diffs, and parameters of a net are all exposed in pycaffe.\n", + "\n", + "Roll up your sleeves for net surgery with pycaffe!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "# Make sure that caffe is on the python path:\n", + "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", + "import sys\n", + "sys.path.insert(0, caffe_root + 'python')\n", + "\n", + "import caffe\n", + "\n", + "# configure plotting\n", + "plt.rcParams['figure.figsize'] = (10, 10)\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Designer Filters\n", + "\n", + "To show how to load, manipulate, and save parameters we'll design our own filters into a simple network that's only a single convolution layer. This net has two blobs, `data` for the input and `conv` for the convolution output and one parameter `conv` for the convolution filter weights and biases." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "blobs ['data', 'conv']\n", + "params ['conv']\n" + ] + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAlIAAAHNCAYAAADVB5V4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWuMZdl13/c/tx733np393T3PPkYDUccPsQZiaRkCYpE\n", + "CYklOwYhfwjCIAEiJDLswAkQf3AQIEoC64OcIEDiIHESBAiCCAkkJ4GtJHCM+KHQjmGZtmxKJBVC\n", + "wxkOZyac4Uz3dHe97q1bt+7Jh+r/rt/517499ER008xZQKGq7j1nn73XXns9/mvtfZq2bdVTTz31\n", + "1FNPPfXU0z86DR52B3rqqaeeeuqpp57+SaXekeqpp5566qmnnnp6j9Q7Uj311FNPPfXUU0/vkXpH\n", + "qqeeeuqpp5566uk9Uu9I9dRTTz311FNPPb1H6h2pnnrqqaeeeuqpp/dIvSPVU089/b5T0zT/RdM0\n", + 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+ "net.blobs['data'].data[...] = im_input" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The convolution weights are initialized from Gaussian noise while the biases are initialized to zero. These random filters give output somewhat like edge detections." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAicAAACbCAYAAAC5xzv6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVuMbVl2pvWvfb/FjkueW568VN5dXSUbl4sHbBBYbYRK\n", + "jRqEJW7qfkD90MItN4gGgQC3QHYJiwdejJFfcNvgRtBuaBAPyA9gt5FBcrnc1bbLVemqPFmZlZdz\n", + "TuaJc+KybxH7sniI8839rxFrx4lMU7mjKveQQhGx97rMNeeYY/zjH2POleV5ro1sZCMb2chGNrKR\n", + "qyKVdTdgIxvZyEY2spGNbMRlA042spGNbGQjG9nIlZINONnIRjaykY1sZCNXSjbgZCMb2chGNrKR\n", + "jVwp2YCTjWxkIxvZyEY2cqVkA042spGNbGQjG9nIlZJPDTjJsuyHsiz7x1mWHWVZ9jezLPuVLMt+\n", + "7vF3P5ll2TvrbuNGNvJxZKPbG/lBlY1uf3rlUwNOJP2Hkv6vPM/7eZ7/13me/0ye518uOzDLsrey\n", + "LPuL36uGZFn2lSzLXsmy7KUsy/4wfLeXZdn/mmXZ4HE7/s3vURv+8yzLfuOqXm8jH0m+X3T7Z7Ms\n", + "+2qWZZMsy37te9iGjW7/4MiV1+0syxpZlv3q4/sfZVn2tSzLvvQ9asOnRrc/TeDkM5K+ccljc0nZ\n", + 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net.blobs['conv'].data.min(), net.blobs['conv'].data.max()\n", + " for i in range(3):\n", + " plt.subplot(1,4,i+2)\n", + " plt.title(\"filter #{} output\".format(i))\n", + " plt.imshow(net.blobs['conv'].data[0, i], vmin=filt_min, vmax=filt_max)\n", + " plt.tight_layout()\n", + " plt.axis('off')\n", + "\n", + "# filter the image with initial \n", + "show_filters(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Raising the bias of a filter will correspondingly raise its output:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pre-surgery output mean -0.02\n", + "post-surgery output mean 0.98\n" + ] + } + ], + "source": [ + "# pick first filter output\n", + "conv0 = net.blobs['conv'].data[0, 0]\n", + "print(\"pre-surgery output mean {:.2f}\".format(conv0.mean()))\n", + "# set first filter bias to 1\n", + "net.params['conv'][1].data[0] = 1.\n", + "net.forward()\n", + "print(\"post-surgery output mean {:.2f}\".format(conv0.mean()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Altering the filter weights is more exciting since we can assign any kernel like Gaussian blur, the Sobel operator for edges, and so on. The following surgery turns the 0th filter into a Gaussian blur and the 1st and 2nd filters into the horizontal and vertical gradient parts of the Sobel operator.\n", + "\n", + "See how the 0th output is blurred, the 1st picks up horizontal edges, and the 2nd picks up vertical edges." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAicAAACbCAYAAAC5xzv6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWuMbNl13/c/9eh6V7/uvT1zHzNDzgw5HNIWNInpMCEi\n", + "2wkCwYElBFASBTLg2DCM2LATSAkSJ5GlWDJi5EMAA0ngL/EjkQPFcuIQgREEcCIbAkJD9JhDgdJ4\n", + "yOFjHnfuq2/fflV1VXc9Tj7U/e3+1+pTfe+MqOkmWQtodHfVOfvsvfbaa/3XY++T5XmuJS1pSUta\n", + "0pKWtKTLQqWL7sCSlrSkJS1pSUtaktMSnCxpSUta0pKWtKRLRUtwsqQlLWlJS1rSki4VLcHJkpa0\n", + "pCUtaUlLulS0BCdLWtKSlrSkJS3pUtESnCxpSUta0pKWtKRLRT804CTLsk9nWfa1LMsOsiz7C1mW\n", + 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make Sobel operator for edge detection\n", + "net.params['conv'][0].data[1:] = 0.\n", + "sobel = np.array((-1, -2, -1, 0, 0, 0, 1, 2, 1), dtype=np.float32).reshape((3,3))\n", + "net.params['conv'][0].data[1, 0, 1:-1, 1:-1] = sobel # horizontal\n", + "net.params['conv'][0].data[2, 0, 1:-1, 1:-1] = sobel.T # vertical\n", + "show_filters(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With net surgery, parameters can be transplanted across nets, regularized by custom per-parameter operations, and transformed according to your schemes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Casting a Classifier into a Fully Convolutional Network\n", + "\n", + "Let's take the standard Caffe Reference ImageNet model \"CaffeNet\" and transform it into a fully convolutional net for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional network (convnet) structure by amortizing the computation of overlapping receptive fields.\n", + "\n", + "To do so we translate the `InnerProduct` matrix multiplication layers of CaffeNet into `Convolutional` layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 $\\times$ 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1,2c1\r\n", + "< # Fully convolutional network version of CaffeNet.\r\n", + "< name: \"CaffeNetConv\"\r\n", + "---\r\n", + "> name: \"CaffeNet\"\r\n", + "7,11c6\r\n", + "< input_param {\r\n", + "< # initial shape for a fully convolutional network:\r\n", + "< # the shape can be set for each input by reshape.\r\n", + "< shape: { dim: 1 dim: 3 dim: 451 dim: 451 }\r\n", + "< }\r\n", + "---\r\n", + "> input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }\r\n", + "157,158c152,153\r\n", + "< name: \"fc6-conv\"\r\n", + "< type: \"Convolution\"\r\n", + "---\r\n", + "> name: \"fc6\"\r\n", + "> type: \"InnerProduct\"\r\n", + "160,161c155,156\r\n", + "< top: \"fc6-conv\"\r\n", + "< convolution_param {\r\n", + "---\r\n", + "> top: \"fc6\"\r\n", + "> inner_product_param {\r\n", + "163d157\r\n", + "< kernel_size: 6\r\n", + "169,170c163,164\r\n", + "< bottom: \"fc6-conv\"\r\n", + "< top: \"fc6-conv\"\r\n", + "---\r\n", + "> bottom: \"fc6\"\r\n", + "> top: \"fc6\"\r\n", + "175,176c169,170\r\n", + "< bottom: \"fc6-conv\"\r\n", + "< top: \"fc6-conv\"\r\n", + "---\r\n", + "> bottom: \"fc6\"\r\n", + "> top: \"fc6\"\r\n", + "182,186c176,180\r\n", + "< name: \"fc7-conv\"\r\n", + "< type: \"Convolution\"\r\n", + "< bottom: \"fc6-conv\"\r\n", + "< top: \"fc7-conv\"\r\n", + "< convolution_param {\r\n", + "---\r\n", + "> name: \"fc7\"\r\n", + "> type: \"InnerProduct\"\r\n", + "> bottom: \"fc6\"\r\n", + "> top: \"fc7\"\r\n", + "> inner_product_param {\r\n", + "188d181\r\n", + "< kernel_size: 1\r\n", + "194,195c187,188\r\n", + "< bottom: \"fc7-conv\"\r\n", + "< top: \"fc7-conv\"\r\n", + "---\r\n", + "> bottom: \"fc7\"\r\n", + "> top: \"fc7\"\r\n", + "200,201c193,194\r\n", + "< bottom: \"fc7-conv\"\r\n", + "< top: \"fc7-conv\"\r\n", + "---\r\n", + "> bottom: \"fc7\"\r\n", + "> top: \"fc7\"\r\n", + "207,211c200,204\r\n", + "< name: \"fc8-conv\"\r\n", + "< type: \"Convolution\"\r\n", + "< bottom: \"fc7-conv\"\r\n", + "< top: \"fc8-conv\"\r\n", + "< convolution_param {\r\n", + "---\r\n", + "> name: \"fc8\"\r\n", + "> type: \"InnerProduct\"\r\n", + "> bottom: \"fc7\"\r\n", + "> top: \"fc8\"\r\n", + "> inner_product_param {\r\n", + "213d205\r\n", + "< kernel_size: 1\r\n", + "219c211\r\n", + "< bottom: \"fc8-conv\"\r\n", + "---\r\n", + "> bottom: \"fc8\"\r\n" + ] + } + ], + "source": [ + "!diff net_surgery/bvlc_caffenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The only differences needed in the architecture are to change the fully connected classifier inner product layers into convolutional layers with the right filter size -- 6 x 6, since the reference model classifiers take the 36 elements of `pool5` as input -- and stride 1 for dense classification. Note that the layers are renamed so that Caffe does not try to blindly load the old parameters when it maps layer names to the pretrained model." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fc6 weights are (4096, 9216) dimensional and biases are (4096,) dimensional\n", + "fc7 weights are (4096, 4096) dimensional and biases are (4096,) dimensional\n", + "fc8 weights are (1000, 4096) dimensional and biases are (1000,) dimensional\n" + ] + } + ], + "source": [ + "# Load the original network and extract the fully connected layers' parameters.\n", + "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', \n", + " '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel', \n", + " caffe.TEST)\n", + "params = ['fc6', 'fc7', 'fc8']\n", + "# fc_params = {name: (weights, biases)}\n", + "fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}\n", + "\n", + "for fc in params:\n", + " print '{} weights are {} dimensional and biases are {} dimensional'.format(fc, fc_params[fc][0].shape, fc_params[fc][1].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Consider the shapes of the inner product parameters. The weight dimensions are the output and input sizes while the bias dimension is the output size." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fc6-conv weights are (4096, 256, 6, 6) dimensional and biases are (4096,) dimensional\n", + "fc7-conv weights are (4096, 4096, 1, 1) dimensional and biases are (4096,) dimensional\n", + "fc8-conv weights are (1000, 4096, 1, 1) dimensional and biases are (1000,) dimensional\n" + ] + } + ], + "source": [ + "# Load the fully convolutional network to transplant the parameters.\n", + "net_full_conv = caffe.Net('net_surgery/bvlc_caffenet_full_conv.prototxt', \n", + " '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n", + " caffe.TEST)\n", + "params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\n", + "# conv_params = {name: (weights, biases)}\n", + "conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}\n", + "\n", + "for conv in params_full_conv:\n", + " print '{} weights are {} dimensional and biases are {} dimensional'.format(conv, conv_params[conv][0].shape, conv_params[conv][1].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The convolution weights are arranged in output $\\times$ input $\\times$ height $\\times$ width dimensions. To map the inner product weights to convolution filters, we could roll the flat inner product vectors into channel $\\times$ height $\\times$ width filter matrices, but actually these are identical in memory (as row major arrays) so we can assign them directly.\n", + "\n", + "The biases are identical to those of the inner product.\n", + "\n", + "Let's transplant!" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for pr, pr_conv in zip(params, params_full_conv):\n", + " conv_params[pr_conv][0].flat = fc_params[pr][0].flat # flat unrolls the arrays\n", + " conv_params[pr_conv][1][...] = fc_params[pr][1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, save the new model weights." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "net_full_conv.save('net_surgery/bvlc_caffenet_full_conv.caffemodel')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To conclude, let's make a classification map from the example cat image and visualize the confidence of \"tiger cat\" as a probability heatmap. This gives an 8-by-8 prediction on overlapping regions of the 451 $\\times$ 451 input." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[282 282 281 281 281 281 277 282]\n", + " [281 283 283 281 281 281 281 282]\n", + " [283 283 283 283 283 283 287 282]\n", + " [283 283 283 281 283 283 283 259]\n", + " [283 283 283 283 283 283 283 259]\n", + " [283 283 283 283 283 283 259 259]\n", + " [283 283 283 283 259 259 259 277]\n", + " [335 335 283 259 263 263 263 277]]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAXMAAAC5CAYAAADavt/0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWnMbWl23/Vbz7T3PtM73aHGrqrurrbdbbttuulYVhqT\n", + 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net_full_conv.forward_all(data=np.asarray([transformer.preprocess('data', im)]))\n", + "print out['prob'][0].argmax(axis=0)\n", + "# show net input and confidence map (probability of the top prediction at each location)\n", + "plt.subplot(1, 2, 1)\n", + "plt.imshow(transformer.deprocess('data', net_full_conv.blobs['data'].data[0]))\n", + "plt.subplot(1, 2, 2)\n", + "plt.imshow(out['prob'][0,281])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The classifications include various cats -- 282 = tiger cat, 281 = tabby, 283 = persian -- and foxes and other mammals.\n", + "\n", + "In this way the fully connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself.\n", + "\n", + "Note that this model isn't totally appropriate for sliding-window detection since it was trained for whole-image classification. Nevertheless it can work just fine. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (This is an exercise for the reader.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*A thank you to Rowland Depp for first suggesting this trick.*" + ] + } + ], + "metadata": { + "description": "How to do net surgery and manually change model parameters for custom use.", + "example_name": "Editing model parameters", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + }, + "priority": 5 + }, + "nbformat": 4, + "nbformat_minor": 0 +} + + +
    +
    + + diff --git a/gathered/examples/pascal-multilabel-with-datalayer.ipynb b/gathered/examples/pascal-multilabel-with-datalayer.ipynb new file mode 100644 index 00000000000..34aa94e334d --- /dev/null +++ b/gathered/examples/pascal-multilabel-with-datalayer.ipynb @@ -0,0 +1,541 @@ + + + + + + + + + Caffe | Multilabel Classification with Python Data Layer + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multilabel classification on PASCAL using python data-layers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial we will do multilabel classification on PASCAL VOC 2012.\n", + "\n", + "Multilabel classification is a generalization of multiclass classification, where each instance (image) can belong to many classes. For example, an image may both belong to a \"beach\" category and a \"vacation pictures\" category. In multiclass classification, on the other hand, each image belongs to a single class.\n", + "\n", + "Caffe supports multilabel classification through the SigmoidCrossEntropyLoss layer, and we will load data using a Python data layer. Data could also be provided through HDF5 or LMDB data layers, but the python data layer provides endless flexibility, so that's what we will use." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Preliminaries\n", + "\n", + "* First, make sure you compile caffe using\n", + "WITH_PYTHON_LAYER := 1\n", + "\n", + "* Second, download PASCAL VOC 2012. It's available here: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html\n", + "\n", + "* Third, import modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import sys \n", + "import os\n", + "\n", + "import numpy as np\n", + "import os.path as osp\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from copy import copy\n", + "\n", + "% matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (6, 6)\n", + "\n", + "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", + "sys.path.append(caffe_root + 'python')\n", + "import caffe # If you get \"No module named _caffe\", either you have not built pycaffe or you have the wrong path.\n", + "\n", + "from caffe import layers as L, params as P # Shortcuts to define the net prototxt.\n", + "\n", + "sys.path.append(\"pycaffe/layers\") # the datalayers we will use are in this directory.\n", + "sys.path.append(\"pycaffe\") # the tools file is in this folder\n", + "\n", + "import tools #this contains some tools that we need" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Fourth, set data directories and initialize caffe" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# set data root directory, e.g:\n", + "pascal_root = osp.join(caffe_root, 'data/pascal/VOC2012')\n", + "\n", + "# these are the PASCAL classes, we'll need them later.\n", + "classes = np.asarray(['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'])\n", + "\n", + "# make sure we have the caffenet weight downloaded.\n", + "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n", + " print(\"Downloading pre-trained CaffeNet model...\")\n", + " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet\n", + "\n", + "# initialize caffe for gpu mode\n", + "caffe.set_mode_gpu()\n", + "caffe.set_device(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Define network prototxts\n", + "\n", + "* Let's start by defining the nets using caffe.NetSpec. Note how we used the SigmoidCrossEntropyLoss layer. This is the right loss for multilabel classification. Also note how the data layer is defined." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# helper function for common structures\n", + "def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):\n", + " conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n", + " num_output=nout, pad=pad, group=group)\n", + " return conv, L.ReLU(conv, in_place=True)\n", + "\n", + "# another helper function\n", + "def fc_relu(bottom, nout):\n", + " fc = L.InnerProduct(bottom, num_output=nout)\n", + " return fc, L.ReLU(fc, in_place=True)\n", + "\n", + "# yet another helper function\n", + "def max_pool(bottom, ks, stride=1):\n", + " return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n", + "\n", + "# main netspec wrapper\n", + "def caffenet_multilabel(data_layer_params, datalayer):\n", + " # setup the python data layer \n", + " n = caffe.NetSpec()\n", + " n.data, n.label = L.Python(module = 'pascal_multilabel_datalayers', layer = datalayer, \n", + " ntop = 2, param_str=str(data_layer_params))\n", + "\n", + " # the net itself\n", + " n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4)\n", + " n.pool1 = max_pool(n.relu1, 3, stride=2)\n", + " n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\n", + " n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2)\n", + " n.pool2 = max_pool(n.relu2, 3, stride=2)\n", + " n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\n", + " n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1)\n", + " n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2)\n", + " n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2)\n", + " n.pool5 = max_pool(n.relu5, 3, stride=2)\n", + " n.fc6, n.relu6 = fc_relu(n.pool5, 4096)\n", + " n.drop6 = L.Dropout(n.relu6, in_place=True)\n", + " n.fc7, n.relu7 = fc_relu(n.drop6, 4096)\n", + " n.drop7 = L.Dropout(n.relu7, in_place=True)\n", + " n.score = L.InnerProduct(n.drop7, num_output=20)\n", + " n.loss = L.SigmoidCrossEntropyLoss(n.score, n.label)\n", + " \n", + " return str(n.to_proto())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Write nets and solver files\n", + "\n", + "* Now we can crete net and solver prototxts. For the solver, we use the CaffeSolver class from the \"tools\" module" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "workdir = './pascal_multilabel_with_datalayer'\n", + "if not os.path.isdir(workdir):\n", + " os.makedirs(workdir)\n", + "\n", + "solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, \"trainnet.prototxt\"), testnet_prototxt_path = osp.join(workdir, \"valnet.prototxt\"))\n", + "solverprototxt.sp['display'] = \"1\"\n", + "solverprototxt.sp['base_lr'] = \"0.0001\"\n", + "solverprototxt.write(osp.join(workdir, 'solver.prototxt'))\n", + "\n", + "# write train net.\n", + "with open(osp.join(workdir, 'trainnet.prototxt'), 'w') as f:\n", + " # provide parameters to the data layer as a python dictionary. Easy as pie!\n", + " data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'train', pascal_root = pascal_root)\n", + " f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))\n", + "\n", + "# write validation net.\n", + "with open(osp.join(workdir, 'valnet.prototxt'), 'w') as f:\n", + " data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'val', pascal_root = pascal_root)\n", + " f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* This net uses a python datalayer: 'PascalMultilabelDataLayerSync', which is defined in './pycaffe/layers/pascal_multilabel_datalayers.py'. \n", + "\n", + "* Take a look at the code. It's quite straight-forward, and gives you full control over data and labels.\n", + "\n", + "* Now we can load the caffe solver as usual." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BatchLoader initialized with 5717 images\n", + "PascalMultilabelDataLayerSync initialized for split: train, with bs: 128, im_shape: [227, 227].\n", + "BatchLoader initialized with 5823 images\n", + "PascalMultilabelDataLayerSync initialized for split: val, with bs: 128, im_shape: [227, 227].\n" + ] + } + ], + "source": [ + "solver = caffe.SGDSolver(osp.join(workdir, 'solver.prototxt'))\n", + "solver.net.copy_from(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n", + "solver.test_nets[0].share_with(solver.net)\n", + "solver.step(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Let's check the data we have loaded." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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LsnYgBhwRxUFI4W5MafBLwQLe6UAraRuS0aebiWI7nIvb2x1QRVo8BZ9vA9YZ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\nYb2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9ZrNesbnbo40lp0iYJsaL3LuYtFBPqcjKkfKAJzDEnsuU8UmhjEWlwMIcUjfP4v/H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ms0IlK897IZMylGQxwVP0Mx2XSjDQxEDb7/jHP/8jP/74HVkNx1YjNFM1dm/7R56fQIVe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\npfLk1EJeqVQqT04t5JVKpfLk/AsNeRHC6zkzzAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "transformer = tools.SimpleTransformer() # This is simply to add back the bias, re-shuffle the color channels to RGB, and so on...\n", + "image_index = 0 # First image in the batch.\n", + "plt.figure()\n", + "plt.imshow(transformer.deprocess(copy(solver.net.blobs['data'].data[image_index, ...])))\n", + "gtlist = solver.net.blobs['label'].data[image_index, ...].astype(np.int)\n", + "plt.title('GT: {}'.format(classes[np.where(gtlist)]))\n", + "plt.axis('off');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* NOTE: we are readin the image from the data layer, so the resolution is lower than the original PASCAL image." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Train a net.\n", + "\n", + "* Let's train the net. First, though, we need some way to measure the accuracy. Hamming distance is commonly used in multilabel problems. We also need a simple test loop. Let's write that down. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def hamming_distance(gt, est):\n", + " return sum([1 for (g, e) in zip(gt, est) if g == e]) / float(len(gt))\n", + "\n", + "def check_accuracy(net, num_batches, batch_size = 128):\n", + " acc = 0.0\n", + " for t in range(num_batches):\n", + " net.forward()\n", + " gts = net.blobs['label'].data\n", + " ests = net.blobs['score'].data > 0\n", + " for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n", + " acc += hamming_distance(gt, est)\n", + " return acc / (num_batches * batch_size)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Alright, now let's train for a while" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "itt:100 accuracy:0.9526\n", + "itt:200 accuracy:0.9563\n", + "itt:300 accuracy:0.9582\n", + "itt:400 accuracy:0.9586\n", + "itt:500 accuracy:0.9597\n", + "itt:600 accuracy:0.9591\n" + ] + } + ], + "source": [ + "for itt in range(6):\n", + " solver.step(100)\n", + " print 'itt:{:3d}'.format((itt + 1) * 100), 'accuracy:{0:.4f}'.format(check_accuracy(solver.test_nets[0], 50))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Great, the accuracy is increasing, and it seems to converge rather quickly. It may seem strange that it starts off so high but it is because the ground truth is sparse. There are 20 classes in PASCAL, and usually only one or two is present. So predicting all zeros yields rather high accuracy. Let's check to make sure." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Baseline accuracy:0.9238\n" + ] + } + ], + "source": [ + "def check_baseline_accuracy(net, num_batches, batch_size = 128):\n", + " acc = 0.0\n", + " for t in range(num_batches):\n", + " net.forward()\n", + " gts = net.blobs['label'].data\n", + " ests = np.zeros((batch_size, len(gts)))\n", + " for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n", + " acc += hamming_distance(gt, est)\n", + " return acc / (num_batches * batch_size)\n", + "\n", + "print 'Baseline accuracy:{0:.4f}'.format(check_baseline_accuracy(solver.test_nets[0], 5823/128))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### 6. Look at some prediction results" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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oaWdkMB8GUkqEcSRME+Nmk/2wAgHT5AIwnJ4wbDdIENP29nt0VhgHZLJ3ZRoN\n6IYA42AmjTCg04Rev5YFqxJGIZzt4NEF49kFerqF61t0M9n7N64zfuDLkDSjn36V+JnX0Hk2oFJB\nU0Seusnw4Q8SXnwWvf+A9PEfy8MsILdvINPGBul+B/fuI2cXSFL02ilcv4ZcOzGBNCc42xHu3yNt\nJvSpWwzvexfh2aeQcSC+8gb82E+ir91BT7bojWvIZjJXzLsPkLMLNCb0xg043SLTCDEic0TmGYaR\nsJ8JZxekkw26GY1nClzMjGcXSJqRYYBpQ9pMyBjMvDInwtkFcrFjGMRWG1FhzmYGxWxxEtDB+l0E\nSEoaTZCKAiGQUkBn21LMMpMhmL0ZhrpKKDO105GCMAJBE0GUoEqIisyaN0wFGRqQexfXdjparP/K\nCiJFpjAQzxOP3rrDPiqcbNkGIQRt87KHjUaXSieglyt8VPMKqs3rBupeS1/Vy+sfvxYv2n5h1qVu\nl+u6+9W5HxabsAHU4uRUaZ14Ji9cwPriFijsu0YOOurgXcpyU5wIXtBaaVvvmKXnTXMh600N/Zuy\nfHBpWsiELqtIPwACygBsQ2AzmpabkZiQbbQyJ+RsxkwBZv8N0wbG0cB+O9lE288VxAmmZc4xsYvR\nlrfFBJLt6ZmbpBRJc0R3ZhoI40AYTVuyfhd0NnNGGEb7LYhpz9HARIIYgG9MwAQZcum5LTevo889\niz59C00RYiRcP0UenSN37sOde/DcU+gzt9FB0EcXsN0QXngWmffowzN0M0GMENVAK85w+wa8+DzD\ne19A77zF/NMvw2YD16/B809D1LyqgPTqG/DmXQPz974Lbl6HaYT9Hhkm6/7XXkf2e/TaNeTF5xje\n8wIyWp/oq6+jj87R2zfQ2zfgxjU4OUVefR196z56sSe87z3IzetonNGzPQyB4doWOd8j9x7C3QeE\ndz2Lbicz44wjYR+RB+dw547RfusmcnpCMuXbNOa7D5E378K9+7CbM88DzLaK0azN6xhsD4MMpONo\nXk9KFoCBVEwsxbSSVyaI7TGU0S4KMUWry7uHSi4LkLzA8GtvySsCUbEVmLr8NHt+CUM7SCAkZX9+\nzpwEHWdbWFLMMks8aG6Sl9rA24SjbTKXFffilYrS4l+zmhbYdlhPK6BZLxqda+mKgNz5tmaEKsqq\n1O9eLfcvLzXs9eflxQMG1/cc7Kh2+S5zhzyIZXFZKioIvhPX3+sPEB4eufdlNhm11OqpjBBsGTsq\nbMbAZqIHyZpxAAAgAElEQVQuiTUlRLK2FBViQqYRjRt0jsh2Y7bNmDf+TiYknhhgDMHAJyVinJk1\nMU4Tw8kG3W6I+71p29NAGgfm83PmOaKaOJWADAMxJfMVCBkYhryJFxPEGTTYhD3ZANnUMk1mUlC1\n8vcRPb8gKcjzzxPe/27SzVP0/kO4uCC8711IjOide/DSq4T3vhuef4b04AH605+B/d5MRdH4MkxT\nWxMNg23Y3riOnmxtJTMMyGYyMH3+WeSpm8yvvka4dsr04vOk1++QPvky6Y27DB/9atiOpLv3mF9+\nlfDuFxheeA5eegU++RI8PDNtdhhMwyUSNhPcvMH8/G10u0GfukV4/3vQ26foa28S7z9i80u/gnD7\nJvvPvEa6c5/h9g02H/4A6eXXSZ9+nfTmXcZf8gvNU+jVNxievolMG+R8R/x/fhS5cY3w/ndZ+RcX\nprG+50X0zn3Sj7+E/vAnSBd7W9FMI7KPsI/INGR7vxPYhLyhKU4zDQbQc0JCQuopxjb2QzbfCIru\nY+enLYL1yWBnFyRI3YsQsolPnYko7c38NA62EkpqAiaZyjQEsf2NPGZ1F/P412qK6W2xi7laQX0R\n6aTLJt17/aEow7WyMVyETXe2wnv++E/aikjiiutW44fpSk92NkwyKr2/cRsJSl1qdNq2K+1Agq41\ntnczktWyWgmrYuFSEM+y3JWp7l97d2Eyqpr8opjLaJKWRS+h0Iu/IAUrbSNKYjJNZxxhjJhv1wD7\nGSWhYjZnGczeKOc7K1GEVCZh9pSYk5pGPo3IyRaubwgxf95MZpfemEmEiz3hxjUDxbML26jMNmZR\ncwKLYmYYCXawqGzMCWKmhJg3Iqc8uaYBUSWdPSS99GnTOGOEaTIBlfKiKhg4SIAwz6S8ugATZJoS\nUYLtCYRAEEUfzAY0krW7OaJnF8yfeR0ePkJuXTegm7ZoGOHkBL1xjXSxZzjdmjnibAcPL2AfzQy0\nnWz18eCctNsTNCFJCA/OzCSx3SDPPE146iacnpiv/90H6NkObt6Ek2zXPT8nPHyU9zCGPCgSut8x\nv/a6ge7ZOfL0zdwXG+T2DbNV37tPGgIpJZgm2yC9toVb15AhEDQL9zTbyB2D22zOYBnyuFOFYmuH\n7Jlj80OmwWgdQtbaswaS3Wg0a9p1CKeIZlea6k1YBEd+JygGxKLMRNjvzCZPIuT9FoLzFJGAEBiH\nidOTayT2MNjmem4QLUiVn6c9dhSUciqfs5r2rqwN38v8LxijPYAvVfEqS3LdISuWiIkbr+9qp+B3\n6cq8VtYkS9XGPaB1TOg/Lu1HyzL7JUkvTPt6D1C2Me6AZg/RTiD1Ruw6dvudai+1GxmluYeeKZ5c\nOaTfDcw2IkyjCALjKIRJTNvJk6NpOmKa4fkOndsmmKpCirDDQLAIWk2mOQdhP8/s5tkANxRZbKYQ\npoEgI2P2dOFky3CytYk5J2QMpnkptpxXDMA3m4xLMU9GG8HdGYIg2SVSswkmog8foftsBx4GeHSG\nXuzgrXvIw3O4/xA9mZD93jQ9BT07s3y7PWwm9MY12E6mjStma5ZEevQI7j+AR+cGSmB1jCMSE3p2\njj54aOA5z+ijM+OBCGGzMXvyg4fo3fswz2g2H+k8w8UM9x4aQJ1uCbdvwrUT680Hj5DznQnOcTLx\nPM/IxQ652MNuRudo/RRndLdDH56Zhpr7UpJp1ez3tucQMDNSCOYddH4BZ/n/lHJATQfQwUClqMxF\nAwcqwHaukuW3YgIJThkrq+/qh16AS5utXfrxXYG81pnQWUlqJjRRkBgt82B2dJuvpf7AOE1sT07Z\nJTPfWcVuqq5MtypzChZ1E86paUJ1gT3Qv1xbpGVcaGP5eSnR62YOS6TKPCf8VtKV+ZGvR5dbobOa\nDLR+X4O7tlFAFaxymGNRmXvhwAZzUMCK8GndXAPteO+SS9pZ00JILUOadnV0trZ2erWTNlL1BEZV\nhikQTgS2o03qpGiKBshF4ueJTox5TkbSPhFC2avIoImARNOKLnbsLnakFInznv3ZGXq2I2Q3PU43\nDMAwjoSTEQSz3W5t85Ihe7eUnbEQkO1krd4VlzmzyUsRcCJwukGy7ZN9JMgIYSRd28IwIkMgvfEm\n8tY95PW78OiMJIruLwibibQZTWC98Sby6AzOHiGnW/Rdz8CN67DboeOAbkZUZ3j9dXj1NXj0iDDc\ngmlCblxDwmCa5GuvE1/6DPLaWwwXM7z0MvrULeRkg7zwDEoivfQy+hM/bZvKz9xATib0/By9/5Bw\n94H13bUtcn1D3F2gD8+QR7tshx8Z5oicnaNDgF2EOaL7PfHRI7jYoRc75PzChkAYQBSNkXTvvnnl\nfOa1PDhu20aqmH82b91DX30L/eRnGM/3bS64YadZ02WQNuay2aRzrVVqf9rM0RYyRPJGZcmcvWUk\nmzNFbd+GEKo+bDLbxntUSCGPwXkmne8Im9E20MuBIrX83YGkIRBkYhRl2s3mIZWFTG+Y73Uwj7eK\n8aDMvaJhNzhv87s5qjizUiVHasOaH36rv309VBt9Vq88LtMVHghagtzjyMz5D8qpr7WB0vfRQTnr\nwkIP3HqKNt9pIkUbl9aVQFsudoRc1o4maWtdRVgVSe2zrzbHbOnqGl9ipwTMajIBU4BxDDBNMEWY\nEyElzKQYSTNoTIgKwzjZBMeWqrrfw2xufOwTMgoSzGq4m/e8eXaPT7z2Eq9f3OWfvvIp87XO3iUE\ns9MHEYZQ/NHNhjlkP29VZYhqecYRGUdTAlNiBCYJjDISxpBdEwNhM9mSXYQQ1cBtHNEpHwQJAzoN\n5umxn9H9DD9lNnwdQjYvqfnKp4jsZvT8Av1J8wgJMcHF3kDl4x9H9hE5uzDvjtdeR17eIj9+Sths\nzZYczDwSLvYmXB68BScbEwZzQmIk7PbIwzPT5E+3yBtvgARkt0fu3DO3ybfeIt69QxrMXCIK8ui8\nmmZ4eNfmyt2H8OAMXnkVfeM1whyRB4/g7gNSnE3QIMjLn0FiRB6dm7AIAX10hr7yRuVfBOTRBfLo\ngpRMQNs5jbwyE6mfSUrCDuUUMLcVXNaaxU5eJrXVlPVXVcApK70aE1+0epkMkPsxGI+Kolank42R\nVmcW8tNo+atvYHkvCx23jh2HiVFGCub04XUXzr+Z/91qX8qeQFHwtJbtZ2i9Xq5M86USVwLXrdpH\nChU+nEQpyJtt11XyqzOtNM458FtoBav5lz8sQO5xGnB75cDs0vFIFiyT9azrVT2+/iJKOrxfil7I\nA7KYO3ylniapJkhxNAYRNiJsQmAUO4gj40iYEmmOraxiLsmF6D5avdNgoC5qoFa0KhFIyjzveXD2\nkE/deY2X7r7Oj72yNQ2r5FWtE7poVpKFXdWKkhKCMEhgGMy33CybylaE68PIjbAhZG0wZLMKIdix\necW8JFKqk1dESENgH4Q9Fhem9GEKNoHMLu/CFmmqh3FCxgTbEJPqTx2CEybZlbI8G2KyY+SIbcRl\nDRaFELN7ZsI8P6bBTECKmTBiqrTN40Acs/tgCLCfISbDjnEAxIB7Z66SerIxGmIyU8towCbZHBZi\ntFOReTwU4LQ+AeaIxOz+KcFMPDGDdOkw8mlQbLwklIgdCFLN2mqZBHMk7WdEAkMQwiDVbdWKq6he\n46UAjEkJpxumayfcDltuTRNT6YeCm06LDYPxMOQ9leWxywLpqkDClAcJhLy6K3mKq67N47wJ6+ce\n2k036jt+jnZVU8woZW4vDxaVeXe5surq8r+46i6Dtys7EFSWKYWRpQvQohUs33kyQD+53lbGQWmd\ny6HvrnUJ2PWPrD1de7eVuy58tCkYS+1dG/1LH9flrnoANgG2YWCUYP7aw4BOI2m/r1qPiNSohwp2\nJHoI5omikkFpsAMXGWA0ReI8c3ZxzpsPH/Dw4two1US2pDY6fYvVdB9ZjOTyTg2yJMr1ceL5zQnv\n3lzDRSTJAJI1uxCIyQSTBZ6yY+uzwMMUeZAiD1xkDS3RoAp9fiVnnKfEIDFNsZkDlAz+WTANmBtb\nkMAowiTCBAzZH0Lyikkk2AGV1LTdICEDogHhrJjQkXxqsnBNCsi0oVn8PkQaiJYj64XXQUpeo22Q\nwACMKBsVNghT0UxzewPFbJLHhOneBDXPiWQyjQtNXGhin3slOd6A0RmkCP4ivK0NVUgWIZy1owFb\nkd2+cZOPvu/DfOj0lOtcdyAubR9GhLCZbOWXTTEtQF7WTeoKwL6YsMqCuKwWnJZmvCyW7v7Upndm\n8Vpym7/9/NbFo85kUwK3Cb3QWIF1Wfl2aK7p0xXHWskQpI2Fl6aFJFtbnXRg3xWnVWsD6uEd/55T\nxi8Xe77Eg+O1BZIOKvd2mkP6yiDMeczN2kZAKRdPUwbhFoBnMSoFBhFOQ2C7DYxTQGbT/jRm98Zq\n6xTz344jJR6IHa3GAlGVOqfBvC+GgM7CLiXOk8J0HRlvEaaskVM0ulw+xQ5qJGo9zK511Jfltpl7\n7J0Xn3qGj77nffyy93+Q1DyEsx02T7WTibSPpIs9MWXdW2AeBj7z1h0+8crL/MOXfoJH8y5PcNc7\nVTkscU7sQeseO+VZw0b4//PsFITNMPILX3g3773xFM9sT/KKIu8TStbqI8TdjhQjSRNzgH20zeJ9\nTPzEgzu8fHafBzrnqm2a1/gt4vWadlDE9Trm4VAA3vIFDCRRYQjCzWnDVz77Lp7enLCRkE9c2klW\nSdmUEgJDsENcZaUTBgtBsNvtefnOK3z60X3OitjIvCvCAy3j0VaTPlhrSIWBJiDKYR8VYbvZ8mVR\nSe8p/ZxblVcuCnZYbRgpG5ktZpFUkBfyJivF40PqidQWo6WspqXjX6G7CzB7oHH5Rw3aa6GZjqqA\n1X9KLd20X5Tqk8+l9d3HpS8qrxVLXqTVf1ru0sldzjUN3j9esE0Oj+1/1hq/LG15a2Qsd7R7u7YH\nd6NXuoIEzafjDmWLl9C+KMlAfiLCNA12am/e2zIzZ6w+qcFOSUrMx9yD+XsbmDu+jEN1K2MK7IEL\nRjZPv59w+wWmW89RfP5MY87gq5o9D6joWQJXSQHxclKvfFbluRvXeN+7nuMXvO9ddtIzqdmSixdD\nELNFz8mAfM4+7MNAGgI3X7/Dg+kW18dbxBgx73UnPMhlasoHSDJsqhOQXth3dEJZJUzjyAc/8F6+\n4umnefHkWr48QLMJOANdTKRH56T9TNJEHAL7uGcf98xR2b32Cq/dfZM785zrzaQVOrNQLJS3o9rU\n1YaQQZtyIjH7cmNlnQThmevX+fkf+DDvvn6DjQhpnut6RWaFMZvfhgGdLX4JeWURY+L87IKXY2I3\nw/3cxuqtkrXMYjIpQNfMV+TwMi5vbtccI08lAQmMw8hQNkb9nFWp3jClPBvQLVRCWU1IVmYkH3Qz\nk2DqzGn1hOjBJCq2cDpsLtR4Qd//LXkWCqF7o0EyLC0OJT5RzSkOKTp3lst1zCvzI5cVtjTU8yB7\nqOVWJtWZ1YNx1bilMHcN6NdlYjNtHKZONjrSsqLhOsh3oK6/b0Q0EKvaQtswqbR0NC3a4nbJi1kg\niLIZlDEfq07znmEw75Gi0QjZ3rgZ84anmi/1OJrr3H5vNuIg5jY3ZJo2IzsJXIQtJy++l/FDH+H0\nvR8ixewzrBZ4STxDtbW+auw0MNDCJ7XNzqf1ITe2OzZbg1g7IWqHQEQwj4RhQoOiYSDtbMXAZgIC\n128N3JTbPP3iR5hkskNHih0iyWFkSRafhSx4yHFIDPBTFUBaNNfilqeKaCSocnsIfPj9t/nK26e8\nuNkYWGXbt2aw0H1EH12g+xynZRxt41BnJAXeuvUin37zPvfnVM0B5X8l0+U3CVuoRVpY2jIVCtNT\npVVVuTkI7751nY/8ol/MB27dZJvU+Cn5QE9UO/Q0jWbnzzF2NJvd9ruZ8/tn/Hga+anNM1zU+MOa\nQwSnzBccjw71zIqXRDPpxRm9eMi1TeDZ67c4nSbGEKqapsEJ4KJwpdx/IjUqZBlm5dQmYgfaZAjV\nzzyLuQaXue2AHcnPTC1hiy/TM+vs6wT7AohravQd4NfCKlDMO06Xz3TmcvDIeJiuBshb1EgO5ViP\npKbYCJ12mwd2D/Su/M7G3dfgyy1i133sQNwfiFiro6u5ZtWFIDjQL/pvWYMwOhw9BslW5oKmLq6x\nR/GcBoQTsc4VQIeheiEIIJsx/50Yoh2q0Kjm0TCVeB15ciIW+ArQqKR9ZJ4jUWHYnNihn6wJtXs1\n19rsW6+1bOinQQCmODPKDPNFDhlgdEmyyHzVRznlQGCAZJfERCKpEDenjNdfZBqmPOBSZWTRb+sq\noX5fo7aAaukje2dUuIlycnLGKLOtakpDSuTIhK1wTjekAHqxR9Pe6lQhJSU8916uPX+Np6cTkgrF\ntNLGnOIJ64Zi01ccb0El1XGkCLc0cnuITDdPEGKOi0JdlURVQrSQA3Hn+kU1Hy8MsNlw8sGPcPvL\nBuZxU/llCvniejJ1nx1r215OtHEVZ3ZvvMIz91/iaX3ARkY7mIQpA6nwfh8NnIt5hAzWc7RzBWIr\nkRKdtGysSzYXlY1ps7r0ZpbSZ72SLB0M9Zp5+cH6yqF5NWu1nij09mPL7yGVypueVp5Kj0Vg+1as\np6sPY1s+1iNTnqsNqHtsXMDiAWK4B9WEcgkZSzou+Vb6ffW3rrw1wdLe6iai3yuoMsy1N2uSLlur\nwxXU2XLFTktu08ywF9tFq9qomk/wmCPzhUDYbkyrnSMyhLpxmBGhhW0VARJxjuz3swUiGrfIMNXf\n8yv0Iq4NzC75pa1SJ1lAGZMwZL/hchJUZIBkYFOAXJPmoF/J8VNIIqRhQranhHFDYW4ZZ02H6FdL\nHZNrHyzJzoetFLYpMjATJLa+zXGy67uiZrYIBgJ6savYnEiwvcZw8hzb0+vE7CJSBIsWwdFpdY7+\nFa23rQzz2k6ETdyz0XNCOLNQCLGZkmqkw5RIs8IczcMjhLxiyetnCQw3n2IabnKyOanKhMF+qtT1\nbF3yN4Mf0XzA48w4bLkVdtw+SwzZ2G43OjmznBhfJWT+ulZXs4RQaSIq9a5TMbNMECnn4urYLuO1\n0/hzWV7JWKBwp0CZzJLKc0o7Fy13r3fldALOvbuCIksrS5eu/GKJMhTyL7SLERqIgyysFoeuQSJS\nsa1q22Uwu/cupan8u6LxePtGq2MhrjvOrz6krSL8zw5kcD21qMdH3az3MNYxnN/O69mgic1+h+wS\nusmXNZSQzjmeRd1EKqf9MpBqMo2t0FAmA9ltbp4jF7uZ3azIaLFZqrztJkDjQdM2fN87myfqJhgM\nKRLmaCEZR0N5GQQNFiIgBCHO0Y7ZF7OQKARzgUwCMZ9M7K1z0rG4C2i01l0Z7PwmWZvwipBs5eKW\n6caE0H1UUULYAJB2OWZ7Dp1g3h9S+aSLMVLYL4i3wDmiXXtotNVrzwIWmiGphTGY88GwULosM6TE\nLJkjOmZBGbMQUczs5OOhrIzxzhtoqZXXvAHFhN0QYNhuubE54ca8zQK6eA5ZGaqQhhzIbQjVZGXM\nNGGX0LxH4Lya1O6htSrNtXV046zMutLv5Vg8ro8b1c30UuHe5UlZoh7Mdl3M98onutlRhJF4jFpZ\nERyW1NIV2cjLh14SVem0aHyzWRXJ52zLDiUqgEsezJrBzde7KPsA2g9mdM/Gxuw8q0Lp6JJrUX4H\nyMsBkv8pWqL3dyoDQ2umWn7VyN2grFNSbKpc04iMA/ut0ThEIcwKu2RLUlVEczTBEpNETLtKpi5a\nmYNU/+eEsteZXZrZq8I45ciKh6kuXTMCGrj4DNKa5nhuK4rFxlTudsnIrGBH1BW7XCGp2ciLi2RI\npOglHyhhfZ9hAYYdiJY5lYG0btI50JAYQaN5+YRg46365tskjfsd5di6bCYL6hUjaRYiQpLg4mp4\ngdCPpyBtHBTCu30yqOaHNiYCMJuwy7HKRSzMgYGc2lH/wiqFcvF2CDY2yrgy0eU2GDE3R6+b19ja\nAv7C4UJPpTWbPU7HkRvTxPVxgjCQhsE2KS2aOjKMeX/GUF2S8VVE2glh10fdqsXkRj4kZ1fGSe7j\nMre0kuX6vcOMctCnjRMTMm08h8wbO7Ha+sLfFYvvu25uN+40CdPm9kFacc2GKzWt9CDeS7SVT6Xl\n3cRbAVz3qGnmBz+t6RNtsJU8S1BfahmLcbnsHLdiO6BBfG/K8ldXn4jL4obbYlKXfhfsVOdpUGSO\npBSQmxt0D3oWkTO1YFfYpGdoFz3UOxkHuxTADhLZxALQlJjnmX2MzIBMEyGYx3USoRtiC3AuS9lV\nZvjval43Q75VqAZBKtK8DPTsvSJhsucl1KraRMyW84OVky767/BzQ8bDDXHxRZluWdz3EkjIGnwy\nd8piT7cveQU05tOIMeZ7O413RVWs7pCy5NdixObxb8NjOQ76SWDPiqki+8No8XVfDGstwy7zPAjk\nyyEKnUHsggs/LkvnNSjt/ulYXBQfATZD4GQc2AzmnW99nHOKbXzKEFCNrd9dNxVB30JWZAqK9MnF\nBbXI6B1PpeamJ7zxzv9UaBf3bmNdodn1l0r/SFo5tXy1NvjeVTe3l7C9MouAK7x8WZZATlViumeW\n2gaijxtehJMueKiu7LW5WnUq7btn2ckHJazw8NB9sTn7HzLd+wG7BuTvpg3p8pU8UL3QaPwqA6pM\n4IAyiXA6QrjYozth2J4akFwkO1GZ45qkGM1zRQQZBuZ5NgvFJCh2VD1MY/bxThnII3NMzALTtDHf\nXhb9doDomuk85EdukOOnCaJBAppttXVZ7T0FxJ6ZaWfIwbgw33IxTVfzLJblpFiLqwNu9Zanprbf\nuiTmlTGIhUMosUWk2Jtto8HoVTtinvJ1aN7n2twMs57vx/WCJ+s6mG/Bgka3CSfeVAFGV41xk09o\ntrCAFt6gaoTmvleQyzTfsiLM47fbtO4FUGda8KaLKnBgHAamYWAMA3U1HgSVUPujmGxUc/1F2JU9\nn/Kel3G5n9AcKkLErHR0Pd7vV8phGwq9y722Lv5dxR8vFaWyqMyPshlazDiln+2MgxPHld9aWEfd\noL0kfRHYyMuzbuxSmOG16jIdS74Q/LJnHbwXtXQa2ZN8yD1Atb3FpRDy+d3AYNkeT1kDOw/KkifP\natmrPBOnSYMdtrDj+dfHgY3uCecz6a0dYQZ2mqPv2dI+ztEuGJjMlS8wZr/tfIdktolqaku9Oe7Z\np0hCkHzCTnNj64TuZTCX6xE9gBeNbhBlkBw9MQ+MYgZv+UMVQISBEoaXZB41cwaEw3q8y9QTINLP\n8PrIOrh4QQwku1JMFWSqMdYNzGOO+xKJoswowzwTktEREVKQasOtK8BLxlfWPtpKrWub1q8F6ArA\nCcVclQVMjG0IqlqM9yLuZruyjXxHaQVroUU29FAY6rqDxXGaDrbb9JH2o+SQAeY4SIzZrz0MyKi2\nMVtunAII+ao3yPbxcsUftu+DzYmQN8PNF942h4JI9uKS+q92tBgPV1S6A1t3L2gXW5XliLL69cli\n/op/qw1sb4l3JDUXSzpNt0tX6rWyBOD6Q2384S8d48QPEu3ysHjasX0VYJda2sGYe+y0Xy7hH3fA\nSNw/ndZBK6K3fYojUftynGBDhEBiEuU0CONgd2xqjp1S/Gg1RdJ+b0Gl9BRBTWOMMd+Wk/2rkTxB\nygEemGO0uxQJ5rFSNjuX3HRrxWWfHPLO960yYjccaZGK4vhfViHlgmORuomidmsdESFKaB43biJ2\ny+D1nrmcTtceEWjGAPcwa7Gq7XZogbwXq+Zlg2lhCYgSSDl+zGXpYOwdDNfFmBBqYCbN8mwIQg4r\nX32lNeYTnClfLF2E+LzPlxoIkiTf/JM3nqXVsVhPHnLNE+4nkxYim0Awn3Vzy5R6OXUWzur5W+hW\nKGEhQm/WE6cVarGpYyY7w3iHPLKcUYd9358YdxPTldMiQGREcnDQrcIdx5a1tpJ7jPMmh8sw6Oo0\nctev0JYP/VP3Tn2hj1LgG9+/+RggXQXZx88kKUS6Z48t85LiSjuCA37Jg0skH2L0gL2Q5G3Gq3tf\n6gQLKKMk8yOfRttcm0Zkv7ciNE/YHCGwXpuFwjxXrwarqrgfZi0wJfbzTFQlhQHGqQax8kfgC9m6\nmMRdjuWGjtrvgcCgZntGhHIKFC+wVdsSv2jsgEpAEaIIM0Ot0WyQReS7sg5OzTrp0yVlqaUZrzM4\n5CvRRMpVdn7VpjAIQ1Q7N5OktithJqDolAsvbmpzPUWFsXVjXP2LzgTQwEakuN5JlX31+HvUBpgl\nJv2+hDzIl1dkbxY2bkzmjUxFHYBlrfGyaeKxXNqwK+admE/vipJBPfdXPphWSDGDfT7nMA75WL6r\nMLTCTaCGrJErwQ+6hvfNbOfY3+RNEzZVOOViDMTVac2+vc2EUkSd1P8aU3oR6HDBPWrQsw7lV3Zn\nJzQAK2D0pNvu68tl3HeNPYTwbgIsSVgwvs93+FTr17Y3XgHKz6eFuUZxWkx92wumoi3l7zFPJqTD\n7Go9Ce2ZJ9HYKATNIWwHGLYWpU/vnSMXSoh2mjOhMEQIQiqa+may0LUpb3ymbJtOiXR+QfGU2c97\nZgWGDWHcGHhVIdybuQ42Cx3YLA85FZ7YppSZzcplAeYulzIQZr/xqHayUzDXRMkn/caRNJjXSgH6\nfppkseBtYJU215FPSBZF0QDSNL1s4y228ewLjWTPGmYLbVti2aREUrsrtZ0mbKOkUtGT2fGrixmU\nSa8bZ9LMBybnlRZGley6aX9jtuOHcuGHArNdZN0OvhkvJTSCqimlTkatfD2026f60W92FhA2BUZJ\n0Q55laicKlSTjvEt7+uMo+1357ZW80s2w1UBgZpJBhglNaUJmqsz4uaw1CBd4mit3eNAvAKwLFvr\nsEjad7+XUMWGtl96E1T9sf7ayjtMV2ojb0TJ4vlyoErXgMVrGV8PW3gZiD85z6Xw3y/LFq/IQQfW\nH7nw7pUAACAASURBVHxX5N9b9wzZp7ZYCZIKJQJoU0C0yJFVGkt5I8IkMEmOhBch7PJFuZo102CX\nIacxgwruTsUMiJLd6cgC1saVcjHv2SMwbgjjmONbFyLCKuf6WBI0kFGtPCiTxGy5imgy00osszLk\nCap1dVs09BqHIy8uZuwcFFIUhTbhOueGTuQv+XqZUmGVDwITmiMXlPGaoLA6H6+vYzNrw9lIRc5K\nlKzcZrA92PFZI7GycUFbNx/sn6oRZi22hpZVzSaTZJc6007mmpO5rW7MFVFr8ClffufX/rjJlvtB\niwoujTLbazD3yxJTpyBxdWWkhc6VIEiOUa/RqXfedz8/S9nUVdZgQ5kTlazLT0p2PvH9Px2vVwvw\n/ZAHqluvNJBWOulQnTdW6VlW3KcrC2PbP8BpdT5fFZPtJW/v1CW4fS7E5L+u/mV0xEbLSp7aaXKQ\nF/rj+v0JTKnAbXH1DcyjmLXDXNPUQL1sOvUVHqwbBGHMID4Kdfkc9uS4FJhmlCdDBeG8gdYds5dg\nN88LlPV4As7n2YB82iLjWO2Th2YxrX8NSw/XR4dAZGqUaMo3wljMDFTMPcQQwdxFlgyQ0hS7AmG/\n+Lnc4NRw/bJJ6fuxI4464cS08TEDQ8gA5ZfedeWgdjtS0QyL257mzc8Ej7WPH1TveOfH6aHXVBbR\nkiMjFnNavoC4CRTNm7GBFCxEbw2BIJKv5dMq1Ot+gJJXHZfxsaF8GR9SjtCXfsy+/pLBXPIlyuVA\nTylFk51tEBHCmC8XQaCcVoZ8nqOQYfWK2sEjkvXRSN3S7fq4klvaUM1SONt330Z/IG+JBQddt4Cx\nA61bTdB44VF08W7/44vtiL6UW6y9aO8AD/e9imX3Y/nYDyJh8c5KquFanVa4fEUuqaPzeFk4l4j/\n1w2EZS6BGhO5jBm7SUcYBwhBGbJiss93OxTg9wBUB7vDzwRMSdmijGBL5XHIbswmHVIyu2Jx45Lq\noWB80SG0/YqMemHI5+YSXOz2RB0I04kBeQV+pcYJcRxrfFx+X+mjDHCSzAceTSZMkp1ElSHYDUTj\nYL7Mc7RgXXPKE9YYFxHmrE16+bIS06xP1bYK1JvWC91FQ7detJVDalEHy/uZZxJzwC9V5OzCjrwP\n+URkdvczIM+be3lMPn7Xk0vAu3zOVlg3pMvheVGFfcoeM2YimYOQBuvzFMRugZLAOI2EcUAGIYzB\nbMCB+r/nXzNPFz70c3kpvjsjhNSFIio5vnm211fOqxJjsj2IHJmzmMukCBmaYKs3GIWQL8XILqCa\nmFBb7WWXTC2Ajxf2ZZ4X84trS4+rrh3i/6yn7LKqkK+la+2r9XWCsPG011bXofwKbeSuS8MlHHAN\n8P9C3xxZDB6vvCybvzxh+TjvkkNyfIct3yuD4hDED3zmC15kJE8oUQVN5s2QFen6uWya9FqZ+OKA\nDOQC2yCEKQ/8MCAh2xzSjMyz5c/AWBE6teiF9RSgFFptWRtj5Gy3IyKEzdbeDxm4gBagf0HjGi+d\nRtmtYNQiILKfifNsN+uEAMOInkj1ErHLJQQJI1LulMyAq5p9c6sG2bTDOkFXBbinsB9hTaXKQC52\nwKRcNtF3seQwCKA1NgIVwOvvQ76urIZjlZ4e9++hHtPztmhwB0OzmOzEhK0dzc+3CE0DKSjFKV6H\nbNLb2N2qQfJJ2RwJsUyzzu4rjr6Fm95aqgtrccpC+Z8MbGr7NhYXfbYj+OPWInMOg9VW4tLjwDXT\nWMRuDTkwKyIDw9hW8M3dtxSSx1XHvjaa/bRrXdB6SN1vJQSyLvL1zoWuz6oAqQVYDnUMqyxf5+/V\nxiOX+oV1SdPPsjqBcfwX/62X+MsO8X6Yl1ZJGZzqTKttkC1XAZRyBNbG8Jp2X0OcYnpfVPLN5zay\nFBtoqSzrahGZ/rqYMQJDIUZhI7AZsAtqywXK2VYoZXms2PdpNLDPy1qjIR8IidqOvOdDInOMnO/2\nzAgybbPdtPH/cTbHNX54Fkr+rdzbKfNM0h2a7Ho3GRSmIXs1JKstHxQqyxvr5YRqgfvcKQcnxnDA\nv0LIshHdOBXsKjjblB2y6aKX3wbUUo7EjyMVYUpRIT8PQz1WvxQdSxIMLB4jHHG0Oy2xyQmtD0LR\nDgcoXuSKEjUxhsns0OX4fbang9bxJN7mIG4+amXxoh1968oZCKPRbapmjdr4lbJ2Xswqg+3l5Gea\nJ4myHFeZmpTs3tOoEJLdTX3AMGq/+hOyZY71PHeNWyK8639vStEn9VeVbI0Gv7pyHLu0HLiyMLZe\n13AiMX8tA7I7oemv6YL2Zn1NumWWz1Pm0MHpKPexSUvqaFyOjaDNnuKD7JideOUd5AC4yru5UmIG\nlc7lsNIkl/KoARc18FoQ2ARlE8Q8JbJfsAyCBVwZLOzsYLfAyH5v12YNQy5wBp1hnilxTRiC3dqu\nypxmLuY9s0zIZlsnnyj1GH/f/Co+C8kLD87WOqNQs/ukgaTGREw5VMCQCNsRjWYGqOFzM54YaIcW\nzU8GOltKd5qo/Altukj/Y/e1A3cboEE0b3a2DdtqL4MSrsSqDdkckBJ67jR0pJoVhOKl5E79lV53\nivzSA3b18FUZECId70UzqBfhl0BmZRxG9g8fEs/PQcRuricgU35Ti0afBaYU7XGhvzo+eaFSci1X\nFQX0yiXd9VKJ7PVj0b60xRkHKKuDpIRkHjeU1V0R0EOwVSZZOZFg0RZr5cajRpNkv3k3y3Ibe/FT\nBpvzKKsdpXkM569S+kLyz/6Ua3tPLmFYxaGFqe0yQL+6oFn97HG/LQB9HXcPCjzYbPT88UzKXOwE\nIU1QrNZVCnHqRt2IFXte+/WJS8v139c12X6iPKFgACaF7SCEkw3NZFLv2TINEDV74JBdDNHs7pU1\noJgvNK7LAtOOUkyc7yNRtoTNtm6AEQoYrtHqwPqSplTeqRJItlErXuPKm3KPLuz+0RM7+RPyZcMF\nsDSlfLvNZD7lHljzB/UVshw3dONGV3KV/gvABmXQZKATpJ1yVHOPNETM2m49IZltuZj9vAXKagv7\ng1Wlp1ecIlAoW/K1CoZWlrTcNX88v2B/dk5QQaIyjiMyjoz5cmmUHIegbHQuqCu2iaqESLf4WWrl\nXjhWEM8eUgdjQzAADmp7MQq6j/kkbOZ1slqamayBbClfymZ5pT8/d3xpQTGtD9yIdfR0InEVvg6b\n4F0XG+BUeFp0nQntiuKr6HhZunrTimbpjj4WBJerm/651C+1E7qj+2WnXg8ERDdh6yR2HF7xtmjE\nyKXgtN6GzyLzJakGOyrAtKBvI8rJKMi1rR3w2ZNd+PKAVs02z+ynO8/UyZjyxmdMdpGDYoIAso08\nsZtn4hAI06bGQdEcKZHyZ9lH7p8uIlx5nAWjACOJcRDGcUDSYDbOlA+snO9siZ8MJGUzoZup3lrP\nbPeSJsUmPOLGRFsZ2LdlXyz1Rjrp0/ypjY2DKBtNdnhJbHOteHjojEVnzEf3lZQ9hpqUKKdQc+GP\nmbwd0ZWWYiZcrMwdODjhU35wUkwlj4PdHiUwhGAXjWw2DNNIyHaI4jVU7Nlt38ERVPm54OrqF60f\ni2m8mlmqWcV+lJDdEofR9hvm2W6rcocpavTD3M7Kj7I6CXmHVoqrI62uSpa6d91vjvSy7irCfrkn\nLa7i5fjuvucHa1CwMnXcw8djx9X6kXcDItsJvRAseXBaiNABYtUAFlHX6yAu/SKlo3uG1MntJeGi\nfA9Evg2PO8C0bOvj0tpu+GWukB5QaskFcFTZCmzHwHCyQfYCwdwPzS2gxadgnkkxonuLtyKnW2wp\nakA+nIz55qC5lp3mmV1M6BQI48ZuJ8fmSTWF1W44gGt6ch0CZW0nqDCqsJ0mNpwSNgPp7Azdz6QE\nYTY3Prs5xny07fYg86wo9n07+k7BcaNNqYApB2p5g3gvxIspoRKqVS1oGnmx048DTCNpjsz7Gb3Y\nmWfLEMz1023EZbXdTi96gKga3GJz23V403KLeCqfy1hsrDfAt1+DkkPAYlanYWC6fmp3dO6sj2UY\n7GxAPq1bomIW7b4gb9sEL6y7ZIyro6k+k9oXRf8trrj1TlaUYRxJ2XRiMjCvziIWM7+0O8dn0SEw\nFIRNmDKSNF9rODAQGKXVJdLuNSrjsV6W4bRGyfsEXpEsC5HWTG3l1H5qmZbcEehWLl05ZeVQVm4Z\nl1q/rvP6iyBoll+q9U2XS99ZAcgCwuW7Piavf1b9UJ1LUJHQteBDwNYnlLtuKml5DmBO1rvIm4la\n5ib/ysC0oaNMKTLNifTogpBi3hgyY62ixBTh4RlEi1dOBm6Z8xVgWYssdn+NOTYIZgrYp2Txs6d2\nM1ChyH3ksDXrA1DqqDaNaRRl3JjWFXdKnLLNdj9DCAwFjEMOv5tPeeTbzex/MeAJecapZJOGHyPd\nur+fVXXO+gmrLa9gNvyx9E/IB2gkkAZFx0CaxFYNwUDTXDznKkztftBQ2138yaUypdGi/osflUsT\nS+mpxftGYxlHRVQA2415gYS9ZRxsz0FyaGNKsLTid35grmq0ejaVT20zkyxMcw8lCwGQ0kxMZ+x0\nx3n27jnTyHmyC7NFCqiqnc4UMSUkm1YoAbRQwhCa8MtAbeMjuCiW2ikRnrdaCP3/mXvvJ9uR7M7v\nczITwDXlnmszPdOc5dItSYlSxAYlhf7/CIkhsxuiluSO62n7+vky18BkHv2QBolbVd3U/vKImX51\nLy6QSKT5Hn/OQ95M1XUPctI1lmSUP8mwmedLUhvznk94UenQ5bTd3LWTEa6Pj5c0qzqWi6CafKof\nqsG6Z/A8aed+AyfPWwyQLChhYZsWm/pkVufOPt7uw5cswf/042PonxurwP5UNyoJFprgscOA3zmy\nvyzGEDQwDSPj4Yg99lHMbGKYMyHmXVHv58i6uPLQEDN7EzSWeQuBYCKQFy8YZlH20RcvLzlvluWG\nkhK0YQioeiY/EZwQPPghEhhjItdoG4dpbMmnHosqU/yR602y5KzvDT310C842kfuEKKnioXkOht3\nWeYCZ5ejxMVni2YiiDmhjqfmdPNmPQGB9HUJJFKkg0UPC9CfsPGZyJrcduqXtdBZmJInUKXHz4m/\nchwDkoHcVNN8f4/NwyfVt4D6Hj/sGYcDOu5R9ZgwYQ93/Hh8Rxf2OBFuRuHtbuRMG846S9eZlEbC\nYtXSepi8MnoYxOC9R1Rw1tFaR2saWnFYiWBvTATyEo+RBxp9OJKyxoGT93tcxlxMwCPrRk+kpdNb\nM7V76Px8z6nWJx//BjjyyumnlllOEFrmm++3V665/1uRRvJE1lx1GhUjVXhyvRRP1TCPBWPc68z8\n8UGThVReLvmd9MFXO2k4BzLke/OSixKEEbBhRPoDwz6kKuIOrGOaBg77PYfrWzZtx6pbY7qOqR8J\n4xRxJoN4EukEjVkTNXLmfhwZfUyYJa5JYms1X1J9ngdt8XsBhVMORGJ2wCYEZH8kHPeEqY+6fmti\ndOQw4pzDrhpk1aSApKiXzhs1JHDULPXk1V/5uOvJHM8Jk5b9L8RV6rnJQrMW4lmMxeOInQIyTAy3\nQwTOzqCa1FPlfeMMjsQEXzmfyMkiqQdx7t/pIqnUKPFDpa4hSSFGyZWsADSEWOTYpFqiooR+jFJa\n4yD7W2tAjC1vGx8fQT7LPzHgZt5gJXla+pw5cL//gd27r7l98zXT4R2iI1aEG698EMvvGsvZusHf\nvcTejHz6vOEv/+SSz5+v6e96dOdo+pZLs+LmMPH+2PO+HzhMAbBs7IbnqwtebC74bHPB1tqoSlFF\n1ES1HaSEuTOY/9xRGyyXrObpvelspTaq+f3i5SYkP/OHW8kL7hSnyjp9BCM+KkdecxfxU9XL+wTx\nQVDMl1aE7/Sm5d/F8+tn3+vYw9eXix8B8keO+/7TFXT8/HpacFun6pZM/hxK5wNtzvGhgvpAmAZC\nmDAI3WqFFRMNhI1FWgfjFI2JKfcKk6KNifrypGIJwTNOnlFBrYsc+YLuPgDiqeNZBT3n8liCeCZO\nBqUFrAZs1ofeHWGasEEw2KiVT2HlBZqtLAiQItGPu6idqk3BTEyo/3KyvqqJyeNdO/NFNVB8F4WY\nU9wISEyt2p5tySqBnOBDQuTkNSlIY/ravGlPMq0olYtbPrvs3ywEVe9Qq7pI+mCS6ilLWkXVkgDG\nRre/oCQpy5R3yv7aZVxP133dN1n8goYJq29Y8w3PPnnHXXPLD8Fg3DOaztF1DY1ztM2KVdfy5GLi\n+tUNh/e3nH3as/33lrPPhItgYRRkDDT+yLgfmfYDsh/4w7d3/OGPe77+amRjG666DZ9sL/hy+4Qv\nN5d8ubnkiWlBXLSfFBHjdERnVnkh4SfQrZf1Q0T/5CbqQ5nVjpnEArMq7cGjZhrz38ev/nheK3L6\n+X4nTznOBZF7pK1ZNDqlBQ8JgvW9p+Hl5fTJl0wxH+7LQwTlQV36A7f+3JE3TtE0VYvHEEP7W4HO\nGlzTYlWiATB4jFcaY3HrDdIPiE8cZddGQJmmqHtMlXiwNqkpotjt/RTLvCmosTFFbslAtCBLD1j0\nT1+kBoNZlWVI1YHI2Cf4IXKKRlNypUA0zhXNhSAuoZrGZFSRezSJCZakMsh51e/3c0mg64+S/1/O\nZkNpDNGHLEqJjc8kSSliJBqTfQxeyQCa71fJQL4MaFpwXz85iEtGpO5+/VeE0tfsvVIiKrMKKPcN\nU0Lg54aSTv9Ed7zYYzJ/y31SDQy7N2zsH7g8/z2//MRytzWoOWNyHefnK84vOoZJCcFhraO7GmhD\nYNQR3w4Mqwm9slxdNtGO4KOXlR4n3HFie/R8E/a8+fYt//fLDxiBVdNw1q74cv2Ev7z8lL/75Av+\n+/NP8G0T14HcfwdYosOp/azs+gqnMjKV+3ObjzGSJzs+MwX3Lr+nBeBfdfwbUK0sz9eeIEt98n14\nvtfKiY5qibVJzMqDdw9s5FFgnvHytD9J13ii916oc0Tu9/O/9ajfL4v8VSCC00DbOlarNavtGWac\nIrctBrxGQ+Gqwe96GAe0bbCbDmkcfn9MRYFnDlODJwTF+IAfJ4ZpxKsg1mGdi8anRFhMrSw6feFH\n9IKFKJJC3RNXG0tzWYwJGGPnSjUiMHn8/ohdtVF94gSZMjBFrlNTvc8aIJedmscxemTGDX4yjct/\nTzZ/fPVQ8mQj6ZmEklY3csIaVVcupjMI5AQ6hkD0aIkgOg/SfUN+zY7UUoIuO5ffKRNHjS8oSIlA\nLepLAVL+HU19j7lVXBmTWkaofbUlEaTknl1UK6UyEoHge25e/iOri9/z7Fd7zp/9Cc3lGUdnePkB\nNlcNnz03/P4P17x9e6A/jrzZWsJhz3h3w4cf3qG0rNbnPL18hm0E6zzaKusm0Kzju2++CkgzcRwG\ncMJeB14fb/nq5Wv+y+p7/un9S1Z/8fd8/mQDblVWwpLZmA3gQsqamAczQA72yV5APMb0saATCyZ9\nmba5mrp7dz906COf5+PjBQRBRb5OKDqnm0oyZi7ArOih7pE1zc3dZ6hn4foesjzkcfLY5q4B61GR\np8a0PHs/cdScTf3ER68n68wiEDgUZ6KPs/FTTE+ash2a9SqG3HcOGSf0OBCGEbNZxVD9tiFcH2Og\nzaqLJeBQ1FowwhQ8/TgyETlyY92sWsnsSs20la4/OAnzZ9VyqxCTGxkNSY+YakbGWHhs46LhVpiL\nHYwWtVGXqwqjD0wGgjW1BuFkQvI8zyHnpxLbUkWRX2UGzlh1h+Q9k7a1ajQca0gAHdUUODcT+JL0\nyRBCTpY19yl+rDxr0m96b6+c9K1aOkUHnOdHtOyJzCDFvWQwhATKgto8FqDZOS9oKv2W7wdJ7Uki\nFLmPQhyPaX9L//Yr3v3hP+M33/BsDVfbnpubhj/+88CP7zybv7xidX7JxeEDu5sj++uJ61fZKDlh\nGFlvzri8OMNaAfUEnQgaC1AM48Sx3/Pdtzd8/90d/TAiHlQUP3pkEF6PN0zB8/tP/pTV5jnOnVc1\nO6XMyalacN6GMjMQeezqaXmAo66PfC1lny5nrdYY6Ml9QKUTzwRkqWOoj49Y6m3e9XPHT4BT5vP1\n93Jj1YCU3VrRysItLM4uAFmXJxddy9zcPNiZ57wP5qc9e3CCqx9qtVF8txOA+5mjbP7MTWrUkTvR\n6E87TdFAmD0PmhbJuSpWLd579NBDCLGAsjVoKkMmVf3DWBPTMAXlOE4xq6CJovBCzVyc4atJrMGc\nBQldcOkZvGyARkLO8JE2mykZ96SJfTEhg0g0MkquUp82eSBxmGWa6o1Qr6PIWRl5eLxPZala5WFQ\nHKGAoqKVp0oaAxOlF2OjjUF9mEvjqRDULozsS6GwWlsyA08t7i8W2kwN6xaYMV0rcK8ASm0cvywZ\nLHzeoxSruVpPVvdULOWiCwhWAjq9g9t/xh6+wbp3MKwYbt9w/VJ5+bsj7248784P3Hwy4O9uCbue\n4Xri2Mdo3aY1WBe4vvF8/7KnP4445zHW4xpQHRmHI7vdLT98d8vrH3f4aSSXywy9ol6ZmBjDyA/7\na74YD3TM718TT4XZjlPOxjEqQF/etSKy6XiM8TuZifLpIXzIMtl85QKE0tQ/jgsfSbWyROxTFxs4\nZV4zp35CD2U5QIX7qyYpc/Nx7+R7Z4v+3EKYwbW+v56Zqk8Peb8s3vHemUrPv7juERB5jMt/sP24\nwazGPCW5NmTQQAgeM8Ziu4aABjsHPoQQudqs15VY7T0Ejx3HqLYxBozFa6CfPEFMLK9l51K2pUen\nXdaZE8yIsjTy1lweOAMriSl9s/pdJCptvAYm7zE+EaZVG0E+gXp+TNDkfmjMcuPdG8+KHOcOMM/l\nvQCtvNWS90u0SaToWNUcx5JUXUkAtyapWYCjwugJJhXz8LG2aG0Im9fjcqvf0+WX8ZsXgpYpqMR+\nTcQ47wGRlABfCEaLB4cIYCNBNM5GVVC+Pm3V7NIZ250jK2Nel5xnR2lMj+VHNPwTv3y248VnHb/6\n1SV4y931xN2NoR8tr97AP/9uRHvD+6PldvD0h4A1HjcGcMp//sf3/O6rG842sD0Tzi8MT55a1usR\nI0fG/Z7XP96xuzkiwWOtRTW6G4b0P4tyHQ7cak87GwsWaxEoScRO/b+p1s4po7kA/eqOPAOKVGv8\ngf38k7/N5+u4k8dUOh8pje0DIEne0EtQrwdPZH6dGtRrDitzIOV7xTbMaojT5yrUNScXPz9GOOpE\nWT8NunWbDwG3VoD2EAlYclnxZSJDpKV/RiI3a6PmNVrobRq/0SNdun5ItTuNxdhYcxOlFFzGT2jf\no20bvVhsrIk4BuUwTQRrwbkI5pmzUVLQTR6vJZeWe5p1qzOAQlEjGMF5aAlV3YiopgCwxqKjj7nI\nmya5jMax06AEowQNqTK9LYmqZF4mj06MPDDxiymtDI+ZPFiUhsyU6BzhKHXofTxnrCnBK0VylOx6\nmAicLiD4PuFZgPyCV7l/TdJZF2mnPlIqASMUIhQg6vqNxKAgLxhPCSLSkjkzq4JquaHKr6LKefuB\n7dO3tO2R5q8+5cXnZ3zxJ1exUlW448PbD3z3amTce777+hZRZRgC3utc9i2A8Yb+Thl2I9fii4aq\n6wzGTUDP6I988xLW5y1/8yVMe8/1tfLjGwiT0BhL2xn+39ff06ye87fbXzEGjbns52Gq5ioTrwXH\ndm8u6rGfWcPclsy3USnsChGk7IX5EVVHagnrdG4ld/r+8fG9VqoXnH+bX2wW2+vLpdxyyt1XrSzb\nT18WoeH5fpZN3O/SCeGom11s+AdEpsWEPzwJi4msF9eDr5H7qwsthlHoRGkkVkuPxWvnfB9Z64H3\nkZu2NurBkxoFTYmyRKIBLAQQl4p/CpMGeu8LkGPMnEde8lycdLtKEVfUBguiStJAxFVqUZowRdVK\nBvIkjUkCQuNiruyswig50VP7ExBK6sF50z22B+5z3tV8pXmpiVI2LlvAaaXiIeXzyQWEA6iEUp9C\nk2ERyc+c/d1P5JRqltPc1v0+XR/59APrV6r/lieqRaUUo2VRB8EMZhnIilqmelbal5mAWyYuVm/4\n/OmOp786Z312zvnVGWcXa/r9yJNngU8/O3AclbvDxGHXM3llSkE+fopzakzMhqgh5iTPRlkBjDFM\nfmQKA5Mo+7uG1cry/BPDcNszTSPh1RhnywjGWr69fs/59jXPf3HLFAJGJGZNTGNQxktOh3ZWwcwS\nT438M2GWahIWeWfmwSpDNmPPKWmuuJzqKPF5J/NcHx+v1Fua/HtA/dBmq1iL5YLPqpQEkw9KHVrf\nWLZMfGS4tygXzy335w09e9LOv6VO37tf5/fLXZWq1eyfKlI+I/P1p1btmlvLwmyRYDRO5FqgawTj\nJG6AYUC8R8SWG1WzDlzQFBlJVkCYmOpWXARvTSAeXRIDUwggqVhFLrdV51hdcBUzii6ZngTSZVPE\nMTCSKu6ECZK7W+1HLTblj0k51MO+j6DjYn+zsD+pEIjc42ngWSE4nBxaXyMn52p96py6IXoJaQqW\nSrrUXHpMs241Zj300xQjZ9UTQipEQYyULH7mGeTLSFVzX6unHpEuFlkHdR7ruD20jE+8OENNHGOz\nYCtJtT0DopETNzkUPoH97PmlZF27wdOYnifbH/nl5wNffPonGNfhFY7DyM1xYJSJ7aVwtRfsDez2\nnn707HrlcFSGMdp0YvR9nFMjEdgbG//DBPrDxNArAYdTR9cIXi3S9IjdMQzHVI3J4YPj9njk9d0N\nL3cfYlrnohGYp/qBpBnpHSuYPQVtzZ46SpEsy0pK9pSKw86EIsDJldVzHzYI/ozP+cesEJQJ/D3W\nHOA0DH+p96tBX4GiFjkhBpr+WYou971Wakq8HL8TnjwbTiuiUESonNWvohtGoElecBpgUi1FlRec\nTen73BGpgC6ezv2eJQIp10UOsbNCIzGvc2w8RT2msGolprXVEItFhHFEQhcNXKbiFvzsdmY0lohF\n9AAAIABJREFUPjFABHIT9eMmJVYqxW2zeqH0N42tFmXQgpBV22MONlHFhimVcAuEEKK+P3g0CJIy\nHYYpFUmTlKEv5LS7gk/gaVKSvCyILJ67EA1YHgUrpbpY0rrJ6gpJHHmIpfLSu2v9NCGWoBs9WlIJ\nx/ZisQQhWEcuWJ2ZlYVBeNGv3P8HuRWkfh8zMzVCIpIp30wE9tKV+E9KLCVI9MMXSS45+aLZuycy\nD7M/vqYBa8zEWXPH+WpECLz7oPz46i1ff3PDN19fM02e/jhy2A8cD4Hh6DkcPXe30cg5TrHYc9bj\nK4EJSbECAWuVxgmr1iCtoRHwo+JH2N8qx35ERDgeVzy5uuIw9lFLLoHt2rFqLSb59Nf7p4xDGT5Z\nSG61XeveUjkF2zxPFRjnkorVwjvBGLn36XSG/zWq249afLkIlIlF1+qFH9hfy895w/2E/uoh53wp\n27oKrEGoc4qXQdf6vmqDU4GozAxVPJ/Lf8XqMa2NT/KiECSnqF5w1zPhqfSPJ5xXLQ2X56sCHvU9\n07TndrjhG9kxSUxNyuRpVNi4lq07p20cRgNMECZPGEfaECLwq8bEYblauo3VgSQFqMTCEgFsLLcl\nKeqPei4jRavmT5a/18CpM0nKso5RaIrrYZ7bEMElVYVhCmg/kqND5xwVcQMFwDihbQzBwKSxWn2a\n4hPW5tQfOPe63szZyJkIN9GYbInunpK5cjLHlVUTEvuac9jkPNqR8sTyfikgqLi1Sd2DmQOsd/6D\ne7riQvKemIdZyhpd3GzMXOXbkuZaoufSqQwvkShGqQmCmZ8ZGRwwOiDjO27ef6C/fs/ubuC3v3vP\nH373nm++uU7gr4UIqIdpDByPgXEUgppZXWcN1tlUqtCkXEExaZtHER+loBA0EusAw0HxXvHe0HUd\nKoEpTIhVWiesneFMLIfElCwklLKpavZi+Vs97PXWlGrc52mQ+cLq5pOvi3NzX6TywFvspIJ5Dx0f\nPSAoe0ucou9Dknr8WQpLXN+22I6PzYRo2fjLWAqtqGbVmlTtpVEs+szEmSBEI36IlNcIOImFlBsr\nMWxbFSRyjSYB+ZQMTfFpWl59XgO1VJIpe4I+k6IydUI4EMI7jrs3/PH9S279jk0YCcHQiuHctTzr\ntnwRPuHp5ox1a5nGHj+M6OBZDy0r62gHn3yJDUGkpDPFT1E9oIExeDAdhd1NGevyNMSIypPVtsDN\nOcSknnNN2fUM0Col4ZGojZw3GkG6H6L+fpiwzhG9R5KuOXuPGINrLF1rmIJk9WqMUC3kO41pPcnM\nRLqsAs3vVv0rGoFcFZc0C3kAJOfJVmaduKfKvidVmoJUbDgzMWl95h4Vr5G5N5xKqvMv84BLpeYr\nT83TInldmRigFPxcUckKEiQSyVT8OAf9ACVQK3v4zKMXx9UPR3b7H/nNy5fcvP2O77694Xe/+cCb\n13cc9seZSSXpwEWwArkKawobiME4ztJ1De2qpV21NF0HavAejr1n8jGC1xhh3UHbCjYY7q5Hht4j\nAq1paSxgBpzAxjietyt+FMOYx+n+Ul0Sel2ukOw9kpmbB4/MYKR5mdnDGXOWnnBVDxI3/1A8y6Ij\nDxwflSOHeTuV8wklsxEze2dkrWN2e6gYpupDxcnlr3lF1teeJACf8XJBZx/ot5SJKE2jlORAeY8K\nBBUGL5igSW1aUW2Jy3fOhzwTGyOl4XIENOorBSyezvRcrj7wdPuGq+0rhuMNL7/9wH/63Wt+NJGb\nAYv1BjMJMgTOv/uOZ5dP+cVnn3N7+MDt9Xvurt9zdrbhTy4u+Q9XT3mxPUdUOB6VZmfYjB1rBTd5\nBh/og8aCE8UXOnU7cXBl+ZUNUi3IBCbzCFcUWCT5ZSsdSmMtVh1BU6rdtLj1OJTGpImeMyTpgRAI\nAkcRejWMwcQKPJkjrWKoizRWLZ24iZf8uRGppnUJXiWFSnmpxN3rfG8QUm6PTLDiC2dPkalM9TyY\nWvWxdK78MB+LpGNySp5qcKg/zoxDsdFkbxSb8qr4SNAxUhKpacrdUwN5ISwIBM/u7Qfe/8tvuX39\nR24/vOLm9sjNzZHQT1hJAU2JOMXI3fhf7l4QUp55RcfAcZo4HnqMNdjG0rQtTdfgWocYB1j8pOyC\nchDFYQjG4NqUlVKVyQvjAFYazun4dLPhnTWze7EshidN48nez3t+Hvg5YyWZ6NbSfMUYnEpG1fnc\n1hIME5F8wJ05z9tjWP5xk2Y99mXxbkvu+XHPj3st1qSUGR3lgXb03q3LZmagPZE45xM1IacEws1B\npgU5sqGoBoble9Tdro2enR3Ytrc8P/+R5+dveXb+gcvtHdd3ytA7Lj+9YFqtka6NzhKhIQzKtO+5\nmRyct6yeC8MovG+U744D+7c/8t2ho3ef8pdPn2Ma4cOxZ3zbsjId59LQTfD18TU3esS7C6zNiyoT\n13kM5uE8AfH8u96fQUkSkVNoEaw1GLWRa9WQihiDTtEIamwy0tZ6XI069RFhwDBqlBhmL4FMvKsN\nyP1pP1kpNb0q85l1zkaiaF+AOsxgmfUZWWtVIUHSBGlUUZysm5mXSDclRiZ35FTQPP2ahrjcHt+h\nlim0YnCkkq7ibxp8tIPU6oHMtaOzG3bVZ+NH9LCjf/OO6x9uuL3ZM44eMymtCCbFIYTUqZkjj3sg\n5HfM46ekwK4pAqCBqRmZ2gjkxrXJ88rFtAc52lezSpMoBXmLhoYvzp7y5dkTrtoVrgp2q5JKzMD8\nc1bFn9BXLzhpfQB0K8JbQPwh42bmzKtbZ4L/8PGRgDx7AuiDi3im9acDXXt5VPhct1E9I/6pwlzv\niaun9z1CJOor00Nrp7FTAS1DXHZLi31dtBI3Q2mr3rRxF0YjVQ7NjuL8RXfL55ff8qe/+CeenN+x\nXYNxZxzlCZvnW/7s7yy62WJXDUZHvHSxZGffc+wNjYOzC9isLJt3LXdrxzf/6SW3uzdwuIX2PY0E\n3tzsePuqx/iG83bNKrRcjz236wmzvcK6HtFjGgsHmvop84zNo1ONrmZxs0xNAoXZpa+TVJneRkul\neh/HSEFNiPV4hchBmtiIJqOjBs+kMGrUjUeGWpOLH1X/Tud1PjGrU2riM18vCFZjFK2NiTgKTJZ3\ny0tPJKpbUtSppL6UdZLzuc9cCrNReF5X91QnJ/3X6rukdTcT1ySRFAZmvqkECOVGQtQ950GWJEEI\npMCrrNKbpQdRxYxHmnHPWgc6DUzG0LWG1gq99/Q+0KfsiyH3w8QkaHORZAp4zUMu0QF1Ckw+ptrt\niakaXNfRrNY0qxYRwQcllppVsNFIHwIYLH/z/Bf89bPP2DoXU9tm5kOz9Djv6Xl/z8OyWCdpDJcu\ngfdBWE9w5jEIlpM1eXq+Ig38FJR/RB35qWdK7U0S/5H5R2br00mgQ7UyH/aAqcTOxXjXMHNP2H6g\nz1SzejpJUghr1EPeJyi6uG0Wcpf9LQ55pV0rSmNHrrobPn/yBz5/8hUXmw84MzKOjmmcuD0qNyN8\nmDx3r3eMk0f0gNeY+IrpQL8fmQ4ToQ9sLy0TE+9vd4hRJhW+f9sz/T9veXpuuDwz/OnfnnF52bBZ\nG8bjwN3NxIvbwF7fMLh/ZLC3DPyKUZ8zcl4BxzwfJ6M/v5nU7z5fboiV6REhiBAsKaRdk3teJHLl\nBg2oJ4aEAiImejqIKeA4E8zMDZ08thJja8Npnc1OUDBhvj5EoHA+9SFVqxGRWFPURzUPqVBHqcSU\nQ/gzsTeWkDyG5hVwQgDvifonw5zvqRi73EIBTSo1kCUF/hALSvjYr0CMOfDBI5gScCWeOB4keJPK\nqBsCdjzi33zF9ONv8TevsGGidQ0qFj8csZrUWili2OuUpDKHSQFrMZo4St4hkcOQcDakdRLS/m4k\n1nW100A4eIYxFowWY/HJVdIHw3gckUlZmY5fP3nBLy6fEmqVhVaeOMiML2ndzLVx55Vab/+aoazn\nIwP7Q0jyWD2Dn2QudKlQeQj04d9CQFD+Xn2umYSfur4GzMde8LTt2NZ9ruZnQfynL0mLOx8/RRR+\n4rws+yMSWLs7Ltdv+fzqe55ffsv59hVWBrz3eB/1fcMBbt5c8/W/7NjdBUbvUTOgNtoaRDzTbmC8\nGRluAm5jkJWgXQAdUFFuDyP9twPvOuHqzHF9O/Dik4bnnziunsDFubIalOOw527/NbvDB47+R47h\nM9DPGc3nqFkj9ZJaED3lnjNsGbTI6VkUm9j0QDKlRoY23ZoMmykvyIJEJHDxEo21S+L/APo9MH91\nl2ujtiaVgjPQCJxZZYUlyIrBaKrsbiLo+5T2YJqQKeZikZxAi2jwVg2oSAxcyhV3FpR+uZqL6awC\n6Xl7545X95Z3mSU7g5Tw9CL6RxSdVT8a5uCg3JpEL4+wAK3o2SLHHXx4yfjyt0zvv8P4kcYI3jk8\nBuuaWElJQpRiNL5nDvgRsQuGJuruk+2oSBJpDRSinCTYVARFiETVGMUTi26PxiDDxFnT8ouLDU/P\nNqy7ll2Vo17zoplf6mQ91JL/bOQ81YHXqhGBpb68OrS6/hSrTrHspCMny+FhpPtI2Q9PFyrzhk4d\nL+qQdC6L4QtRphqUOmhoYQl+iKup72Fe2Mvz+hOjqwv1Vs08LYxQ1ebUBxuTe59zwigkYKXnfPWK\nzy5+zy+ff0XX3WFkZPIB1EcDYTMw3d7w4Y8H/vAPL/FDQBywEdzG4TqHcw49eHQXsHtl2E3IRuie\nNqwbD06QQel3A/u3nh96pfcTT543/Om/3/Af/37Ls2cO04LVEcMNMnyPGX6LnZ5j9E/x7f+EtL/A\nuAtqt8TlmJ3yHhHBlJQChJi5EeJmnlIgi0kbfFZBSEnylOMCskw1GUlAXhuN7ou6i1moQFDS8wRK\nyLiI0Imwccq2gQsjnG9bRn/B3koxUFuicduOE7IfMGYf125rI8B7TxBBCYQAPj3JIBVInK67gvLM\napx4vib6emLAr0aYNBylDiuqSIrelRz8Y6NePNe+rD1WlEhUsyyBKtYHONwSXn/D9PYbwu49nbW0\n1uHVoypYa3EIXgJtiMZS1ZjywWbuWdMri0/ceHo3qfT6tTcPibMVMGJK3IRFCNPIGAITUa1z0Tm+\nfLrFucCIT+/IvXYLk8Ep83gP3RfXSHWuZhl+Kq/K6fWn1xRHiqojUtl2HrGDfhwgfzDbXHHslAcH\npRaVq4YWOZlKC3nGgcWuKIbTmq9ZMo6LqRKqdiWJmDOVzpdGCh/PmxxPnVibmdrO6pTyfhEtMg2r\nCBpY0/Ns80c+OfsdT8++pm2OGJ1iZBoTRixiHD5Yfvj2PX/87XvG3YG2EZ6cOT77vOPy+Yazy47V\nuqE1qShDD0FCFOcdHP1E308cd56bt5abdz3X7wa+e+/54duR63c9jR341a8d2zO4fj9y967ncDMw\njQE/vWHQt+y6wPkv/1fOnl8s52L5ZflR84YyWHz0zdYQC0uoYAIxM99MrePacTnSJ/5Wu96PRI68\ngH6+uXLJq3kiY2b5L8OkAZzAtlUuWrjqlIu143xl2LSKDZ42CIM2DAkArAibVYcTYQqB6TDE7JL9\nhENgf0RuDzS7I6JKGDJHntQ45r7vfb0UZ44vXmOY11whksnNsdYSZCJnMIVJioYTLW6GpERoxqbq\nS6oF3EkEgGSPEFWMCjIeI6d++Rluf007eobhK1bORYfRACPKGCZEA401ONuAEayx5AJyEzAYGL1G\nV828J6v8wyUNQmaMJHLzzjZ0TUfnXPR0GoR+mpiCsnEW40fevXnNP3dfoaz49IunhclYGjvjmBfB\nhOrZiTOfSf18Lv+eOXFN8/SgCuVEF17aLExfBJLMtGoNBmkmH0uYBR9VtXJK7R76WOuS74N/DcD1\nFUIlVZ/cl4ekpowP0IIIvHWDeZvV+ZfzYGdAWhANWdADhQW43OtV3qVJ9BbtkekbGr6jNR8wSKzQ\nM0VuZxwDw6Ac+8Dr1xPDAVZtwy+/2PLnf37BX//1OWcXLatNQ9taGpfSPfmog1RiHotJoR8C/X5i\nfzdye93z7s2e33xzy29+e8urV0fevRk4v4hFFI6HiXH0DKNnfzPy4f2Rm73naP+ZX7Z/xvry32E7\nV8ZjXpT1rFLGIJ815BJvMZozg2ne1MZEUqhCSQUrRI8GEILGzCw+VXqvN0l82jJmsv49V88xEjnu\ny87wZGO4WAnnLZy1sO0sXSO0FoKPucRDcLHQhiQ3vq5FTcwg6TctYVzH6E6Sf/a+Z9r1mHc3jLcj\nFodx0Z9aq56WHublVK/l6vOCX6kXWrlN5nWX3f+Cpj5piWIzLubdMdYgQWOMQQgllUMUgKQ80fhY\nZo92jTxpWYUR60fYvcP7gA89E0JD9kiJxb9DcvnprEtZOj3DJBxH6EcYTajeN4GrBvzks00UMQZn\nHI1zNK5h3bZ0TYMzFlCswDRObKxjJQYTPEYDSmAIvkpoNnO/9/bpydpZDHEFzLL8YQH+j6lQ6uvv\n3f/APC5+exzHP56xU047noewkkBm1ztZ3AswG41yE/NGzeug/FY1LSwnIzVWQHTu0D1oJjMEJy8z\n/1rew5y8V6b697becggSiIMSfM/h7iXj+j0aBoYedseJu0Ng9B03Hyaurz27Hbx9a3Bmw/m24de/\nfs7f/Q/P+Y9//xzXRN1h8YRJHnvZj9iHgDEtGsCPnqDQHwY+fLjjl795i3OG3d3E2MPuTunWRJG5\ndbgVTNfKmw8j3788MukPXPziHS9+3eO6Zpag8jxJTQTzO0shukZiWljnYwBQ9OozqaKOYJo5Fk9t\nBpgIoDkLog+xMHSu9F6NLIu4AWZOyorQGOis0hl4vhE+v7B8+bRhs7K0LrsaJk5JA95ACDG/R1CZ\nq7VbmwhPwLYNOBcFh9SfEJRhmJCrDf7dgc2d4YwYoDIpJeq3LEk5WX+SmYBZBMln5lWoZVzzKo4R\nlSl/jdeku0++5EYwrYtAbmKOHslpbANRcpMUwJ/UVdZPeOOQVYe1hsYa7Lhnevs1fjwy+Yl+glYc\nRiyti5kpp8RTrp2lNdGjZPJCZ+AgEqOfhUS0DQEYvafXIQlUBmMs1rlU67Nh1Xa0ziEiBG0Q9Yze\ns7KWTdNwuW759PyCi9Uq2o7QChfz+quxY0ny/zWFj8vKKuqgxH5W4r7UVypL/Ku8gO6hw6lO/pHj\no/qRQ+5cBtE6qEIeHbgCetT7Vepxgmoxz0zyHFu88Fop96Y7qptqaJ/bqX9Zfjy549H+lz6KUKqQ\n580bIAzK7W1Pf+kZNfDymxt+fBN4/c7x/kbYX3uGXUxspCOE0dK0ytu3B/7lv77Dtp7zC0fTCiEl\nCjo7a3jxYo011UKzkcP3GqLvrnhca1lv12zPNnRuz8210A+B77+faJqRxiVgazvExk0KElPjllqZ\nzCiUFrgpw6yLxQtzyLtTXVSUV2XOAyMxKtFYW2ikhAAhZsoDCMaR86urzCvBVKJ0VBEInRUuGvj8\n3PB8K1yuhItNw7ozNE5xxd85B3WlpE4m6metRN100AiKk58KwRYxYALBp1DxLDZbQ7ja4DYtfzbA\n5VF4c/S828O7Xth5IeicFC0kkSa+vlQc66wiimORIN3MTMhizWWUCCG6dKaC1VGqmTk+MbEgd/b0\nkSTJCSS3ywhuwaUUDiFWZzJNh1ttaNcbvJ/wOmGDMklMTzFpYNSAR6NqhyhYWSM0q4azdfQRHyZP\nCHB+tubQD9zeHblJkVPGGIzYqHtvHG3b0TUN1sTgL5FIDAcf2AJnmw2/evGcp2dXbNyKXrP9Q4tR\n957Qw7w2YcbRErVb7eH6mFUl843mxPtp0WjNkNZgXV2ycLIp5x/GlY8bEHSPM86c6/yt1lyVvwtR\nI3PCQuXeMONxyc18/1m5D5rarHuxGPf6b7nolHY+dMcD7/zQFeX1Zr2ZsZZ21fHuw8jtzQfe/Hjk\n9Vvh3fuJ3c6jg2DU0LWO7VbYbi2uEdwK3t8e+S//4mlXFmPBJ/F+s7U8e9ZFsTbln+66NqpeWqFr\nDC6tiIuLjmdPOy4vG+52ym4HPniMAyOxWsuqc4weVusWz5Z2tca17cm7yYLwyWIe59fPhRqM+liy\nS0Os/hPl+rL4F9KcKohJOtxo5PTWomKLFLJMtZr5V8PGwbO18OWV4cWZ4Wpt2LZC28a6oUFTwE+e\nmyLbZxtPYjyUqPMOyXVOY6SvyXneVQk+lPtEBE3cb2c9L1rlbCO82Ajf38KPe+VtL1VfTxiG+qvM\nbq9Lzm95i0m2BSMUT5WYhz4xDwnIcxWgmAUz9luSQTS7YRqil46k+qoFoJqO9vwZxveJaNzCOMV7\ni7HO0VjhvLGct5Z1I6h6fIiZNb0oPunCrTFM/YgNwtq1Uc6VVNfVRq68bRw2GTDVBxCDs471as2q\naznv1jxfX7Ft1rQm6u6zP/08XjWUx8/3sfIE6os+5qfumZ+xUDPMP1SfterLQ/Obo2oe1638m1Ct\n5E9LS+3JSxfSVt9b8eTCDOLpurn9h4F1YYjIclDFQeZnzNi9VLQ8Pqz1vdVOy4tHs8g2vwWJAhvi\nhnNNFA3f/NDz47cfuH3vuL2zHA+K+onGNaxWLWICbmVZXUK3MkyT8v6u5+XbXQyS8DBNgaBC08B6\nI1iNOmfnDOtNw9mZ4+K84fKi4/zMsdnaWJF8Zbh6YukHpT+CH2AcDIejZxgmViuPAbquQZsrus0Z\nrm1RQhmrgnnzkBRiNYN78ljREGuNpvFR1VT2MiJxDrcvxiZJIJ2SLfkM5KZSi0mt2omqlM4KL1bK\nLy/hz184NiuhdRKNkinzHpqIiEr0x05FkyMHmzh+heStHZ8TNJZFy6sjATkaz3uJUauS1phXz7oz\nXGwEc27ZNB4nymES+qClclwZpPLhPsd4eszWpWjktCZ7gmhyOawD9pNKJmgERNWkrgpYT0yuldey\nkBKqxTkxEgmB6dY0V5/Riqcl4MKIHI6YaaQPcb6ME1zX8unlGc/PVlx0limM9OPEfhi4OR5AokSy\nuzvgB494ZW1d9Msn5tqXlFTLpeImuY4nIjRNy6pp2K46zrsNT7szNq6jM5aAJCPrqe/PMpCrLNWi\nApnXal1Hczkx9ydi4WlVMZxxa0g1+pVSuALBfNlsBl8aUuvjoybNKmOky/Pzj0l0rF78lCPJx+kl\nWZKs2eiZYJxQvtN2a4ieFfWzGoglptfn5x/vdXfJs9congFe0gkJqB/Y799z+/7IzeuGu5uGMURx\n1jSWYIVjim4cjp63fWCaRg53E8fdyHAcYyrYKeC9R6wkl+UQ9aQhpTa14KyhaRyrlWW1cWzPGi6v\nOqwR2pXls18I1nR0rdCuHT++uuWbb655/erA4W5EbOCTXz+h3WwxzhAmH19Kak5lHrDFeKfhbYLS\nhIAJvqQrNIaYojYI4iIBlBCBKOfJNhJ15AEikKeMgoUYp/ZFY1/OG+XLS+HzC+HZVmidxjEJxMpC\nIUVohpQjT5IOPOd0T3MXMToUEPTqGcYRIbrd1fxV3IzpSjXlM2LxKlgMXeP49FIQ8fTe88NeuB0N\nBlNSJAuGObT1/hHXtS5+r2GoEJhaJZPWroa4VsIU1UfonI8l74gY+GQjZy7RuIwxWLdCtlcInrYJ\nGDNyIQfOVjEX+M3hyPWhx6jlou14sXJ8ul1xsVkxhJH9NND1Dkzg+nbP3V3P0McapzYVhM46+ky4\n49zHyE/vPeM4kguMeAKbdsWz7TmfXF5wtdrgbMsocb2YChzvbdJ67B6hkvdo672jwrfqnpm5kMW5\nMk95XZ1y5rkNfbxPH7WwRKGL9/o2v+j8dQ6FZvnrAsRrsKjhtX7EaVRW/F1Ovtedvf/L4nQe4AeJ\npTzwad5EFY4XXXmYJob9gfff3XHzVjn2HbsD9METZKT4nRjF3ChoIEwT0zgxHiam3uOnmHskAmFI\nHFTa7Mmlb47Xi+HSxoFzlqa1rDYNXWdoG4MYy3rtODtvuXiyRpxwdtlyOMY6i8MkIDYWA0heI9kn\nRcunepyTioIkLgOdUVoBKwZJHgWSEvSRaouGcUxVX0x0lavmNxADV3KVesnPqpbLWSN8emb4d88M\nF2tYNykvx3xJ/FeJOtFEiAQtATF5muMwpgRYIRC853jYY62jW60wYtNvHu99sSOEJG1M3sfK70aY\nJsPkJqyxnK3hy6vIkU8+0AdJ3Gj9ttU6eowlJ66nJI+QZyQLCpK8KkTj+lGVuaBElhq8Ft26EjMT\nanILzIE9ChgruLbFcsY2nGPHM/ywYbNuuAwbej+w73tEDKum5UnXcdG0bJxD+ol+9Eg/0IyBJsSg\nKy+KM+Bt9uo3BcitNcWLqR9HDv3A8XhEnMVZS2ctL9bnfL694qpZs7ENIibm8SGD5smuzJvwoT1c\nMcpLA/SJEbPMkpR/IXPxiQhVV0tiEue9MXPtD871ozP9EQtL1EEzuYOhHswCbPmYN1JmtWtjT/5U\nBqUAa/WARybpX3Hqsdc47d7PHNVF+f2rf7KBw/vA4TDy5qXncC3se8vt/kDvR7yOMZe4xnDqEAJh\nmCIn5X2pvSlELlU0FYeoB7peu5k5TIvqmH2RgRx4aJxltW7ZXnRcPdvQrixBlG4dA44mdVjnkqEz\nq77mh0jqR3ztedSMpAINIrSitAaccyBJXWFANEYe6pgkCzWRMEFJAZv7G0SK2G8KhMWXFIHLFXx2\nbvj0wmFtzhuynEetAoxKtj9mbleT+k7TuUwsNSh+8qBSqs5riH0OforeKOlB3gfGcYpcJIFRlD3C\nqlthjOOim7hqlf3R4tUw1jaBahWdgnjeGzVzgM7EaE6aL0WizJ4s+BDdElWhTWM4hNRlX/ZcqIlk\nGgtrFOeEhpb1Zo3p14y7FQbHRQPNOhpMCQKTIONEk3zWwzDiDz3hMNCqcNY0GGI+lxBCJNA5dbVI\nKmIV15kPgWEYOBwPHPoe4x2brmPVrvh0e8Fnmws2pqVL9Vsb5qRfp3A8G8bjmMUrtHBoQx94AAAg\nAElEQVSdeW/mX+aApTgv5W7JdgMhG/XvEY3cet4b+dfHiEj+7SeA6eMEBNVFCKuEzsvgnplU1RlI\nFrRqvnUG/3R+OWRLUIEHOJnq62mK0Pp8ufwnOKGfPerXX+j748Ix1uLcCjFP2N295u3r99x+iIVq\nc+pPDRHIJ+9jngofuUaT2oy6ZIqRq4yCPNiNsiBLX5JnBx4keIbxyLjref/jHWpjUWbTONQL27M1\n50+f067XiMwVc6Re2HkKinXfFFvklKQEA7GOqE1coigGl6QKxa26+E55Y6W+ion5VbwQixJYgxNJ\n6YSjTNCI8mxjeHGW9KyagLxUU4peEcGHNC/MY1HmSWZpTmfdshLn7Pz8vCzOaRijJwVgkjoi1+cc\nvGcKgaZZodOeaew5DBN9PzAF5W6/R6aWC7vBq+UuWAYVpKhYHl5QNZ3W6oQhRsxKCHnRUdhSERgm\nwnFEhwlpG4yz4FL1pZQX3ho/G5U1pSPIY0hM6yvWIpstcjxDXcvt7Y4uGNbnF1xenuEah58Ch9s7\njnd77vZ79sORnoDZtDy7uGAaR+7udhi5A+0xOjH6QInmdAZMVJ+MXhl9wKuAsTjjOGvXfH7+lGeb\nc7bdOvqbG0dIKpXKbh4Zgczs3R/OeyOdh62C/ByDVZjOuKbvM2z1WpqxShb7bnHfKVcuiz/3jo+W\n/fAhpcc9XTOnSzTrj6QMZp6LxXgtPj9m6nzgcpFHQfyh7/UzTh/7rz1OnoAQMMYSmhVNe4HQYMeJ\nKydYsSCOPoDxHmFEvKZgi5QPo1ogwqy6oMZo0ZMhyhROEkBpGTfJsmTydggEJvUx8KaZsMbg7C3X\nb/7Am+8/xXYt24tnWOuiPrUyQMc+zdLC3CUpQUCZydakdoq7xyBWY9WYdI+xdvYekKQjV5gwyfw4\nJ1ezAmsLZ52yaRXRbLhMunCTAXoeo7p0n5b/FCW6FGZjbNAwMw5VTHvuuwCqgWmaokGOyJGHoCAT\nmlUow8i47zn0Ize7PWJacBOXXcswrhiDo7CFVGvxFINO16JEkLUQ0wTk3CpJwpBJCaNHfIgSFQI+\nlqLLjoeaVV1E/261JqpdCEhKFYEIo+04yBbbXuDXF/RHxYeedj+wPVNWm5bNtuH87AzfjwyHI3f7\nHTf7Ow5DjzGCHydCEEKQxNQkI7QmFDYGT65YFfO6qwExhnXbctWted5tMAHGMUZ5ZtfEmO4hRXlk\nXDhlbKo9XqPOYowrnr6+WBLrvDCgPnAsYaQsnoozn1lXPdmrj6HMR3Q/TOYgYSZrpZN68m3+UjvZ\nl5YymOdr846Uk8kp50++V+f+f3PahSI/8vMjHH39BpLGwuBpxx0OZTQTd23DWduiXcP52tG1HR7H\n693AfhjpB0ujMMnIFDxBzKKwa4msPiVIzNxFBv6oNqDkda4vTj4aiMRNZQIMIUSDlBGOcsOHH/6F\npm1QP/DiV/+BzeUz2vUWZ5qF4PTQ8Kpk90MtnFKpniT5nmjgygYtMSmjYLouoEwKE9nLBPKqsAKb\nRlk3SmOT6qmoTELipqqAI1WCj+JCaUljbvSggRB8xkFAlyrAxO5ln3kfFD8GhmFknKZoSJQIJ1OY\nCH5inJRxnLjbHdkdjuyOA8aMuE7pbINRi+AqlcnJ88pynxmhev/MEfYa67H6kPRuaU36yDyYlG8l\nhMQdGoMawRvBd2BbR9s6vI05ZYx6TAg0CohlFEvvBdNdwuYpY79i7G+R4y3ruwnXTLRtx3rTYddC\nOPM0dytoLOH6mt1ux/7Qsz9GT5ZM+KzJBEXwSJznBNBIXN9iDa1r2LiWc9vgx4l9f+SwWnFmA060\nEO/M6BQOWGf72wJzFvJPYR3rrbGE6wLmCcEe8DCpl//Mymr1PUnTOoP4zLw+jk0fKWlW+iuGU7ol\nC5COf2NeiaVIE1+sWrSZgzt9CFDuNJndOxlCObm8aqv23jzl7WuZQk7Oca9P9/M1ZA5VFax61v6O\nT27+wJkMBJ2AHXbj2D9/youN49nlBSqOf/zjD7y62XMngUlahknpPYx+LiEnRC4spy/NuaWzz0PC\nw7JQAjGqL3Lfp2MZjaG5jkPGEe995EyngbB7xfUf/neGt1/x4Yff84u/+p/55Mu/ot04aifM3F4m\nXnn8nMaAIGNMTFkaJHmO5EELhGNMCSudK1JD3sgJo6rIzpmrcUbZtkprdU5KpplI1NxmcmXzU/wv\n6cijr3L0khBjy/eibkkcbsb22IZPBtIQ0ymM0bMi+AmX0q6GaWKYPH0/MowTx35gGEesieqDvj/y\n4e57+vMV0nWRG5Z5/c9cd7X2kgomw5IJORlZwnwf0CFF2OSFoFH3H/BgYqUd3zjUGnxrORjL7szS\nPdnw9LxFU0VxQ8Bqi0weH+CIoOMKH46Mt8/Qywum4yXX/R3hw2t2h/dc3dzRJskqeM/heOD2bsfN\n7Y673YH94cju2NP3Q/RaQaMqRaMz0xBgpKrDKkSCI0KvyuADJiQPruHI7XHPWbNiZdsFXhhkrtAl\nUmwYC9tatUMWjGPaMzUfKlVby8SzNd84E4NFizrfW76nGIYSSZCZikcYzY+ba0Xml3oo5WjNZRdj\nGRUk1gBcPuvMXd97aD6X/ZDTdqjaqcG5/L5ookqYE1+kXFukpOUj5ztleS5rkkSE9njD5v3v6f/r\nP2DNyOpswy/lyOqyY7d+wvOzLc8vzmmc42zT8Zuvf+APP7zmx75PdiphDIExpOrzJMOQiYllY66M\nnBM2Rma2JiVfgiiqSuQggxB9ost4xnY6Y3DGxKyEEtU5gwpit2wuv+Bs09F1DWb/lttv/wWHZfXv\n/xbTtJURdB6DmmZbjdGdZf7ShtLsSRFisibFlgLRkjaTIniByUAwptJwZElH6YxiUtbBgIlJqnIS\ntFg6npidVaNXjGmiqsHEgCMphCFFeTJ7spQVmfVCIpHwTNE7ZZgmhmnC+5ACWgyNc6zXHcPgOTqX\n1oLinKEffXTDO3qOvsGuPdJF+Lkn5p+qWKo1lsfXoEXeCAKjESYBugZtW3COsFqh2zXhfMO0XeO7\nFm0cU2sZreUglm235rOmLV40qU4y+BDXDxLdXXct4+cb+t2B492e4W5HeLvl+sOPfHj1Fu13MA0E\nPzKNI/0wJGI20Cfilm0/XmN6Wk9MUeyRShqa39T7wE5HXpsDXx/3eOOwTYMberbDkSCOsckMQJqr\n4so572GpNnEp4bgY77KbK8Iw441IXpP3MXfGmBNO/aELK7zLAFXz7afHRw7RT92U+917CIRNHiAo\n3M8CDYCfVY1I/THPihR3rIfaKQSi9PchKi1lM0KtnZinT+aPpY3sedGNd2yvv2X4/ndIK7TmBett\ng9k6dpstzy6e8PRsy1nb8MmTM7YWGqIR8McbGHwPqnOS/sxlA1iZK6OQGTHBmagFDZpBKVO0aEC0\nUAhCZwwra+mcY/ABxePF4toLVk++4NM//R+5OF/TuolpuIub7rhPREWT/jU+vWBONUZOA1ZnDjxJ\nwuRgGk2h4GIlVtYxkvS0iRAlFUDI1ixmcdmgOBMz92lI3i2hykOTNmVI5eKC97HYQghMfmKaJqbg\n8VP8HF0KA96Hsrni/NvoUZESZ5lUD3OcAsM4EUKga6L6om0sm/WGqQsx6ZM1rNqGTT+wOwz0h4E9\nnuw6JKUc2wwED6nM6k9la4igBqa2wTvDAaVvHGGTwLtt8dsN/mJLuNziNytC16LOEazBW4MXYaVK\nqzl6NaZLcM6W7yqR4w/jhml4Qn97y2F34HB35PDqjN0PW3bfWfq3rzh+uON4/ZZpGJh8jO4M6iNw\nKwnEI+c9pVB3NaC2dh2e7SAalMM08YYjdn/LJAbfNqhfczb1YBuCaaMqrcx6WmjlzEwhiuuq1jTj\nhKOWE6bu3jyU7f7fcCzxJbf7WFsfyf1wueSW1CsOaeQ9tLpFFu8Vr5GCmFJm5l/z/Hv4X4k3cx8S\nps3Z++SkEV2+h0BM4qSVaFWJwvMiSQAkma4LLgS26nnx5IJnLy45++JTfnj7GhkHLNCayEGvWse6\n23DxV3/Grz55zpf/9Sv+r999y29evuO9TDRGGINP+sUIagETrfZI8iGf3cmmEBi85+gDQzIeIcrK\nCJ21bK3DGaExQmMsq6ZlP3r6MOHU8MkXf8Xnf/2/8Ku//Xu6zQYRRcOEqsHYBrvqSv4VwyxSZuk1\nj44LHhtiMIp6UtCSlrQsgmC6FtomuhiSuJ/UiEqs3YjYSrhLwJdrZxLwChaXhibmxbbOAYIPE8f+\nyN1ux+3dLR8+XPPh/XvevXvLYb9jf7djf3fHMA6M48g0TUDy6lNoXEPTNjRd9Px4+uw5n3/2BS8+\n/RxjXASnARqzxaxXOCM462JAVmsJ4Yxx9BwOI+erFe9uD7y+C9y2LT1V3o7TDS7VGdWFi6CGyCyM\nbcPNes10dcbu6Tnj+TZy3uuWqbFxTJ3FOFuiQMu+yN4ppXanzXqp4s8PiVAagzgDrcPZgN10tE+U\nqy+eo3/zZ/jbG26++Zbv/o9/4Lv/83/j4Hv6YWTwE0FDrO2ZpCxFUrWnHBsglWQejdaxGElkTAYN\n3A49w+0NB4G7dcMgW85NLFOiY49Xj5EAYpMTQFojUite5mfMsF0fVaxlToNcxIR8T8a1tNcrtcLP\nuV8U19B7+dIfv/PjBQQxA9psLMpiz6xOSQxFTHYj81CpJs+Hn+HAS+ay9MT8nKzf1Oq6jNdFO1YT\nRRF+5lGFwtcgXr1ZIQySVQcVsBtncKsVl08vEfF8eP2aD6/esPMjftWhFzFThBNHZyzr7RmbbsPV\n9oLnz57x5Vff8k9//J7vb+94fxwYfHp+2pS5IjwSkt48cuFTUMaQjUeppJZGMXZUoQ8Ba1KghWto\nXIv4AbGwXV/wyZd/wS/+4r9jc/UkAmJetQV1UhKtBYhLBcQx93ijiktuhll/rpr9vKvx0oB4H8+F\nFDpuDJpKfWmuCl+eAc4oKzvR2vgeNoFV8CO3d7e8fvOWV69e8/3Ll7x7946b6xv2dzuO+z3Hw57j\n8YCfohrAj2PyOkn+43kWNbov2hSsJEbYbLZcXT3hsy9+yadf/JJPPvuMp5dPSp4Sk7ImGmNi8FPQ\nFGHbst22XF6NPN2P/Di0vB8Du8kwJi4VKs60VjumdZorGq3WQvf0knFzzs3VBn++YdisCG2DuljE\n2iR3IZPWiyb1XLUdi2osSnMZvCVt0hSc5QM+5ToXFLGWbmXpjMUKME1MmxXWNNy9esX7r7/icDwS\nhiF6HPkY2KV5vnP5vLSXKHsyrWlMTORlwFlFpiQhGYu0LeurLZ/8yXPa8yvuevjwYc/1zXeMTJiz\np0i7QsWlVLs1PpwM6s9yiLVO9eFr5SfaKd5X6f6cmXJGD/nZ9j+ejjz/rcZAT7luqVyGRAsgBIrR\nPVUVkUdkjnl6lgz7A59OnotWzPRjlDB3N1PQ+ny98OpHSKbWs1CPKKFZ4bdPaKcr2L+n3+1xYnBI\nFPUzV0rMqtaahs1Zx7Mnz7g82/LiYstl5/jnH97w9fsb3u+GqMsUgzM2Zv8LgXGa8H5MeuFaX2ji\nGGtyMpPo3jepxox1yehnjUOMxxhYdx3nF5dsL59inSvcST1u9wXEiiNnfnxRrSQgnx2PKuKrRN29\npKyPIQVVGCFYi/embP58GFFaE/OMtynxdz/0HPc73r9/xw8/fM9Xf/yKP/7xa77++ls+fHjP/u6O\n8Tig01QMrpJS2ZrcoxMivZDoZJ7rtu344bvv+PWfv2Ucjvx/zL3nkyTJeeb5cxEqdZboqlbTGAyA\nAQiKI5fcWzu7+//P7NZ2yV2SAzHgiJ7uLp2VKpSr++ARKap7QH4bhllZZUaGdI94Xv28+meS0SiH\niFOxpiIEVBCxfZ93XVaMR0lPri2jdkXrtrhWgsxBZniR0Gs2+2d7/0xlqWRcKGaThMksgVlGNR0Q\nshQv1XEOeM9e2e3bc7wE9kRfCLoahih8+qKn7gdCXwjV0eVKBFLpLj1VIQkxz9+l5JMpg9NzirNn\nyOv3UHexiK4Bc+g03adxlX60I5OlRKiYoaQDJB6kdLEKOS84Pzvl9atL3ry5ZFQU1JuG1rfY8gpr\nKpQtSWbnqGwEKsUEgessAUS8hhCePrud9v6R7v7Ue8CBD3j/tB8+80duXI5B4iDp7j+8/MQa+TGk\nip3K2v8Le5wRe61A9iPcPW17iX0ED/FjYPd512psV2d7cCGH13c8yscf9yr8p2MUR9sfXNORRD6Q\ntd31temY1eQF1t8zVJAPPa/nU97e3fJhsdhpu9GXKwleEpyARDKbz/kyT7k4n/Hqm7f86zfv+N33\n11QdI1yeZLEFl7NsqoplvaU2pgvmdPwdQtK1C0ARfepKCrToswUCqQskKuazJzgSs8Wvbmkfb0mK\nIfQseruhPZyD3g0Sx+TI2AldZ6CuQrJf73v2wd3DLTqlvKMoCPFIQYnoy5VqpwyI7mXUAnItGOUp\niYKmrblf3PP7333Fv/zzv/C//uUr7m7vKDcbhI3gqUQcA+f7fPF46ki61PebDMSYqdxZviF0QeYD\nPnRrDHfX15i2Ybtc4ayjKFJO5rMu26LjabGGdbllvdmwXG1YbzZsy4qqqTvBppBekwxfQH6BSU67\neeMILHqNbjbWvH4+4PTZiGyQIHT0dQchdlZODy+7/OfAzoespe7AtJtCCX1muRQCa0w3zwIpY+G7\nRZIoFYWfc1G4d++sqyrauqGtm0j3m6bo8RSZJpEAK3QpkbtMpd6VEtW1mMURdhTCQki8CJGrRgq0\n9ihhkTphMhryF1/+gr/+8nPeXM5xOEyRk+cZ1e/e0tzc0jx8z+TzLxmfvyYtzlgYSelk9Mf3kydA\nHmQjyZ1PXOzGbRfyPHzUu+8HDpbux04AiAO05mDn3Vex3+4/uPykGvkuoNCv6+8v7G+iH0RHVBNE\n2A/WPslKHByVPch/8qx7belo2YlfsdeonsrjT4zr4T0cyoadtSXEbv1eQOwleq/tWJVRZ3Pa0Rnz\nQjPRgXRSUOuASxQkKd57qqoiNJZWN+hEQylpXUtVl6zXKxLheX0+YTrMaVoLSNI8ZzjICd6zWm/5\nt/dXvH9Y8lhXBKB1Mjav9cepi1oIEilJlCBTCYnS8TcBWgRCXeHLLaGuuvE5zqo4UMx3c7QPFHX3\n32mGOvgdF3nU7ujajvUPdTxO7zfsG3D0riOHoO3NcSIApdIxVA2JLXn//p7bq/e8e/cDb9+94/37\n99xc33B/v8C0NXhH1hXESBkFQfS97oG6VzZUJ+SUitAWQgxuBiFi/0glO9dVlzbnPdv1hg8/vMV7\nT7l65PqH75lOxxhj2JYly9WG+8cli+Wa1XrLtqqpTYt1loHWDJKUPM0pTi4ZvvwLRj/7e0Q6AKnj\nOByUsE8GipN5yuw0Q2bghYvl8ISuuMcTpNx7dEUf1O4Ee9grTXConIj9q6JUVKjYv5Cx4jbysQQ6\nmly/J27rFS7TtJRVw6YyNC6CcbQI/V6ZCx1pGSCVZFhkDPKMPE1oW0PVWkrr6CQLyitOplNevHrB\n3//D3/I3f/UbXp7NKbBUTdVxmCe8fnUGIvD+dsnm3bfk0nMyksxmM7ZGsSgNG6tpiQHenjWzL4w7\nAmexf5OfIFCnu0TF5VNw/B9JytgnY/z5TeEnzCM/hN8jvfwoErwH8/j/AMUPQPfIRfUfOftT5Z1D\nkP7YGSB2v/eT93T69tsdKOz7LJuDWT6sSerHISDwUtPqAWV+gkeQJg0qkQyHA6bOU9mAt4a6abGu\n3rH+td6yrUq2VUnTVKR5zsXpjNcvUpaLNduqxQnJeJiCCKTCc/uQMkwUrVFYws7acTtzufOxihhg\nTZUk1wmp0vScI0r0CqFEKL2/oSdzsROIB8JR7O56XwiU4FH4TrkM+xfhEG26xbOvApVdOzLfaYRB\nCJQMKN8gq3uq6o7V5obfP77j22+iC+X91RXltsS0hhBiA4lU7dM1lYxFRD50f90l9NpY7+7bxR7o\n3A0i8mhrKWLgznucDxgfaOqG2hiqqqEqN9x8eMdoNKBualbrDQ/LNQ/LNY/rLduyib0nXWxIPEpT\nxnnGbDBgevKBZ06Qnf2MZPYCqSJtcD86UgrGA8VkrMiKyIkTrO1c2SqW0WvFvjQmCuDebXSoiRxy\n5uySUQ8s4L3idVhvvf/r+WaCs/RFN0IKvLXUdcumbmmt37k0ejdZn61EiKmgWZown4w4m4+ZDgdU\nVc26rHksW7Y+UBlP1XouXrzgL/7y1/w//9ff8/z5BUWaYLebyJ9uWpT3PLs8wVhHVbXUTYlb30M1\n5nSWM80yCuG52TrWTlOjY4bTkzd8l4IsDh7N0AdiD5wsB/vulMeD/Y78Kwf7RAWnm4X/ILD9NFwr\n/Su9C8Ad/37IE75b13tTjlwoYfejeFodunsen47WAZLuj97VRTx1Th2j/f5yxfEv/bUdrNuB/+Fp\nwsExdjZr99AjMEKzUFNmtmRcrxBNzD1OkxwrYpFKawxV3WKspWoaHjdb7haPlHVNlii++OJnzE/n\njEYjHpcbru8X3CxK8iICVWM87x6WlE1LITRlsJGuVnauGvaVgFp0WrkQFCpyVqxNgwseJTQqyUnH\nZ6ST0y6heB/HOCab2s9rCPJoiAUC3QO5CHug6KlUfT/icQ/fuVOQ8oDeOAJ5EPEvlZakeWD1/f/H\nv/3+f/PNN3/i/d0Nm01F01ps8B3rXzcpncYliUCYKEWiFcZ6auepO26UPm88lZIERSYFmRZo2Wnm\nHQNkENAYQ208lbE0zhEEaKcwvsS/u2J9vyDNFK0xlHXDtmnZtpbaOOyuR2UEwjrEOdFKEe7vyKbv\nmT9eoUfPEJmKY9QF2ZWAURFIk5g+qYJAW4+yLo5VnhKUxHbWRegskN73vweYPSzHeew1j/3aiLXd\nnB+kJe7+XOcqE9EdFaTfWWHOBRobaEOsyO1P1Lsw+hcuSRTT0ZDnz0549fyUs9kU7x1l1bJ43LKy\ngXVjWW5q/uH//j/5q7/5S37+s1eRYhdBMpnHtt7bDa6tKEZDLi5BipS7xRoTHKuba05Pp8wmmlmi\nyPyWq0pzbwtaobs+sFHp2L/rB1lEvZLSAfqnll0sUOwJuo5A46Md/tyPHy8/Hfth/3I+tUk+3vTo\n0y4x8AlAhl4qHoLnE03uY2vmcMN/x9zpELsf3iMxIPZX9nTwj7R28fG63bFELFR5SOb4AHc2RbZb\nrFQE4Rn5K3So4rl0Zxk4jw0tXniSLGF2Nmd8MiUdDiDRXL5+QTGd8GpTsV6vuF8sWWyW1K2lsbEt\nWcyCiLwZiVJda7O+rVfMNU+6rJUgwFiL95BkE+av/4rJm1+RTmc75sPD+zoe09CNXz/Wey6TvdA4\nGMUDrUWEbqPOtSYEEdE7qRhC6IAchqJiff+Ou+//N3/4p/+X9+/fc794ZFvXtDamZQZiMK5/n6Q4\nqD0NscrPOI8NEYQynZD25wmBLocK4wXax7TMTOnotwWMd7TW0TqP8yA7mFRdTrhxntoYECEGNcXu\nbSAmi3ZaW+jcIcFjrGNTNwTvmGyX2OU7shc/J5UTai92Y6qEI13XaLuEB41OU5JiiCoK2uCgXEVL\n4tlJzFzpSLH+3GMfOtDvA5F7BNoHQnt1ZGetBuiuHsI+Rz+4uC7NUoajISsdGz7sGl30YyFj44jh\nIOfZyZiL0znnJydMpxO0VFgH83PD99cPhE3NYDbnzeevuXx+QZIk3XMa02mTYkAhJapO8EA6nDI6\nPeeF8QQh0UnKcDxECjBtxVBumHiLMZpHNSfIAV6mHHjPn+iLgp3Pt3el7GDgUOnsheFeAexB6aMK\n98OTPD3UJ5afvGcn8BEu/9g2+w2fqrl8RM7Uf36iU8MnJuOpIt4/UMCO3vXgbMcg3v8/BJ7dNRzf\n1ZOrPrjWThsSklIUGC14dBnClwSlSEUL7SNFWKNDVM8EInZcSQXjUYZQCbPZFBEE1bqikQ1aSObD\nEQOdkgZPU7Ukuowdx7vgJvTBr4CSgazTziOQx0pOraKJWbuY46uSAcOTl5x8+V8YvXiDynMOxdOn\n6YL6yTkgB+qGSBLQRN/zzmSn+7wTAAf79CZsIPqvfIDg8aaiullw/cNXvP3DP/KnP/6exXpL3bod\nCAdi0Cxm5uznq+dGV6LjZkeQSqLbgQh0nr4VmUAoSaIl8yLjZDJiNhohhGS53XC3XNJYg3EqFl2J\n7thKoLXugmbxwVNItFAoIXcUq10iy+6eY5qop7UWJWC9fuTh3dfMTl+glUZlZzjRdZYSntBUVJsl\nZVsxGA4YXjynyJ4TtCYS2zeI2RihFUh1MLK9MDu2WvuAXq8kHlIu9CXpn8rk6B9072PxVF9Q5YIn\nG+TMz054GIwwZYUNLTth37VsGxYZp/Mxr59f8PLygvPzU4rhEC01jXW06y1CbcgLwfzilNlsRKLB\ntA1ta7BdeqiUGp1mSKlojEHoQDYcopSmd5M4YzC2JdiGlJphqChbx0NT44szRDHrmotEOH9KLhLY\nC7Te8u4tGw7HTxyM6A40urens0g/VhKh5wH4z9UhiH2Vo+ilfb+E4y13n0T4xFPCE0Tst2WPpuFg\nsA4G7uOUk+O0/49Y5D61T786hKPLOGRP/FTl19H3w6BSt8okA0wy7ASUx7Ur1vWQ4B/IbWz+GwCt\nBONhynw6QusMRMb65pFFdYNAkKcZSkqctwgRGCcpz6ZjtlWLDVBbi+yrGbuOQVpBriQJMmZoiNhW\nq7Se1jq0Sijmz5l+9ltmX/4NyXQe082keHJ3O9vpeP0umh3nRobIM6OCjwx9MvKNx7Q3TRDmyBvW\nR/x98DEDw0twHuEb7PaKb//4P/nq69/z7t07to2hcRbXJav25dl7nhuxO64UoosJRO066SyRREqU\nUFgRsCF09ASCItFMhwU/e3bKF29e8/r1K4L3fP3dt/zT7/8YC0+kobExYCplzFPLbOQAACAASURB\nVHeWKo5rIiQpMSjakyP1AqV3H+7qJDow9CH62x8fF/zpq38iVZIX1jL5+X8jyAwlon++lI7H9R2r\n998xmky5SAWXz05JJjNoDa6qUKbjrlc9kB+zXkY5c2hB7fPyHRy4LCW7fqad9n3ozowEZH7HwV43\nDa2xFKMBF6+ec/fHM2xZ04QS79NOoRAkieZkNub183N+9cXPeXZxzng6JckyrHOUD0veXT/wuC7J\nRkNev36O9C3rxQ1KaZomusOECGT5iCwboLOC5WaDMYYk0SRaEpylbWqapqVPT5J4UuFImhXVu7e4\n+RvyixSRjuhz5o8Ta8UBJu8tzY/V9/3z9kkc+vTHeFTx6fX98pP37DzUtP6sWv5kUA5N8MPvH7k2\nDvnOf8x1Iv78b/3yVBbutfFjKSKe/O+3/YTM2R13V5B05D4LBCGxOmczeomr1zTbBZltEd4QnMOH\nmJUynkwYDKeYWUu7rGgXW4QH11jaMma1NHWFKSsoG4SJnWtcZwBHtOj+VMxskMSKOk+sAI2NHRTF\n9BmTV1+g86IrQ9/nxvfByk8OJUSt42CFEKAJMWMlhCdk++EALwQi0fG/c/vCIRlpVcv1kpvr7/n+\nm3/j8eFhx5zX2S4gBErSadyiqxqNwB4rLBVKKqZ5zvPZlFen0R87Ho5Is2zXCd56h1KSPNOMBinz\n0YjT01Nm8xmmbdHSgbckWcbN44Z1ZWIQuRvbQCzd10qRJoqAow1N9MFH2kGEiARWAfYd7qETYIG2\nNWw3W25u7iku1wxCZGZUeHIa7MM71PaBqRK0t1eYTOGGGUEqaBoIniC7CowQOiVpzwu4e6cOMrmO\ntM8uVTFeUn/dfdzhQKsP0f3nbGzbJgJoEStpfZaTDEecXTwnbRrq5AHnYxZSliZMpiNeX57y5uU5\nr15eMhyN0FmGF4Kbm3u+fXvF1c2CfFRwejYh1QrbtJTE1oXbssG4gFAS/1iSpDn5YMB2vYwcL1rS\ndBaf9x5rHE3dsi0rHhZrHh83LB9Lyo2hkBnDyYD5+A3bkLO1khBUfD5FPzpyN2aHFn3/hu/cU934\nHuLB/gX5cU11hw0/glM/HZAffufHvhxLvcP9dz8f/r7D8iPdevfTj8mJj5gTn5yxH/RPGQ49WO8K\nlUM4AvYf08SPgoFHQu143wB4mdLkz/D5Pa2+I9tuyIIlEQKdZqR5QT4YMBgN8ElKoxSlc9Srirqp\n2W63bMoy+onbFmwkoYoNgUP3GIrOPy52bHm9VuyTMUmeMZwlCJEwefVLBpevkWlKVxZ4dHM9YIud\nrckn5jsGJiWBpCsGEoRdZeJuw54PVojY7EAKRIgVn/2kBqBuW1brFevHJXVVd/7RyPFSpJIiz0kT\nhZIS7wLbKo5FCB6tFInS5Erx8nTGr1+95MtXrzidzxmPR2R5gZMe4yytaUF40lSR5ympTsiKAVme\noURgNCw4m015ua0YZBm1cQgkwQZMa6malsZFThGpJcYZBG3M7ugxkD5wG3bvSh8AjOCqEKpAFmeI\nfNalIAokhoSK3DckSQLDCYv1GqqSsFnT3N0jhEInXZroIfjurJ291bOrhw4B3drODRy79wTdZXQc\naB99wPNwCaFjguyYMqWUpFkWowbjCc9eveY8AVZj2tYhpCQrUubzKRdnU56dThmPx6gkobGW+9WW\n79/f8OF2gfGB+bAgSxOWixWbZYnumjKvtxVIwWA8xDtIkoqmKSnXq8jiWWQ4FwPLpo1ZLOtNxWq1\n4WGxptzWtCYmGkyywFnheDETrCzcl4F1E1kYe3Vh/9z/yFsvjtcGIQ6arOxz+ne/f4QCxx+fLj+d\nj/zPBRaPluM0qGgd7/S1PcKK/SF7DTd+eapN7Af9WIB8+nrEwd+f08iPgOpQiz8sAjg85oHw2GfL\n9OlseynsO3CzyRQ7fEFdLtg+/sBcBdI8Jx9kpMUAoWKAJ0iPH4A/0azKigezZNlsaYOjkY5GgVew\nK4/txk/KSOSUK0kqxV5zTVLC8DnDZ59zcv4CnaTkl69Izy+gL8mHo2ftUKM+HttwMF1iJwATH4Fc\ndkIwdGkzvS+a0AG8FEQSKdVZuAEvIvOe8QLrolYthIxUssGhlWY6GPDy8oz5qCBRkrI0fHd9w93K\nEkLMUsm1YpgofvHyOX/z61/wqzevSZOMJE1QSYKTgcbUbMstjamjoAPqtok0BnWNbQyr1Za6aRkN\nC0ajgiJLmQ5GBBuotg13ixU3jysWZRmBILg4HiHsGh7sS1BAhJjSqDrtUQpBlg8Zn7/mxa//G6ef\n/SUiSZEhoKjQoeRkOiVhQtt4qu2WdDhBJRnbq1v0bIo6n8dmC72b60BN6d1XfVg/CJDGka+qXWMK\nrKOdDrBFipNdRko/p94dgXn0bEYWSB8CQmnSQpJoRSYE2Zs3nF5MObErtqsIvkmWMpwMyLQi6Xp2\nNq3hbrnmn//0He+uH2iM4+RkxnBYYBrLu+/f4oNASUWWp6w2G4pRzsvPLimyFIejbktWi3vyPGeQ\nndHULavVlsVizf39mtVyS1k2XbNyST4YcHZxwsXLZ7x4ecqry5yqFVwtLF/fwdZLrJC7WodAB9B8\n+v3v3+woN/durMMAaTi0WA8RYyfkP738ZBr5XkL1j8CPA/tHnYOONL/j/3sTZr/tIXTvI89dPmj3\n29My2R0Z18HKI0GxO0a/pr/Wp9e93+eJ4rrbfl/2zU47Ojz6LuCXDgnFKVbmLNstJhhOkozUQ9ZR\nfiqhyNICPU0RQZMNRgyWSx7XWx7XWzbrLcELNIqhkjvTUBIYJDqm1glBrjKUyAlqxDqfkT7/nNkv\n/yJqh1mBVMn+Ady/xsdzshvG/RzvH9z9vSkfffWRCGn/QuwoRzunozeGnpxaAEGJHdOgKmYML7/g\ncrXC/PA14eEK1wTGgwGXZ3N+/tkLRplE+EhKdb96ZLGKLiMhIUs0ZydjxqMcKTyPj4/oNEWqaELX\ndUXd1hhvGM2nrOuG2+t7ru8eMM7HgLALLMuKxbaicZ6T+ZTnzzJkqpDSEaxkNi0wOJyEdV1TtTHF\n0AMmBExwuzTK+JoItJKkSpGohOnZc85ff8nzX/4dp69+TjYcdQJQ4uqK9faacWIwVclyuUHXDUFu\ncP4dqnTIX/2C8OoS7x3K+y5WHBA+VnT2547PhUQGT7LcMvyf35JfL5Fli9WC1d99TvX5M/xkEOfJ\nxYYbfREMApRSpEkalS8XqOuabVmxvL7C1AYRBMoblAukQTGaDsmzSDympATncKalbgJvb+757sMt\nb6/uqFuLUIrNtqSuW/BgWhvjD1rjvSfRCWmS4I0hKEFdV5TbDVIpqsrw3XdXLBarWHy1rWgag0Ay\nHBZM5xPmZyfMTk4YTabIPEdriQ+GNBjmquZ1ZrkzGUuXU4k8cuAfI8ETjNljwEGi58H7/QQ8PrWE\nH9/kJ0w/PPj6RKs72vTIzoan5CVHWck9mB92dnlyjt15w8H2/WGfnOroRAdH24PQ8cGeCqPDvY4n\nYK9xBwLy4LxHvv/9O4GWDpmK2Po9z2jaLcuqxorIh2K9Z+AceZKQSI0QiiQvKCYerwIukRgFpXek\n3mMEiC5wGjW9QCoVWkbAOJ9eEKxm1UpEPiaZnFCcPiN4tzMnRTiei08NnXj6Wez0cRACRSAJbt/J\nqK8u7JtcsMNxQmN3K4TWICRByFjlqTTpaMbF61/EdEwl0I+PDAcZs8mI+XRELj2ubQjGkMg+QBWi\nP10IijxDSkHTNDw8RlqEEALWGOqqBC3JxgOETlCpQOgU6wXrdUld1ay2NevWUroAUiOSjPHE0FiD\nqFvqbUnTGJw1ndAKtNZSGkPVpyuGQEf2h5SxI1MiBUWWMxqf8Pzz33Lx+V9y9voXFKNJx9Xi0Fqg\nfYtYL2i2S0pneUSQG0t+fUf27gYbBMnZjGA/j8VPXZD6wHh98nz2pr/E5QkuUYREYXONT3W0nGBH\nM8zOVcBOECklSdMETIr3AdsaRNVgbhe0dYPLM/QoZTKccqItg1SilYzVoN5RNy3v7x75+t0VP1w/\nsN7WUUBkEm8cNkiKYsCzyzkqkSilYp2KFHhvqLZb8Jamrlk8PKKTHOegLFvW6xJjLVJKprMJ4/GE\n6XTG7HTGdD5lOB6hVUppDK1tqDYrtDdo1zD0LcuNxZaCrSvQo1NUMepcjXEMj5SaDl+OFLojvNuD\nwKFSGZ78/GNQ/xNp5GF/c8c//OiFHup++4DYPhf4yCsuPgUo4Qgou5X0e4uDdfvu7Pvf99uJ3fbH\n+eC7K6LPzNiVVAgOM426gMgezMXBh723PfJo9CllqTSopETlFck44aGSPJYN99sNq7LkpBpzMh4w\nynMynSCRmL69mJTkRcbIWWpjaLzD4RF1BBTVsfCJromtSgdcXnxGWRoebpeIvEAmmj7RbN9kQdDz\n1/R5If1wfSp4HA2dAyEoIhVA5mMuNV1TZyE8uO5svbDwIQJ5FxCVKqZQBilxWJxrEFJw9vINeZYw\nLQakP/wRKQODPEMLSfAO27a02y3O2njFAhoXYqk4gqppWK7XVCLegzOWpqox3jE6nTGeXKCyAeNB\nSj6YMprMuHp/xdu373h/v2JZGwwKnWq2VcvDcsM4V7j1hs39I9uqpfRQeYFxnm3T8ljVrJoYFJUy\nBn/7NMhMKJSEYjDg/PkbXv/FPzB/8XO0SsBbvKnRSYrWikJbCrPB/OkbmtEI+9lLHpcN4cN7/Ps7\n3GzC4PPXjFYrsvmUVEik0CBjrWzMkOmLdgSyM4/cbMjqH75guywRxmGLBDvK8InGO4+3Nvq/xaEw\ngBC6gKqCJM9QOjJ3ytGUh/d33F/dshgMGIw+w51eIP0S4WpwsSWeC4FV1fCvf/qOH24fWZctWmmy\nNCFPMnKdMjqZcfn6Bb/+za+ROp7cu0C52fLh/Vv+9Kf3VHVOVTbc3y2xZom1kbNfSMlwVHByNuez\nn33G5eUls/kJKtEx1dQa2tpg24qmrlg2G1IJwTnaTcn2/RXLmyW3JmHy+d8yvPw5Okt3/EnHb8CB\nprjLOT8sZNwrb731H7p36kk26CeXn7D58v7C9slX+5+P03vCwQ3uB2jXJebwsJ01vg/YHPwW1fV4\nRNHD7pOzh0ORwV6KdtfwNCy6O8LuXsIO3Pb30v3fTcixeN3TDu/HQAhIFYwyz0DVuPqGzfIHto/v\nGVnD+WzEJNN89+GWh8cVi+Wam0HGME3ItEYRm8865/A+9oysm4ayqmlbE1Osktif0flA1VpaK6iM\nofY1v1/890grqnIuLz5nImJX+xhs7MRNb8YcCMi9wNzNMj+6iJhDngZLz/wROuKs0PdkPNjWtS0C\ngUx0JHcKAXygti21FNg0QwpLcXrBSZKCbDHNhlQLRNdr0zoXO7c7i5CSaVqwqR3LxvDH+wXT4RAN\nTFJN2sUAfBqZIFd1yfrrbxDhLaa1NNuKZltSlxVlWROqhtDEsvNaljhT0TYbqvWCoZRo66mNpfWe\n2nrua8PNtuKxMRjvO/+0RAlNmuUUWcE4H9LUa3KdkHWEUTHYqTuemfjU5Ykhy4A8Y9VUlKslptzS\nisCjENQXZyR5jhKaWevJiiEqTeNj5/e59WE/E9Hf3b0JUincpCAYS+sspm1wbYW3DhkEHk/rPVmS\nIqTEB0+7LbFti3eWjhAebx3t6YTx3/2WyV//mkvrKYY5ephRmhS2C7JqhcJz8/DIH759yzfvr2iM\nJ8ty5vM5rz57xfMXl5ydzshHQ9IsRUmomorWRqbQ1eKW26srfnh7RfASZyPAZ1nOcDRkOptwcXnB\n2bMzZvMZg2FOkiYICdbW1E1NVW7ZbkrqpomNL7KU+82G2+s7/vCH77i6uuaxajHFKT8/+Zzhs+iq\n2r0HP+bQfmLZP30n9lizB/M/+x7xEwP5/ttTEP/EHh+tfJKnfGi2dLb4U9f6p67iqf9dPNlgP5Cf\nPsDedfP0nvb/j4TJ3lF2AP7H55UCEgWzoWGUtBSyBi1xG0fTlpi2YTbMmQ4ynPO8u13wsNqw2ZQk\nKpriWkDYBZ4iRaoxse2YtQ7rPNZ6jA+01tNYR2Vg3XjWraNdPCKUZjCcU2wfmK3vqNcLkrwrpBDy\naFR6AOfAIjm2sMSR/7/fWRFIvYtUqj4Q8JHO9OnoBeiJo3fiW8SOQJVtacjxaQZ2hS7GFDpjun5L\nszQE12BtFF4hEFuJeY+QktFggA0NW2N4v9jwdXZPsJaX44JBlpJojdASnSfoNEEphaksvqxw6y2q\nbilsiGyBxQCN5CE0lAGctVR1w1IGQqLJhaCxlq2xPDSGD2XNY9vSdFzwfaOJNMkY5UNGgzHj4ZhK\nuqh1SxnB1btdUJLgCa5FA8UkI3nzGe3tAv+4jK6hIkdmOUmWk2QZ6YtLRKJpraHdrGPmkjME6yLB\nFbExCUCeZVRViWlbEhGrfpuq5OHmFidiI2qcRwmJcZbatAyLIUIKTNtSr9e0ZXxeZZYCkf62RZCm\nKZlOMa1lOB5Szaf40QSbjhggkNWGdw8rvr+6xSE4vzjj8vKSyxfPuXxxydn5KZPxEKk1TV1xf3vL\nw2OkqvABlje33Fxfs3zcIEjIiwHT+ZTTs1NOz044OZ3HY0zG5EUe6S/ahqasqeuaqqqoq5JyW1JV\nNXXdULWG65s73r2/5od3t5ig0KMTRhdvyMYztNI7+pHdI/4Et44t+b2lf9j38xAV9m/Anwfzn9C1\n8il59ARUD++3x75Pchk89VGHp8PJ3uWx96ofYu9uIDvuiEPfeu+j/TjouvOH7Fd9ys9P51LYAdk+\navHRBMrYlmyUes6nNQkblLcMR6fUy3s2XmKbBjnMmE6GjMdDkkTjrOft9T3GtIjg0B0BVN9kOYTI\nwme8j8BtPE3raJyndR7jArWFso1A471HJQFlG1aLGx6vv2M4HjO9eI0cTCBJu7noqtEO3VE9Q+Eh\nuB+NWT8GnUbuLLKjCuy5OYSI3WeCdQfzKdg1Pe6m0iuoKkubSEgyMAFUgpAarRUWh7EtdS3J00jN\nGohl+FJK8jyncF2bsFXFH7ilaWrc6YRpnkbGvSLnpEiZz8bMTmaE0tI+VjSDLVQG07RUbUNlHeNt\nRV6WPDiLkaCSyOHuQ6DxlsoZFpXhqqx5X24xYe/KU0KR6pQ8HzAshoyKAUWeI1yGVBqpFc62eNNA\nlsZ59Q5nW5SH4dmQyZu/IhnNqBaP2KZF5zkqzxBpAmmKHg4olWN79Q5rLLZtMVWFrUpc2wCCsmkI\nMnY4eri5plwvyVW0UDaPj/zp91/FR7Vz/wgCpjXUZcV4PAYBVbXFbjbU2w1VVaIGBQiJc4EqQNAp\nQmdYH3j++iW/+PJXfPnlb7Hn55jRAN8G3q1q7ldbLi4v+PLXv+QXv/yC569fRuEK4D3GGartmofb\nK25urlmtN7Q2sL5/ZLFY4l1gOCp4dnHOZ29e8epnrzk7P2U0GiJCbA5i2oqqLNmWG7ab2J7OmhZn\nLKZpKNdrbu8f+f6HK/70wxU3izWqGHH++ksuf/E3vPjNf2UwnpGkKcIfIMRTZW2nre9qZbsslZ6u\nO+ys8nCAZfvl0zXT8FNmrfSfD9Va9gGCviIzfj8w1MVBkcgODz92h3zyXE9S+w5+YO+IgkOE72ks\n93h9uOeBtvjknD2U9dqj2B0gdKQ5cYc+yCc6l0yqYVRYJkUJ8g7nLcFLymrB4uoHlu9+4DSYXd51\npjWvL88osozJMOf7D9fcL5asa7s7PsQqwUipGrDOd/9jpaILYAMR0L3vKGJj2luqBYSGzd23XMsa\na2qmL79gcHKBcMR7ORCCu/Hcz+5uDA6LfYSM1LPSB7LguwbRRF8r7HzjIdA1u2DHztcXyTgfBVBw\nIKRDKIuSGgsxg+Jg/tvWkWkZuaulQguBc5bFZhuFlogB303T8v5xTXCWi0HGyaBgOnIUg5y2NThr\nSfMEZrHhc7lYUwMNglZBojJORilDKdjWFVVVI10kw2ralutNze224aFpqazBhd4ED0ipUDohTXPy\nfEDWZUvQ5WIb52J2Seg64QhFsBWuvmNYPGcymzI6mZOPxrimxdYV6/t73v/wlre/+5b7uzuatsHZ\nGPxz1hCsxRuLM/FzQOC8QynFaDSiXm8wdR3HTGusMayWS5SKaZ50Yx06F95KdUVkwcfjdS3cfFVh\nvacxltoHKuupbUwr3dxesbr+wOOHa37127/iszdvSIxnMJ/z5a9/wS9/80tevHjBbDZHEqi2a9qm\nxpkWYw1lucU7Q5rERhbl+pFtWYJSPHt+wWdv3vDi1UsuLs8ZjUboRNHUW9q6oq7in2kbTNNg2hbf\nNghrUdZA21B4w1QFZmnCfHqCmL3m4pd/x8mLz5mcvSAfT5EqibDV+1aOFNWAeIobvc5zBB3HvvGd\nBvaJIz5dfjIa26P/B4ruUxfKJ4NmPSB+lCL4iZOEJ9+fbt8P6KdcIwcX+lGnn93hn/i7Dv4dnjrA\nwSRFH7OQkeRIyYDWnjT1DDJFlpQkeomUDQQRgzf1gtXDLdViwfB8RiIFeIdQmtEg7zqxgJKBRAqu\nbh8jOZaLpPyOA1pWH0HdHwB8bPnmcCH6apXWTGdjnr+45NmLC7wPrBcfCDJHZAXpYEyq83hTfWuq\n49zLzoKKo7Qfyr01Jgld1kpHi9sLVB9iwVK3/U5eiiOZHotNAIREBA++RRLwpsWaBkSC0hopoXUW\n57quoTJWeQbv2FYlqVZoCYM0IRCorWVRNkjvMdbTGIdK5I7jZj6fkRUZ4ywjHw0xjaFtTex9aiyl\naVmWJeHBUJeOpnVsmoZFXXO9iZkttY3dgFznAlR9hF4IlE5I0owkzZBaxqpab8ltS88K6YzB+SWp\ntsynmul0QD4sINEkeY5uGvyD5ermij/+/nd8/bvf8XDzAVPXEVzb2CVKdrEG0VlDrivqEVKwSBKC\ndUjn0SK2sCOAcxYv1S5Do89YIUDTPfChD4gLETv52Njj1NoW6wJta2mMRSqNUZJtovkgJCkBv1ky\nL1JSDecvLnjx8pLxaADBsF1vaOqStmlihaa1bNZbHh9W3N8vuLl74Or2nm1ZUwyHvHh2wfnzc87O\n52R5gjU1TdnSNDVtU9PWFW1dIZxDhkAWYsAZFQOiVoLUApdp5sOcZ8mMwegVL379XxjMzkiynFjU\nIDuFZq/chQMUEL0CF/qnf0cM/BGmROz5lLv3x/3EPyGQHyKt2IH5U0CEj8F9v8EePH5UVu3A8985\n3pPg6PFxwyeoAA6vov8k6PvtxUndWwp9o2DZuRO09Cjl0NqQJpYidwyHMMoTrFtTt0sECXiJtZay\nWrLdPmLrmskwJ091vN4uh7jIU55fzEkTSZ4ovLHcLUs2dcs+ZLW3SkQkVyG40BXUeIx3sdRbSsbT\nMa/fvOBXf/FLnr/6jKurO/7t67fcXX+LGowpRjPU/BKZpp2AOjBdDkdJ7LWRWMm2l4oCUHhS/I4w\nKgS61MNwVCl4SNTUl5Z3HvWuQ00A20Kw+NZi6wqBQHXNjYMxsRUcMWc75u97msaQiIRUKabDnNaa\nmFtNoDQefINpWqwz1HVNU7d4Lzg7P2M6mzI/SSFIrI2kVtuqZLFcUr2v8d7Sti3LxnK7LbnZViya\nlp7I+bDwJxbV7KtRtY6NENCK1kfgDsZ0c+6xbY2zJYNZxsWzUwajnABUdYVKM+xmw8OH9/zz//jv\nfPWP/4v3335PU65QwZNI2XWAir1DlegbZMSxtz4yP5q6IZUqkqjJgHQx11wJEQnIvN+51GIII+y5\n27tenkIqpJSR70b0DRpjYVOiNFmWMh0WzLIMNise/vR7xMMV7vyEl5cXTJ49J000bV1impqqqmk7\nq4LOpfO4WHP1/p63P1zx4eaOm+UjxlrOLwWvkgSdKJw3rJY1rqlp65qmraNQdBZhDZkQ5ErHIiQp\n8Q6MgJYQ+Y5cwmyU02SvGJ39mvnlZ0gp8aHvq/VEwTvsgLP73lP+cgDUe1/4R9QWP0J18anlJ+Ij\nfwrjvab146AcFbsn5e+H2R+BT2rvnzreU2X9qcb4qShp78I5PPdR1WaveYqnJfo+atzKkyY1WrtI\nTJUHksQhtUGrQKo1qc7QgHeAC1R1g6s8pmyo6w1SOYbjlOFoQDEokFrHKw8e33VAGKQpF6czZIBv\n391wfb9k1ZFkORHdKKLLPsELHJEP2nrwIfZZHI6G/O3f/zW//bvf8Pmv3uCCpvGW4c09V4s1H97/\nDuNqXn3xfzCeX5AORqiOGW5nNu6tx52ZeJSa1qG2Cp7UW1RvLrjOteMjn3V3wbs2cr1C3rMZxgrO\nQPAm8n6YCmEcbNds796Rq4Y01ciqxdkoqFpjEYCWEitiDvsg1ZznAx7XG5wxFFowKRIyFUm5ltua\nujEsVhtu7xaczaecTGcMsxwpBM55mr6V3nrDzWLJ/XrLoqxZGsuiaqiM6bQtv3M1ySB25GQiAM4R\nTI0IJrqQ9AihsgiiIloRwVmUEgwKTaYD2/WSDz8YVJpGtsx8wObulrdf/TO/+x//yLtvvqFcr0lE\nINOKQkbhF/3bxACkj4HvxkXKXNMVKUlhSZWk6KpfU6W6VncxzuA6K6S2ltZHUFMyVm4WaUKuFZnW\ntN5jnSdgoiUZIifKaJDz4vyM1y+eR8oJGXvHru6umRYJ9cmY1cMdUnTB48ayXm1iIc+6oiwbNpst\nj4sVm7JBSsV0MCB4x0hJmuWCr3/3FZPxkOFwQJaorimKJwmeXCmKoiDbxZMC3llCiM9KIMYBlFJk\naUKiZYw/iUBXdNyRiLELge3qLI5cueIApw6xRXxk8T8BnQOs+s/mI2dvguzNiD+vWYtDZDhc/6Oq\n+KfPe/jleNfD4z91ej/5unOnP9mGjrNEebQOpAloaUkTT5YFBDXO1ThnGQ5y8lyjlCbgaaqGxWpL\nkWR4DMEJhIGEFKEFG+fIp1NynTF5dkmRqC7Q1ZNfWfAxJ7zIEk5mI9rWHC3slQAAIABJREFURO6U\nhxWbJhad+F4YhoAXXSPr0D18QjCeTvjszSu+/MvfMD8/5X7xyLv3tywfNuhcMBxLlKqw5gN37yXb\n9RnFcE6ejVFpjk5y0nyA0hlCqP0cfWxoRTANgcRasJZgHMH2RT8diPfXK/bTIIhCyeJpnGOzfcQ6\nRyo8tBtEWRLWK+TqgWKSQ55Qdf0jrY1AroQg1YqqNUgRGCSSi2mB9oa2jg0jzidF9LlWDW1tuhRN\nGwF9uWFc3FPoNHYHCgETArUxbJuWZVWzaQwbE4PHlbVdiqHYveyC2JRCdQ+xFJAqyShPSJIUZNIR\nFGpUknYMl3EElM5QGprNIzc33xLmKWnadXmSmturG775wx9YfXiH3a5RzpKnmpFWDJMErRO0VCjR\n88x7auuorKEWltp62s5CdQEsIjZt1nFf7wPOxfuqrKdxARtbvxKCQHqBdQEXZTJdW4ZIZ+uixaVU\nzzmeMRsPoDXUVUVjWrJckejoClsuHmkaE0F7W7PZVpTbirpqu65WkA2GjKZTkjQhzTXNZkVbl7TV\nmodmi63HJPKc1CekaUKeaDKZRNpmFSkQeoXId+4rKfqWfpHOOdGKRHiS0FJIh1QiKg8h9tGNz3S8\nnv69cqG3u7ont9PUj7Dn31O8exnwZ9oF/YRcK/2Hw0DAU4f28fdeKT6MqR26Yg7l1XGo4UkBT3+g\nXf/Hj0H+aHR32+837gNwUdMMOxM1EYI0NRS5YzCALDHkeSDPBXXjWa9rVus11k4wJsc5jTGG6w+3\n3N/cc3F+xmCUkiaxeCIrhtjM87i5YfLsJSdJzvTijNTU+HKLbRuMaTGmwQQfg3lKUmQpl6czMh1d\nC9eLDWFb43smOtFxmBCrGqWS5EnCs8szvvjVzzl/ecFqs+Krf/lXvvrqD4wnE169esF8npEVKTpx\nbB7/hCk/0AxmDAbn6GxEUowZTc7JiikqjZkKomvEcOx0CZGcK3i0dWBi0M17tzczDysFd26I6NmP\ngbOWsm1YL64RwTNTIJol1fKRbL2CpkYOUoxQbLMUFwJ1a2iNQUtJrjUrEQnCci2YDTWYjFbHebyc\nj5BSci+gEpK6sRjrWHdtyu4e1yTIrjoy9ni0CIwP1N7SWEfT0f9aFzVcJcSuO5FCRppcKTDBkWjJ\nqEg5mYxIihHIFGcaEiVRqiApRjF/XCt0WuCDpVmsKL/7V05fDdEZ+LahrA2rq3vW378ntRXjRBGU\nYJhoJnnOpCgYpDlFmpElaQxiWk/VGjZ1xbpp2bSGrXG0XfehRAkmg4JhXpDqlNo0rMuSTWNxxMBn\nqvtWeRItJD4IjAPp4jsTeclt1MyF6Phj+tfNUZdbHh+XWG95NXtJmuUEF7i7XfC43PK43LJclzgT\nA9mJ0hTDAaPxmPFkxGw+YzafMJ2PeLx6z7vvvuHrP/6BTW2xeUqmJAMpmWjNMM/JEo3sgrKRWZK+\nBVXnQpIEGauqtVIopTpKgYpcRoKv4EPkN+rSfLv+J5FDPkiMD9gg8DuAEUeYdATihxr4YfLFodb5\nI6D/k5Xo7z92IC36i++rBQ/dE4dXv0fdj7JQDgCeI1Dfrz8+R6d7H2nnB9ZBf/ywP15P1BU6f58k\nIHFM85TJUDMeaKSuUdqidEw901qSJLHQo21arL3lq6++Y7UuaRrLZrPl4fqeZlvzX//hb/niFz9j\nPnnGcDRDkdO2DdPJGdk852R8xuDsFL28IiyvEabGtC2mbqnrCiETlErIEsMgLxiNxpyfn/Pu6o4f\nru74/vaBkoB3ASEDUgkUniJVvP75a569fkY2T/jq9//K3e0DP7x9z+LhkazICNpjMZydTXnx8jmp\nGzEYjEnSjLryLBdbtuUVdv1IzjO0nBNkDmpIIO1GsNNcRMfv4QPC+eizJxBkx28diP8R0Z3gPSG0\n0dxtPdvtmsdmzabdMmpqTvIEnWu2ZkuDo1KaByVZNjWtEowGBZVtoYo584oO2LKURIC1nm1VMcxT\nZlmKJvBsPkFrTaYSqk4bXG0bWmcJIbpFZPeSORFfVhdCpAYOXSZQCLQd818st1e7KlgZoEg1Wgrq\nFs5HBc/nIybDITbNYmqirRikGVmWM5rMyQZDdJIgpcc9fEBvbzgdBN48nzMdZ7RNTdU2zEeKWe75\nfiDZbBuc9SjnOZ3MuDw953x+wulszmQ8IUtzBALTGlarFVe3d1w/PHJXVVTGIgSMBwmvX79gPp2C\nCTw8PvDu5pav311zv22oTfRZJzK6R5RUXfPi6GKwzuB6bvidmyx2Q9ps1rx/LyhXW6x1ZEWOlBl3\nt2sWt1uqssVYhxeQpSnD2ZDJdMLJ2Snz0zmz+YzxZIJSEmtbqu2aYjxmenrGs8cF5uqGJHgmieJk\nOGCQpmgVm6dEBbknoo1uPCUVAomXgYBF2IhSWtD5yx2V61oT9p2qOrdqjzpeiE5Jimiy45nfYdQT\nBfTQn77DsAM8Cwexh08s/3myVghHF//n7A2x3+kTx/mRfY7O8dSV8/Rc4UhIfBRBFvFFTLVjkAlG\nRcLJqGA41KSZp/EG6ysCEi8kDklwEo9DJp5ilDDc5CwXK27fX3N/f8/D3T1NXTMcJrjQ4IPnxbMM\nXMt2vWXTNKjRAJGnVKkmLXJSOyJvNWliMUmL0hqlE7KswDvf3XTk4j47PePi2T3nH274cLfkbrXl\nsWrZNA2jQUIxn/DFb39GPi3Y1hVvv7/i5uqe5WJJ8AKEpHWOsm5oaoOWipeXzxiOB6AE203FaCQw\nTQClSDOB0g0+GESQOC+ojcL4aKZD6Bove6Tzu6dXSNH5eSL4BTxCeAQOoQwhGII1aGkYSAfCgfQU\n0kcucOlxOJxrqZoGgmOYKEQ6ARHiS9pZU0pKijTBexdNamsphjnDJCURgslwgFQS2zoGacaoKBgN\nahbLDXVjIqdP9272WYTRd+/p+1bGpgoh+peTGPiruzZyiVZdv09I85STQcp8kDAuNDZPsUFhUchi\nTFqMKGYTBqMRWZogXQ3bK1R7zyCDLFORcEoFpPLYcUZ7MmL1uI75+UFwNp3y4vwZl2fPOBmOmQxG\nUcNOMqSQeO+pp2NmwwFnszF35QYnBGmSMBnmXDy/YDIegRdcXw0YpArvHfOyxXmihq8ViuhCedhU\nrJuGypnY69UHEBKpiGyPIsYH/n/m3rQ5jizN0nvu6kus2AiQzL2yuqtnpnskmclMpk8y05/Q3+5p\nWc9MV1VWkskFJJbYfbubPlwPLMzs/poKM5BAMOBEONzPfe95z3vO0Dm2HGgPHUVRUhQ10Qv6PmCU\npJpMOalrJrOayXzKZDphOp0ym8+YTCejTFNx2O9Zr+64/vCe9XrN3c1nPt/cs2saJnWJIY1N3tHZ\n89n9nKdahczlWRR5Cjj3vNK4p8zsd0owJIGKApFG90PB2MzP2JL3jjyJxR439I8Q84RuzOzAsyL8\nV+j2COi/9fjdBoIePn/4g+fdsF+94Dd6w0+R+6Hx+OyA/8FP8Wvwfn7Y45kW46rJw2ZAihyLNq0C\np3PFi6VlPikxVuJEQ990DOFACPmCkXHceidHkAPTeUlhXiGcZHfb0OgdZaHxIfHuwzuKSlNPSyaT\nmqEL3N3es9pvEFqziHPuukgtBmaFoSJhlEUpgxASpQuqEACJNll+p5Tk8srz1es93351x1/fvOOX\nT3d8ut+xaVrErGb27SWXP75g3/dsbj9z/fmG+7s1QzdQVzXa2HyhJ03oBaGPTKYl1dQQRCAJyWK+\nwOiCpLMaJnlP8AHpC/yg2R00WydJMe9jJBGZxqnOY5NZyZFnTTnQOfn8EQcQOVBDpkBdagpbMy0E\n/T5ijUZIhVKSnsAuOobgqYKgTolkFH0yeYeEJuBAQGk03XD0ZM/N4nldo7VmMpkggM46VKlgKlgu\nPBrY7Bq6wY8c8tj4HpU0D43Y0WogJVhMSk6mJf0wsOs9fYS6tPiQHQOnRjOvNFMrKXUk6UhSGqUM\nyCKD+WxKOZ2hjSLt1+jhDh23GCsYXI/z+oHr1UpSjwNNh/1ATIKXr1/yzatXvJifUCAxSSK8J45U\nB2R/lUldgFxQLUuKqqAsCgqpqaYTbFmitKHZVyymFRcnc2azQFlUXMxPmRgFIdI0HX+7vuV6s+O2\nObCL+0w/CIUaFVxKjvRFSHiXUNqyWCy5OLugqiZMpxOmsxnL0xNOz05ZnC6YzmfZHVFJpADvA01z\n4LDfc/3xA+/fv+Pt27fc3t6x2Wxp2oZZVXB5lkjRE4MnRoWSDyj60LdIAlJS+f4PIEUY4X1ElBgR\nSIQ05Ba5HMNQHgEipadKnif/B/ka+RWGja98qMITPISvjJ8/f/1vV6u/E5D/1nPiV58/arRHkB3f\n3fNA3+M3PX3Dxxlv8QWd8h/9VP8e6B8JgcdfgZEwncDZaWI5D1jbEZWkj4kh7nCxxcWO3oVRcKGQ\nKFxsSQSUFJTVhO9++JGLs69ZrT5ze/+Ou9U13ifOz884OZuAGljv73j/+T332y3zk5JBzri7eUtt\nDae6QETJDIVVFmtBKQsiN8i0LpA6X5gxJqp6xmx5wsXlFf9pf2B7aNm0DdtSsl1a7potn24+8f7j\nNZ0fkEZihCVKsKXh6vKcF5cvmS8rZsuKKBM+eoSKaKMgBRwtYVSeSCkwVlFVktQncHtiEBANvZgg\n8ajkUTGnxyBBKAFaIo1C6gIRHEOzo902DLstpbXMpjPy0IlCyES/3SGTxChLIRVJSUxp+ceqJqWI\ns5qgFathACJKQvD5Bq2MxnmPGCvPxaTibD5FKcPJ8hxiwreBYfBIrZjNZhTacnuz4vPNPX1IWR4Y\nQaSQuWAEPoncqyWrM15fnPD1izl//vkDkJgIlTXUMvcqKpObbiJ63H6F7z3alpSFIkSFICJMlS2E\nBaRuzcR6kg907cD9/RqjBMt5TYo5iWdal7y6XBJDZLPp0EqPqVEDh86BC7laD4Hgs1Ry37bs2g5s\nwYtvv0Lpiqbp+Ondz/m60gptCz5+vuFmtWHT9EymBYtpxcncUilFHDzCS87nNV3wbLs+JwWNma9P\noxClhMJqThZzLl5c8vVXr3n1+iWn52fM5hOqusIWJUqrzEmnhBs6Dn1P17fcfv7M+3cf+Omnv/Hz\nuw9cf75jtdky9B6jJItpyVeXl8xncwbnGIJDhzzhq8UxqxVIIdcSYytmXNaQQo1Ne4EPAVWYTG+N\nWafj0s2xCf1oevecEn7wYHo2yn30uHkCP+J4zCdwhhgXmX+fdfjdMjufP/F05frtNedZof2rx3Na\nJj174a/plF8d+3jw9PTEi2c/z4NqRiSMDUxmA0UdkOY48p3VFill06fgA/3QZfdCqdHK4p0DAkIr\nlIV6PmM5ecFkWjJZapaHCik008l0TEWRCB1RFqqpQRaJQfY4HG1MrH0ieUBUnAiLtiU6gZBZRqi1\nySZLQEgRqS3SWJQtqOczTgfHGsfP7YZf1p948/YXPny4oekapBbYSfaSXkynfP31Bd9/95Lz8yuS\nhJQC/dAjTMTIfLHneLJx0SQihcSogsIWJKHQukWmBuElpCUag04RScThQAp0oZGFQuq82fUp0fYt\n280aEwJRK1wYslwvBpSLoxxO4Xxku2/ohgGlJKf1hOgdjRzzSb2HGHJCDDxUzZXR1KXGGsVEK+Za\nYYxiWpYYW1IZw2a9xceIKizTyYT5bM7JcsmnmxX3u4bYDzjIFThjM1kIlFYsq4JZXVIazaQwFEYT\nhaL1fqQXJKUWlFpilCB5x9CvCcYykVOiTwQMbr+Fw47SSGzsqEtFSgbveu5u79EC6sLkY2qF0op6\nUjKdlvR9GL1DDtRJ4puOMHi8C7hhYL/fs9ntWTcNpqw4vbhAGosuyjx0tliyurvj0+d77vYH7vYN\nh8FndaiW7HZ7PoT3FFIRXaBrHZtuoG1bhjDQB0dI8aGSFkBpDRfnZ/zdjz/y4x9+5PLlFbPZjLqq\nKKoSBIQY8ri/dwx9R9M0dIcDu92W+9WKt7984Jf317z7+Jn17oDzAWsMr19d8erqgm9eX/Ly4oxp\nIel9hwt5ECvFPIH64FAa01gZi/y8ONpBZJdNEiMfLhE6e6pkAM8Sxd8eGHwc+nmCySPMpIfXHDHq\n0cn1uczweEf9B6KV30u18mT5EU+fe15pC34bfsUTtUn++vFw+R+O1fN/XGU/fu9jtf7gMCny6Zfj\nL0FIgVKgVKSsBsr6gNARnxRZ+Sczz5giMYxA3vVoKRAajDCkkBUXEUEyAa0VZTnBp46gFqjaU0+m\nVEWFUYZh6KnqkpOzBbM4oZwWWfdssn68DZ4heaqomSjL1OYAXiGzvauSMie2x3xxRJH1u9IkrJLo\nsuCgPE234pcPH/nw7iPr7Z4kBEVpqSeKqir45vUVP/79N3z19SV1taDrBpquzYDqE0JZBAIfcwKo\nVWbcRgqUKNC6yIoANeDcNcN+wIcTlL1Aup7Bt0QRQGtMKVBFTp6PQ6A57Dlst/T7A9V0gpQC5wak\nVIgQEC5glAIB3eBYrXf0YcCWFmU0SoFNgaF34DwixnGK9AjkkdJoamtyao2ESiQKEaliYlKUnM2n\nlMZw6HqilNR1jXqhePXKMf35PeWnWz6ttqSmzVYvIaJlQkuFkILTeY2WgsEF5nVBQuIiDPsBJbLk\nsDSK0mqMklmq2HUkP5BqzXDoaA8ON5T0WKZ1wTytsTNQpaVvFXf3GzSJ03mFMVlqKZRCW0NdF7TN\nQNsc2BlLESF2LoO487Rtx/1mw/1my6breVFWyLIg+MjQZ+17PZlyd3PP/XrHX95/ZB8gCInWmrLQ\n4B3b2zsMIg/TuEgXI1vnaYcel3y2szUq53ZKwWRS8+rVFf/wn//EP/7TPzKbTen6nv2+YbVZcTgc\naJsG5wdc39E1Dc1uR7Pfs1qv+Xh9w9uPN9ysdjQuUNc1y8WCq/NT/vDDN3z39SteXV0y+IF+v2Xf\n98xjyMKUJz4fYpQbHieHHzhvme+lB7hPj0Avya8/ynmP/HiOcDti0ZFuO2LNI8n9QLM89V96gnuP\nSHicD31qoPXrx+9TkT9sL76E6MdmwVMQ/5VZ1dNjHQFYPIHtJ6nkR7OsfJrS0+L/yTGeTBCOY9iP\n5E5ena0WzCdQVh2mbBGqxSeIDmK/Jw0CJXOjcQgdg+vphx4vsu2r1QXeBxKZbslDBx5ERCkFSeCG\nQCiztEpKhVSK2XKGLS2khCrGwFctEEllkLaKwxDZpcDc5lH9x588a3fjMadBQJIhT3SS03Gu91s+\nrTd0+57JbELUkqbzqKC5ujjnD3/4ij/+6XvmiznWlNTFjMlMMIsdu/YOHx2xDUgpcTH7ajilUSmS\nVEnUo8+1CqB61nfX3FzfIETJdPlH+hY+bq45P5tTVgatEyIGUooMQ8/tuzek5sBiNmGyWGSPjxiQ\nIeRYPKWwKrvvDV3LsG/QPlB4QVN32EIjhYKuA+dy/uSx2ZUEJEHXewqddxVOS5yIFG7A3txTBYm5\nOoP5grryDCFQT2fYukQZw9VXr/l0fcPffnrLv/30lk+rNeuYUCHlqk0k5qUmuJ5D4zmbT0gIdu3A\nagOkhFZQjIMzUgrafiCJHMogU2C/3rHa3tDGd8j/+c9MZjMuzue8+qfXVJWhNBLXD6zv13yuFbPF\nlLIsMcpgtKcsC+pJj+s8+2aLCB4rDISsO0cpqumEs9KyABYnJ1ir+fz+Pd2+pT20tF3P7rBntdvj\nnSf6PIcQiNyuBvqq4GxW41zIHu4+MgAHN9AODaVRFEJnFZA2FIVlfjLjxeUptlSsVrf89NNfuLu7\n5+5uxf1qTdM0tF1H17UQ8/SvEoKu69nsGj7drmh7h5Cay9MFP3z/LV9/dcWL8xPaoeft+/f8t3/+\nV67v75nVhn/49orz+QI5yf77UkpkSsg4lsMPen6R5aLpmFv7aJ8tRZarPtSOY8P2EYQfjyPESPJ+\n2cQUT0D7SCMnxgzbx9DwI1am8WBf5qE+ffwuQJ5DZR+/fvY5PH/jD9KeLw9yrMzH1fNJk0B88XEE\n9qeUi/jiRI5L8bNXCQFGwLRWzCeaaSVBtwTpGXCkKAgpMYRhXEgC0stxwZBIpbK7nHDEYlRhjPRM\nEmkcJsip7EqNo8wx4GPAjFSOVAJt86XiXCAMAV2OY/Gj9Gm92aN9z3m1QBbZOIhxKo103KrFcSso\nkVLjSbTJ8fb2hve3N3RDT5IwnU85u6h4cXLBy6sLrl6eMZnXCJWyAkXl1HQ39AztMPKciRh7Qsq+\nLkkFhNQgFRFF5/bs13f8+d/+hZ//8hcOmwOLxYINmqoTVO09YtXQHgxSyXzuEMgYkUNDJQMTArJr\niCLzpNJaxBjDFpvM8+oUWZiCoKEuKxaqwJPogstZmz4QvUdrjVIeJTOn6bxn3w18vNvxYjHlpC4p\npKQ/7Ak+Mh0GRJEHf5SQVDOBVQZdFJjplElVslzOuLw85/rzPTd3a+43Gzb7PV3fUmhIIWCE4HRa\n4WIihsBSZz94LQVa5inJPoU8eSoVKQT6Q4tpB2Ztjw2Rkxgp0gD+QHdZoBclQ9sTnccrQes8psux\nZWocgirLgtl8Sm8cMoJL2aMkeUEKeULTlAWTyYLZYs5sPsdoTbtr2Ntd1rELEMETQ2AQYAaf1Tci\nUVjDbDbl/OKEZrVlF5rMjfuBLnq0kVwtF5SFxWjFMISsrQ6JX959YLs7YG3Bdrtlvdmx3e7ZHxp6\n53CjR0sOB8+7TOcD3eA4NB1KaUprsGXBZr9j+Hng7fsPNF1Le2ho9g27tuXqYsm3l8uH3k0e9pEo\n8m77McAhjc6YAhFzSSfFY4atJGV/mlxhjv7j6RGlxSOhcsSwpwKMJ1zEs7+P7qjPaWEeqJg0vub/\nVxX5cfUTX7xhxm3Jg8PhsRJ/WAHF82M86+oezZiegPkTjvv5CUgcF87jYiCSHLXh8eG/kUJQFYnl\nFJYzQWk1A5IuJpKLD1V+FBGpIcSBto9oKzCqwJpA03tCdqjKlaEEJXM1jcj/h1YaYyzGGELMEVdH\nakKM5Fki0bc9ro/MVQE2W7gqIdh1DekQeVmfo5SmKnNTi8S4YBwvrvG8y8xnNyFyu92xPjR5HFtr\nFicLXl1d8cM333JyOkcVikNzIPqY/a0JtF3LZrulax3WWLSRDC5PuimlcmCwsChZEomsN595//av\n/Ou//DOrj2t0Ukzrgt3hmmqA0+Tor2/ou56tdyCzZepMa74vLfPCUCaPaw/jFKrEIUArRMyTmil4\nTBKc1VOSlBRlydKW7IVj53MTrwt5cMMWFjNkRU0i4oOgHTyHuy1fnS84m9bUdU3jeqadQzU9tq5I\nxpC0wRYlVqicNTmBqio5nbzk9XLJ/dWGz5/v+PTpmpubG1brFQd3YOgCOiYWRtPEwKAE50Zl21Ol\nGMiZpB7wIWFEpuda5ymdpwKSlHxrNFbAtj0wfPjM6lDSE0kugNX0LuJ8QodEkqMaRymszZ4wcrRB\n6PoON0SiF4BiUhiqSc3FizPm8znGGNzJQLtc0GwPtJsdu82O+f5A3basmp5107JrGmaTivl0yqSu\naHYHBqCNgTYEhJYspzWXF0smpYUYuF03bFrPvum4/+ntQ69icANdN+Q4vJgdOmNK2eRMjDYGjIZv\nKRclxmqszQv659tb2ran7brs2Q5okbXsbpgwuGzjcNzlSzny9aMdc7a1zUWeGIH7y79T8AQ3kFLg\ngZJ9NlH4uJfnCGFPYOfhL/FYdR+/K37x/UfcSg8H+bKafXz8bl4r8IT6OH7x+NcTymR84suVD56D\n+APgPV/tHkyixu9/KLzFlzuB4xRhFhxJoNCR2SKhy2ac2LMkGRHCkMjVs5RQ2oK2azk0B7rdwOXF\nSyo7wxuDF6CSQGKxCsToq6KFxUpLYSoUisrUNKaidQ3RgyTnZ0YJQSYiQ/ZG3nTIqKhOCsqpwWjN\nTsK6a/l4/YmyKCmr8sjE5VX+qApBPNhsuhQ5xIAuaibVkhQUtha8+uqSP/zwHcvFgkikaVucC8yq\nBYv6lBBhdxhYbToUCq0sIlmIiaIsKEsDyVPZOdqUeFp+/um/8+f/9v/y7q8fmNRT6sUEqRNBB4JU\nxIXhzZ/f8/O7a/7WdnghKKzlfDbh/3j1iv/14ozZwmAQSJ8YnKNb3RKMRitN27SkFNBSspzOCQKk\nkhTWooXJ/iEx4kKmgGZVwTAMeO9ycLPWpJDYtwMf7racVBVXkylOCfqUde9q36LdHuMDRevQsymi\ntDCpoLRIa5goRakkZ8s5r/DcK/ikBT+vArveZUrDBboYsClxaS0ToUgSPgfH4AIOkavBFIkucOg9\n85iYKkUFnGrF0hZIo3jzac3tbaKpDLY2xEGyvd8xmy/R5QxrNM1uS3vIfiQxwbQsqcqCYd/gho6u\njyAMSUnkVufw4pA4WS6YTKZMJ3PiRWRoc+jCoW3Zdg2f79e8/3TLm3cfMFrRti2/vOty9GDb0QZH\nURmWiymX5wsuTuf4oefufkXXD+wOPet2wCc/au7JPZYYx0lZhVbjvTkOGWmR/VCOVWtKj+fKdQ0q\nQm00la6JMY5DZNlHBmBweV4gZ9RmADgmreWJzGyqRnzcsT96hGbQ6JuGvVqhhw5bVA/Wyke6JB3r\npeP99wRkjoAsn6DUU3iTDzJGnv37QyH6Bfw9ffxuI/pP3t4z6eEDTfJQiYtn25WHt/dku/KrNyie\nnqRHnuYxai0/8WxNEEeHw7wrKA1Ma6hKRzus2Xcts7qkKC3IgJARHzwqSUpTEQN4FYl4cpavpLAF\nUtnRZE1n6ZYUGKmxssSqOYWeo+gobUNlZjgPWlQYkYdRoESIAhdaTk8KTmtJYaYE00EccgU+neKb\nxPqwp+laZsMEjlFopNGpLlumJpHwJA4+seoShVlydVYQTwO6cixPpxSmhqBICLQoKasls2qGUQV3\n60/cre/YHQ6czk6JKSfuaF2gtUVrgxIVUhe0bcf19Rv+9m8/8fEoKnf2AAAgAElEQVSXawbnOZsW\nLM7nLJdTdGkpXYT7hsoKVAzsupZkCxbLOd/88TumL684aMnbvuXUJaokM58t9dhrcKQUGJzDS8l8\nXuFSwguB1xZrJJMUMEIhk8dIyWQxoRsG+sERPNmTm7ybaHrPoR+ywZjM7nd9cMxshUqC1A3YpkMn\nQWp60vZAkGIc/MoDQDlgeI867CkOLbMkqLRFxEhqB4yCUklMYXmh82Rp7CN3MeLIKhoBhN7R9I5p\nEFRacVUWvJzPqZSmHXrmPrHpBjZNi9trylnFaRJ8/rSid+SeR4A+SVwUWWYYE0JpZos5wxDY7be0\nw8C+69k2DZvdnt1mx+ZkwXw2wxqDSOCH3Bht+p5de2C739J2DUCWAu4HDv1A5wNCCcrKMqsNi6ll\nUlpiyIqiz3cbNk1gfWi5bzqUetqRgqMtQx7MyRWvHE260vHjyfb6weM+jVW2IFOeAuKIi1aCESlX\n0yGD+dEC4sjlPucG8lcPjpwxjdLJxND1tGwpmy2FVlhbPLg+xpETeMSr59XosVh8JFCOC9KIg8+B\n8eFneu5m/tvkyu+nWhlP/HHFgbGTe+RDnq5qvwLdZ0dCPFnJ/r0V69lJ+qL6/7KM1zIxqSTTCQjZ\ns+/uaLoNqAmYJUZalMymQCDQ0lJqTbSKvpTZZGh0qMsZgIm+95TSIIVGojNYyxqrKohgVInVBUY6\ntChQoszRUSIAGhJMZgvK5YRCLNgPt7RxhRSSsp4xzODQ7TgMHW3XjBODx8smjXab2fZ1HzzrfmA/\nQGnnaD1HW4WZDNhaoVQJyaCFQGtJXc4RCXa7HR9vPnCzuse7yHwyJbmIipLZdIbS40UsJU3bcH9z\ny1//x5+5fndN2zkWF0vOXp1xcXXOdDZFGkO56wibhkVpOSkN1U6ip1O+/vYr/uv//k+cnJwgmwN3\n19fIzQHhIzOhMFoTUyS4ASESPuYbbW40IeZE+gGBLcsc1jGpMN6hBczqkklV0rQ9TghSl7fJ07JA\nqzxC70kElRufrXPMJ3M0iiA7bPDofiC5iJdjryLEDBQ+e31LP6D6FuMdMyExRYGMsPaRoBXSGqJP\nzEzBXAq8DAx9Tx8iWht8jDjhGVxASc3MWq6mU5aTGpUE3nuWyrCKkegj+y4QLcyC4u52x7ZxFJMK\nZXS28O2zXr+0iSgk08WCvvfsmo5d29J1Hdv9nvVmx3q14eZmwmxSY7VGjsk+IWZTsH3Xsjoc2DYd\nrR/Ytz3bLk9xWmuZVxXLec1iYpjPasqqoOscq23D/faAKCZIo7N6yydgNBMbFSJ5GDnTGUoKzOi4\nKGX2O4E81yGPRlZaoR5CLvII/QNgkwNDltM62yOEkPsDYzZsvufTI+akxwWEkTaJcTSmiymHd8cD\n3f6eeV1hSvsQzJKSeAyiOZK8T8DqyAb8mu59kkr2BKePr3kQgDz/pmeP30m1EhBCcWwkfOm7e6Q4\nnn790CT4okv6jEZ5+IcnK+wXx3m+zo3PPhb2KBITK1nMBWUdud3v2LVrOrehconC1ShpMVhQIGSe\n8NKyoC4rhJzQdVt8t6OoJUl2uOjYtC3CzFCmQMU0cmxHfwZPTP6Bv8vEWBqd6bL6QeVlHoFmUp6i\nlUIPhja1CJNgknAOtjim7ZZ5URLFKJ1K2Qs6A5Tg427DzRCIFNhCg4/46JmXM6q6QBcFhS5HS1EB\nWK4/v+Wnd/+dt58/EGOkKiru9p+xRlFXJUtbIbTCp56h3fHpl8+8/+k9b/78hmHwnF694Ie//5bL\n1xfMZhOEj4QhIbaOftuzkIKLQnNlDfWLM/7ww9f88U9/oKwqgnO056cc/vIT8vMKfWhR2uSdjkvo\ncbPqgMFamral7TtsYXHVAllWnLQXFH1D6nuUyv7tk7qkbTsQAqMVl/OKi3nFclIRoyBKjZeOJuYk\nHz1efzoGTAogDeV0mpuuiVwFxkAMjtA5bLNFNFuSaykjmCSQOMqq4GAEdz4glWSmDD8UmsN2Q9v3\nRCXZxUgY05qm1nAxqbmazvGjm8FiMkUaxWmqeKE0EwVmUlNNZ3nBfXvLer9FKEVVWGZVwemkoDSW\nOJPosuL08hJhCkL6yP39LjsLtj2r3T6DpMwAinh0yfQx0ceYaaAYCUR2bccQHIURvDjJ4/0niwl1\nXVPPZ9h6wvb9J9reI5Lg+29fcdEFqo+3XN/eM3j3ANqZjwYl1KMdbmGoizF2ryzQUmGtoa5KppOK\ncgxf9j7r4r0POSw5CUbSBKUEldXEBP3g8DbksAxk7pMdK70neCTIC0McfxcxjmETrqfZ3uIXS+R0\nihDyoSEZxFNI+WL38IQ2eYp4T7MPnjEr6WmVPqpWfqX6yI/fBcjvPn+gnsypJ3MYo6Hgt6vpfF6e\nbnyegPTT9yWeL1b5pPx7S1h6pFme/GlkorCC0xOFsT3tsGa9/cy+3ZDoQUBMAZkkdXGBCz0+eoKT\nKCRKQmk1fR/wcWDwAmQ2mmrbHcoEoiiJyWKVwcWW3h/o3JYu7BhSQ58a8BEzCJIcCCkyxB4XO2Ly\nKAx92JFkwpqSEByUBVJEpBHsm47bfo8hYW2Bkoqj48OQPCvX8efbd6yGyHR6QVFMMUUkpY5t39Bi\nqHxNefKKwlYQBbv9nkO7p/cdSWTjoyEM9L5FKIOPis63dL6haxrWn1dcv71jfbOjKGd888N3vHx9\nzovX52gtSD7iugEtS0Jq2XeJWcimUpXSnMwmzGcTrDW5CisV6lwxCEEsP3D/5j3VoaMQCm0tloRx\nOaHHdT0hBYKVHFRET0rU2Yy56KhWd4jPPUPvMErlEXnn0Foyanyy/jt6xP4wNh4FqOxDIqWmnE0x\nNlNIqAKMyZVkytxnIBGiIElBWRRMqDnsBrT36ATnVUlXF6ytYOcGGHX2J0pyFR0HBRskKiWKaoJ5\nMWd5ck51eko8OUVMJihbYIWkFomvypL5ZE4YVUkxRG5vbvh884n79T3GlpRlSV1ZJoXkbFqynFfM\nZjMEidnijMXJObef7ri5ueP2bsWh63DOM/i8+I/2+A8UgktHi1YIZFoJOc42DInVwdGLntNiQnAJ\ns2tYb3YYa/nmh++4ev0KoRSXL7OZ24frWz7fren6HgHZ+bOwLGYTlrMJs2lNXVgKO4ZhHxUkModT\npCRxLhG8xPuEc5GY5GiRkKv9qPLvp++z+6UPnihlNqGSGQOOWHCMGsw8e3hi2hbxztH7HXJzQ392\nyTA/A21GkyweF4RnliGPGPOlhPDfweVRqHDcUY+YNTIZv/X4XYD8/vaalCLWWlIyKDVqfY+Pp+z+\n+MRTP/LHLcfzZgE8XQyOqpinr376yucVugAKLZnXisVc0YeOfbNi19zRuSa7Fyo9Kl0EVk4RaEgt\nvR9AehKRmAYSAzF5gpcoJUFEmuGA7kAoj1QVkzjgQk/vD/R+zxAODLHDpR5CxDgQOnt5uOhGIDco\nWg7DJnOXKmuutRZEofFSszl09Pseues4OzlhUlfj+5Ts3cDPmxs+7tY4aZlpgRcdPnV0YUt/GChD\nidE5Ck1JhXeB9XrFarNi3zb44EeHvwAi0bYtbhioypqhb1nf3XP95prDxmFkzetvv+aHP3zD1dU5\n9aTMYbzDgRAgusSh6fm8PzD1AaRkpjXzSUlV2vzbiSn3FqoS/fKSNqXcgP3lA7XzVFKTxsGN5AYO\n+z2uNsSJpbGCqtTU8ykz+5LqzS+IuzXb3QFlDWVhaFqFVQLvE90wEL1CO0fdZZ4cFFAQfUDYAjst\nUMYgpcngpdTY6zrGpeXKTQkw1lKKgN5D8hGJ4qKs6AuLNInbyqCKGqkrZj5x5ivuUmDnPFFIVFEw\nP71g8c33VFcvSacnmJMTbFmiEZRKUdY1V9MFIoYMMl3L8vaGs/s7Dvs9tqwe/Ha0TswMzK1gUhfo\nTChz+arnxYt7bq5veP/+I7e396w3W/ZtTx/Cs2zXkBJR5NIgCkFIAiENAkVIif0gaVNADS2D6Tj0\noIms9j1VXTG/eIGdTJlNKl6cn3J6smA+m1KVn7i9X+FGr/i6sMzqisV0mikeY7JEd7RfJiW8y/2I\nlLIFxZH2TlGCyA1NocYpZyMpjCKExDCMskYpUVI/2umKo1f8WBWOC8ERxGMM9H1P03aEO83h/DWT\n5SVmYjLoike+/zlX8pxFyBf208beIzZ9SbnkOzcdJ0J+xV4cH78LkB+2K4pCU5YGaUrKcoK1JTKN\n7YB0rLzHxkd63H6kh+fHk/+YwfZYuT9bBL6o04/nUTxtOOTPZ7XhfGkpi0BzaGmHNX1oiCKgjKW0\nJdoYItANHZEenzp8avApW3W23R4Xhzw4kDRWavSYoj44j/P+SVZmTnh30eFC/kgxEkXAhQHtFUhB\nSPkmlVISpacdGpLQCOUZ4o4QE857hv7A3WHD8HnN6t7xj3//95SFzRmEUnDXHPjn929Jesbl2RWv\nX73ip+v/yafNRw7DDm0sr8pXLOZzClsQY2LfHPh084FfPvzCh/sPDNFRVQWlzR/v371ju95Qq4LD\ndsenj9e8f/eJi9NXfP/Dt/zTf/1fqIsFVpZor5CqxpQHLDs+vPsLb6//xs93H6hjZGkMFzbTAIXV\nuXqJfrTByDs3c3pK+BHutxt2n1ZMDgNW6+z97SKr1RozOcUu5+T8gogVIM4umJxcIMw1n27vODs/\nxY6JL1YKWh/Ydj29FRS24GWt2SUPIRK8YxCOaBO2KnO0nNQwNlxJeSYgjQIyjQBriSKhRZaShhhR\nSnM2mdJbgU89SyEpT6aoeor6tKNWlloanBvoXIKypDq/Yvp3/5n59z8wPV1iS5N3WRGicxkmpCIi\n0bXFzqfY2ZTLb75DigzgITicG0BAIaHWiakJGJEgBdzQcXJ2wdVXX/P19/e8/ekNP//0hrfvPxK6\nHkcgBXKaFI9NxJhEjtpT+SZ1PhEpiQ5CN9D2dxRWY41GF4YgClSb8OsDAcnpfMr5i3NOTk7443df\n8/bdR+7u1my3B4Z+IPSBzWrPYdsyMtcIoXJc4tH3XI2xeFphrMVagzVmlCVaqqKgrEqMNUiZSM0G\n5x29G7BSjcEealTKkFOTFKO6JcNHinmn43yk6TrWmw41DExOPlGfvGReL8YFhkyzPIky/PJxpI3T\n0Q7qSYGajjj3UHhnm18tEoUcm7XR/yam/i5Avl29IYQ1bXOL0hUnp69YnlxS2CrfJF9ozI+uco8V\n+QMDxWNF/YQkeXoGHz5PR9pp7HQf176EEoJZWXA6nzCfGYa0ftCpptE4R8vsLS4E2RkkdARa+rDn\n0O948GhQEkXWB4PiZHLF1L5koi7woUWoAcYV/mjmJRG5MtAGpUKe6pQaJUx+lyJnTyqpcyV4rDxS\nNq0K44CJiIoQYds23H34zIuzc+azKbPFhJXv+NQdWO17fvzhj5ydXtD6lkO/RynB5fkls9mM5ewE\nKSSr1T3r+x0frz/y8/u/cru7pXEtUSTmesa8mlGXlsqWdKqj7wfubles7rZM6jnffvcV3377FRqN\noaQ0M8qywLsD29Cy2d9wu/nE7WHNVkQO311y0gTO3txQL6ZU1jwOQsDDSLPSGjOdUX73DYck2b69\nZljtCd2AiIHaWjh0+EOHWC4IRuNDIOwaLk4vePX6Oz7e/wt92yNJFEpQKkk7XifFEJhFwUU9QYWO\nrnNEH0AohLYIU4zX1aOSIuvVJEImhFIPDXgtwUaDEAKjDUVZURQVSiZmPrBICVzLrgctBnahpxs6\nnHPsDwN4w+WwpzKJui6wVY1g3PKHhJD6QVGRh38ghoQUGmEk2lhsoYne4wYH4jhaHnEqoFTA4tBK\nUZiCqigpy4qqnnL64pLLt+9588sHPn6+Y3VoaMe4nyCOSgqBTIJAQMUcfSa1ySPuCQIFHovAEIMm\nHASHYc/9tmO97XlxOnB1MWNSGBbLGT+WBa9etbRNS9t2mVaTGonMAc+kLAAYpzIfQFwptM4DeEop\npBIPO2cpc1MUIIZI1w8oD31hMjWnJFrlginryseBsxFfRMzDZt55hsHRtC37fYNF4JzPirCxGoen\nBeJvgdHTXt/ji572/x5olJTQAlQKpKHl9uYD2/vP7Pdr+H/+719h6u8C5OvbXzjsPrO+m6BNzdAe\nSMGxPLlEm7ylDmEAQEqNNROkeiTDv5xvEv/BV/nxOLJ/JNMfrXQTWkkWU8t0qjFFomn60UMhX4Ra\nJIw0424pEMhUxxAO7NsN99s7YhRYa5lOp/R9RwoJQ+TFfMqinFKqKbvDLZ1bE+KBwff0oUVGwxD6\nzNn5SHCeQEJGw2S0oRXS4enGfEqPIdujRnK6et5WCqTINq1DCHzc3HN9d8f52SnFsubN9o73uy1a\nTzg7vWQyrbnZvEdpwbJecH52QT2psdIQfaDb7bm/v+PTzTX32zuaoSGIPCxVFxNOJqdYmahtSWcr\nuqYnuERdzji/uOLy6hWz+QKZFFrpnJupJb7v2e7vef/pDTerzxxChz6ZYP70LcUAtdTMzhZUZfFQ\nvTz9lUohMNZSXV7StT3tdsdqvcU3e8qYWBYWicJjkLMT0mxJLKfQOy5OLvjmquNvf/4JP3gG4SgL\nw8RonFbZCyUI5kKzqCf4NldDQeQ0GKTOE6s8sR0dm93INA6ViYeqSkkwSuVJ0lJl32xjUQqmwXOC\nZr1v2HQdTR/ZuI4m5rxIiaAqLK/PZpxPNVObcpABEGL2+05Cj7QC6NFAJrpIjJlSSOLIrEpSUoSQ\nNdpJawQaQXZ9LLRCSY9WGm0KqsmU+ekpp6dnLJYnnLz7yKe7FetDy74faJ3HxzErdQxgEMYiiwlS\nlzif6IYuS1KVzUoTmQOQfRgIQdC0is0uspxDWSiKwlBOppycZB66G3qEyJO0JEEcw5aNyRa2SmYr\nWjk25J9gKUcr4UyH5KIphEQKCRcCfQh0XU+hc/UeLHkhVjnpiJgeaBZSJPjAMAw03UDXDzg/oELE\n+wHve1LMtOBzWclvk9+/7tp9QaaMsmElcnM9up5uv2G3XrHZrmmazW8e93cB8ttP7xFSIpXGFiXd\nYcXQbUjpv2Crmpgcze4OElTlgrOLHzDCPKysR/H9Yw0+noyRUnmmbHl21kYZD+MU16g7LTQsFgpd\ndPSxZQh7QkiIVKBlBVKhpcFHz9HJxCVB0x24W63429s3KCSzyYR0/oLb9S3eBabFjJcnB+qypiwV\nLlg8Budg328IyjOIJqe6tAcOzZ7Dfk9wgkJ7lsULSjNB4Wj8hjY0OOVYlBUhisxXRw8IpFIYYyiK\nPCyyCwOfVvdcrO+pvznlXz684eYw8PXVjyxOTgii59BvODs/ZT6fMZ8uGIYe33t87xCpoCpqFvMT\ndv2a0EYIHSlF5tWc89kLmvYWqwyFNRxWe84XF5x+d87Fq9ekIGibyOyspJwrpHHsDzs+3vzEz7/8\nd/781/9B07VIa3j5wyuu/ulH5lEwaIEt5xRViXowD37E8yRyLF1RVswuL3IizGbLerujHYZMJc2X\n9GcvERdfoS5eoeZzpHOc6Bu+XW3504tL/nLziaZ3LBcTFnWHbgd8FziXhqUqKG3JrO0xMhIrMEJy\nlJOJdBSFiXwDK5WTjWIA5Uh+QIS8LTYqq3qSDqhCI6zBSMk0RC5SxW6z4n5oOISIE5JG5dT6F4sp\nX3/3Df/X//m/cXK6RCtH61soa5LM11CMmQJAgDC5Qo4+y19lzJV71/T4ITK0A03XUNSG6axCYAlR\n0COZqVw3Zx8RgdASPcsh3C9eX/F36x2fP37m/YdPfPp8x91mx6Hrs+UBAl2XLM5fcPnV9xhb0g6O\n1XYDST1MTSYpcCFP4RKzAkPLBMoQRU4TwueoQmMLlDlOA43hHD7bVBRWo1WmQgBSyNx1cAHSkUPO\n7+WI7MdxfG0MWylxztN1HZW1uKIkq4glQiqkVCQRs3adzJHnSnxg13TEGLEm7wCG4UCzX7HwLVrl\n738cs/8Srh8LkqObbUaSR5V4SongPEZErBaICPv9nvvVmlQuOf3mkktrfxNTfxcgd64fp7IkInl2\n62tSbDkcbqmqGVobQvB0zQ6tCnarO5ZnL5nOTinKCWo0F3rWFP1C5vO8G/zY9xUiPqhdtAzUE8F8\nJsDsaHzL4A50Pjv7gWdqp0Q8xkD2b80J5NYYBpNDcsuqpm0a1tst/WguFLxnp9Z8dfk1WktCEDjn\nEElQ6IIhdHjX421WTmQnukjXDQSXkKUhFweZm5Oixjzo0h0hZH4+6MzB5gAGDVoibEFQktvdlp/v\nPuP3JzQ+Mpsu+fbb7/OOwrcs5zPq+YSqmqBFiUsREUElKMycWFq62nGYb1Glpo8DKUZenn/DxfJr\n/rJaM7RgRMWL11cUtqIoShQaO7WY0tDrHZu+J+w86+sVf3nzr7y/fsPBDVTzGRcXL/j2m+9ZLBeE\n3YGm0qi6zgEGYxamFBIl1CgGyIu4TNnaoCgr9LymnFbYKKmMwe8PiA/X2MkUO19i5yeIsxNUUbJM\ngh92LXdC065u6IeUG2l1Qewcr5dLLudLjDDM6wWVcQTvSVEjU+YpH8o/KUBrMAa0gpAbwAT/0I7R\nyrCYzem6JldbwSOSxgjFspzycuyR/O2wpUmRQ5K4KPnT3/3AP/ynf+Dy/JSynCCMQquBg1ekqEee\ndQRxLfBuvB1SIh4NoFLmYqWQaGspRUJocPGYAJ8tmvaASRFDopR5ulGm8aOQiBOJsYbF+QnfHloO\nbUfbO5wPBASqqrHTBcXsHG1Nlvg5T/QB1zv6wWXTNCEBDaOHiZICWwm0Hs2oSKTgcENDs18RQkfE\nZw25kQ/n3cd4TAvPwDkGJnvnGHpH2/SEmO1qwxgA7UPKAL7fclJq5uUy7yhE3vVLlXe/8kFKmPNF\nB+fYHlpuNnvum54WhTAGYwr2Nzd80n+mOr3i5ExTlVPiQxbC88czFuEBxB/L0BgS3jna/QZVWqpq\nxrbtEOWExYsx+1bpTD3/xuN3AXKt0khdJBCeod+xWTXsd/eU5ZSymGKLkmZ/T4yRoW3ZbTOYL5cv\nmC3OKar6Yfz++TYmPTlpj93gR0omHYso6jIynymms0QQWwa/pw9NlrHFnExjlCQJi1ESkkZhKFVF\nqWcMPlAUNZPplP3hQNsc6LuWru8JztOlA7v9PbPpNPuOjAkjSmi8D0g3ENwwRo3loQM3uPzjyogL\nLWLIW2klyzFt/RhLFnLat8pmSykJtFbYsqCazqgWS7wqWMWI2O+op6ecLV5y+eIlu+6eJBSni1Ns\nPUVJk10cB0nyCo2m1DMoKoY60LgtdII+9pS25mL5kkl5QtdG9puWlCKTr2bUk2kOAFARVCDKSBsc\noVe4xrHd3nNo96AEpy9ecHZ6xsurl3z99TcIEWndBhcS2hYY8/8x9569lV1Zmuaz3XHX0UQwvKRU\nKtVZNVXdQPUMMMD8j0H/6AHGNKarK1NSyoZjBMnrjtl2PqxzyVBmZX/VHICBMIxL8p591l77Xa+p\nThks9x8yL0j3Q3DxqdG42rFadCyyEybGMFDnwurDR5pHj7BXT6BbQlWxRPMqFn7yif1fNMNxS6Mr\nqspTajOLhVqsdtja4kzFFD0+arI1RKHzyHekHjxz7ikTudwH+KJks1l3K8Gvo6ekRMnSbdXKcqZq\n9tnyo4+MPjDZhvXFBV9/+QVff/k568USY2uKMRidickTU8FnK7bIRozVok/3eSoFGdDFIENKEOzW\nGDEISzngumo+4WpGFL4UXIFiCk5ljEroLP45Va0wzrFYr0gp4WMSA7ckeDnVgmRboq6xtRV2F5ro\nA9PkmcIcPmIMVlfkPEvrtQjgY0zkKPYJOXqOh1vev3tPTBPG5pmGOlsbl5MM/kRskPc6qzlWscAU\nIzEGmY3kh0IeYyIrsfcVqEzYTlrr2Sdd7mcmwhwUPY4T20PPzf7IwUcCFtu0bM42EDyl3xH6HXlz\nKZtq+bTm/I+v02dqFDEGxv5I9CO6NlTGiHlX1dBVzf2r/T0DxN+kkK+Wdl7rckzOpczG8ZE4ZVJT\nMGtJ8JjiwNtf/sy7dz+zPnvMy5df8dmX/wnnhAZ2kqF/WswfQlDlDHPqzsXSNmNtoWkzFxeFxcJh\nrebgD0zpiE+BWDKJSCqemAPW1hhdQa6pdceyWtPV54SYOTZHVsslr9++YQwe17ZoK57M2cM4jfgw\n0jbCiU7JEcKEHwOUTKzDaZo3S4gjVV1RNZp9/566WlGZJZVezIPOTMoCS2kUvgRKkqe1MhWrpeHy\n0WOevviCrmoxy4bdMfGH5//I88evWLRr0IFUHMYpoCGEhPc905AgieWu1S1drUgLOIQtxzDiMzy7\nfMXZ8hE5a477iQ/vb4hh4vmz5yw3HYtNS8wDR78nDgFXOZZtJyETVc2zZy94ap6xXq/ZrFYsuyVN\n1bLffiBue9R+orrQVNpIriMPIhE+wT4f2EuFuhQ2i461QVwQKTQxcHl3h9tuUcNARmOajvax5Wq5\n4MtcOKD5b//vnzBKPM1tpci6kACjZ9aMhqQtqbHkrkZ1NcU6VFbYWHA5o8cJVTIqRggeYhSoxYg1\n7sq20BWmNJJCRpUk8WZjwKRCXTQ2F4axwLrlP3z5Fb/77DMeX15iTIWyBmUEE16WLNBjyKSkxLlP\nK8bJU6JACLrS5JjoxyAsCp0pJIiFYZDszM60qMaAVRQNPsOUFV5lljrRzoX2pFakPNi4OqMx2sm7\nbzWTaslFDN/sjDbpIoPgpq6puhZrHU4rDOLdX3QiqcA4ZlJI5JCgUmjryEXz/c9vSHhW65bHF2c4\nfQKzTurv+UQykyC0NtRzNJ42mjhj6tqYWTUqsEkaRZnrNBKFaKwMadVM8yvzZpEzMUb648B2f2Tb\nj3jE0bPpOl58/hwbRrRxNMqj52CZv+aNn65P+eMzkHKatKAVhGlkv72lazWVlfBnreWEoJS6Fwvq\nX7/s/fWbSfTvMa45+DYlBRlCHtEZRgXaSDKe0YkQJw7bW81r84kAACAASURBVN7yLUZLKsr501dY\nbedprwwztFZY6+6P4BKvpGfJbaFyiW6ZWa2RQFarScWLsCaLmlIpg7IK6kLK4qtSVCAli84Ntb6g\nc48IjWJsA/t+z4tHz1jYhn440jYLcIriNcZWJBK+jGjbYoslpppFu0JXmaISEVDW0C0XEgZgDG3T\noKzBOUvjamxpsFoc7KYYMA60jXhdESqP1oquXRJyxNBh/nmBxuLqhmbZ8urqdywXKxIT1jmYTwEx\n9UzTxDAe8eGALoakLUUFitZk5TkMd9wdbvA54qxBkUhxwDpNCIH9Ycsxbnlzk6iPFctFh6kcVd1i\nrMPSUbuW9vEV67QjlIFcMpNHDN3rmjIaOlpWzYrGWBF8zAVcA2oWo9wX8pykI02FGsXKGM5qDdlg\n64Z6vaJ+/Bj15Bl5cwb7W8LtHenmI2p7x+Ptji+dJb54yrhzxJtMyTvu+iPX21sety3q7Bx/vmRf\nFaJxUFfYpmYqGhWhnhLr2wPNMGLnFPkZuJZlbi04izIamxXBZw6HI41tcLbGuBpXErWtaL1l07R0\nz57zD3/8mrOzM5Q2KGU5pQ2VorC60NjEwkRKMUxBM2wV0yD0QldbTDSkIFL1unEooyVirUAwMkwt\npRB9lPdvadBFk7NlCnLfvYZaiQujLgowoOcZgZYNNitDwtEHmEqiaCOfVzQlz8/4zL8uSTpmPeex\n5jmph8RM30z4kCgxsrvb8vrNNdt+T91WPLna8fLpBRfrBeZUaE8noNnQqiAQkjaadtHhvafMiURl\ndldMyROTR+dEmmcep81Bwr7VrNz0eC8uirvjEeMMZ5sVw24kBE8ej/ih59GjDevVhmzBzgQE/TcZ\nm3+LGTz86TTby+ToCeNAvVpSV0ZCbJSYT3NSfP8Prt+kkMcgHtxKaVJMhFiIaeZwktEZ0gzqi7Vs\nIsUEReGnlpuPP4MuRBJ11Yp380yWr1xN0y6xtkLPvgzqNMixhbYLdAvoWktlDbkEfDwSsyfnDEj0\nltJGFFtNnqlvGYoci0t2GF3TVEsWzZquXnJ1/ozWLrjb35FKJPlMMoK/xRIhD9TKkosiRXB1i7ES\n/YYW0YvRHU21EEzY6Pk9svdiB60rjK5IahJ/CZeAcB9CUVcLbIlUZx3n7RP2x5GYClXV0HYdusoc\nhx0hj/OijuKAN/b0Y4/3vQx1bUUsEwVFLCPHccfhuCOWjFGZlCe8LyhT8F7yIn/48WdWZws26zXO\nPWfVtvL9qAada8g1CodmIIeefhowxmGaCtU6jHLUpmHZraiNwVCQvuQT86LTyYU5Tm/Omiwh4Yyl\nq1tQClPX2G4hgpXdjvjjD4ShJ1x/IN98RO33WBQXBV7lyBbFvhh6D/s88L7c8cjUNOs1eVEzrgzZ\nWLAyF8lZic9KSVR+QB+PqBDvQwTUPMAq2oMxmMqhyVRRMYZMmWmKtqopTtPGwNoteHl5zuazz3jx\n7Cld10lDcRrw31NVBfroTGIKgWFS9F6Rc0JZUClRkvi45JPopBRIRUR3Ss06hjRTZCHHjEbCq1MI\nTGiyUkQydTE4mAuU4d7bX2lSMUzR0k8DhykQS8HZx1hlyVmyUXVRmJkeG8lghdVTciJHSdPKKQkj\nK2fCOLLb7vlwu+Vmt8U4w+Q9XetYdY2cMDgV8lMRn09qukgN0QIzljLL7wtQ5oi32XNIzYNdMxMe\nyEUglTQX8mkS75sYaaqK85Xl5hjJcWQYeo77A/rqnG7RzKZpDwFt/z7h8Nd/I8NP+d6EipxQRJrK\nzMHilspqCYT51ev8+yX9Nynk0xCwTopPDJlxTIRUqOYABYue8WCh16XoSTFjbMNifc447Xnz+k/c\n3b2natYY60AlnDN03ZrV+or12RV1081hAIIPLtpMu5poGodFjs5T7On9HSH72RVN47RGFYXVhkXT\nEkKQhakgxMDgR5YpoC2SvtKuBHJpz7k4f8bN7Uf2+x1TkS4ppUBJAa0tIUoizdK5OVlEAh1AVGaN\n65BDXkEbR/CBwzDQmAqj27kTR/I4tZGHgSxJInmmrTUdFxdXfP/6Zz7c3tIPntXa4VTibnqHD5Kc\nTs5zIR8Zh5HoPcXVpNLg8xGSJqSeceoZhiOpZAqiRh2mRFaJ0Xuu394y+H/l1e+e89UfKrRxVK6h\ntQtsXpJGxXEI9OOefnxLP95wDEceXT5htVzTrRdMYaBqKlbLBU5p9Dx0nV13Zc41u9ZJXRIq2egn\n+n4kugV2s6FpKkqWwVZ//YHp7VsmPzHtdsRhIId4qmtopXjsDJ3RNMeBd31iKIH3U2aRFI9ePaGx\nZ5QZFsNYshGf9DwFpmHAhwnrJ3TIcyPwIFbJPpFTwlmHqSpqY9GmxdgaXTl002FKy0ppLs8PXH75\nBWe//5zFosOcJoCnQpSzdI6piPmVybhwJPeJ3htcU2HQ+BDR92nvWuYvMYmRV21IOeNzIOCpdIsq\nhtgnbMV911+0IpqCT5oxQ4Vi4SKmiNe8VopUDD4p+lDo+5Hbuzs+7vesFitqV5NyRlkhNNgE4xRE\noKU0rlKipcgy4ItRtBDFKKZJ8kMP/cDkPSZpbj7esbs6YzhfY+rqfuhdspidnSC3nONpkjInZc36\nCoSXbSgkrTGUmTvvqIzBIMVUxmKZ4D3BTxIpaA2dseCgrQbIPcNxYHuzZ3915Oxsg1kuUCeE4T6d\n7NRt/9rV8CR4ZP6eipJTnFGFpjIsGkfX1nRdTTOOxCkTCkJvnYe7/9712/iRzzzblAoxFhmaxMI0\nm8GrHJnCRAyRVIJMs2MijD3bm7eg5Q3bbW8wpkFRyGWSIaZ1WNux2lzgqhptLM+ffMbvfv8Z56+e\nEJFoJ50MEY9PA1Me55snR01rnOzyKt+rvRrT0NUXWNMQ0sTt4TXFDIxpj668mNZr5rSUBcbANA3U\ndSU8YqspKWNtxXopkuPaKWyT8NmLoX1xVLRish89/eCJ0c83+o6AwqqMMtJpFZXQNpHiSEyC0Rrj\nJEz3OPB//+t/Zewnvv7ya0I6EEbP6HtilIdIZXF2OyUXKSM2BNZZwR2thPgejwNxCjSdWPjm7Mkx\nUTkr5kWrjs+/eMkf/+EPfP7FK9q2Y5wGxuMA/oY0QQ5iPKRcoDuraWzFZrOkXhpCObL78AuL2wNP\ndD2nm5c5B5G5eKv7AAKho4nB/ziOvL65Y1I9RHh1toEkToIhitNd9J44TiQfSSGQUyTGRCzi0+6s\nZZkTuXYch4kyeD6ULe7bH8EY6uZ3mOUpA9SgcsQME4vdSDMllA/0kyfHBxzTaPPgz2EzNiXhUyuh\nvqqUUTlxPOzY3t7iU+Bs2bHerGe9gtgpnyxOKZJcc4JuSkpUKlGVEcaR41RTTpgqlmbR0C4advs7\nVJCAbGsN0zhyPO5pnGW10NSVWDCnlFBGo610seXeIKoQUaBqKlNwRmiVhyHRh0LAsD47p1ttuPKR\nzWIlNMiSSUXPWK9Gkcg5E32GxH1xTSESvBdMW2mG7Y5+e8e6rhgGwxgSxhVCyjKstJ924XKCBmYG\nz8mPJJNTui/2Eus3Q17znyujqQwYlUU9nBFhXYyitk5ij9FUFl9kGGyMkU45B8Z+z+uf3uKDZvVq\nwVmbabsyK+9Pc7vTvXuofSd8/ITtn9a21prKQo6BkhPGqHvs/mHG97cgzen6TQq5MSf61JwlWdSM\nmxWUVsSU2B16Of6pQl2Jsiv6ieP+BmXlgUg5QpGuI4YJpU4cck27WOJcRdt0vHh8QddBuzBMvqJS\nLZVZ4MvhvjCUotHKzsf2TCzC0VaqUBmH1i1O12hlyNlzGA5MecZ7VURaSKRYO4WrxGWtlIKhonEN\nIUeUdrhmiaGi0pZKF0g7SbkJ4qNsjaMzNbpMTLknlJ5QjvgsZkpG1WSVKSXi00BIo/BtscQYOOxH\n3r655fXbn1lUCxZdTUoTPh2ZhpGYpYM3mHkRGZyrKHOSjKsczlYiy06R3XZPDJG2XqENFB5wVpSi\nbmpevHrK0+dXrM8WTHFkHI4EH9CpwpYKp2uqpsK2Nbq2qBoWyw5jFcfDLYfr97hjoDp/9kAtZaaS\nzuu+zNzoh0IeiZNnHEZ6lRnHieg9BlHmWWPucx6tsZSum/F1Ga7HEMghEnJmkRMLZ9mqA/0wcRw9\n2/e36MUKtT7DrmrKokY1FWqcsHc97vqAOvSk0RNiEGoqzHwy7o/+Oc1sCaWwbi62WRKB7m5v+XB7\nw6gVrutou/ZeWCJH9SzPx/xeyM+fyDFIFF4YSYc79tGCa2nrBUUJXEdJ9HvhS3erGoyiIP4ku+0R\njXgcaWtQRQo0Wt07/pUiHjSxiCd40Uri/FKi9xFfwDaGytXoGQI02tw3BznJwFNpgVFPCslsRPUq\nHO2REIMU9NEzHnYQRy5WHdtjz3EKjFNgdxjYHXpaox/Yag94230BFYhV7nGZC0z5hBOeQkI5gzMK\nC5KROeP4pCKngxTldKWgqgwlKZgkj7aUjNGZykI/jtwePZ1boYw79eD8qnL/TQP90KE/gCUFZzXL\nrqKtLI3T1E7P7qMCGatPBs7/3vXbFHJXCYyRZeeX44cMtqwRbuvt7oDWisoZtHLz/CgyjHuUVmij\ncE4RM3if8GOYMxjlEYjR0zUNS2d5+eIRV08uMNZQ5yWd3dC6DWVM6OxQyVIZ6ZoVEOJI8T0hSYSW\nsZqkZbJubKKowpiPfNxfM8aermtxzqBsYQqerBI+Hdkeb+iqJeuuonVnlNADDqcbShZRhskFxpGw\nGxmOE057zs7OWJ+v2LTn7IZb7obAkHeoKN14zUboymWiHw+kIuwQYzX9vueXt7/wf/xf/w9n7oIX\nl89ZrTpxNhx6hl4UokZbsCJpds5gXAUqU9tq9qhYMBwG+uHI3e2WYhJt285FVni74xQIIWKt4dGT\nC9pVhc8Dd7s7hkHi4Tq3oO4alssVq8WaYkeymSg60jQNeQps371jeH/DpjjslZ1x4U97FzX7XUjm\nZooSEJBCBJ+ocqZzEr5QVME6R9U0Yi+rBLHOgHNWmE6mJqdA9BN+HAi7A+M4csyRlau5vtvxZn9k\nO3q4vsGaHzC1wW463Nmadj/g9gP6MDD2g3R9WmPnjl3P4hY5+ovHiphWCZ9bWznxpGniw8cbXm/v\nUC+eo9oGW1ezSlB+esF0JeTj5MiXTgUxRtI04nc79hO45TmLbkVKhfEwMO6PRA+LJx3L8xUhF2wv\nCtv9YcBq8UFpXCuFXyvESTCToxh/pSgzomAdJWlUgugjUzIYp2kXlSha5xOej/6eIqji7Iyo1MzP\nzoTJk60ma0UpkXE6EmMmhcz+4wfiuKerFVfnK663e653e4Yxcv1hy6qtWdUO507DcH1vdaFOIsHT\nIPxTv/F5MFpyIs0nZ3sSdp3gupQpSSiHOSUoYrplrRG73pwYQ2AMnlwyZ+uGaBtoF2yuXlK1S/4m\nupLTGJZfiRRli5Z4OqWlBtZO40zLxbpls6hpnZlDwsUYTzj4f5/S+JsU8r73p9MiKPEKVrMU98TD\ndM5ymkinPOufZgaDUdJxlShdmlEyvCzzNDslRY5wvnnMP/+nf+HpZ89oV43EtzVLKtVRULNoAEqp\n0MXhbDN7qoxo3WC0QCc5gE+JKQ8EHUh4+rLjz9/+G9c313R1y/MXz7m8vGTRdfg0kY6Zu+OOpRs4\nWyU0FUp56fgZEF4b4BWdL3x8/Z7vvv2OrC3njy65ePKE3Czpx5FhvMPbWy4uF9jzCmfXKKWxuqKt\nO4qKaCvF73A48vH9Rz6+fs/65YaiMtd31+z7D+z7G47jFlfXYlFLoWBBQ5b5OAqFUyPOjNzcXfP9\nj3/hbr+lXQi9zAcP6cD2cOT9+3cc9sfZrN/T9wdSqrDGcrY5o65qls2aVXtJV5/R1h1JDyR6Qhkg\nJPbvP/LLn77hfPC0iw4QnFYYKsj3WPJM0RZnwZLk1HQKcxhTYps9N33P0mp8yeSYpICnk0eHYXV2\nRrvZwLKhdCuSu2ByFW6/ZT0OrFJh4T3q/TX7f/uGEiJmf6SubgS79xF1e6DKYJJAA4uulRBfY9GV\ng6qiuFowaj+AH6XrnWmCJ/+PPMMK70LijXF88eyKarXAnMIVZt8NqUVispvnE0mKkRgCKcpHTh5r\na5G51xXj5GmWS9arFSprnHPESTpYg6TatEvLar2kWbbzkDMTwxyePYdoK20wxsma1TCOnjwXOlfV\nVK6ihHkgOzNPconC9AJSyaiY0chr5BTx00CJCqUKcfK8e3NNmkZMiZRpZFlrOrfmuNvhhOpNKYrb\nux1/KVJQn1ys2SxbKmsf8GYjWPNpjpJzvmd7pAw5JYKfSN6DlQLJTDPMKQkrKqW5GxeV6Em/kGMm\n+IAPkTTPpMbkefXl51z97j/StpLmdcoO+BWUov62h1YK2XDkW0dpwxQyu/2BpnZs1pGmUvdUQ3WC\n2YB7/+2/un6TQr5enIsAJsuCOfkGAzNNKBNzIpfThFvNqSEz4T6fWMRALsIBtZoY08zuMJyvz/n8\ny8/56p++YnXeUTmD0xWVbVFFEeNAZBK5t+swWsQfCg0lYTRUTkNVUNlSqRanWlKKhBQYw0GEStsd\nH8aPDMPE9m7Ho8sLiilstzsOh56xEROkQkSpRNYBXzykgC4dteroXE2lHWkMjMOeaXfg5sMN1cUj\nsgIfBw75A3WjeXQpBeHEVKlURUK4vikmhn6gPxxJ/cR6ueb88pKqqqlCQ13VhCyRcyLGSOKdXcS3\nRSlNUpEpeqzvudvd8u7DO8ZppOqsnHRCpB9u+fnn9/z0/U/stzuMU3jvpZOBGaox1HVF09bUTUvT\ntDR1SySLMdRxYn99w/sffuLN9z+yaS5wa/twdCxzN1NO3HHZtE8fOSVKCuQQGEPE+4jhjo0qtEqG\naVkZiBFdCrquyJMneU8JHtW2qKbFNUJBtVOHDgKlBaMZt3s+Xn+U47iPtGgWWrj72mpUpSla44zk\nXGojir9cObJ1aFNB6FBhRJ0GlRSYaYopJo4h8DFl9nXF5ukVzaKbhS+a08jsfjObN7SU4xygIMyK\naRwZhwG7XNA2DW3b4pqabtGxXi3JUdLqSy4oramqmsVygWssTVdhnJ6ZI8zPDvf548aq2SOliH1D\nkGe0qgyuFiWk5FzO0MUsVjuRZShZ/OsLZCIxeyY/kUikGBgPRz6++YXGFC6WDVWjqSvL5PnV6VoB\n0xi4KXugMA4Dj8/XPDrfUFnh13+C00rRm38gNc+LY4hMk0eniMpZILCSZ9th2QBSkuH0qRYxs1lS\nkijBMQRSkZZn10+Ydsnq4rF4088F+69hk9Na/vQ6RcSf/odSmpQL/eC56yeexIi1s2iKE8z499MV\n4Dcq5H/48mt8mITrOUthc0koLYViGCYOx3HOvpwBf13mHwzBwZR0KXMjKrJkiVinchVffvUFX//z\n73n+9RWuku6pti1GW0IamfKOyAHjoHPt7LAmKSNSeAtGV3TthlqtqPUKpxyjP+D7gTwF1u2K9WLN\njze/8N23f+GXn37h4vycxWpBzInxMBFWEymNpHwEPZLKREiB7DWYMxbWUS8WnD9+wtXTO25/+pm3\n76/Z/vyGp1/01OuWaCJ3+xuevTynqitJT7EWbUAnMxtnCewwDANh8rTK8uzqCZ999jnr1YrFouEw\n1OzGijAvXrJIg3PJZJ2wVuTJMUUm33Pod+wOe9KMnQJ4H3j39gPf/Olbvv/mB4Z+4OzRinGY0Bja\npmUMgUwmpIgPE8lJejkacooM/YGPb97y+pvvePf9T9y+uearl+t7poH4Fp/kEsynt5lylkUvINCK\nJ/mJaZK5gB8jL2uLbhuscSTrcNpQKaibGpQiTBNZa0zdYKqKrshcBusoUYQiF8uW5uVzfEgcjz2D\nsti2Y3G2xtVuVo9oUNKzyZRdg7WSNakU2taUtoGyQMVJjo8poXpPiZEQInfTyJZC7DoeP7mk61oZ\nzOmT997DVUomF+kYYwyEEBjHkcPhyG5/oFs/kQ27qlm0NXVj0ZUwe1ACRWI0TddiKmFhGKvn1xTo\nSs0VaGY6YqwUkjhE9rs7wFK3Le2qvRerkEURmTmdgBQUPUvJBVrKJYhhVpQA8Sl5xuOBw81HDte/\nsL7ccLU+o+1avA/4cbyX3ZcszpKlFPwUuL6+Y78/sNv3aGM4W7a0zgrOPUMoesbD1dwhlywbZwyR\nioQqGVOydMXzYBTU3Bx8MpxU0jzEnJhSZIjh3u1w3weOQRGUw51W6txofuqAeHovfwWb/7X6c/73\nGGE3BvqYpFlTp3lA/pvX+OvrNynk/+V//y+knInzDpiyYJ4lS8Crnzz9JKyVEAIhTKRZ9ZhiJETx\n7k5JpLQ+eI7DQH/sKaXQrVr+p3/5R559foWPI7WtiTnQ+54Qbhn8jjFuZ14pUAyVrkkx40MgREn2\nNgpq46gaN/O7HYmESy02dKzac56eGaxa8v76Dfv9ll9+ecuqW6GtJaTAfrfncNgyxTNiHmWISmII\nAZUty2rNVCoWFxe8+ur3bD+8Zz9ObHc9n0fFRV1hVg3LR47z8/XMVhEo5D5s456OBbuPdzKky4ZF\ns2bRbFDJUpslpUlgJU0+xkiKQYQaSOfE7DbnlMFmR21aFs0KZysqW1GS4i/f/MAP37zmuz/9zHAY\nCSkxDJ7rtx959eo5i+WShZHvTyuotaNbtEILJHDz4R1vfvyW1998y4f377m72dL3CYVYBZ/6OzlO\n3uel3+PDOSVyiqKenAJ5kMLYGcOjRcv5ZonLWYQb1lAtW1zb4qxDzUfzsN+Kp3YYMc7SuGqmPCZs\nKfIePL7g85z5+OGWfrsjJoF1MPa+cBetUPc2asz2xMx4AChjydrJ54UJFQtUDrQmTJ4Phx5VOTbn\nGxarNa6q5s0BEd7MCsZcpCPP8zMTYhBPn92Bt+8/8O2Pb7iyK0IxHPcHioK6rVksFrSN2F2Y2mFt\nQFeGuhKZvLg0zjj8XDhTlshBrSGEwn7f8/H6Pa9//pZmecbjp8+5fHwuroPMLBcUMSSG8UBOM3at\n574zC8yZggwaY4r0+4Hbt285Xr/m8brj+dUF5+slMSV8TrN/epitJ2QTOxls5JyZpsh2N/Dm/S1O\nK9yyhXzyHBLwUpUim6rmPks1xUTWD4wROezMO9fc+Co1b9LayAaCPFd5zkooCnRds372O1YXT6mb\njjLnhZ7K81+V6Ye/+1V1/xRPn08OWkkuaCrE2WxM2D0Pn/l3kJXfppB/9dUf5oV5ekDLTNWa/RuS\n3PB0oo+F2QRnDlWNUTqT06ILwTMMwoVOOaEqzfnFmmE/8v7de7pqgTWt2LzaDHUEJ/SimArRJ1Kf\n8KOfTwkz00EbWttQLh32rMNYWRzGyU3WWvjSm8WKEiO6GK5vPtAfPTH0DH3Ps+WVbD5hmnfq+chM\npKhI1hGvMqo21JuG0lnMpqapFCwUbuNYXy05X1a0m1asPbWRI70pBIRpo60FY2mrNcv2DHfRsWjP\nMKolRdC0WB2w6igCJJfAZFRxYjiEF6VdTlDAmY6uPmO1vKSrlySf+fD2lt3Nlnevb9jdDeJkpxQp\nKm4+7DkcxAytrZtZ4GRwyuJcLXLn/ZYPv7zh+ue33L7fMh4jqjQs2o62WVA5d7/I5aFCuLbzMCrP\nm3lOkRwDafLkwaNiYulqnqyXnJ9vMDHS9wPTLOJKORKVxc0QiFWa3HQUZVAhSLo9zKZmQh2sqprH\nlxdU1rKdPWyY7Vhn/1q5j0qJB0tVEdtOWBo5Y2ehZ0my5SpboebNRI0T/nDktu9pFy3rx49om2b2\nFJdCouYqJD73Ao2cmp8QIt577nY7bndHDj7xsqklb9UptLU4V6GUJYRISD2MCuWS2OjqBlWMSNSt\nDJfVvQozzLatkeMY8VOComnqdqbSinLTMKf1zPBPnr+v2YVKnkOFoMxRCzNs3oiPux27D9eE7S0X\nL37PZrXAGSMb9Pw5cp/n/zOLdu7nBXPKzzCMeB/F0vceVpH1o5HhK1pRYrofkN/j0vw6Om0mhcyu\nhCfK1ENOaUqSkIRxdKtzXn71j2wun2CMmzeFTzr58jDgzHler1mox3JS+bS9/gRDV4oQE8MU6cdA\nzHNmQWHOUHhwSvzr67cxzXLyZWUjFIzoHls6HetOQ58ZWzwdtB9+kPkBp8gCTPIxxcBx7Pnhl+/4\n4afv+OX1j1BO6TyJ9XnH+vGS7qIVUUkIDIeBu7c3jIeRFCPWClbXVA2dW2N9S02DrRt0V0hlwscJ\nnzy5JKyGR+ePqVxLSIXjvmd/t+XD63f88dXvIWb8OOHqSgIjAGsNxkFxmaBlIBdtwl40XOorNqqg\nNzXqqqZ9tmF9vkGm8pbaNDRVJ4V8Oohizxms7bh69Ip4tPjB03YbUtSkBNbWqFKRokJhcLrG1IZK\nt6SSmdJAiB7vB1L0uHrBojtntbigqxdsdzdsP+64vd4xDZGM5FzWdUvdLAle48dCCgqjKxb1ksY1\nwqdOln5/5PqXN3x8fc3htqdQ0TY1y7qiNUvOzzbUlbt/GH/NIS+zEjDNw7ZIChN5nKSQh8K6djxZ\nLjk/W2OA5tiz2+4IfhTbAqVw6zVuvaZanIGtIAXY31HCjDn3PRSFsY66zTRtjbs4Y+UcTin0nM95\nDz/MFMNY1wzrNeOjJxRrscFjb27QuyNMnlhb6Jaoppb5y+0tA4XdOLJ++pgXT66oKitrXuv7TpzT\nM5FPsyNpbEKMTD5ws92xHz1useHp8xc8ffEUWznqdoXRjhQKd9st/fHI5D1ZRZxzONdglaNtFzSL\nDtdUWCtfN8fAcZzY7g8chz1NtaJdrvhi/UdUJfbBOSHeL0pL9FsWhSb385ZITAKPyczJonK6hzEO\ndzcMuztc9GxWC5q6noNRHu496RSxJlBGPkEmc8EsswYg53yvi9Cz2vM0jziljIkdxVzs55OCLqeT\nH/NaewC2T016KeK5HnMhpILPGVXVrC6v+N3X/8TZKFPr1QAAIABJREFUxeN7e5AH7vj8OrOfeZ6f\nKe9H2m6Nc83sac/9+lYnL6EyC9lGz74P+DTDRZ9c5d+v47+RIEjPRzpksClYmnQFU99TUqZdr2ch\nAQ8m78ibe8IT5O8LBY0xQuVprME6w1eff8XV+WO+evkHPt5e8+1fvuXf/vwnvvvTtyQiqjZzVqhM\nkPGRtq1pFxVFQbtcsO7WPH3+kvX5gqgP7I/vONxsOQx3HI470A6ra5p2TciJkBOXlxcM+x7fj0x7\nz+31npv3O1bnS5ZrhWsdpjK0pkOjGfOBWrU0TcdFe8V//t/+Vwa/Z4oDWWesE263VWIIVOmOpb6g\nsSuKjlhzQJckO7ZSbB6tMVahsTRLQ8hHKttiXSabgo6glZmFPw6nHZaCylClirqy5ORZNA3mo1gO\nD/1RsjBDEEBHi+9GVTV89vkrvvr69zx/+ZSv/8PvefnkJZVr6eoOraH3N/ipZxoPRO9puyVPXtQY\nV7GsOxb1mmV1xtXxHU0a7yf0p478NNYW8Yc8jCkm0uTxw8jYj4whoZCkH50VrqnRtqJyNT54ilZU\nyxV2scQsFrCopRj0keRHhv2e6diT+4FUIGpNNIamaajNXMSbGuWE3qjm9atSls3gmIXh8egZbM5R\nVjNk6bxVSuQXL9CXl+imJux25N2e28OR/TjxYtlxcXmGUnP2qJkH/HM9OAUA5xTJIQjlMIhn/Ifb\nPVE7Pv/95zx7+RkXF2ek4FHKMo6e3WHP9Yd3eB9ROCpnSTmRp4HDtGdX7ai7muW6oa4anBUrge//\n8i3f//QTUwoY1/Hk6jn/+T/+E20tISEKM3eJGWXkvTfWUtUtMQbpQkNkmvrZL90AnnGcGKeJ490t\nuiTOzzesVkvqykkSz5z644wW6p20+rM9wOn55x6LNkpRUiaGeM8Zn2M0ZrO1uaDPg09rLM4q7MlI\n6zR/U9zj8afhZ85ZbG6LImWFz+Kl7jZnrC8es1xtcPbErJvX633s5FyvcqGkgB8P7Ha3xOhpuxVV\ns8Tpk4cOfLLK54Frnn2fZguBE8OFkxf+316/DY989o+QY4NYTZaSKElx/PCRMI7UXYey9h4rvB98\nPTQq8qtSs5+COKtptHCjrZNghOWGs7Nz2mbJstvwww/f8/rdG65vrxn6gZwipiSaoinLlnbTsVrU\nPHI1j0PiMgy0wx2q9Kh4JI87sh+wtqCtwlpN5Sz4TFMMZ2dL7tYd3aqj6RrevHmLceBzz6svXnJx\ndUZjarSCQiLmgTHuMc7Q1WseLZ6RyyNiGhhDT0gSJ2WUw6qKSi+onETiJeI8FBQ3wJQ97dJRuTOc\nqsXgSk3EUohxwucDGX8faiOeG9Ln5CLQTyEQkud2d82b9z/x+u3PDONELhptahYrx3IpR+lxd+D5\n8yv++A+/4/lnT7h69Ihlu6IyK5yxpDyQgmeapNs3zrJab1Ba03SNJLRki/WJSitskbSXk+Oh1hLw\ne2J9nGTYKU4kHwjDRBg9jRUb2+VqgTYGbaVzNq7G5iRReVUFTszU4jiQxhF/ONDvbhn3PakfUN5T\nUCStScagS8E68XEpVlOCIRt93y3f0wTrmrJcgtYzhxlyU6MuL9AhkRctxRrJaRUKBTYnNsuO87MV\nq1Unm8PJ1U89bF4ng6l7/vysVPXTRMiK5eaC33/9NZuNFJbkJ8bDgX4YmI5HdPLYuSCVUBinHu89\nw2yyZZymahyPnlxxfiE5popMDmJSF9UWpRTv715wdbZh1VZYrYkpEnMCnclJPYiBXIY4i2+iZMlC\nFH+e0RNCIE4Di7biyYsnLBcddeUoSaiClbXUztFU4paocoJ52C7FkrnBk6LrJ8+gT6f7jJ5Dl08+\nPUoVDDK7UErYMHoutPLvD3Xpk+1CnqmZLRVTZgoJHyLr5ZqLq6fUrhHb3ZzvcYJPm82HQX0ip0CY\neo5F8P+ugG4XWCsmeGqGTchZbHdn9t19oXsgrnzyVX59/TbQin34slpriHEWOCQO19dM+yOPP/8C\nZe3D4Of0+erhCMS9vSkPbxwaiiFrcUJzlaNbrnjy5AX/8Md/5rvvvuW//+m/8a9/+ld++fkndrcf\nScOIVZpGaVbG8GrRcaUM5zHS3X3AFU/pGrCJHD1GK2LXEpWZE2sCKnm0SbS1ZXW+YHNY0x963r59\ny7HfkRil4+8cdW3EOEkpii5MfoezjqZaYtUSY5dku8TqHUMYCDGilMWoGqNaWUB4fD7OToKJTCIm\nj6scjW1xuUEpRUgT+35PUj1ZjWQ9yftVEioq0FGglTjO/ssDx/2Bw+3IN999x8+vf6SgaBcrUcrW\nLV1boUh8+PkdT56c8+T5OY+fLlnUFquhsTWpBEIc8aHH+5GUI6511I3DzgrSnDzj7o74fs+l02hn\n505JPQg+tGgL1LySc47EOJG9J4wTYfKsqprz1ZLVanmvTFRaQ1WLinhW9iWjiCXhjyPjdsthv+N2\nOFDGgIoJp8AojXGOqhYGiJ29OHJKxGmcDZ4yaI22whsPdU1cLijBU+5uKVrsmc3lhazXw458M5IA\nNQ3Y4cjKKF5ePeLR2Zq2qSTv85O1XOahXcmFkiLlfjYQJZx5HFG6YnN+yWefv6KqqzlKLXI87PHD\ngIqBdd0QbcGHImykfs/usGWcRJZOFoW0Lom60oS6oaktF+sl4+GGox857G74y4/fU8Ir1Llm2WjG\nMJByQBvh/UsAiKTrFCV2AkUJc0WU14ng0+yRHlmeL3jy/Iq2a6itJaeMUgqXInXlWLYttdGYnISP\nXgT6OWVmkGftwtCTU5hrg5CHjFJYJX5NWkOlmambYrwnk4fM34wj55PQ/T2Ym4cQpZDHUlienXF5\n9QyrzcPJ8dOXgHlwPA9eTyeqHBkHmUMpY7HzyesEIasZLrrvyE8cznszrpme93ewld9Iom9+9Xtj\nDF7BbtgyxUBCchmtk5zMk6m8+tXoV65T9uanYwCBm8zDjZiNebRWfP2HP/DyxUv+53/5X/j22z/z\nzX/9P7n+7k+8WFa82Kx4dr7icr2ga2vcSYVHJg09oSSqEGhSZNgWRmvpreVgDfsY6P3E2Asepm3B\nNXOyymbF5188Y7npsEZsRiud0EajZ4WXznM3oSu0chQc2ojwJKoDqkSsarHGkMrI4Pcc/S192N6L\nTUIJNNphdIWmJkbPx9stf/7hezYbw2pd0a3aewaIONdrfPIchwPb21s+vL/m3dt33F5vORw86+WK\n54sz6s5Rt5pu0XD56Jy2rfj5+59wznJz+57NZc2iEgl/VAMh94z5lpgP+DjgYxA4Yjb693l2pNSB\nto44Xc3UO2EaoIWGZ7QofYki2igpkXwgTSP9MLAfJhbGUuWMGgbG4OXIqsWoQi1WmMUK03aUTUdx\nBX17gwotJkFdFEVPqDrjlKaqG+rFkur8jMY6bEqo/kiaRsZ+YNzumKZhtmm14mz48SP67VvcYolu\nKqE/PrlC1Q15mkg/fU8axQc8FbA3N9iSef70EYvVAqcN0kNKQSyfVHShHeZZDCQBwmEKHPuecRyp\np4k4TZTKobSlqjtW55bYebKPUAQSGGc21pRGXFBgarpmTWNqxu0Hdj//zPvv/sx+8LTLjm7Z8er5\nc277Hp8VaX/L+wS79x9xKHwQDUbXdVRNTV3XuKpmCsNclOWZNc7ilEWhGPvINE7klCQdqBIOvrEO\nMweGuGipa/EjWtYVNQjDa/ZkVwVUSiQPw6FHxchoDcrIO2g02JnfL+HMELQYysWU6EwtBZ0HQGMu\nJPJ3nzJYkHUYcsKnQrvecPH4GReXjwXi+MRe9r4qFUVOnhAnYvDEMOK9ZwojztVCh5wm7sJb2nbF\nev1kDlmX+y12IeJUaZQMlMVGYP6E/z8Jgv5a7WStJQZPfzyQSsHUtSR2zJ4basZ/1ScduLzQ/S8n\n1Pz+9+WT3VbUdHo2valYLVdcnJ+xbGtW/sDbtOeqq7hctpwvWpZtjZ0ZDjNQK+6HGXTRVGi6kBhS\npp4mVEn040gcJoZxYvIJJlg3Kx7/4ZLPvnjGV//4B84352xMw3ICMwsT5LitMMdMHDL9ciJoxZD8\nHMosCUJWN+ybLA5vpSeGAzkM6FwwKs8FMKNq6VxCTLx++543799we/ORxXJN0ZaQIrFImkyOmWm6\n47Dfc7e9Y3+348O7a96/ecdhe0QpS9OucCRcke6IUAiDxeqGbuHIOXLc3/HjN5nXfEDnVvDeMgID\nWg3iMWItTdtinUMbRSqJWBIqe3T2GO1kQ1InBSRyF0sWAcdMBzVzcVcpMU6B7TRhK5kIGgopRRQS\nD6eMnPZyDBK/NgzQB/Sup4qgrMN1K3LdzuvEYhRoYyjTyLQ/MAwDcb9jOoocfxxHSo6irHUVVV1j\nfcROExwO2Ep8y9PxQLaOEgP67hbjA7pIt1imiWw0bim0QDG6mjNAT+yHT37+nMVwLOcyH/MDh2Mv\nkyUrqfExB0rMTGNgGj1+8kQfZvEcwpxxjna5pJhCKprWLam1o9WJd28O9LsDuSjImbquefz0KauQ\nmCaxb27qhtpVWCVYfikCmRwPI/utJBIdj3tKKdSNWCfXtYQl+9Gz3+447LZEfzKCmzFgLUCHyXJv\nnbUsuoZFW9O42SN9ZsFYo2eRlue4PxBGyc/UWmGVxMcZo6mMZHtaOwc3JCnky2pDykWaw9N6m1Xl\np5PQiSlCEZfNMSaitlx9/pLHT1+y6FanbpGTL87DdYLFEiFNYqnrHNZYou8J457j/h3K1YT1FXWz\nwprVfbDFKdJJmVNq0QPh4+/BKvAbFfJSHgKSS5E3tZTCYbujFEXddZ/AJvID/DXGfyrup+QNNQ8/\n56/wK5qOtRat9cydFtphpSuePLqkPHvM8uaSzhlJE3eGomRc8jDXlgeQmaRfFUNOmUXKtDHgfCDs\nR/ww4UNmDJkVNY/Pz/jsy2e8/Oo5L373nCUN3THSHo7o0ZP9SEiRVAqpOpDaO/zqmh2B29BzCCOp\nGJTusO0ThnXF0BVyOWJzoCmwokaXgNGRxhqq2mGUwYfAz2/e8f76mnpWWVpniFkGZcFHwpjYb2+5\nu7lle3OH7z37my3jXU/xUdR/RqHCyBSO9FtPUYmb9+7/Y+69eiy5sizN7yiTV7kKTZVMZonuwjQK\nGDQwmN8w/3peGoMBCiOqsyuZ1CFdXGHyyHk4dt2ZVTnPzAs4g2QI9/Brtu3svdf6Vj6FVQZjFGNK\nvP/xPYdPM+PRI5OgrQTbVnG5Nmw2LZvdGnO5o1qtkCjmYLNZaJhhcoi2WeRsPLWUKUvNCBEVI1qI\nPEMtSialCVIwpESbIklmIJVXWd+shEIYnYORYyDMI6I/gLUwzhSmpNCGtigJKbtjPZI4DfjTCXs3\nEU49tuuZhwE72UzfA0yRF8VRZqWUt44gBNZm3o8C0tv3BFLWdMslDX75u43B40pDYRTl+bDA+X7I\nvygXlacifv7wITLOlkM/EoQGrYkpMbsJbz3TkDXms7VLbGB+iOrCoIymLbbUq5YYJUoYVAJRZ6a4\nA6rVClMVrLYbrp695EYYrIsMw0zTrqjKbKrzfsbakWk+0R17ulPHcX/g8LAnxkhd17Rtkw1sSuGd\nY+h7uv0DbpogxGVYIB7PY+cDl9YqK5rqirYq6eZ5Ga/EfI0ISN4zOsesxEIKPH8sI1WpMnRKZzjf\n7HJ84/NtnR2sSmVTk1TLrPs8TInLbiI/OG2ITCmRqoY33/wjV89fUejiEVks4HEx/fjPlMc+s+0x\nygABSeTU3zP0D1g/sdq8RKqSoT1SGEOMjvPWKpEeI+jkci8skn/+8qHx9PpNCnmMcSm6+WkTl/HH\n6fYBLTRV2+anWgx/cZE/nVbE+Q96+jmxPCDOn+RxgSwW67HEmKy5DiERo8AvJxzvAzalHDZLQj7G\nGUhU9vLyGPqbd/QIqSlFbq9LXdCYmpfO0ztP5wSqabl4dsPqZk2xrdHTTNMNNIOjnD0x2HxxxvxU\nD+OEP3W4j5/Q80QcTrw93vNhnjiYCvfZPyBffoG8uiEWilSUSFXSiIRMmjomblB8yZobapRyCLPm\n6kLyuzevEHUH0oEUHG4PPNwf6A8zGk+ZBC82G6yZuCwLXlxfMNhAu9qx215hh55fvv+Od9/9RDcO\nmV5X5oVUVZdoo4k+8fCpY9pPbKXhszeX/G6140oUVLOluD9gjj1oRRRgFoJhNpXUUEZSkW+KmDKR\nkuAWGFMu5EUSYArkao148Zztxz3rTwdKFzF1TbHeYOpquUlzsn30eSnqDweCy7F6RldoPMk7bEq4\nacJOc565O5cRtzFAzLyNarVhdWkwZYkuSwqTVQ8hJmY7M8wz3Tgwe4e1Dm8t0eZxng8ZERBTwpOw\nAm4RyJsr/vmz59RSsFoOLYKnyz38yj6eFlCW8w7nLN048enQ06k1VTfx/pePFI2hrGuqdk2z2i33\nymJiSvmBIqQkO6IjpBxzprVC6cTq5ppxHJG6QC9jj+ox3CKPkIxRpJiYxoCzBYUrqauKwqypm4HN\n1Yb2dkW3PzKdOm5/eYuzMzGGLMGXAhF8fqiSFq07nGWC565bKUlRZBbMdrdhb+fM+MmzpizRBILL\n/BPhwah8yDo7Y+1ygI2CPOP2gUjW4Uul0KpA6QKlTWbhEBYZtEQsck8bIv0YCMWKdnvFm6/+wGq9\nwfuJEGNGZkv9yMw/F9l5OLB/eMfDw3ticszzSN8d6A97pmkgEKirS/zcc9i/g2gznmMaMqAr5b3A\n+eEkBI8H1r+pZef5aXa+2ELMZpHhcOT65gWr3WZxmZ1P2uFXIxaZ51O/ru//bvP8/9+AZJMPi/FI\nKg2mwClNHAeCFYTCEItICJGiiGglnz6VEMR8QMQtTJgksqypkoIkc0u/awuaVcu21DBb0oMn7RN1\nUBRRoM4t2aLG8dZmCt84EV3AkLgAvjQNaxR3QvPOOj4ej/SqoL664mvd8Lpu0CI9hkVHEt/P8G2a\n6K3juG65ahq+XO2IQ0c6nIjzhLi7ZzNM7LxAR4dKedEnZUEoDZPy3PqRsqjYrdcoA8+fX/AVLzgG\nx+1p5O40YW1CRocxkcoYtqsaXdVcFjWfXa54Xlc0QqFcRLiZlKZMUViMYNLlTMlQWuLmilCd2RiZ\nYnd2OZ5vcK3V0/zy6oov//A7yqpGHTq2V9ew3iALnefozme2yrI8UmWJLMt84lM6jzhSQoaQZ6ra\nEMoyS8/SgqlaFq5a5k4t73P0kmWZDwGmLDB1RelzKLF1lnmecKeesR8YrKOfJ4YQ6GLkEAO3UrFa\nr/lnU+Rr8Fy9l2viPGR5KsZhYag77DzRdz2fHk7MpUHfd7z/+ROqNDnrtchmLLkcdv4yFu1p5CiS\nRKiMsJUmc3qCj0g5Z2WHBCkPi1BCZjOT9Av46qwbz9JhH3IkYsJjTMFqvaEyBWNRMJxO9N2ReRwI\ndiZ6i/QWrRVlWT5+nY9LbrkY3oymrSt2mzX3s82GuuAWRUc+iEkB3kZm75ljXlQ/KlKWuufJqpMQ\nA1qJjGzQmVKppFri9AQinFUxS/BbApcSU5KY7Y7t9WvKqsROJwY7gy5omw1a6uV8+TRbl4tpTyKZ\npplxODH2J/ruhPMOVRRMY8dx/4F+2jP29yQPc2+J5SofZNKy8CQ9/fi3NloZ+iMpelJ0ED3zPHH/\n8Y7heEQ+f0FRmsyKDmJZdManGbnMp3ABjzNV4LE1y61O4rFpW4r+mW8gpVzmygJMiV5tEZtLutOJ\neconqrl01N5ThYLCLHNTmcNQrc+QexfAxYAQgqo0zC7iXAIv2baGjRZU04gdcoyVlAJdNQhtCEJk\nizhL3F3MMq15ngmTRWjDqqpYb1e8SZF9hG+TwJ46jsLgN5e8kIb/qcib/SFEDt5zHxw/Ost3buZP\nfqKpDd8kwYMdqe+PqIdb4vFAPYzUCLZVTZgcyXsUibZpQMJApHKBMIzUXUcbJz7b1JjVG+Za8f2n\njj+/O3LfjeA9lRJs65L1ZcGqKFmXNaUUaJELZQo5qi8sLIu4bPXjnJkag56Znw24ZpVlciKRFKBE\nHpHIfJqUCczScmpj+LyqePb8GdPdAxs0zpTIGLJ6ZJ6Js8tOS20wbY0ySxp7CogoICSM98SqenIG\niidQUZ51shh/lozQmN/PSL7eyqKkKCtaAT7lBClrJ2xZMlYlXVmixpHkM6+D2eYA47KgLiu00o/X\n79OMAc6ulLMbMviM3Z2nkX4Y2XcDxlzifaI7DXBSj+MYsUgkc3FZ+v4FZvX4SjwxY9TipE1y0Vjn\nX5BSxJ9HOjERkyUlnw1BCyhLyoSQegG5ZaNbDrIuqFYrEgnnLH134HTY48aBm21DWZVUVfUoZGC5\nn+WyAylMVq5s2pamH4mxQMQsvZxdHnORNFMMhMU4Uyj15FBNT/hiAK0ETaFpq5LKmFxolcrz8V9/\nT8jh2yFlm7xThmJ3RXF1zTyemIYO52aq1QV1UcGShoQ8VxwB5HqllYEoFrJkfhjlr1Mvf84IWjAU\nt0gKUjRUulzGOuFRfppplOf9yd/QaOXnb/8FNx1w054UZ6bO0u093T5x2L9ndVdSzVfookAZg9Im\nt3hLHl+I+YbLrHL5FwVdiLPqIV9oIvIkGF1ukJyFWeCEYPfqc+Zg+eP9A4dPH9CngaYytHVJU1dU\npaHQ2QihdOI0zMwuYIxBak0ScBwd/WQhJpqiwOgMf7LOY0NAakmpC6zLhojzKUcZgzEF1apEmpIk\nDPv5lv54wj0cqFct2iiqlPjdfEfUFWoc+LdVy/+pJMfo+aqqqKWmkoqvTMVKSkie78eZcp5QQ0ff\n7bna33IxDBQhMIwj3nts3+GdIwHKmEzOM5pGFVxOBfvDAw/v3nPnHZWGTaNZX694udqw+/0N0zAg\nncfEiFnavxAiznl8ypZyGc9Zp/GRd5Et1Sl3IdbjI1w9HCjrlqqGqPWTZFZKhFCLjljkLFUVc1em\nNFIbiqZCTI7TMCP3HYUPlGVJsVpzlq4KKUlKkaQgRpHT7m1eOHufjTbJhyfpI8vNHEPm0i9dxNl4\n9Sh2ICfLuOCZrMVZS/B5Vl6vWjaXF3xeFkQhsCFwOnW8HXr8ZsWqNEhx9gTGMyssX7PxjCWIROdw\n84wd+rxwtY6kNF/87jNev/6Cy9Umn0zn7PjMO6W0MPzjkqwEIJdiuYw0WZ4Xy3151lnHEBf2UX6I\n+JBwLhC8IIQc5OLc2Qk840PIIC9vyfPghUgqMnWb4Lm7u+OwvyOFma++eMH2YktRZK44gicpn8x0\nycIYNnXNuiiZTj1FU7LebCiN5NSd6Lqe2br8MDAFaMnFqqE2GrHosSN5cX6OgGuN5nqzpi7KhY+S\nn54x/goXAngiIWXlW6wqTNUSYuL9D/8dn6BqN1yXNVoIvB2xbkLpvFwFwf7uFz59+onTdMJNI9Z2\nzG4gLUE5KY5Zbh0VOI0MCSXzz/nZZlVWWEiLixkoe2Xi05v1716/SSH/f77vKNIMPnDsRuKhg8GT\n5Ia7hxPz9z8SwlvyLkdmmJAqEapEqxKpFWWZi61WeVkBuYCLRc6oTLYFR2uJqMxu8FnGpFW2Qzs7\nMM8jow18nBL7wwT9iXWhaauCtsnZeW1V0JQldVmRx7QKoxWzD8w+5lO6jxiVlRJJiHyS8R5QGGko\nq3rhn+dCFmLMTOmQ07u9c4SUxz39bPl4f2D+dEDVJWWhqGZLaWo+myNOlVyZhpv1Bc+k4lP0fGdH\n9sFRTiPqtOd/uf/Iduy5nnpu5h7TdYTJYn3e+AcpoMhBB0IIhMomD3wuK4VRlAL6eabrR2YpcHPB\n7CLCzIiy5GJTg5L4OZJsIPlI9DHD+1MutiIu9voQ8IsEzvuA95FhGOjGmX72lJcfiKrg5iZRliWl\nKSAZkhJEnbIUi6eH+WMuo1RIY4jaEqXCpYi0Du1z9qsInmgdzlpsAp9ynFeBwCxjlnm2TP3A3A8U\nSmKUwugse5xCoJ/njFImyyOBx4J/NvKkpZ8vCoU0uX0vypy2JJXi1A10+yMf9nsO3mIqkxkvKWuu\n5XnB+av75Kwz1sZQFQWurmkbz+XFjs9fB775/ee8+vwL2qrFjT4HNCzwqxhSDoVgWZj6gHcZV4xI\nZ64X52ImlUJpidZxgVyRlTLkB3M/DMyTy4vdkEdI1uaDSQg+87rtjLUzzs3Mdsbb+VEDfxwGJu+o\nleLq+pLNZs15aHneDQiRHwBKKbTWbNYtV9uWRkB/6jnEwPV1doOu2gYfMmNIGUNRaJqqyOlFh57v\n3n3MWbeFoTCSpjRs25q2qSmLpTOTZ3PXE/MphKfv3ewc+2GiDz9h79/zcPeOol1zXUjqImK7W7ru\nxGBHmnZDWdWkGLm/+5n7ux/pphPeOlKSmHKDEiXBjTjXI6JD+BwYEuyIKRrKMvNmUvQQA0pA8pZp\nOKJMgVKGvEr/j6/fpJB//zGwq0pKBD98ssiHE4111FvB4TRxsjPdvmOyHT4tKfGmRpsGo1tMXdC0\nJdt1RdMUmFJnA4bO+vOyqjKAKTrcODCPkvE4MZxOjClRVAV1qZmnEyFEulPPz3cHDocJMTo2RWLl\nBG2QtFGxjpp1iqxSoKpyqopQWTI5uYB1Idt/jaGsSqRU+BSYvKNcJERS5gIfhUAQ8okvxCV8Np8K\n3WwZreO+G/nh7sgdGn+xpdy0PHeJi+BZp4GX6T03zz7j9WvBK11ysCMf7cR/G458sX/g7+4+8T9/\nfMvGDtTRoaUgWo9zAedCbpOlQiidqYAkfPT044Sc84IF0mObG0NktonZJbp+RsQ9plCoz66YI/Sj\nIw6BUiq0EMvDKSynWJa0+0y0m53D2swPPw0Dx35kPzhU+54gDWjJum1p6xpiRTARnQwmpUezh8rH\nPYSUaJORqUEoglSkQhFGix0tcnKIyeKHgWEcGUPCxkRyfnkwlxipmGJi8IFhmimVpNSSMuWuYHYZ\nzhTT0hGos+osv6csxVyqnJijpaAQglJJjMj/MBImAAAgAElEQVT7EDdZHu7u+f7dB344HuklXFQl\n3uYZfogRmc6r/zOJnEcFR0WeJeclboGXBard8vXnz7l4eYEpGtyUdwpS6QwJC/Ex3SctEKp5zjC6\nSESrXy2ZhERrgykkykRSUESfJaxCCULwdH3H2M/Mk81UUmuZrWWc5tyV+KwR74eecdG4j12HnWe8\nn2nljqoyrI3g2bMbVm3zNMpaFp7i7MqUEmU0q3XL9eWWZ6uWb99/4G7s0Qa2qxVNXaK0pl23tE1F\nU2RZ69jPxClw7EduHw40VcGmKSlUS2lk7rBN7vAfu3SWbjLm3VgKEHxinC2fHu55CB+xKTJ0J1YX\nVzSrinG443B7x93tLVNwrDc7yrLGBc/d7U8c9u8Yxo7gIsq0NNsNpVHY4BnmGXRAIJdMBiAl6mL9\nxIVJkVJpkpvoD5+gMBhTY3T1V2vqb8NaiRYjKzQl/RBJJ5s3wcVIEVpqYShk5E/3t/x8e0s/WJqy\npClKjNZ4CboSrDeK11/uuLhpqNeadmUoi5aq3VCXGVA064n51tH9sOfnP3/k/3r7jlRJNruG06GH\nqEhR8v7DHVoq1u2GuF4T25bUNMSmYtSSIBK995RjonKBSoFUmqISxDTSVobdesX17gLvJ8Z5xEZQ\nwjPbCY55GRZTTl4pq5KiylZ7HwJD13M8dvzx+5/49sM9b+fE9PIZ97//PfbVC/5we8fYD5Rz4JOT\nxG5k3fd8dnnFN2WLSPBuHHjT9Tz7+JHil1+IGmxhSGVNUa8xK4UOiXmemJ1jdj4vbp1jHAas82iZ\nL3ZTqIwSTdmoMVnL7GOOLCMhu8jbhyO3s6ebIk2Cz68vebZbkcKCH/aRALiFv22dY5xnxtkyzZZ5\nmhlGSzcF/vjTJ7qkccrw6nLkersmrDfURUlR5JQooyImb+FIakndERKpFm6GLBBa4pVhUgrrHTI4\nopsZU8IZjZcKVpJRSNySbCPbCqqCYr0ihoAlzzxVcGgr2ShwIebdhtGAIJIX39Y6onWkYUJ6hwwe\nHSOVlNRljs1zSXJ3+8DtsWMUii4G1JS7ADtOqMYijMkLN5GRqQBaSLQyFMpQFSVt07DbBG6eXfM7\nG1CVJtkTLnoSJagqR9IlgQoR7/KZWy47hpjEY8RiodRSvBbsK+KMWF98ADC7GV1ohIKLtGEesztx\nGiaCXXjofU9ZVZAS8zByf7zHBY/ShuSWpKboUUpguz2pf+D5zXV29saEUE8n4nMykVRZStq0DTdX\nl/zhy8+4Px64e/uWH4eeqiyp64p2lSWOdVVS6XyYG2fH3cOB2/s998cTh5PkYDK64LrRaCEyBXMR\nJpwdlRmtcIZrZSrqOFo+3d5yjAGhFSl6xsMdH74P2Cnn3wbrKMs1c/dAjBmmN/QHxnlg9gEZJclO\n7IefKLQiRIedHKJUkHLykDCJukwUOn/fz7iCptQIP3L4+BN9tGhTUpUN8L/9h5r6mxTy+cMfkfI1\nq/WOZ2bGX5TIqAh+pNWR63VDLxPN6UR16Bl8t8yLAoUwfPnlC559vmL3IrG9KanXGlVICiMpS09Z\njhiZNbRGB0Y9E+2J4XDPeH/PxWcrPv9ig0stx73gcK94WW1Zr3dc7HaUdUVdN8uMvMgSxBRJ3mVr\ncAqMJPTiGAv1xBgdMgnMOEO0WOeZQ6LS4lEpkURO0pn6juP9Qz7lqbx0GeeZT3f3/Hx34ENveRCa\nwzhzMgXp+XPeX16QXKCwnl8my8/bmvd+4NQ98J/qFa90wX9tN7ytWn40JY3SvFhMFRhDjIl+mpm7\nTGb0PmQJYVMjlcpdTBmXOekyQZV5KVcUltBPnPoxj6aWGWxnLQOQtKSoSgbvuD0OtEbTDTP94ma0\nwWMXDMNsl4WyzSaPaDRF02JXDXc+8P2PH5nu7jnu1lzcXHCx3bJetdR1TWkMpdYYpZEkVMoEvnwq\nzvsSLWSOLlOapBSh0Ni25jgMnGKkj4ExhcdWPkZHURlSgpHcViugNBKVCqIVzCrhAbRCl2Yxl+XW\nPLqQHZTOUymZjT+TpRawMoZGa/CwKhRfXm55geBuHBCVJk4D03EPRZZKhhRRMSy5n/Kx/T/b3yUm\ns19iSR0jKQo8ARcGPC7zcqImijNpRIHUWZkiJQiVZ+Upz8IRAZ0iIeZQFTirR/Ti73Bk5AUQc0ci\nU0I5B8FSKyi2G5TODzdfVxS1wTqbyaILFtp7R9PU6E2LiddsLlqKolzGUiwL2TOy96wlzzPs3XbL\nN19/xbvbO+4OBz4ej9g55w8c+x5jVMYoLHpw6wOncaIfp8VEFRkW+WZTaqoiL2PlWf22IBDiGRkL\nJCFwMTJ6R29nBu+QWoGIeAKh88zvpqyGkiZ3Qb7D2S6jC3wOylEq5vSglDuhfGVFlM5mqpQSyBzI\n4bxlHHuMdIvsNOGDYJpGjof3PEwP2ZQo/nrJ/m2cneMD0u2o1Zo3G0Vab4g+MtzdcoPnUoKra1ar\nDZfrLcr2TLNDq8jlheLv//6KL/7xgtWLmaQCUcQ8+3Rztsb6gYIGo9aItM6z1CUUQiuTbecvn2F2\nhttbwcd3CiEu2O6u2O42CCUpinyaMkqTxBJIYHPKiPceHz2WBDEQSsc4HpnchB0DKiW8h9EC0ZMT\nU843lEIA8zgxTjPOR6KAfpr5dDjx4TjwMDuOMnF/v8cdOxRw++IZXhlUiLwf88z6l2TZnx4QwO/L\nihtT8v/WLferDfV6k8c6EcLs8LPFjRNzN9J1AxJYKUWwAVFKdKGBJdbKO1LMbG6ts9Y4JhitRyxM\njBATwxyQjaZt86hr7PJ88qqtOfQTx34ghMAcAi7GhUFBVgOIHFasy4Jm3SKbFWWU+IeO2wfH8WFP\neTzw4uaKm8sLLrcb1nVDU1WUZYlKCa0iWv4K/7oUc6E00hTEuiS0NWxG6E74KXdKh3nICpMUmYLD\nGJnfA++JIbf3hYaYJE4o5qQRWmNKQ1WZTOgzmW9OSATr8LPL3cJkkeNMqyRrY2iVQtmA2ja0CbZC\nUp5O2JADU4buQDCSqDQ6eFSZXcV6ecBL/VSkskkkq3jUoqaJKRJSJKQZHy0+SgIaJTRKGpAGooKk\nciFHLYu+lJ3AZDVA4GmXJh75HoLoM7QqLrsGnEN7iwpTNt5UZSZiCgGiYKUbZqeZrWMiMCKZpKKs\nS+piTaMlukoZQvZI/2Mp4nIZWaVHldhqLXnz2Su++fA5tw8P3PUdk3XM3jPM9hFQhRBI5OLE9LgY\nlnDmjEVQWrFt60WxsnzSmMdOZ+55SrmICynwKTF4h40BG3zmyItIIOu85zABClNUmLkg+B5nO2Y3\nAeLx7yFkWPY5EYHPyh4hiNHlTkgnEBIfHMM0sq5yB5wLOczWMgwHjqf3zNNI9JG/9vpNCvnV5/9A\n0ZYgI29evaHpZqpDh2m2FP3M/OEDD4WmdpbruuTV6xf8j5/fEo3j+e9LVq9HXOX56cORbhiYrMX6\nQH+YsL0DD8+Lr9lt/5H24nP8PEPh0e17zKVmcNe8/f6S9ZstSTVsX5SUuqSpskoFIkIpIoJpCbJw\n3hN9Ng9lHkJchPoCnzRRb0C07FPAzz127rF9oOjv2SnPs3XFzeUlm/WKy5s1u6tn9MeO2493vPv4\nkQ8Pe97tT+zHicM4cwwJF4+kP31LuL7g7p//iX2VTxGhLgBBEIL/expwAv7VluiU+LNWHLc77Pic\n99/+md2Hj8hpYFdX7NqG3WbF66sLKmPQCU6nE+M8YafANFuGYWIeLW1dsmoqSqPx4ZxUnheA2V6f\nw20bLWmMZPIzp8HhhoC3jt5O9NOUtdU+EIVEFxWqbsBkLb8SilYKLoTgc1mx0xopND+eDvzw6YFf\nPnzg+cUtXz675vevnvH85oqL7Y5VEggVs1LAJEqtKZNALyAyueioU0poU2Dqmna95tmUkbzH04GH\nceRhnrmPEUciKDCNRkhFjInZRbphIriA0pJ2VWY+eZEjzpRSCK0JITLGwL6b+PhxzzgMiBS5uFjR\nioZCFPnkKwWl1mybiqLdUrlApyR+nrCnA1ZqjHWYqkIXBq3VgonQKJP/PY8EzkU9K3nOZ28tBMWi\nP84BwpYQIEaFjwKXcqK9VCWYiqCKLK8UGY0qkyCKlN2q0i+4WElyDhE9RmXedylBrQ0yOmQKCIZH\nRVySCdRMJBG0hNowOMPRSeJqmwc4wTMAOiWKBDotkhWZgCz1BUAotABVFAit+cM3X9ONE9++f48L\nPT7kZHmfQl4WL1ruECM25ieSJI+UkIKqLNit2ixRTOS9QSLHBnqIcYltWDoCnyKj8zlCUiaSjPnX\npTxP1yogJFg/cb9/jxQBkicmTwy5e9FKYP0MAlSZgX/5+hR4mz0oUpGXLjLkz3MmX5JVrzFBCIl5\nynmnj3mi/+71mxTyi0bQbGpUWTH98g757hZz+0C7PxGjZSw19uaCVVNRbzdQveLLzQuGdOLW90z/\n/Q75bxPDdELISJSJkARuBrykEAVXa8NMZByPfPj0ibtPe/axpr26ZLW9QbTPCWKN0CWlUUgENoId\nc2uT8MRlXhlDWFK5n2hmIuWtciRhF7B9lnkKkqxIpULIioP1fDzc8q/vfmFl3rOpS3ZtTVsawuw4\nHTv2XU83O+xivECFrLBJkfjzW8L/8S9Q16TPX8N2s2jmZb4QSfwyT3QuW/ZVErxoNlw9l0wny4MV\nbPZ36HWDqAo6BIfTgJtmbDcy9T1aS9pVToOvioamXFEXEmNE1rSS8CmD9WPKS9uz3luaCtNeIozB\nHT9wP91xPw4M1jK6vHRrqop2taG+vOHm5Uucd3z7b3/E9T1FyiqVcfIMQnCKgXFlYLNjR+A4eP7t\n7oG9tbzpel5enXh2cUHbtBRVTVHUlEWi0rmgay3RgoVfwtKqKyQRd+iJH28RhyPFNNNYS/SOsdJM\njUI0GtloUllgyszIcC6f0pUxOeTaB4zKig43jHy623N3t+dw7PIJclWz27asVjVaa2IEZx2n2RH6\niQ+TxwAliVYILp1m6wL15KmanrKpcydYFo/gOG3ygs4Yg9YLaErp5XR+Vm2dZZMSqXKBTgogXych\npZyrKWZS9FlnnbEepKQJMacChHPhAoqgwM2I4NAqm7SE8iAtyQ54N+OtIy7BHC5Yoo8kYUiqZpos\nD3PgkxXQHDFVSVUW1GWDVBrtJVoEKpU111LpLI8UT3mVKSXKooQ3kWmeePvxE//ypz/z8+0dLi2R\nDikuuu9l5p/OM/d8v9bGUJeGXVstaGeWXE+fpYeErPkUT4Y06x29m7LKR4KUCXS234cU0eTdjFzG\nrjH6RZ21kA/JaICUcl7q2Wh0phlmpWIeLUUH0UXc7BExZ4pmQ1p2l6costIt/I3JD8NaIbYlSijG\n0x3m01vC2zv83YHBWR5Kw8Nska+vkHWDVRuu3ryiTgM/ffoTn24/MJ4OHI8PXF421KsaWdUY1VDo\nCq1KSDvmKXHqHvju518YrKVcv2C3WtNuLtCbHUlVj8aGmCLWn40r+ZsdE7il1c6L5Yj3OehYpkxw\nSykrIc4RUzHmhaBSFVpXuNbx0E3c9feE/g4VLJWEdZGTgoJPeARRZnh9H2EO4Pwih/p4SxxmZLNs\n+X//FaIs0GpRRyDAeYbk8THxEsUzCnbVjtPla4Q3VOWKoi1JMnGaJ47Die7o6PcDOgYuWkOr6xwO\noLMVm+SY/cQ4jhxGy2l2DN4TUUs7nL8Xl9qwbjdQ1aTiwIMPnIaRwTqsy1zll1XLypQkZfKDynrs\ncSAej8wxMjQNsi6ZC82tkZSbDdWmplGCd+/3HIaZ7uFA7x3HeeIwjVxtt6zaNU21oiormqqkKQvK\nZCiiwpzhWoJ8ipkt9nbP+Msnpm7I72PwxOhhVGhfstItslYkqfFSUgqJ1YrZLrp4l/PbgnTMs2V/\n7Pnhp/fsDydCTLx8cc1mt+LyakdZFKSUF70hQXSBKXjcPC12dLKZS8AAlMOIORwpTElVV/mjqimr\nirLMcK6yLJ8K+rmoL1r6PLL7NbdEPlneOWfYxGzES25JnGdxJYql2EMQkG382T1J8vnQEnIGKzLv\nFRyO6EdCf6I79YzTzBQjSjfIUiEKwf1x4sOx58PoMe3Aartms9uiVMUcJEOQFMmhyywnlqrI9448\nsyDzl2iMQYtEcK/5r//0n+jGkUPfc9vn4IqcZxBxMRfzFLMpMC6z5pVSlEZTqNxtuRCROi/Ko4wk\nFbNhcDmhxxCY5pluGvF+KaqZapVzWWHxJ5xzc8WT6c2f34C0pN7lh2zwLF1H/sgywtx+nLG3MYQl\nezQsKGb1+L7mw2V6lFr/+9dvUsi/fbnjSyNZjQNV4VhraAXMbuJeKd4Vmp/mGXd3IFlIFXzW/iOm\n2aBjj4+KsYv8/N0Dpduwki9pV6/Y7S6piwq9GDRGmziGnodpZrW94ovf/4G4IF+VEAjhCS4uc9Gn\nZJDkQ55HIojOYoosKRzGmXGcsS6L9c9tmEAt2ZKZVW1ULoZGQ9lsuHwuKaqWTx/e8en9W27fv8OO\nPVKIzO8w1WPO4TDkm8Jal+OuUiQNI/zv/y232+s14vUr1lJyieQKgTy7/5znmQ/cOEFtA1u1Rl5X\nqN0LfIrZdThPxGJANgP1rueq1ty0BRdtwXDqsPPE4CzdsedwPHI4njieerpxZnQ5JCCeZ+nW8qWI\nvG4lkxF8awwnUXLnRsYp7xKkkLxA4KeJd3/6Iz//jz+C9RTDRJU8TiS+O3n+y/olz55dUN6s8FUF\nRiFk5PJmS9eNjMPEvXcMD3s+DAMv+p7rzYqLVcuqbNi2G/xqTRUqKmOotMYohVIC4T3h1DE9nJiO\nAzZFOgV3RvBWgRKRrYbXukDJkilK9qMlPHLA80w2Lnrr3s3c3e/55e0dH+8OlGXB8+eXvH55yWrV\nLJCmpzmvMYm6zq5H7yLDMOVRnZG4puSkJLefOuaHE7hA29Ss1yvWq1Ve9DY1TVPnhW+V03zKwqD1\n0wn97FIUQmaLvpSPp9uzcxLOFnLyoeBc+BdQ0yPUVfilI40ko8itp3g8/QoUXhakNGNnx/tPRx4s\n2PaCq+svaC+eIYuGo/2OQ/+eIezZSI3SJUZn70dCMnnB3lkqU7MrS7Qyj9AxfnWqFgKSUlxs1vzn\nf/iG97e3fLh74O2xy+/LMub0acnX9DF/7YBMIo8RpWIcHQ/dSNI1rTEUVZMRsXYipHE59YKbPd0w\ns+8m+sHlsVta1FEsDlQE0ecDoJQQfFYJWRvRWuZc34WTEkPEzyxwrfNYNuVOUStSiEgT0WY5DIbs\n7JRaPnZhiDyjl1rw116/SSF/9/0PrMqKHQKTEuPlCmLETh3fucAfgbfjjJaSVhRcFQM//PhnvFDY\nbs92U/Hi5jlyHKiVwvUj08OBIWlSGymKiqreYQFnZ65ffUZd1SgCtYBKiqWdA6HTsozIo6pzelck\n4iMMIoLKF7U2EWwg+QAxEZNY9LpuibhaUAJMKAWFlphlUYgymGbL5gZ01XB/94m+6+hmh/I2ByCH\nkCV/PjzCjkiAC7A/UP/wjtXzn5HNlquV4EZqLkJCuYAMGSq1DZEmgAoRkkRIA0oSUgBhULqmqlYU\na0fyjkJJZi14kAkrVtnhGDxq84J6HAhdB12PHAZU39MdDwynE9M8IoThp4ee8Oe3RF2w7yfKumG1\nyLec7/Eh8mF/ZOx7Su9QIVHEhFmcdMoo2rpm/eKS9rMb/K4BpfP33zvWUWCUYlVXyOWkZpYotoP3\nzH1P0Q+s+55td2TTNGzbFeu6oSwrjJaoGJGVRl+tKHEgEvdhYmQmGsV2VXGzu+T58zeUmyuSKBh7\ny3B8YBxOzHbKi1HvOY0j33265f7jA8dTz9XlhsvLLc+uL9htVmiVaYQixkfDkFYKUeab0ntPVZUI\noCgUdV2iRS4Mw+x4+Ljnx31PU57YNDW7dct202Y+d1PT1C1VXT9+lGVNUVRIFf6CVyIXw1J2MS/O\nZ7G48iEXhQhycX+mc5yYlAiZsmYxLVrzJJbZ7CJnFIJSG+RqhdYC1Wx4HgShbCibNTbOHPZHDJbL\nyxXbqzVV3VBWLWVZIbXi/vYj3f0tF42glc+42VSElLKKS8q/wG5nt7bAGEW7qvnyzSu+fv+Rf/nx\nl0xADMtCNorHGXZafp/Riq9fXvF3nz3nxc0lu7alKjQEi+2y5d+NI9ZZXAgLQykx+MToFodnTESf\nT/pFKTBakhKLIzRz/dMZ77/I0yX5wS1SIiz4DiHTrzqdhFQRnQ3KKCNQCrQAsfCnlEiZqW4yjteH\nLE38a6/fpJD7H36mLyoejKGeZ0JTMDzf4eeRD/uBX/qZd7OlQLJTE2U7cbv/gclFVkrRVM9oqxWv\nb65I85jNJ+OAbzbYMhGMAlXmiLNk2bQNq0KxEZa1UawLRaNzIX+84JeOJUaB9zns1YXE5BQ+kU9x\nMlGHxDEGBiJzzHP1sCyYQgQpNSFYsBEnycTFlG3eqqho1hJd1kRpSOqesD8Q/JwXqs4t9nVxtrqB\nECipqKuG50lzPURU59ni2QhofUD7iA5QCYE5740g34PpzJlRj6oOY0rO+ZeJxJgiY0yksoYis78F\nCbO2NJsJNU2YcaDoOtTDPeLhHtWd0AJ6Bd/vLSlZhgBSGZq6ZRozUN9ay2kcEV6zqgqMdxQxUiws\nBVNV7J5dop9fIK+3eQmeEtHHbKJC5jHFuTAm8cjPcVLkLiYEphAYnWOwE6PNbXFd1tRlQa01jZGI\nqzVFrZFa0KSRrRwp9cz1bsvN5XOurr+grC+QQZMOI53R9Eox9EdMYfApchxHxvsDp6pn3iRevLzh\n8mLLuq2z+mW5uYXwS7aFRmiJSsvyVWfJ5HmEIJfxnKwrYlXiyoJhFghVkoKke+jwqiDpiE8OG2ca\nWRAqRRIlSdYkWf+K4X4uzmmRaC67giXzVC3/HyIqJWR8SssRIjujHznd5D0IKT1qm8/IAyUloiiQ\nOkfqrUPEIxHK0o+Oeeq51JF1aaAwJCToxXmtNd57+mNHJQzTODGPI1prkjGgFzb9r8YrcumGisLw\n8vkNX71+xbPNhsE7BueylX3R3y8DC0ptuFg1/OH1DX9484wXVxe0ZY3UmojE2TxeI2RXcggRv4yY\nImIZt4gFVRBzV2I0OTzi7ASF4Bb+06J/z7koec7PWZfuE498tPMuLfF0wpfZNayX9zEszmglRHai\nn/MZ4t/QaOX5pwMiPnCHoKoKqssLit2K2HyJ/eUW+cstcX5gmC3+ZJmKmePhnuA8c3uBLAauW81O\nVlyus7j/JEuqzSVpfcUsS5wLxLkn9A8YN7Lbrvj68oaLTUFTaAolWd6nR/gcv3qanxclwQd8zPPz\ncfacRGIvAve95RgkncxqAi0lPilEUTEPyyIoRuZ+yoU9OlCagGKwinJ1xVYWSF1y2t9n0mPI7W5i\ngTqJgJCJuml48+Uf+OYP/8Rnb36PCSXpGElpJgcqZKBUiIHA8uZLuSxi8onijAeJyS9X+hletYDs\nkQsgSiBSttSTJOiKoi3Q9Zr24hk3L79YzBA9yXuOpyPHwykHL/QnnB8RQlGWFVU147zPy9S64sXl\nBdP9gTROGKlBScrLDdXXL+gv14SyQAiJ8wu+VhVUVZEvmhSJ3i9SsUT+7UshjBCS4JRkTpef7jDc\n0qqCi/Waq82G6+0GsWngYoWpKr5Yeb6sZoTsKaorTP0M3T5HzAlOI2LuqWJijok4TbSlYbvdUb18\nxXXV8Or5J/7cHahXq9zWO4dzcQkwyIuqSki0Idv9Y8YnK6Xz6W357xAzddDZgJeC5uqCizdXXF9c\n42bLt9/+GXX5imK3QyAIhSatLyhevEY3K4SpiarI2N7lfQ3BLwjgkA8o3pHcBN7mj+BIyaFSysVc\nJJTIXan+VXrX02ECiOlJdbFo3FOKBGcZDnuGIbNPyqbFFCUvWkMKMMwT+9M93ejw9Q61e01RN1xd\nv6QtGioGBJLucKKsDbGsSEXMI6OznDQ+USi1llxcbPj81Uv+/s0b9tPEYRx/VfLPhVywqkt+9+KS\nv3tzw5fPrrhYrWnqGm1KolJ4FwhrR5izIqSfJ07jRNdNlFrSGINMIlMrRC7Wzi0O2gXMF0PCzjFL\nMXXm/suFXBhDPpCEkE/sIoFU+eQdU9bKV2U+woskEUnkIAy1UCsXTLcQGpJCCr1o9v/j6zcp5F8f\nZwbr6JznFviq3LC7bDnMHS8vL0hJ8vF4YrZQpMh1SFxryZw8vfvE/sMeX5WEVY0tC6r1Gr19SapW\nOG/pjh853H2kIfLZbsXXL655ebnmclVSnVUN8KhhfdJPpQVhmr+RWZYFCjBCgFaopqLSmk3jOc2O\n4zizH3r23jIGSaKhKkqsLphstkPHmPBRoJVEaahKjZ0DShnqdoWQhqLqH3GXIVmEiJiq4vL6Oc9e\nf8GrL75id/2MJBTRBaSIOWxaJLTIkP7EcmoVEREdJvNWsdZnK/vCgpnmzHURQiIe5UyLjpYESWKD\nyPuwkLXyeaYaid5hfcBFQcIgi4bVVrO5uKbtjhz299zd3WXXa1FR6Jkvnl3yzbNLvr5a86/ecztb\nBIn2ckX18ormxSWUhgAYJMHnZV1pTN5bhLCQMPONnUgorZY5arY4x3TujPKpc06Jzvbc9ife39/z\n1csX3FzfsLm8pHz+DF0HhMxmFS1apGoQUeGGE2LsKZLPvJmiwEhNf/+Atpb62XNuNhummHAp8XEc\nscvXEGJazE4ekPjZ4+asUz//vI9PGH0pJWmRt3rr83VH4P7hA23bcHP9gv/19ec01dOMPC4L5EPv\nOU4nytLTNC2FNnk2rsRjslVMgSQU0jTIepOzLYOH6JHCEaYRNw7EqUd4m3HG+twp5K6BZbkWQ+Ac\n3iIXtHMMATtNdF3Pqe84DSPu/Uecy7NmVdZEbZiQvLs9IlaRS7nji8trtpc7ttstWnhMmhmSJZCD\nnJP3AAhFHkcsLBuh5JI0VXJ9teO/fOrUyZQAACAASURBVPM7fry/48PxwOgsv2pE0VKyqyu+vrlg\nVxoKsVBdpMwdksxs9agEaIGRJa3WqKJivUrsXeTTMHLrRga/gMZC7g0k+aECiSgCcpUw2lCagros\niTE7omMSIBVKRIiessqn6hQkUeQFqkjZYaqSQoRE9JmSOHtHUgZlirwLEYo5gif81Zr6mxTy1wfH\n3Tzj7IxWko0ouChX3N8eCC7jG0mJVfC8iI5vVMC2ikNQfPQTfonzSmbDqGucKNEhcfj0kWE40t1/\nwgTLy8sdX22f8dWzLRfrhkKeuQ4LsjTJ/JgkLSOI9LTJj+ecxEV6uPAwCqnQpeD/Y+69eizLsju/\n3zbHXR8mbVVl2e5iD9mkCImaESToSQLmTdAHmBd9V+lRIwkEMTTd7C6XJjIj4prjttXD2vdmUdOC\nnoRiFAJVGZEVcc05a6/1X3/TVYZFpVjbzFp71krRz4EpnwgmM1UNY11zGAV3d06266DK0ksWYcbW\n1K2Y56Scid6TlMRd3Tx9zovPvuT5p5+zvbmhbsT03ySxnbUKLImuMjRGYzXURgkXN2WsaHyYdcJa\nwe8ymT6K3D6j8JQFEeCzJmYlnymLz3TpKnTOaBIqBQk5iFk2+kmhTYWtahYridR6PBzQPmBtRVfV\nfPXiOb/94gWfrC2v7+750Pe4BFdPdnRPd+hFQ8wKfCQlhfexqGGTbPGTOPhdGBnF40QSx/N5wJDw\nhpQusVkhRfwwMs0zT3ZbblBU7YJqtaNugeSJ8wDRoFxCuZG435OHQeAlq2m6muVywf7tgT4m1u2S\nqutYGcMKxTsfiCpjKglQBnF7VEomhRQSEZmhQ0yMLgrLggJPgEi1Qyj4tiLEkWF4JN1c8/LTb7C2\nxVY1pqoZTz1+csSQMEr42DFFQjLkWAqDzqUpkTFMKznEs5ZwZOHwR1KuSMkQkyLnCeUn9OyojKI2\ncqjqAr+lWBwnlUJHgYP8PDP0PfePRx5PJw7DyHgaGIaJwUV0vYRuRahb3j+MdHmmngMxiuLRtJWo\no8PM5EfhsCPXVyqLwZpc3m9536211HXN1W7Nn331iv/jD3/gD+/uGPcOLsvbjDWGbdfwcrsQWK3c\n2GfOiOa8NxAxkzIKi6LTBozl5e0135xOfHd44DjOqATTOBGR17hSkqmLScLGShqdDWouj9MYbGVp\njSW4wCH20jAGhUoaVRd7AmWwymIw6JjJTjJYvZc4QaMralVRY3FKQln+1McvUsiv91EMg3Lg+dWO\nr9Y7usWavp/4h59e8/2He4Zh4tc+8Vur+KvW8/eLilmveeZaTL2k6bYs1td4VXGaRu5e/56fvv8j\n+w/vYZ759//13/Bvv/mCv/r2K5SVxbtGSWCBcK5IlIBeXQp3WWDkJGNNPrsUFi/tmD7amSoFi6pi\nUTfcbLe44Dmejnz4cM/BHZjMkrB+gtG5GAopQgjkHIESS+YDKVAsLTPGVnTdAtW0rNc7/vxv/jue\nPH9G10ngQWOgNRmNRyHy6ioGrquaq7Zlu6hZVprWIIb3JUIvR1PwPBE2jSYyzJ7eeQYUpwx9ALIR\nnnjSmEthlGniXBA6YzE2gvNM80TynhwiLvTY2qKrhsV6QwgBPykWdcOXn77kN19/zoqe57dr7oae\nh6RZvrxhcbXGOXGJTCkS/SjKRaUIzl3gEyhTUnkfY8p4nwg+ylSiRQTkQy42qlk6mibRVBXLtqGr\nDZZMmBxGd+Aj/sNBRC85CwSyvxcHv8rSrTqaVcWWK44Pj8yj4/ThgFoHxuGIG04YLZ7kpm3LUtuj\nbU1lDdbI7kUrYTWkKKlEwcciH4+0JYLvvKRsjOGmqhiGe96+1exunvDJyy+pbMPhcc/D/QNKa7bX\n11xfb2mbGpUhJeEbJ59wMXD2Ug+uLM4Re9hc+n5llYQeL65R3ZY0TcT+iD+8o4mZThs6LCYLf/ys\nk0AJJz3GyDDOPDwe+endA/eHnsF7stJMVOwjjMeA60dindG6oqkaCVsYHJWesI1QeMUQbEmqKnzy\nzGkiRLHEVUjE3xnWMdpQWct6teCzz57zzcsX/NMPr/lp//jRqRrxklnUlm1rhHSTM0nlYoOQCl9c\numuF7FpSeZ5JZXbLjq+fP+OP9w8cDj1unHnTzyQFJmtskkPeNhqvIqejYzyJwVq1qlluOm6Wa24X\nS4JzfNcH7o8T3gcao1lsLaayJGsxqcZkjQmQJkecHMnJAteiqbEscgVVQ6r/FWHkp/nEu+T50cBK\nRZaHR9bvKuLhiNofWY4zV+sl/1VW/Ga94vbpDb/3PQbF1fU1x8nxODnePH7Pw+Oe0+nAOEi24pPF\ngt9+9QV/85uv+fzFLbaSTD9FoaQUZzGgLIAUoClVvGygc/FhloxEVyK7UuGQKpSkkRSKF9rQVKCX\nK2pt2Ywjj9PM+4fvqUYwUyA6J7Mi5yiziHOiNIzBkaOnUpnNds3VdsfTp8/55HZDV2eIPeM0cjrt\nOQ4nquR5drPlxe0VT25W7FZLVm1DYw2V5hIPdaZwYQuemTQhGhaVZdMFQoq4IOGyg48cnedhdNyP\nkSmZMuYK1ndeifUBYtRkXdO0mTnDMDsOx9MlF7RqOirbkCr584fTwO9ev+NaOza7Nd9UNW98ousa\nlMpUlSHGwoxQ+SIGSekcvqyKda18HRRVJf7wodIEL+9VjkXOV/jjGkWylqQUh37gtD9gtSUcTyzW\na2qj0GMvghatsbqlspXEw/UjdC26rjCNpt1sOKUDPw4DyWYeU2LfLUSAopQIYQquqaAc2oYsRj2y\nONNGfn6WTteaBq0FL/c+lnT3XOxdDdo0GF2L46DKuMlxdX2NrSqMMeQE0yTwzTx45tHhJomYy8hr\ndpbdn6PURB8h3h+zmxingWE4MU893g1EP9FVlkVTs6wboXAqMCUGEQBdDMNmT38a2fdwmgzDLCre\nYRZ/fp8dts4sVg1X22tavcP3hnc/9Tw2TtSx5aA25rwABmsSjYnsWsX10nC1VLRGIFGlDNZUNHXD\nYtXx7Vev+PHDPb9/+4aT87gYISs2i5pNY8R3PosXj+ySlFgbKC5GXSVoTjDVQvm1GnbrJd9+/gVO\nt4xzJP/H/4ibDhglzUOnLSkb3j4ONCiWbUPdKtrVAlvX5DmQtUdh2axuyYzoELjSkOpMqgxR15zG\nkZgTuq0R17IsO7UCdW0XS768fUYwN6iu/pM19Rcp5H10HA3sm4q0ang99dy/jRxOB6axJ/uJrq3Z\nXW1Z314zrVrCKclCoFnxePcD797dcdgfOOwfmMaBFALbtuPV7TX/7W9/w7evXnB7taKyGmuUdNfF\n5UydL8hitp8v5jnFSD9lMcuPER+kkPsLX1W6AmVUCY8t/hDKYOqmeH8oYnScDke6AE0UMyS50JNg\npCkQ/USlE7YYZygiTS0Wu42Fef8WfxBTH6MSTXR0BNaLipfrmpfbjtvdkmUnvGKj1MfnRv7YRWVF\nymc5t3TbtYacNMEEvMmsdGKpoI4R42cep4SfI2lKKNuAqUEZXEjie12gGWVsCQAp2I221O1CljJK\noXXFhyHx+/cD9+nI1fWK28WK/jSIZ7yScVmdcZOcpUiVZTPnzX5WGGNKoVboyqLLYjqmQhEjXcJ3\ntVJyMCvNnDJvjyfqqibmTLOY0G6EpqZRmlDgsxAiylRoZYnuxHg8YZoWdEWoLUNtOAZPTIFewdzU\n0gCEVGwbcrHWFV/rmBPRn1ksqgyCGqU+HjghyVQxO2kWcpbdR0bjnBy+3kcobI/FaoExBj97+uOA\nnyPTFHDjzDw6/OQvEN5Hv+0CKoinhAjbYmCcRvrhxLE/cBqPDOOJYZTGQpGoy8FTGSuQnbz0koWa\nxIPfuSBRayFIkEoIxAgZMWLr6g3rxRWb5TVttSI5zSk5TO8/smM0GFNweSuNlzWZvjMMk2V2iU1n\nWNWathKRTFVVtG3HF5+84M8/PPB3v/8Dv7u7477viVmxairWjcVS0r2ULfma5/eh0ATVxxCR0tJd\n1LwWeLa7wmxu8dkwfniHP3UQPVOAOWf208xwmtktWrZdRaPharejqmv600Ei7dY3vPj6M4H9ppHq\n9Mjj8ECfAo6KuZdcg5QE1lG6mJMpiiGYJSVDt1iyvLr+kzX1l4l6M4rYWPRmQX5+w0/JMb994Ngf\neUiy+JhPR+6+/oLtZ885hpG+XpC85nAc+P0//RM/fPdHfIjFylTegOfbLb/9/BX/7rd/xnLTUbf1\nhcsZgoyznLFWFJS0bCKXpVoIQaLckjAQJIcxFIWeLNmyFQvb8lOKNlROdaUMuvh8r+qGyfUsdaQx\n8NOHOw7HQWTHVrPsaq7XLTob/JyYJkd0Aw/3jn7/DhVFsttZw5fPb/j6s5d88+pzPv3sOW27EFmz\n0lgtkVtndgGpZF4WR7ezyi1GYeGkeF4gSrJLjBGVEguj0J2hRmEmyVpMhxmzfYZebMna4JNE0s2z\nI6dEV1nW6y1t2+BjyaVIAaWVhEhoywfXEo4Vr497/vpqS9c0+PtH2q4WT/TKog0YnUhaFROhhI+p\niFyMFKVSyJXOaGNK8AHCIjAf4QlTusiQHAEYQ+CxHxlTZlbwddfhpgGTAuv1jpzk0J6dk+g1XZFy\n5v7NHVQVzW7HY3A81oreNiSt8UnMpmxB6nxKZLQkuhuBicbRM04O52OhIipISgRVwDw7YggEL5BX\nTpEcA1NwOJ8x1Y5xiqyXUHc1i0WHtkryO93M3bsD/dHhnXCPz7oDpWXyU6mIfAor62w2qJWhqSoq\n27LoNmw2NxyOe97f33E6/IGfXr/lw/0dLjqMttS2pq3rErMni1QfAiGKCjqqwt9Ocgh33Yqr7S03\n15/w7PYZ11c3VLaRDlyJm2JKSVTxunTIUQpuCCVdSCvmMXI4Bj4cDE83hue7imc7acwMliY1PHty\nw7efv+Lf/upbjvPMfhwgZxaVZdXULNoWYyqMtlhdFdpfoRwX58yoxO1Q+AyaYfBMk2eOiabqWDY1\ndbek/vYVebzB+cibEf7uj3/k3d0H+nnm6aplU2ly8Nzutmw2G6YHmOua66++4S/++/+Zq/Waef/I\nj//pb/n9P/xfvL+/5xhhPwTcMBKzJptadk5KUZ9zP33id6/vuXnxnC9e3v7JmvqLFPL//VnLSWV6\n6+HxNTklWmN4+dkzrr94zpQiyUWqbz/j/XrJP//9a+ZQsT95Xr+5Y/+4L2+GOAnW2rBua/7LX3/F\nX3zzOYtlR21rrJJxCq0wlSYrg4rnVHKJ9oqlG0sl9y/mn2HipbCHkqweZSOKVkZ8F5QqlD7pclIx\n8I/ekwqVsJwdKCC6GUNkteywOhJcz7v9HfM0FAN+L/45WmONprGGv/zyFX/zm1/xZ998zpPrK9aL\nBVVti3opiKtiUZrpM60wi8rzjOUrrbHlMaBEhi0USwl8mKaJcZpEDJHFeKg2wrc/Ks/bn37PbDrs\n6ort1S3NesHUtszOE7wjzE66/ZyEx24tilTSxsGoiZurHS9e/hqfJt6/fsub1++kuBhNipGuE/64\nmxxN18hzMpqmaVG6MCS8FzVsZXAh42bPPDmqqqJqKrQCNwcm7yFnKnVmWgrW/34YiR/umWJkU1tu\nFgtMQg6FGBmHA5WtCfPE/v6RP7x/j2ssKxWYU8Yp8CBdqAvMrizYyrLMoGiaiqauCNGT0ohzCXXe\ntZzj3LQwb7wPeBcYx5nH/QEN1JVltWww2RPDyPu3P7LqxKMkZw0Bxn7m4f2e/jgTvcANMZWffeYu\no8jl/Sz7+6JoLpCgAXRGGahVxWa9papqlss1T25e8O7uNd/99AceDg+cZkn3ETqgKTsL4fSrok9Y\ntB3r5Zrd9ord5ort5prlYk3XLtC6AsoeqrCjzpOj0h8zUiUImYv81ChwPjP5xDBFHk6R+1PkdlOx\nbRW1VjSN5fmTK/6bv/wN3z9+4L7veX04MniPi4m2rkjThO970mJFBsI4khCjMTcL66afJpngQuDN\nuz0fHo6cZkfz5Irb44Hrqx1LmzCrhhAzqU7crVvysxtu/803XK9bbI68f//A9dWam90OvaoIRtMt\nKtqH74h+i9ENTz//NWE+oRQMb+/IysjEVbxbjBZChLEWtMXUCz759bf8+ttv+cu/+Os/WVN/kUL+\ndmEv3gWpeAsYY7naLFjc3qBXS0KKLDYbxnnmqDLRTwTn0D6wWayxVgQ/GsWua/jiZs2ff/2KT59d\nY4uUlos4UvBJZRUQydlL0S6BB6EEPqAkFDnGVHIISxF3XjDyLPi4tQVXy6L8ooTyxhAkAmt2+Nnh\nnWccPS5AZWsqDdlAV8PYD/SnI6fTiXEai4dLwegRIdFys+GzJ9f85a+/5PNXn9C2LQqF806okWWp\nJ9Y6xQoiRtLs5LUqEmAj0TplvM9FlVr+HWK5mI9iC4p4pIeQ0CliU8AfHznM94QP9/jxRLe5purW\ndG2Ds4boLSZFxtOBeezlMY4jOkU2tuFJpXneVHyy2fD2/Q8c9ifGYeR4OEn+qlY8ud3SVPZygCoj\nSeptV2GM8LDnqbxGSKBDCAmlDMaakhQk3VZyklgjlqRaTKaqjMuZ/TTBHg7WMEwzPmWhi04z797c\noYwFlUnJ8XoaGb2ivjMgnB1ZFSYuAdK6SOKNEcjt7H+SMxgbMFXA5ESYRPAlij5ZPk+Tw4fI5DzD\nMOOdwxqND0vqWuPcxMP9W549f06IG8iKaXIc9z0PH/akUCA9IywSmTJ/1lgkLkHRGYVKiqTBcE7j\nodAJLXVdcGRTU5karSt8iGSl2B/3hTUksIYxutwHNW3d0XVLVss1m/WG7fqK9WrLoltitJVDq3j4\nCxR55qOX353PdrYyIV+YJBGSygQFhMw8Z8byeRwT1yvDts00WrNeL/nm85f8xZevePv4yPvTgNaa\n2hrWdUN2nvFwordiguZCwOdEow3TOLM/nuSgMhqVMm/ffuD1+z0H59lZg9OKYTyy6iybRUtdtawW\nFS9vd1yvFnz56Qu6zjK4kWxgubAsG+iaJdkYjInEd7/n+NCi109prl6xaGpqoyUZzAuDj0pfEoJC\nkoPOVBWL7ZavXmz46utf8fL5yz9ZU3+RQm5zxbKuWbTVRYTTVpZ1VrzYXLF79Sl63fLw/Y+8fvee\n1XJNPz6yNponn7zi6ALHGOnJWAyf7Zb8zRfXfP3yltWiJgVPVJmsLEoJ/nqOWTvbfIqAQzo67wNZ\nK7QVpkciF8xUJLzBe2EdAFqbiwl9TBKOmrIUxOAFdhiHkeHUczqceDj0jLqhWm1Y1DU2B4yK3N9/\noD/1smxJ8nOVMaggh0Ntaz67ueZXn77ky09fUDWN3KRRLAK0FvGAVUkUZCDfd444TsTJoUscljUG\nVYohyGJOZcF2c4x45+nHgYeHoxwiTYOLmWmcCSFgc2Y6PPJu/z0/fP9Hnn7yipeff8XuyQu6xZqM\nIswj+/u3vH/zI3H2nA4PNErzzfqar+qOl0mxngJ3QwCXqIyhP42chok5CfRwtV1RGYsPgUpr2qah\nrSX70lqDd55pckzTzDjNGGNp21Ziy4qmu+0alNbMsyKliLo8f3uRN/cpMc2Bg3PcDQMLU3F8PPCP\n//h7nFJ06wVPXlzjq4o5Zcaf7olBQqq1sdRNRde1dF2H0VUp4lryOpVgrDEW6KWqqJCi7ebi0YOS\nwINxKk1gBGU49iPTNPH+/sDz51dUVcf+cI/zc7luYX+/5/HhyNCP1KYTu2VpxT8K2WI+7+0v9LqS\nASTvVRb1Z754BeVLelAqDpd1s+TF81cYbVnW75kmEXppbbBGk1WmaTqudrc8uXrCarmSJayu0dqi\nyETxuuC86FBlkWuNQDTamEIELJdvPlMJ5H8RwV5GFTrskBLTnHl/CCw7xe3W8NlOs6trNrslf/nV\n57y7f+TvvvuJVdOybjs2dctpnhkOB+7nmcPhxBg8QUFrLNPoOBxHUmNYLDrqqhZ9SEgcMFhq+v3I\nj497lq3h1fNbXtxWmKbjs2fPqLXm5dUWXRv2fuDISEUg+x5dtaJWDQP9m/ccZ4/ZfcJO1UyHD4zD\nkcFPzMOReRyIdUWcHc55htkRU6JqG3ZPb3n+8obb3VXJAf4TNfX/j0L9//XxP11/i61ksXF+66w2\nLHNF/VPE7t+iWo0+9SxGw7O0ZaprIFBj8StFDxxihtpwvev49HpFYwQ/jDGUCyCJLaS2IhpJiTDN\n+Fk+Zyd0oJRAKxn/ZN8myyDvz2k2ToJelcZoS7RBQmi1uOGllIlOOvFxGDnsDzw8PnL34ZF9Mpy0\nZ+pHjvt7tNZ0ywVttyZlSSTP8RzKHMg6oVOmspa2a6nquuCKJcQ4CG4u1qz5oygmlhs3lzCIpcW2\nDbapxZjnLHpCnl8Mgo/PsxTFYZg5jRMpQx0TLiQOw8z+OHAY5mIalZimIz989zs+3L/n6dOXPP/0\nFbubp0KxmyemoySHx9mjtCUEzdv3D5yOPbXVnNJAthXPPnlK23YSaGEUm82Kpq7lps0ZXYIcUMJm\niV4gjZTB2IqqylirqWtR1HkfmFzpbrzgzposWa5a7IFtLYvGmCI5aSYfeLt/ZBon+lPPwQWubnZM\nteUff7rjMAZ8kg56s2pZLjoWraVtG2wlOLjSRhSwKdNVkq2ZClU15FBizyzL5UK6bSc2DM4HrDWM\n48w4TRz7Hsrid5xG3r0XWK6xlYTxkgkxcDwe6U89ZCVLWqI4BZbYOymAmrNJZQEwLoZaYqMqNrwZ\ndfH0yUhws48JHyWWb5odVdWw21zhmq7smAQa2u6u2G62bJZrjJFmKQYxVEN5MYtKEv7MeamoRWwU\nreyQrJIGRkYDc7EF+GjyVa5Z9REXTDmTI/SjWLuOQ2ZpA50KVOsbvvnic/7dmzek6MghF5m7xlY1\nuu2I/UQMUQ7lusYkhXGJujEl8StR7a5Bd7jJccqKu/d79kPP8+dPWH9xw9PbT2lCJOUDMTgGN7Ay\nC2ptqbuOPimCarH1WqyMgaldkNVMmGbu//H/5P7xnsdh4HAaUVqzXi1ZLqUpyUkQgqxyid4TkiQ5\nE9O/okL+rd2UJaUSnZK4+IjQ4OjJQ4BW0ZBZU3FjDflqAUrGwpjARTG1Sa2h3lrWiwqjueDaKieI\nsSTHnE11ImH2BfZw4m2SEtIjFR/jUlBDkEI+O8mXjCGWZY26UKYoQokQAm6aGPuR07Hn8Ljn7mHP\n64cDQ7cSrvY4oYaeVbfk1lhWt0/w6UaYNNGLM6GX6SDERFNbdpsNla2IMYkwIwRSCJdNu0RVnRWZ\nstw6j+7GWExdi8qPsyS8PDcf8MHh/Cz4+DgyDTOxiFeSc2JH0I+c+pHJzXITA9EHTsMjh8MJP81U\n1tDWlmaxxE8902lP8EFuHmXog8cfTzycBP+LNqAWClM3NJ1mtVpxdb2mbhpSFtxbR5E8gyzVYkwY\nJfJ7kJu/OSf1WEkISiljdMRlX9hFWehsJWFHFX8TVCZ5oZWeJs/bB4G35klCJOoYaZLBZYWyllop\n6loK8Xq9YLVciNGVNYX5IPCBKfFkGkUkEZKwL6pai9UsIHhBKrarsh+oKktVlJJGy+ItZjieBozW\nbFdL3r//wGKxoa0t/alnGkasrUnaUtkkHh7nQi7bkgsuzplaV+LiKGv5y260/EXp5uXvgS7sm4q2\nXci9ET3ZS5jHarVhtyme8NZKmHcKYpWQ5p/ZXBQzrsJEqqqKumkwWV67ixgHhMnzc7HLz12zyn9K\nL5LICnyCEBTjGKh0oNaRzlrW21v+7b/5NT++eUNnM/PgCDlhitFJQsnjzUXwJ4OpcPtDBG2J7RUh\nWCZ/II8TDk17/ZTPf/Nb1p+8JK6W4EdcHJiHkehOZCO7h9ZUhPUOu3rCev0cXfYXZg7MD+8YDw+c\njkfuTz2P48TkItpWqBIzmAq0mFK8bKej89x/+ICKgbTxf7Km/jLQyixYoLYap3KBQSg3oEZXmpws\nVSvxaDFBvVtiFjWJjD858hB4OiXCUhM6TTwbvBdf4NJmy4WifLloE9ELh1a6GUhKeCeJs5ozXcKC\nvRff6TO7wBS5sjCYhP3hncdNM8MwcDqceHw4cNofeXcY+P40oncKHxPj44EnVc3LyvJJVbN88gzT\ntSSjyX6WSCcn3fEc5bHfbISXHuZZ2ApBundTVUAx3fm5SKkwzLTWaCscXTKkECVZ3TncPF8wuXme\nGMeBaRhxs8MUfDLGXLr0iXGcZJzLGY0W1kXMRDyn/sjx8Eh/2GB1xg0HxuGIxtA1C6w2HNwoomYl\nBvvZzcQ5EXtDUpr1ds1us6VpK5wP7OOpYLBi/+m9Q4zDBHIxRuxEm9pijaT15CTWoY2SBHlyIhtZ\nPNqSFpRkwSEdvdFMMdBPMx8OJ9w8Er0Uqv3hxFZvuL7ZseyWNLXFWCUS7LahWzTUlS2Hw5m2ds5l\nPXOThRXR1Ea+hoEQcQjS4EtqEkqJE2KGvp849dLRKmOZ3cxxGHnYn/jn774jY/jk2VPGYWCeHLlW\ngv3buuxBzKWQlzauTGH5AqtckuNz8dm5hDAIDKRUxuqKyla0TYu1mslakZwfIz7MVJXl6mpHbSti\n8BzdCEowc5UVsxf7X3JCa+mgU0pYW4NaUDdNyak9W+9yUVdTune5yc76o0KfzIWZQ/wII5GYgydG\nkcRXBq6bBX/1mz9jWRni4z37w4AvC9bYtkL1nAMhehpjmOZIPzkm72XHWrecbmsmlRgjTP2B9dNn\nfPXnf8G//x//B0Y3cNi/JXWGk1Pcu8R7N5Ay7EzLUhvM7acsP/sNz5/9ihyl+YnTyLvf/S2jh2nK\nPEzv2Y+ehMLWNdM4cjgdua0XIhzLkkBEzrhx4sP9A9NwIqd/RRL9/y38iPGSO6jUxw22DhmTxVhG\n1RW2qajqispUmGONsQaVILmA9okqQlffUOklWltyDIX77bFWFlRZq0vnkc9GKlmJAY2RmKtzxNMl\nK684Ec7OS0EPQWh7OsPsCDEy95DPVQAAIABJREFUDjBNM1M/MvQDYz8wnAb6fuDu2PO+n3iYAysX\n+Xyz489f/YrPdjtuliuWbUdV14LJG01UDd4GfBOJS/HwSAqq2rINmvDYg+ZSrJOxJJ2LZWe6jMda\nn8OH5fmkGMlZHrsLM9758ulw88Q09IzDgPdeVIWVJWSYgoRU+BDxQWiK3kdG55lSpFks2V1d8cUX\nX/HJJ5/QtQ0/fvdHjo+P4q2sFdE7XMpYram1dMVV1iQiy7Zm9/KKxbojRce7t2+5fXpD09ZsNwuc\nCyKs0YquK3sUMjmXA4yMd56goqg5nS9FOpeD1wsenjM0NVVVim5hj6jCfLFGEbxnnOR1ySHy9MUt\nNzdbdpu1ONmVlPfZR9BBQqbHWWif1lBXVXGm4+Jn73xgGCT8V4amCApsZfGH0wXjD8FTGyONRxYK\npPOhwH0JHyLHfub+4cDV5sCT7YYUpFvzIWBqysErz8laMWQS9mHpvIsoLJfKmCm+JRSBTxb8POVC\n4zWZphWHwBgDbbNg0S5Ydkv2+/e4eWL/cAcoKlvTLTs0Vq4R57B1jVKZEBzORaq6ZrFcYo0kHn20\n2C2DOKXxPEMIhU6cz5xJpFtXpMKSEWwfpMinKEyzHDw+waA1+3bBk+cvOVnF7378CaMiV2LGw+9+\nuuNhmMBoPnEwuMT3jwN3YyAkha4TaXhP0AodFKejZ/ukorENb396yzQ84Ib31LvMu3zgh6onMjEM\nni9Y8sX1Detmia4aTjpdmGQ5ah7GwLvi839/GphCxDYNzjuGkNgPjm0s06QqYrhCiQTYbLd89urV\nn6ypv0gh/4fqgE5iSKV/RsTXBjjzS70V2aq31KYinXLB0mWzbJKizpov84obvaDKZwphIEVLKtLf\n88VL/jitaaXJuoyclAURBX4oiSPnaKUQU+H5RhRRcNqUcGWpOZXPYRjph4l+mHicPb0PEDMtnhdY\n/ovVDU8WaxZ1g8VgA+icUFHCiEMyxKxQpozFZ/xwiKS7AzMRaoOxFbWxMhXkwl8vS8ysRF6ssviM\nnEU1stgVS1nvBBef5olxGpndTExRDL0qi0qJOaifvQaRcfYMsxcoC8t6e82Ll59yc3NL9I539+95\n/d13jMcjC2O5XSx4slizaVq0glYbGi2pPYGI2lZUL3ZEo9CVIkWHnyc5WLW+5BJaLSZQ5W1EK83s\nAsM4MU1zWaAphmG8mDzNhZIYSzyf80FS6fW5MChUyfY0Rn6X94KZrtZLVquFfK4XciAV2mPOgtEf\njwOVNRgty1RrzqwRyk5FhDHxbIYVErOL5WcVUy+tqKwp1seyMG/aGj1MpanQLBcdm/WSp7fX3F5v\nWHY10TlSdKRCO73gIkghPFPt1aXDFZz1PL2lkm9JBqVk6tNZE1XhcoM0xbqI3oRciNWaqtLi6Okd\n/WkvpmZNgzWZUKDHrBRt7ogxMI69hEerBTk2VE1H2zQ0bUtVN1RGJiql9c8KeHHs/H8UchHLnQv6\n+UYWeuWZrpe1EBmmkHiYYFVtUFvQvmZdJTY1NJVm5cGfRuYQ6LPhfg7cTYE3vWeOgIno+b2EdeSE\nHwNx3+O+f837ydNkT4ujOVmussPHjE8GcwrMeiLVEX1/JDR3vNFGGsmYSC7w3fvXvHv7luF4zxwj\ntm4wlWKaBsZxZJhnbK1ZLRu2ywaVBWJcbdcsdi3Pnj1js978yZr6ixTyd60riw0ZnxVcusgYUknu\ndnICZyBI15VTpq4NRmsqZWio2GjPRiVsLEnf2RSjKyM8WT6q20rZlmBebcpFLq50OaWLh0RMwhxJ\nWYqZ95F5luCI4DzzNHE69kzTyDzNBD9zGh2naeY4zMTCDmi0YqUU66xZBnCHgWgmefwFU8OoIqgo\nWGLBW42WlJE4JsIRjq5H75a02xWtFYochY+cy5PLWRwApUMXXvG5U/JuLoV8Zp6EHTHOMy54IFNX\nUjRT8fsW0ZC8F/3o6GcvsXe2YbO54vrqhuQd3//he97+8APzqadRmieLJX92+5Rvnz7l+WoNOVJr\nI1054FXksFTcPak4uJmkMm1TEYJnHAQumCZ3EfboopY0RsQRQ5joT4OYTBUvmf1hkO64toQCh3kf\nLkstOQSMZGFWghM3TXVZyPkQaeqKZ8+vWa8WkivZNWgkxXyaRE4eQmD/OLHbrlCVHBq2OJNpJQKf\naXI4F6hqcdebJ8cwzufRE6Wg6xqaukIbhZsdOWXRPgwz1RwwJnG9W/L86TWvPnnBi2fPWDZL/DhA\ndBcipCp0WK2Ls6bRxZpB/FQkGgxALJuJ56slX+h/F2k6cq/lYmEcY5AJFpn0qrrBVg2gmMZe/qzB\nnTKHhw9kYLHZ4qJjnkaOpwPL9QajYbaW5XJD07QslxK+ce7MBQ46J+/oy6GdOGPi55MnI1ayUszP\n8npZAalCl3SElHgYHWNV0y5e8OTrVzxbJK7qwNI4Fi8G9seex/2R+4cDMR1gjEgUaakB0yDMngy1\n0uiHPf4ffsfwwxs2yzVPlkvWHxqWZD6jIumavR/JNjJwovI/Mp0m3vWPeKsJWZhMP/z0Bx7f/Egc\nB+r1hm5ZQ0rs33riOBDDTNMZdtuOm3WHjgmjDZvrHbvdkt169a+LtaIoLAwofuDqX9hvogXTlM5A\nTmy7qJE3NRQbyZa2WqO7ikjCTQ6VwFQyVKachE1Q8L8CuAkWf7l2S/VDKInenbtWh3Mz0zwzDCOn\n00Dfj8yzo+97xnHEu8DsPL1zsrTwAuucDfjP2N7rGHB33/P9aS/jbzGEqpQqLJjCdVYigba6BA+U\nZBmjFLrSxF3Hi1+94tOvP2fVLshKi4+HymXSKDBRlFs4FTFMDPK8/DwVps7MOA2M84yPgjde6HlK\nyQ4hBEKQAud8ZPIOYqRVmppAfHzPmzDhhoE0Dqx85MvVlq+vbvj65gmf396wWXQ0tVxepgi3cop4\nnVBN5MEmqpQkYzHLxR6CJ8+O2UeUghCDFFStSGlG5cw8zWgi60WNrazkqnrpwMlRlLxal/TxJNeV\nuKLhvSMlQ1VXDOPM4+HEw/7E6TTCaoFGOuXaaowSf/dMpqosdS0yaWfB6Fi674j3SpacujQIxUog\nBpHb17WmqpdFrxBoNi3BJ5yPrHXLqDWjmpmmmboybNcLurbiyc2W3WZBjp4//tPfY5xilStutjds\nujVD1dEtljRNh62sQGkFPpTutTwmXZaOUQ54rTRK21LEucAuGSVJSkhDFIotrLz2gXmeUFpSjiCR\n3MA4HRmdI6RI1y5oVeZ4f4dzM1orhv17NInNZoe9HDbmow0usqxXZ9YNssPI5+3m5RgWuEHOQl1q\nSMaajCERdcKrhNJW3DpTIObI6BVjyAyjJ24NT54tWXQdL26vSTEzB89pmtj3Ax8eB/anmX6Qe985\nR5g9KmSe1C3PmgUbW9NqS6W0XG+UCLgMVW5lLfc+kPcPVK97bv/pDUkLTz4B2w8TY+5wRtPUG3Rl\n8d6jdEMyDY1JVKcZ93hiPA4SwB4j7njkP/3ubzE607QNr/7D//Kf1dRfpJBPcyiFXN4oazUoU8aq\nsmHXuaRmFPy6dMhn0QM6Y4t0T1zzvEhus5EcPZKEJiQZLy84ec6X7jcn4YLHwlBxs7t0qv0wcjwN\nHI49x8OJ02lgGEdOfc84SmDCFCKjj/TeiTQ9Z3QSr4xz/z/mzNt0EiFKwQXNBR88S4XPCehcDKP+\nxdeNJt9VfKNBLxbc7K7KLG0uhVxlgVQSRZ1Zxu4UxcMjeI+bZcHpnMjyZXzVxevCEApzRyCBeNk3\nZO9RQZgjJiWm/SP+dCLME2tl2DUtz+uWK2OpY2Q8nfDzWLouXZ5HOWCt4thpZqPxOREVqKiorBSN\nrBEKFhTutGzzYwxIJF9mtaipqhqlFS4mmtrgXElxUeZiP2wqXbBwczmzswKtIrMPTGWR7cruYJ5F\nWxBKdN80O5yXYICmsQVrr8rjEN742VVSK6F9ai2huuMUZeGnoFtUBG/w3hBzYsqeFBOmrulPI30/\nMhRxUNNYnj7ZcnO1pqkqXO/w93vaGZ4vdjxZRWYyo5IDxlZFXIcUcYHkfsbFjvEjc0SXkUCd2wy5\nTKWzzx8X5arAM1kVxbLcI03bsd1ek/zA3B+Y+z1uFIViUhCOlvH0SIyRumk5zQPKVizHgW2QoAtR\nRhfYJAusdF6WXtgu56kBPuKhSg4nuTIiSgWUikAEEgFfrByQ1PlyqGmVeRgdral5cVNz3UInwzop\nZ7arjtvtimcbyZmd5kCM0swFJ8ZnC12xVBYdIM6B6AIh5sugkDJopFmR69WDC9h+vNSBjMLGRNA1\nsTLYKIdZSIq4uuaGmkNw7OoNt0eP/e4tM78TYdDdHad//idGL9g+/+E/r6m/jGnW4GWjXoJL69pA\nVgLwl8Xd2WQoxYxS4nuSknxdk6hUROcgaRzF8IqymMpEMppzJJPYKX0MjEhnm9okeZtntdc0TYzD\nxGkYOfQD+1PP/iDCntOppx9GhnFimGemEBiDJHfLtr9s7ikLnNIlZyCkxIC7PH/FR3yeTDFJKh0K\nZUop3ytmbUQUc1Ox3mz4s6+/Oo8yclOeu/JcmDdZkr9zkuy/lGNRkEnRkkxQeSRnJZ02Iu2OOeOT\n8NpDoUHpGDFB7HcTMM0CjRkFtmlpEbz/w/HAw/GAVhR4SCwSKlWwW6uxtiLtOmZ2uNZIyHLO1DXU\nxQzLWDGTuiwKUyIljzFCBayrCoXGR6Fknt0tU+FGCyxQmCy1KESt0cXgMkl+KQU6ujCVPP04Mo4j\n4yj0wlM/iy2uNiy6quDhhhQj1la0VS0wkIK20cJmMRU5wzzri3tjU2nayuK9YZgdXscLA2qeHY/7\nE/0o3OtFV/PkZs16vSTHzHycWQfFK1Xx14stCsPbEPjBevEoM6o0LOp8cV0Ku0BswkGXnYAlY37W\n9V7KebkWJGfSWkMI51zKdGFKdO2Crm6ojeHh/WtUiDBN1ESa6FH9EROF/bQgMYSAGweGwyNuc4Wf\n1/jWU1XVx2E4g3Db5T69UGkLvn9mYgkbp2DkeCRKL1xgwJxDmb6NTKtZY5Sm0oZHn/kwJr47Qq0S\n1opqOUd5f2yCdVWxUJpQGbSqyKmVfrHpQFlSAjd6xuNE7B1qiiKoSx+9m+SxFxQgy1JWnzvIJPe5\nNVZ8mryEvDcafrW75ZvtEyKJaDIMGfPPPzE/DESrseNI8+YD0zQw539FrJUz3q0U1FVFiqJumzmv\nbvIF9sjp44kslCpdyqBmaVtqKzeY1vpfuL3JBRhK4s85lfBnGPLFn9sz+6IYHEb608ixFPHDUT7H\nkj9JiNLZeoEeMsIWyNpgs3iNZF08KJT6GRU2gwaTy2Cozvt6eWL6wii4/A/y7XJRC91RMR563ry7\n49SfWG86dNUUuEguplw68XTm7yZRbqaz4Kh4YEcfJWkmJHFMPI+7UUIcQjz7YchzOS+lP/6DKAEz\n3KWBBzfxu9Oec4LMRxl28YBBFWaCfO3q6TVfX3UsF0sZwecRaxNWN8V0qjBQnBeYBTBWeNDBJ3IM\nNI3YeaaYmefIaRBsXGmFcxJmrbQqDKRYsko1xsr+oaktbSMF/lxUYhbF5/E04aMkKymlaVrxgQlB\nFr85I/FoTm4q29iSdK7KoZAYRk/fSxRf29a0jbBb6sagVC3mTP100VOc+p6r3ZqbqxXPn+5ompoU\nEq3S3ISGL0LL59sn3FvLnbVoXRcaXuEc52IHq9WlA5eJQO6lrHSRfReoUdpdztmR5QoqC1M5sLwX\nwuTZy8e5Ga0Ui+WGuul4en2LenzH0zixSIGUM6Pz5aCEu0VDnzJp3jM+vEEZQzaSx1lXzeVaEfg7\nFSqwfFymZmSS0BdrunMnnlBoSUFCJgmyJicwJBKalA0+WhbNkpwCr989sjkEsJFOJ3ldJPmYnOHu\nzQd++uFHBu+wBpZtzXa3Zble0S4WMgUuLW1j0C7je4+fPFmy4EozlcvjkJtXXw4r+Z7KwtLhYiue\nIRWYq3DOswcVAio8Qs50MfBKLYh18y+EfT//+EUK+ZlSppRgktp8FNiccTCltYzK53V6gbmlW4dG\nWzbtkq5q5ITPjUAWuqjbUiapJAXnXHz4SBGLRcgze880z4zjRN8PnPqe42nk1A/0w4SbXUmWT3jv\nxTe6SL1V2V6lgsnqpGU5pz525ufiBhSbWTh7ap9rueVj0T935qVhvpR2hQQtHI9H9ocDm6s1urHS\n/atcRmEuJmBkOfGJHxN2ZBo5L5EowiYpQLkcbKFQ9FIJ4DBKUV3Gw/N78PHQySmJFSuhNIXlYCpP\nT+Vzp/jx+YaD5fphz+Kqo17UEiOmIFGgFMDNjtOhL9Q6SRIPdYU1lqrKVE198ctxzl8gkm5RY6wG\nn5hLw5CzFKcmW6osDJZU3sOzb4hw1oVt4pyXsGwt6eXp7EWfFd4HjLES4edl+Y4SplMqzYlYIcjr\nOTuHORi6tqauLblAC1rBMIzEGEX8tV1yc7VivWyJzuMQ1tJi1RBrRx8CBz/zUFcM1mDK/ZDKNKqN\nvvQH+dLqlseVz94rH03cKIvOmBQxCPc9ZURv4F3ZO3gRl1lNVVtSUTfXdS07gBCxMfLpas2tlSJ0\n/n0pR07eM4XARGbfdYzANI0YbYhdomlahCp9tgbIF2HT5Tmcl58lIBk8Wke0LjYEhVkWgyrXsewE\nzq9FQhfbBMf7ceC21jQqolOQFZoxoCu5Tx9mulGjUoXWUCWDUpHoZnwPuYmgxdPcnAOYs/oYvnY+\nHM+NqIJzIDSldp1BI3Xu1MrfvTRsZbLXCNvFpIQJmRYx0Pp/qeO/TCFfr2T7LQUtXxJgYhnfdaE/\nxeKjrbQY6OQyRhmjabRl1S5oqobKNtIFlp+lypgD6sI5PdP0zi6FIQgX9yxFHoeJfhg4DSP9NDGM\nM+5MqyojsnhGS8q93IzSaSctyzpNpo7y284F+4KFlz9fZMiIbNlqxbqqqfQ5dEI6w5AvQIsc6EqW\nwTZm+sOJYRypl624Dp4LueICGamUSjp4hFRw0jP6KL+80DBFtOGjLDbloDr/TkGrKn023CqF/Nw5\n5Vzw1oJply9r1CUI+OPlKnNUypngHI8fHnj2yTV2u5Bu1cqkFYof/DjMHA998aDRKK8JAeqawtYR\n2MV7Rwxe5PIK+VkKxpQZhlkOXaWo66o0A1JIQzFGk4M/E0NkHJ0UjCR00+VyUSiK8eIprjIFFhGm\nkUAbmXEWlpUs51Vhi4jD4eEw4nykbS211VSmIsXI6dTjvaNpDE/bHTe7FYumEopjU9N2Latly71y\nDJMjTIGp/pShtaALhJakEColS/aU1McpthwmoQQAn31dQnlNUhLGVoqycE4548Msdro5gUqixDSW\nurIEFS9c7n5/j/vwmuX4SLv+lKtuUUI1SnhzaSxyjvic+b7q+FHXvAuesT/JfW4kuFxS6mVq1MUq\nN1MaDbQU7BDJJTRam4wxJUO2RA+m9BFCDSlhlUHpSMyBlGK5z2ceQ8MmRJbjQDa5CAYNKinaBC/a\nK7knKAeiV2QP/jSTbOCSG2v0Rb+h4DLxQzGkK/Xngi6c6Tjly+pyI8qfk/pYyA1nwRNyaCjK1/K/\nmFp+/vGLFPJzQE8qnh+6qPWEF1qw8ZQvRePMbCGXFBgouXiVqMMqg9Y1mlzM7zUI8CEXLGdRhHhM\nhIIBx7MpVjHG8iHgUyCk8HG0y+fOKjI6Mc3Xpd00ZZHn4fLm1YihEKr8vTNE8TNoQSP83KWxPO2W\n/M2nX3KzXAsUUBaTwXtClmITUyRpRQCaVcdyiqRBDprGWJnfyrWSigxcpVxglVBuglT48qX4ao3K\nsuz1xXN9nGbmYlugS+7gmQqZy2t6wfYRDPC8OMvIBSdd+BkOKhNWGd0F78/gA4f7A6dDz2LdUVnZ\nZ5xHaTfNBC9udG1XFxbK2YNDQZab23vh9Wst3u5KK9pGRCfeRx4eezJQVQK95SRsDaN14XqXkN+c\n8bNn/3givLiiqgytqWgaK3aiJYtTa0vXSWJ9TPIktdK4KdD3E5CxxmK0ZXayR+ha8cCmQHralIWn\n88xFPRyihIuI/YMFI0KjpjJonRkt/PN05H99/QNfLhZc1f83c2/SZEmSXOt9Nrj7HWLKoaq6qyd0\nAyBAEQIrCjfkgiLkH+OPo3DNDcnH9yAYiEYPVVmZGcO97m6DcqFq5h7VjbdN3JKozIy44dcHMx2O\nHj0aCFE45YQLowY6VUfI5VzIqRl4R+uLMqRFax9ZNWBa1uVo3y8sa7LsQ+G16Wj87xhZl0WHb9fK\nH377D7jnD7y7P/GSr3x3FWpKqqDoPFryteu2zG6cBobo1QAXlUAOjp4NiggpJ0pdTKBOeeYIWrSt\nBcj4oI5LaqZaDaia/HQV0ajZFbxPEGaGcCDEO47upAyX8qRwiGhDmRNU7hpt4W/yv9XskN4gnV0r\nJEq/Nt0HTdWxoyj7BqwWmTs2uNE3Q+0M+tX+D2XxaPm2Zb06KxdCNfEz1zGZV68vYsjvTw/kkkh5\npfRmAGe4kTUI+dC7GatFGFU8xMbuMBW1RmnyQlNXa3pqDYrKLW0Tw5GrCQNllacttSBO0+8hRpMi\nzcZrdQbDWLEVjVIF9ZKDbZaA02kk3c5tkIrr37cJPWbM305n/ubhG/7uJ7/k/d29yfpWalbjW4pq\noFcEiUYviw7nRiQV0jozDJPi8naxYgMl2tfmuGpP+bWDtRorpWihd1Ud71wrzntTxdN7HdAF+eO0\nrkIvltrSZnSeWz9wiqoh0tN6gQwUKkRPLI71aebp8cLxPHEImp1o16KOK2s49zgOWji1NnTv9DrX\nlHmZV67zqnBbUMzbu0iaBiuOKR1wHALHw8D5NBGcRv/DEFpoRCmFl5eZWpUNcpgmfHDEQTteY6sj\nGKsoeKE41fbRLFE1pEWcZnEGHYYQlAZpDjHnyjwvfH56YU3aLJRSoo6By+VC9I7jYbQ1rM6tRM/F\nV/756SP3lxem5Z4Dkcv8TBUYYjSxMFPqbOqHJrerRe6Fdbmano/q7OSS1PGL6RDVSq4gBEIcmaYz\nN+WWko4k77jMF+bLM+vLZ37/3b9Sro+k5cgHawTD1k5wOsi452oeyukOefMN/t1PmcYjSCWvCyWt\nXS4A3AYNOo/zeQvgajPk+qV896R/igU7jdUWIkgGt4APTKNGyWldOBwn7lLhbl6ITmtEwXsqClO2\nPqRmO3xb306NdePuux510zBUgFd7xKyWWrR2mA6Sq7xDFmG1Kl4wWKx6lRtuv12ksloEvw+k9q8v\nYsjf3b/VCuxy0cjVaHa4VoGWbkw0hd60lUtVRcNpUB2NEExzWwGpHaRhXY8i1AzeugUFsYHKGpWU\nqhGqj8ovPtRKLkJaCmtIlq5qyo9sBrmiEgMAo93INsigIeSCFfncRjcMOAKe6D1fn27523c/5Rdv\nvuLu5qyZh1gEWxXjFucgeFzUbZEpfJYXPubCvC6MTtkhOp9xA9qdYcMVyLYYqiWAipdqZLYmpeIt\nlnIL9Gk7Lbr2ssFBQIdW2v9brcDhOPrIz6YbfnFzy8N0IElTAlTpgUyleCgxsiRhvSz4KeBSUG13\nUTiiGFQSgk5NnwZNw4s5p1oLyRzRvCQtyFmDUIxB54fGYHrnyow6HQdORzWSayl9ZqTSW4U1ZcPT\nlbaYS4bqGayBRdUDrfsQpfm9vCSKCMMYOBwipUBazBC5YLBGCyA06lXKoQ7dWFNhXlZyDXj3rJFn\nOVNzNRropFG1E5a0cLm+GM585uXlkZILh+mGuQ0nSasV+rdBJ+vywvXyxPX6zLrOrHlhTWkz/iZt\nUUQoeIobidOJ27v35LKSpxO+Fl4uTzw9fuD54x/49PgDyzLz4emRgNV/eoGbPjAZNHsOxxPvrwvf\nhpGH+3c40Cy4qJMHxberRdm+6Rqhz1qpi/ql/85W5FWacarZnk9AXKC6CC7gfOR00Ole83rl4B8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OO8yWu6zq7RYpL+o9HK2ny/UivVbRNDELHNYhGiNK2V2hdVFcPD7aY6722SeDWM2XeD2Xnt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\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KJteAqh6jvRi8GYUhYJhlDTdYUinxIQopk6pI+XWbVqVU0ztoTWcdO+OEQ84aJ+qtqXmH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wInswnXFye8fnnBD2+ueXY+Y9YMjZSz68nIKMmTrglLXWC5jQFamsI5ciuB6GHfdqzWO+4eNrz/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\nndiSd5M5/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SvKcDPnh0NgrylEdRhKqwVGW/KrVSGq02wCtOTKq59VCGgTs5MJfCVDJD9Gyc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\nEBi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EhlEArk0dqVxB4TTGASbTLmteWketE9MxovNAVTiev3jF88++ZLFa8u2vf8vXv/41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RRY1llCiUUucAYVRI6pFEQVhIuCQJTODTBai46fyuf7iqvNIrfkrImpNACdp+RzKMTyE/I0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\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sqYRGZJ4mcs5Y55jOI8pqEhnnGlpjcArGZWFcpLdPq0Y8JUYRo6hzUpTaudJJiewSAomC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+lyPjMACKM57a1lPgxBS9QiH2RH5hoZusZHWM14Y8FsMpxkzMuQyEKU4Wt5nI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AppPEqMVhBOIr0noiQRGzL2CKWx+oxGprSxpM8kzkYoJNo6/NCRm5aq1yg9I05mSB3T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Rqq5o10uu1plFbVnWDt+fxMeaIux5fOT5acdi6bDOCBSQIiF6Ygrs9s+8f/839vsH8WTPiW++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\n5DygCKyaFus8cU4M/UQKMM49MSWstTzcv+eHj/+Gj+9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3ixQXIuegiOFpNfSg6XvIowRf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CzKWYN5VyeC4pUdigk+rM1HkDUY6evohkolcTztOZxUEJHygZxnWhF82NBqZJ4L//A//pH/\n+X//I3/89z+xHE/UXHSD5ErNFsR9W6XxSOu5KAC77cSrFzte3e7YBE/t2WgxDxV0Y6gzYVgrkipC\nLjq2qxbdeMMQV3WsiFCppKKv7das0ducx6ZNYlu4K/UZPSibaFWgAb5xPBz48PYtVz/+yIvf/mcb\nDOI6GY7T/sC7Nz/w9qe3nFLi9Tdf4aaJuVRSPRnGe8aS++HcRCX2OWfmpHM3a07kdJ7BWYpqBmJw\nbKaRUmfzWnFK3TS8u+bKab+wf9zTUmJ+/MyfvnvHj7//jqdPD/gYzENFiFGHK4fgrR9wph/qGuEZ\nuql/6SW4SIdLBNfa6lvtLFj2DF/FQQLeM8RRmRfoCL+zJ4vBTa3RWsERzfNFA0NzfRQYxBjwQ0Ca\nDvoYd5Htywtubq/AN57uH8g2ri6VRJFKpZGOsz6nEBnjpNnvkhQKcN4si2GcRhXDUJG5GuwptATH\nfeE0z/gJLq8n6t4hkvXZlcqS1PwqBMWTj4eF6XAieK8TiJKw2Y1MVeHB+ZARH3Tvzgt4he9qqtRT\noi7FjMUEPKSSlP9tFUoWEwUCIUajoqqVQ0PX++l4Wp9pbeaRMg2Muy3iPW1xxDSoyKdUTQwM6qhN\nYSdcM3TM7G4tSXLBM2wGnRFbG614nOtjKlX1rInYuSegbVJt2mJ9nb64HOf48HPXLxLISymIU/61\nGgwFvNcMNpekkIr4FWd1RpvQQ0usaRFwYcCHAe+jus+JZbZ2032IalfaBN9gsOZFEZhLXUURvcmw\nJt0rzGL/hrcv6fi4Juqmtz8+cjw+0KQw+IFSMp8/veE4C5/uT/zwwyP/9k9/5s2f3pGOyzqsOM+F\nbBaY3qFQiVnGIsrjjT4wOXhxveGbV1dsx4iUQs6FXIpOwekJrDVItE/Qm7xWmeAYQjT+qjZfxCqS\n7tymZfsZPul9dO9sKJPGDHsN6wyILv5iGLhrwv3bdwz/9M9cXL7g5a9+w/bmFqKnVGH/8Mif//07\nSq3c3t3y62++5nK7YXCiPP/n0J+9TmlCy5UlJU7zzHJK5HmhzDO1ZFopWkI3EzIFLWfFGnFebACB\nj0zTQAwj211k3DiWw2fefbfwT//nv/H48ZHolRMcB1VsatalC6IjRM5EGt5pgI3B8Mv1jnV/H11P\n+tdzBQOagOCV1eTFmETizo00o61J689AgSVQaoW0oodGCNa0g1ohOsfF3RXX377i6cMTadZMchgi\nc0q8+/BISE4rOadmTWEKbF9OMAolQ6ueUirNC37Q5jVFsX4XPHLSZKjkZT1gShCCFFyLhKLNYaZG\nXSpLKuusSxkHunXBNA6Ijxz2lThZRZUbQ5hIVJYoyOVkuacjtEgtakO8lIxoM0ghihjA2X58POKd\nYtBFoDqdkOTiRBgcgytIW3CoPmBJaUUDaA3xQquF0/5Jp245pxz8EC2G6qg7J2ok1+FehRatKqYZ\nmy4gFds8Zx56jycdbuzAoVtZcn6F0/T1HK49V7Cck8/n1y8SyLU5oAOoggtK9QomAGmCtPOHFZxy\nLm1CUA9a2lyIqlx0Osi0B5pSVRXpYmW02lXw+v1BaC6QijYCEVYs94xKGizDs9vmVIWlzABjYOTE\nkhNDVO+KUoTHpyfe/HjPd3/6yL///gMff9pzeDhqhiPNAnmipgLSdIPYAAexE9eLeqPsNpEXNzte\n3l0yxMCc1ASr1mY0MF0MvimroyCrvLe180EUg7emnfYfqmiGJ0U51k5k/Zxu/dPgAG/3xQLb2oBs\n7fw+RAgYF/sP3xHjhsPnB+6++Zq421Kr58O7D/z0p++pNbOZImX/yFOaOYSgsnF7VbVr1edbnVL0\ncq7MuVJyo6REOp1oVTH/VnRzBaWXI6hXR2vKjnBOxSsxBm5vttzeXXL3+gJP5unTgXc/vKEm9ekY\npmDNQFY+rwbnDo10QVAX7/SN3CN9h1XOINFf2SJz9vBwoSu+tJJ8RvdZ/1QYu7+HXrU046/rPqnV\nBl5vN7x4eYubG8fiSEWpdcdj5pgrOz9q2HFCSxW384TRq7OiqINozola1NsoxgGqNWCdwT6nwjJn\n4hjxU8BFFX81AZc9YQA/QrwU3OxpRXFsL9Hk6EKYRggDqXlqFmoWJFdyK6RayVJpzhGjHiaxeaq9\ntzktBLEALWpbQNQE8Hg84VolJ7UQENTqwI0DfgyMPrCcVHOAiFlTWK9McV5qqSynI8M0EcZRA3Dw\n+BZW8kLfJ947xAWtwAy69B6GQQU/LatiusMiKw7+bL3oJu02uL2/Zc9/bfr3nszPB3H4hQJ5agkR\npYpps8Gyb8uYO0St//8lbl2qmDMaOKfMCXGKHBXEsnLFVl1QHDoOmrX7YSRGpxNK5FzK90lEf9nw\n1L9Xw57Ng8JwRRc8LkR8mNS7YRhpCxzTyB//+Il/+5c3vP3xgXJq5HkhzSe8j5RcSEmbnMEgkdX5\nsfVpIJ7oA7c3E3d3O66vdtQslmm3Ve6vAqCGdLikda8ZMVtVg11CsODSA/GZR94HWD9XLnr+IpN8\n1j1XzLPz45v5jBjlsxQePnzg6fGRH/7tX7l58ZKrly9x4wX7Y+Ljjz8yzwc+v/uB48efTF3bV66a\n7+uQCk8wwcl2s2OcdvhpB5srSkocTied7FKrslVaVjhkgFwWcs2Ig3G7xVFVQ+Aar7+64m//7hte\nfnVLDI6PSyKODrdT/DzY3NHuZLeG6N6cMln+8/6THW/P2FI9aJ9zqOdXh156BuacwmPV1Ll6hGoP\nRp9JXOmT3fI0OB1qIcFDhSiCLImUE6fDCY9jiBO1VpZjVfWi9yy1sNuOjNPIUzrAohPjj0ti2WfK\nviBLISV1bmHjOS1JR55NOqDCjUFVisET4sC43RhTA3J21Kkx7DyXt5GwBOIpczoWXI60U6UulVI8\n4WJkuJxos0KNLTdNrhahzY3j/MTF7VZ9d5pnXhpFMqVmRALORaKPjOOIHwM+GdaftRnuZFy1D34I\nOhkIgdlpRWuMJkQbzs7pOVNzQwIEX/G+rmI6FwNSTGnpdPiL94oIBBeRrCsh+MhmGHGi0M3KroaV\nmKEHcd+fJulvup/CsyrPeX0+Dd3zPbn6uesXCeT7wxPOiXl5bJ+tecMEnwUdAB+MHRA0K64i5KrZ\nmveBzW7DbjNycXXFtNkRh0GHMATPMI0Mw8B2u2FzsSUEz7jZEYbxfPLxZeDq+7DfuPPEd7PCrFUD\nWPAkaTx8fuTy4pbHp8IPPzzy0497Hj4ulJOQZy0bvdcmVPcB14cn56arZQSgzbTdNPLrb+94cXdJ\nDEEZLu2c0dG8sVU03PTSfOWn9saXHVA6Ek+ZLgo99caL/lwP4OYuYB5Rz3nzrM6Rq91orXogotJ7\nLwY/1Mzy+IkPpyfev3uDny6Zi+PDh3uW455A4/DTO8YYbOFaUMOD00PHBQcRhjASpwuGmxe8/t/+\nG/O8sCxpdTyUolSuaQpsd5794yPSMmN07DaBaTNxcTHw+vUlX39zxdX1yGajNNTdxcjtq0sO90+U\nk1aJ6juuzfaVGeIMwRSteHTQ4/MD/0v4pPv/PHere27R3JugfRpM/1GFDt3qDyTWVO8yfxXNKBYe\nB5Xe4x2uVJz3HA862egq7hhjxIdA3AbaBG5wjCEQxoAbPGOY1KclF4PhlFVUU9Y1GANuEKbtgCCE\nQRgAFyM+7jThCh6qMGwGaqss+cTNZsvt1zuuX1zw6WHP8DgzPCXqUW0jchZ1L2QghJGUEmnOlFNi\nTnXtHdFgOSxIrmz9hnYSfFV2Tl8rDs80TYybgeAFmZUt5ix79d7hB+WGt1qYl6MlL5Y09agjXUzk\n1j6D89Casshap38O0SZkCZJty0rFOx1+3q1C0jLbqdDWRqnzbkUSNHP31jjXHF284Cpr1aU2JOZZ\n5L+s6H7u+kUCec1FTyL70J6AHzqU0i+PiHKSnYPtxZbb22tqrez3R2rdc9gvxGFgGDy3d1e8/Oob\ndpfXhDCQcwbABb1hwzCwmQZ8CGx3GzabnTahnmXmf3kprtWoTTNoa60otCKajc4lcf/0QKmO+49H\nfvj+I58+HDjuEzU1bcq0ZjRIDHZVaiLWMAzBr5OOvHNshsjN9ZZvvrrj6nILon2FUtV/Wixr6yV9\n7yXIynTQplKMwTJEpUqWkg0br2cjKsP29I4bLu66LJwzRrD+D6zTmgwWQMQMukwU5KGVhSWdWEqj\nDU+k6jg+niinGS8NfzwxDTqzEN+DmWOtzpw2sTwet7lgOCYufvVbUpJnDaSq3HEvTJuBzSbw6eOC\n1MrgPF4yU4xcX2345ts7rq83imsbvW/cDNy9vqEuC3vjHmt6FtYAvv6pH/yMfjy7L71a+et1ZJxz\nCxJthQWf/QrvzAHCrVBYiFohSG0WyPUG9R5HjN5EStZ0k0ZFpyyxF64uJ6LBRH7ylChIcMQpIl7I\nreCClv45dRMojwueiqxMDp1PocM/FP+VlQwQBg0drTb7jACNm6tLXt1dc3k3ckqJZS60CdKS8dEx\nToG4Vbw8n06kw4wkhRNP86xU0ia44MhLpSVRvDm3FSvuYpuuFQk29CQvHVX3BLTp6ccINJ3jepqV\nvbWasPXSSg/KMHiGbUSiW5lk2sDUXgutN8AdYQosc6YU24leX9dHr7Tgor4pYupN5x212kPvis1u\ntNbXiSnDV5X3ihg8q/r+Iyk7N+OkGHmrPD4+IFdCiOo/rJmvYl+dgYITXr684+b6EhA+fXoghPc8\nfD4wbSbGIXD38gXf/u53XN++xIdBHdUcuBAoRcUzXoQQI7vNyOXVBTHqx//LDehsirdmRIViUuEY\nddBBcJElLyw1cUwn9mnP6bTw/s0jP/35PfuHA2lOtKTDDmggMWoQbqgTo7U5aFU3KcoQ8B4uLye+\nfn3Nq1fXbIaR+ZRJKa2BuLa2BoQeIDTINPNLCQyDiaFaM3GMDqgQiyZSZcXuekB3Ng7OP2NCmMTU\nAo6+P7pvuYPQ3duagHHHw+B0LmPV97fUBEslloUhwBAHLnaTctQ79qt3ns75x7Jyj0fGET+OCFBa\n1RK6dhWsNsunaWQaAylnFYOIY3nYMwXBy46rKxWBlNw47TPTVo3YXry64fHDAyJ79bkxBFNwiO8u\ndai9AILgTYfgjHBip9AzWO7/LzFY/52+ic36Fu1zdD3REALB6VDgktW7H1RsEmIwDnhD1Y+NnBJL\nS3gfuRy3ROfxrqk1ufWWqlO+cypqTzFsRpYlkU5Zx7cNA7IVlppoWT1YaB6xXpJzjeIcrjTKbCKZ\n4HR/tIzgGOOG1zdf8/JiRy176r7SjhDKgCyFgcDF5cSrr17w8WnPn9584HSoXF1csru8IJ0SKVdK\nVytLgBZIreGbSvD6esTM6Eop+CyqVDY82eEYfFC2yhQpTQeO+CXbszPltvTUQeemDruB8XpkmRNt\nEVz1jJsJhkh1sMwnXPSM08A0DTw+HDjsF0qrxOCIQ2CcNkpkaMVsQTShDMY1V63HmU4tVYidzup1\nbTnbl7V0y2Qs/cfEYX99/SKB/OriBpFGLolD2xsVR02teoGvmyQwTRtCuKNd9hpFGIaJeUm8ffee\nWkdojXmeSctMSQtx1LKoZzvSCo62coCHcTQjrrBmUt1yFgeYV4SI+qGnsmjQAKpAblkXkIvEMOHd\nQPAbpB45Ph7JszJS1KmxC0acDjlI2WxV9SGJ8XJ1+IS69t3dXPLNN3dsNxuo2LTvQq5Kmyy1Qx3O\nxqcBTu/XMKrfifcKp+SUmE9H5fE2NdKS1lZ2C421UnDRGW/cP2vJPPvPBCyCaJOz2cHEswaePcEz\nCUPQubsCU7fIdYRgxmgd0lo/Rqd4AU4bqiE6pt3G7I0LVay6KBmRxjBEmjROi/ZElMXioVXKXDg9\nzXy+33Nzd8VuOyjD3wnjJvLi9RWf3lxy/HxkbmBM8PXw0jitlZmzLEmqMTmcZoN9U1aeB3I9Djp4\n3pMpcee7Kp2q4xw+sjY8xaFsDImU0vq3aGU5eeII3gt0ypqxdqZhYDdN64BsJ448V4oPMA408ZTU\nOB0XjsfEUgtN4GKzwY/eDuFAPmYkCb5FSm245pnctPZThikSYkA81CLMqRCGSNwF/vTdn/jxDVQK\n7z/tSUlwbmA5LLhSWYJnGjcsh0Q7NFz2lCQs3gJeDIA2cluuCp15oUiitGQwXyU3pWiONRBRLyYX\nHDFObB1oH9npXE2a+ues6lvWfe84Q1utFDXwKqJYeamImwl+g58mNpcXOBoZoS2aVDmDvwhBIUZL\nLtRLhXWQC/UZecD1A51n0FuHVm35IYZGmAGHQSz/oQL5Ztwo08FHWmsqrX2WxPTmovdebWeHwGrp\n6NTw5vXpyOHwDU8POw5Pe+bTkcdPn8hLZhhGBM3UhiFSW2EcI+PFls3mku12w7jZqlTfXqsZjKKH\nSiZ49UXoKtDaKm2ZKSKaMXjPZtiym67YTdc8fUo83J84PB5VsGCinRXbqo2SCzUrW8VE62CNrY6b\n7bYbXtxd8frFNTFEFbdk5eKWqg5vTdakWDMzp6ZXMfoVF280lVwvC/OyKNWQvojaGoS1QlD50TlD\n0cXSA7gHuz8anVdoQEFd/d5Ob3QdbenUOm0UDsHhJz04e5OPfjhwZoMgmGmWNVRFy/thM5kWoFBb\npeZEq3ovQ4yUUlmWkwZ+c6l0TdkQZc4cj4mLazlzwr1nHCK77YbLmwu2u4k8L+aJ8eUG7wMgvN0C\n13oFYxCUW2/NOWL79QatQFiz+9URqW5s5pxY9aFNL+etElkNXAAn+ABhYKVH9kaaiFJML7Ybrm8u\n1b7BSvEyF9oQCG5Qt70CZa4stVK94EZlnXjvdaJWCPjmlfmTFE70XnTepCmIve+iJkccIs05xm3k\n+sUV9WnP4XHmcEzc3+8pRWnAJak1RPaB6D+TUqUtBScDNTeSU3MztaY2zNg3fDCRV8Mqog45NDJZ\nhUzeE8aBMOiaHoiqtaiZtmSF6ZoytnUugfSTzu5t72tASY2W7bAWbUJ78/AJw6DxoPWxcHRw04gP\nUQ/ipsG8WqIkDoirtckAACAASURBVIUyu+Jz3UtOITTpyZRl61ItwZIvLBp+PoTr9csIgrz6KQ/j\nyNVw++W/OQ/ilLLk1WukZzgaPCrTZuLrb77i+vaKw9OBn77/gX/9p3/i7Z+/p5SmU12cYxwjm82E\nC3D36oar7bdcXU5cX1+w2W5xxl3HWApVbMpHOjLELTGO4BwhDNQqnE4nilR8DGwvL5F2Rc1CScJ3\n//LPfP/dO/aPB9KSlGaYjXkg0FrWrLg2O21lhUOg4tBGy93NBa9eXnNzc4kDUi6ktBic0GymobPG\np0mQzYpAZejm51Aqy6xBPOesrSHnzcK2nRulhtutgdoWVc8YOh7Jc1zcnpP6uejy8qZU1NFidWXP\naKFjuKK5UvbrTHpkDVj9mLB8FkHZQWEcKaWSSlkHj9SipbLzjpQW5tNR4Rz0sBITftBETZScI4xm\nAuUHQpzYXoxc3mzZXo48farP2CRuLXFB1kxobQiLYprQBy/3qqJvOM2unjNYxCCo1X+masavlb1o\nT8Crxw8F+hGANKtS1KNFxXKeYkHDCWyHkZvrS158dcfhcNAG4qJNRL8ZGcNAcIIXhytem5pbtVvI\ntTJFE8LVoglWgFRODFE9T5Y0n8stcQSJjGFgO20RX7h5ecmv//43uIcjn99+5s9//oC0J+bjgrQF\nkYJ3geIix+N7PI4Q1Dah1UbW0bI6iq45Sq0MIaoLZ4y0lllyYnVcRBuNLnjiODBuRsqc8SIMceSx\nJFoqSK6rHbBWx94iSbPfoypWZSwFWnWUXNBehTbFXW1qUhfVNS2EwOiF6jPFZcQ7hmlkHCdYFrKo\neEt5vlqNN0BJR7rnnw+HF5S9FkPA49VK12xLMNjRrSzGn4ftfplmp83b7Fv23LywG27fJ3Rp96Ni\ngyFYlj0RQmTa7Li6uERK5uPbHzk+7jnu98wm291MI1zuGKZIyxPOF3a7yMXFpLQldw4XempqNj4v\nT5S6MLQJkUr0I95HUprZH/dUJ1yWE615vDhudy9ZDoWHz3tSqUaX1c+j5VwfKttRVs7NRnMzicFz\ncTHyzTe3vLi7ZBgG5tOJZUksSbvnq7SXjo9bttqtRs2HvZTMsizqrpiKZm292WbBTQfOainoO6VQ\nTOLgO3PCrf0CtVJQbLpPYkKsMeqdLkIn9vqFPoyjqxr79dx/pI+2+surHyS9OYjzuBDIWT02fHO6\nSYs1o5xjSZXjaVH/DOcZvFe+sc7XI88ZncASlaZJQ8z4/eJmx+Xtjvc/YF75bs2sKiqvltrHbwnO\nDwTsffWg8OzokS9WcD8fz418dd7TYF5NrBZjh7S0SpLSVqxfGUnVsje3UtG8a0yD52o7UYLn8nLH\n7es7fvVf/46HT5/54d+/J5aFFoRcFo7HE2mZcb4xjoqLhxDpgw3EsSJL3jum3bAutDBuSSkhrak7\npHNov1UoqFZht41cX94RvfD+/jMXt5cUAvN+wTe9n6klpUealYBzkPNCSZUxQnCRKEEPzBAhRkrw\nFB+oBJpgh5Kn1ExOlfmYiE2gVJxruFAQl/EexmGjPjJoj8yLJYpStA/gVFw19jmztdHKWTLfqmiG\nTMGFivOazBQjEBADwzASNzoftczVCAOOKmexnahT4DpZKnr9jN3/CFErjeBHnPPEOGp/ELcmS709\n8HPXLzSzs5ecdkJawDhzcE1UIX0CugpXxInhlioEcj7SQmC73bHdTDpguBbSnMEaabWOBJMm6mQT\nExqEHhD7RtRTMKWZOR2Ibaa0gdYq2+kaiCs2l2tCDpVp2NFq5Pi48Pjxkf3Dk7JCeiYqrJlsqXUN\nYudErxdmEAfP5eXEV69vuLragqjv9rIkUsqU0syUydkhgDn6hTWIg6OUwjwnlnk2vnq1Kt9CiQPf\nqwGzi5T+9WcYnj4g47I6a0AKK1xy5laryi0GZ2KknrV/eTg/v+TZ89eK6Mt/7yGx+43gNZB3g6xa\nKnnJ1Ko4tQ+RKkLKlegCg/NEOtSj7zvPWYUn4iA4gwbUsMwPER+HcwNT32RH3bRHkZtOU0KDRQjA\nEA0rt5XUIRY75PT3db8V/bJm4+fMXB0PLcv33l6vQMqIDeCWVmn4tRzXZdUIXkfayWYkO/DSSDnx\n1YtbwmbicJr5XGZOvrG0hdqyiVpEsf0mSG7qRhoUntH5pOoIKN6p0lOUeledZpYBWyfm+tQEgh+5\n2l6xiTDtjky7idtXjoYnLRVyn5Xb1qrb28CKUqtVHcre8mKOkna/suHNmMYkeK24a1WF9Mk5Ylbi\nQAjnA9M7zxQHiuhQCSd28Dez4W0aGbvLaV+bIZqcGQ0NTUTJErXircnepOiQaHfWvkgzR81ObVwX\nsu6zXrmp0Zn2t3q867a6OnD6mVnWOSdYI+bPXb+Q+6FfA/dzIc75crqQmmZMm81ItY313P62VLOf\nbAJ4hmEgxsjsMs05WvAwRNww4EK0BfQM4+p+0jRwjlIyc5pJeVFZf4VSFgQhhp0KBcZATnBc9gzj\nxLJU/vD773n35h2Hh701P2SlOBWbiVnNJFz3vFGWXJ8jCcMYuLre8vrlNbtpVCbCvLDM6vJXa1On\nN8vQNFFSjDJExRVLaSxLZp5nlnlRkyVkPSDPgoKuSHxGYnSod7I/f/3cMncr7awiq2ObcyoNj17r\nimKHVvfFOUMKz+CUZ6yOzqv+60huBbAOUDTb3EiZGyl386xs/jiBMA5wVG/0IQxEEXyr6hljTdi8\nZErSwb46WBfwjVQS81yZF/VxD83YOEb3Wy14m+HzxQ5H55mGUT0MnEVpwaAyWZ+rBmIzWhNrltpB\n3wMVZpSG1808n07KDRdVCtZWjfgq60FbWyWifRG/US75fNjz9s0b7n77ay5urvn2P/2WH58+sZ8f\nKVIYxogvlZIy3nv1qZkLYRoZNpHdMEEbOORFXTOzUj0LOoi79ErMYCC10df75hi52LxgPjywFM/2\n4oLxakfD8Xh/pLQC1a2Oi955e3aRSTyhOoagLBlXhNF7ltrIeaE53e/BB1wwe1dbNWlRlo0fYRoC\nflSmCxJxrmqCIx5x1Z59QJruldqUcBAI6oEkWgJuhg0EO2y9GSR4wVFpxWAWKXYQBPV4WrTxXlM2\n61/FGtSMssOW5noZ/Pnw7ywVO/C1X9YZCM3qd/QQcD8XK/X6RQL5ad4zDCPjMHHe8OZtbZcKg/RI\nU5vbLhZxq2Q4RvVSGacdm4tL/DCq2Y1za6BPuTJtAiKemtHA0M+1NUPWutJ77ajj7miSWPKBx6dH\npI1sNp6lJh6PT3x+/MTj4YHry0eOD5V//Mc/8unDB2qacbUYdNHW01lsfFunGIodHBpk1UFvs524\nurrgYrfFe8fplNTxrWPjXQ7fueLGn9UmjWLSKVVS0vFmKz6NlXS9Qw/0Y75bxPYmU1gX2NmGwCNn\n87X1zmmmPXivVDnvbLizMSxss/am5/PrC5vg9a3oofFFkO90PJQ2F2KkSOK0LBxOM805NU1r6sWS\nckZEZ7LGUnC1WEauWW5eEinNtJYZpy3HQ+LtT4/c3+/58OaBh7efOe4ru8kTB8OjrQxmzUMHpAh5\nycx+YYyDBpfoqDaY+HkK5da+Tj/AWAUifTBDT9VLLqRS1ekxZ7VPkKa8d4TooVJxQYM5VahNfWak\nCeMU1fZ0KTy8+YBkgUHnro5twAWhzom0JFKqxI3S+yKei80F2zgxop7wc11ocyXtjSWCcJof1Rt/\nUisDPDp+bRoJpbA/7Pnn//WvKlSi8eLVNX6EnDM//nnAO5TUUBqyFKKPxGGgBW3aeiu+rP1PaA0p\nghStmujQiHfoTACdZq82yoVGsXutk4nE9srSkh7EDvCmiO5Vsa2zVivtpBBk0JREn70DiY4wjbgY\nEZsS5L3TIRTNUJdFPdE9Qmg6nara5+gHvArszjzx2myegPReyrM9Yiyp8+XW/fdXSY9dv0wgX57A\nXzAMEZFAZ+6ep9LrRhfaM6oRUIsOZTZ6YpOGEMhNu9f9w/oeLLvXcoOUG8fjwjwv7EoxaKUhUqii\nNKzSMo1KjIMGjOOJj/efycVxWRupwmE+8vHxnp/e/cgmfGb/sfDHf/+ep4e9ntZNVql8z8INuaAb\nMSns61cZ+DAOXFyoFH8zRZBGMgVjKWXN5vrD9t4ThqBezVb+5pQNhjn/TGepYJWMQ7OZVaZvb8x5\ntWvtiXQT5fH2qy8+ZUApQ8ZhCkOv5XC1N+hAS0agR7GedHfY4S/Lzr8K+NKzWzQ7j4E4Djivdran\n45G8qJVtSzOFmWVZcC4wbTb4+UTLvYLDxoZV5v2Jzx+fKAt8/rTn08cn7j+pF07az7gkTKNmmipS\n0vsX41ma7wSWRShFmE+JaRwJzgKF0QmlVz2a4Cnb4hmsJtaPUHWtls9NlNWk0vFiHjINEXvtpt79\nKRVi8NSkDCYVf0WmQRlYVRqP7z7oYInJM+eZMHj8EHjaz6RUybnRXMWjY+wu4ghz41RmIg6yMOBp\ncdSpNy1zPJ1UWeoUemit6iSq5hDfOC4HfnjzZ4ZxYrOJ7DYenxr5sEDu3uC2pqwxH8YBt1GaYWsN\nQtBGbgBfCkEqXoRSlYXjnWLL1YRxPkS85NWFUtCh7a0kzYi7mKfPNfCW9LRuUKeHaKtuFTYRYIha\nlebakBDUgmOKlEOvqlXnUsV+Vym06lBGetT5m72YNeis9aSqoYI8cZrciTvvlX68yFkI5qzak+5d\n/2xfPr9+kUC+5CPDGGmywbtpXdit9SETYo27Sm0avFPNijM3xxD0Z1I+In7g4fiZ/elJ1ZyCLQYt\nRZpAqo3TnHjaH3k6PLK7uiUOVzhXEcnUpr/7uBxY8swwRI7zgYfHBz4/PFBaJbWCCxfs5yOf90/8\n9O499fCBp3eJn374wHJa6Go8qSBFJ/74ENaY1R+BUriCYnY0pu2O66tL7q4vGKOnLJo55ZQMo8SC\nsS4GHx3DoKIfgFqKDiOeZ8vGNfCuM0gd6n+t6Bu1VQueGghcsODsAZplltGqFpS6ZQpbHxzBBQKB\nYTRqYtHDy63rsK0Zz5o/2BdWgy7phzVr2dnhojXqN2AAF2zjR4+UyrI/sjwdOO335HSEg05Mn+LI\ntN3QaqadetPRDtIiHO5PpPyJXD/z6cMTT48nWgFas6lJajzW7HDrB2AMDoKqZcMQcIdAWQrLXMjb\nonYCwRqVuFXYcx5icn4v63llhxXVfDbErf4xrJCXNjYRRylCo+CPi3qyJO0fDUNguxuULhsdUjNP\n9584vX/PLIX9lbB9ccVmu+Fz+UzJjZqFnBPDFJjGyAbH08OJ4+OJaVSvm00cGC4j1Xt8TuzzyaoB\nPfxLqZRWcFTc5Cg1sTwdFd6Kkd12oh0WHt4fKE8LLetndg4Gm9YUp4jbeepxIS2FGDdMcWJonraf\nwReqU28mJ2qMNgXH4qzh69VPJYg+O/VWL6QlEYiMcWTYTJDV26k5VTU3yTR9Q1pxWLWMB4IwXUTS\nUihLUZdV10e2GV7eHINEsAEvToRWPeICcRhxkpSFZIFXmWKqkREBSRUXgo6XsxilzppYBSy2h82a\nxBs5QMd0/GxM/UUC+cXmhjFOemJSKbUonawVvPlqq8eBWHMvsMwHjsuRtCRe3L5mt90xjhe8efee\nd+9+5P7+AznN9NOrT8pptVJSQtoGH4Q57TnOD8RxMtZKJdeF/fGBN2//xOeHD9zdvsD7kRgHvv32\nb2lSOJ5O/PTxOx72B+4/7ZkfhcPHhaf3J8VfjW5YUlGcrDVi7D7qIN7KaHF4CXiBOEQ2u8jViyte\nfn3Ly5e3eOcpWdWLz+l+OLdOcPfBqx2tQM6FlLUcrzaYYs3+/BpGz4Ida/4ZzLlOnQ9BrT2D/e71\nJ+0A8Q79IQv6+k/VJht1HxGFcL6kGH4Z2KVXBx1a6i/Q4S5nFhVWhTgR6unI8f0H0vsn8ruP1E+P\nyGGhLplcCtJmNmNkEzfWrNODC3vPysdtPH56IH8+UBg5nQo5aVbaitoHB48NrtY12Jk73j6/anA8\nUk2Ik22CuwRiiBTBbBJYvXm0apQ1WemMHZ2hasHcOa2UYjQevk3A6qwHsTvlB06nohldrkTt6DI2\nNUGLIWjDPy+0pOPNJDnKDI7GaW60psEjnRaaCGNQnn0+zJRT4nK3JS0Lp9OJJSU2l5eE0bObRgpJ\nJ8svRRuPPuDbAK2Z57vjdJyZc6ZkRz0W9nOhtGIGVmI9HlbxEUEZHa1gPSN9ZiF4GAebsYlqBqzJ\n3yu4nIvSKXE2aatXO2cxW/QBFyM1ZU6nI80pJNUb/OdU4/z/NQtSNFsefGQk4LOQjwuSKq05Tvmc\nnXuj1Yrip7iqRIA4RNsbKBnA8gqFEbVSO9szmMDO9Z5VT/vOOPm6gX7m+kUC+Xa6VOK/lQ5apmkg\nCja/zhvOqrLxkdo0C+gskFbF+MOJZT6xpIUm9RyhQAOLQQqdYaGj2e4RL2zGDSLCkmf2hweOhweW\n5Yh3r9htLglxQwMe9/ecHu/59PCB4zGznDJt8ZweM4eHmZIKOalqs48dc6IZHK43D3swVcvQ4BxX\ntxvuvrnk5sU1X33zgrvbS2jqOV5KNYvYL/Ez3yl9okrAnOsaxL8I/KCL3nVYp8PyTSlazlgnaEOt\n48ne9UaUPwdb+94+XKHTILuh0zlI2ctaUIczgvKX2PgXBmUdAupv2wRLzd5/zon94wP7/YF5Pllp\nrRhts880BMd2DAwBZZLY8++VR2tOZ31Konpt0Kn61AYQWLaYi5zZMNbw1LVjB413VnU1tVsVVcn2\njIpnwaQn3bIGMTkH9dZZCpZ0BFTIhI6jE9yKqeJ0OEIcJl0bzZpi4sgNTrniU2G09V4qFDwlBppz\nlAJyqmoVPQhC0bUpUHNlOS6afTYYY6QsiZYq9ZRpMRHcwBg8QdSXuy6NOpia1Al3NxdMm0CrhZJm\nUtIst4o6Nk5DwBsLRFCWSNx44hZyUJjRuwEvOsU+OIcYq8M149WLIs6l92HotExtKNamfSjQ7Dz4\nqBWWCfCooj78q797B3Q1qxGzBZAKy7FoNuyjcsnFQWmY+b7i8FWIPhDNQA2nsakbZ4k0peraulf5\nPWvlWTthQTqz3a2V6RcbmA5trhjpz8bUX0jZueMsiHmudDOzGl+VVWBv3PtB8SIcm82ENKXmKZfW\nhuqaYg+vC6NP84ghMsRoUmoh5YWnw0fm9MT11R1NYFkWUlqIYeLu+jVfv/wtFxd3CJHP+3tyec/+\neOA4z+QkSEalxadCOiUNpqlYdqiVxAprIKw+49GGswpMU+Tumxt+8/dfc319weubay63W46fHjXD\nXgN53/ysyYNDg0WtTTFxC+T9cq4Hzi8BDssV9f1wDj6+B2qnJWZnbPTOOnQpseJ6GvfOUcoBfRbh\n8+t50HY9Y3dnjvrKWvkLeJwe6JTXxiKV5XTgcTlyapkcoA0B1wa8G/EhsZkmLjYjY1A7YzEwS+9T\nXf0qXNNS1fugbo5ZBUbO1mIpkAu6cfr7dRq0+mkYo9BG5f83p5LxvtnOhkfnPdkPFdCNizXJxHx2\nXK8gvdo9VbO5xSs9V2dXDsRhBBcRF7SZFh0ZR02NNleGpgfwUjx5DNSLETY675Zc2U5bsnMUJ0xt\n0PVVG8uc14lKUxzIPhJdYJSITw18Jo4Q/UAVVT8mCnGoXIyeb7++4upy4nDYc3p6pJWC855hjEyX\njh2emnQoC9GDE4bLgbBzZFH/ozGCb+Zp6D3Fo7bR1QgD6CCH2lRVHdyZvaX2CGoFoUZngSEOOJya\n8jmlxioVta4BVJ+NZdIYd7w0lkMiDANhM9qouYZk7bn18XYqcgumTlflp6CDKcQ0F6Ua1dJsL7x7\nlnmjiUovHH/+MohNAudG53+gQO40pq4lkvPOuNBeMa6cWOZHVQ6ip9ecjqQ862YUjxsd280Fr158\nRUuN/fsH0lPGu0yMWmrFqGKZYRhIqfDx4wOMgj9WhMzT/snUnZ7d5pJXL37N5e6GVzffEuLE/rjn\n/bsP/OGPf+D7n77j4bAQ3YZ80jJ9Oc7UrNm4VOWJtib2gJ1iuu2ZgAaVNofouflqy1e/u+Wbv3nJ\nJkR2ccCZl0jKlWR2sXqPzrBDx5WrjXHLOa+jzuAcPFczp56RclYJ9pLeoTaawXuG4FeZvqAUPR91\noAc2EcehXs5Kp2xnNk1tK1wiYLRHvgjsqzDoLzIKPcAbf/lFj6M5T/WRpTmWpXD/eOTh/onD50fK\nUhmi4+Likuubl1wEYWO9lfOklX6Q6IEVUT+UORcy2gSvWW1bveGupWojszadW6oUWdADUKsRH2CY\nAj6q/4igEJd4GzLQM5Nn5ZBCNM4wVuygaqvXhlYlznKz89BwHOtIwyUXQowMccJNXicM4al45jZy\nmlGYsjX8tGG6fcHr33xFPs08vfvIvP9MWRq1KryCaK8kxpHkTSXbnVPxBBe0U9JQl8RhxOGUUTNU\npsFzdzdytXPc7Dy7sOFD9DyeEstS+S+/+x2vb64ZPfz09hMlQLyI3N8/kKXBGJRWN2oPos6ZUy5G\n+2zmV6Rre23498DoVZDUn7Pzqs4MgJPGEAatyFwfjGH4mDFkvA2EqBSyqJJzJQeAKt2CjpBrSZvl\nYgc+Yl4+9L2m/kFUMcIDdIql2H6qUjXD94HgWdXVamDXKatuhT5tI8DaDLW14P5ir9j1y7BW0kHp\nPVael6rTc2Lc4COkZeGHt39myUcEIY4jx+OBvBxwLNzdfMMQN4zDhjhOXF3NXF/f8il8Qk8/TO7s\nrXPtyaWxPJ7ILrG5hO2lZ5wWRJL6mqOuhmXYUWsmxolxmLi6uGa3uWQIEy0nUqnM+8xpn0mnQslV\nH4SAw5sUHbqVsEjTQQBDxA+OaTtwebPlb//+V/zu73/Ft3/zFTs3MB0SfHpafVV6Nt5s4bhueelY\n1ZM553Xw8Gpj0DNl0GDyLMCq6MB44F3w4JpVNh271tdap7NHj1mfaL+hGCWyQ5btywOkp6FfZOeu\nH0Tn5mbPXFeYYf3+M37YxNGaYz5k7u8fuH/3wOHphIhjvJi4e/2CV1+/ZDtG8sM95fGeYRwYxlGd\n7+x1alUWAc1D89Ra9K/N2agvvbGtwxIddnE2Ps65jsPYfQmEFqhNtP8iSnX1RD3IWs/IZYW31s/b\n9BmohYG+tjgzW1oz+e4qst6SFSbyURko0UdKqjpwpKKWyU0nzKeadULNw8zVVwHaRGsTJQ2kOZNy\noRa1cYgIe5dIWacCpVpWUZstYLw4pjCSRS1aU04QhZaE+VT44Y+f2G837MYN6VFoR6UOXm83fPvN\nHbd3E7tXkX1W/Pyw95SjjperOVMPibosFJvrqQEYXG0EEbPI0DrLrV7wGOxkMJ9oX0WHbsRVRV0N\n5ijK0dLfgV/VqUUEke5po2u4uUIMAz46csmUpVBzXXF3j35/s7kEymoKlrTp7+kMny8YS00Q3827\neLZP9aVtJxnjxdvMUXfGxZXD+LMx9RcJ5IfTE+M4MsRAKQs5L/RNggukMvPD2+/5cP+WIpnb2xe0\nXCnpRE2PjHHDi+vXDHGk0BjiwLTZ4l1Yb06XbjfD1qTBkjOP8z23dWR3eUdwnmSeJMswWBMpczFt\niGFgM2751Te/5bQcmJfE4emPPOxPHB4Ty7GR5kZN3XbS4AfndQqRk5XiFIJaX7oIV7c7vvntK/7r\nf//P/OY/fcuLV7dcyoblzXs+vv2sAoxabBLRc5Uka1XVqmbjKRVj8uhCcWvAeBYU3TnItuZ0OHTP\nVeU5TGJNFq9ZuLcGqI8qeMhWeaRloRbFnCH03k3XVp0hIPnrYE6HiDi/T83Iz+pHHfSsw0OKNGpu\nzMvM4+eZ/ecDOQvDdsvlyyt+9V/+ht/87teU/YmPKZEePjNuJqbLHWleKMdZcVGbb+8M45UCEhQa\nWd+HHXjViUq6DfZx/pytO7D7otlc6bir8fa798z6eeif1YK7DXCota6DPpxZlLUVamTNQDsmT7WM\nPqgSMgwBoqdW5ZtLM/65DRhf8oykQi6O6eIFbhjIS6DmgeUUOM6QF/254CqUPVIzUwzsjydSymSj\nAremVsWbYatBrWaDISPp1Lj/NPP202euhi3fvn7N4b7hSmAXBzbjwPXtxNe/vcS/KLz9+JEf//xI\nPSXyQyNlT15OzMtMXhLVVKTN1m00Ln8MntwsCHttAktT2MWZCyUilKKy/jFE7RuJzh+VVtX0zu5r\nAFX/OmWN1+Y518xCcxVCIwQhnzKyZCjNhNjBrIGhSgWvgbyTKywSoFYOfUO01RYCw/HVDMv2ncE1\nZyFQV7z7FRtftS/u2Z56dv0igVyZXbrjfRjZ+FF9GJZEoXFaDuSaOcxHcl3YXlzRzFYyOthETwyQ\nS2af9tw/feT+4RNLmvWB9SZID2pNS8Zh1KzGAXmpHA+POOfZjRNf3X3FdnPJOGyZtpfrUOfduONX\nr35NTYVlFv7l4U/MT4+quCxKawpeVYhnua7CKc3YHD4GNhcj40Xkq9+84G//27f89ndf86tvv+X2\n5iXxWPngHki528OaqrXb2zk9IBA0wFX7PruH55Bh/QbLClZxoTsPXG614Xw429HiqE0Dp/qPAEMw\nNWzACRbA1S5Ajf97si9rQ9RSXsV2V1MIv0I1nU7HswC3YujeQa0dfDLuv0Ifh8OJY/Y0B5d3N0xF\nYZtf/eZXXF9csDwduH/7gYf7Rx3gux25m24Zh8D7735EcoHm0O2r5XA0fFI9ONAbJqhc3wm5CUUK\nzfVgD9hG7QyWLpbqe1cHULc1ED/PFGsVaoFS0BmVJvFuUm34tceZ2x5iPR6MN9w8VG82C56yqN9N\nDTrHsrbzCMJam062b4AXakkcHu5xcaTMmlXWVCgnHQQuUmk0nmrBSWP2nvLTPa5BqI3JNWJztOpY\nbEp9dwxrpuIZd5F2GsB7aqhMFwOX1ztubm65utlycbvjxdcveP8vn3l6e+Tjd0/MDzPHp8JpaStj\natxEatIZtySjogAAIABJREFUn9VGNYIG4IyjtmQWA+rHgkBuCWFRfjmeLKrc9s5pkJWm82xFAZjo\nAslU5cEmdmHQp+4HrczCMChMOhdIhdCazpUV2yft/2Puvb4kSa40v58Jdw+ZsmRXd6OhMbOjwJld\nLvnAw8Mn/vXk8pwluRzMYIAW1SWyUoVwYYoP95pHNoB97gmcalR1Z2VGuJtfu/bdT0QMQnMu2Wig\nhXbZVoNbinbnRsCybMQipFDItuCyPCtFCR31vFDfK0WvMWKoJe/WkvO/Ix65d2JCBSJmEVl7YN8/\nEHNkGHcYA+fn5xhrON9c8Hi3IxPYbrZYV5jSkcP4yOPxkcfDI/14FOFA3eHKCX8Vb5AqlzWQLTla\npiGxWa+4PHvG9cVnLJdbvGvx7ULf2zQX5TglxuNEvx8YjoNMw5GJei5Z/aGFgpXnLMyMby3d2rM8\nb3n9+RWfffWMz7644vrZlouzLWerDcNxRwyZaaxQSVaTqnzyM9ZuIuZMiJmoSs+nxzYxrdLuHNQx\nzVDqv6+E9LkzPg1RZ66sdzivpvymht7q+6qCp1KHgPXIJ0Mm6dJrh2Hmn2WebjYVZoH5PfAEDqqF\nsBZznGG5XdF1G7Jt6PuRGCOvPntNCBPff/uOu+8/Md7d4cOB+xu4vLxge77lfnFDLFKkajCjGBoV\nTBZNAuXk75OyISJOfFNMVHZgrkdcbQ5SqnbHMsgWJ8MERnByY4xutLJHhJCVv51Ik3TP+Ylx3CxQ\nQbBrq9dUYPYCGqmndBRhzjirJIky21TM+gHrwHqca1ksRCY/ppFh6JmmnpykAakOkSXKzzE5Ew9H\nnDE0xpCVOJCTIcdArsIoJx0pSiFcn695cX7BL3/2GQ9393Tdmpcv3rBcZc4vV2zOJPR6uV5SnAPv\nMV5OsrZtMWRcMIQkHv7OGU3aEXpnKBm8JB6lEBQcMTjfEMtEJgm0UQSLjgqlWeGhzE2E1S5XzPc8\nRqEtq7hGNlCswbctYAhj0ACWupY95ulC1cKfUvkBlCpCRnl2rfPMViQYmZE5SwkKnxnkHpQTqeFk\nqV3nOzLqlfXy7whasbaZTd5jDJQciXHgcLxnyhP9OOBbz/PtSxrfQnLs8wGD4+JiC7ZwGHdMpWF/\nPNKPvTiNoUdhzAwZVF8LdIds2gWLbs2yO8f7ls3qGddXbzjbPmex2GKtp5AYhiNTGPGN59gfub/f\ncfP+lsf7HUM/qmugBedEUKDmSeJFJLxkawvdumV1sWB7veQnv3zFmy+uuXy2ZbtdqtGXJwwjYz8w\naiEXsUUmPsHcMmL8E1IixjxL99HPSBaHtx/I3Gd+alYMtnCilstiqdDBjP2qy2Q1cBIsPmqgQ11c\nZsbR/7S7xkANftb/MC96GeaZE0SU8+k+1bdE1Q/IhtGtFpxfP2Nx/YKQCo8Pe/ph5PrFM95+8z3v\nvv3A7v099A+05cCHuGPRNlxcXtGuO0iBEqobo0BAzhRIkZwEM58/hDazMWXGKUmWpXZsYuhUr60M\ntNJs1VvmYm6NRKblKP8tZYSeGsTwqwQ1gtMB+ImKmH5wTWVvqwPqKH4es3mUFCNtIwWj1xPGfC+s\nxVhP1y6YUiTGiUO/Y5wOlDLJxTYyL8laQAqSSWsMBC1q1nu5RwFsHSBaKydQwBTL5fNLfvbTz/mH\nv/0ZH99/pFue8frNT+n371lvIBNplw3dpsOvW8wi4gK0TcYvl1hVIhfEbRJr6BYdUzAMk5hm+cWC\n1lvSsccEYbj4phPv8CKS+rrBllLmoaLRDbeuv0rftd5DiHLNjaeeG2t8XIpZIikzyDHEYn2DQymI\naT50ceLUVLV5OcFjVuEhIzCkOIVaYbhVwGQ+iqH3u8xzEuWEzY3QnzLD6utHsrHtAYe1jtZ3hGhI\neSRFGMeJECPr5YZUMrv7Pd9/8444Hbm8XLJevyYWw/4YoT+QLfimYbFY0LUtkx2ZgggW5mGnYpTW\nNXz+2RvefPEZL16+BCzdYslqtca3jdyUkkkpcP9wx/74yPnFBdY3tO2Kqc+SI5jSfAMrPbBpTtmA\n5CgFo/NsL1dcvthy/fqMV29ecPXsnK7zrBZnWFr648Bht6M/HATCCJEpZkKSgGm5f3L0n5JQHGuR\nqB1ZRWDmDrv6Y1fWytMu3VrgJLn3XvIOG1VOylDN6M/IjMOkdMgn/uJop6AzojJ3tfrnnH/YeXOC\n9sRHgnmDqVVUlqueoJSeZY3l7OKcizcv2bx6zWF3xHvD4SA+8943GCQwIWdhN4194PF+j/MNzaYj\nThMx5oowYih4V7AhC5xTvF4XpUUiePg46jDOi3IQezr9yPuvn1kLQ2NmuMton5ezYPw5y3GblChZ\nsGmJJjtJ9Cul7U+5+pUqWs24pGUrep11IFwglrr5qzAmjZQ4MvzuSCpZGoVhLxGIpYAe1WtiUd1s\ns96xjBRBVyR6kZwwoZBMEU9+LCkVxkPgl7/9Gb/+xU9YLhZ8/mbN9vKKZ5+95MPbPQ+PH3n3/37g\n//vd13z39oHDLpLGhEkJX6C1GnRsHLFYshHriClOBGXgpCJhNM2yo2AJe1F6ds2CRCKEnikE6cC9\nmOfVYYzYG8j0upQyd98xJZkVGalDMq+QE1R/6KU5SuCNAxLWZxabhhwLccjC9dfGJlEgZZJuhLUp\nMVRfKDklpeqJpKHOFBEdVchODo3VWK8CgbXI183h31FHPoWDWkd6ShaOZNOu2Gyucb6lTQHjOm7u\nPnE33RPCyNXlBZeXGyngppWC5MSsKVFoulawLSMRZ0L9qUdfCW6wTrCxzXLDs6sXYBvkUmWO/QFr\nR7xrZ1tLay3Hvufm5o7vv//E7ccH+n0/T7D1TkioupECFqNM/5vOs9wuWGwazq5WfPb5S168fMHF\nxRZLYdmdsezWYvczBcZxYJokDehpnJvCt5Qi3i0pRomhysxH86eb9Hz6AKn/xUgVrQu7Ns4KhzTe\n0XiHr0Uc5g4+xsSksEot5PI9ZHHZJ45scqRXxac5QRHUfxooOtR52lXMdq5P1scsonCW7XbD65fP\nef7lGz68v4EYlWuMnChMwXUeU5YYdcvrx0g3Bly7wPiRVCacqe8NGmdprDA2pko9K1aP3vJ+pqkw\nTYnUaNDxPIiEyiKQzy4DOIuciIReKF4aRdkpp9NwYb5hRWXbT6yMtcacnBJn06U8H7+llmu3X9Bf\nUlxDTsQisvaSDSUNHO96KQApyXBU8BqBDOdN9IlVAlCR/0hhzJIN2rROOvYqUiqWHDLjfmLYHxj7\nnrxZ8fzlazYXG6xLPOx3fPf2ho8fH/j4YeDhdmJ3e2R6HMQQrkApjtZYbLE03lekGBqLs57WGAiZ\nMiWSCRgMvvXzILExAiNltY6tm2GO0kx479VQ7dS8lCKeLB4P2pFX9W/RzThrwxKLzE2sBb80hAHK\nVOGaiq/nJxh3bRpOcYkUedaKUVplyVToUWAi7cbnQWaFUBS+4Ymb5r8nr5UQByL6IJQG65d433F+\ntmC5WAvW7VvuH3ZQ4Oxyw+dffslqseDd+/dier9oWW08YxykeKq8HCOUMnKVQ0vn56wMq+IUKMXQ\ntku8XxJyZBiPHI47rHV07ZLV4ozlcknME3ePO7755nv+7fffcHvzQH8QWAWQa1pqnRR+dsngW0+3\n6thcrFltWy6vt3zxxWuePX/BarEgTRPedjSuw7fikjgNoxTyGBX/ziJV16N9zkWm+jGRQ9YOQ4rj\nDwIaSpkLjcxK9Ig2f0k5YeJWpMPVFErqi+J7KZGSQCohJvUBUQGR1S4jq6+yQjOlMDsG5nIq5E9f\nFQM/QUC1Oj6BOJDv49uWi/MzXj6/5uWL5wyPB46rFaUYHT4lnC90246ysBA64n7HWBxDLKwXC3AD\n2Q6A+mgjFGHvDM4mTMpgHNUHmiLFagpayBcO05ycHCvjzTzZGKSpLfOJLkfISY76MvAWCGw+yehD\nX5TSWB/o+ifBeutxuu7mFUeVzTBmKbSxSLFJORGJhCIcHQCyIYwyy5E9JJ82IsMcYQfzVjKXDqO/\nD6VgizAz0M7W6o3MITPGiQ/fveN6u+JsteLVZw3FwO3dDd98944//OE9tx8PhGiZjpn+sWfaDQxT\nENvjsUDX0XlRUVpdD8ZbaTCcw+RIHiR83HZifYsr5H6SRCDriN7rSaWu/QzWzgHrlRUk96gQoqQL\nSUyiV2OuJ8uRyiNRTQSWBCRksFx1oXKL8rzpzhYYnODGCufMMv6shbl+7byp6rpAGlDM07txWm9/\n6fXj+JEj0VEpBrGNjQnnEtvNOZv1OTFGHvZHcswsVwtev7nm5fPPScHw/sOem083LJZHLq+27Cfx\np57GkQLzUKEKLgriQVyVigXBQGMUwZCzBu/FV6VCB06xxeNw4NOnT3z77Xe8/e4dQz/pgyz915wA\nZAtpkkFr27Wszzo2lyvOrzc8e7Hh889f8cUXn3F9fUUKifvHHfv9DlMsLsNw2DMOgxRxxb6zFvGg\nvisxKsMhZ/EG0b2kGEO195XOV3YXW8UllRmiXXTOipPrcNZoF1CKIcWMdQZfivrf6KaSJBgjRSm+\n3lmM8Vgrcn+5tqdu1jkHqYZMQC3oZV6kJ1OgWvyl8CsEZCxtu2B7dsGbV6+5ODsnToH720dMMbx8\n8ZxjzDReePnL7YqSDWmKDA9Likn0xeFLQ25XsEyEaSTnhC9ZBCFo9qQpiApGHyjFrEMUKp94njek\neBoYW1sfc+ma03zklY4rxEiaDCnk2TNHBpqVhXR6EmpnVj3cTycqTbLXylLDsqW46OAbLea5UEoi\nkkhGxF8ztIajenUUPX0+rUGmlNMaUNgLKt3NkktiShH6gcZZHOI/UjCUmJniyOF+4NPNJxabxMQO\n7zseH3t+97u3vHv7if39iPVOHCptIeTEmCIhJ8iDrOuuxblW1LYpkUKia1oa12C8ZAWUAo1vKc4S\nQ6CXHRNMoe0a0kyckGFyRjI3ayMjCFFWCEMKaa0X8xcYQynqTU6FDx0xGO4/DZQUMVk2DydttMyq\nTBWPOT1Fa5i1eRpagUImJ0hrLuIzTp5nfB1OStVS3A9OrX/6+pHcDyNTCOQUaJsljW9ovKjGrHVY\nJ4vde4t3hpwk7i1FSybRtBbnC8P0SH94pD8cCf2kvE5VraE4YxZOedM2LNYtL1694vzqUvyFTWHo\nj+z29wzjgcViTdMstFAbQszc3j5we3PP7n4vKTFPrmbV4ZUkB6nlZsHVs0tefP6MxaYhpp6Xr57x\n4sULzs4uaJslxRbOzwrL5RZy4fj4yHg4MI3ChJFCLhzjXItoyISQpfiUKsTRSm4sNs9jsfnIlykn\nA6m6BKQKnABrJwsm5UxMYubvnaNt3Fw0areYMgL35GqBW7FAi2/sPOwVpqQRKfzTQ8LTDrTokXQu\n8k+oooCxBd9Y2mXLvj/y/Ydbpvue97d7zrZrri7PSbcPtK1ntd1gvGSqTsOEyxtyCgRT2EfAt7Dd\n4qLY2zINwqVXNauzim0W8fY2CA0x5cw4BcbJkZKEg8vGGhV60z1Tr3yFwXIsxDGRk1G6p8xUqm3Y\nrH4tkBWnNlb44aYYZdKcTi7Cfjj9f6H8oJDX32NAYu68Fu76vkSiP6/beiqjnujmr+R0x5WqqX8n\nx8J0DOC9hLXo3zEOvJ5YjJOmZr/fMfT3fPyw49OHR467UXFjWRyucSQjSUtON/+QJvIQaJ3AQylH\nNWYTSCnVoOMEeQhifKXMlHrASBaR1WdEYVky4i1aP7MRloypSauJKU80NHjrcVZhnVKDHjLZVEdP\nPZGN8mSJU6IMk+WE556sZXtqquRCURuU6sGjBMP58+WSdROtfj16zMdKk6ERd/Wz/qXXj1LI98eB\nEHoMhe3SiwoKVUqprNV5Q9s12AM8PtwRxknM3F1ktW2xrrAfHglBdknqUVQZFaXUrlzZKr5hs91y\n/fI5q+2GVCIxT4xTz7E/EOLEYrmlaTsSmWEK7PY9Nx8eeLzbM/ajYMQVf0autc3i1rfoWl68uuY3\nf/MrXv3kJdjEx4/f8+rVK549e8F6dY63MqBbd2f4dsG4e2TY75n6njgFguLSKWqqUE1AShIeXDu5\nrPj4fCzPJ7xTtnz92uqhDTyBW58c4KizM6EeK+3QOie+zk8XsQ4Thbcs11VEC2nuJquf+VzMFVeu\nb6u+55lVQy3j5gcYJgbaZcP6Ys2u77n/+ImjXXJ3GDm7PGezXfPp9k5ocotGvEdyoJiMadxs+zml\nROMtTbPAl47iChkNATASitE45XmTZmRTzluFfoz0YyDEVplQ0qlna7DKRhCPa/keOUMKMsso2aoM\nW4p5MQgTyJyuvXTiDucaXONm3cF8awuz6jUXocfVe6UjULmGRrrnYhwWkRWXkkUWXrt8ynxSL3Mx\nl3uStaDod5t9SOrNKBniULAt0IBtpKX3jaVbN7iuzp8a4ph5vNvz7ruP7O56wqAugUUk/9bLQNMY\naKyoK0MKjCGR3All80bi1uqcxirdNR5HUkkyYJythi1Y8TKXWMcExtbcY05sf4NDgi8kaD1gNRqw\n8Y3EoqYJWzQYoj7rmlAuBgAV3rAyKDUGpyfRWfaBUnN1jpMV8snICVoGrpXdciricIK05PdVumfI\np1bgL9bUH6WQf7q/ZxyONNby4uJzjsc903TH5cUzmnaJsY62a1ksO6wzPO7u2e92LJYrzi/OGKIh\npMIQEmfnZ5xvDNPjxPf3k4YUoxJdoc9N00RhRdt1pBR53N9yGB65OLvGtw0XF88YxoG2XZCLDFXu\nHh/47u0Nb7/9xO7xKPQwvZT2KXsBoRhdvzjjN3/zc/63//1/IZvEsd+zPet48fwZz65fcnF+DdnR\nNgtWyxVgKcMElQecarhxEv74jIkXxdQKJanisJSZx3166E+7ueDYEjwsvl2ZTBUp1KMbYiHayvGy\naaBberCW45CEfheTCjOYwbmcpRtLBWIsOFfUX6aWZXlZpcCVunPo96iKxdl7pWLp+Qmu6Axnl2d8\n8fMvecwNOxpSkqFj11raxhLGgbE/MB0OpGyYhoFxGCghYik0zrBoPZ23tNbgTSYkyzB5RjNhjKUp\nhU6P+qkoE1FxaG884xQ49hP90NB1jao/LTEVTbnPs6I1ZTPPEuZhKPIsp1zULK1uYmYOFnHOize3\nd4gGVVknmNn5z3BiR4lSWTaa+tDL7xwFp78XPPfkhS4vZ50W8ie8Zb3muZy4/kVZUkLZdBjjBUpz\nHt8Z2pWoH/3C0W07UokcD0d2dweWiwXjbmT3SbyPJPw702ykyzQlKTwpZIeUkpgKYjSr09N1Ldvz\nM8Z+5DAcGWOYTegKELLkZRoDrfN0vqVt1wI5moxtJZy5jo7MPMioHXH1QamKWUtbGiiZlIQqmOdj\nUcWodQnr9RW5PWCcWjxDsTIPmYVj1s5GetX4+WT4JRTGpxMJg1xzip0PTlJmTuyu0///8PXjSPQP\ne1FjtS3TGDgej6QU8V4MrkJK3D/c8+7dO95+/z0P9/d46zlLhYuLDUY9hfsQWHQLVosFa7Pio/sI\nVMxVCldK4ndeJdPH4Ugwo4QpOMt2dcliuaJpW6EIlcRhv+ebb7/jX/75D7x7e8Pu8TgbUxkdPNXB\nj7OFRef56S8/5+e/+ZLNeUdMI+1iyWb7SsUZii1bh/cNzjXKO86EMcivKQgenvKM4ddfORZR/lVJ\nJYUfqEayKMvKk4Vr1Xkx5yJm+lmsV9WUAotI9o3mJtqupY+Z2I9SxItIiGX4xCxkqAv8yUx+PjbW\nSf+fvuahc6m4L1R6YvWYEi9qha6y4fDY8/brd4TFirw+p2k2pOnA1B8IY48zhdbC0hVs4xiLpU3C\n4HAUGgetSzQkfM7Ykkk54MhqTSuBuV0sDEmAolIkA9QiM4eYAsOYOBxHpQVatVM+DbarpQHUbtmo\nLzZz4S2UeTidsqgp581Yu8qcq3VvmTu3Ys0PbvfTR1nGHPURFwZ21ri5glG4qDJpZGGYJ0UpG4W2\najNgkP9uoHLlZXYg8xBDy2LRsr1wbK7gcX/AesfZ2QZnPYfdwLf9B5xxPNzvebjZc3FxDUtHfxyZ\njpExjPTTCFk2SmdbUhqlGFqLcxqq7iRQG8THxlpVVCaZNMcsbB1KwZaEc4EmBdWkSHCzUSvjqeof\nnsIbPOmagZjTqTnT/+aezBXml6nTA00oyxkjV1hmIKomxRSyMdhi57lbJgoMaovAMuaJqM48eZbr\nD6q/SoVZdJP9y3X8xynkQ39ks17TNi37w0G8G7yT4aOz9NPAx08fufn0id3uCHiMacnZMhwndmPP\nIQTGBK1xtCvPZXeOd2IV6Zybu9rKSxY6XeRwPNIZh28t++ODYvKepllSgHHoub2955s/fMu//csf\nuLu5YzgOclwuRShjohTBUlitOl68vuBXv/mKz3/yklRGnCt0iwVtd8bh0GMM5BSx3UoFR0b46jFJ\nhuI4zS6GQYt4COIwmGMSo6uMVopMsYjplX2CsJR5bDYLfORYl2eDJioVThWeKUExlmQsY4bjYaTv\nI+OUcY2l9ZbWG1otfI21mBosq8W9UjxL+SHr5IcPTpk3VWB+IOpD9bQ7lCmvYXe3px+/ZvHiGV02\nuBVMh0eG44pp7Fl2DZfbBf35AoOht4FDMoSC+r1nGiT70aREjpEyBYryh6XDs7TO0liEt1+SiNWs\nE3FQMUwxcxwibZfn4Imo96ScECwxc1IxVSnIvStlniUUFWzFHCl1HGlEjZhVWBTrkLswmz0V3Uhr\nezZfVyoOXNRJpsz+NEX9SjLMEYimWKxt8I3HeE8giDJYFDhqbVE7ynrUr32kwRpP4zxd61gt4HiU\nArtZdnQ4whg43B7oD4HjfmAcAs+uGrp2gc2OtD9SEuQgYcdYof5lpbA6Z/HezYWt+usDWOvUf6ie\ngo383SwDXhsNbhxEGVpETt81LRSEeaTK2FOhtvPpTwzPEq6UuehSUJ9wNejS9Uw9KclCpRTZOKWQ\nRynkgocJVGwk+Bk0j9hk/cxe7r9VQzp0qlXKDyu1AcxMEXgCyf3560cp5DEM5NQSw8jdIbBarFlu\ntnIMMT19v+PT7Sec73j9+ies1yuMsYz9kffvP/L24/f0cWJ1sWV3c8twdmDz+RJDEXmvM9gkN1pS\n3aVw9v1AN3YstlsWiwX74yP9MLDb77m6+gxrPYfdnu+//sB3f3jLx7fvmPpeHrSCyJRjhCQMkrZ1\nfPb6Gf/5f/1Hfv5XX7K9WHE4HjjfnLFcrGkWC4xb4PQY553AA8VADZEN06iFPOnQMYsgSOPTRJiQ\nRS1ZF3EpcuLTzrj+qso1p7aauViV6ddiW6hgaymFEguRxG6cuN2PPD5OTEEq9PKsYaX0rLZ1NM7I\noDXqAK92pUVsUb1/Qv16AuGcAiTyfC+MQixZxR5Cr1QMsMgmkdJASInu6oJpf6R/HDg+7JieXUDJ\nnJ+vMJ8/Z90mHm4/cRsiwU6kMsp8AaFHGozwy4eJ0I+kMVOSU0xYHvoGEf2EIowc7wSvrj40IRpi\nNBSnRlmIhNxag288xQlH2DiP8S0YR5gC/e5ADEENlhQ6K5JDCyoCyQZjpACkUmcQCANFsyZtxV2R\nolJPNlEhlkQh68/IRX6PAoGppFMh9gu2Z2cst0sGesZxEnFO8Rz6I8PQz8/oaVAt5cM6mIbA46eR\nMESO48T6zOBzYLnyFO+JU2T/4chxP2HwvHv/ie2mY7Nuefl6TT867u4Mn+KecRRWUEyZxlta7zCI\nT4olMw29RCXqPmap20rVO4gFrTUSz3ccxdCrFAmsbp0MqEfN7yw6LXSuVQhDsLxcRBwkxbYeWAU+\nqT+7yuIrXbbO4vS8qoytuumdarEpSjs0BfQkJjx1hVisw/lWVNPKR5/PBJaTP1apm3ie2Sx/+vpR\nCvnrl69YLdeslxu8XeJsRy6Fb99+x2a7xjrH1cU1IWZ2hz3D8EjTOob+yLv3N4SUaLuORbekibBa\nrFlvtkgART3KqxBIi8Y4BQ7Hnm1czxzl41HyOdtFSz8e+PTxgW/+8D2/+39+zx//9Rv2Dzt5ELUT\nzinNLJi2hS++eM4vfv0Fb948F5O6ENmut1ycP2Ox2mCco2vlPbS+wfkWUEpUDIz9gX73wDSNSvWT\n7kE6XmHdmCJtd866mI3RKDtA6Y8SnCwbmPMWq+GxdSMTSMZg8FiFeZKRwJOHQyDsBnb7gRAMpYi7\n31QiMbSYTYv3Fucd3hva1qvjXyJOWZVrwugw0l7MjBcpOsqMKSe1IKoXqpFu9fRkQGEKXbRkinMs\nz87ZLM4YjxP3H2/5r//Hf2HZWRY+s+0C3WVDw4rGJh4aS9+PhCDQ19QH4nES/UBKkBTn1MGqt4bW\nQmMKYxI5fXFAVpsHKyW0qlCjeq87Y8RUzMnAM5VMmCZKiMJmqClLXqiYtdUzSe5FKRKAPCtBjQp8\nqCKfOAuMHBansJlcviK0w1rE63UGTcCp4AtgBE0vxpKKKIPbWOgWKxbNEjbSobY7z35nGIZBbH4r\nvEKiEMklCmtsMpi2oc2Z88UZX756w/l5x3AY+fbwCYt4swhUIlS8oQ8stx1nXYdzZxweBqZhgJyl\ngJtCigGMwdlGZjvZ0FgLjaOkrJFuMgfo2kbK6RBEnFaAbHG2lW64VB+dcvLGRyETHbCWnCU0QgfV\n4n+ffzAIltCaVoy7UtCrDdqaM+t4TR1KnsIjpMBXbP7EKZN2X6DQ+n1mn7n6vbXrNzOAqfCLdXNj\n9KevH6WQX2wvWC5XbNbnrFcXxFh4eHzkfvdIKonNZsvlxRW3949Mwx0pBYapcDzs2T3u2Jyv2J6f\nc3Zxzca0PNte0nYLMCIbjmoVmotiwUUSyMcpzENAAGu9ynMzj7tbfv/7r/lv//X3/PF33/Jw+8DQ\nD8I40K3bZMGNO+84O1vyi19/xa/+6mecnW+YkkAkq/VWfq3OZICWZfjhnHoWY6RjDBNTf6A/PEgi\nfFTvlkbnAAAgAElEQVQ/Ey3kQnqVYl7JhZUPOw9xtLs1ylJyXn4ZK7O42umShTUg3TokI1LuMRWm\nfeA4DDzsDxg83nraRoZQOSiEpN7uzjU0Xj3XFVOt3tViLapv7gmsIh3OaVxT01FAURQE1y5qTFUd\nBefxjmtol2sW23Msjg9v3/LuD3uuLztePlvy7GrJsmkxZx3eFtrW8bDz7Pcj4TAxDZHxIF26M1Vv\nV0n44KyhsYbGyMOUQxQ66UwpQ9+rqDVTzsKTt5X7rZBShjiOpCIwi287QJLes9O1k2UTkGlAjSpL\ndQ73hI2iLIVS9YKVPVF/lHbipii74ocslpNukHoVKQjFchxHcI4FC7qFp2m8ZrSu8QYeS6EfIagB\n3amQB3JqIDX43GANrP2K89U5z89X7M2B9/YBZzzOZazzCnEWpsNEd3Ss1i2LrsE7R+OcFEBriWki\npSRJX6q+pij04OXZK9q4YKwYuiGqzlxEyDNrOoxVwZ4yoRRGnDvd+evKXBjtTIvV4qytsLGGtuuI\nMQgJQjfzPy+lc3kXeqNuLqYGI+gAvX7lDJBofSDnJ4XbzndOtcbUDt01ThqDv/D6UQr5bn+kaZc0\nbUvXtRiTaNuGs+050zSy2+25vHqOt7L7np1fcPd4wy5NNM5xttny4uoFz19+zovzS1rjOe4PajZV\nCEmP7EUNfpQXbophu9zQWkcaRpZdyziO3N09cne74/f/8ge+/v03PNztGYeRGKKEvhrZIV2ROrVe\ntbz58iX/4e//mp/98iv2/RGmxJQjj8c7npXXEuuWlcaEFDRbRQcUSoqkMBLGnhirDD6evEaqYdMT\n1gHaLYgRj9D9iuJosobNKXS4kkUUqqjRVI0zmJyZQmKYInEXmEKCbAkpgC14BBvOkySSl5x0IGYp\nZJZiYIeLVOiWysio2N/sJaLH1aJH1piq2RdUXzf5VChMI8frgjAsrHFMYaK//cT7dx94/8e3TIdH\nppdb6FeUYcXZ+ZpuueTqfE3XLWjbHsOe230gx6KFuQb76k+bL6rw7b2z+FiYMdA6a0IGlCmIZUAt\nBvU4LbNb/YCyc4NRc6ts8EqNwyYJw67NWK5H9pqNOk9I52YawxNNRMXEzUwWnLWfPzzP66cqp6P5\n/G8kn3Z8DDzuHlksWparlqZr2CzXXJ5dYiPk/CBKRyN4LyRKiZTiBS4oQvXLU+T2wy1nywVpdITR\n4FxDtzS41hFSYBgicZoY3x5YbxYsuiVkw3q5xvmGMQwcx0QxjsVyhXceiiNHlCKYT8piRD8y+/wj\n17OYDEa83YtCWLW7zvO9lmZI+OfiFJpyxhmP916LdTUmi0otFS1Lzk4LbJlvjJhtuScbfsGoirog\nehYJZTbkVCG0kyixDr5NUk2vrnVj1AxNG6W6OeAcruvwi+4v1tQfpZD/6x9+z3E4yqWNEWsbnC1s\nt2cMoxzt9ocdl5dblkvHFA+MQ4O7vOaL6y/57PVLrq6vWa7O6ZqOMA4cdzJFt95JRBiSsGGBccoU\nm7BN4HAccJ3Ft7BcrRj7npt3N3z3zS3ff/OB+9sHhqOklaQoMTi1u3EFrq42fPnVa/7uH/+K7dma\nKUy0XYPrNsQSCfk0lJEpvGT+pTp0LZmYImEamMaeOE1MUyCmdJpw5xPL40ROLaf/n3FnWTzOnoZF\nAllk4qRDuShwgnOC6GVjGUPgMAQOfVDLXQmebRW3k8IBY0iMQZLqj8PEoR95drnCbDs2rce20rUJ\nciA4ZDZZaWKnIlMP6RkZsErA8UkQZHWGMXus6IZRYuH2+3d8uttxnBL3Hz8w9SNxKHx6f2DaD9x9\neOTsrGO1WdAuGlIx7I8Tu/uB4+OROAVVL6qqMZ9Uc9KdSbH0FhqbCLmKvorMMFLmOEqsWNso115n\nENZIR5XiKTlJKTjEMOqR20laj9XeSt05izEYgWaRwe+TI7huclUE8AMhELXz1s3jtDvpmmAu5rXD\n1/9Cqpa+YpDB0EdCmHDeM3WJzjWEKc+c71Si/hwdqdZ7VApt22GK5+6mp+QPApFZh1s0khfQeKZh\nAt3YU44SoefluQghMQ0jxYgjqTetrA8VRMWUGONEiKN2wlYphYUcJ+p8qBgvnbNviJqTmbJQSg3l\n1B1zcj6sXuRjmsh6sk0mydfKysBYR9O1rFYrSjlw6NN8D0wRnNsZ7Y7LybGSp4zvIkVaYukspVhd\ng2a+ZdnUr4OTO+hJvFRnXykX0jSJGvYvvH4kQdCRx90jj4/3rLqF8LeRJPFUBBe/v7/jbLuhbSzj\nMHG2WtGePeN8+4LryzO2my1dtyHmwuOUCCHhmobFZk12C8IkykhjDN1qRdM2dKuWVDwpO2yxTFPi\n/vaB777+nj/+6w0f337iuDuSpkhJWaTD2tl4Z1h1DV/97A1//Xe/4td//WuG8cjj4z3bsw2L5QLj\n1sRS8Fb8jI0eBU1h9qcoit2nOEkRH8fZg/w0tJYlU6lMtXcFOMknmSEVq1RKEUJJ9FwKwkGv0W54\nweYSheMUOPQT/SAblTXglTngrFjY5lJmGuQYBsYpMk5ROecNXdvQNBXDFwuBrNeqpjPV91l7R4EA\nVCUafyhDrl7agPScBXKJTO8+MBlHP0am/YCJUpiHw0TsM8d7eFx6uqWj6STZaAoSQTYeIxIto9iD\ndlx1CGuNnNiMsTOdMeU8C54KiSlJTmOrdr+ttTNkJRROcbyLUyTF00MWY9JTkMe5BmPK3CFWymUt\nsnWIWecIyn3QOl2LuHxlvY7175l5vTz5X6VeUIGAyt9PSGqp/ruYCDFiTEMaYXSNQFvF4q0XOfuT\n/j+TMLbQdI7lusUYx93HIyEXulVHt17QHnuYAt55huNAAZq2pVnIRmtdg/WBbCJTjFhvaLoG560M\nX7NAglMKjEEcEJ16pjjNj61OkNZ7jM5o5PliPmzVbILTtVJOfrWTwMiw3kQiEHTTMkZ4+845mqah\nbRuOPZQ50lvKtPDus+6jRu2AdY3pm0g5qUmd4na6odRTWb1DokGttlin42D9q/OwfEqUMP3Fmvqj\nFPLP33xB1zTkbGh8RzHQT0ce9ns+3Hzi7Xdv+ePv/42X19dcX13Q+MKXX3zF1dVLcllw6A+knHn1\nYo3FkkLmsO+xvuHq2XNedls5ymCkW+1abOPFVvZiw2qzwHnD7c17vv7DDb/7v//I91/f0h8FqxMv\nDjPHSllbWC4bXry85Lf/6R/429/+He2y4Y9/+GcOj3fYPNK615xfXXF+8RzbNHrMK5o1iJrmKGvD\nChZcYiQohFPUM7kuRkM5Ta6L0WQfhGNr3ZwLOg84FZuWlBhdVUkKrTEaSuwMMReOU+Q4JkKUXnke\npAFGzYvqCF8m7YZ+SOQy0LQDXbdguVxwtlrgXcG4wHQ4Ujm/ouq0+t6fFjDd2IxF6TSUXIgz9Usd\nFgsqbc8QItl5LJauRKyNTC5KR1wMJcJwyPT7iVLUolVlzWj3BNKlnTrXomwCo54X4k3TGsNkEqEw\n+6dkpMueQqLRlKCUCs5lonP6wGbN4SxzfmqMUQqMi3SdMkqAULniWQyvtISLerLeNn2PlRcxK2GV\nrli79npSPJXw/KTUqL8IJ7ZLxdxnlNY4LM0sLIspU3LEOUvjOmKO6vNfv2vEtZnNZcdy2TINiU/v\nd6yuVqzP16w3K/ZH0VykkDkcj3hrOT+/4PL6Qor1MLHzPdZbPF5OCR5854jZyUkyR6Y0itrWGazz\nLLqOxjtyCnowldI35USIgXEaKcja8s4TwqRDfrDF4K2hse6ESxeBLnLJTGUkFGEXOfVKaVyDd56c\nArmcBp1Z/qJ+D7nGzlhSUUuBOmspEgBjjZx+CkVcFnUXL+gzZ9CTt/mBj79w+u3sF59ynk8Xf+n1\noxTyV89e0jUdXdtxOE4cpj0Px3se9g/cPT6yH3bio7Fc4XDcfPjA1flzCne8/XDPMD5ytlkRU2C/\nC9x8uOW7b7+hFMNyfcbl9TMppMofb5cLcEIVsqUhDoZQMsf7wt3HkZv3e6Y+gIYw1/Fina+sVwve\nfPGS//g//T2//pu/5vnrzygU3nw+MQ3PWS46Fqtzlpst7XKphH+Q43n1Ds9zOEPOib4/Mow9KacZ\nw6tDmfpHY8zccRkdOFonvHfrCs6JlYFz2mHkE29cNSeUUlPgJanmMATGqehQTmCfbCplTZz0KOlJ\ndqebMfAYMo8PR7rWYR2EsGTZOolOc5aig0+j3hj1AtY8w9nD3EkOJEmFHVnwQelL0rzILQg0FPXB\nywUXM03JuBk/1OtMpFq0itmgqhdNnVKcHqC5F5qfC52BmGpSJAWwYvsGYS1MAcUyC9ZmrI1P3Cnl\n/la+8kwzTBORRC6iCJ1STY9Rf5YnUm1NFqUYgwccwnGPVXmqHeZ/n1MsJdtwgrROn1b+/kmAomuT\nCEUyMRMS7TczN54Ye8zqUmPxi46L51viFDnsB5abjqvrK15/9pLbm1tyGPG2IaQNMUaKyXTLFigc\njgFTaeSl0PqGpnXyZy/7uy2Gbtmy8kuMMYxDYIoiVCs5i5jIyLA0FpHbZ21WvHVgioja9PTROEdr\nvczGtDESlriIqJLG7lncyYslJaZh4DEGUQzPA/taF8oMJxaTlQ8uS9XoOirUuUpNk5IFZzGnDbmO\n9f90gqpD2ZrneTqh/eXXj1LIX1y9wPuGEBJ//Pprbu4/cpgewWcOQ08ukavrC54/v2bdLTXIwfGw\n2/H1u2/o+we26yW+sdzfDNx9eOD25oazswvWm3O6RYfV6XWKibaTZJOUCjlmxhiJIbJ/nNg9jBx2\nEzlKmvxsQ2nEJdA7z8vXz/jNf/g5//BPv+X155+z2mzJKfH8xWtymoTO2Czw7VKmgMrzLaUQUiBF\n8S1JardrjCWEUR3dylxMqPiYedJt6T+MJs9Yr3RDZ7SIm5nylGtnqKwXGTwKBBNzZpgiu8PIqOZf\nJ66qdqaqJEyp4B1zmok0tRmjdL7H+x5rDTEk1suGrrGYknCAN4DafBYBSeQzWXWNyBIiLMU2oexK\nCsIeEJyyclbK00mpDtqKoESlKL4tLVABqerZilGUdtRQKPWhq4XZyPHdGPn2Rr3rDTIEs0Y588hD\nHUthTNJdlVBl8+LNImwW+f6xBvyiW0uWQiG2VY5cDKH8UJySSFrYkxZxmWALHqzD0rnTzic4xmhB\nrwXBVMoaP3ja66ZU6/Jc/hWikKN+BhMEOlERixQR+Zxz6o12hiFknOLgq4tG4T3YbBesNi3j2Mns\nKif6XoLVU5HA8H4YpDt1FlcKrhGLAqsNCb5omo5VRllhJBCC2Ng6NfTJFooVbx2Lk4GkEXWo95YY\ne0mAMmJT27iGxrWaqyrwhi4dTltjnUQjARDTRJgQEoBetur5YtVltJQ4n4Dq3MIgz45rHIvFkpIL\n++NhLsRKxH1COazv4SnYVv+t/NPMBl5/+fWjFPJnV8/JJfP+/Xv+z//yf/Hu/VtsAz/79ReQCovW\n8/nLV3z52WuuL6751a/+ik+Pd3zz7jvGcuQwHpjCEfddZvdu5HA3MBxHSc8eDsTQY3D64BZiHAGR\ny0blB0/TxG5/YBgmUqqMkjoekqFK4yyr1Ypf/9VP+cf/8e/56ue/FFqZyTgHq/UGkILhmwXGNkJ9\nLEFw8ARTODIF9Uwvha5dsmzXlV6qjBqhPBk1YardaC3wxYiy0jqDcQbjmQu6qz7HuYbvQk7SfRqn\ntCUPh3HgsR956CeyhDHijAGrTBNE+eewGP37QldUZoauepNgPEzc5czUB5aLhrbz+AY264bN0gvM\nk+XvxXQK9UhWwihclqeoGMBnihNTsKyYo6giE6mE+YERschJ2iz4umxw1dnP2EINSa54Qt0SBPLQ\nr8POxlBSvLVQFtnMHYUGo5AQxGLoc5HZg4m0iEgHVUJWi9lYqrUsFMuMLpsUtZibirDqJmKIJhJN\nUm8g+ayuuLngOiVkZpWCF5xcJaOQzCy1t/NnFvdOKQyuDsb1msm+XTc5ue9yUkm6OWUaOpxp9OsS\nxRSSyRiTmKbA3c2BizPHcuNZnnUchz139x952K9wnWV9tqZ1LQnwbcM4BB72e4bjyGHXY9HsAGsp\nOKxtabwjukiDpC1lCofjwOHQyzMaMxaHd50IxihEX1ifbXBYjmGn7JBC44VWXIx6z1iPdS2NX+AQ\nS+YpTsypWcZiVCSnbjeIPS1ioJYKFDvDKLLJWn0fUSA9FSlVIkLjHNvViuur56SUCZNkieaSyCXK\n99B1mFVXkAgY62ZmUDLSsGCK3scZlf+z149SyI1tSXFgzIGhTCQHXbdgvb6gW3TKwLA87vcY23B1\n+RIaR7aFkCcZtjUN1jdcPltxsTXEIdLvD9x8/47Dw272Dffe03QNBUMIid1hZH8YORx6DrsDnz7c\nYEqcd7uSwZpI1zZcXV/w67/+OX/z27/li69+ivMe8VM4cdArjlW7ulQiUzxy7Hfsdg9M4aCpKo5x\nfOR8e8mrZz+haZf4ZoWxjRbv2olqP5dPXaQ+nTKA8UYYKFYzQw2UnDRVKKvk2aBUXDKGKRmOY6Ef\nJWBYTJCkQ3Z4bF2gFJxBunyNEjKlsk9Kte0WHjmGEgvTkIhTwbhCHCNh2bBZL3jz5edsLq/Y9yPH\n3T3Dfkc+HLC24LwUveIcJSvvVyaHFIUeUtRwigzyIe0MLVDFNpVqWZt30HtQNCJMBrBZKWlyglXM\nWIfIpaBdno7zSiYVI1BGLYdaoKecMWpi5vS0MLucFM3NxOrRPIsdrkGKAE7EWzbTGY8tEFKSYTFZ\n8e/67gwZSzKGoRT9vefEYK7rws+6gtoVVtM0+eVwVBjCEEvSa5JP+HkxVZ9IXWp1rXnbkuMkcx7t\n/KcYeNgduLt35LIgKWNj9zDy9tsbUrDEaNg/PAobKwgsloKYRDnjxa45SAqWc+J66IzHYYgpM0wT\n/TjSjxPjFMgh460MYL2RWDaKfL/UKzauzaw1kgpkndMN/8T6wRi881KsQxS/cBpM8XraUdxb52PW\n2bmhSkXDaZDvR0kKBTKf+CpkIzOZxBgDtw/3s/+N3DqF/2SLnyU/pa63FOUZK7phoMI+85Sq+uev\nHyezs2QOw5H7/SPRJNrlgs3ZGdvNBdY7ocPZwjBOmMOBxWbiOA70YSSWSNd1rBcrlss1m/MzmtKS\nhsg3//JvPNzes7u9I8aEc47lYkG3WIA1jCHw4WbP7d2e3cOBFANhmjA5UoN1hS5UuLjc8tNffMFv\n/9Pf8dNf/oztxeXsGlcHmaV2NBhyToQ00U89IQ3sj/fc3H1LTIN4WpmOkkcWywWpBIx3ON9gnMd5\np97WspBO3iMqAFLVZuPl1+z9rd2dSPuFKge6AK1g4CEXxiEyJUMuTsU9Xif3Ql+0VlgrFoFqvCYt\nAbLwqqeLwg7eGbwTChdJIB2TC1OR0NvV2vP8y5/wxa9+wX6M3H38jt3NB8b7HePhSL/b8/jwiI1G\neLTGiGBGf6SxDmwFKMqfLf6iD1rRzU+ky+rXnWSgKwlLiv8X2Tik0wEQCCdrkXalPpCZKWcNMq4u\nhBXWgJg13q7ojGH+PplYMrEovqpQVzGWbA1Yh29avGtobKFJGRMSORtsSTqQrg+70WGwZkGWpCVa\npVImz3RKMfeqJ7es7IeiNORTKW9si3eeUiKhRKYcSFnpmOa0F9YjvTFyf72zRJyaVMlVCClx6Ef2\n+4mmcbjGk4tlHBKH/cRyuaYkw+72cfb4SakQg1BdRcUo3OocCo0V13BTRBSTY1IjvZEpBtEVIFQ/\n79S9sT4bWCnIWLzVDOC2wVs7zzoqZBGz0H4bK0rSYnX4aJxqHaIoWnWTk82yzHCUnmsE6avgSIX2\n9OQ/z7OQAfgwjYwhMWdTSfuvqm39HPX4aOr31xMbCWMabVJOm7T979TUHyfqLY98ur/h+49vKbaw\nOdtwdXXNdnPO3d09fX/k5bMLSikMY+T24ZFP93c87B9JObFYLTk7O+fsfMvV2RWda4nHwM3btzx8\nSgzHI8M44b3HkCXKrHEUIuPY0+8PHHcHSg6C51YZO+qf4T2fffGcv/sffsM//NM/0q2WIoWOo+CG\nAo6iem+KgZgG+nHP4/4OTMvh2HP/+BHMqCwFz+XFc5q2I6RJjrJOfpZrpLucU2QUt67eMb4xNI2j\nbUUmXzuygkiQY0jqnKicfG9wjaVpPWVMDLueghMjLJugqKLNGqzNOKOduLXCBrGWbCp4XbRr1IVk\nrRZyKyZaepp0XuT/zjkWmzVvfvVT/uZ//o8k33Lz9t+4//CO8aHn49t3fP/Hr9n96wBHKUoO8Z0x\nMJue4SBao54tSk2sCfNU3q7ACylHdZOEFMrcTadSFOKAhLBc6oMo7oCCf1uESmiMbEahCNBQOA2l\ninb8kSI4v96frJTOZCTtPVMoRaLjjPO4zkFjWW/XrJdLGgxpd2TaHXFRYBRLns2TDFaFIfLTxLnP\nUc8SBUnEcVrwi+L8CoPPRafiuBZH6xoWTYe3MKaJYxgkoat+9cyGkKJiVQizWnpCdozBzDh5KuLz\nPoyZcSwsbJJTYJH0nlcvn7Ff7Xm8vSPGQhkmhtgzDaP4rWdZZ956iol0raNrxCwvZNE+hJBmOq58\nrcMb+fSRTERmK9a7+ZTVtg2LxVKj3arl9Am7DilwTJklDcVIw4H1WOOxzpBzwBQZLBeT5fQSArY2\nCDD7AcmmV0kRynCrTQgCS6acCFNAoKOG1nVyB2eBl2688180YKyKnbK+R0v19D/Bgn/59eN05GGC\nlFg1HT978xOG40CDYTz0eDxX2ys+e/kZXbdgSombx3vuHh55eNyTgW7RsVqtWCwWDOOR5ALLrmOx\nWdAuOnFMdBbTiD2r0c6h9WKda72ICGpijV5DMAXfes4vN3z1iy/4+a9/RrdcU0ikPNJYr9FQmqBN\nmjvfGCeOw577h1vGMHAY7pjinrPtJc4tiNHStWtKzhz29xxuPnF/f8swTkzKg48hkUJRH3vFyp08\nVG0nHZLUV+3Fc5FEmiiued43NIuW8+sLvvjqS2we2d0/Ytv33D9MHPpAiAKo1MxOqyILq11YdYST\nrkFW0OwBZ8o8XK3q9Dp09R6MNazPVnzxi6+4evmcxWZJNpbLZ9csVi0xZS6/eMWzLz/j+s1LPn7/\ngftP9xx3R8bjwNhPTMMkizULo6WkBBGx4DV2drQE5iFnypDEy0zYAchxNxcVUiBQi3Tkin1rl54q\n1o5QFSPqdaIDKa2RM6sHhC42O8LkE4SVtblyjeHs7JzLF9ecP7+g27RkEjFMTIcjoUl4k9inIuCH\nfp5ci7kBi8eURs4gxs2QkNGCRlGqpxzZoDWY1mIXnouzDYuFqCBjMLx89hmfvXpNCiMPjzvevv/I\nf/vnf2Z/PM5FShhSFnCyGa9anj/fkjkyjkeFB7UvzYkQAevZnK/o2iW+8fTHPdYkWm08ukVHt1qy\nOFtByOxud9zd3FNnQr6TFChjjIQxD9K1o+utImdmNgOLTFkGvtY1tIsFLhZ8EghwHAaiZtCip2tR\nekbh9Ks/uCwv6auNlXBpbxwyfGKGWEQABfOZRU+lpz9XSKqefyr3W/9U5Jlw1tGoepRcT/GcBtao\nZUN1u5RVPW8Uc1wj1bfxz18/SiFvXcfF5gLzCj57YegPB2IMrNZrrGlYdkueX17j25bd8cB3Hz9w\nPAz0/ah5loJBhXGk5MRkDJNriES1lEOwaydm+Kb6HWMk1slUXJD5uGONZb1dcX55xuWzM16+ec35\n9TWpJGLsKSXSLTqqLLuUNPOipVN1NH7BYrnlMOyYwiDf13aAJ4SBnAJx6jn0A3efPvFw90h/FBmz\ndCFl5iMDYIUj3rTSjWPEnyOj/sy5yn81RLZ1nD+74Ce//Ip/+M//xPRww4dvviFORxrf0+5GDsMk\nHaMx6tmsi1an+5KslLFKlaIwd5+G/IOwZWMtxlthMHiD9YbNxRk/+fUvuXz+grZdCDNic45vPPvh\ngY3b0HWOi8s1775/y+PdI2HIDPuB/d2Oh093HPdHjvuBw2FkOPaEcYIQxctFXRdhPjCQkiVlLaoo\n2q1vXyxhlRY2r8ByogBShTkCXkTQJJ4/9dSQQp1BYSDZ2LL5/5l7zy67rjS/77fTSTdVQAGFQBJk\nN8nu0fQESR5L9it/fa9layzJ9ow8M+pmk0SqcOOJO/jFs88tzKj1mrpcXABJVBE4Ye9n/6NCW8mZ\nL5ykW9ZNwRdf3vLyq1dc3FwypsA4jQxDz9iXxE3DtO45Lnr6vqefBsYQsGWJdTYfIeT0U2QFllai\ngx5HUVAU1kjgW2HFaFNqgo1Em9gsGqpKo21kGDRv3rzlqy++YvvwiR/+8I7dcYfJHMj8Z58n2/mv\nwhnWm4a2rTkeC/opnJ8DUmAYJ3yEZlVjtZPvEiLj2DNNUtxRVgVXFysW6wWnxyN/9D9y/+ER4zSl\nsyirUYUl+oCfIiGXctgMifrJS9ooGTiKoqBR2mC1prAFTmUqePLEMOWmJs0cJfsEkWRJaN6RBbby\neSM22bSXXbdpPu3x9NDMBMIsPfn8uqn8C58wqvyl6gznfc55GYzg6p8t2xk9PEMssySV/PsBcRL/\nD7WQbxbXrBcbvriNcowPnhAmxugpbCXFC0HIgugjYztKc3xQpCkxDSPt6UQYe1xhsqX/SHdss1Ro\n3rs089laRWkidGhsAh3kKJ+UBq1wBbx884KvfvWGxbri8tkNiZLjaU8MHc5Z7LLIk58HAjqZTFCA\nsw2bTc1iecXkR4apz+5IRT8c2O0+UTgPoSEMid3Dgf2243gc6bqRcZKpOsTswktPEiZXGmxh8GHO\nrM71YlEeEKUs1ihcbbj98pbf/pt/xd/8b/8rj3/8BxZF4PDpJzF5lAZz0JJDnh/AmDIsEWVSnwO7\nZmnW2T7MnL5Ilj9qVNa0y48aVxnWV1e8/e57Lq5eYFUJcUTZhn7o2D++R8VIU9Xc/vYrLt+sSO4q\niXgAACAASURBVEmzqJ8znQYe3t/x0z/9ng8/v+fu4wPb+x2fPnzieDgyDhNxTMTB51jSWZ8PMc1G\nGZmAzhnp/+yArc5k6BPKml8UNcM16fw15wuQzvO6/J0dmudSCKNxdU2zbGiakrq2XFzW/NlfvuXV\nV7e4puL3P7xHTwWLVY0rNpg+kI6B8dFzOrUc25ZT33H1/BmLxRI/jvRDi60sz15co0gU1rAoSx7v\ntqiY2KyWLJqa1WrB6qImWc9pPLFtdyTvZ5CEYXK8en3L89sLOv+Jdtxy9/CeyQ9nLTvMxqknB7LW\n0DSW1aJmX9ccT2MWXMq02A8Do/cUTcHYDig0dVVzPJwYeikpN87y+ouXfPtnv+Kf/u6fuP/wSExQ\nO0dRl+jSMAQvnETMUlAti3zjlvTHlq5tiSoyBS+RvElh0RTKUCpDYQ0pedp+yJLfLMFNkTl98NyT\nGhPJCvQRQzzLRFHzJpWX/nmIUXL6DJ/Ba/AkADg/JvMGN39mwjlDTgSplQtJojDO2TrzxvC5Xl+J\nSm0+eSqyQksJJ2DUn0bJfxnVipqPJIILayNdkSbmTIyULc4oKldwvdyw/qZm9F/RDi1NXeOcuAd2\nxx0f79/z84efKXp5oa2zwpInYcF9StIAguRIGxFJizbZJKrG8fKrZ/z2d7/i2+9+RVXXPHt2i9WG\n/f6BonC4qgJtME5s13PKmfeBfuqZ/EQIWYurJfe47yIPjz8yDB0xBu4fH9myY9j3tB9ado9buq47\nt7X7kKNbdSazCo12oh+PmcCT+553dB1JVlMvSy6uV7z85jlv//w7XvzqFYdxhyocZbPEGotRPYVO\nLAvLFOciZTGazCFQKS9a8gCL9Eqrp3Q/os4Ji0qmKSRN0WpwRnF5dcGL17esLmQC9z4wjS3d8MDh\n9IFx+kRh1ii9JEZYL19gbE3hlqRGsVjdcP3yNbvdI117pD0c+I//4f/k/c/vOB5a9vd7+tPI1AfG\nzjP2nmmYEK5CGtUT5AhTjdHCkZioMEFITJEI+kwy2UykqUwQioXbWoN1DmsSfvK0pxEVcwaNlv7X\nsiyoFiW2NNTriuaixllDaRWX65pnzy9oGk1ILYXydKeeUxewJvHmxS03X18SXnREnWj9wMeHB168\nfEldVzx8uqftLNoZrm7WrJdr1osFy7Jie/8JpxXPr64p6iWulDrEn3/6Pf1+oNv3nPqBMHlUUhhb\n8rj4CG7k4bBjeziyP7RMYc5En5ewTHbn6V8jeTLLRcVm2XD/cJTyBBIpWfw0ycScDJvrFevVgs3q\ngmka6NsBo8Xgddg/8E//NPHj+4+0vqNa1RKENkz4fpDSaRLGapRVJC/vgcoSQKUUU5zwOTDsXAwd\nIY2eoETpkZhD2uQ9wcjokZI4KxUqm31kI59TUfNkInBp9KLCSU/odzpvXk+E5KzfJ5PvAs7IQh7n\nTPEkC7lVBoPJV3eWJ4gB6Uwxq1m9bjiHPCgtmfogYWtZ+aSU+5Nr6i+ykM8BPPMWp7WI/2fJWwhB\nbOhasaiX3F7fEJJE7SWVNckx0A2t2GC5o207dGwkQ9hapuBJSSRePgbMGU4Q/BSdKCrHalPz7MUF\nb799zdtv3nD78hnaGBaLksIZqqKirGqca4RMzIlnxjiCn0SPftrSjyfGqWPyE/t2x/64o21b2v6E\ns4bLixtWiyV+nBjiSHfq6U4dwyBhSz7L7vjM/WgLg3UmB29lTPwspUqCTVeG2zc3fPHrN7z97Ws2\nL55Trwra4UDhDMVyRVUvsabHoihMjghQOVs7CSyhMu4+vwB6DuOSY0uuNhNFjzaiYQexPhfOsFgt\n+PLrr/nVb37D6vICYx2THzkctzzsfuDUfiAxURQVZblE6YK6vMC6GpRBl5ayXrC82LC+uaIfDpwO\nj4zqyPWbDfvdnh9+/xPdcSB5Q38Y2T+cOGyPTN0kKZU64Zxh6Eb6fkK8BIkUA9oHdMwbj00YZzDa\nYJXOud/iiNXOUNQli/USZxNd23P3cc80yGCgtZHnoimoVzVF4yjXBeVS1BJNYbjYLFgsa7SGvh2Z\n+oGp7/BDwBYFy8WCF7c3mDBxHI/Y7kQqIs9uNywWDc3ScGprINEsG5ZNw2ax4nK1oS5Bp8C6abD1\nAm0dUwwMPnA89jxujxw7sacXRtPUib7fczwmhmHieBo4HFq8j+dT2Ywni6ZeNuWyMCybApsWnA4d\nVlumGGRqRkj2cZg4HXpubq64uGoorWXoZWgpSk3VrPAEfvzxJ/a7jkSkWpSE3jMNkvNiMyyYcnmL\nD3ONnj47NmUal9OTlacTIvh+zBLWkPtuc3JolpSkpM4ntRmRCJnf0FpDyP1JeUKetfaflzeciU01\nj54zNv6kbsnyA2YRwkwaw9OJYHZ6ngPPmIf+OUJhloKqDGcK0cnMg8QZhf8faCKPcfpsEZdMDJht\nreLwKkrRChdFiXOO3f6eGAObzSUAbd8RHyOrZs2iWlOoCqdLSTKzE3oSSdg0eYJ3JCc6aFQiqYCy\nifVlw1e/esU3337Bmy9ecX11iTWGcewIvsfWS66f3aK1JZLo+hPSvi6t6t57uqHLZc4PnLo9p67n\nYf9A13UkL8H5N1cv+PrLb2mqJd2h5cP0I49xJyTn3MsZ5RbPJydjhDCy1kp63OTPDSaJgDGKonas\nNgt+/btv+O6vvuf262e03cA09UxjoqjWuPWaenOF+3hCqw6tvLgFFUDWwBPxMcspFeeCWGPACNxI\n1CnnPMczcQQK6zTNouLZ7XO++4vf8du/+msWmw0paYa+Z7t/4Od3PzD6Hbe3tywX1yyba4y1OLfA\nmIKQRoR0Fo17VZX4cCKokbfffsmrr67Z77ekBbSnEacqum3Phx/vUD9p+sGTUsQVhsuLmsdPBx4+\n7Ek+DwYxkgYvM5GzuFVJ1ZQUZYFThoDHewkwc0XBcrXg8tkFRaU47I/Y6hOHbUeaxFJeOENRO4ql\no1xWmFqjC7DGsGgqVpsFyiqmKdG1nuO+Z5omisKw2Sy52Ky42KwpdGL3w57T4URdVJTG0tQVm82C\noVvhR8Gagw8kHyhMybJZMnUn9rs9xZhQrmAMgeNpYLvv+PRwpB8DhTWYxoH2hDDQd44wJvp24rBv\nxeRC5npSks0UhSJQWMWyKXh2vaIrLcd9hzU2Ry/ISTnFRN8NPH7a8evvrnAu0Z72HPZHhkGkiW9e\nXLE7SP+tSg5nHUVVcBymcwWdn7zo8UMiThID4UPARwUpZKLa5wXYPEllIwz9gLOyfszxyFJ0nPOd\n53WXOa4hEgOSbKgNY5hduXOhRHyCV84LNqjZPDdDIGpOtM8Q7iw3nTNTVFYCqBmuS2dMnDTLbDMb\noT4nTp9wepPmZE6E5P9s4/hTn18MWplzgyc/yrFOW6wVra3KmlClOKfixfCkDTbaUNiC9XKN0prn\nV8/54vYr1JAYdh2+24u0LptJUsyxqQqM02yultTLgi++esnrL5/z6osXvHrzBgLsHnb8/NMfuXk+\n8PoLx8XVM4bxxLHb8nB4j8ZQFwuWzSVaW3wYQHu2+0/c7z7Rj55jeyQlaIoFF6sNm2aDTQWb5gY3\nHrn3d/g+Mg7+HLsZEXxQayk7KKymKmy2yCdGn6NwIZOKC1598ZK/+Nd/zhe/+Q3V1Zp39+8JfUdT\nL7i9/RLjCqajZ3W9on5nKfaKKejcwJIfvIy3q0waSYEEWK2z+UiLmiYfIJVSIjU04j5cbZZ88fYr\n/vrf/Xt+/Rd/zvrZFZHE5E/0w47T6UBVbLjYXPPm1Zc05UtKt8ZajVJlDstSxNgDEzH2HA53HA4P\ntO0h50UbloslzWrN3f4D93fvaB9OHPctnZ4org2utKwWBV+9eU7xB8sQAv1R0hoTwOjFUr6uuXix\nwRSiJKhcwfX1NcYZ9rs9+/2ecRzADDSrBWW1JiXNY3NgbEd0AK0TtjLY2uBKhassrrJ50Uh0Q8f2\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fOvaPHbcvr3jzxVd88eU3fHp3x/F44Nju+XD3HlJEBUncDCmcQ7XkVTYoowmDZwqJqirl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RUhR5HkR/N1fDvdeyzMZoKG9Y6eFXIzU2a2zgwvU+0lf4lgm8cS2uHLk5clpT\n77a1rRufN1MdrM97rcylz59ztWEwkp4blVwpImuLfO00+G1okUvsg+TMZq86PFbpo0egawMEwbeC\nWMN4WrK3N6ZFiMuW1kdIWV/1wRNKT1EYJns1bdusLFg1/YxOm8mNflZkVRvqqkJMTb1UrGkx4onR\nEzTLEHloajGFzZE0miM/Ot8hyRAjRF1QFRVV5RiVjrosWDpDbDNJiiZM8lRiqS1YF8EJk4ljb1qT\nUqRtA4tO8b34bC1gpB+p5BwphTGoE5xxGFOsZD8rMCqhrhziHHlKf479jkFpmkjjFQ0JQnZ4Kgox\nS1hGekem5GsaMYzrGlLEt23Wr2PuLHJx8ggl9bHiIrnzCDFlgg8RNYoYg7WOmBI+KJoCKQjJC0SL\naSJJc56WlPIoLJpIR4fXjkTk4OgQUxT4oEz3Dnj91Tf4kc//KM4qk2nWbN+7/z6F26Mqjzi4MWKx\nvKRZzqnrMcFH5vMlhSup65qqqjZYdvs1+lY4Qa9p7S/+/jqDXPO7MDzbFVkNIyRVQoj4tiN5z8XZ\nGadPnjE7PSN1DeMSQndGNzuhvTjhfLnkqXGIGo6PH6EiFOUYZcro6QMmH9xitDel6yInJ+e8f/9r\nnJ7scXryjP39fS5mx5xfPGV2eUHXtXjvaYPHFEpIj7DmNyhHR9y++xqa4M6rd5hMDdaZtZIgV2aW\nDoS6IXusiXfb+t3auPVpsHjXGrVu/+rPcV1HujrD1jPa1rt7A/M5y39dri0Nm/VxCoiu73llr6+s\ncdkg6OvKeYXp+4axVbYreDnOzmGii7UYyc67PsKpH+ZnMk8x4ROkaDDzjmQMo72S8XhMxNKcXjDM\nJw6A7zqcLahqR4iWEAIxgdUckmhMJguxICbP5BRbUVU1tthDU0EMS0JQEoGg6wRTztg8O9LmvCeh\nDRhjQaFtWowKXqCTQO0Me+MKJE+1HxeGSW2xxmdCdootharMk2tmrWe2DMybnAfGWLB9srCkOQ0B\nCk7AFI7COURMHwuem44VsKXBFDlOMPV1GSMYU5CaSFBg0LJ1Pew1mrNt5M5OcqRJbQle6TqG6bS5\niZo+Z0z/vEwe8qAYQh+G2YVEMtkBCzZPQgqJGJXkIQWbHa+kTN4iiHGoEZJLLMOCk8sTpHTEIIzG\nY47qCXv7+3zm3md5895nsFaYTscg0HnHfNmymAv1pCLFBSEoo3EJKqQYqaqSqq4pypKBGLdFzav4\nVksuWyLZ0r+YAAAgAElEQVTsx9hv2Jc+2ijPxg2tx7cdXdMyvzzj9PiYkydPkBgpDUhsWV4+o509\nw89OmZ+eE5qOGCJdt0CNQW1JCBX24gnFyRRXVUS1zJeB09NTfLckz7WILJZLlkvFh5oYLTFVpNTg\npMQ38OTREw5uWOpqwtPHjxGUG7dvcnB0sJrZvO6arspNa7nk6ozIq/x7pctlNVy52hFs1vSmjLFp\nrF8rbchGJ6LX7nt1ss7zxNoT/xDYIP3Io+8QBrlyq3N4QUezjq66rmN7Hi+FyAsRSucoyiLrRtIS\n+3wikAkm23z5hmJSZotAE5eUPnDn7l2KcsT5yaLPHJjprGu7rHMXpifu7MhMSdA+54kh5TwTkpgt\nLglJmSKMRjUGRzCWtk0ko4S+k9GgYIWqqqmrEaiwmC8wqR/etp62XRCbSxah5ebNQ0ZHU6pxwXjk\nmNaOvdKwOD8l4KmcxVWGROBiGTi/7LhcJpZdlkDKSilR2q6foJSUqlCctRRFnrIfe8s3quYQx5jA\nGnzyfaRKbkjiLLWraFOHDYqEHPK3MvRU0dg7mF32KbhCwQSSxt6Df1Uzj6B5RihqEM2hMcPkpy7l\nSUYxGGJQghdiyHJBjHlEYzTPYFVJJFHKkcMU2Xl92c64/GDB46dPKNyYu6+8zq1bdzg6OuL27Vvc\nuXuHuqo4PDrEGMN77z/li196m/sPH/L42TOKMlKVUJiGoi4pij6yx5pVaOL2ULqX3mQQpVZp3D4U\nW/HB17xlm/HHG9+u/9okhQ2LSxiiivpZvikSfMC3LRfHp4RliyTlwf23efb0A2aXS27fvo1vI08f\nv8/Js/ehO6dIHcQO7X8Kp0QiMXWoJpaLhvPmKSEl2iD4aDEypq5usb83Yf9wn2pUUzdTmsbTtQHv\nO9puwY0bE0aTCmOE11+9xWQ64uzZB5yeHfPq7HU+P/phiqrOzvaVjJLvdE3mqbd6DYpdZVHULTrO\nNbJtKW/U2vq0awViIFztrex+u6ye72AcP69vPE/kVy3lzf02y6Fbn1dHbJLx0N6u7HvdJKq1f+S6\ncjyPl0LkVnK62sIVlCOHmJxAynf0qVPBGZczISZQNaQE0Sth2fHs2QloTrgV+/aQE/clQudZEleJ\nf3TDGiflyTEDkSfNOTtU52g0FIUBKSmqA8ZmBEXLYrHIERUa8N0SZyuqssK5EvUdKXlS9Ah5mnnh\nEsZ2lHXBjaljMq0QTcznC2YxYFJCPUxHkh2YUfEmPwnb54IUK3hV2pgt8dI6RuOSuipw1qAx0XrP\nsonMWyUag1iLOkeIkRACGrWfxJNIoSPECJKypR/p24iFGBEkE+vqJYt4n2deqpAnWqG9DJOfYYpK\nnnLUx9dKREXyc4xZ7zaS8E2eEAU50kVsfg5NF3KdmTzUkJijgSgM1kh+x1Ok8wtav0Q1sLc35snT\nx7zz9lscHhxw795nuHPnLq/ceYWohukHjzk+f7JKwnZ2ekwceTTBfLHkzTff4M03P8tnPvM6k8mY\nsixZ5f54Tun4KMt5PbT+RiYYXTdLdFMiWE1HUGiWS2bn58xOT1icnzC/OOXy/JSvvv1FZpdzqnKP\nvVHB/PKch/cfspyfYbWlMkoMhiSWZF2W8HykCT5n/yRPOLOuILaBzitIZD474dRZLudnRLWIKSnK\nKftHhxRFgRDw/pKuu8R3c2azx/h0zmw+Z7J/gO8OaJslxjpMYdb32xOZkUFaMCsjbGtq+9WQvr5S\n1omq8gO7jjtXDuG0LYoM+ybdCFHcemS67gw2T7hRrk3d/jrZ54VcO0g0et0lr7Q6XY8yrks78CK8\nHI1c83ARVcqyyJ533yFJV5kLrRFiyjHNaRiepEQMiYuLeY7fNga1hiT0PT99Aq6EOJuvJdkyd67A\n2YLYNKQUGJIvex8IvsVQUlUFrjBgKlzhqMThQ3Z4+i7iyf87m3K4XfSoBqxJlFYZl8J+VVGPlWKU\noC5wVaJrPbPQ0ErKUTEGWklIymF3XiFJ9gOUzhGS0oWAT4nC5JFLlgUMoinnXNdIEkUKB0OjNVnH\n96kPEettoeQDUVOmXJN1flIf/pny5CMjgjOC7RN+hRDI/cg6cY/0WmdKkCf4r8PfkvYJvKTPPxPy\nZCzfKUY1z+40grUOTYLvsuNWU0JiTsxljZJnQK0tp6iJxXLG8fFTnh4+YrlsefbkKXdeeYWmW3Bx\necbewRGT/ZI79iZuBE1zTrs8Y3ZxQu0KVD0XF2e8+15OuzCejLDW4pzD9A3n+uSzH7M9f52Tg1b7\nrpx+vdGRhvz5CsasaleTErqW+cUJJ0/e5enjd/ng0Xs8vP8uguPWrTdYzM44Pzvj+Nkp0TcUJuEL\n10tgCdVETLAMStMpWJtlPoHK1iTbEb3HJ49vZ8wuldYn6ukNRtObFFaophOmkyllITx6MOfi8pLl\n4hnmfsQWhvl8wf7RLRBlPJ7yyt3X2ds/oCwrlsslmsA616fjyLKLbk3eeZ681hb35izOFz2qfny1\nklR0w88Amoac5HmbDImahkyQV58Pm6Oq9YWH5zWU67pybPP8dqcjm6fbPBfDta475/V+nQEvJ41t\nTPiuQwphT0a4QigLi4nQxZy7OqXUk/h6cQY0k4IPAWegKhzGlqgmUoxYm3OUGDM0CNNPeDHUVc1k\nPMUHiF2Toy1UiEGJIWDNkqSRMpUYW6DGYm3JZLLPghnNosm5zLsANMzmlxQm4oxSVZa9sXC0V3Dr\nYEQ0nmgUb5VZO2ex7JjHDlcLtjJIbWglW6q+E4LX3krOklPXeZZdjtWtK0s9qXBlRUwd3vcRO9ZQ\nTwqKScnpZUPXBESEJCYv/pAUk0wfOdD/W6U1hUFOMEaxYnC2fwYOMCk7G9MQOSG9Nd5P4WdIsC+r\noTPkvOZJtfcjJFLIlruVnDPcSE4lYIzDFcpymbJjNOaMj7FTouZJWlmztzhxXFye8VtvfYkPnjxm\nf38vx+9XysnZY548e5/9o5sc3bzF4c1b/Mi9H+Lhg3f46ttPWS7PGI3ucO/ebY6O7vDw8Qc8evSA\n119/lf29PcbjUe+P6V+Sq5EP3wReZKUP7Xj9d39p8szmmLIPwzlBbK7Z0agijEsuXOT0+D3efefX\nePdrXyb4wOHRHeoRzOcnXFycs1x4YlC8c3hKuhSJ2vtFkqUN0CVLXY9zOKyzjOqaugpURcO8WeDw\ndM0FZxcXvLa/x2Ra5hFWZXDjkrJ0zJolz46Pmc+f8PD4q4TUEkKkrKY8fPiAi8tLfuwLP8kbn/0e\nDg6OePbBY5IK0+ke9vCIoihzvZvcQlFdkbjqNqFvat2qL+o0t63l7aihXkZLwirLo+a5LANJb+7/\n/OnXQ6U1+a5HBs8/440PIhvfbWo4w/bt/dfrXVx/ny+yF15O+CFZr0oaWDaLnPoVkJRyljzypKFB\nJ9TB/lPytGGTF2Noo6dwBaIGTYkQEw5w1hBFsxUSEk2CwgQKGxmPpxRlSds1LOYtGmNP/Imua4jR\nUZZ7WFdiXMF0NMbhsDpjsVjkTij4PIE/5Sn843FNYWPWkY2ybDsWMdDZnJ1RrVCOit5ZKiSE0AZ8\nl+gaesdunk5N8n1q2Zz4v/WJi4XHGIs1OdQPIb+cKGIthctT6EMIECImJGyA0mR/QbKGqFnWcdYQ\nNa0iUYq8CgYhpZy1MPb2RG+pD09MyO02x4nn+6hKh+vtRjGGQJZbosmNXGOf5KzLETPNAkLssEXE\nWovYrJWrClYcxoAzmrXqvsEaYDKuOTy4wXg0ZTIaZycmkRA9TdcgC+Xp+SPC28LdO58h+QVKw8Fh\nRUwzLmePODicUtV5qvnDRw+YjEc5bcN0muWC3jpcW2BfH6F/XGllGNVkKSWt5T8xLJdLFouGEDz7\nB1ME5ezslCcP7nP/a2/x7lt/h/Pjd7k8e5JHpxHm84bHHzzGh2dosuzfOKQwN7JvCZjWhuXylMvL\nPM/B2ZLSOfb297CpRWKLiKVwiboCxWJMJGjAaeDs+CmXi4jXisPLBa/cfY3XX30VHwWVmqI6IiWH\n04bC5RQSyTf45oLUzTh7dp9HD36L3/w7X2IyPeIHfvBHGY9qCmtB7CoqC2Cjj1sRxXoizbB9U+Zg\ne+f++9yJ9qmkGZLBDRZ9Hp1oSn0Ybp90rydlEd1qB1tata4nzbH5fd8ZPEfWrDsVXTnX+xMM/c6G\nUzPvv+E43Trfh8stL4XIAYaprF3bZosuKc5IL6Ok3hJnnZBm6LGT6Wcta04lqwbTp4RNMTv/kqWf\nSJMrI0al63KI32g6znHGrqBdRrARWwhFkWOiQ/SIL/rGXVG6mmJSMK5GdPstIbV0ockpakPO5xK6\niBJwCIsGZkvPIgWCE4piyDzYNxhjECwpJLou0bYRI9I7AUEIqMnkaIwhJGHZBsqioyoSVlJ2EDJ4\nwj2QEM3LD5mUsCkvQFGqwSF0G8NMY0FMTjCmIacLMPSdiaZ1sjEVnBjU5lV/MnJUi5gcdWSN9BEv\n65TCRgxBsu6Zl35TfIx5IlKErsudddGXP7fynBcmh4ZGjJW+eWRruejzlccUaNolKXnaZkGMHh89\nxbzkcrGg6QLz+YKyUExaIGHG+UXCWKUNgS6AqCN4z3hUUY8qRqMRrg/Z7G/xW2WUfyiGSIauy7Ni\njVjOzy+4OL/E+0CzXBBjy5PHj3j8/ns8efAeJ8cnzM4uaRctKUIMwmLREfSYkAzj0QGHB7fQoHSt\np+0847JEJEc1xaRoCNkoigHwqLYEH1b5/a2JeWKXCuOqQIGuaThv5iwidDFRlBVFNeLm7ddJ6RYn\nF49puwssntDOaJczjj94j/femfKoKjg7f8Y777zPzVv32N/bZ29vSkpKUdYYV+QJRHJVF9fnSHyz\n7rb33PyVf1KKqGbpLqfNsFhbcH5+ytnZGfP5gtu3b7G3t09Vjxie/ybxbvXNut3RbBLqZjTL1f5l\nc9vK0dp/sZkO9xq5/ppGwws2vCwi76esG5OHkyQwSShcQTCKkmUFlV4W6Csj80NvLRnAQkgBi6Ww\njuhzY/VdxBT97DLJcdExKU3bMd4bMxqNqOua2awhqqesBFfkWOeuS3TdAmcrjFhIhv3JhOlkRD2q\n+ODZA54ePyKmJW2nzBvPctbiLLRjAbEsInhrELV9THwkhkQKgows46okkCDGPv1tziiosZ9S3y/P\nU1iLT3mdzqbNy6EVTjNpSw6lDNETQr+UWrI51DK7JhmJhSQsOt+vWpSdwqXLblX1iumTW6FDVEm2\n1p0rcM5iJFv6oZ+IJMZkx7IB1ZxCl5TDRaUgr5+pATEWsRaSRV3W1WPKo4HgFehHF6yJPGv3Od2t\nc7l9CBbVjouLU1KwfWeeaJbzvKpSaTCuRE2e2WtdiVFPCjPEzznYm3B8eszf/uKXqKope9Mb3Dq6\nS12V7O1NuXvnLpqK9YyoQSqXb5zRNxemeC6lLmtCUIX5fMFiPseI49nTE87PLum6yNMPOpaLc559\n8JBmNkNE+Mwbb/JeWNAuLonBEiN4H1h054wne31Oncjp2RkX55csm4aDpqYqE04UjTnnStsGhI7x\nSCmcZ9mELOtEEHGUhaOuagpbYSc3WWrF7OkJZ2fHLLuA4Hjz3j3uvZ4T0KWvlpyfPUbSguQb5pdn\nfO3tc06e3idpZNHMUSkRlPvv3WI0ntK2nsn0kOnBAUWRo182nYmD8bomaNn4YpigYwY7fOtRGXIy\nvq5taNslMUaqakQ9mvDVt9/hi1/8MvcfPOR3/s7fwQ9+7gd5/d491gsbrJOqbS65Jhudyvp5bhPz\nh2FoBnLVqz5MiGRje1+Ozeid7WS8z+Mlrdlps0XoE4VzOfZZIbSRoP0Uct3w3G+9BIrtQ+FSb9VE\nFGLCFgZJJi+M2Q+VVYWo/QQFC8tmjrFKWZS4AqKP+JhQA12I5AVShETAh5ayqokojW9ZNBecHT9l\ndn6OTwGNBlWHjz4vemEMyVpsv35ozkeiqBoCWQ+OXWA5X0LKckJZZJnIOiEvKt3naQna58ft85Kr\nImJxVlCNWR4h5VzQkqffL9qASUIlhv2R43Ccl58LrcdHSP0aoWWZJxeZar14ctelfjFkSGoQ4/qU\ntyH3oL2zM7s4+4UrbL9OaN/ovOZYcRHy89U8C1as4gohRUHjhnNPc3RBzgNvkSRgBVNZqsJSVxYr\nlhiErvU0TYsmg7EWZytijDQLzWGlVhhNLKOiICVYtIb5LLJsF1RVwDiHbZZcLs44vXjC0e0Rby5f\nw8eWIpV5SUEZlotbtbZvuq2v06Bun09V6dqODx5+wMP3HzKftUynhxTlKE/E8p7L84bHj4555dYR\n00mJ4FGbQ2RtPeHG0RivLfPmHFfmBTj8WSQS2TssOHQFPjWkPnOmqwrqKSTrmfsIhWVsK0QNxvb5\n/UXwIRG0pagmlOOKzhuWyxnLJuGKEVVZM9nbJ6C8/e5XeXbyjK5bUhc5g6dvOxbzJe1yTlEYjDOU\nVaJdPOLhfRDT8vDhK4wnt/j8D/8YN27cpqrG+Z1d1c+Ge29lxcEq8xubcd/r6o3B0y3n/Mr/9Zd5\n552vsFxecPvOLW7eusV0OuX//et/k3fe+Sonp2c8fvQ2Dx78BH/P7/opPvPG91CPxuSRc/av9Vfp\n/UwwODYHal+V7SOe/+bT7wWcDU5bm9+9QMMwl1+3jvpwvDSLXPu82M4YqroiqWG5vFzlAtm4zW0M\nTy+RZ4Ya6UPIDIUpMD1RlM6BdURxNF1LkohPiWXbZqJOORzPOMmr1GsiYcCY7IgRSJqjUhbNkvnC\n45sZi9klBI8hL+GW19+UvJKKsyRMXgC5X3PT5tvFWUM9yhqcIebkTSrEKH3USR/3HvPkGR9yeoGi\nAFdmwnQmOx3jVpIxkL4DCDFRiVA7w8GoYFwY2i7mOPjYW4fSWxaDjtiHA6oMzlBBxeSwzpin8BvJ\nUSfGZmsirZpY1uhtjs4f8t72s0Ozbj50oDEIJggmJCIpT90POVYdVaKPq3j2WCqpICdBM0ryidAo\n3TKgmlPmunJE4ca5o44QUyQ0ifnlnJRCdqR2FTEakjr29kf42ND6BV3wPHj8Hu+8+xa3br7Kvde/\nh6ODG4h129Ort1rg1Zfpuhf44+rk2cF+cX7J+ek5pyfnzC9aNJbsH1TU9ZilNiTNKyn56Fk0kfn8\nnIvFgjYozpSYqsKmhHaJLrbgDcQsidQjhysT7XlDCh2ub1+mtFgVuqQso6BesGpzRJEYxEDnPRFP\nAcwvjlkEIcQ5gpDSksXilGVzjqJcLk5pfIOmiIrBFBWpscwXLaFtqOuK0XhCqx1Rj2n9IvtjyiPq\n0U2KUvCf/X5u3bybjSYlT8hLiaqsKF2JamS5WNI0i2xclRVVPaKqeklEe2emJkLwLBdzvvr2V/j1\nX/0bnF884/adGxwc7lNUFb/15bc5fnaaO8qzp1SV4+bNG5Rlxc2btyirGufqzd48T7obwhrZJPLt\njjk/W7n2u5X0Als0PqiLKz7fcIBvtbGrztsreEkrBA2LFuQ1F+vRiKSW9mmeoJMXRNispM1ZTtmC\nM9I744xgnaO0FaVxmGgRY9mb7lGN9lBb8+TsKU1zjveLbAUulrRtS0wph6BZaIJHjFCYgrLMVoqQ\niCkwX85ZLuaEdkZlInVREBGWXSCQk0WNakdVWVKSPFEGJbYea4XCgSuEyV7WK70POCs0AqFLvfOR\nTKgRfJu1c+tyEi9nhbqyFFZyyoGw0dNLTuNbFIKzntoK09qyPy7QGOlCpGn7hamNYJLgvZI0Ykh9\n50MvdEuflTJne9Te+kk6pFQw/ees9weFAkGNxRgwvm/mKTtZi6KgKAtImcg7L1gfCZrACWm5zLle\nBGIIxF5iaWyeBBW6iDWO6IW2Ad8pKeWwyGq0x97eIXU9wXeRy8sL5vMLHj14AgjOlEzGNxFjKQvH\n/v4B8+UZTTdDrOH+w8cIfwthRFVO2J/uUzi7mo13NWpgs/2tsU3m63dYnnuhV+Gb/fEheE5PT2k7\nT1FWTPdKfBdYNi037twmcYGroKiFs9kJ89kFHzx6wOzyBI2RqiwwTXb4LttICC2Wgto5XFFhbCSE\nhmY5g5QwrsyRSOIwpf3/mXvPJkmuLD3zucJVyFSlC7LRunsoZgXNaPvfueSSuzO0ESRnuhuNRqNL\np84M6e5X7ofjEZlVKHD3G8YNpSMTmeHXzz33Pa9AZehCoG8TpdYYIlpFjNW0ztEHB92a9vwCnxXa\nWLE3yCtOT79lNMnU4xG6FIO6hHiVl9WIvh3h3DUoScQqq4rVdkXWG8p6TRcU2pxjiyk5d7h+DV/9\nmoPDY1xUtJ2EnBzMDjFjQ06By4szTk/fsV7fcnR8zIOHj3jw8DFaF4IXZkgp4vqO9WpBu1mzWS24\nvbpgsTgl5ojzgb6PWGWpq4roItfnp/zp6z9QmJL2k085efCQ2cExxlayMaH31tFyx++bc31/M/9Q\nJPYxaur3P+ped7/jTt6v4ffWnnwu/b3P8OPY2GYZuKSs6FwkrVpiVPgQ95FiGkmL/9glRU+6TAZu\ndlkW6GQwqqBpxvzv/+7fM54ecXm9pnr1gvX2mpTWLBeXxNgPx3pFzDL0S2oIu8hCjdRYrBZL28OD\nAx6dHNKvb3HtihR6lNG4foONisl4hLXyNcWYSL2k44xHY1R0QlE0SpwQrWZUlkQvPPBKQUyK3otS\nU1ktHXoCawqsBp0TKSS0LbBa4WNAJYVVBl0UVLWhLjK1cVQ5MdJKxDjKEBR47fE54SPQMQxFpbgU\nNRijMarAZy/vp5XGQORJFt+HfdeghuFkTBnnM8kF2hwH6EVgDmVE8ZlipmvFNkErUVbGrseFAHGg\nI1rDjtCYgpKi3WZiiHRtoqpkSFUUJVVh6HuP6x1dGzmYlzx58owvv/iS25srXrz4M6/evsGamocn\nz/jNr/8tt4sVq82C2WHF9eKUxfISH1pSKDDUzKeH1HUjcXFDoPa+yxuu92LH7j+YH2Wq/HBXft/L\nwxYFR8eHNM2E7arn5nLN5eUNLnrabsvF1SkvX/+Zb198jcqJ4BzbzQarLfWooa4qtusFfe8IqcSa\nMbPJCQfTB/TdDev1Gh+XoCVGTxuFd6JN6KNi6wJd54guYrXoF6wVuq5PUdDJAUTLOdH3LcZAjJqu\nS3z3XWI0GWOsxfc9vnf0m0Dst7i2J6cGR6BkTDZztr4lpA4bAj7foNQCxTlde83tzSmvXn7Ls08+\no2pm2HLKbH7EeDQihIr1csHf/t//lb/927/h+uacx08e8+VXX/Lr3/yaL774iqOjhyhlub295evf\n/47/8h//A9/84Xfc3N7Qu57o3ZBTm8lJE4h0bYc2irevX7NadXz9++94+PgJn335Bf/L//q/8fT5\nJ0ym831DqXaRk/t7PBT2D2imdzU73xXe++vno+sif+TfP/yc9z7fR64fp5APeFcGWhfofCvH4yEh\nCHY90fcvxQ4iGHC0LAn1rvOYnCmbhtnBAZPZIePpIZGaZjpltb3i+vo1fb9ms+4HL3IZmIgq9M4R\n0DmHJqOrkmY0YjxqsDpCJxizsgXNuMH1nq3JTKcFmShYcCXfB7uBJFmGglkTApAUQWWC8zgXhX6V\nGZSMDJgxxAguJFQ3wEgRaqMpSjUUYpnPmAhp2MxmI02DokQCIdoIfQaPknzMIN28GjLzlIJEwhYC\ncQEYk7GlGG4J+9sMX5P8UFrtwxp8SHgvwiYzWNMaKxCQWC5EQogYk8QvPmuc83gfQQ8DTQxJRaFb\n7jYJP0A8GJTWjIqGyeSA6XjGernh6vKanEvW656L82sO5kdiz6BKJvUco2qmzTHPn/yURw8Cm26B\nKT0xebq+ZTxqiD6wXne8e3vOzRe3PDh+wHTaDB357sf7nfXHV+P//MrDy/Yc6WHhGmOZTKfUdaau\nA1pXRDLXNzf85cWfeP3mz7w7fUfb9aQYxGs8yU0fFRXT+SHbzYaQDFCjVMN4csjDJ0+4OncsV0ui\njyitJe0qOJwPhKQJWYbOMQciEWtK/E5IpzV5SNfWGIyxoBLebYhZo63B2JKu9Ti/lDkIGqMN1pR0\nfktKJc3omJwCIVtu1p5NL892SYZtjyKhciT6DX275ubqjDdv/0TZzBmNj3n4+DP6rufxg6ckF7k8\nu+DFt3/h3flrTk/fcXb+jrOzt/ybv77hp1/9guPjR5yfvuObP/6Rv/l//oZuu8L1LSHJzCCrjNIG\nraww3bKCJE6p243j8mLJu3cXvHl7Rt8Hvvrpz/jk08949vQJdVXvOed7GuP+Bn/45/vIyI7yeK9L\nV/ekZ/kHVtD9JmIYju45CT8Ayf84giB11+V0LhBjeC/BHQRH+vAZUoDWeuAgK3HYQxFcot9sKW3J\nuJkzmUy5vFqwacFWNb/81S9YrS/53dcbXr8xhBQJPmKVptAGoy0gVKVMwAePIlNVY2bzOSoH2s0K\nt11QqEBTl8xnI7q2pSgio7EYWGEyRa3p2p4cA96DQjjnyWdCsuSAFPCBKhmzImQl+4gRMyqfMi5m\n+q2j1xlnNWpkGRViiUuUAYzJmTwkCWEyk9IysZoShXfgukQbIj4pSfWRNguz67aUBEbEBFGDHSCg\nohy84lEi1CmMCEuG9CM7ZGO2rSN62VTqwghOLjV6YEFknA8ovKjolMG5REoDdVHrYTORQa907YpE\nksFuIbMTUzTMDx/y/MknLG6WpFiRtWGzWXN58Q2vX76lriqaumI6mZGiwDEpWB4/ekxRP2frL3l3\n8RKjDSfHh1xdXHF5cc0//MN/5/HDZxweHNE0TzBG/LR/qIi/35W9/+/vPWP7DYH9A/0eRU1pqrom\nxJ6sMs2k4jgfsNre8ru//2+cX7yhd1tm8we4fkvfb3Gqw3tH1prpfM7N9SVd3xFzQcqWohpxeHJM\n567p0w19KmSQ7XuC63HOo7QMXXShqVBQWpqqott6ui5gTI21DaasQRXitBkiRmlSNpR2xuHxY2KO\nrLq1nQMAACAASURBVDcLLi8umB1MOZgfMJsc0K57VFExmUxQCpabFefXl2QdKazGZAh+56WU8LQs\nvWNxe8l3L78mq5qqOeLR0y9Zrda4n/4VJ/MTSBLSknzk8vycm5sLvv32G65vrrm+vuI3v/7XvHrx\nmpfffceLFy8YNxZjdmpmuafaWBQFFoNFEYKImGL0WJtYLpbcLjasly2vX77jt//qt8xnY4pCWE2S\noJXfK9Tfv/HvF9uPFt79qW7XkN7vyNW9Ai8zLcVdEf8XVcizHgr5LoYrD3Z6d+Nh9g59Sh4oaw3G\niAd33dTiRxIDIURCjiQVcaFntV5yeXXOpu2ZTA+ZTg9ZrM65uHrDdy9+x+3ikr7v5WhvDSEmso+U\ndUnInpiCxJoVFVpn3r55ieu2qNhyVCdMqYkxcH55A6pnMlVUI0UfIj4GOpclZCJn0J6iUqAhRIFQ\nnM+020TwQ99nhF55dyJREgIxdLUpgI7gS4FfCh/RVjOuS6qyIMTMsmsJWQaoZWkotcTQra+33Kwd\nrUukXRcOg4hndxBQIgFPiXpUUteWolbE4PbiIK0EfpFiJPdGA2VhB+takd6DCC282x1HNXXV7MVH\nMcF8OmMynjGfz3l3+o7V6laKtTXY2ojrYoxoayjKUjoYo8kkptMDHj/4jJ9/9VccnRxwdX3Dt99+\nx5+++YbtpkVjef70gBBkTtF1S1Yrg3GKPm7ZbLYslrc4d8t2tSUFTfIFX3/9gunkhKKoODycMxo1\nGPMBHr6jjynx63i/0/rIIHSgXahhPYvoJA+zoSQc5wwvX77l7PQKaytct2WxvKGsLKNmzsnRE375\n659zfv6K09OXnL57SSgUIfW8O3/Nul2w7Tf0vacZzVmsr3nx+s+cnr6ga29IKdB1nuAcMXq0VlRV\nQVGWOB/RhUWnTPIdKkbqouLBw2fUk0NMMSZmw9npG7btmqoqGU9nPH7yKT//5V9xdv6O129e0m1v\nqQqL63vOtxes1q2cPrTjyy+/YJ468ilc3b6lDx3ZZ6JSGEQj0HUOaxzGapLS+OxZto7zmyVlOaGp\nJriTnnfv3nJ1eU7fdZhSmoBM4Pe//ycuLs75/T9/TY6K07dvsSbhXUe2iqIqpUHKwhojiS9RUVi0\nDvK8VA2Pn3yJKSaEZKiqirpqRCw40JdzFiOzPZ4Ne0rgx421dtYWd8PJvF8Lu9ep/d/tP4adQ+Ld\nJrHjov+LK+Tw/uzVWkMxxHsFL7zoPCB0GoWxhtGoEb+Rwu6ZE4aCrutJGQwIruq2XF6fMXY9yijq\npuT0xV+4vH7H7eKaznWQxctFhCAQYsSENFirail2Rib4q9WC4FpK5aEq8CHR+8DatzQjqGpFtuIb\n4rNsLDllkbyXGlPIm++jJkXoPbRuN3AUZoYtho3NDEIgCzplYpLINz9g6AmxqdU5YArBPXVOYkuQ\nBvaOFQm8QeEjdE646mZgzhRaY5NYHWQtYqGI3AzpkLVkeiYt6ek7z3GZc0qHk2XPLa1Fl7LZKDQh\niqDIGiMqUDRaWbmPw3ymKCqaesRkPMOoS3LSKC0PT1GKOMQ54aEXRSXUTGPQRotyFcV4POXZs085\nODhBq5K+dWzWK5qqYn7wgIODAw4Pj3jy/BBtLT4K/c5YSYvqNh3tdkuOBaX2ssmh74KnPzgr363V\nO6vVH3ie3l/he1xFPmeMCeccy+WKrhP2xatXbzg9veTJ4ydE32GU4uHxMf0mUNqaUTVFKxExtd2a\notBklWh7T9uv6NwWHzJ0G65uLvGxY7m8xftWoI2QydlgdImtLLYsMFZTZhFm6ZRxIVJa0LZkMh7T\nTGZkXbFYtXJioKWsC5pxpmo8MS5w/gbnbvB+jekzKVu6bSREhTEFLiTWbYvPHSE4vHd470kKfB6e\n6+Fkbk1CxwBmmOnEjrZ3vHn7ilE1493LU968eUOMgfFkjDJZGrfe0XYXLBcrri5vKYsa17Zok/Fd\nT86GelRjSosLnq4PTCdTRmVDoRTLZS+2HFrRjBums2O0qXC9w/Ut52dnvHtzitYVk+mhrIEdw2QP\nqdxh5XKKu7cC7hX+/Vrac9PvY+Dq/ivgvrHX7n/5L7GQ75VMWVjJZTE4ChrLer0WqGV4E5QW/5HJ\neCRy6qpiuV4RQ6CqSknMSQPPWYsE/GZ1hTKGgzgH5bi6fsdqdYMd0ulRisIYitISo1CWetdjlPh7\nqCxOf4qEDw6tEoWVSuZcoPOBdUjo2mKVuDT6JOHIISSsUpTWMKpLIgOHOxtCyPiQCUnglBBFdVpp\nUYBaq8hZU2TpkGOE5DMxKeG6G42pCvLgXpcH4ykFWKtJShEAqzTKFIgBlZx4rDU0pWFUGlTvhVOl\nBXxzsA+xCF6EUTFmGFKb9K6QI2aFDMyUohDGT0hSwL0bxERFSc6Sq6opJK/UquHrHDbsPolhly4w\nRUEzGlFWovdMSqGUlc/jAmaAYG5vb3BtpJs4nnXPsbbk6PiI3/zmV/jeo5WhGTd88eVnPHv+GGUC\nq9UKv96iTKCuLE3T4LZhSIhX1HXNwcEBBwczisIMR12Bf3bQyocKv/31Ufx892Df/7O8LsbIarXm\nzdt3LBZr5tMjzs4uWC4XfPHFJwRtSb6E+QHn9pqubbk4PeXs3RvOz99yc3POfD5G6xIFeC/qT6Us\n236Dvw1suzUKcA56JypZawu0LimqAm3EfM5oEYMpEkZDVRcSlTjY3PY+cHl9hgsLbNmhC4eyhk2b\n+ebbBde3V1xeXbFeX8sGmyvaDup6SlHWmLLg9OKctl+yXF/QthtS8gT08HxprBFSQMgZFSK6UGCl\nYy5Ly+L2mj/84XeEFta3G4qqYjIf43zHul3RbddkEsFHnAvSguU0UHEdqAKlDdZW8gymnvn0iOl4\nBlFiH3vfo7yn9R1THakrQ9f2LK9XrFcLHj16TFGOaEZT8W0aqI67gGal2J+0IIvL6u7O7wVOd8PR\nYeF8sEburZ4Bg0v7GemuQ//hIg4/VrAEd6k0aRCQlIVlOp0Qg6NrO3E0RDrHFOQmpZRZrVas1mtC\nSlSxoixKAPrlCm0yppTOcdsuubg8JURHu10TfE9MXo46SmhsKQe0MRQUdG2LJ1Naw2wqZkp973B9\nR1NCWWrKShENGC2Rakll3CCJj0gUXEyJygxBFNnQOydBtVrhUyLlBEYk9TsviBREw6SNgB3Zi1hK\nCmlG6YQpFD55ltsodropU2qNNVbELkYyL5dbR28zo8oyGpdMJx7nOuajkvm4YD7SqN6KjDtkVO/Z\n5kw0Gt8Huk7ERQwzCqOhrmXTK7SIPXyf6DpPKiAFKf4hiy2qLSy2qnAhYnTB0fEDRqMJWim6dsOk\nGVOakhSgtDWzCYymNarIuNiz6bc4l7BG4JEUFdt1z2W6Inbw4ATm8zmtW3F1dcnlxSX/x7//dzx+\n9BhrKpYrz8s3r/gP/+k/8+LFH9hsl1RNwZdf/USSgsyIyXxG8pdU5Yjf/OZfg7K8fP0WF7Z8+ukT\njo4Oqcuasqwwxgwoyd2ReXd9+Fyp/d/ne7+7e30IkaurGzbrfmDoRKaTKePxiE8+fcLl6Rlnb17x\nj3/393Rdh1KZb/98xduzb7m6eYv3a65vVtilWEDnlLG2xAVxI+p9T8iZ2eQIWzR0bst6u0EpR1kk\nUvI0paLQomVQSaGyJkWNsSVKw9Xygu3NBVsf6fqOyjoK29N7R7xds1pVGNNIdq3v0DqTQ6QoLUdH\nM4qyZjo/5Oj4iHW75O3bFYvbBWl49oxWGF1IqLbKpOTEzCoZCmUxSoam8/mclBI3t9eEVj6mnjQU\ndUkkYX0pNEs9sK6Mlqzd4ETnYQR+cX2gbdeEnNG6Yts66ioxHk2oxhNCB17B2fUZN4tbdMp415NS\nZjye8ub1tzz79DGPnz+kUjM05t6dVgPLTu3vdE75gzUgRfz73fluidwbhN5fReoOXrnD5X+4kv9I\n0MrgnocwHFKIdF2H0YpMoq6sxLQNvMqUxM/bhUBW4HonXWDKVAdzClsCImgxO+FKSGy2K1zfy4Ao\n7ZgYok5MKoPOFIXBZkXftXgXICVSqrEFknBjRPUpR0KIJDHk0tD7hG9Bew9akbKV0GUltqxdG0AZ\nGcgGhetFRQl5YMpIxy0knCQuhIP5iUeGnimL0tRYCDGw7RLBB5TPmGyoa0tZgUqZkII8VCYwG2vQ\nidHI0HWW2aTkYFoyHwmX23eR9SZQBI3NmWwUXR/wXuAceRAE7nG9dHalNSRtcCnh+0QOYbg/QwSc\nlm4lxkgKkaKumEymnDx4BHmYNzgHVtNUY04ePCRER9aRLmzJoRfNABZNiaZm0kyke8OgsfSd4/Ly\nnJg9222L0ZrZfMrzT54yGc+4uGh5d3HO7WLN1dWCxfKSTGC9WaFQGGVoRg2Hhw8YjWbUowk+RN6+\nO+XV6285O3vMJ8+f8ezpcw4PjxiPR3sI7v+LpXJ/ZHW/E989qyEEFos1m02PVoayrvn00+dYq6jL\ngtXiltvrK0qtsHVJiB3b1SV9e0uOWworg0vvMk4XFKbCmopRJcNJ5xN9n6ibY4yuqBrH1e0p2+0N\n23aNJuLahFWJwgwq26QkMNl0oD0+Q5siLomTZVGIEVwmi1VDyOQkbBijFVVRSMORo1gmFIqYHbfL\na25X1yxWC3mWB653yGIOJ/Cc6DaMqQbLDj14oxiMrolRgluygta1tK6jCzLw7btOQlesRiwwHD44\nUvTyOQCfEtuul2fQWIy1tG3Ljbqlcy0uRuIwI1pvNxCWJNcPXb2h9x3//Pv/gW0qlLX84uf/irqa\n7N0y8w4qGYr0x8rsfWDu/iL58LX53k97qEV934jrh1bgj9SR5yHLEqyWSLaNEwl2VUpxSiESfSIE\nyShcLlcYa6iaihiEQRF8hPkcayzGWBIeVMaWhqqq8V1iuVgMg1KN1kYUnQMdKRuJXBOalcZlYVm0\nXc+4qLG1RneKrBOBRJdEGBDIEjIcRPmYtKjQFBofFB6FzpHkI+NJQ06avhXxRYgihhlMBwleot20\ngcpk6kpjlEz2XRLzKa3UwAQJ4II4MHZKhDYo+hjJKtK7SAqJwkRcgKwMdaM5PCiZzUrGo4K6FGaK\nIdM5hXEKlRge1MH7JSnBtxHPaNdHrIXC7grTMIiNcYjmQyiJQE4Jt+3kaFgmtIa6Kogp0vUb+m1P\nU40ZPZ5wdHyMj46b20uCF9+WqhhRmYrKTpmODjk6OMRqS4qJ6XRM7zouLy65vLxgNB7z9NlT2RzI\njEYN05lmNp1yOD/k2dMvqMqKy8s3/PH3X3NwMOP46Ji6qjk4PmE6PaL3EeMc3WLDyxffcHl5zma9\npaknVFVDWVbCWtirMoYOa/8z7HDwHYi6Zxzcu3Zxd85FfEiUZUkzHnEwf4hRidurS96+fM3NxSUP\njw+JIbDe3NJtL7E5Uhhx2oxR3nsfIuRIURbM5kdM5yd0fWZx2zKZPKKqxmQSqtCcnwVurqWQb6NH\npUBTVeJOGcHaDDpI4IqBbEGZLCZmFEI7VFYGtRG8j+J8qQyVhRAgJUfXr6CA7XLLarNhsbql73uU\nKQacN5ER+E2nDEmjVU1RlJS2wFiGYgwxWApbYUearuu5WV2y2WyoipIcZahOztgoQ3YfWvHaZ7B9\nyFKgfXLU9QirFUolun5N229Qt2qHPKKU5M2m0OPdZlj3hs4Frm5/T+elIXv6+DOKowqjq+E5gO/j\n3bvfvT+s3P/7B7/uRyn7hbLbINgLJ++W1g83Ej9OQtCQwj5QrQfXuwFvGkQlRaFJYXfDZL/LIeLb\nXhwOh8luThljLU09YutW4jNsLI8fPyZ0ibM3F7jeDf+v4VikARvxqcNFhVUFSkWUlodt03WoUlEo\nI/mZhUI3BjsqIEa0TyI11zK46fpEu40DD1yTrTg0Jp2IWeM8bLdBil9mwFHUwA2VQl1WmvFMM2os\npddkk1l3mTD4n4SQKAbMLSe155orl/BtJETx8TZaoSpDxhJJVLXh8GgkNMucaKOjSBFKw+hwQu7X\n+M4RBhtDPWB+DOZZKikZLvmMd4kQ7733w4lJDxDMzmnO+0hRFCTXcvbmBddXZ8QUWSxviD6z0Vv6\nznF4eAQkrq8v2XQrxtMxzz/5jFF1xPHhM54++pwvPv+Eylq6tsNaxZu3b/juL3+hbkpsqRiPa65v\nLrld3nB8ckQzMnzy/DGkf0P/819zcfmWP3/3e/7u7/8TSnty7vBsmRyOmB8c0LUS4hFipqkP+PzT\nn/Gzn/2cp0+fMhqN9tDK/SsP6qj3Cra6e4jvjsh5+E/+xtqSk6MHlGVP1oasLcvlhuX1FX/6wx94\n+d0rLs8uuIo9h7MjhO5kiA5in4kqYUxFqUQTgDKYoqIZTSmKCWXVMB4VGDuWwapveXDyGNdtWNxe\n44MnBlC5pKxnYDKZQNSKuimwBfRug7YZM8Qlxpzoeo93juh37piGupIQcKKADb1vWW2WhNtrMAXa\nFvvhdNZGXEUVQhEeTdDKCttJF5gs4rbDgwnOO7reo1LJwyfPqaqa3/3hd7jB6ZJhbSrEZC8NYTQh\npuGBAOG1Fyj0PtQ7JJmD7byYlbYUpsBaEZuNRw3tRtKYlKlJEWG05cRyteL03Vvevn1FVdbM58ek\nuMO+h7UwFFmt9Z0K9INfd2sjw/7178Ep7PsEed8G7D3nYcORD/xoTf2RCrl4c6QoboVKC5vCFCWo\nRMji7e0zhGFn2lHmdEzDmyH+KNvNljJWkseIxKBFF1ktVqioqcqSg+kBzjlWm7UUdLFhIesojAaV\nKSpNzBqcdLddcHinSToOVq4KF8UqNgPaauF+B0mdCS6Qopw2fDHcuJRpu54YwHnpeCQAWrC1XTK9\nD+B8wgVFrTKm1JSNomoKEoGcEr0T1oVCoVRBIu0TlHxk4KxDUYu/SUJJcj1isYvOQo/sOmxKaCwJ\nQxcTfcyEFPeFaDfAkRmyRMbturEo1iioXRybkg4ohjRM3OXrSErje8cqLVDbNYkkD1LSZK3Ythuq\npsYoJYEUA42x0CWlrShMRVHUPDh5xONHx9S1IefEs08f8+DREd/86U9c315zs7hhtVkSh+/xYH7M\naGL57PPH1NWIy8sjbOH54x//gd6vyCrR9VsuLs9wAapyxvzkIeORKCUzkRij0CGtGWhuH1wfeaC+\nP4jasVXuhp95GJI1dU1RVZRWc3V+zeX5lQiVU5K5TLehrmaUVUlZjamqKV1oCSENDpVyesoqozYd\nSt9QrjOTyQnzgynj8YzeOfyiY7na0Hsoyimu3wqWqzTb1qKIaG2oS0vVjLGlonUd5IQePIR6B20X\n6YbACo2lKkpUPUYpiw+9yN0J5M6z7TsSDlPWsiYyoveQnV40HCGK1EyXHM6PyTkQwhbXd6CE9eS9\np91uCSHQu3YgNUi9sKoYrJ+HY/0g9hGbH4UtKowpiCkTeifePhnxNYK9B3oIDmMCtujxfYdzPT5k\nqlqyRmP2BB9ZLDZ8991L/q//+F9IUfOrX40obMM+Yei9dfB9gOW+VH9XyH/o9Xl4wZ3mIO8HoHf1\n/vvF/MeBVrQebFUzIUm4gbEFZVXjo8d5oWqFJH7WVgkUYBgm7YNLXciZzWZLTJF6VFEoKzevC5xv\nzylMyaiacnx4JA+Ic7jQghoKuUoS3pygtJqiUqThlBySxzvQNhEHKt+mEyxaK5HaJ5Q8WAMPWyT+\nArnEBNEnfN8DUBSC36shmSH5vA+cdlnR9YpNqyjqKPav2lCPLBg1+EV7nB/MwYpCVHk5oYy4y4kv\ne6YoDUVpQItCMviIaRN5VJBCZNn22CxWuCHB1kWcH4ysGAr0biEN5vw7V7oYIYbh2Cf0n30LEYOo\nueTlihiGVJooVMmsByiG4RRmhY+fB2GQHjzSu00HcUtdbOj7DmMN88MZxycTIHF0MmEyq/nmu2+5\nuLzm5vYKpQM+9UQcX37xE2aTQ6bTGQ9ODiiqyOnFIVVTE7KkQLVdy8tXf2F8teDps8959PCEojBk\nHGdnb5hNGx4/fog1lmIYpu+6JAZY7QcZB+w2wnsfM/whpohzPU0zYTKqSdmxWa1Zr7Y04zH1aEw5\nGmMKTTWZilf6pGATN2xDy9Y7gg/03uOGFK2uW7NaR4xZc3QcsWXDeDLFWPHTub1dEZNmfvCIxe31\nfpi4WbaEsMXYRIkma0tG4QIQE3ZgNbVdZruF7UYar8IajK3BTMhK40JkPBpR6oi1HtVtCVHmNRLK\nLQXIWkNKELxYA+icKAtNVTWk1OF9ZLVeUxQlxhT0XeDi7B1ZK9rthpwlFMNiqYsRGkXbr8kqAlLI\nJbvXUpQ1SmuS94Qc8ClitUXpGmsNCkMI0Hdb+rSFfWCLpiwbmmaCNSWuj2zWLTHAxfmCf/zHf+b5\n88/58suvKCbNnlUi9/4OYvmekHFXxP//FvLvsVw+vkHcv36UQu7i8NAbSVnXOWNyHo5tBTlrfAyg\nFMZqLKBjQPjIosbMOYvtavDEYDCqGbwZHF3fksl451i7FbfNgvF4zONHj+nf9rgsXO+shHGRvScZ\nCRDWVsIt0Ap0FkpjEKMuQ5IgBaXo/fDaYVNIQyJLdIkQZEiYs0JZ4VBry50QLEHfZ5wT7wdtEW/1\nnFiuJdDZR4O2FQdHNcYqnN/SbhxtF+kHUy2lQBvLqDRkVaBzwuqELTTNuCZpCKHHR4eLEvTc+kSt\nFCSJrQs+Dj7oQvFTRok1d07cJWHlQQEnEI9sWjuK5vtQg7CR1IBTRshaOjOViSRKaymLgvFkQvSB\nznlc3+J6Twob3uUzptPIyfFjPvn0EQ8ezXCp5dsXl4zHDZvNmtOLC6rRBFvV9CEyPah5ffqK2//z\nnK+/fcpXX/6cr778OarInF6cc3F1gS4KsVj1Hl0YNpsty+WWtnMsb28ojGF9e8tfvou8e/uOvvP8\n9V//Wz77/FPm89mdLQQfQijfv74HxcAwfxDIaTqtqRvD6ekaa4V6eXl5ymgy52e/+g3Hx3MO5kfY\noqDt16z/c8vp1SW90/ho8EMoSdv2YtjWwLZt6fp3LFZrTs9OqeoRxlhOHj5gPB5TFIY3r14yqkaM\nmglvXp1xdv6S1fqCTefoL25IObBtN2gdMVaGoD4WuFAMdFixeU7Zk/KWsihRuSBg5JSqCsqyJvs4\nWEdbCQmxGVREKTV09A2uCywWK77++o/UlaaqoW7UELTR07aJ9UqcEtu+xRrxj5+ODqnLETFGrm8v\n6PwKHzvikBCWcsTGiHc9zjtc9KS+panGTCczHp08pK7GeAevX75gvbkh546sogyOmzkPT55ycvKY\nqhxxcXFF3zvKsuLTTz7n8OiEGNO9+Yhc76uAPzit8f0Cvrt2Te3+z7smYdj/xWzt/pr6+Kr7UQp5\n3BcOkYnHnAkxo0MmJk2OFoPBFAajIpaMSga1G1Tm3fFIdqrgA+22FcvULAM4gS4SKTk2mxV2GJRa\na/Bei2kV4vutElTjCmUjIXs6Jwk8srXKgjRak4NElBmlCEm8L1IeGBsDXCZCAYF5tNFDIk9G6wFf\njgKphCjskH2Lqg0ZzXrthTOeRe5va01ZajGKHdgwKThiTBSlQWlFVglUEuglZ+LgpU6OQrXM0DlR\nk8akUVZhtUbpzNgAEdywee0Gq2L0L19HjOz9xcPwPTN8z3stw34hy6rbL+ws7okpC6soqoh3jq7d\nDhimWDQI1q4pi5L5fMZo3OBDy6u3L+h9x8XlGWUt/OkYM4+fPmTTbfGxA93jworzqxsW6yvenr7j\nm2+/5Sef/wxjSrZ9z+HRCVkl4VmrxGbTY43myZOHRC+sKW3klLRtO05PL1itNngvISdDfsw+7OR7\nQyruOq/3Oqr9U6mwxjCbjSkKS3CRvo1YUzCdTgmh5enTx4xGDWVhKcuGbdtxtVqw3Hhap6maQ9x6\nhVKW0WhCCCuqesTJySNSloFdiIHzy3cUZUHdjBmPJsTsKQpL1/dMJzOmsxlHJ5719oZtdwsKylp8\nZtbbtdzfbFCqQOmCoiio6ineRUBRFjV1M0ZlcO2G9TC/aEYNRVPT+cC27Qk+UpUV49mYlKOEmPcB\nrSwpBZzzkMTKOWaN0oUUyWw4mJ/Qth3b7YbSltTViJOjB/zki1+iVcH19TVXt1eD+V7aM2q0kc3a\nBS8sLuT5MxbK0jCbzyjtmPUq8ODBc6qyYbW+JhMoqpLZ7IAnT55RVRNcHzk+ekhdNxwfH/HlT37C\np588p6kbtOZjjfN7Nfx950MRC70/Gn3/dXvdAh+gd/su/YevH8drJctPWstRPmdFTIqcNCRNqQua\nyZhMR0qtuA1igESKjuAEVgCGblHCGqRwSlJ8jgOGmxLbdou1hqyikISyIvSD414UjnfdlOgy4lKm\ni71g6YDKhrKoKYuC4AJ26EJTVLjoZLHkoSvdd2xSzHfaADU4qKUkxTzGHV1vN5sRg6gQNZutl0GW\njvjkMWVGWT0k9Axw1DDU0feSeiBgtIiZcpauPQ4Se6U1Xui1kOVE02gt738n2HQIsvWr4Z5oI6cM\nUT3KPdtxZLXavVaK/X4zutep7oRXKLVntki3IRS69WpBWdYidkhyPNbaUJYVk+kEVOb0/C2nl29Z\nLK84v3oHGsbjMQ9OHvHLX/yWx49PuL254OrmLc71bPs1l1dbXr58wx/r73j54h3Pn37GdDJjNj8k\nK7Driq5dY7SlqmoeHB+zuL1lE3qUVRR2xGg0xhYl2lhQovhE3THC3w8GlkdzN1fYvQM7RoMaXh+i\n0Ea1znRtj3cJsqZpRownDZNpycmDI5qmYbtpcT6x2LS8ObvgZtmCbnhw8pCYXuODZzwakymp6oaj\n44egFX3fsVwvuLq+YNMlym7Jal1TlCVaa7p1S1EYmqaRwlZbkbDrQFU3WKtR15aMHCGNrYQGaiqq\neoTrPWRFWdU0zRhyprOK6FtIwiWvrUFZTUxi9KUV1FVJTAmjCoyK5ADWRqq6ZjKayAnU9zjHgPts\ntQAAIABJREFUwKIpODh4SGHXYgsdvbB8RmMePnyId4nVeoV3ogKPgw+QNHhS1GMIZNKg4DaD5YIj\nBk8XerbbwGRyiKIgJUPGU48K5vMj5vND2q1nudxweHDI0ydP+fInX/LTn33JbDoVm2t9V5XzbhYy\n2Inc75533fju2rFPPnRNfK+jVx+U7V1l/5+gKz8OjzzdYdHFEAemsTT1mBwVk/GMX/7yF9wsTzm7\nfM35+RZbFGhjyUkRQsTndLdrZXHa8yGgtPh3G2WGYz+ywMl0vpMbHDJx53WSINlBdFQmtEl3g7wk\n3geFKRk1E1SlWa9W9K6nrEuS3w3G1BCoIO6IIcrwTrM7HmXxbE5qSN8BU2VUlA3HWitBui7Qu11S\nvfDGUQkfFEUlg86iVPvoJGMUxmRKYzBGURVCGYwh4J0jZz1sKrJodFKDpW7JvLKMjaZwW4J3rHwC\nJcZXfuDXqsGjHC1ye+nYGRwQd7a/whvPuymnEhxfKFQCtu9OJihQRu6L9z191w2dqnydfd9zdXOF\nLko27Za3pzUXV2csV9d0/RoMjCdjHj56jCITfaLd3vLm9UuWm2tidlhrKIsGhWG1kqI+qpdMpxOM\nGaFVz831GdEb2uj47//tn6jripwim9WKw/kJh4dH/PVf/1ueP3/GeDyG4fu5Gx7snPDgDiO9W973\nMxl3A87FYsXZ21MuL6/RyjIeTzk5OWE+H1E3omLb8dWruma1bklkbm/XGNvw5Onn/PKXP+Xr5p9Y\nrRZUTU0yJd4HlptdfmmP8y22zHSuY7ldQtZDgROaabtdcn72hvFoStdtUVo8SBaLBSBwpdJZzNJi\nZjqfUBQNbedw3pEzmMJiCsXR4REPfvkVZ29fcXb6louL0+Ee6yFURBK5Ni/XZGUYj2fMZwfkkJmM\nZ1RVzdHxMVdXV5yfn6KsFgdNU+O9BkqqcozJDud7zs7P+Lu/+690bcftcsG2XeFTBypSWLs/Je2o\niXK6FAWCdz03XctmuUUxQjFmPjumLGoODh+AgvG0YX4w4ep6KXTYFLm5XfDJJ5mDwxnzmXzNd1mi\n388ZTfed/3aDynt/3l0/5Kr5Q8EU3C21j14/jkQ/Jkn2AbIGowyT8ZSffP4VhR0xnx/wi1//lP/x\nuzWvTltRrA0BqllJzNlOMs6Q0iNx9YJ9J6AoZIiWsniwiMIygpYCIzJhQGW0ygTvUCHvHRVVQrp6\npem2AZ09k9GEupqgsbi+JQXpttKwgJRR2EqhAqSQiCFj0IhjphKPZ6swhcGqhB6M65NP+D7iXJTs\nzqHjSxlcK8PdqiwpKkU20KsISlLsq1Jeq3fJQ1o6db+jBqIotMSY+WEPKEtLPaqZVAVdF6l9okyB\nMPx7ZoifQ4lZVpIirZTcL97rM4bFpu9+2OFjchY4SDI+1UAx3bFc5Ic1gpkXVU3G4IPwypXJ2PKI\n7WbBenlD22/Qhcb5FudakndoDJv1lsXtNc47tDEYM0ZTk0NJt4nYnDA5QupompqD6SOqL8asNyuc\n7wVyaLdsNy2bdce4jhRFycnJMaPxCGPNve/y3hrOH//999Y6kHOi7zrarpNu2BSMxg2z+ZjRuMIW\nmpgSznliEm+Zm5sl11e3pJg5OX44FCpxOex9po+t6Bgy+JjwQbpaHzoyEa0SSkV618kgOmWMsmyi\nx7uWdisWtJLPamRAHWVeklMm5ABREcItSq0Gy2KZW8UY6dot68Utq9sbbq4uWdzesN601HVBoQsA\njDGgLCFm+s6zTluiA6O1eMAPNptFXVKOGmL0TMYzDmYnzKfH3FxfsNosaPutDMZtou1XrDcr2naF\nMYmirClKS1OXgq9HoTyCNFS980Q3MHxIOL+lqizzgyN++9tfUJZjVuuOEAPz+ZjxuOJPf/oj3kvg\nR06K68UVN7c3A5Y91Jv70No9ZlJ6b2Gwf07uEMj3rR92fHH2//6x9XXnQy6/mu+tsx+nkKfdFyyR\nb/W45vjwiIcPHjGbPGB+cMjsaE5UgbZfEZIT3+tsQCdZpDvOcwTUwHpgAM8Hqo4e4qvyoA71Pg2K\nyoQ1GWt2YhbIOZCHIaUk9WSSl8/hUkSnQFMqqnKEUZa+dfuOfTc4VUpJdFwewipSwmqDyloglSwC\nGasVyigKqym0ZeucDBFDFLny7rZniB5CryAKG8UUDGZDWpzbioGGlZIwagZ17O6BLK3moKlQytD5\niPNecEWtyIXBNJais5Qu708RKEXYfU9D960VaJ1JWmhyOcq5crfAtFYoMyhC74VjJJIwF/QeaHhv\nYSolD3zTNCQkeKJ3W9quxPuGGDpR6w25qt51LELLenmDNQVGF6SkaKoxzWjGbHpEUTYUtmJUjTk4\nOGIynpJiomlGjMYjmnHF7fKa9XZJSoHLi3O6jSgmYxwgpaGzkods+Lr3C/jeWn6viO/RzfdeL2yF\nRFFa5rM51paUVcVoUhGiZ7Ps6Pqe1WJLyoqDg4MBnw+MmjGjeirDex/J2RAiuE6aG1sUNOMxaRPp\n/VZCR0gYq6gGs62kRIizmy2FkGhDL12zKTDDSSANQ+yM/Bp9xq96UpZTYwa0MTilWDvHzeUVp29P\niT6QksAuWhcYU4iveRYrgbIqyHEz+PEEkjFo7Wi7LYvVAh89trLE3tNMRhw/fMDJ0UNi6rlZXOA2\njpiC+MRoR1YdSjvKEsqqoa5qmqZg224IfjefKoSC2Qd0kihEcTLMlIVmfjDiN7/9KcZOePnqgpxh\nNC4he25vF/jomM1nKDSd29L2G8Kgdv0Q5rjPRtn34zv1527Upj4wwtoX87uu/WNB3fvP/xFO+v3r\nR6MfDs0wKHj04BFPnzzj+uoKqyaYouLNP7/izcUbfOowxcAc8RFIYGSoYawiDrujcDqj/A+URIdJ\nBJpFKY0LHuf94EwIpVWMaglDUFqhS2kdQ9TCPOkjyWeyilTjMWVR413C6oLC1oxGU/qVJ4UAZn/A\nFp+Se++1tQIdhSBDsxSVBDsgUWoxR/o+SNeUd0VDdv0BmiVFaLceazJFYxiNCrRWQ8p8FKWsymgl\ndrXBRVyf0FrTjEuePzjCGMP1asP1es3taoP3jtZLnFUqDVm7YYNTqMJAPww6Q0Zh9j7lCeG9e6Kw\ncgZMXRmF1iLmEl5v3gGCAsMgUVwqi8OjdPoiIOr7Hm1byqZhMpsQQmDbtvzluz+zXi/3FqwywZYT\nh1IBYw11VTCqZpwcPeXRw094+PAzxpMDmnrEdDzmwfERs9kUkuLqesF6u8UUiulswu3imrPzc549\n/Yy6qLm+uMSYkq51/Pm7FxwcHjKeTjE7GuYHkypJ/Hnv7Lz/3Ycn5+lkMgw7p5IJq6XBePP2HW/e\nvGG5WnN2eklhK379q18zP5jwky8/QZHZbLwwjKLj+PihSNb7DWa9ZDKb8OzZE968fcn5RU/v15Az\nhbXUTYVWmr5zeOeJPmGMoi4kti1n0Rr03XZQSge8D+ySniBT2JLC1mLrOmzsxlhSleh6x2rdMWrG\nFFajsqcoFMUgh/fOoa1lVE2ojODqk+lMhpGuo/cd5xfvUEbLKbWyJBVBR55/8gilA61b4XLHcnlD\n57d0vQbtKCrIUWO0JobIatmSCZB3hp0ZlWWoPx5NUYgNR1aZlAMhbimrjDYJWyTmBw9Zr5a8efMG\nF8LwbGWq2vLw0SHPnj+S2ELYD7vfq8y7Yr6H2/J+RjTwevejpH2R4gPo5b019CEM8wHM8sH1o2V2\n3k9i2WzXnF+csV46xuNDTp4cc1wfMBnNMbohBb/vnHf5viABBjsPc6UlSDgneeMCGRUjKgWMtVhd\nkpQSK82h2FqraBpDWRuwmt5n3DYRXCJ7IEIm4bqewpaMGlH6KQ1VUzPJU6wz+NwTs5MA2jxwVqzC\nKivCAsRQ6s7bO6GtQDoqDh3Q/RuVdzc/Ywooay1Ku1Kjq7tuL+aM6yPGSMJPyJlSG+qqpDiUo+S4\nBnJPjIqcPJXRQ+qLZdPDpnMsN0H4w8PQToy6BGIyWssR0WSC1GhJXU+DuhDZDPWOf+4FZ91N9dPg\nf2OMRmczbOKSzel6L0HTyaP9llxkqhJ0AckH+l5CfUdNw2w+I6tM7zvafksIjpQcIazxLlOWIw4P\njzk4mgw+KQ9oqobCljR1xXw+5vjRERcXl/zlxUtefPeCi8tzcs4czOfMxgd8+vwLTk4eMRnPiFFL\nd57yQMeE/dAFBrj8+13S98ObpXEZT8fUTU1RFlJ8YmTTdrx7d8bbt+ccHMx59v8y915PkiXZmd/P\nxRUhU1ZlVauZ7plZgMDOLhbG5RrNljTy/+YTiYc10gyzMwMMgJkWVV06dagrXPLheERmdQ8onnqj\nra1URmTGjevHj3/nE58+YzGfc3FxjLXgx0DwAz44uqFju1txe/ee7XYtghgf2NyveBUcq/U1Y7+F\n5LFaDt8qRWpj0FWNzZakZFPW2qJ0Q1NPmNsWlTV3d3esxxVV3ZQQhgprG2IQxXBKAVNJqtGzZ8/x\nznF3vyKkKz7/4gtmsynOdfS7LUPfM4yelBDhj6l4+uQZF8+ecny85OtvvuHqckW3W0k6GJmshYY7\njo4UNLP2WEzRbu/oxh0hO3IKbPuOFIIwzapG8nFzZvSB4LNYesRAVTsgoWwk63SoC0ollEqEMPDP\n//w7hhFev7nh+PiCvu+4vnrP+v4aiAS34fknnzCfNhwdzUv4+KF9fuiyC0aisqydx/fBnrZaxOuH\nmrd3gP24LmdpgsiPuQPlbvrxffX48dMU8sPgSPDDzWaFGyNjD+vdioTn6fkZ88kSlRtSKPaz5vA0\n6fAUBfuSv9EaspZCmFLCp0jynkaLn3WlaskFzbEcFyva1tJOLdloQgqk6EguSfJ8UqWQDxitmc0m\n5EZ8XaqmYpImAvEEJda00RGTBC5ba7BoiFnChsvGpVThwu9Nsx5BDForlBK8UmkpqlWTaCaadtZQ\nTw26yXjvS4HVBCedvMqZEDVN2zBrJN/S+R06CU4qySOR2cRSVxPQFS5mVtuB9U5EWMYqEYIkCY5A\nK5QVnmtQQtM0iF95DjJUVVkKlaEENkc5a2SrUFYVjFyhK41S5iD+0UqDyXLUDomkPSFpVFDYypJy\nkHALEm074fTkHKVh2+3ERyNrYvSMzuP9BmsrpvMZd/cfOH9yjq2eMl/MhKdfWRZHU2aLCud33P/2\nlrdv3nF7e8tyuSBOEpN6zheff8XxyTHn509YzJdobQ8ZsgfrZR4X748HU3+2W1IiAmva9uF5ORWf\nE+HxW1Pz/OKCo6MF8/mU6bTlzZuXfP/qBW/evGLXj3RDx+A23Ny8x42O2fQIozS77Ybry7egHJkR\nnSNtU5NyxI0jOYLBYioDxhauNWRVUdUzidCbLvA+s9nsBL6zNVU9oZ0sWa9uGPotKUUqaiAxaRsU\niaoy1LVhvpiyWC4ZfY0PgbAb2PWj0EmRWVXbNkynLU1jCKGnHzYM/RqfPC5GCTGvDMFlVGp4PfnA\n/eqO27s1nevLaS7TDY5U0r1qK1bTiYiPntFrnMt456lzwFqBUl2UIJmUAkonYnT03Zrf/f6/0veB\n+9XAbPaOEDz9bktwXqDWuOPZxSltY5jPJqVhkfWWCyVNPTo97+Ma9424NEalkPNQtCXMPD/8+6OK\nrfT++fu/LN9HS/vzr41jfiI/8n1mp3h5jM6jVWA6XbJa3/Pu3SuefXpCdB43iImOMlIE991rygqV\n9oqpPZ65987WZC/cdD96Qkg0TS3MF62IQQKJ6roV7C+LZD6njMpaoI+YDptvTIGu+KRffPKUo+ZY\nwhNihgCTegJ1hfcDm9WGpq5prERKjcOIjpI0zh4+2VP6UiZ4OV6JA52hqiqMrbCVpbKamB2mihyf\nLLAthCRsj1kzpa0naOWJThweo1eotqVtZyxmNdfXjnEIVDHRNJa2qTitBE9OuWbTRVTe4V1i9Jm6\n+L5YpcQ+OCZCzvLjqlxwck304j0efEG9dUIZGRCD0B2VLhz6SmL5lCknKIRPnrWkLelix2qsJaEY\n3ECdLGEMuGEQh74IGosxhulEprn90IkTYHDknHHR8/b9G+5v/zdevnzFX/3V3/Af/sN/5PTkhFkz\nJeYB7z3b7YZ37z4wmc553sxQZIyuqeuWxWLB+fkxn33yCZ99+iltLcZSKQls9IiAyOMi/lH3tf+K\nR4D64yZ9P+DSWtPUDb/8xVf8/IvIJ8+fisYhONbrFX/3d/8Hf//3v2F1P7De9pjKcP7kGD+OKOS5\nk2kL2XN1+T3TmUWbhNaK46Njdrue+9sd0UNVTZg0c5p2Qjf0jK6n0kasoq3i+OyEm9srbKXQWgKv\nJ7MJ88URm80N/dhhlGLwPYPruCvD5ZQyyhhefv8t0+mMum3YrDdsuy3d0DNpmrJuI9+/fsGbd99h\nLKzXd4xDB8qhCNL0mApbCeNoNl2wXJ4wDF7MutL+pKpxTpw1IxmrR4IX/Hw7DoTQEIMmxoxNiZRF\nnxB8T45a1rQKhDjS9zvevb+knS6ZL09IDMQ0khkFtjOZusooRupKM520VHVG64jCSIdfwrQVghQo\nLc2OIA5wgAseVT7KqXz/xEPBL/eHQKYcqv5HG8JH99XHj5+okJdH2bqeXjzl+cXnVHpKznB7e8vf\n/d3/zosX3wlFjfTR0Amks0mlcJBlUKO1QluDNbYMOCV0IkYZ8gklTkQyMcPd/ch259iHpo8uMQyR\nGChDR3n9nDMxR4ah5/bmjtE5qrpiHKWQJB8wATIRo4VK6JJkco5DwvuCl2WRuLshoRoJsTBaUVWZ\neFjsmaatWCwXLBYzUgqEOKBywo0jLnqCF1VfihprGuq6QluwU8t8IQsqa4jM8UnsBWwWr5KYxHul\nrRvqas7VfEc/RjJeqF9aWC6TqmEMgW4M1JUpnUIqhyKxGFZlkpNR4plTTP1Ldq8MfrVs2DHKxF/8\nMDLaVEUoJDz0qrJlKBxxg1BErWlRxjCOkbdvr4TmWXQCGIWtKpQGHyJVXdO0DcZqumHNuw8vmf6x\n4uLJM05OTri5mbK633L54Y7F4oyjpaGpK5raYrShrmqm0ylPL4745JMnnD85FozZCPc97Qf0h07p\n40zZx0Orx4+DKu/h1i1QoKJuak5Oj8kp0zQNSsFut+Xd+7e8fPmKD++vqOsFT588AwXb9YammTOd\nzDg5Pmc3rIkp4OPI4EYqK4yh7aanHwIpt7TTGZ9/9iVffPYlKSRev33F2w+v0DoSkmPbrXl3+ZpN\nd4/SUsCOj2fMZjN23RY/9uQQqWeTwyB9s7sn5iQzCjthdDtG14lVhQ+kGLFVJitPCD3DqHBuJKZA\nzoEQPd5LcpC2EvBsahFGHS0vuHjyBb/45RfM5g2JDv96Td97gvfkIGcjUUSCzwJnGl1z+vQCrWq2\n6w2JDSl3pOiJQVxBpZCnYieRQRtm0wmffvKcwY9sVitiFKhHEQlp4G51zbcvvub8d79Bm4rJZMrJ\n8TGffPK5zDoo0GPKxQvm8WafSnn6GPdWJSlCshGEtKGU0JK1LjqNPSvvz9XNP/P4yQq5KoMTpRTN\npGG5nGNVS86KzXbFH/7wR1brW7x3ZXD2gA3L8VQ9JNCXC3kYorLfFYtQowRJ7E2HUGK+s9qMh/Ul\nX1NYC6lU9gOeJZcwxig88nGkmTRChSQQcsAkwZONEgwyRWTo6CSEmGKWxV4pmSXiTFmDRXbznIXD\nrkoRtFXZ+Un4sGOMjjEJWyENIzHI4KpSNVY11HVDiIZtJw6TgTm6rlAmEvGkNBJJOD8U696Kymom\nrZXgaKuorKGpJNdxsx0YOynkexxcZ4miSzaLA5xSB+wwF3aWqaSQl3v8QPfMURFjQmuDMpLNmYp3\njqhj5fVyFv6vbWqqqkGrGrAYq0FHsvLl94WX7wO2klOMrQyD2/L+wwuGccW7d+ecn17w5MkF2/VI\ncPDZJ58wmUxom4aqtlhtqWsp5CenU07PFiyW03IsfnCh289pHihiPwwI//FS2z/vhzNRhUQYTmat\neIRoTfCe9WbDixcv2W47bNWyPDrl/Owp/ThyfXPP2ckJ8/kCYwqMER0QitMgQMVu2xFSRV3PmC1O\nOTv/lE8/+0oUlUrTjVu64Z6UIv3YMbiRfuzQBnIKkCM5errthuCcFE4lboghBlwYH6ikSrxfvAu4\n0ZXZk+SthhgYXUJpKcJizJawVjJYQ4q0ppw+m4bl4ognZ+c8v3jKJ8+fEsPI5eWMpmpwY0XSSdaF\nsVitoNBYUYqqajg/e8qknXPf3rFaZ7reE2N8sGU2ImNOyZNSwqjSVOiM0RlbQV1pIhIr6MLA3f01\nf/zjPxOjBmV58uQJv/zFVzx7/gRrrUCEuTC0EugCrwgVMR2w7v29Uuae5aEeIFW9RxPkuj504g8O\niHuI5s89ftJCLrqKzNX1B4LzzKdHfPXzXzCdnfPty38uvM19WAQHtWV+mJPK79O+iEh3GEIoUUwC\nxxgj21tIEqYqP0TGhT0+9aDAygVvBmFoKPLD7JFiBpVGUgxkm6HK6FqgGjJoXTObHUGE9bgiRkdM\nqcANCFZfG0xh02QUtjGYuqgkc8YHx+3dDavVHQhlnOlS4/KIi04CmzPEcWTYJSo1odGRpgr02y0p\nJmazGafnJyxPjpjoxNBdEvNI1Vb0Xcd60+HdPXdrCcU4O22YTiumbUtbtQQvLKF1ApuVxNHVGqMt\nzmS0CpAHcQW14gYZnNywVW3JWrxVQkyC56NwXqx8IRHK5yMbQWb0DrIhJ007mWJ1jaFmNltycnzO\n2dkTTs8WbLpbPly/KtFhMkKufcCHSD/sCMGx291zfZN4806EZucnF/wP/+k/M2tPOD0951dffsbi\n6AilDOt1x5OnZxwdLaibSgbHdm8f8fFgU7xCilCqzDxyLroDVTazwyzs8Ynu45v/cc9mjBicpQT9\nMHB1dcO//NPXTCZLfvHLE+bzY4JPbLYDKRoW81OsNVxeXtONG7x3YvmcowRcA6hAVU+YzpfMFkt2\nw8CrN+/55Ve/4vzsgvXqhnfve1wciumbwBupqtmsN7x+9RpjGhQVKmsqXTN0I+hMUiXlKstgcRhh\nHEQJbbQlOIdLipQDOUVitKTkSkOmqeuGumkOzVTTtNhmQttOWcwWzKZTJm1DU1WM/cB2vUGhmU3n\nKDUV3N3W5BClg84ZpQyVFUjmeHnGtJ3h/Ya+25CiiAObtmXSTghuxLke50eUgtvrKza7NXVbiZ6h\nKsSI5BiGgRQDf/yXP/Lu7R22bvnLv/w3nJ4eobKwyHQpuDlmgaoeETL0I9ogCGx5ABYeFfH970Ub\nUwaean+HHA7zcv/9K5X8pxl2mkJXs/IGxFY0cG/W5JRomoZxHIAHVaCiYOHqz0AryJtsysTdO1ds\nLx9WkNYP/iJi8v+o09r33GWHfnwGPkyLH62+nEX+rsw+3ml/oUX8c7w8pbYNlop7dc049hKyLG8E\npTM++OIHA7nw4K0pLBHEA8a5iFbiEZGjpa4VprGMMcjGkxIpRppJzfHsmOPlCbfXt6zuV9ytd+xG\nx3zacLyYUFVgaslsdEoxpMDgE9Satq1YHsuA1GqJ34opEpMn5UQMjnFQ4gapI01TcXJSM1+KQEss\ndQU3z1lTtzUhCZwV+uIpk4ptQrm0IextBhQKI4u8arC6RUWFURW1aVEoppOGZxdn1K1iOwSGccvo\nOvZVcRhGnJPNO9iK/QaekrhMXobEP/zu9xwvn/LFpz/ns08uOLFHTGdTmrZlcTRjMpPuXJXFuOf+\n5vyw0KB4wT+uxnBgJDxEcz26hfixU2IuGHsuQ1RSMWWzlqPjY7788pd8890LVusNQ+8Zesc4Bppm\nzvHxOU+enPDVL37GH/7lN2y696R9hGDh6hsj/t/D5obOybXSOrPantINHaPzbNYdSQeaiSTG28mc\n0bbc3dySFKhaoZVhOpmiJhoXRibzlkTkdnXNEAZCSJADqmS7Ho7ImdKeHqZ7orRWBo0WvDrXaA3e\na/HhCY4P4ZZhlyFYvvj054zDngUzUtUCpQmMoghknHOkKJqS1A+8f/+ezXpXagXUdYMbR+azObPp\ngrZpWa9W4lOEIiHWEDE43JAOLqhkjcJiTQNK7Hx9SIToub1Zc315y9CPLOYZU8k9nYwqlMdCujio\nOtVD4ebRBv8Rr3x/3NszVh5cR9XhJfbd+p+v5D9RIaeo/eTn994zeg+pL3hhzTgOVNZSVaYMtGKB\nQDgsklyOqBwWTsGhYyoKyXw41kpatz4MS/f3W84Pi7JcOw7/CI8u+KPV+KjrErxLTgQRsSqtbPGQ\nOIoM/YaMR1WSNBRTKmERkegz4helqCuD1RaN4NRKK3TWGKWoK6CIiZQ1jGXhJhXJWokXtE6YyjKZ\nz3BBOt5uGOjHkd6NzJcwM9AoQ9SGaCLRZKpGWDvNRJKJog/4cWToI6MfJR0pZaLfD3eE7jibG2aT\nhoh8duOY0EaMv6rKSEizLha/MRfDsHyY4qdyXszFkthaS9s2NM0Ukyw6GVQUXD/4gb5f0bnIentP\nP3aHDEiyiIT2njDjGEqXLMfkFBXBb3nx3QvmkxVuCDx/fsF8Kd3q8nhBO2kwtREMnkenPh4zBdTD\nPZMeF3L1cLo8FPIf4+X/qnAoy/zAj45ut2UcHPP5EfP5CSEaqqpmHFdM2ilffH7M2dk5509Pmc0N\nL17/Qbj7hw5PmEEhRUY30o/iHxOjo6krrm9PSD4DWgaJlaKZWFS2EpKtAikYqralrqeQDIv5EVXV\n0Luek9MjQvZ0bsBtIj6OZLJ4/ABZqUNkW1VZBGnT5XM3GGUxusjuTSZlcShMMUNIJDeQ/ZrWXvP+\n3Rvu7ySAeuwHcjYoZYGMJxNDIMVAVdcYXRGTotvtGMeRGDymsEsUGltVtJOW2WzJbttjbKSqGrJO\nJBwxe4H8QiZpIx2/SSKmSmBMTVVNSEnjxkS3GwqUJWgBOaMzZF02+cyjIv6xEOjhxnn9o5Z5AAAg\nAElEQVR0YntUVA7Uc/UA4T38WfGv1PGfqJCXAVhO6SAWyTI2oOs2DL1QAc+fnFHVFdfXH4pxUTGl\nYc/Z5PBuc0oM/QBIJFp+VItTSqSoyEaJT3gQB8JcQF1hERx+usNRZv+QDWFvDi8X1GiD1sIFz0WI\nE0NizI5xNzCrZ0zamSSQ1JbJvGJMI/0QCW4v4kF2piRdRlsbFJm6MrR1Q1O1aBIxOXo3yvdQEq3l\niwuhzom79TW7bcd22zGfLVieLjg5P+Xq+pr71S1Xq3s2QXOcK55OWiIWXWUmFnJVY60m5kgMGT8E\n+p1jvfJ0QwQrarX9QWV0Eb0bsTWcLxclSSUweunGlVKo7GSY1xpsZdl1I7HzpJCxRqigWskAef+o\n64q6rbG15dnpBWlM3F/ekWLm6uot769eY6eGpBwhRZq6AhIheJTOtHWNUYbb2/UhEaquDFXVUmnY\njFvurrZstwOz+ZLZ4pij4zPmy2NsZQ95sXswMmGKeEnuiccDq3w4CZYuqkB6B7w0/7Bwl+flfUf1\n0JGDWEh0ux3ffPOC16/ecne3YbE44fzJc9qJ5cW3LyErfv7Fl2LSpgPbfseuE2jFWoumKTMay2a9\nph8GXBDFZGU1q3XNhw8TjpdPWCznGFWXmVLNZt2j84AfR1KqmM2OWCyWDP3AYrlgOp3RDVOOT47w\ncWR2f0u32xGSQxkjjqB6TzU1VJVlOpkwjJ5QcmCNNmhVYe2U8/Pn4ulyt2K4u0FnLWpXO8Hqmq4b\n+P3vf8v93ZpxGPHjiPeRcdDUTYVTQBKPo5OTY9pmIqHK7ZRhHLm5v6Yymr3hxDgOhBTF4dEYbNUy\nnUxpWkPvNqy3dxJskg3GNCwWSzabzHa7IcaE1Q3z2RHBQ1tPsLYqoc+Cq6ssJ7e9udpH3uQ/ALYf\n7o0HyOWj2lhUkvvC/VDi/jV0XB4/jY3tI+60erQjyX/yxm1JxXbO4V1EW1NoOb40R3uQnMOa2vv6\n7g2c9m89RREI7QW0+2SeB+RbqrjWJdtTicNgCBK2UFUi4thj2EqDrTTGShGPSUlyUBDI5d2bt6xu\nVxijWN9vwSTqiRKXtSxgf3SJFOSornIihVTc5WSTqrQhKE8OATeObHqHmVj0pCImEIe6ICpXnYlx\n5G59xWYrroJtO0VXlqOzY9p5S8oDMSd2m0STLVNbUTWWjXP0nWfjIipmgksMY6AfEy5B3A+Ny+zA\nWsE2d7uAuR1x3rMbIi6Ila62CRvVIcVcZK9lEEwusrssMJKxxf9cEYKn63cY77hMGTz0fV+ut0Bx\nY5extRV71llLv9vS7zqx5w0BCCgFR0dL6mqCdwFDw2J6zCfPPsdWUyazBbOjE3yEm7sNzsOTJ2cs\nlxNqK9dVYuzUj3qm/Z8eqGXIKaVsyGl/J++biMwPXuHj6p5QqAz94Lm8umO97mmaOb/+9c/pR8/t\n3R3fv3rF5dUHlvMF02nN1e2WNx9e8vLNP/Htd3/kfr0iJS0CphwBT05BpOjGMGtnTJsWReDq+i3R\nR+aTE3725c8Z/cDoR6KLBCdeLSlFjo+PeXpxwc3NLSdnR0It3HV03Ya71Q3r+zui9xiEAmyVkfWx\nXyuVRStLVWksNVlnoZBqizU1x0cnzBZLnj13fPPtCypd8/zpc/7qL/6aq+srXrz8hrdvXxNDpq4r\nlstj+nFNyg6jrayfFMgq4YLD2BpbNdjKUifJE51MJuQU6bpEVZdmatrwn/7H/0TfOd6/veT27op+\nCChtqStpADebO2IYJU0pB+aLBV999QX/9tf/PRdPPuH87JhPPn3C+fkZdV2hDvdDQZJ+VG8/1hfs\nw5p/tM8fHg84utpXx8zh1/3X/PDxE3mt5EKvOUiDJPgVmfRqZAjkRhkUxlAWc3mOYFDsT7uA/L2x\nRTsV0qEzAvnaVIQ58lx1gKX2xRwUVVXTNA11ren7nr4f0AV7r+sarRTBC8xTVaawQcoALxXPlZgZ\n+kGO9nWF8wFCYhwCuqmK6i4VX/Hys++PURlylNDbpjaoHMVaYPT4MaLrCUbNaGwkKEdSI2gnH2uM\nhNjh3CDeEEPHfLmkaVtm9YRxzKQwsF05skmYxlAZCC7Qd47dNqCTFJaUlaSdKx7YNjEfDLBSzgxj\nJN8PuJhxXjzlMRmdRHAisw1zOOmICCgVZ8eE2gdiWwVa4b1YAitrCG7EJIOKUkwlwELhfJZjcj2l\nti1d7Bm7cvoqqlpFEnl622K1orFzzk8v+MUv/oLj0zOqZoLPisElbm43BKeYzxbMJhOyVgXn/nix\nPSy6fQPy6MxbYJz9QPzwnB/h4nsoUG68/OhrUhKB03S6YLm0fPH5p3z38jW73Y7b21tSijRtzXQ2\nYfd6w5t33/P1t//C6v6GlBOTyZzgHePYMQxbyBGjZA01usFkTXCOYRgxymJVzXIx534T8H7gk2cX\n9H3P7W3m7k6giKqqyGQ2uw2D64vj4B3rzT0hDFgD2ta0TSunI6XwPjCZTalqsSDQVUWlLCZp7u/u\nxSsmRfpuR9M2VJVlsZjz9PQZ/92v/pp/+1e/5h//8I+8fPmC9WpFVdW07Yyj5YK0HhiGkZxTGaIG\nQvDsuh0+ZKxuZPidIrU1HB8t5d9362KTnNA6c3J6RFWNvH93Td+PomGpNEYbvBvpuw4/DiiVMEYM\n5pZHM7744oK//Q9/y/nZCfNZy2zeSvZwAZAeQeHyaT/CxQVvKBIg9XBj/evFXO6tPeliP1P5f3r8\nNDa28WFxyiLYg/3SVUckty8EEfPkjDgmlgJsjFSHuB9mKlA5005rVM70XSKE/IB/l+lxykLmV4VK\ntTdGyhlR3zUtR0dL5ssJd3c3pOwxuqJpWibtVLi16zVuHKjrisgogpWQhUJWLvxsOeP4+Ii2adj1\nO3a7LWqjOJvNqEyF70Phvwq0YKwWR8K2IjjPYj7h7GROt+uITrjZ1mom7ZzZ/IxsFEPeMsYNIXfk\n7FA20TaGOIDvA8OwISbHZCaqO6vAhcx2M+DxDDW0E0NPoO8T3UacGtvG0k4sLonQJpcw2ZwFPtrf\noykl3M6RsiYXBzNZZAkVOKQ+ZfMIb1aKHCMxiQzfKl0gjYz3gRwFYAtpoDENs3ZaXBRDCeIwGG1p\n7ZTsEmFMBEeBNBIQUToy9Dty1Cxm55wcn/Ps+Wc8//QzPvvZ59RNy6s31+y2AyqtmE+OZOicZDh6\nGG4W2AQezVJyOTP+IFVAnnHoDB5BKBz+TDltJh6Wd0oy3KrrmqcXFzw5fyIxgpXm/bv3fP/yDTkm\nnj59wqeffcri6IjV+p7b2ytictjaMKsmHC1EKHR7c8Vusxb1oVIYDATwvSe5EWU1Q79la+5ITWJ9\nfwsq8zd/82t2Xc83337D9fUH+qHn5vaG6+sr1usVw9jJtTXSIU9nE1K0tHXDydEJ1lpG51ltdpye\nPxWvcqVZHh9xNJvTGss//eGfuLy8ou+3/OlPf6BuW9rpnMl0wRc/+5Rf//u/pq1nhJAYBkdMmYqM\ntUqETGPNMCr86MvcJDAMI/3gUWwAS13VtE1F21acnZ0w9gOvXn5HryJDN8WNA9+/fMlm3XNzc81u\n15NIVEYTo8J7yUwNysnrNC0pBvpuzeg2XDxbcHK8LLAqiPpn31E+LrSlUfxhC3CQ92dkoPqj6duj\n13nMeHnYJP6bEgQRpWOzGFKKhT740GKnJNLtvUw9p0RKEiFWV5rJzKAU9H3AF8mxMlBPRFqekiZ1\n8eDJsh945rwXDtmS9GPIWURDOUeGsSPfR/pxTYgjpsrkFIvEvAJVEZMR9gaAtiikIIU8QhIz+95t\nqTzoyazEXCnG0XN/u8ZYeR+TqYGyqfgUoYJciamRVxJRl/DoOlDlRNCZmAaCGzk6P8cEjXKQck2M\nHTkO6BSwJlNPYdJqIomsRvp+hQoRnRPLhYUooqAhR2xjWNiKtoZFa1hMGipT8e2rwKaPYoOglZiJ\nlXQjY2WoFUJAmYwxMr3PhfKhlSqUT0WMQRg5tXiaGyslTdeQlWxmCqiNwtYV9aRmDB6lEsFKcnsM\nkegTxArX96zu71nfryScdwzkGJm0lumspZ7W9L1jHDbkAKAFlqvg+v6S5dExbTNluTjl7HTJ2emS\nprFkEqE4aQrr+OG4J3X40ekxfrxu9xu43jck5MO9B4+gltJZPYxP5XtoDG09gZxIUaC0ZxdPpAuf\nNoCiritxCawqJpM5s+kxxtRMmwkny1MuP3zAj5ngU8FtEzEHghrQWe6zStcEP3J3/4Hb8IGhCNv+\n+Y+/Z3COq6srYnbE5BhdzzDscG4HOJqpFZaZCfi8QylLUgYfR7p+Rz96ujFyc79jScPR4phxzHzY\n3jKsV7x//5btbk1MCdtYfD8y+I4QA2/efM/vmhnb7cD337/GxcDJ6QXbzQ2XH95S1TXbbsfQe8gJ\nheS/ehfISqFUxOhEIDIqT1Y1L19+R46BplKcLhfoHHjx7b+IKdoQ2KwHRheYLuY8vXjGX/31X7FZ\nr/n2m2+4vnxHik5YWMnz6tVrfve73/Dlz3/Gr375Fzx98pTJtC6FeI9jH/CQH7XaD8X3Ma3wR5DC\nA4S3bxrynk0nDLtYHFIX8/ZHJfUnCpaQAYFGciLTYXXAfpgUUsRW9uDyt2eG6ErsW7XOYjta8CNj\nFWh5DVWyNlV61DPtu/IESiUywgihJOWEILmUKUmStqmywAhJEuZBsVgsST5J0LFSzBYtMQcub64k\naLasYxdHeq+wAVSdsY3GjZJgb4x0oQokKLmpqbLgy0lL9+2zo3MBnRLaQqM0MSdiHhnHLdEvxNO5\nmuK8QiyzNTl1ZLwEXFSaISRCdLgxoEKi0oq6KbxlWQ5UtVgDTCaaRa2YWo1KWlgnQZSX4qdehsQJ\noY8ixUzvnQ4PfuSCh4sL4h42yuiswIgBmHi4FLpWkf4rhB5aGUPMkUQmJE/ISeYVPjG1DWRwzh24\ny03V4PNYimIuWD7kFBjHsahvA0ppRufYbDeEODK6AYg8OT9mu02g5iwXc5kHgNwv6nE1Lr+Uwebj\nIq/2VCoF+yn8PjGmvNRHUM2+55emX/5kjRAYowIdI88/ueDJxRnTacuu61mt11xeXx58162pqYzA\nSn0/En1xqdQ1KY1yMoqRISka1dDoCVUlm0E3bCU4IWd8qnjx/Z8IUZK0snJ0/RrnBKbJ2WFMlkAT\nI5F9Pji0yjiv2O7AjYHRRYaQUbsttpkwnc5x24HdasX99Qc2m1tCkA4oKiNeJU7jQ+S7F18zDp6m\nnZJ1ZHmyZLuObDfg/MDoBkYfRB0cRXGTUiRFikWynNokljAzusTVTY8hY0uB7Pue2/U9ZE0IiJ2F\nqaW5y5q6mVE3gbpuODo+Zuh3+NERI9zd3fPdt9/ym9/8Fo2lspamfXpg6zyGZ/9MHefxJ7/XHXzc\nXpcB+aNinpO4j4aYcD6y2XTc3Ky5v9/xv/7P/+5H3+GnKeSZQ4KBtZWEDCSJYVPFGjTljM0i5qkq\nK+G/KWH2iSf54birS5iCcwGShAzvg5PJP45VSilBCIKNGckmjFG8nnM2QpcqSUFZwzh64iTx7Olz\n5u2M1WqF847PPn+ODyMfrq8OC1cpRUyR0TvMoDC1oppagVJUwvvI4GXYp61h2lZMG4sLnr7vyCke\nnAh1VEwaTd0YYoJd7+i7e+5uYX58QlNXuBFq22KMwceAc3KTG0D5Ys6UFdknESxgaGojXjElBMLW\nFq0N0Qe2nSf0ATdGgo+EJF4jReEsRbtcX114rTmLTN4YOW0oo9mLGiqrJREqZGKSwZOtLbY2BepK\nGA1+zMV7JrHPMPTBy8KLEpHXzCc0TYvRlsXySPDznOn7jtH1bLYjXV8sjk1Dig3GTDhanPEXv/pr\n6knLbtjx/vIlb1+/ZzGd0zSJo+MzLp4+Zzb9UoRblGG4WD3ysPQUlGHo4VZ+QPc+wjIfw0nkdOjK\n97CLjNjzR01czhllhOE0mU8P9gxD6Nl0K77+5k+sN3dEPxK9I3jPdr3lQ3/NrG5pm5bZdM56N0oI\nRJLhta1r6qalbmvGrWNwA9ZWpODphx7nu7IuMkp57u8vS1h0oDKGygqDJ4ZMKHRSlR0uO3Zph1GG\nkGD08r3GoaHrKtxux/r+ntu7G/Erl/GuDPW1RinNZrVju+7Zbjr+8//0v7A8Oma1WvObv3+HNjCd\ntZLHqxUYSyYWy+csdMsyO5M6IFfSO0eKEaMyjTHc3d/jU6Qbh+KRYtHUNLbG+cj791f89ne/Z+i3\nbFY3fP7ZM/pdw93NHTFmnAtcXd3y29/+nlk74/joiCdPziXsA2lUHuZxD/fGx1mcmYP/4QF2yQUS\nTKWIKxJ7KFM68K533N1t+NPXr/mHf/yOb759+99OIZc0koQOibqpsMaQTJROqvC/yRJgoLKiqmXw\nEoKk6PSd8DdFyi24Y44ZV+xpM9I16gzBP8KZCmdZdkApDsZarLWEoGjbOcfHpzx5cg7asxvX3N7c\n0m96ohtxw8BifkRdt1zdXrHrO/riDJcO6lLIAYJPjM4Jz7RK6CqzT/FVhQYp72UgZoNpFLOjCj8m\nUoiMLhIHcIOmsoIj77HccVjB2mOqBh8SWStqnal1RdXI8TylJAcUnyXMOWWyVtiYUSFhy4Xqu4D2\nSPD01hP7TOih6yPeZ1GsIkwaU2Av8sOJqlxOrLWiDVCyKe9ZK7aSjLdk5FgYkRQnyViMZdIvQdVG\nW0wRGGgjKU/j4KRz1xKs0MUOlcWfYzadUdcVvRtAG0zdyLVKEKMhhooYKnKusXZC8CJqapopysLN\n6or/8n/9F37xi1+Rc2bSzJjPF7TtFGvEe1oglQdhxsOK3XdhD+VbZ3Xgkz8s7MzeXOmAjudySnm0\nHg4iofKyq/s1l5fv+P71t+y6NV23I9NhdMD7nuurDwxewhzaesLTZxek4NEadsMW5yRgIuTEbuiJ\n93dMwkiI4gHke1EtmoLRp5TwOUKOZCLoRFWBIhFTJg2ZiCqqRSUdcJLZkC1qxhgC/W5DDI7N6gZy\nwg0O5wYyGVsZuU+SzKdyTkwmDUfLBdN5w9v3r7i8fk+327FaXTOOW4xKLI5mNPUMjWXYdazXK3bb\nLTEMRX2rDmtaY4T0oMRraQieMcaiYE0YU0GSDfbi2RO0beiGkffv3uDcjpxGrq4V03bKyekZu634\npqdseHJ+waeffc7FswvJGfh/qXGPH4ct/nCSK92siofPXmY0wmLarHe8fX/Di5eXfPPiPS9eXnJ9\ntWWzHf/s9/tpOnIocV+JlKQDM8aQQjoUcSj876QPDAiVxPBmGDOVlW4apAs0VhOiLyZMuVjClp0w\nq0N6/Z6nedj1UkRFEQqdnB7x859/wbNnz9h093y4jKzu7gtHvefDu3c8e/4Z09mCM6tZr2+4X63w\nXlSFBxZCgOgSvqbACBnbQBj361+6vBgyziVMIyVRq4SpgKwIXgsbxGmC0VRNRinJFI1xENVnGojR\nkEoBsTVURmGsFUMu5VAqkFwiFarPPhO0imDr/ekmEl2iX0dCl8gOBpfY23fYApNk9UCZE0lyYbIo\nCqZcoKucQauidpRNV1JbtAiZYkaXWcHeb2dvrJWSKBQxSjIcdSVeL0oVP5JI9I6qqkVWniXGz8dI\nXQlVrTIN5JqUJyyW5yzmZ4Rg2Gx33G1WjKFjs+nYbO4PnHOrak6XFxId1oin/L67OgTq7mHN/PGi\nPAyi4EBi+Iiy+IOz9n7EtRdH7U8vh/tHZXZ9x7v3b/n9P/xXtrsbvB8xSuNdZBw29N0WnzKLxTHP\nLp6zPFqyWa0JMZWfW1TGWWfGGAhDh2OfR1vMygpdV2dpkrzzxBTEZlWJnsEaC1nhfMJQFdFXxvtO\nfFlQhCCNpUq6BCJ7+l4+35xFum6MLV4nDzmmmYyuFEolnOt4+/YlMUT6vmO9uQM8k7ZmNj/ms0++\nYNrM+fD2ncBmQ49ye9gqH8KQ8/70k4toLqUyYpb7tDJaMkVzhuxFO+F6Nts1MY5Yk1ivE5NmwvHJ\nKW0Tubu/xaiKs7MnPHnyhOOjo0Kt/f9Z8/YslLyvAQ9NgQ8i4Lu63vDu/S2v317x+t01r9/c8Ob9\nHdc3W8TK5s9/358IWpE3kGLEjUH8u7VmLBSyPbUw5b1oR4pBSpqYIt6XD6WuZMpfW9pJy+DE2jSK\n7h1jDbbSkDRhjLjkH2CpsmF67/Fejnonp3N+/tWnzBcL+jdrhlFoV9F7vHd8/c2fMFXDz5ZHfPLJ\nZ9zfX3N3v8K5h9fNGXFa8wodQFcSvFA1mugT+5xEXW7omFQx2fK4MFI3YCpDCsJbj7FCq4paRWzl\n0XiJhMsyiE1Bi8wfUBHs1DCZtCznU6b9QLPzqF0iqRrnEt2uk1i5CiZGUxtxLtyNid02kYaMjppQ\nLAw0itpYtBKedCidTUyJpKVgKyWilrx3c9OqxMIJlKS1iE+s1Yw+HVSddVVTG41KURJwXCSGiLZl\nQGkStqlp24qMoU4VITp8GOlDYBil0xsGEYJZW3N6esHx8oxJe0TbHlG3R7STJdtd4Pp6xfXtNb2/\n5/rqnmHYorLmxYtXtNWCv/zVv0fMzCrS4fQmJ7v9SW9vqSpr8TGu8sAjl0r/Z6v3oyLOA7Ra5gvC\n0JH/vR9ZrW959eprrq6+p+s2kBXHR2eF3hqpjOX89JR/86tfcn93z2qz4ur2Wobne7aRkZlESJHQ\ndxhjscZQydQZlRLJZcbeM4xliq/BVoq6tsxncxSG1apDmwnKSFD0bhfwWbjhfpD3b40mJGGV7HUe\nSkvyT1tPICVCUXKmJF/jg2e7XTMOAzmJdmAcB1GdNpaJsjRNxRdffM7ZyVOS99zf32JKwDPI56Gt\nBi305ehDqR0FutL7RgmaqpAcEtxdv8MncKWBVDofmjYyLOZHnJ3MyVkTc+D46ITFYkHbNv8fStzj\nzz/vq/cBEi5XR1hfSbHd9Xz4cM//+fdf89t//JY/fvM9622PD5CUlVOQNof3/MPHTxQsIR1YzpLh\np2pb8C14vBb2eLcbvRy9jcFm6dxCyIyjZ7GcM5m2VJWlHweZhahcUicQv4j5nNE6vF+Ls2ROZfCp\nDgtJ68y7q9e43/Yoo1jdr1ndrBl3AynEgmgGtIV+2PDyH77l5uaSnDJtOxFpcPSHBZxjJo7CQsEi\nKXQ5F8hHFJDisCPXQJdg6BiUJBNlMJVmsTxhOT8hq57RrxjcmliYHkZDVYPOhhwyu27EaJi0woFt\n6wrvNfWQefL8c3TVcH9/T7fb4lxPziM+RkJOBf+W6xGzwDBKyQZbtw3BB/G22OO6RhLP2TPxEgdX\ntxSyBFGgsY0pSUu52MUqCZ7IEMfIqCIqZcY+4L24VNpk0FGLR7syEgNma05PTmCe2a7W3Nxcy+LP\nJWtUW8LoefP6HcNp5OnTirMnn3Jy+oS6ntJ1A7/+d79iMvu3XN3e8tvf/oarqw+cnx/z6bPnfPmz\nL4uy1KCtKoNOGQlL1ydcb8ExxdVOHeymczmVPCrgen8rlxCBUrSVQk4XSsmppUhmU3EGTGl/Kt0S\nw4rd5oq+u8ONA5Vtca4nRC8NilIM44aXr7/l9evX3Fxf0bsdMcumuqdKSrxZLp4s0hzllDDlfXWD\nZxgjzudi4iUdbkpyAlIyEcTFkYywyYyy1JOGqplQnUyJQTEMA+PQMboB7zxJZybThsVyWbrwiLFW\nGGK9aB1SFiGcks5NILeUsEoTjWIcR65vrvnt737LpJ5ydXnJertBGeG7ayP35+xoRgoe70bGoT+4\nnBprSDFSacWktrRVTWUqtDKMPrLtRtzoUEpslFU2KBTrdc+Hqxs++3TO0fExzg386euv+eqrL/ji\n8084Oj7iIensUW37iHb6sDHLP8pnEnMup5pE1498//qGf/6XN/zD71/y8tUVN/dbdn0i5lruIaUQ\nAxD3kX/U48dPVshBOu4Uk0yj8/7Of5j7ZgQO8T5gsikKv+L/Wwr6Xkyz9wPOmYOIxShDbRtmszla\nDfSd4IKxKDsfGio56u/cCn/TEVJk2HrcLoLMaArDBTKBrl/z6tV3tG3D6dkJKSy4vLwUb/PD+RsZ\nChaj8Ryz8J01JPPwPnN6EAYlpQo0Id9TKTCVpWpaEgpMIqnM4BLeRbnhjcJWAlHEoAkp0Q2O7a7H\nKgm/GDoHSTFtZ9gnE+7tnci73Q7nOkY/4rxQPLNSZC0DQ6s1VmvBVJM4GWb9QM4wWh8KlNaax97j\nOUI2SjzGnfis5JjBCFMp+ogjCRRRDLdiTIJM7N0Fc8aPDrLG6kRVWYHNjHi+5OilTEYx5nJ55D5K\nKHM7mXF18w5TGc7OnnJyOmU2Fw503X7KODqePfuE6azh2dMzzs7O8Mqz7leMyUlYdnSFHZHRqkJr\ni9b2oYsuIcU5R8j74OrSuSs5uUhhlu5TldNKZQy2sJdSCgQ3MPSdKCujnLYu373k+5f/xHZ9yW57\nj/eeUEmAeEwJQawzu92K0Q1cXl/SdR1JhXL9kKCPMkOiBJCTC9NLJeH/IwZRJb9bfkalIGlikKzY\n/ZA3xihNhNLU1lBXLW0zYzI5IidFZTum7YTdbsv9akVOEatr2noiJ98k26AxMlzfF/eco6zFKJi7\nggKFJsbRE/yKsRev/6EfMNrQTBqqpsF5T9XWHJ+eMI49/Vbhg0Nl2ZSquiJGT2UUbVNRa4NRWmY8\nPmO0uF3mLHRgrSyTtkXpml03cHN7c8haPTk5oW2bHyBlj3kqj4r4D/E0ymk9S93bbgfeXd7x3YsP\n/OnbD3z99Xu++/YD6+2Ij/ng3JZVFu1FDmQCe3X6Dx8/TfiyLIEyOxKsPOsfGA0V4DFnys2bMVaj\nK+HEyp6W8MFjnIQ0xCjTX11EKEaLwm86naOVpe97vB/xDjGpT8W3BSXYtBZvcV9YM/vopkJIAQ0u\nDLBL3N1d89VXX3Jx8RRyZtdt2HWbAyy0j3DaqxPF5lOzd3HcDw2lwCli0oSkCquQTpUAACAASURB\nVC+7bByAj4HeDaIya+aYqmK388LxHQJ1BXYpkXVVDWFwbLuR5DzL2ZShh9Xthsn0BqVbjs8vROWJ\nwZia0WfBnYdAjsXjocroRhhDJmr63Yh3sRju7wd7sMd5994zyhRMFF9ogAqSwg0R7yRouW4rMIjA\nJxaRURniZkUJmywbecoFw1YYbfFuJPrIMJbor3KgCiXEOSmBZvphx93qitvVNevNFf9G/SX//m/+\nlpvbK95dvuPTz37GL37xc1L+mdjA1opsM+vhnu7DlpzEfGsYOpzzxJipbENdt1R1KxirAqWiuGwW\nAyf9SLEZSfgoYcYhhENXVlnxIqkqQ2UU49Cx26y4u73EDTuieAFzd/ma68vXbLdX9N1GZikmE+IW\nZRS2Fu/9wXWMPuBCQJkkuPRh8P4wjKb8Kp+PFIOgEmhLW1uMDxBCmXkIqyaEjAsjGkUzmRwappwT\nilo2NyVQjTIWJtDU4iG/68S9tDI1FkvKER8S0Xl0LeZwWon1AjzyKYlJVMRJmi0fIv3o6LYSHZdS\nYrk8YjYVL6OrmxvQimY6Ecm+G0CVQbu1JXFLYzQoa0X4FSTs3HkBw5qmhdySc4UxNcvFkqqEU79+\n/ZrT02M+/fSC//gf/5Yvv/w5bds+6rw/LuKPTfgeF7N9Zx5SZHCe129v+PvffcPf/Zd/5M2bezYb\nJ35Q2oAVx0UypBRIOZCyQ6n4oxPA/vHTYOQFU4RSIHM+4IP7fy+I0uEpqcjfRdRpDpFIwzDK0UwZ\nfPJgE6q8q5QzOSkWiwXT6YSYA94N9N2OvuuEGx1jubGVdCpJCeQizSOqUsKeURBV5PL6vcAsOXB9\nfckwdlRGNon90M9WunSNkEZ5j8rownct/yeZEWQyvhRAF+R4XdUwmVqOjuagE7vxjpzgyBwxnx/z\n+ecL3r97x/3dLQFDCJLkMJ3N6NniuoFuSOToGZ10WtvNjnq6pZ2fonWNrWaECCenFcvFMUPfsbkV\nEyZlM8lId+jKcRxdRDKk4qusiqm/dMhKR4neo7QdCnIoMI0HlVTphCBHSI6SxMQBcsJAtoLD5xjR\nCYiK5DPJJy7Lpuj3aTMpSpcfE6owEVIM9P1Wwi9yxrktu25FxHF7v5LOPWxp/m/m3uzJkuxI7/ud\nLSLulpm19Q5gAAyA2UCREs0k/tcyvtP0IJqJ4gw4gGZAED1YGhh011653Xsj4myuBz8RWYA1nxtp\nVkBVdVVW5r0n/Lh//i3DDmM9sWRiGhGT8L3KoqXRIKdzs2pFGwVrNR3eWL8WCVa088G1VVAkrdQ2\nbS4AeyuS1qivtKWS0sT5eMOb119ha6LmifF0Tzy+Yzxek+cT1CVezOK8XvZiFPaxThlIYi2kSoxJ\n2TO1uYCKfh21jfNrg2Gap4cXJBRsbwnSqS1sK+RSBYNfA1usFTpr6cKAN/pnb29vuL+/pws9Q7dj\nM/RsNj1Xl5ccLi8J3iI141oRnqeZ8+lMKlEvpNC4w6KOmZIyJZUG/7TnvrkKigpQ1N/nfCZlZYal\nOakASAolJaVNet+MrTQqsIhhTJDnSImJmgsYDSEf+j1/81f/K5v+kvEcef76S2qphD4QfODJ48d8\n/PEzvvvdb/P06RP6vlv3JPq+mj8q7MtuZcXDW3c3z4nnr97wi89/x0/+v9/wy1+95Mvn6vdTRSEt\nobYdlLK7VABVsSIPmpqv+fiGWCtaAPRwWLxXpWaq6SGM+E//RuuQSy76MDSHmhSLzmFkhQSMBiGA\nFsppmjidTrjWCaWkr711jr7r1EI3zq2L0SLelPsYq/J5Izoqiyncn+5bp145HY/McSZ4z5zmxrIw\nKk5a4J6CYvDLr1sRV6aXNL/v9g9mq9NJKTiTYSdYLxgS4xgxdwZrPE+efsCzYgl+p34wVgCHsYHQ\nO2oKxHlkToZcKn3nifPM8e6WzeEtuTngb/c7YjpifaHrNnTWMI5nxjiSjHmABgy8v51TD23tPBe1\nek314eCiU0YtlRlZpf3WGUqSxmjxGFOp1LW7tqLMlOCcGjKVSqqidMwcqeVWl1ytczfSLo78gE2L\nVOb5jFj11ziPhjHek2RiipEudFgT2e4usD4Qc+Y03VNNZHsICtOkSomiUFwG6wIiBus8tsUfDcOW\n/f5AP/RYp3JrsxZyDdVY8G5rGlvjvUvONLRdSiRGlYrP8Uwc7/XH+ZZpPDb82DH0W7r+kjmN5Doj\nzTQKaTyGWqi5FaimtajwUMCtQnt6gaA6Bgc+gA8auOKdpURpF42+F84EZQu158sY7eiH7YCzYV04\nS82cpzu1xbWW7X4DpjLOE/N4BqnM80RKEzFH9QL3egUaWfZZFpzXS6QUfQDXe1AvTKlCzomYHGIM\nuSZijuTj3IRlCmFpIpjBJku/2eHDBms7rtMbohSE2mIWA5ePr/jf/v2/48Onn3K8H/npP/0jFc1F\nePHiJSkVbm/vySnp5bnww+WhUD0sN2X9tQhMc+J4Grm+OfLi5Tt+88VX/PwXX/Cr373h1buR8ygY\nq5eOWYRNaEqTxazfk7LCKib/GUErC35krMZCdX3Ampa+0xSe8r7xePuptJHL2AzOUo1tXZ3eesah\nMuLmjlpy5pSOvHz5gu12i/eWlLLGP4lh6AfAMKekDJCqUWbSDo6xYL3ixdUoGyWmBFnfqBgjMSW9\njZ1ggsX1HspSAA3V6P+LaZ1oRQMWGqQAeilY6/DWkUVtbieJnPuJ3aMtoXNUCre3N+RYefT4Kc8+\n/IjLy6e8eP6GUieQSikWHwbYDMTxmiIJYwvbreU8Jc6nW25vDEUcm8MF+6unnK7fUuXMtrM8erql\nO0K+mSlJXRoXq9/2yEHr1AQadfAB8y5Z3x/nDOI0WLo0Opwx2n1JUd9yHzzihCpNsVcFWw0Bz7YP\nih/nApJIFUqupHmmBIsLFhss4gxkg2md+fK1pjxRx7iG2M7xxLu7N/jOc9gfMHVktzvgfCCWzGm6\nw7jCxeONwjxRyOfCeNZx1/lew75dwHUDGM/ucEmpmQt7SQiddmUoplkpajfbEkeUk24aXFBb0QLr\nNEPSdYGLq0vevT6raCo4ZlEmT8wQuoHd7pLLy2e8ev2cMo1YW1nUjFKhzJESC5KqLpNb+pQYfZ9s\nE39pUYDOa8PhvCF4wXiQYKjd4jejgh3ve4oY5mlul2UhpZEn/TMNa8jK4Dg3g6/u7Oi6ARfg5uYN\np9M9cRpZhC8VTQTAagGvTeOAWIxza8A3RhsFqYLkoktb9ExUrxGBldqw/qKZsS2xx3mj1OYolGo4\nXO25uvqAvjswT+pqWsyEC5bNtuPJ0wv+9sff43vf+Uumc2KzM7gQcM7zn//v/4evnn/FV1++5O2b\nd3zy0Qfsd1uwjpWeynL22vMsOg2llHnz7p5//fI1/+PXf+Dzz//Ab3/3kq++ekuWQBKHWLWHAMEZ\nAckYEs4WPF6LuWhghUKRma/7+GY68qbA0kD5QqmaXdn1HZFETvmPdgh/TOWhQTFVu933hRltcSi5\niYQanjeOJ5wzDMOBzz79lJt3t7x88Yq7uxM5J+WSW8EUacqvusI7aYkqs05H97bxt0Zpe1L1z1pv\nFYvzXimQpW1tHIjVjUDoHCYrRicL/o5igc7SOjva5FE5Hs9ULwwXA4eLPffXJ473t3zxxb/wne98\nl6urZ9T6lPPpREwTUhI4hXVcmDBFjcJqzXS9xYZMnO6JxZIkU22hSqSWmbvzGYsmfuwOWzhbSjSk\nOrNw+xebYNuk+N43XBxV+1Gl+awY1tgzefg+s7Rlqa3k1G5b3f7ogs4JpipOKsZivSX0QeGbbOgG\nFR3VFtAhRRAr+CUrtU1opWpPbJ0mMlUDmEKuleN4y/OXkaHrlfVg1SfHeqGWgMNiMtRZkGxAVPE6\nzYlqHb7bEIYdzlpuraWUrNF0ff+w8LX67y9d2/uOinr8zeL/xqIUzQK+69ns9jjpON++A2Pxoedw\nccnhcMV2OxCuLcU7+sG3VPuo4cJVJx9d5BfddGqSL94ZTdhpyltnFOZSJpgayDnUTtgPHbmoHbPa\nVpmWWq9GVSLafL29faVc74pmsBrDdrfheL6j3N9QgZgipWG7i0+3iDSmk84kJTepfYFq28Rg1OZB\nGjSlrCRWbYj3ntB5nDd0vQeny0DlvRucVKZcmg5FdzSmWi52Bw7bC8bjcYVEHVDLxIsX/0qOM1Is\nP/7xD7i4vGIcE+9ev+WDZ0948uSSjz/6iO1m+0dxkUt9qn9SyG9vj3zxxZf87Oe/5vNff8W/vrjh\n9i5yPiVq1QlPL9UWWI46h2KqNqTG4JbfK+r2KCVR859RIe+3PaWWFsaqCwBTLV0X2iZbb5//6Ufb\nKGnHY1ljg5YFZVnGVx1NUopKiSo9+/0HzGNCpCV7Fx1RJYNJAk6LA+inlQq2ue4JGhUnviKlLSHq\nAgVpOos1TkfbWlgSkEzDtpzXnjZbsx5s0KJtjeC91zxDdFpZLGMxhv3FAamWu3rH9e1bhtcDxlr2\nF1dYJ0yzbcKKSJVCv9mS5mb76TK2xczFqTIli8yFeY5s9x5DJs8REYO0Ym5FE12MxOWafDi4jd2w\nyI8Nyk5Z4I3aCrcqLFn/Xmnvm+FBVm0M2M42lzvPsOsUMkVZHz54nYiKvo6FQi5qd2B8uxyLxVuH\nFYuYGduix7zXYiXOUKw0bxhIdSaPM3bSQua80Bm30j7JArni6dRbJFbteLFtShBqikyne1IcefTk\nKYfuEVUsC/tqmbpNe6+hoVPvQTC1/dxYh/Udm+2ezltqPKNBqE651NuB0FlSmhQiaRmfRVRsU1vE\noBg9u9Lwby1+qpDtgtoIB29UFCOGXIWYdDoNTcvRVhZIU+2KNeDUj6cmFdw5McQ8UkzEVIPFaRNg\nLdM8kloUYRUwpuI7i0OX0pKbEdsSKrw8r1XTpBrRBlngKnjYKTiF3S4u9nSbgfM4srB3rHkQquWV\nwqnNR/CBp0+e8qMf/oCSMuNp5Pb6jjwbohNub078w09+oqEs+0s++exDvDeIJDbbwHcvP+Wzb32M\n7yx/eP4HpmlERPjoo494+vQp3ntEIOfMeZx5/vwtX3zxFb/4xRf85ncvef76lpvjTMptL1QUerUG\nnH0v3m3pekzBiE5cJRcka3NbS/6jzv/9j2+kkG8vdsxxQuKkPiui6sqh6whBx/FSY1smLMk8S3vO\nQ7e+tHygXNfm9VuzwixLES0lE+eRcfTknAB9GB4Wjw9/j1QxocEJRtu5PuzYbA5YCVqI08TxeN2U\nY0vnaVGLXKfe22gxN2LWLtV5tVs13jRcV9+3kgvWeJyzDJsdPjiMgyIJ53Ux1fU9+wsVdrx795oX\nr76k1MwPf7il3zpct8F5x831HSkmumFDqbdQK5bF+KqSZpgmRy6Z8/0IZUvXiS6ESiHGREzCpt+u\n+OTywi8FXS9bkFLwluY1btpFIGteqjJ+zHqpmqXCIVjXuhhncMHRDZ5h27PZDuScSDGSS6HrlOEh\nUolRWSCxKJ+/6wJd5zFScTgtKsViq2Ct1TCLxnSqVmGPRt8mTpGUE7bCYLWbD9Zgm4GZ2NICRpRp\nUVNWVrlUklRGkeZDn9huOi4fX7RialgcFBdq7Krg5I8X+OszaR0+9CrOCo4ZAddhfaDzAe8Nucyc\nz1NTL0OaMwnRy8A14NvJiqcuxc9ZzaHsvKWWgjeGzltKEUrMjLMgov/dGyHHhPeuLXUF21nMohZ2\n6pmzQBoLV75kFfbkosymFfbA4K26g3prMFmpjysVegHyl8e4qTILOk17r55AtgnPFIbt2R92+C5w\nd39HjolSMsZZhW2ktrQf/fy1ZELwXF4e+OSTj3j75obXr97y6vkbjBhiNFy/PfH//pd/YLPp+eST\nj/nhj37Aze0dd3dH3t295rPPPuZwOfD81Zf8/ve/5/mL51hj+A//x39gt9+xGTakVLi7P/LVi7f8\n409/yc//+bf8+lcviAlSNWQMUjXfdDH2t0bjFY0IpvkOVcltw5IpKVGynr2SU9sp/hktO4ftQCEz\nJwNO/bxzyUzzjOFh3FMZb8Oh1q3w8uumpynaKVZReEJEyM3pT6QpDoEUE3fXd3w+/VIzNQ87DYmo\nhSqmqQ318xtpCzWr4/2PfvA3/PCH/4bd9jExzrx48Tt++tP/zO31DbVM1NyKVlNp+q5rGHrWNtRZ\nPJb63rnVma5RE1dc3jaDI4UQUrXkOnO8n8j5Fd3QEzaBzcWGEhM3d2/49W9+ybMPP2J/uKQWuLx8\nxKbfcHfzGt87bKf8+zgJ01jVSyUWclKp8v27O0IHzinFczHHivNJl371PR/y5RCZ5esWxcZpvPHF\n3x34U3/mZUG9qD8FhaNwkE2hD54shfvjqXnXZKpUdWI0rQu2ioN6YxpkpUfeIu3Aa7e+OBiqU6Eu\nk4sIxitO751HegemKsshZeKYGU1mPwwMXYftBu7vEsfTzP0xq+eMtdgKcU44Z/FdYDpdc3+7YX+5\npd9favp8w3O1gD9MXgaahcRyrelScnVzMU5VfMbTDXu6zZlaEnfne3JRxsd0SkgR5aGHZalesc3K\ndblM24Ei2IAzVqGLpDBTMdJM0RT66TqPFYU55ilROqFDrX91qlA9g00OmjNlKkktHIrBFqe7BaRB\nZ7D4AJaqy+hcVQjzkMzVXpGF31vrH52V9b5rnfhyUVVTuDveAoZ5PDPPc7NWdphOqZAuOOYxUbO+\nLjfv3vLTn/4Dv/3Nv7DdPGaz2fHd7/4Q7w2n8Zbb2zdMswaOP3/xiv/zP/5HhmFDzoX7+3tCUOW4\n7yxTHDHARx98yI9+9FerJ/of/vUrfvkvX/Czf/4VX311y81NIsaOnFtj2tKrLMr+WeC3mhOUBDVj\nTW0NTiblpAHtOSsUK4XgHNaGr62p30gh19teW2Z1GFQoZU4J36wh3RposIylbdHWDr61huAdqTZ+\n89rxtHG1deeyXM5GF03jOGJ3gU2/XeWu69FqFqi00V+xOs/jx4/57ne/z4cffI8v//B73r59oVOE\nXXxN0Mugpf44G5Cgl1NtJlG50bpqKyjOaD7nEo5Rm3x5ntT7W+XGhVx1w59iZSiFYdfz6Okl0/lE\nHGeub95gHMzzjDUdu+GKEDoOFwfuT+9IxeA6MN6or3iu2BGIlZIqCZAsuKCoQMU0aKs0gZU8QCjW\nrI/gMhaXVjSWwv7+OmNlk644Q9sDeF1I2WDWYoyFIpUcizKFnMfTlIntc9cGxS3vtL6/rTA2KMpJ\nW8C2wIolVlDxan2/XbBY7/HeUKKBrCPsdJrwVaBXLv95ToypEJtxljUqCDIlQS3agEzC6faa61cD\nT70m6ziny0K9y9TPh/VMCkuZWy40ocFOumzB+o5u2BL6DeNZJ8gimZgTtQpd6Lk8bJnLDTFnbFVO\neDW15eG2SahaatI9TzEqmLOiS/t5KhQMNnRstgdKSsx5UgFRU1QHr+Z2tXW2xqps3zunF2cuGqxS\nGp12eZIWagysZKe1TLclpwi6D6l6max+Nu2JFGgZtcouMWZZgFZlyjT2ykKAkKJxiepsq+e8Zt2f\nHFsYzPW7Nzx+fGa7ueRwuefiYsvbd5Wbu5dKtY1V2Sn5d+z3e7qub5YBM7lk3KCZoY8fXdINAy9e\nvua//sN/4/b2yIvnb/nDl2/44st3HE9CSgFHryK6Ks3eGVXyikJIpWR1aswTphacFUIXlMlWMqVm\ncuORI4tF80PO7fsf3xBrhVYYrM5Z1iBVBRSI4Ix25SonVi/wpZAv/FfnFFMvJVKaeQ9GRw/vDc7q\n6GtoI6wDYyzWaEfmQ9e240YfLvNgVbp0+462dEEl8Yf9JXOMvLu5JuW5BQwPOON1zDYWylIkLD4U\nisyN36oWnmqiZXGdo1rF2qUtJXOaqDU9+BIb2pgv5FARowX30ZMrvM8cJTLd6QE9n050fku5EC4O\nV+z3F8S6oUaPCYqBijGkZOhOlZqEkiq2om6NCLYza8GxDl26skw/ugxTrrbuJxYb4faGsv5SHmyD\nl4K1hBS7YLSQNoYR9kHC3oiLyuBxzcZgHdX1squ5GXPRsl9LBacjqrUWCTQmjVbJZdKymHWxqt+L\npwue2gXSeaTGqLF3qFjJusJpLkzFUm2HerA4jPU4KpREmWcMlfl4z7HrePz0CVZ2eKNhEMtCU9py\nf7nMlldm4Rcv7AzdpVh86Ah9j/WBXNUKWC80IXQdh92eD55e8e7mhKkjzgrZKrRYWiGv1VIxpKi5\ns87RrIoVCpkj2ODpuw2Hi0ecj/fMY1K8txpd+mPIOeslUmawQgievgvMU1XnzdZl14Zxm+UZFf1e\njFOfE2mNlTW6E9JFPOt7s3j5LIw2GoxVq0C1bYHepsB1kdkM96zeFOv+wiqNtxYwUpnGMzlHutLx\n7voFiLB7tqXrDdZpzul2N6gzojj1Ru8L201gszGAo04j0zw1TxeH9YHPf/Ub/umffsnLl++YxkpM\njsRArj2C17jEBv86qy6RiyujSKGUSExnchyxUgjOqkeR0SZOLOC1GBmaMdj/xKvrGynk8zRRGuWu\nNoYIS/dSdMPtGxVJZet64N2S2N3MsPzgYU4rnO07Q/BWPbDJaibV/m6t+rmNdYTQaYp23xNnZZg0\n5g+1Jaw45/Rwxcrnv/wX4tlz87bwi8//mS+/+pKuH/C2EGzP4XBF32/JqXJze0NGyJLoLaRsKFkh\nnLLczJ1lGDpqp7LuPGt3tzi2yfJ1t0K2dKvJJc7HM29fZzAJYysXVz2lQo5nxuOZPFVyjHzw4RN2\n2wO2u+JmOmFtJRkhIww7S7CGOgFSKVJIoh2Q8RosTcMqTVWVoi5rdVqqpVCa3fD7/Gl4KOILNrzs\nIowz+N6pItGiXWrWwt73YV2aqsgGvYS1zV+hmprQeLf0UBSXbtwpBVm7/WaX6rxXg6as8JAk/X/n\nMqFfgrY1Zg8j2KJNgFDVMC3CnD1VdAlfpFJLZOMqplZ1/0uGwRqudj3bzhBMxZT04IW/eKm05W2V\nsi62FFJbLGxrI0tZutDjfKBimFImVdMmJMPV5YEnV5fsDz3He0NszKxNUA1DLiosS0XZIFIsoQts\nN6HRRZs9BQXfdRz2B54+ecKrFLlbJp6m8a9V91epROYU2+WuHP+a2tKurre3vv9VD46x6o2kdae2\n8HR9vWMspKRy/yW716JirGWvsP7E6qtXqkBzzVzIEFLbhCeaPZuL7oi7PmDajiqXTC1Ki7GukrPH\nuIh1kV9+/hve3byhkPg3f/1jvvcX36cPW/7Tf/q/mKeZsst89um32e8vGaeJv//Z3/Pu3R03N3f8\n7osv8a7H2Q3B7ei7C7r+ktAPIE6RIolgKtYWrK1q+SC5hbUYxGh8XhH1aLKdo5io3kem4vrQqJwe\nZ5plhvn6Sv6NFPI4zWCWXM6o9DNp3XDr6Ar1QRxkzNpNB9+xO2zphw4TNB0l5aSim6CFwViQrJ/L\nGau0t2bIY22l7weuHj3m9etXqx2lwTasTg3r1eK2IsVwd3vPy/CaZ09fUQpcXj7l4nHHeHdHHGfm\neeKTj7/Nfn/F6TTy/PUr7k43xKILzGRH4jwpLawl7mSSFvSLQJ4sedbx3mJ1ARwXPLEJD1pBynPi\nfEx4VxsPuE0VVqGj0+kGqo7Fh8eW0G2xeUCYIST8oK9v0PEFycJcTAvjUJGdtdo91YVmGB5gsLXD\nFtZknPd3Vw8fZmXrWKfudK4VcRFBlkDtapSXX1tnbRf/DxVmpahcabVfdZgqmKJLUljUu8pIqctv\nO2lGT1U5w7Fgsv6ezZVTHRu8otJtR8VLpUO51rYZgo1TZY5CThFMaA29qnw7J/QhcLHbsu0D5EQe\nz9R+wFsHVbncDx141UKFTjyLH/lKQzSmWas2aXpKlFJwbgk9sfTDlmHb47wQ06lBjGp4UatQMqSi\njpqlth1Ts3UWo7oK62w787o3mGPi/v7INKkhk0GVoLkkYlHhTBVlS6x02ywrjCilifqcwWNItTQo\nRhDPA3zabKtL+1qttZhO9x9LqhPtDlk8lWpTOBYdNtTYrahfv1ojFBUpSdM2iDJoutATJ/VjN+1g\n1looxRDjyM3Na3KK3N7fMMUTxhqmecRay9XlJVeXF7x9+5bj/T0vXzxnvJip1ZBjZR4V6ki9oe87\nNkOg93uM24MZkKoogyHrJW4XbqVO3EUK0tCGSgVXEVfBgxs0r7UPHoLF9gHbeQ0qt+CxD9THP/n4\nRgp5ignfaW7mYhBFVWxLWz7a4dU3B0yDRAJDv+Hy4ophN1BM4fbmjimOYJV/iV3wcMUJjbHQLDOl\nCMWqac92u8P70MaWh+ZXlHqg3YyAQ8fPzbAj+IGPP/oWj59dMeXXvPzyd7yennN7cw0iPH70hE8/\nucT3G7566bm+e7PSi5ZxUA2UqkqUQ6DfdVgn6t8RRbHzuSqXuqK4PXqQ2/RMjqqykwx0WsyttfS9\nZbqfuI8q33bdJWFvMLankDAddPsGYLSRzzioyTALiEP59E5HVeugOtYAife94tvL9bWYePtVK/r6\n9flgYcFcl++tZV+WXDFesKGxJFqDVwukScUf1jkcFiseRxOIWCXJPSwTH5gypUKtrfOLBZP1sFcL\nNWUdW10TxBjorcF4w24wDMGB89o5RrVabnoWnWAAYwObfuDy4tCWa2dOt7dYF9gYB65XHrs1reNc\nPpbGQfFSpftp92lZtBDqNJlLwYWOlGd18dwOhOAoEjme1dytYiliibkyJ0HZhG69dNQFUXnWKUU8\nAW+DxulFoXLi5atKnCblmbQ9TmrB50uoh3GKgZciCOpTRAGq4DrbgkVsa0B0YrOl2Tdg1HCr1HZZ\nWJwL+KD2rFShiVT18vcW550ahZWy7mMMGgaTc+uy26VnxKhBGK27N67Ba41NtYSZ5EqcZ97lt9ze\nXNMNHcYoV/3Vq5c8v/wKJ44uKBpwf3fD9btrNpvX+LBhPsdmS+Ho+h37eJ5AbAAAIABJREFUi8fs\ntk8YukcYBhCvecQawaEFmqyMlFLIeWyxkZ5qFGdyvaWzgS4Yhl2nnkmbgBs8EqyyrWxzzATsHz9k\n68c3Ushrs6p0VmPcapHVY9uydJfvoWXG4Jxn2Gx4/Pgxh4tLwtCRaqLrB9x8oppMNUKWgpWG42aa\ns6J+HmWxVMZp5u50JNXyHq6ryz7T5PMGS3Adu/6Kv/2bf8vf/c3/zl/8xY/Z7D33p1f8989/wssv\nv+B8PjKNZ371q88p2fB3P/73/OhHP2J/seW//v07EDWUGoaBOiVqw9vFCIVMat9ntW0TZSqmXUom\nL/C9Lkf7vqcbPJVEKfqgWwfQOvCrPaSR833i5u01YmeGK0PdJsRXnIfhQg9HrtJMtxy+mhUmsQ2n\nxWpf7IylJBX8lKqeJmtQNl9/qFZ+fAWMijJK1WWUqM+wuiqmSprVb8b1DlsrpczUvFglmPWBlSw4\n1+OwK0HaOL3Z1sg5IJFYcJe05Dyi9gALSdpbuy7/UqqkIiQMprPsN+CMY+g95RCa8CwSR1269SHg\njWO3GTjstgQXyDkzp8itvSYV2GVhf/WYzg3YZvCmxdwqVLiIyhwKvywJQu22WC7aBXtWbyFPCBug\nkHIkx5HjeWaaCqUaYjYU0dfF+RYmnZMeaFPBZFwA60ozo2o0xpKY04xbecyydtGYtoxEqYA69TST\nu1WZLKRoqFJ0Yd6aoSqosK9autZdayOjM5xt0++yH1gAcyNNll+NZrXmolOLbtaVa64xResluUB4\npTYdxb2yfBYKamkLx5L0a3DBYjoV7mlbZbm+ueEX/+Of+dcvvuB0PnM+nRnPEzEJ05ixriPVplIO\nDmP0/ej6HWI6kKBfe4lYW7Qx6ZbdgCC5IlMmlUgtEdupod/+sCP0l/S9YzM4sAVxheoqkQf1KpgW\nqP1nVMilKG3NILpwyhVqJTi1XV2NhurDF911HdvNjqHfsdteEoZAzBP7i0uizIzzffOBkLV4L6wm\nY+26XN1s9/T9RkUYVmmOy5rTGsVWt8OGXDPe9jx7+hl/+f0f89d//e949PhTDleet9eGn/8iMc4n\nTvFEJvPu5hV8AVUyf/vjv2PoLKYm4vlMLrOOqLk0WboWu9xMcZS6qN2uMdoFNZ7S2uZaC0M/sD/s\n8Z3j7rb5io8V3xucVc+Sw2GLN4V3b4+cj0ekg24oVCmAUKxgB4s7QFfATgKzYCuUpLxkt7AqWr32\noYVctItx8XpeOCzt2f9jvj8NKrP6UNfcfMONwh/Oso7mtIe7Lt+occ2DJi85B9gqQAY0LNs1tpMY\nUdGP0KAEA0UxXiu6JMKBw7Y9RLMGRfFX17pmWyFnuLvXAO69FIwdGHrD4MF7QxDLPlg2fc+w2dAN\nHVOTrneuU2uIpNADqIBM2TKVFYR6L27QFO0mVxGOqM9OlUrKhZQLIobd/kDwAedso7RWpDiKBMTq\nUrUaNZoyVEgFSbIudr0z9N7gOw1UiLFCaupNKxALXbAtAFoVlZK1qAqlceP1IqJBWbUxTpaUrSqN\nZteeQdqkRW2KaVku/7aMFVHG0wIzVKU1mhbvaGpep1hYsHJa+HK7bJwuoK0xuGqgTfGL+lFYIuX0\n37eik/1iR1xyojTdwDjeY0piDnekVHUaa7TiVGcomWIsBN05+BBwLmBYlsMVWzK2zng1etewFKPi\nNUvBDcKu7whdT7/pGTYd/bYn9KFRSdUwTsu3ftg2ZdI8h2r+M2Kt0PjHSGXY9yqAQdhtB02bH1uG\nU8PNMNIWnAFnO4Z+S+g9uUY2uy3bvCPVCaQ0P2eURdHwR2ed4p7Bc3F5xf7ikmHYMAwbNbzSFpm+\n79ld7Ll68ojj8R7E8fFH3+azz77Phx99B9/tGHaGcA6UPDOlkbnM4IUpnnj1+g/c3b3l4kJVl3k+\nMZ9PpDQ3/58mVa6t6DTCFm1MtWXBT3XExCmjZqH16WJuw+HiQIkwnzNxPOtCxRWmeeJq8xhvLLc3\nox7UpAvVii5SY4UQwG3VojZlfY2daGTX0hyZxdSMSnBecU5h7aqavAdof9YuT1rr7le8Sl0laQ++\nC3YNyXXO6teW1SulZoW1FshNUtZChMGJjtZryDX68IhV+mGtVZlDWPVfwdB5315DLQ7FtBHdNLmK\ntbjO48RpvmksnKdMISGhsj/0+M4SHHTBs3Oex5uOvh+wfaA4w+1pxPtGZxX16rGigjAavGNramlC\nFlxzzmt02NpYK4raqBlPldo8w3Wxf3Gpfi5pnplPMznqvqAS1jNC66BNASkZEnqxeL0Ig4EhtDSp\nVPHY1f4XQyvWilEvy3aDoS7MoUXz0CCmBdMWFny6QYfOsKRGKZNFz7x9r4grmqo/s0ahg+oeiq6O\nzuahkWsTYNt9t4lB4x/V1kIvbIfVS6Z1cbqgVvZLKzwsKmSpRn3y2zeTamaWDDWQE9TqEaPGXCrB\n17xZZwKuXewGmjKuUlNsXPCIE9WvxByZqIgTQoCuc2z6jt3uwDB0+KAXlO/0bMaSmVMiS6aauk7b\nVL2Y85zIMX1tSf2G6Idq1l+xDGj+Zh8Gnjx5xN3NkRQLNstKR6q1klIkpYhQsB4Kieu7a7JkXPB0\nXU8tSY2qUJwvO4GqBjihCwzbgUdPHnP16BG7/QVPnj5lvL/neHOHMZWrRwc++c4nPPn4KV99+Zx5\nLHzrO59xcXlBRZPir69P3NzcqUAgeKwHE7TjlyiM5yP/7Sd/jzGGm9sbYlQ7WAyYoBxyyW0qtdrd\nBoIqK2OhtvQg71ipZCIq/z0dz/iuY+h7+mFgvz/w9vVEHDMiGTMIqSs413E47En1CKLilUpplqay\nFmkfDLnd/t6pYGmulTgpPGExqsjzyqox9eFBXlDp1meq/atocX+womgeLO9BNt4FrIWcE7utQg/z\nmIipaEJ7zKAeVHSdBlsEY3Q52/jBcyrkqSwCOQwaUkBVWb4WCNhve5wHKSp0wbclKkIslWrBh05V\noUUQtADbDghODbKqYM3Mduh5tj/w6aMD5xg5t4dO1I+YEqOGYOctRgqSmsOfZEqc1GrXOOzGIN6p\nTF0KzW1jBV8welGGrme7u8AHz+HqEcEH4jQiVbHuaTy3ombbM+Ewpa7h5QtldGFrxVQ1Ycvpc1BN\nhdPMOEetyuah+FmrtNvSOm4t9ll3GhXtPtFiqpObFkPNqWjj1nIGiu6HSuuqFkuCYsrKhjKtybJi\nV7U00qAetIiaht/TMkNVx0/7sWDvDWOnNRxabNpZbeSFrOeMaKk16yWj7gy63LWAaR236ZjGiLFO\nE4vK3PxfDLVsoM5QI0Yc0iiFrhNSmphzYsqVERBvETz9ENTX3nmkZMaosFbYbqjWMOfE6XyiSsYH\n6Ded7lFKIo2ROEby/GdVyNuoVeuKhYtUxvNZlWVpaVEefpSSSWUmSeQ43VJd5ZzuyTWTa2pvvMIo\nLlimnHSZ0HnC0HO4PHD56IIweKY4Uo6s5vNLunfoLNjCmO7JJMRY7u7vOU8nhMJ+14ETUn7EZ599\nh4v9pVIoTVFr46rMkrvbGwQNr6jlvUDp2rqk0p4bszAvKhKFGiFVacwby2I2BLpXmMeZ+9sj3geG\nzYbN5sBuH5nLiVJnUs7cne4ILtPtAvFoSclQk0NaNFhFF35F9OGNVVF75zxBZDUNM807vLU16sEk\nCrc+qGBlFU6t3+OCgdqFR6a/uTRaKbUdwcI79qKXYdFPri9JXvHNh9rQllhevVNiovHZDSIaQF3J\nSNYUlVphdEIILQS6inL2a+siUYvhKUWV8mPV8znov1GxHE+JOqsN7W4Y2A+dQkKmkNGuaegcnfEE\nRAv86Q7TB6z3hKEDCnkcsaiTYHWRKoHibVOeNj9C064YsVQx9MMeY9XzfLPbqjx96BinE+VomEqm\n8wqDpJQbbNNwbdpkVUVpq1mI2WAi9IPXKcNYOskU49aiWUUFPiUr11yVyMp4MVXhEmmQn779rcuu\nK/UGaEe27bWq1AahtdvfKEPF2PXLXTy0oJ25FasXZd0oRNaEXjwwHheztfV6XvYKPIjEhHYOm7it\nFp3OFCVq00Y756bqD6vGLnjb0Q8e2l4o14SlYEukpglLZuiULno+T6R5ZsqJnCZiKWQxZLFIEmap\n9GRGGZEUwRT1m5JCECjGMKbINI3KUDEBk7UWBBfoBoMv2oB83cc3FyyhMKbiaFZFH/d3J+axNOtT\nWeMPMbpMUUrUyP2oIQ9zPlFEFV611HVjrQy2jO88Q7ej7zds9jv67YZcC3enO8zpiMkJMRUfHJtt\nj+sssczEu5k5z4h0vHz9ktdvXvLJp7c8e/aUMOxx/gOmb3+fR4+eELqOKY8qufdgAqQcm2WtvPcN\n63RARuPj7Io86EItCZKE1KLhXNcgcsvKh08xIcczYHnyNLDd7ri4eMz9ZJmykGXiOJ0IvrDdXpBR\nvHaetN8TY9ex3bRuKyMUYwjWElBerjd6IdaiQbYWs1oWVKtd9joaW9vGaBpnuq0a2wJKC8EDlr7E\n8zlj2/K02Y96wVkt6s5WlcC3js8b7RCNNXix0Dl6BmrVAm6azW9KE/PpqB1gFk6lYb8LLJSVuSTG\nqJEWkGtpl4b+G9ZbshjGCOc0YzJ04gjeYo0Q48SUZ+aSKVXonGOwDmcMtSTO5yPZaZPQbXqMhTzP\ndK5HBoNNHWIbG8HZtV+xmPY6WUQsw3ZPP2yaj75r8FXTVViD6Tr1PmkRectqou0JWeKPQRvmlNVe\nV3yl94XqDDZAKAbaUrmUQl4KedE9gjS61OJJr7YDdr24De1ML3UaWpMmKqDiYQe1NFu0Z9vS2CZF\ni++qzF+hu/ZdGBQ3f7+GVN2PmGLWC6A2++iCYHxbiFsDpcGAy0S5fB0sDPZGhV1cxtp/tcYyhI6a\nIUsiLLhTLZQ0IyXhrDD0jhhhngvnGDWBSKpa1NYlcwByLEQRJBX9Z5wB76iSKWJWbY33luAC3gU6\nr9CeCQGTMnkav7akfjOZndasaSoxJnxQA5nzOSK5cT+rdmIYu3aPRRJzOlOnSLWFXCbAIqWQ58LQ\nfM1rLVjj2OwOPHn8EdY5Upl5/e4NPjSHxVKxtTDHERsc22bEE1NhipGcldf74u0Lfv37z3n09Bmf\nfusTdt0juu4pfOt7PHv6MbvtBdN9BFuxQR/gdK4rfZGlCKKYn2Qe2pAmKwdAlDddBXJUyMU2Wb20\ncVuKmhqdjyf2mz27YcfFxSNM7zCj4TyrJW+pE/GkocqmVm5vMzuj9qXFyirCAAPBYoJBSsVZR/Ae\n4z0YQ5wTUaouP42DYHE2MyddxFWpK0+crCq7BY50Xjnjte1mjNFiaZyKbvpex8tcCs4Lvle1rgkN\nhmlFo+s8tj2oRXSp5vuejz75NiEcKOIQbyiSOZ/uef6b33K+u1eoI0NpVExy6wCXt8Kpish5peop\nK0fhpzFmjBUchg7lac/zyLEmkhWOkhjbn3Vug7GCd1p6Yp6Zj0XPYFBKXnCezbCjOjXHsoR2HFoQ\nr6BZpvpwqOhts9Ov2wi1avjJ+XTH3emIWMeTZx8ST0dqLjoVomckL8wQozWp80EdOylMsZJNZJa6\nKnTbfNy6eIWucmlfk7V6Pp2B5rporFJdyyoE0uO8XBzLcq5W9JloXvTCg0hMF6Ygpil2cyu+tmkV\nFlXumpLT+PsoA2zZGVWp5Ix6lJhFcKXl37bLQG2W9b0xYhqkJWrL2Ramzlt1iOw15CZPghp4Fbz1\njfZrEOM1OzNXSszEeWaaRoxRz5eYEjEv2a1GYxtR/YKz6jfurdAFCF3A9QHX99B1FOPYLPshFP7s\nh6DNDRrDF9PEze3br62p30xmp13k15CiepbQuMa2Uy7mNGpcEwbGeW6dZKHawpySHhIHec6kOZFj\nptvtCcFpR4zDexVxbDdbiHCeT5iaNZTVWVVPmHZj1kqpBiMdwW8RqeQsTGnm1Zuv+PL5bzmN/5b9\n4cDQdVxePubTT77Dtz79C+pzmMcjOSrhX51gWwe+FMwFrBNZvSkW17rFSGrVJFUgyyqJVzywjZpV\n2RzjeGYcd1zt9gzDjmISlZE5ncklUWqkNkhCOygLolxli1L7UkIDCKzSNs2SIOMMfb9ht9siJTOP\nkTgXUlSr3QUKSCm1cVXW3fRimLQUTe3MLda79n5q8IRzrv2bKtlXrxTlEHun2Yo5F0KLVDMY5hp1\n8gqO/rBht7/C+A1JhFwSJgS67RumU1SOdYN6Vnm+sHaYiu86xdcbYwIE4zzG+HZxVn1APEzzhMuG\naOFEIbZJw4vDiiHmzBQjE4VSk763xYF3dL7D+ICXgnNGi27Nq4hptUduVD9rVBi2mH9N88zt7TVv\n374mxtimkyXYoJmAuUqxSrnLpWKsIXhP3zlKLcxJGTC5gv6PUjqliXuU4bHsblpH6sCGVszNIj7S\n7ta6BaPWIAyLX20USlbWy+JGakDhQlmEXgts8mDloNBHRerSwT/I9Z2z6/uHea/rFyUMVFnQ9IdJ\nkPYZFPRf/u77nkCC+spYZWeiOHhn1JvCCXgMmxAaRAanGDhOlTlPZFuJc+J8HsklqJ+NNFKNEbyH\nfuMYgsN5y+BhawudVXOzLE0L0lhqzvc466FdbvqitZCSFJEcOU9Hjue7r62p3wxGLkr7MsYyiyb2\nWAehd/ShU4ZBHTU1HZhybPQjFTbUlNT3wVnKnClzoqZK73u6zuuBwOF9h3NB8xm9Zc66LFr8GZLo\nrexDIAxbtrtHbHaXEDZMKTHHCSQy5TPXd2+Y5pPaSfYDw3Dg29/5S/7qr/4XTPB8+dVvublOmmLu\nwVZNWjEoBllrVryudSPAWt9xrNAMpS3pNamM1axqoTYAUgvTOHE+n7goj+m7HrEHiowUyWp1in5v\ni+CpFqX7Wat2rzULaRb15GhYpClVu4hWzIehJ/gtUzdzOs5USQiZKuCqJRllBKyQefumFlaKEkz+\nhGHQWDm5LkHZCmeoh5peeoYm4ioa4BFCy8oUCzmr/7at2N4Q+q7ldnpCKnTDDuePGBPbQ9+G5wWk\npzaanrIdmvds60CbgMwou4NYFYLo9NKJAgadEEvDjlMtIBHEMJVEpFKMweVIFYsU5ex3XWaQBlGg\n3PRaC2IcWKcMnNa9GaOLVyN6s0/nI8e7W07392x3ai3ROU9yHmcd3nrEFqorFKfsI2ctXfCamZma\nXgKFUUrWgksRSAu8qerdJZ1nVfl2S/6svAeR0F4nhUi2GzXSqihbI8VCbsHGKvxqxbsJ3GDZb6iv\nkkEVnrXx0ps+Si+OZeJbgkvs4unTzhcPqzTTJj7nbBPc0MzT2h9oBV3qchG0L7CCM17DNRr2LqI0\nsi44hr5TP//OEiUSawFryLVqBKGdlQYZHN6qr1EIlW4DYaMhINvOEPKMyZlSNJ5O4S6H1+RdXBiI\nuYK12C5oDmstqiSNI1McmdOfEbSSYsbagO881i9jodD1nv2+1+4raaBBqQox6IgnzPMZ47UYyGzI\nU6FGtTsNviN4zzRPGGDTb3n86Bn9dsvGbhi2gXdvX5HiTIyF0/1EFcPF5WM+/vTbfPjRt7m4fMac\nLVOcifmMmJE4j+CKsmJqM78i8Jc/+DvEW3ZXF4zTiZubdxRT8V3AWQfGYnHUUkip0ZRQvPvB7c2u\n5lGWQo2smKP6qqsy0kAz49cDHOPE6XhkOp3Y91fstweyTIyzfs3GloYjaJCwnCtBDL11uleIlflc\nIaJUL2OZUkGsJ2A5z2PjaG/ZXR7w/QYbRu5u7jUcYK3eS+stK06LWTjdglh9OGqt5KLYt9RKmrMu\n3pxtbbxi73HOGFFOpDVCTgUZoBs6Or/Bx5kpRubpyPnc0wExKytAbYA3+G6DCRGFIRsOaxpnzghY\nNVlTwU1GjGkpN2oDQRHyWJC50PeecAh47widpQ8dJWWohSSVYvT7qVWU3mh1goo5kzJtlHfKkqnK\nl7ZFYYEYJ1zocV2HNDdN5RJngnhd5JbK+e6GNJ652Gx4/PgJ/bBVDHpOMEfKFMEGxFaKKWQrylDp\n1IO8NLhFHxqLZEOOizRepf1q9+DUkXIpgh71zjdtWbxMDIuthVXu+e7QK1QmulfJqa7wYClCioVp\njHoOc13PMFpDV7OwhSaocJ1tOHdrCqpOGRrioH85Zw2IeRj/2o6nU9rpOvGKxsFVERX9lTZFQouW\n08+rU2Ymptz4/JYr/4hqM4VCt+nYskOCNEMrVMjX6WvX+6CmYr5g7USuJ3yX6YIQPJTpyHw6ch5H\nkqloWyQM2wu6fot1I+c444aBfadECmcNphimnJCacfahZXr/45tRdlYeFFsNs1Ins4JxGtHV7S2h\n2+Bdx74IMY6kVkh730HVcNw8ajYfRbh+d00/BB1XbIc1AXDEOYNVRVuc1ZR50x3YPXuiD6Ux9P2B\nDz/8hE8+/S7v7jRC6zTe8uL1r3WceSX848/+EWc2DP2Gvvc8unzGZ598j7v7W35x9TO+7H7HHFXq\n7IJju93yyQefQhFev3rF7c010zSBmJWqpdOx0ZBlZ9SYvwVAlyzK1vCa6LIU9JwVW43TxPW7t/ih\nZ9vt2A4Hzv0dMZ9JRQuDt46+8+SaVaE5q3FUmoTpWPXSyNrxSFYjHxszm90WsT1zsaRZIZRu23Fh\nrzB3J3K9x+SszVuLl1uW1NYu9NqGZZqiF5VTCMEYizdqgCbAlDIkoaZKmho10hn63mm3jOK3gwtU\nU0h5Jp3u1akwRqw/4MKe3gWuri6Y72+Zz/fAUrvVBpbWbS6dH43hoB4yurTNUamxZW7LZ1M4TxG7\ngUDAN2xbStV81dbFG9cyJ1GjqZgypdHl1FK1arc6TTBLm5xmoGBsoWQ9B0Uy8zTii8WLpbMWUmTX\nBYX1hi3GeuJi4doM5G1jXNRmgVBRiMM06Ks0h0vV8OvS2RnbOmKF2kpVmwItFYuo7mFpujLDGsZh\nRZrljXqz5GVPAmDV+C4YDQwJvWceE/OYyHNu/jgKMy2slYWBog6QHhOUx51yUuirCpJz23G9B8ss\n+E2bKHwX2vO1KCsVJwfbrHEbVr7QZozqOytWFdemqvc9M9fna7bbLf2wwXeObb9h5wbCpsN3B3w4\nYMNW2W/O4KhMpxvO4zWpnPGlI0dHNkI8nojnkSlmxBvEW3CO6TwynmZyrkTJhE1Pjmfybk/vO5wI\nEmeCVDb2wezh/Y9vThBU1FRmsbdEdJSjhRUbD902MAxbwHJ3V8nHTM65HV4hTZkSG72pGo53R2IM\nDNvm5+x7vOvV0L4t1pTdolSww+5pWzBWum6LYMk1g6l0g6fQUWthGs/MxzP//ef/xKcffItPPvyI\nTX/Bbrvj0dUTHj16xrNnH/Ho8RPO072GWdiKDYbtYYszljmeOZ3vMPPDGCht4al/VhkbNRvNjGys\nF1sbPOAtnddROufKHBNVEqfzLdvjHt97fO/pfE9wvXJeW8evMXWNj3/WbjxO+nNqbVi+LuSFghkT\nvoe+9xjbk/KM946+C3SdLrrmOGukl2iB9s5qQarKPjD6pDS2S3tIQ+MLO4MTp7JvamOZVH0vZ90N\nuDZCl7a/kFpxaHFzRSjjyFgKKUY2O0uwPc57tpuOrncYL6tiVt3zcoMDzIqdt6OoX2MTEOaUFTtO\nCo8ZWVwAlRpKVv+WOReiFIo3KwvHGKOwSxG1T/AeZ71OZVVIMWLPZzQsOFMlasFBU4eKVGKOnE5H\nXDEErE5QeabrOg77HWKcdo3zzBx1wZazErxL1lCVUgSphRasQ86ilz803ru+Lta1CWLZyxSlnUoT\nDCyvy2JdsUB7OkDVtROO7SKOMa9waC2CN+CcQmuh961bz5RkHmAOqQojLYwYFpxb07YqbdGIwnga\nnN7eP2sas22BrFi78nX6WhesCreYZumwiIoWVkxFX59qoDTqbLGV43xEPEgAlwqbXc/2sGV7ccD3\ne6zbIATNEDWVMp+4u73hdH5LyhN28ozO0htDPI3EKZOLYILTZef/z9y79UiWXXd+v7Vv50REZl2a\nEiXNyJrBwOPv/yUGGBieebEB681DSZRMkeyuqsy4nLMvyw//faLEcb83E0g2QLKyKyPi7L3W/1oC\nbburaHur9DDIW2bUjdAqLCdyiIx9JwynhEMd8adfvxBG7jNvYlqsTatULlpvWpf2dUy1iruz3Su3\nt03SnoccNXVvkzg03APWB21v7DFweV05nV85nV8ZDLbm7DdXUM8M3nELnF9fKCVj0fjNP/2G//Hb\n37Bezrx++EBKgTUHrHXev7zzz+1/8Pvf/QPX9//EX/zqVR2bKZNT5m//w9/yh7e/4/df/5nH7UHb\nd96u3/jH3/6G83KakkjH48ToIvjsB1XXog52KwbV9X0cRB1yUah9nqobe8DedjxsvL//EbfOh88f\nCSOQyWwj0rdBDbAHo6wLo3cebzuPdx2acyzh0FXj6ju8jwZ247S88PLDB+6PGyklllJotXM67+xt\nU1ZMm9j/VDiErp8V0IHsFrAlEosummUpMh+NIfkcA5pxvz0Y1hnBSSXhydh9EOpOaQn3FeudOAZx\nDOGwrVJbI5cz+HlOgxW3LszY+yTQhghIAuZxWrsP/iA+E+W8asL1LllbSYFzKVyWQoyD2gePttG2\nxt6mlKw7hK6p9t/wBWZGCgpcK3lh4NwfN8ZoWBjToFWxIBlmHZ37due+PUSsDiO6YKkco0qog1P7\nzq1ufL298e32xn270+o+FTOylR/xs0rTlECjd+Vcfz/vJqk4jTr9mf2NDsDZSG+z37N3bb1S9uuz\nouTIyPW+05vglGVhbmQDCTx1KOaUp6TxoCUE4Tzny3Fkzsyfrb63yVFqazhcsDanbzsOcv8uJpA/\n6zA96NuiDuze58biQAhyVs6+0xGNYTIpkqO05hboriKLNjS0fPRPpLVQfCqrIgyXhNW88nh85X5/\n436/yvtS3zGHJWWJMqp4qdCcPBT78Ng2VRvOFFcbjWYO64qbIrixHdfsAAAgAElEQVT6vmvz+nOK\nsdUuddyiLh33y8LLxxdG72zvG/tt583feNw2eotcv91pm5jAKor7ufYd38osHljrbPuDkOBXf/mJ\n88uJH7/+jus//iuPelU9mTfsGnh0kUfraeX99sb77Z28JD5+/MDldKbtD7b7O99+/JH7Tw/+2//+\nX3g5F/7dv/+BEFYe9Rtfr3/gn/7lN/y/v/strUubbllB8u/bV+6PNxH4sZFPkdaB5DPCcka0TsLT\nFgjNFO26+TOkaCSpRCwEPnz6QFid+64grBgrtb3xx98/cOswoFAYrUpSNhU0KSTOKUvd0ndl3Ew2\n/yic7j7wXnkYfPvyhRwTHz59xEJkrwqUiikQ0yAuzlDLl+AV4vNCCGOuzCqNxEoipkhJeU7bXfh4\ngOKFfMrs95163UUaze+SDI9w324kE0VZ1hUzEaEjRLZ6x+4/kscJp1PWyOn1RN0ftK2q5q0yXTKC\neWwm5IE9A6DMDWvfVRXmgxBcOSfZaDRqfVAd2rPl5jCL9EleMsn0KDildiwf+uuD7D4GmERvktFu\n+862yb2s4nHpypPN8ujhksW6JI6132ljp46dvVelYo6Om6IPlNl9mHqkCIr2/ST3mRkeQhAkM+bg\nBJp0J16veAvl0bStaWuzCKYJdtt9hpsZoxkNpsFumTBgZ9sqLWhKb7VPsv9Ix+RAuZ6XiiPPxNMi\njN67Z9StHf+TsyyFYEZtbYZjdfbHPg97cWk5ZW2HRwzyTCC1aXzz41KLSZe663XPQRV4AYNd8R/t\nVmn3ztg1sXvbweFxv9P3O+/ffpLk1RP1UTHLqp+LBRZtiykWat8J0UixQHbWLGhGf19tvycCsTZw\nKCFh64Xy51T1JgXKVBGY3viyZKRd7mx3ZYRsPNgeO3U36tbVwcfhLNSH7kmwwcRnA8uSCclx22l+\nkwfHb2z9yt5vWs/a4PZwOneGnQj5zGP/xvX2Ezw6tZ25nS/kkNge72z3d673d/7+//4/eX1d+V//\nt78jXy784dtP/PPvfsO//O63/PT1R5kUgj0bcEattDqFtUGlErigo45svVMIoIstQ1hsOiiRnd9V\nvbZLWiFFVQzkYsRFJu9eN+7vNxE3OXAqJ+K6yPq+d7x1CJElJJa0QDIx7viTwfe57nZ3qJXr9UrO\nicuHi6SDw9n2B63vxATnc56pe/PpCvGpEshADoJSPCc8aRJOpkZ1vEteGSBbIq+Zuhb2dddBMkne\nHAKBQfPBo1dySMScCXOaGzi1bfjDqX3DRiIV4/K6sm2wR6MFoxPpW5uX14w+ndvI6GNK0LSC64CR\nNEyW8UEgCdO1I+V6RtMepO7MLXHEAYQUlB+/V7awyR3bnbzyJ/zI9hDkt++VWif851PbHUymMHPq\n1ri/XWkMtm2jPu6MtkuFxVHyDSnosG7PpECbb422XqbM0IOUJ0cLk1JVBeF5nGSnaQKMQVzFCJMg\nDEpXxFUXeGSBGwqkymlhXcv8XG1srpKL3pxRdRof5L3NNKynwchs3ndHjPP3Q1t0kj1z7mMMnJai\nz9zusGuDZerjw1SnBXepeFKkIT+Cm2CKEJNUQ/PitKTX5FxOXJaTnpsmx/a+7ywWyR5ZLCt4bHvI\nxzIavT5oj02H7XKhbzMWISVKloGrpMypnLje3wX3BZMzOEdOp4VoYS4TCvmiassLFomhsJSfP7J/\nGUOQaf32IRAvzJS/r1+u3N829nuTFXoozGbfO6Mf+Nn888Ok6Z/60sMWvK6Fv/j1J9K5UPnGP/zz\n31NH476/c73/ROUxrbuyQtt0DY4xSHFjWQd7e3Dfdvb2TrLEVt8ZrjyQf/mXf+S//3dnfSmcf3hl\np/GvP/4rP379A210YpbZI+BYglwijIDXxn7VrVOSVuW9V/r2eGLhPknQsBohRoYN6n0w9k7thtdO\na0768RvkhhUnReVF487wxuNWWZeFHz7/JfGlcL8/+N3vf0/1RqPRw+Byusgu7m/sdUcEBc8ccp+H\nehs7t+3K2/tXPv2QKKfCH3/8I6M/KNk4nS7UpvaY2oZqqZIOvDUmlpzJpdCjyYATpg1+KMZUE+wg\nJUgIdhmXizrEvTF6m4e2wYTb3HVYCdOcNXB90DeVMZhLrvbyupCXwF4SdW34ybl+vbK/74Rmz8mr\njY53QSMxSuIoglZJkbs33vc7a8oyK6WgmNIAR9SqEEDDepiY75wubNBcGRn3mDivK+njB5WipEAd\nncdjZ9uqDl2+R9eaS9sdzOnFqO3B9VGpU7rW2w7bTh6DNDmIblDdGCbSudlxwUqrr/zvOf669NOK\nyNDkmkIkF2Vlqwn3MPdoc4uT0I0hcfSnquxcv28skdP5xOVyYlkWNXJNqNSGsQ8FQoUjJwUpRg6e\nyia+rb5eFVwc/+4DQw/zec05siyFZVnwqfQBYCjTPw4nYZQo1ViJkfV04rEFWm8ovyyTy0pMmet2\n04VtxrJmfv3DZ3716Qe26wNvgqnutwd5HvC/Ol14v995f7/Rtk0kfuisObEsC2M42WReiiFSSuFy\nKlzWlcuy8u0tcHtctYGFwHpa+PDxlWiJ9tjZ7g9G7TP+QK7hNCsQf+7rlznI538euk8zYXBitTt9\nH+TFhFcOn6SZJg0ZJ+xABP4E83O04m7bhp2d29b4evumlo04KGvgY3iZAVDGkgIhNvZ2fTr7Yuws\nIUjDaeBeleExhd2tbvzhj7/j//hv/5XzD2dYjdv+4Nv7V7nTYuS8ZD1cqH/SfDBapdWdvo0pYXRS\nChQSPQpm0Z6igzkWYYOS3srCb13T4+3bDVuceHbiOUAcpDXx+a9euH590PbBl5/e+PT5B86vr/xN\nKvz44x+5X28KehpXcspcPl4Im7H3qhYlm9PMscMGIAxu2zvhHUrJkB60esd3+OGHX3G5vDJG41/+\n8CPbUNCXxTzJz6kCYGaZ+7/VAI95kDthvp+YEZL0zL1CHU3KnQmp5JQZFmTGYTbZDLlZ9cdnZdhU\nTgwGlqToGDhxCcSqyXU5qXmFdqz7Aw+dkHWZjd6xAlYGngdeBnGJLDERzRl7w/vAeiAcC1cy6q6f\nV3vTATrt7cOHAtHioI+dtg3udadWqamYMkZp3J0QZVBRtImBd+pe2epdHbU2CNaUZFmUiT5c2PZE\nPvA6p94IJMejJtNoRqsimMFnHEIi5cT5ZZXGeTb/jNm7GWMUjt2cVqtConoX/lskCWx94K4GHVcT\nNiFBzoL1UjJSMXA1N43uchKbNo+cEiklQjCRom3WI/rk0FKilMS66gAvJdNapTP4nF+flY3RI751\nqH1GBTvLWri8nHm/Xhk+yCURcsZixi2y3jOPeqNRWbOR48B8J6BBJyVj+XAi5ZX1FCmpE3zDfOO0\n2PSJNDwMliwl0+hFCZd9MPp9bk2CqtaTemNbk/EwZ0jWMB/EMEjJqGOQUiTFyPbYdV7kPyOy0w7G\n2aak7nmQN+rEwWWGgEPypBD5uZYecMpz7dL/z2G6Hu9w0fR63xrLaaWcEnkxllORoqJDnI+71Czq\nzdNKqQCh4fqgPkmzqaB4f3vjN7/5f3i5nciviR6gzjaUEBJLSdooepeMCpGLMRl9U/vRGJ1QlOVQ\nTeTXYMbwMjHrZNgyIYBjZexjSiiFwZfqeDRyiZT1BKw83nfutzvp9s7L5QMvL69sj51eO7frlUff\n6KWzpAUrEOcUavZ9NTWHtETiEmi+cX00tm7EtBPSzJLpG5fTSlkS367CUIfNhxUT4da7Mpbnmxcx\nos8MaxUbTgOgIIkYk2R8QZdJrbtw1xAIJSqnZJpmam/UXhkzNA0mxzUlnSqwcUYAj0Y6B0IoxOY6\nyHPEGtQ2VRwhEC0q0KsZSzHWJbKsgXKO5FWKm1HAW4IpEe3DVa3mcL/v3G+zynCJlDVL1ZMiaynk\n85zamwKYcgJ3hVCFDszM7BiLsl8clT54x3sj2sx0T4mYF8xV+rBvO2HbYatYCcTqWBkYiZiMUHQw\nl2Uh5cz22NgeO6O5/l5JE+6Hj2e2ulHrRojCvMfE/M2N0eQBGNXnlK7KszEGt/vG6bJwOi2UpWAh\n0vYT++Ok+rs5ZfuQYag1QQ8hzp+T83TxooKIfpDH86yIktKWtVBKJsbItm0MOumU5yaDVEJbx3eV\nPDMG61q4XM6c3iPDXX+/qKja4cZLLTz2lb0+WMvCZV04LcalnMWfgExtqZCWzHJy1uZ0N3KKT3ez\nRed0UuxzTIKX+nRjr0sgrxDXzilHSl/oPdCb4oqXkiZvMki7sW8ojdPkEFbx9Z8RtKJhTy6tnKIO\n8jpUqdXGTLqzmXznHMW8gkGGiJWJpxwr6GFzr71zvd5o686IsNU+g4QyI0TWXCg5k0qk7btKbRns\nQ3U8x8VQR6fhpBAViXmYcTq4N/q1kT8OghfcROTpmEpqy3ZNeXHqPjs6jCrT+VY6yxIpp0xyQQlt\nGNvUiIPYdY9AmYf41JczYOwDD7DdIYeKRdhi5vS6EnPgdr/xxz/8gcdt49/97d/y8vrC6EPa+7ZT\nH5V7f1DOgbCE2eAdROxMF18pmoDMBnt/sI+djx8Da3TaA758/QOfPxqX84WSO7bVqUZZpjJoqHg6\nKaTKiMp4xmW46LogaYePIJBipJxWokFJgevtofcoRMK0+A+ElVYq+9jEMzDH4qaDjqDXvE/IKoZE\necnk10LGiEvEYyCT6F6e/EoOSdh7b5Rk5Ag5aQCIecraWiKRSJbmIS5liGO8vz/49nZjjM5yWji/\nnFiXQs5JcQMhSWXSImtICidzZ98rPsJMSVSMqoUIY9C2B71P0istKh7PibavHPkjtVbeb3fi9U4+\nF1WiPZzzeiYmk6HNOy+vL5wvF27XO9f3G602Xk5nSsms68LnTy+0ttPaBkEdGW0cKpRZw9dcTsiY\nyDlwPq/03vn27Y2YMjFqss656PBubWLt0zmLhpnW1AkKkgTmnGcWvbPv28zb0VZa+5h5K5K7MpUx\npxEnQRie0bUWIt50oKeQ6K1RUuSyLpQ3FX+HlAgxMYYpbTJ/YoxGr5siFdyJHvj48mEOml3wTQpY\nyoQEvhaWD8LzgydlvkRXgbg7y6WIL5lwUetNf9ccMCvgidEL+CDFQCmFsiy01rnfH7S2UvdK3zvZ\nF0rO2op/5uuXkR8ekZiTJNq3TRrYOm3DrgxlwnR5zakjWISuGiT3ubJ2kwZ2HCOlml7yPOx7dWrY\n1c1YlN6398rWd1pvpKi6uVNOzwQ4H0agEr0TLenBip2NCujfG9AUEaeJwxm4ByXHuezlakEKhKCE\nxsOe3rvzuO94cJbkpEWEUuzQgrLAYwxER3IlDsZ+GmzqVO102N8HIQdG6dT9PpMUURiWdx7txm9/\n+4+8XF4JCU6Xhdu90b0TFscWiEsgpESKSZKz4/I0cGsKFIpGioH1pGLoXp23L51/+scfub2/k7Jx\nWhOtOvf9Rq9BTeuuAmE9kDI94cZolRjjDGqqMm0BnpIOjSSJYPw3pONjf2NEJQeWnPE4CMuEC4J6\nFr3NtLIjXc7CJIryLHwQ3BFKhKRi7jFLkoPJCZrMSLbA6MSgggqFd0kuGT0RSJipJT7NBDR3WH51\n5ofxWeanaISkP/80rzQorgtBzUvTMWmXuVUcwVDzUI8B84uGluCzSEOfdUlv/YmC1dp41F2bixs2\njDUt/8afMcg5kXKmt1fBOt1ZUn66K5cScS+MsUjrPk0AIQT5I8bBU+jAjikSotNbI54v5CQ7+0EH\na/hJs5nnUMMwD9BZMnKIaWxCpMMJe9T7GhXito9BHY3xTGET8a/PiMpHMBG8qnnTQR1iordEnFjs\nmhMDRRXHJMPgmPVyNiLBT+SYn2oVi1MejRIoK43mHUIjRGc5Tdnk6PhoUsMFkZh5hTE6FgYx5ZmE\n6NRQn+T6sQU3GxAru1canS3t1F65tRuP7UGKiTUtLF5+9kz9xXTkTLhAK7aL0T7yFIzn+mw8DXma\nBsZsSk+ICEhSlLWm0Cs5ImdwzyR21ECjHOwxq7SOrGbQ+hJnZqyekUCORrJBskCLbQYGac0MBiEx\n8asAU0rnLlxruzfqo87kPZuZ2P5M9BvDoenBS7uxnFZi1P+n5zwTAhPdBxY6TtMGoudc6pYG3p16\nh3KRuqUN6VR7lyaWAq3tbFc1FC154XReqWMTFndy8mqEbHMVnFGuM+lujC7sLwRiHuSsuNk+t4bH\n5nwdD4I1Pv/6Qo6B2Dr31mjVdOD3wTINQ89asrluHu0u7oE6cWqGExky0oSomrYgEnjEBlE4esgz\nmMu/R9BGU+iZHeRJZBLPkRzLd6huHuQeFZvlisp7boDJVI/mXQd/mhOVAQzlsPTRJ58xAWm+bxQp\nJqmwgviNFNUbKdnrmFhxeHZcHnCK5NGKCRiNqSEPM5cIhsmCP45858E06iizZKFw9pNUJwQikUQk\nmEPo0/F8bLbLUW7zXTbKd0WJ/rTjQ5dUCDYxcJl1QkxTuifNtiWXImlyQ0zCdvROpymMLClvp2ut\nJZhPaEVbeatNG5ofeTODEZo2oRkNKhjukLNI8RZNEkoz0/NeJ8yZ9O3NnrHNKSeRwQaW9PkwlyrF\nDqlhOB40qN6mkAJG7LP/QC50n2F2R6H6GJ3Wdjx8/50EH/IMNVM6I0huMd9bV1NUnwNLs0aLjZYa\nY9GWMYLT8iCk9rNn6i90kOtFGu7QlFJ3GHsOItQPgekcZEafzdl9lgVEifFLCfTu3K7OqDokvTut\ntjkV8MTS48EAoRd6ILfX3is2o0mCKeCppEiKENzZ0sZhXJbBwEhrnLbcBA7LksECe2js7xv11tjv\nTR+wS+J0ilhU1vbx+x0XkGKJtUaG00Iqktht+w5UxoCdLmI4T8XP3Z8ORK8md2uQfK11YxCIxbE4\nGLVze1zBnZfLhb0vNCCeJYfzYN/JzoiUGYbQijaovRG6DvLucnnv1WhdUS37zjMI7Aj3Gl0Y6Pao\nWIrCvReZX+hjWsT1hLgHWtNrMbyxlKjpyjojIbhlSXRrWFGIlQqpwzwA7fnelVhUIRf053sTZLak\n8JwmLYZZHKoDe0wpXZhbSAhD6iG3qWIxOjJQ9eHs9426D/qAVPJ8P52ciyJoY2IEJf6FCNU7tVU5\n97bB6VIUrWz12Rk72lEWrItA2dpgozFmh2ntuw6DoN/BuhNdG8QYqjSMOUl9h6ae2jv40SM44cgQ\nyKWAS/nl49BsH9VsXdG8SXp/b/4UElgMlLzSkW57r5suLYfqu1RQ3bCuv8++qSzhdDqxrGpj2nd1\n2IYYCB7JYSFY4tH2J9nrU43k7oSxHfFDkhr6vHDmoDWC1EMahVyqm9GIXTGyvUlZFObrAyqj0cDY\naGNKRoOeG7xh2NMoNrr4nMhMrpzveB/+LAcPyZTMap0Up2x0cl1jDJnN5lnmMAPhZlSFxSekOWzQ\n6bQwsBJZy5mVs9JSmRLZn/n6hfLIlYLHnMwDKPdACZLzbzyDdExyNe/yH8Z0ZHEETielu22b87iN\n5yRmQeSSZckTT5eV07lQyizjtYDbganNSeRYccYgDJX1BjOSQUnMfr2ZLVwCZdVhYZN0TEGW50ji\nq+sApKNKspwol0KPTozj++TQnV6h16aHMBhlzdIYe5sHfCRmJ9rM1giOVcer4WqqmoUJENfAqGPy\nBlpdUjKCJcatTbPJic8/fILU2LkSFqd6Z+ydvIghV168JGNugd4GW5XU7yUGth3ud+gt8dgHX0aH\ncKVF5+GwDU3ZMQTO60q0oGlqTPMTRwUZE7dXoNIwV+DTnPDCMhMAI4ykhyWtkm6GYLPnUx+YmDIl\nFZJlEWsmgknFt+hnBph4HR0d1nkpTJZFhpkp5SNKF+1dr7M32B4b12933r88qJus7kpuFFmVZuRu\nLpm6V8qaiTnQR9VHzKXeOGCmEeQ6ZGq7LaAH39RIbdj3nK8QSJbpo85DvhNcv3sgkIKOGUZ7yvdG\nb+Ke9o3ulfPLKvxoDLw28ECvzv2+EZMw/DjLLvDOaA5VmfZmxl53vBnEROvTsW+RfY72jsuzMAuo\no0X2KqOTpaT8d3e2vdKmhno5rQxTC/f7+1W8WdZwlEIiT75ga5W9KSdJWnIEu1ikD7jvB08Co3VK\nTIzHzm17Y7ROypn1fKaggeoYAIIdG9/hHlWtIsO/H5wBmfWydhUbuuB73Wm1Ta16lEorBDzqu0/p\npLwH34dSHdguJdyU4QYkaujm03AdJ1wrCWirjZwypfw5QSvzFzI3aFPkz4QDTFBKQNiXBYcwiCVM\nQ+hBDETOpzRNDp28BOoYyuDOk0RNhvmgLDrw41MBAx5kNhGBooKE0Rq9K88lWwSL89aEHGxalo1c\njHVVpnk0phSsT7NS1IE72+FHddqjq+0+pyeWqy1kTryPRo5GOWVy1pveZuN8zjLSFDf2TSSlLYZV\nQUs+rded2bCSND2NmTVOCKRiytpojev9xq9ePkuB4R1ync6+QYxT3vlU7wQiSbBA67NpZhonouAY\nH0ZtztevjZGckQKkOC/TREmr1uXjIB/TOs9guCRw+2PXpRqVBheQ/jjJhyLoI7oqypKiQoNWLBHZ\nrozzEA3v6sGMFklhhidNZ8khYWWaiYxZlmzTYDKnUtDvPlzF0WOMeWk4fR/06vMS1RYVQyBxTFZB\nk/yYLKGZ/nvjTwwq9HkJeHg+4POYeG6S0nl3VZ3NyX2/7TQfSvibctURRCwPNFinUhgd6qPTd6e2\nSmenpghJURDJIZDo3dkflWAdL4N1XRg2GKaLbmyyhi9L0YUWtHG1WhlAWjPVBV3SlKUitQgImAai\nip73qil526Q28uAQEo4UMdu2k5IuOk25+qcPuTD13ExYhPDMSul9yAnaA6O59NemgK3H7Ya7k0un\nd6M2pVjGrIdwjjwTKorHK/90nUsaqs9Ol4TtCWm11pQ3NMPHnhN2TArAM13GodvM/ZlwLzwRiSec\nGNSf0Eb7Lso7oKk+M2ZCwA6O4H/6+mXSD5nNIRPqCkFrTUD5GzHJIhtNU1RIgfNpIYZAvVfOp8Ja\nEjG5Mhs8sJ47tVfoTipGyVGlyD7UzuFDNu15kluAEjSN4WKbuzu0Sq13uieGZzwkka3TzhwSLCVw\nWoLKgSM4kin25tTtcLtN4qY7273zFh58/Pyq1dvsid97M+q94UuixEBK+vD4cOHxObLmREgn3r69\n8147ozh+VntQ3wYkaCYiZURdZoqbnqFbwUgnaPfGt/dvnD+shGUlp4zP6hzLAaxP2SWYHQSVVtLu\nXUqIrlad9WK0ayMlHZqPrUrrvphgphDJqfByfqE9Kq3vjHYEd07s31CCYKsq4ghMEWajiafEwsRg\no4oSkgXBYC581VIS9GPKOqlVmGbKC2s60YhT8eEcbmKzaW5B7kut+PbclGxOWGNouhz7gAapB86x\nEE6ZnnWY5Bk9ELP+mWKQLnvq6IPJtWw4PhqMCqPh9bukD5hboDD7YCIqvQ3aoxKXeWj1zuPLjQGU\nDy9T0aXDda9N8jsz1g59d7b3OhMtB0TY7xuegKxrLBJ0OO9dioq9s4bIsEY3keiPxwa1k2PRheKB\n6IbXNgevhGoYG147fe9Y9XmwGZaiyod9pnbS2bfO3tqMSa6EkKQO63NSHS6xgFfqGNR9U43ixM1x\nQSADnvr1URu+BcbmtE19mL01tq2pvq81ar+S98B6ziynPGXGUh3lPIuQLIqsnWd6m8Fw2KCNeUnU\nTuhJsQBKmmNBwWklZIIptbGn2Q/qjg3Yd/FAh8PW3aXG6YNSJKoYe5/DJU/RR2vqD95Ho1X//x+o\n/ILOTg5b+DCRei7B/8tl4XxeiDlwfVxpo3J5Wfj88UJJke268XK5kHOkdTXBpNyVqhc29jZIJbKu\nC56NSH+SVSkG9rorGbB1ljVp6umNYX3Cphm3SiQIE3WA8Fw98xI4L5lzKZQUCUklFsGURXJ/2+l7\nx/t44lq9D1mxZ2RuWjJ9q1qdpha57o19ayzLQoy69enIgRaFiW5BrfbVnFjs6YpNZZKuBo0628nR\nrT9mIiGGJSeu8H57w63x8roKEw9GSRGLR8vLvBSmjrfkIthp13SQY2BZA9vqnHPhVAr7XtlHZSRY\nX4okfzFSlkjfHrS2sdU7YRHRV86J1jv0Rjip1sqycmGW10K+ZOIpgnciTg56UPJct330J1H2jFp1\n4bI2H5LRITDVFUETEegCmKI3TdQ2MzbGkPnIBKv00Qnz20whS6fXC6+XKVsbg1orbXR6dzUdxTQJ\nPVmsfRjbQ0ayVh8wdpFwOcIWn8Ua8SjeCCoat4fjj07fGqEbmFF7xbqMZDrwE06n1kYM+h1xZ9sq\n9dGoeyNZEQQYw3c6czi3+53R7vSqiN2IVFJ7lWx3RJcSIwUwY6ML908GKdJNrVJhdDpiDi0oPXS0\nRvRp+At6LVJMpJgJJPYw8N6ofacsCroj6jN7PKt9SgnHGCKW25zEZ9uUGapcG9rqggXikqQoc6Pv\n9XmBxRLVPhad1hv7LphONX+mAaVujHYHd9JayEt5XgBjiF8QnNLoe4NeVSYRkyDgIQJ8r43oDjPX\n3d0VgNeg3yq9yVuSl6LzZROs5W4qJo+JpN+cGBI5JoYvtDEkdX220vzp1y9UvjwP8Ul6SoajVfzl\ncuHTxwtLjny7Jfa+cTplaUBjZA2Z8+mk6bypifzkzql1Sq60DiElRh7sVPahgzBFaaV7/06W5Dmd\nKYnNyFFa5TijWI929aVk+hopufLD50/89V9/5i//5oW37Seu+zfGiLgn+qNz/SLX3kGsH4dM74PH\nY+eoopLsSN9aDTv7o+LnI+YVlb0ObQlGZ03GOGXu3oSLm/Azb+BbZFkLKRaCVbXDT8q4t8ktJEgn\no4/KYzfsOshrJS82g32i8LihqE26uhRDLqQQ8ZhmaJJgpnUNXNbIeY3srXPvgWZOWQ+EFwiVkAdx\nFh8Pc5oh8i1IZ3/JC+upYDnQbJDPmbgmEbsjkMwpZiTkQIpgWlkAACAASURBVIwWIeiBDxbJUdHH\nTAVJID4/W5KSCrQ44pGiS8vsmOITCAwzzKPwyQHenNqgVehNm1Vk6syTWqzoQ0FKvTF8EJHixU2a\nYUk5TUUa+8y8GY1QwWrDY5wYe5j1g00XyRj4e2Xcq+CKpRKTcP1gR0tOB1M4VhtD63w4Si7GLD4w\nTXdTGNAR1OOmgK02i04sGSlIVbT1+lz/Q0p4CnQ7HJtqkWqPq4hJQ7G+k+COBBls0EScJqTWxpBm\n+4hlGCJco0VBmt4VdYDqG2MSKK3XVka8cLzfxOnl0ObapmQYtElZMmKSOiQGY10KqcQZyzGb64f8\nJgNdkMMP2EOKmb3uDHOiJ/EMveEC7HRehXgwr2ABs6RGKfe5SXEgdDMuQOon906tG2NXBk/zCUWt\n+sy42fQqTF4kylWrGsomeWP/M4JW3P25UuqFkaSJECll4fV84eVUuJwW9r7h1hXg4+qSLDFL5xyc\nvKqVfOudlwtM2QXv7Y33epulwPMgT8qIiAwsRkpQuL4ltfmkmX6Wox4AyZHl0Io+eD9X/v1f/RX/\n+T//R/7jf/pL/v43/xe//dcb+z6oW2C/Vd6+7MLVptRtuA604ZNUmmaGQ1rJVOz02mm1qUwXOfoW\nM00cW8MGnHNg+bAQg/G4N3bvWHPqO7AnXl9W4hop7IzHN2IUxrb1hkVEfGbxErVX6nXjEgOxSG2R\n0cURpsihjokXuxLkkqmIOKHL6HwKXM6J82rYlI8GM+IyEBEx6H4nFqdYpLZCDx0P0uOmEuXmi1nw\nQzSaDWm8p1TRhlQZS4gioH3i2uGQ7BklLowZS0sUlj0mhj1mrKslyDGLeJ3ZIEq9ivIvGJJ8jgOX\nbNSJT4qU1jJfkmGx6RB1p87Wdg8uWGgetI1ZADwPmzbmoUnUut4HJCd06c3dd0kdUWRN+3LHb5Xg\nsJ4KadHFFpZEyNq88Ki8dpckMZiCtnRIZpG6tTHmAddNEsmZUwWmdqrggWAJ3NjqNHVZYLFEj0YF\ntqka8zrwx03PRU54H2yT+cxIYmhZPaHdBGdV7yQH751eZ95KUidAXGaSYu8i/+zoqo3cW2WvO2aB\nNWTSTP5rXQF6MdpsQGqKY4iSV5IVsZssKvsomNQprakdyIa2qLltHy7AlBPRZmVfr0STgqbPyyLg\nItRLBnP2GUqWYlRiZXeCcE4d5kMmp2J5cihw76jZyx80G6rm604cg2yOuwpO9FmQHX90p+2NWqtU\nSD/z9ctM5IdUzUTXDNeHvd4aX76+cyqZl7Xwcj4zvHDf3hTMIxJ4lvbqFh2tE7NxzoXWdG8SIyMs\neBzYJFNLMHIwzJtWwRQoySfSJsw0BnBTV8ioFQxOy8qaCuGy8PH0gV99/synDyt/8cNHPn+98OW6\nEKNx/Xrn+m2nPcZUR9jzVna3icVp/V5S0kprXWuvGykmTuvCsizkOLHaEcATg8wInbAmSJG0FK7v\nO+9vO7dWqa6SjetPd/7i9CteLy8kT9T24PG4c2+NLiRGhdWo+DdEdV6WVVVmNCenyKUsej8YNDNI\n+VlYvUzC0dvg24/vfP545nyKPP7wO1I2rfAmyCIQReYliCXByHhy5X6kMaenKeNMOmCijSdkFCzM\n7AyZbuIkMeM0fgWbnY4+aPP1FQSkjJaSpJiRs05T1JhRC3urOthyVi4KkFKWqWjikmMIB48m+AqX\nmshxQkwyqWXxATYvfzM5hUMGTLrxfWx4GMTFKCHjLtwrF8UROM5eq9pvhmSHBCcUpUWmk5zIJPBi\nIrSjU+kKGGPw6Js+LxhpEuQxGi00OuJN2mhqgxrQfECIhJQJMdLroFZJ32K02RQkKEKhaH3CTpqM\nS0ya2OeGOXzQ+i7Nep65KWucW+Ms2B5SH8Wuz0kqcRrEOlRtPK1V7ne9FzqNkw5SBrVXtn0TVBiM\nsizUXtVONBqUTImZ5RInQSzorfVBG5EQM2lEQRRNfpVaK9tW1T2wFjXXpwBzGj4UO+6w3SueAiMa\nvXb2KmLd1kBuTpy5Lsr8CWQidps+GQ88vly5vb/zeNy5tY0WHNaIrwGrAdvuSvyMiZIztiBZZ6tc\nbzfqzGf/ua9f5iBHEPkc+BRq36WceLteWXLksiY+fnwhJykAxqyowpLWojG09ozOsi4sayQhu60P\nJ1tgCYmRlCNRYiC52O9GU4BQHM/1t+SoHI0JhRwLQ1mNFAbJnXM5sRantitfvv6eWu/EaVhR1rJ+\nj3BIluC5yhrC90opvLys9Fap+4RwknE6L3z4eOHysmI+ZpD8hAmI7MOfsZ4pJ5YztO5st4YFGWy2\n6w41sIQzn14CdWy8h8x2HfSjiitL5uczCpQZu5uS5I+Kuc0qO2BQDSwtU98cKTlQZvVcCoWPH1bW\nNfKIg59u79xrI6RMjoVoclMy4aMwKqEYcieLTAvToHLYrgnf8W4lCvqsMpMBK8dMmi7DCSVOqdgR\nayvM36IkeSGo5d6e4jIVHliYBKgDdP2oieWGoMt/zA9omrjsIZt0Q9NfdLzrwp/dxc9uUmHSAnNC\nEfaeU2BJwv0P3bqHQzURGC1iI5DM6G54noFhS4ZsjDDoURdgKuHpdO4M9mnhNwsMG89eTbIMUyEE\nYo+zyARltxzF10GE8LBBzGpuinG6Ml0mnIiRLOq1QDLKEARF5Wj0STZHpozSHZLc2TacahuYM8KY\njtcEBck/ujbYKPyH0TrD1AFqQXg4URBM7VUXqUVJS5PgttBV3qANL8FUtViIeJUIojPjeF0qpO7O\nfq/cbzpAJXrQeyI/hBza5hA8sN0f9AC7NfbHThtOiJk4EsEDvg8e1+uTy1lC1lbYFSt8/3rlcbux\n1Z19VHqaHJrPGGIPbC4+K1rkviySA5umcsO+Sxj/p69frOrNj4fQj2wRwOH22Pjp2xtLlivrci7U\nquQ30E1dm/Kb9+3B2hbcBykELMiY0GuHNB8eImsqlGjECUpX16QSkrDyGKAsuoF1kCuu09xJGbzv\nwkBzouO8vW/s//AjXx8/MnoVAhsO999TfYoyrTkUTcQYOZ0XPn56USv27Sidjrx+WPn46YXLa1GZ\nxZjmCIVXMwbKXegNcpL1e03kJRKy0+5Kpav3Bi3w8vqJERopLtR75/3+RvNNl6bNbShIQWTm5BLJ\nISrvW4JmfeBxEXMkHJsWaCfHwOunF9ZzpiyRvzj/mv1fA/u3d5b1TMkL2TIMwTW9N+KIpDUSVoM0\n9HPn4WdTG3ys/d6HFCMoLZI+SCmQY9R03SRf9OGTjOJ5KcSsVo9o6XmRjD6mjExTc56k5iwa04zA\nYF2D8OIenqXFhpFK0qFgSNlzhJzVqtcpCGqgN7pXMFWVWYgspizsMgcKPOATe22uybCUBCMSMXKI\nbGGn74I4ZGCaRpeAysiXRLSE9UBksN+kLw8h4E0yXjOerVI5JMEKzJ/jnR4QFGOzz9OglCIPhkHr\nleBGtkiJusjD5G3ihI3Ap1fBBDMEe1rRVS+sj8Ded3lB3Cip4Enk6eEnsSAu42jEGnVwxCuEGGfq\nqUvPbYoTJshnEUjQi8qPQ5qqMNBhnoiMKdSUuqdXEeGjD/Z743Hb1R8QjFTifGZ1YnoflFlMsd0q\nFQ0Q1/cbFhLLspJHZokL49756Xc/kS1yyiuWV9yi7qq9sr3f2LaNfbSpI59F2HQhAkug7ZW6V0Yb\n3FNmXRbWdSGvBWyW5/zM1y82kR8Y8rG2uOvGcVPF14/XG+V9oaPrrM4MjRB29tbZtp3H9pgsruSB\nKUtPe78/sCUysnTf/VGpyNCyFAUYtdiJBeFr/buTK0TDPRHG1LC601ul90oYgRgzzY2HNx7tPuNL\nIx8+XXjcjZ/+uM+H/Mgr8albF4S7roXPP3ygJFcS4WMXNPR6olwWlUqYYX0SH67D0w1qHeytA42U\nFKDz8fMH6vs7+22jtc63r+8sy5kPHz5TqVzWyPp3Cz99/T3f7l+4+22qN4SlltPCcirkRX/X6pVt\nqFmmjSH1BZ2QCsGCsjx24Y8pZt42Iy9GXDPltPApivgJpgb3/fGAOU1/eDnr98swFPXHcDkkx0Ew\nZ9nZmZp/fXCVuZHNoHdaa7T9II7DdGeK/E1TAcDkJnRCK69Fh7gI7vNpJeSseq95GfTeOJVMifGQ\nd9Bap7ZGzJCWoKq9o6ptdGpXhqYFmyl3Rl/mJZISFuUDzzGRQmDUTSoNGqUEoouoLTkdTn9NjXEw\n0qAshZQTIYdJBvusJIPHtktjHY28ZlpTKYUl7SbugzgncoYa7DW1i8gfRxCUCUv3rkafGEVCgklv\nPonjhKAVgtNrk8zPnHQq4gvuOyFllRDHxGO/S6KY44xunlLjaDiD1pRNbrMbddsa7FJ4mDdiXoml\nTOlmBzfWtMrHYEYYgu+iBeII9LdZaD0LMVLO5DVC1SXTOtzedratK2qASL032r1qIHJR4tfbTdV2\n6CBvacEIbLd9OjDh8X7HUoEeKG2nl4LfnW9fbiwxY8VYT5mYI8GHOJehHBWPRs4LNSjUj2CUtHA5\nn8kx0bIuC0WHxKleWTSs2J/RQW4TbtAUZBxgslSiShbZ6Hy5qtDhZZEszodcYRaiVt09sNdKfMxg\noq1Ra+dxf5BqEaZcEr2KRjYglxVMt3R0w8PAw5iORqYFd3bjTXXGmKYNN5SHgMOoDEwMNsb5JfP6\n0Tm9XNn2XSz8sQbNw8OizchOWM+JGE+czlnZD3Hw7f7OGiIlOeuHMqdOg2GUVqBH8iTN1rKy5BUb\nGVqi7V94+/HO7Xrj7euV7dqxkkk5kJfIr/868KGf+bb/xPW+z1jbQSlF0NS5MIKwU9WPddWBYeQc\nIKjKIeQuF5tLXtWjPycFC4GcA3U0AlOetz0mrJHwHqBJXxyz5uAjwrXPJh4fnTAnveBIJRKEOcY0\np1Pxtfi8LJNFmguPDnOyPKj/IwAsEjnq1twhLpm0ZKkCWqfWiu9ztUaXtwYzyf1CdMFoxaZbVPJC\n61IbGIHWFbLlRwBZTCgcXJrx4NCHIkk9KAhOdNYMypp5IIxZEJy/S0tD0mc+mp6PvXX2WTOWcybk\npC5U1/uA2zQsmTTZuzMe7RlRYCkSkz6/+lcatQ/2UWfRS5yNXDq8+zSuHFCcIzivdl0E7tAfg5Yk\nWQzRGC1Mkl2JoOY2lT/yb3RvE1KdDt2u6FnfBt5V8DCvJBkjXESolCZO6zvLugpO2mG/Tulj1GvS\n02BsO7V3ttF49J3rtwf73gTbDNgfG3WrIjWrIBc84lPWOPYOYSeFRDFF5bZaaXsnuoxTW924PSL9\n3ng8GqRIYXC3hnkkRlR1lzU8pRAYIUoM0bVV1r2yP/ZnIFmwgDf5U3ymLMQkldPPff1CFv0pDcP/\nFLx34ZQdZyTjum9ad/MLzByFWqvW+VjUIF7VJL5tu6anXfrZ3AfJC2lOZIZL2dAGIUTSUFB+yrJR\nh6kHbN6eTd0ehN3GnLEE5pK1qdxXZNxRJJ3XxOniXF4Wxntn1AlhjON3PDDpQR+b5ISnSIwL26jc\n+oMvtzcuMfPp44nLZZXTdCp8zBOLzdyP1jkvJ87LmZJPtAqPe+P2bWPfNq5v73z7cuXl8wdyKozW\n+fTrHwinF17rwtdvV759vXJ9u5GTLOXLqTBywNBkOqpsytkDp0WbUfVKJJKtEJF7MEZTWYJLWRNc\neTLRAn3M3BIMaDJ2uPI1osWpfdYB1WYeyVa7ujnn95LLNPs4scTpFzBqisp1n1td8yiXXZ8uzulQ\nlRhKTlQdOIM2RHYTAyEnbU9dBK1MGo3tdifNHPGY0lwgpxvVj2KHeVnMz5jIVx1eOc1D/PD8u/68\nHK8ZXMYht6nv9hntOxx61+UVZl4/83PUjuFgwJDbT72eNtUqgmbEMdjEZwetAdtgv23Ce9Mswzbp\nlJ0DZpt1czEq+qEP8Qw2SeIhjDuYXjtZik05It3wHemo58XjHghZKYMxhCcOnHvSEOCB3ZuI59pJ\nQ5kzbWt4M5IJ/x/zEjFXtjxog9q3jfRadFDeOvu14V1Rs2mFHhW/XIezjcq1P7i+PaTXdsEYba/a\nyA3apkiDZEkRHi6TW3OFta1loTbltJgbYRg0fV6u/UbbGnVuLlt3rptqC5MFPMEouiBDUG9vO+S4\nTVLTERo5L+RcxJmMI69Ef7cjlO3nvn6Rg/zAJo+H4wCSnw5pl4Sr5ETKkX3fsT4Ic9o4yMgQlCVi\nONuuCbPVQW+Oh8bY9KAsOT5tv/f3B7FIeL9tO6lEymWhrGniswHrhd4abXRCdkqJYuFjFmU2Gnvt\nehgdTecTCjhfCrfHXeUMJtWD8T3nXOUUO7Vtqqo6F5GYpj6/mAe2RLykCfMMHY6AhUSwiCGs2KJW\n8A9//cKvW+f96zvvvxez/9OX33P5fCGllffbg3ofrEvg9eWV0+XEx48X3r68MbaN3naCrazrQokQ\nuy7F4OIYSk7KS/ep4IiFSNYUbg42lRauHIqxRnLImAcur6r8Gl0PY5JMh7rvpDVQUiGnwm4Kjhl0\n1XiVwpIVr1prpe47JefZIOQwDxs55SI5GGVk9rrP4DMZUfTRkn76EBIdTe0Mn5DCdCKWBR9Kzhx9\nkMpCDklTGkzNv1N7x6KTYpjhbDNw6VCGBR1yIegQV7pnZxwE7cTdfW5pWOBZLTIGozWGTyVVTHgb\n7A9ljUgfrkLmlGRs6qPO11jPVIxq7KE3MMUjU2U6wyGMgfVGHoncMikllpiJp8xjF8xIh0SWsQdl\n1RA1HW/7ru1rKSzLSvTEftvZ9od+/wjWI7V1qjX6o7PkhWSCWEbYn+S240/Lu3lUvyaARbzO7HBT\ntsl31ZFs+WNHmHWQAarvet/qo7FvgqzaGDSMh1eu/c629RlGBrfrLogOZZzvj0a9V2LKTwluMG0s\nbTg0FaEzjHVZpVaaBGQPTk+GnTMN40Zjq5UtNcrcKnevDCR/fgznMRpbb9SxkS0QMtgIRJfUdImr\nUllNMFofg9T+jNIPY4hi/dFKdUzlfuDmLqVASWLJez8CgsBwHvtjAn0iEONc//ZdYfW9OSE6vnfG\n2LGU8ajYUu+O7RXYqXujn5ReV1ubXYXTaDBUSFEI1ACYQB9Gx1uj1QrjkKQBQe3XHz9deL/flHc+\n7d7RjRCcDx9OvLxoguhT6tjNWV5WcnZaahA6MUe1ssQ01RgzQMmOgwEag+FqTw8X4+PfrPwv21/y\n5XVnfxuEfIN4J4QTSzpzf7uxtY30qRJPg5QHl3NgG8qMG71jw0lBmu2cpNmO89/3/zH3ZktyZNmV\n5Tp3UjVzB2Igiyz2S0v//2+1tLBLmEME4G6meofTD/uoIasq+jkSIpAURjIRcDPVe8+w99qCmxXZ\n4vXNvdjYZisUP1LtuDmUSElpGYZhc8aLMFlziKqXKjMpoqyL9CxglFkM2CKRpnfGGGy+qTqHVxpM\nDlmiWyZXdXRX0LC4LkGrC9ONZKCS0mVHLr3LAJPSK/z4SgtKpv/NulQ0wa9ZLsv2dZlZqIrcIa3J\nWDJhJSsxO1//QOET9gB0OSwmvZ+6VMbUxR9L394HPgh54NSuBScXyR+vfM7VNVacY7G1W8y5my6m\ngIv1seJ5DK38czL75H7bA+mqDqOPxZzQrEiDHnrw6UuKjmyBj83YEktEaJVFKZWcpEkvHiiAGTFx\n/LjAVvKXYueljglTVrKEWY6DutOKogOT5VdOq5HFuR9F4eoDBomxjHEOUtelNnxitXKacw6xkFLO\nCnwf8xUtV0MF5EOjX4HpnEzRu7bQKDZw95bjgPcZKrWEV8jUECho/HNUZ2SplAaT5YO0YGSFWZON\nNIT/sJVY5+U+FcKZ7JA0zlnn4jz+WLbyp1XkliMdPSKy8Osg54fkKan9ZEhbrG0J9HkCpkVCyuGM\nq/DszLitbQX/+VxYQdrbUvHquA/m7DGal8B6HJNxHeSx8s9Z8KdlM/I8O0zpfZkLRglnIOStsLdC\n+nXj28c3znHy+QhaW1Jk2L/+yzs//7JTimO26UBIzn7f8A1y0kFuCBd7ZU/+2CHIJXgOzTqxJNdk\nzbSvif/+f/3M27b4+MtBP79RthNLnVbf+O3bN/rnJ2UcvP1i1M3ZWqbcm/YDaJZKQMIu+Rauinh5\nmC0sxcZ94BbW5aSvZ9liJY0HUsrkbPo8bbEykE2kxYDtD1tAZ85D1m5W8Fc0L10uS/OIpPYxRMwj\nKWGGIZOYQqvVdUlyFgs1WzHCsxeHW0o2jSOy86q2I+OWFVyFnAXAsn90H8efbZ5fz9kCzIMYH8/v\nCkASgGcZpS7pqHgo+rteh2Ofg+dxMMfA1qKmHNUrnEfHpoUxSQEGlnXBknkta5PBxexmEViCFM/6\nYA5p7TM6r8dSFOKyRUtVxqVkdF+KW+zR1AcSeOSl4qVCKy0uuYRfnJM+yMvZYzewptAVYCQvBOgv\nXjnhYJct7TYQ5Gpl8KXxViIz+8THJLfEdsvUnAN0FdmXRVX6WPI7jGycy+gWpUYYtmqCaRnPlWRC\nbbRaIFkwexLJlI/pMzDRfUnbXqrU+r5eF/UlspvXP0M7JJJyBDCNj3OSS2WGC1UOUp0/VgoEbC2N\npkWyS3E0ToGzlg35TJLTbTJsMO2fyBA0eiBn0fgwR1W7pPWSC/L55L4XrFRaK0p1kaCTEnrtac7I\nQDXKVuBZSF1cBulAtbBzhFW9ADcrrK4tZ+apdPnTOl5lJhouHof74vx40Gah7jkYKHHJWNFydgmq\nU3Oj7o17bhz9V8iJ//x//87x7Ox749//7Sf+27//xNsXoUjfSxNYJzme1ELOgIaZBwFxXVCfFVI4\nae3PfiqEg0RpsiAnYB2Tdtto/75jKO2FOejnA0tyw338/cH9TeOV270iPbNaS7fFEmXrHxZvxItl\nL7v5lSbj14AsbNhy00gNsUJXPaerS5pRj5X8Mt1Mi1FTOBM9hxTOnTU7c9rrAGDB99+/v+a7mDF7\nZx2nOOAs8MxxngrWzQWu79Hg7JOP3z8YY3C/hwKgJLa8MeOF70ttazIZtIyg1k2XszJpLj/moq+F\nhzFN4ShdSpUkeWTO6bWUc5P2PzUFaFzqNo9/l5QcSQTDNclAskIhiSdyKgglgdj5yUm+/9gwmZb9\n9V4xMsmbxjl9hiooMmKDFjhGj1m3qrveh9AM5pw443SsGx3Dk/gvVBdL59S7R9PYYXWnfx70x8E6\nJ15CEw2sfmrHkws11RiJyC17XcifxyczTVLN6pqHZu0NQbbWMbH5ZC8CUR3HwTkFdstxebslVtMC\n3GphuwlzPH2RxlQ8m6FYOHSRy2vQtAhPgW/woEgOFXk5Vcw0tks4TH+lMkXUi0Z8Kav7Rg5ohYqj\nInDpfWpbw1dmzcCEmIV72QRMmwT+N70+o3OuCHVe5D0x82LaP9FoZU5hSuWqvuKueOmIlzvPs/M4\nB7cb3PaNkh2zgbsAQXM4Y3a8ZmZLnGWR9kw5pZfNUU0CtFzU7i2jnyMOX6Vy2EStK5pLeiTZpCQl\nxvN80qfRVqHthS3CA64FXrpszxkmGgHk5rx/3fg3fuLj48m2V3751/srjSenRspNaM+pTbW/PguF\nAfhQiAbosBtTt/RcCl+WIcaoc+GrwnQ+/3rgx8FWNn795UZKcD6fkklyquogs+Ube7tTm/H5/BDq\nN2vEtJbT12CMrh3Aiy8RlprrIDd7zX8XwnC+FBCpaykbioTre1iBIhUpUJFjKUuRAqpW1nAtn2Ln\nkCwJHuao8n61rXJn9vOUCsPL65+vWATONV7GFYsqG9TprGCSCB+qsDabSppZwb5/yRddqVN26a5J\nlFwhFUAHpa0fqUbJ0guDyhJzXIdFUqxhcPBTTnhoqbfaqCmp4l6OH46PxVYqxzjVpabEdL0vo/fQ\nzCsAZSx9H9kqPhN9TaYHYz1nrEnvTXRIOmldiIdgk69gs6x4IM/VcS8CyE1ndLlWM4JDzXHy+P4Q\nBmMpN+B8PEn7znbbOZNSf/oaIjjG8/V8HmKbGAwfeBVad5jGV/oMi2SlWQft7JPnHHx8PjjmwJOx\n7TspFciLEd8N8TmTCFJjChCcRnvqtIgZWHgCXF3FGhNbM4xPktfmhPjjS6V4Tplaa8TDrsAsW3xm\nkpikHLp6UzXueOAJTnBV2WMORlzQzYpUVa7Oy6VOZZ09FuQwfTDTZPwzVeQeHGCSsin1tgRP2ySl\nPeficXYeffKeC1TXMmx2QY/ihaMmZk2stMhbor01LD4UX+JG11wQzU4oAI8+d6xJjvmvE7Q/W5p3\nRUjwmIPZwfPECrR9J22Nax6Uim53L4u+Do5T9MH9ntjuX7l/SgL4/osyJgeTkgvTJ8c4+TyfsSyL\nMU4wncfZFeySorJdA9DPNOdihNM1xwa8Pxd//68P5gFf3u78y7/cIWmxNKwz6WCTWmT8Aeir83F+\nB+CdO5ZFrzv6k96HgFRJigO3OGxjBqaqUlU8seicI2bJdkGrVL0Yal1XzPpl8a5ixmfNZTG5P0ek\n01h0AWXGv9djJFGUtdlXV2scyiJLkkNqYe6sMWVoSaZlaC7YLcJETGOQOac0yOFaTbmQpgxPa3SN\naizjSNvukciTTdK9VBQakTzFXF0qFcdxn5qxoAoQE3t6TTEzdJAHNKtktlYxq/jSCGMclyEsulZT\nxN3MVVyXtagRAC1+T4/s2ni/IuzBLLDQFCrqtNT5STs+Zqf3rkJHrFjJcE0jPN3jYp+v6I4mKqr6\nMfj47YOSMjVwCf3s4a7UfsvsUuuoEPAuqV0/BedKTcRII70u6utzz0mKo5Kk1BpDIRXHkjIle8N8\nAprRk+TOhhiRRkiDQkyEXUjJXyPKGX6PNSN84xwwFlstbGXjSrqR6Sx2dMleNM05h9g8Sweyu9DT\nNUW+q7vAZHNxemf2Jzk521bU1Z0aedWmMY8vWGOxQga5zh6oisw4TroNxsU3+V9+/XnQrJCUzTlV\nkcdLHgUQRuJxdL49nryPN2lT7UdFOvrlG8svV2Cqk8F+uQAAIABJREFUxvbeeH9rzGNq7n1O5VlO\nRTp5Ak8KUh3h0DNCilYML+DFVLWYjBY5YFPiWvgrEDi3a9HqTAZ9DI510Ncil8bb2536Jk1w3p2V\nJs8xmf3BnM7jOPg8H5S9kU+13hYP7Rwz5uuS+F3htZjcrSu4xK00Hr93vv31k29/e4JLTfF5nNSm\niLXb18zvf/3OWCdbqjye3xnfvuPnwWM9dDAd2o4f/eBxPiIU4tILBz/E7QX7X3OGnT0z5uTz88HH\n55O5nC9fvrDvJazfevCSySEoVYLci9XU6krSN/FYTqYaIcmul9KnnMC1FFItCpZGXHS7v4lOyeJc\nWl6xlmBRazJSJ+XMtu9ssbic85RO3FXxX1rpVDJ1h+6J5ylAUsoaCRwzpGS5gEcM27ys/5EWlTJz\nRfe0egzLA/+9xKS+CHsyIE0alVwKLVcx35HaouwbboPfHx/SytdK3irNpK6ykqhVI5wEDCZ9nJzP\nJ3PEQizpciyBYDB4wZcSlT66lqyjU7JHlmhRwbQmx+eTOZ2S52uBnCyJz54ylcrX+9fIwxVtsIRS\ny6cMQLUWctnY9l3BD12d74zLZp7+WpymUvARZMJ5kIaRQymkYONJTplWM1Tp8VfsGeZ0zCd56Zk/\nh+iCK8U45xzMs7NWictFOAYr0REOY3WdQ8sSE4IV3uMC1iDLikExZR2YRrMf379ruZsz+65OG1+v\nQIg5hbBONrEsOOAV/Nwy3Pc3rC+O5yO60ikJ5jlppapzm9olrn8mjK1uZ0KdcNlz0z8c6B4V0+I4\nB98+DpxKrbqp+nEyh0OTNMxi2TOG5q5W5YpKph/6XCfD1QLpt79MHUQCUSpG2jKppeBBSD98K4Vc\nVdVYhlyrjEEXbFxjddHtilNugVkl0e3g9ENtO8pIfM2856K7qvcxThKDvJJm/ejPfc3F46UosYB7\nbc5ieXJ+DB6/D47PRanOeXb+9pcPfvmXuySOt5Ovv+70bsw5+Oi/wcck+SBtqpC7nzwfJ4/jwTlO\nvry/vyR753liMVNOLl2z49hp4W7sPJ8nx9mvIpQ1F9u2AVGRuXYcKciQY56alyfVTxS51nKxFy2Q\n6eSZsCnN7pyi7BlFBhkzHFEjp694yP3FHS+Xnd+JUYS6gjkns2scMF2qkFyd3JqY6z341qW8pHx9\n6uCvscDrUyYiilpfy/pKkiVqiXCBKdxwTorgm31G6HTMVYf0yEKfRm6k6TMZafD0yWd/srXKrVba\nTSEnMw4vpqrFZPq81akNznORc2bbGnWLhOc5owtekgfGGNNNh5MJ1QgsMce3wtoLDHUX7kZO2htM\nH/iSZLPVxkgy1JSsn/l5npx/70q9L1oQP48TW5nlBCo6Klk0mrwUQjJ2SQnkcRiOsUhIsWNZoQ/T\nBJM6+8H0qX3HtpFcS3EPONo0h9hDkHXRvpC1BNWzFLatiWOeddguoK+pnFIHlnP0DiNhTSKLiT4/\nNwseuUf1fzLG4DhOLqGC8kCvql5h7iXCqFPOWkavIfn0OVjHwAYUCitrH2Fzsv54RP5nHeRw6cYl\nx1IlKkmZHk6QPGl2ZTqWrLlLQtFiSnbRgkCLBWeeg1ZgtQWbvTZK69o0J4NrqUd8CfFB162Q9kze\nMit7SJQa27aJJRHbY41nBJ4ioWzHonmwZ1VSKUtj/XwezLxYyUi2QuWgFszN8QyJQnfxhteAsmdy\n0XLWij6PS/OckirZkqTnXiNAUX0xns44BW86++Cvf/lg236hFl1C96+Ncyw+j66ZK50KtLLLZDBg\nuBCjrxmqadkyV6S5zEjSSar49PBJ8nmOMCyU9Jo/eywb17pS04mke5lblhOXqhJ+UtEseQxVtSsO\n8rwS2bOi6ZZj07H049FdXQd3TimaZc3Bay0Bu0LGD++8JHCR4jTm0AE/HPPM8XkwDlmxlyN1S8qK\nqXMnD6ma5gRbK5ZmFkoM7X2IZaknyQPXdM55cvYufXZp+ruGJtmHM5jBVNHLvbILcVqMUhOrJs2S\nl9CtfSoQYpqSbWYc6toRyGZfSlbGo0v5s5YO8RQyo1SUf2pFpEQCdZtK7BVmUwKVXOSxzxA75eqd\nX+Y+Myj67vp5SoFTEra0u/A1yBoGSbFWCz5Fe3TQqDQ60TFnFHuqxL13sklC6tkYrmSdsRaP48Fa\nk9YEUxPCYYbEEb17JcK6szDWHrWQX1CsJBibNaObLqm+5quLKjE2GnE22QIb4ruP2PdJTq4DbY7B\n8Tz4/HySs57DnCsrYhGXD277Rs5Fz5nJMT5scXQF0zCcMkMMMbUfWRP66X94pv45rJU4CDzE9FaC\nfjdcid0rlkRJioHVY3NvmmOaF3x1xiFd7jqEsSzL2e6w3l3SxGqkStjgtYFfxRg+6N5R2y73VN02\nqT9aom160M2S/hxzWCNkeHqRU06klklFenaGNnKpyhWGQ8k7Nd1Yvuhj8Hgcgm9l2XRzzsgOrIOc\nFJFPNR68UjSLjiqi5QAD5cIck/N58vg4qFGdrql5YD8nx+cn+/adZJn3n42RDkbupLfEnndyvbFt\nidoqiSz4Us3c540RFvs5JdO8EnV0oGda3RUwPAdpOXlMch+0qnDYnBVCnGORPUbHfVJRyk/2rOXk\nkq5WXJpYbhJskFrwYsxDvgBwWgKxqzt9nnKZeqHlppFHSZyr09eJmz7LSwU1lmLg3I2tbQIthb4b\nkH57dR7fHpz9DNelVBFv+w3vUn5MS0yXWeet7UpqKsqb7b7oS+anNRX2kFLm8/Gkn9J4Gxo3qbhI\nUmasjnUpN/JWKXujpYLVStu3SM0xVZrhBMVNo5GlzNmcQxaaoDRxeEotkaRktH3jPB4sEilGVdvW\nSPuOzYFY5jKpBGUX2xJbFv53LsHlzMWcuSSRY06GawHn03mOUxmhc7LVXZfYx5PRByU1bnUHEjNJ\nejh8YvPkPMCPk7OHwqk6xXLIjbsUKSsxfXD6eKXME4qrlBLn2Zlj8Pn51GXRCvW2YQGfs4XCwHPS\n3Po4GM/BPAa1harmcppO7X5qKmBCLmxvOytJUntOhbeMMcEz2cJjYTFsM7nFW21xkGcZvubiPJYK\nLFNR1GcUUMl4jolPp6G9y3VOjq6IvOP5T6RaUQJJAKUMydhcrbtfBpspN1fyDCvTnxP6IKURL1VQ\n717LKz2Ac2aOcYLJ3t5dlL1UNA9TpJRRrIb4Gf22DHGQrywUwFrSuiZf+Dg5ns/AmBpWCpWNTCGH\nXFBOtWgHp8JmPeBD7itMPELwllqptbGVxg6QFpYWpcXG3VAcFVquFQs4EJf21Wit4HNyf9+43Tp/\n+a8PzQqzDq/P7w9aK9R9J90SaauCLxUnlyuVpsl7ai48KlnzcK6LNSo9HDexSWprlFpf1VZ8qZJ2\npUSrmxZmS4duLgoK0DmcWEsqiD5HaGrVZhsKprhgWGDM7oEryT8+XxfIaszEnJkxEvfbnb1smq8v\nmN6lfhCnmJSgnx9KYeoEJ0VVoJGwZqSWXr4Eq8IBuMPHxwfncWqo0lz69ar5b3KZliYCQBVXboXP\nS1Bu9GfnfJzMyGa8JmPZCnMM5pikrLYxYXgWZdNDLfRSvyBJ4Rz+D6OUkzE6930jpURrmxRda/Lx\n8Z1Wi3jzNfwWFsbTKQZKLYVqDePaU5hS35dCEtIgXJ76jn2E/DBwq2bCz2qXovFUn+KrZ9egdE5n\nTmBNDjqYcfbO83iCLW6rRsUfgdLuPI4HZWWsL87zSUkadS4Xb6fUgtUkXk6wbuaQtyFXBVzkWkml\nSoV1LTMdas6sOTifB2tO0QbmeHlVci0SJEk2wVo6uNcSM2gZEcDh4Ri3iJJUMLgnXby1yBFeA8KW\nShVDx+Qu7UMESl0NjjVJa9fQc5OChcML4/D/f6b+ecESpqFGshTutkVuVyiXcWlsr3blPCSIL0k8\naTeR+aSA0WlsFbpPPvsBSS3LylCqFqFWkKA+MhNTqXqJI43eSmJl6OPkmGd8uY7NwTxPns+Hbsmk\nG/g4JxbLk1IFdcquv1fvg+fZWSF7q6WStxJsbCe1TN0LpbVQ72hOOdapWZoRB1hs+12qHTctfzDD\ni3IKt7eNej+lSBgyQFlZPM5Pyjdjuxlve6PUDPvA84oDU6qHZMZIQgEoxi2H4cTxpNmuyJARWFCL\nFBcp6e8Xi9jZwwKeQ7kRh0MObgiAMDZaAg4m2Ir/zkmuw1wXmQSAZhpdXLr16+Y1grfRJ8/PCVMz\n3Pym0OjpMMeM9KOCowPoeJ50hkJyl3M+Ti1tPeO7K9KuadyQihJ4+nlEmIku5Nm73qpa4QRPUkLl\nFqqVJT2yJ+0DZtehIcZHpuRCTrqge4yRStMsPrmB9VCLTJiXbl96GFuKW3tNv0KlA2KEUxJnUmt/\nHAdzFmEPqMB6mfE8Rw5trWwl4bNjE3bTCGFKlo91XVzuoUuPxSmm8RLo+55r0UcnWYacFGThKww0\nPySorrQOcXWGLvk+B+W01+HJco5j0BfYdMY48dp0mWcoeyM1/ayX0QuMfp6ShxYFduRaSFtjPh7K\njI3vumdjjc7xOEk4tcipq0IvKd4wRwgzqHCMJXQyFXJE0SGVT3D1k79UP6Vmdq8KgSdyWMPg6El8\nm/kaI8tY50nnElXjqkTGc1LSUo7R4/wnYq1cDjitPFUN6h8sUgOLODLDWTbl+puDnJx6q0xTGvdA\n5ozr4KtmGIO0Tr0oodpYBaYNJiMOiahua6bVjZIKa3gs7g4+jqfmjGZavsyTMQ9mkpzJKthufPYn\nx1Mt89t957ZLUpeyhbRJy79SYNs32r5JJuczKIiJlC9jQTyUyy7ZjmZ7y+jn5PP7B/veuN1u5Brh\nv9OZNbPuDdubqt2xmK4szvP44PunU/5WuP9yo5aKNTn8PHSeAXkMt58WsctdBpux8D7xadLUFlHZ\nFLkWDsxw2nVD+YMOHsaTS6MLag/NjTlGzKOhtnCP2uJKgL9+L4JpEuYNlma/5lJHbPsWUKTOt+cH\nz0OX78/5JzzpcOy9k39Suv3z8eTz8eTx+RCjfNMh8fH7d+77nWyFz49P8mZspUrt4J1pxroZvuTm\nPG3wHE/yVMhyWgmXgyfGVJpF24pq2vR3P4+Tx+eTeU51D23HzRnnqVFWNojvtHd/hQXDpO1N4ybX\njDurNZEbcmV8a+IApYInYzhYGaxufDyfPMZBHZW9VVpWhbi1Rqr6bHJO9KPj0/XP/cIXF+iLeXSF\nKoxF8sTb/c7oi+fZOYLHP8bg8Xjo8HEordBXPA9L36kSprQVnl5YbOQG9MnzeZDSlEPbjRFsEV/q\n2nN2aMIl1zdJgN1iAW4eh3ahzFB8IRNW3cXPGUdnpCFD3VyMQzPsVjOlbnJudmWrfh6D0hrbvitQ\nI2XqJgf0JcJI207vnbmcuim+r/vCXY7mLRduW4U1GWfn6E9JWmuj7HuYpqLLWiuUatq9WQ2TWCpM\nF+UxtUxKlVr+mWbkFiONV+uoX1foa8TZI333oq9TErXl2FjkHawmUnLqFcRbjK1l3u6N97edeoOI\nIdQDjsT5qQQnuhQ8JfrsfD4fPD6eml+XzP3Lu5abiiuRbXjJDUgJd2Ir3EIhMMZib+Iwu0+hY6m0\nW43DWosniVkks9TGWpXNujStixg3aKa3bDGHKkuSKoUztLAxVcKBkUSFS0uusbVkd55M/Hzy21++\n8et//8J96kUuqUqv+hg4Uz9PkdW9D3Git9pUlZu6i35Mzueg7U3GkrQiT1HckNxqcEqghHJgzI4z\nyCtgxcPpzxNzuG1NJV9o+OcMhY4tPEVFvVZotHXoYrCVQtsqK6nyHMOpbxucGtWQVHGuAb9//6A7\n1O3k28cH3z8/6Us/mwd+N1UjbYpPe/ZHBFfo5z5HIFlLJu0ZG9JWK0R48Pl4kEYWQrYVxiFvwON8\nMHFutztt27jf7iQy432QyTIpWVJYhh5zYQOCU2Ke8RkL4pyY58CqVChF5nalsecSvJJJyRsTZWvu\n+0bbGuv9TdI5pIjonw8cFUt521WZJ+OYU2OJtdhCzuJryig0ddCMMDy1rAPO0nipwNRxdnzGSE4R\nS+IVhRsyTWiRPuXm7LeNAZyrcz4PxjHIVjSPn1F1h/685Uar6h5sq7BlvESOQXRtciZfvoIc+ZaD\n9dAS/MuXd+63N85+cBxPVjbafWPfGvvbToodjc6AU/jo6RxjQBNPfwVoJVsUYasw03wp2tyD5xPv\nEGPhYzBHl1M5LdI0fObIGl0xGvYwsfnL22BV71iK7yMlhcdVr394pP7JwRLxO1qjKwGERMzfLLbN\n4BO9pHumvGXKblh1citqg5MO8vum0OZSHUuqLC5i4nIjFYXSLkNqEReMvq9TVX1JvH256bByudfc\nJf+TqkDb+VQy+9QoQ1pbtTxzjkgb0Q+Zi/TZay2uxJ3r50rpqkgJJ2KwMtw0D14eGGZVHApnER1P\nsK4op/MiFVVxcxkrPDokJfN8//7B52+ffPnXxtsvDU/GZDCWIE1yK2p2O4fMRlsmeCBhUDkXiuF1\nVjUwxceJSSNJnUd1TuBT11DoASEtnWMy+xBBMeVoTX98//qMBrlUVT5jRdss6l4yi5f1WiJICra9\n3ejWw2o/MZPl/jkm63mQl/M4T6XgtETalH9pbuw/3WhbExo3HKW+FIjc14KVKSSNpVwKGcsaBw5i\nxBHu4GXOcR48jieeEqUMdTKWue830oY6rgVrTPqaJAgVEqFIKhiZtQQOI0ZEnjy01lWfPVOHy1Ch\nU7OqP+tO3SopFuYghMBxHHz/OKXj9xQHRCAE3KNLNmwaPgTT6n2+/gyLpJ5cKqU2iOQiKxoz1lxJ\nIbBKWbPeSTxP58T6pFpmizEXObFy4jk7z1rorWOuIGwbi1ZyXMril1y7k5WQu9E1n77GV+rYUxRA\nzryWsUPL+ro10lvh+4eyci0SgVqt1K3isfCt5EghC0iZzRib6H0qJql0ciGAFwurLmYUqIq2qQzZ\nPqW+WpJH+rUXXEO8prVIEPp8U5FX1fEqUo9YdAtAZ4Fy+KNff4788CX6s1jw6MV0n6G5NrGsq1H3\nzP3txtFPck389C9f2PZM3Y3cjHZvr+DbbC6jSc5kGyQS1QI8ZZHfZ8Y5RS+cJgnj7a3x5estHprM\nvhW1cpbxVZkxIwRJml6c8mj3mJCyQngdXnwE8FjEytPo0wJHoMVnSgLglwynH5x9KpIrFqTmmrul\nJhlBPEuq+kvDrGi+mhd1X+xfKvNjyL06lBG5svM4nvz+t9/58veNn/7tLWaxHpFpOiRSVSQVU9u6\ntOJ76iuceJIX1m4iDWajtS0Ob120yWNb79pzlFzY9lBP+OJYjwiLSNx2qXmusZj0Yie9H7Qq/bmA\n5wGrqlqeHnMxjjPko+qOtnsO/MLJ9+OBm0BUeWtSv+RE2XduYXap1aS+yJn7r+8kpGLws2schJM8\nS52kVk7KHjONUWIklEthLo3+xnhSU+VcQ0oSh+N5sqILSeH4a0nJ8z6miJMEb8URiA1BnEqVBPXo\nD12GS8qFt32ntQqbDvjjPPAjULprkTqk4aQUQoJIz9kppHKnJnE/jCwHqztbyaxS8WXMc/H57cHn\ns5OytOs5V/KWmafGgCllagsnbBKHXhm5WY7gktnuN1KRg/F4nvTHSfXEnou6tUixf9vvbLdNap8u\nxdWaTtt2Uku4OcdxKs1ozJDnCXuw7zei1WY+J+/3OyUXxlq0nJhJBcBcS7uxZKSauJWdnO+xn5Pj\n85xPbC1aLrzvb8qDzoNzK1G46TtppUpNMy38IjBd4RWKrEykArM2nnYwlwql7Vb1eS80HuTH7qy2\nwvKpbvutcqUQ2ViUXGilxiEeXfwf/PrTLPrEAm+JDyqWwcvOLSrgYkLO3L9mmjdSS9x/rQq6LZpV\njzR+LHBM1uJhyI6LWtOY4rxmdcMlfeqzY8kotZBrhuB0jJEilEA3ssUS5kcTIXp0CZhOHPGxc5Js\nz9GYxrkOKw+HZnvBfn4wW5AVvikAWS2jBw+GkJw5OQ5zn9EGjk4/n+QMX37e8f/zX/nP//sv/P74\nLk530WXYWuLz9we//dd3fvmPL1hZpBQJSUtuv8VTKSUfJ8/Hg7oyrRRwuO93uAVcKMPyISaI6++f\nUmLfbqK8edjwLYxDS9pngmtdiyLP3FfILqUbHtHalyTOOC7dtiIdNUPfgix3DpESUykCZmWxW1a/\nUXLi7Ac2dUnmSIk3W2y5aalsWsCWLMb8GusFA3s56EK7+2LGmOBuWOJxfDJ9UbbKvu+SkI2F+SRl\nKUd6l5uPSFn6EaRRlLuKYgX3tgmpum0cz844Dwad2/1GrQVLmqVbMvJMrHPyvZ98fPvQTP2Y9Odg\nbEuh3KXpEhphxY+4wLIyrd2lerLE8Bzh5YsJ1NLIqSngwSq7aXncUtVznBSwkEUF4Ti0XN9KZZmQ\nzyk7OVVp2LOc0PgkJUUc5okO3TFUqRoR+qHRUtt0aLmHcqRmVbfrCGe2FDpzLfLKeJFBLqGci7Sk\n/ph9qEgIQcWFe0gJbnsNfEFI+0zfTSt3OVSXs2WpidZctKIO3JpUP713jv7A+2Jvm94RVBgZEbRi\n0rvn5JT2Dgbtvim20FVhXw7nknNwnbTXecUcusFc1NJotWm8Gd/XH/3602bk1zh8uWRatowUBDFL\nIWqzheVFu6Ht72bYPkK36rgnVYIoI9GQnmE5jP4EjzTxMGw4iktSCpEM9znYDin5y8SCzfgLLLAA\n42ChpdYz8D+pBrBwv8XM30V3TIk4wAQFWpEWLtdpkNs8bmhL0UapOtSDuzQ2ChKj+eQKRVhTLrB+\nnJRauL01yn/c+Pbtk8d4RgCwk6ux3TNn73z/7ZPf//qd/Q3lReakGepS+G5aRkuFlSoBcVWAwE3u\nt5ITcw1GV9dRQiKYLJJp4iDPeqIlFcMkYfSIDQsz0+UViHc9UKwK10gmE5Fa2AASrQvroMN/+qSG\naiHFd5hDeWPdlbKjmZW8BnNS80YLqmEu4fqMkZ6+G42lZlz9s8+XOW3VSkuFjNQGwye+YEsbmp1p\nQWsp0aqWlomLK9OouURuZ+ZwFRoeo7ytVbZWWX0G3c4jISjJch6qnnVOznlyPA++f/uOzSSZ7hR8\nqpCoVe7U7goKvn2V6cQWlFikS+qmA97Xovt6XYg+lMfqOMULDQVyp5I514kvdYV0fa65SF10Iad9\naubr67JmOTkHhfJUNzXGNcaU/dwibalmjUkdjTFTSUwz/Kbw9F4TZy2spTHTtjW9T66fLWexYnIS\n83wQexM0vsgB1YoXOhzACTMxydcSrK5ZZvZgLpUkB/kmHEA/Ts7jgD4lLkmBPA7FgHwnKeblkAta\nhN82hsuhWctGSZI2JuA8DzLOljbB6pY62uS6YGupoYI7Ofv5h0fqn8MjzzGbA823gsImsDyCJl2h\nBdmxMqlvCa+Lx/qd81RWYStV21zPpKkPM5sxzHg+H4wpqLJ4KYJS1dTiwBmSDGZVmZZidGGJbSsh\nJeKHpXtp2TpcD26KC2iZMiPxJFvx0swzZc23Fjp81lw8z4NzimJWqzToyVPI7mIc4cY5J/15cI5O\nu8t8M4eCgNforOHQLZJspJ9PuXD/qfD288b3o/E5BWay4uxfCo9vzufnk//x//yVf/uPG+VrYwYT\nY5lhS8aX99sd/3rd+gH8DztxydKArwK+RIcLpzjH48Gzn7gZP/3804/0GnepWWKJk4gE9vA3Z1fU\nm4UE896aTDozEnRQR2VmIiMSDGiLh4fFXF1kvj7ZLGtEcpxBp9N3cI6BzTvFb5LPoZm2u8uWP0Sd\nmytmqGb89vcPnp8HvgY/vd94v+3ctp16a+QUqpEYWaawokcwG1urGi21jXu7UXNVrB0aAz77I8Y4\npkX+6JRsCoXOJaLSFud5srUN3DmeB6B9SikajSQzmX8sB2ERzo9Pvh2fnAzu7+8US4y1eH481am1\nQrvvZEvSfI9TPOySXu/kPzpZW4vIvS7DE2PqwgyHLj4ptfC233g+Hjz7wTmeeE6kXGhNpqLjefI8\nP5lzcL+/8fb1CzMO27Em9WLKYz8gaCXzvn8Nminhho0dGilcpPPVLaWcsVLItXCOwbfP71JORTEw\nA5lQSlVgjSWZyQxJAN3xz5NZnLob7AXfM7MkjpjDi3sk/HM22GrRbiiZdnZJ+x5OBYxovDMiaEbj\nF0lijTEXf//4DctayOYoikrISUvO5Fy4v+3cR5c7+A9+/Unhy6/91v+09FRltEgrUasxFrAMW4W9\nbqQ7JH9SC+BOq5u4E+HaS0lXYClFWlgiLabKinzhU8V22fTgxLLnAlJdms+QhEq7iipyTIc9aFGS\niJxHcrRrqshrVk5fclnGlyfJ7VLBlkD/x5yx5zVaUghvcvDSJKmaQ4sVd9Y5Qru8gs3xY4TRSkN4\n0MnkQX03bo/K8ZsWKwL2D9p7Yb8V9numVC2QU3Eehzg0iUXql25fnJTlLvfgs4fRp7C1EmMRuWev\n1PpzjDBSJFpNGpXMFWMFLSl9dMaKnztVWfHH4BxDRp6tMpNe1JJSLD012hhcy0/NtlsYLbLr+zqf\nT87HSd538Elrha+3e7BxjKN3LbYCQ3yOQQ9MwPM4GH1q0Wga/9Rbpf37ptn5VCpUyZmyVaGTAy08\n5nihB1rJrKVEo947eKOkxNMdbxu1KHR4ZSdvUg+lpFHc9+dTQcRuOJnb20228Nnxp56tOSelZUlc\nq7HWk/E8GM8ne27kvFNWmFvWFOlvDLzoYLRiMCUMyA63urG1wvCTNSaP51NExPOMvNge6Voj1F1P\n1oS93TT2NI2HjqPLzTm6WPSmYOkAL6u6L4WyQd60DLUqSbB81U5eRHCy3s/+PKm3JmTs/JFK4dfI\nyiHn+g/QPRl9clERlSyMcVs4Nsdg9DNwG6rO01SntuaktmvkacxiEMlh1hK0xMzQZuetVvj6BZvK\nlhXbPDOmzhstl7Tb0mhMo9wV3RcpkQJKttaNT50nAAAgAElEQVRknidtywpWTkHIXI48QYNxphhf\nqkv6kaf2P//686BZKZQoXPLD+KKWDvSEWsb5dJ7fBvvbxn4rqogSwKKW+uIuCOB70ejEmFhkcpUD\nsrYSRpYUDrdES0W3dLBAlgtGtPqK8YdJhx5jEIhwC/TirjE5ukKFM4laCvu2xYsdxtHp+FSQhdLQ\ni8Kiz4MRZEBrOjRtLJadoqWtRbttAiO51B5m6hgsWahtxNP2uHTIi/vPheE35nyK9byUwFJvle1d\nI5i2F3JL5Ap5SPJlOWsxGyOPFbPr43zKpWfGmhm8YteY5LqcAov7iu+aYm3MtTArylt0uSiTx6FE\nVw5l74w5afedmSYHJ8Uy1QSsmmNhKwiWfQiCVKT+kMEiRgVhFFoXF6YYpWW2vQm09JQKx5JGd2MN\nHqcWzI/PT/DFfbtpznxr3N92BSi7DD0fHx9RzWaoiuBjaDThL35sEqVvnJKVzSEgWoJJISeNIyiS\nz7IsDD3opXaXP8BVJaekMJQVy1MI9n2AxjxNlqnzGg5jJvqwyLkkEnUuKQl48MjdnXl0zHSgllTo\n5iwTYVBHkrqFfi1mk3N6l2P5KRt5zYVMpY/x4r+7L3IVOCwTY8kYkZYK7VZhoUDrLHPNGno2fF3w\nPAUjlyZX99nPF1iPQAFfwcTq7HV2ZB/q9kK+6XPivQdpsjNGF0Uyy1zV5+A8Ox7qoVQakl4ChK+D\n9ArKIEm2nHbIl3Rwymj2mtjkOMsWWFxogoG5vCImXlToGbAErTYpsfyVZIzF+E3rtUuwEYEUf/Dr\nzzvIjSCcqdLVrDWoh8NJw/DT6Ofir//5HTL8XN/4cm9yd9pSa2+ZsaBXLX0seNFf399Jwf5f8SBb\nupYRoFtD/OqclNU45mCMoXmtJxJyh+WtKpqJqdsxDurH8+T3v3/y979958vbzs9f33hru+aHa5Go\nrGfnOJ6c58mXX3+m1oIbPM9DfBW3V4U7z4Pns3P2Ezf4qfwSoB9jnGJm5NDTPh6dx3HSkhK3c020\nW+brXtn2QsH57ffvfD6enN6pDeq98Pb1znZP1BuksnjfvuKuSlJmHWnL5+w8j0NB0mgpxFLaUM2Z\nW2uquCtyQ5bKMZ+cx5Mxu+SPloKhkiMab1JLwVwOy+f3B31M6bQXrLH4PA/2slGbyHp9ScbIIQzp\n6UMzbBvYT+/c2lcxa/ZNS9QZ+xOcR/+EpuXSsx/qvHKm1sLn+eD3x4Pfvj1CKSOn7W3feXu78f52\nFy7VF8fRGUG0s5IEmUqJ0ipzdMbxZJydhDqMNYbadRZjnoEOMDmKk/CnTClESkoUy6Tbrg5lafcz\nfYYb0LRLgNgBCX/77LqorXjsMxSOcgy5pEuJZ/caGWRYRX/WGoP+8R1/Ak07kNwydd9UVsXzW0gM\nGxpDthtG4ZwPvn/7RnLjtt1I28Y59f9vboxxhjqnUovkedMHziAX4/Ym/4BbknHMEvM5OM8hLrxf\nUxB12ufZ+Xh8qtMtWua+iINJ58UKOaeAezO6xMVxHnz//snZT7koM7zXL3LA+uJxPnl8fgamwZhJ\nC/y1ZJon9ly1JnLgO1T9rxBRqHDofbBM83QrFglfMgLlrOVvype4QWMryRhVFCgrVnF/htR15hoP\ns1S8XVmyF3/lf/3156hWrrHKa84Z8/GU5BScYCus88Pobnz7r05uJ/evO7lVco5xSNKSsKXCscTy\nNp/UnGMZFsupNZmHkkC0+FAY89l1KwtmbzLLqGQUBe2pBJ/ctNi5Kpox5TLct8Kvv3zhtgloNY9T\nN3yRE4tpSgM/Bp+/fafsVdrn1gIslWk5U5YisVYbwvECtW2qTvri+dFprchRGd3HrW283d5Cxjfx\neWKpsG3Gz7/eKRvsn5nvnw9++umdX3/5yi+/vpPySS5SNLhnznOJIzM627bx9uWdMSepyS3ny+PA\nMUY/qDmzbxvb3pSNicIgsosvkVLkJY7FeTylbllAnyw0J7bQ65YiMJQCp4UPTqE6evTF83G+2BP3\nfWdP4MmpLSRwbuz3G/ebzFTH+ZRqaC0UW63vPm9VMr7Q4nuDvDL3vDNnVvhDddymZu6nZG6XsiYn\nI7Uqs9IKNlBK3LYdSmVtneS6jEaN0YqFBjppL6TFIAoSsBuwfsyic8KTns/jUJWoIkMvOnGwtbKx\n1cotaaRmof1miHmScxZ6NSkg2UoO23mC0elTIxp1LpBWFl9lXe/jZL9tlLbDXJw2mRlmTlir7Mko\nm/g8icRkULZMzRu3fefbt9/os/P98cG+71LklEzLhWWTcyoQ5LLC//btG+dnx/tiS1VO61rY7MY5\nT+bzKbMN4p3Mqb3BnIt8Frb7jdKKzHZzRvWPGqSYTbspxu98SnJca6UEmbLsFUO6exk6ECa3D/o4\nSRnmSpSl7mN4Z65BNn858swyPbwd27ZLOpozeOHKeSVCNi5Rg81FMckZncnh4HmQctWIeIFPZQMb\nGmuqk/8nWnZe3PFrwQBXDaXBSM2Fn7++8fHb4HkO5gnH98njt8Hxu8YHpYVt91IeGNCyXI5rUbNT\nU1T/UwtKdzHLX8sUFw53nDNwsQQ3RV+0Zc0obS18qBVdfrGMZRXf98pWCzUlsjvz7ALYp4wNmUpm\nl8FiHKc4DNlouYTBopAXpCH6Ia3qALeEl4J7huUUG9gyVoQGtNwoLXHbG2NqPLFwijlWM/WnnbYZ\nt7fK/Wh8eX/j6/vGbdfMmQV5yRhVDFpJkBr71jSO6CeeK1aTsKABzypVc11PMsT4GrF799eOwV2m\novPsPD5PoXIth91aFQcZXSYJbttGrmGMclWcyxfnclXSU5mot7YpfKNAa0XpNmentkpphS1X3IaS\nbJbrgMJeuxLiIJeFPLHnSrlVuXZnsGZYzNE53F743Vdwcw7XYoxsk6Pw3JwBqaY8TUYKpUiSSioZ\nsbIN2qKpoq+tvmS3HkiIhVNXGMdSLMxnmKlmB6vUWgSNKmr7PQxJxDdhSxyclBPLRyh6jFVRJubS\naC6VRGlGqfG5IAplraFt7oNJDoOegl1UFVddfC6DVEmFWirbVvn2MIVGxAI//H0aRaFOWo7QibuM\nV+pyiJ2KglFWyvSlZVrbNCefU5v1HAYaqdAStQnCJr0/geFQx3R73yUVnZ1yZvkSspOy09qm6hfD\nVoYR31nghK3n1/+dq36PoYvQmZHeE+leOJaNFoEfhlNTY8wfHhTN5pM+uxXCjulRQOpQt+DYsJx9\nz9iKyUAunL3Qzn8iHflcK+SF+n2xV9ZyiiXu953/4z/+G/9j/Ub//B4hrMb5Ad//1tnfNrJlatbJ\nK83ooja9bHMuUoQ1l5RZcftREuM4MI/MxSsKi8w4BBpaa/HTz19pX2S8mLNw9jPGBWG2walVtmEz\ntFCawSYZ0D9PfDjPcrD2FryWRXN0KfSp0OTcNIedi3l2Ru+ULAMNpbKSgnRbytisiggbU1VOlczt\n4nFohFEUTGCFvGX2vfBl3Vj2lZKNmhIJpbVfwOaUF3vOvH95U+RW0mHipoABK1UkxedBf3be33aw\nxWMcTJySinIMU8SIzcXzefI8DoVNPDrlrZJaZZphWWMqX/ossumwettv5KKl0e+f3xhrQkV8aEwI\n1ZaFPag6mI6Pg8/PD+Ya7PddbG3EPTcj2l+jRwe1YodSkiR/FVXYQiVPxtGxhWLIiBCIZNRa4yWU\nfCzlkJJOXVoebj9Z5pVyn3bTMxhmNfOJj5N+zaYtVFb14qXLGNSShatxvXYij8+D3gdznaxVkRa5\nvsBxY81wm4YdnqSKbmm/0jxrUV3ANh3Maw3apn1AKVU+sKEFpty4huf0OsAzFjMMGc1qJA6trOzb\nZNqxLANKojQl1Zd4P1aEOVgGLuCdZb58fWfUSc8n69uTYk5Jcl23vbJtG7Um5nkyz4F3qLeGkaTq\n2gq5RBhMybzqQ8vctsLt6x6V+YxEpA7Iibw3ZQf4Mo6HTG9rOFsr3NAFv65Zdk7kVvHDWVmHeKuB\nT15GPvV93psQFmbgNfN4POldUtqcdXFdktbR9XeqrWDVqFtTPN0aLAbbTWO3mooQDAu9F3/w60/S\nkcd/OpeO7PVfCFGZ2e4K5vUp6H/yDN34/pcHP/1a4UsN9sHSLtelRU1mjLX4+HbQWuV+L1zM7zEG\nv//9YL9Baw1Dzqm8F04/teCLlurxCXMI/3lJA31JxC8Z4w+bMxd4KukQqSuLu9EVjnCpam6+oZT5\nYGNft9iYjN41QmDhSTmEj/7kfP7OOKNzGCoF91sj3XdaEcin1D3m2tJCp5RCGugxqxacJ6H/vrZN\ni9yURIlLYUS6qrJXb3Rp3KGVTGoxBkuRW1ikxnEWY145niYcqXXAXvNKRcOp511L8r77252tJFoR\nOXL64tFPnv1ksiilsH+5vZDGn8cHncJGje9Zi76Pjw/6PNn6hhXDIxSgUIQyTRoCXCO8bFGWo0XS\njIVgweS0S5mMTGE5ZzkpMcYcHI8HH58P3JEt3RJbbezbjiMkQQ5bvl90x38I48hRaOhiEVGtJI3a\niOcsm6IG19LnWqe2Ym1r8kFgkrJdfgxTdymGvWLTPBb3WORYhkwsl8xeGo0qI06yQMhqLHMiwcEM\n/HMBlkknvm1io9eQOa416f2MPzvhy9haIS2orSrq0GCOUz9r0DvHcrrDueDoJ8uh7oW37Weyw5gH\nYx5YvUJUnLJV0SaHxp+JzD5XqMKcTJGoIRcC9hzz+Y6VC8JWGf1UnKCJbbNcbKLttkkgEaHnBnIZ\nucJgluui97XYauPtfRNn3FQI9HOAi6QI67UEr6XQciJbw2phsHiOgzHPYNNI4Ub8fVOotZIVipl2\nS8sha3EqJ8P//uvPO8gNsRQu2h/6D1Upie1eyFWORuVXJrwnnt86x7dJ/8kpW8HSjI2uNuAplgMT\nsUjM1B4xB3M4szvekNcebfdTydhWwBtntN9jdqw7JStBp4RDdE7Fio3zlJEgDoUaBpOUkZtuTvoE\nLJNjMZtLxSyJnbG0VPU5NX/OFdujzUtZ0B4njEYzDk+1uCXAThfmV0swmZIInvslD7vUOHKMqu29\nEr6Jw91YL7ONzFEpzD+KaKtJS5lsFkYntf2gJfI1LzW0s2iGtvQmeqN46kW7gKwRimejFBEoc5Zm\nePpiJkhbBZuRS1l0GZ5yf4JYIQZqZbdGPxWnJ0Z20b83KdlH4Q6JlqQbVxubScsoqNKaIfnD1GFc\nanBwoV5bgxgHiv2jl6mUopHLFS6SkSTPeOEa1G2uGE/oezALZvpaQblLL520wYsZ4ia3VG5S3gCv\nKv9iZ+tyui5gtfk55VeRYOYyppjm6XklSRwtgy3sCiOO9zKV/MpIvVj6yex12edIqLL4s9fSbknj\ngMTb252xRnw3lS2nFyTqUpiMJbmhrcWYndwS2155o0JUqdMjT9NUHKWUXu5tjZOiQIow9WJG2xst\nV8XrZZUjwyOF/jX6q1yh1innyFHV904FZgmWUShhyDI4mRawdf0YB9YSjJTYIflaktpi4NJRZQw8\nlC+X6iQHdsPkFrZKQNxc464S77cvyHKbWtI7KvXc//7rzzEEheMR1+bawqZ/tXU1FBglwojXXGpj\nu1JwHr91nj813r5s5JIhTWZkOrppu51vWZFaucJyhjnmaocUphAyo7TCVJFJaaOMLDfYJdtaTmth\nU0aa4/l8chxPtcehPc22y+nortFGTpQMHknppci6jamlmhEM4HNSbzf2207LRYvOuRgOm+nQXvuM\n7iWRiDCA6yGa/x9z79Iky5Vd6X3n6R6ReXFRVewmW2qZBjJr00D//99IE70okaoCkJnh7ue1NVg7\n4jabmKMSBrNilRGIm+F+zn6s9S23ua+uhQvBZ6PScC8fK9Sq6nF5osxzoB1tkZJCczF1KDlUak7M\nrgM5JYVCxPjkyPgSEByDKnASFghmbMVDn29GQmCnuKB6F7NMD6Y9JZQ5MQxmDIS9cLtnQb3mkBGo\n91eCEuUp8VII9b5X5qgvHIMljZdSFJgLv7RikILC3Bpt7gIUiEnL1OgD9OUUOlCH4ZN/Ukzs+852\n218XnsGruresjsV8Xvr8UVQXPot1I1vM/2Z+GvDZ6xID/jnbn0vEzpSrz+plKglBe5roW0pb9tr9\nhOQFDOHfHOQ5ZhUzw+PwopzLY2n5KTVwIBRXXphfDy4XnmvCnCTLzgOxHyz5oOVq3t7ojr/Ifijl\nmhWEMaQMSylqdr6km48pUVMlNf2eYgmUVBmhuznLbdT+ecYcjLY4jiaXp3NvkmXiirTzIG1Vo8Eo\nOeaPaDr89+1/Lpfzqa4UJ4WiSYCZKSowxRdSYw75QFLi5cyUO1U7mbDEogGpg2ZfzG7M1hQUUfRM\nbW/1FW4inZHp319UfOQQWbMJdxAUSK0n6+/oIK+bDonnotzcqRUz/PSnGz//h7eXSiT5CzHHghGo\n253Hb52Pvx789KebqplqEMUNiSlSc1bIq1fIFtBBnXbNO5Mchhaf1mJlMtpaWA5khzZFwivd46mL\nLimxvb9j72+MYeJwmBCfuol9mWvmmm/J3UKq5BIhKg4uuMAfkxZ8zsU5mmav7lATdyIyZ9eLuhLB\nWRdraKQUqjvggo9TTL/VZ/pSRBWoVEGBXIuWl/aDCjltugRLf5a57IURqLn4BfGUWl1Mk6a9lo0Z\nnwHHcnDOpUpafwZVTylnh3DxeolDhNYU0GsjaxWYIpaLL94UtaYFWXLjk2lG7Yd0RKOdLWffERgr\neEX+5DwEH/dEr+RMxMLkVaS6COnFl0ewidEeHB2w3Nwj88iYSpQPRdX8cv30Wsbylt5MuY7PIIwY\nkLY56NkY/vt87oae46gQhW0maFmrgzSKR2SaTw8TxriU+CzIX+O0gFfGPmqZXo2q/TKYk+totKtz\nu1UHygVGG1B0GCqVXkayFOMr1HotabjNntmkrsJIuvyCGWt0Wdr1qTEbTmGIL/xFCOKLYNrDvN2q\ngAiz8XU8aEfDzLh9fxOz3xlFlgIrRAIiQJaifUuOUsTUpFkyY/mlpY6PFOir05YSw+byEVuMntb1\nRHpczoiJtLNplFSKxodJrnB1Hkm7EtA7Zu5dcO5+dLu+OkbB3Gz8iMPTESEOTAyRXBPmYTP+5vJU\n24B+9wO0WXes9O/9/CEH+X/3P/yFj18OPv524tgFQgxst8T7952375VpzfWn+pluqqmh0s+L43PQ\nHoY49ItpIqRFr5SCoq/RQWCkaC/7L4EfCTlJVa+Tml5GkxCijwx+6M6NJflQydRaaNdQxuCaOjim\n/hmv8OG1iH6gR3ecEQPFtaLwdJX6Q2IKAwZxQmrKEBMxynQgGV/0W75r7JGr9MKunlq2vOLFW29v\n9fmBgF3uhgs++5PESdWv47jQZfB0FErOF30GG/1LkatVM08bg95UrWvmnV6/k+jW9WxPr4CSyVPV\n4RZKdmeqMhM12H/9S8SbCUWaaO12WaA4vrF42zcv2PSimhtnclRH97zkDXVYzPUqIp6n/ZqLPp6u\nU1W15lUSeLWW/OBPXqmbX/72TKmSqmlO/4CuQ9bCk1dFuebTRCRdcjBYYcpZiL7DPpe7k5FO2cxH\nC4ucA8l0sAa/CHD57PNXZM+/DMzJfDYXx3XRjsszI4Wi0OPtwyTzSwOvNP2iSEHmmGQ6JIOPHjxi\nXp3EENptseizcZqq0hQ1WmA9v1K9H89D1UzpQm01rnVhQKJSEN53LcX7DSI2FgmwGV0ZI+loSkEe\niLVeXpEQpGqZ4/kx1cWE4N2lw9h6X5zX5XFtkfM4xeNPmhpEU7U9nd+jHQsvI9jyy+4Zwvx870KK\nMEQ6vWaDVVnmjmvMQ5f9ZDETwRrEaQGCTf1u/Ttd9uOp/W9//pCD/H/6L//E//2//5XRJo+pOLRS\nIm/fNt6/V7Z7pA1VfhaUyTnXcPnVzuhGP43rschbYFqnOSI1BaWkWFpeGXrba6pynku+GJ+gJbX7\nIbnMbK1/o8awp8LmWT6p5CbHxExGnuZfjNrbuaZD4oXdDC4zMtO68fldS2kSXjPTlDLEyDwkJYxD\nztWQ9AIJfC/I1mhNM3oC2ybpXXZeyfBUnuWXxLMilSSM1wO0gma2aiGfl53azfWsC9bEppCwmrMn\nue2cSbGmaq8+Fv0U7yREAfujM6RVU3qqEBGbSn4ZNp0lnyEnLaGmrOB45RbdiRn9MI7Z2TBjsSY8\nHqeqp5TIET0j5vcpgVrkDn2OCQj+OExXGbmDeHilfXWpP1JOlOqzcodX1eQohDmd9re8UtZBrHFO\ncpcw6moiqqKeews/FGW+Mv8dLx87DKXzxMSck27LKZ3G4zhYtpRSUzMJBaUsRyDk9OTt+/5jPWs7\nHTZyh+q7vEajj8YYVeMBU8e2nheMaeYcYyDaehVGT0dxRhfkdGv7chOOXHnmF/vg6g3Lg+aQNMHK\n9LuIU8yWNgcWTZmaQ+86Rc9MxwOdXQzQ1qQvWFPB3WFFrBuWB2FNIgokjwS2XJlIUhwsepfpQSe2\ndCFHH0s6PvbqTTuMEGhXAzMtloN56LNxXqcydnPVd+kyy8WUJj9GYpCKKaD3brBoq/MYDRtNoTY6\npVk26A6MezZdISyhQ6LWtYaeU3UT9rwL/93PH3KQf/s5Mecbaw3+j//tF9Yy3n6q/NN/fuft50jM\ng1Iz2y2z3SKfv+gPrSr9kingWHz91qlvhVo0BthKIYcMK3Gtk3415nFSSgVndtRStRWOmfNx0MOg\nlkgpmt2OMTh6Y9929k0t1eid3pvL9jQzXnMwhsYQ+FYfW4zR3JK+mLORl3TAa5gATlMVe60Ky13I\nRGNLEVLnJYlUjFoW0RXgEEJgXhejDWzISGJm/PVf/8r2Vqm3TXAtR3nGLBzAE+DT+9DCJ0VSKT7T\nNsXfLXUnIUYtyZ1smGIklsiWN/Ef3Eiynrjf7vO9FbgendYaMckpuFhaCipkVEBJg95Opks5i21E\nk574GsP/+fO1OFV1O7QUyhHb83Ol6g99IubKXM6HL/r/iUuLb0uREZZL8eDpNNXSiVdxcw256q7W\nqfsm/8HAZatatuJW9uhjhTWlNCIYz9zYkLV/ScGlorbAZ9JaagaComTE2hjL/1mL67xol2z5ACEn\nJqYIsdYgBvYYXzml9kTKBj3X5ynaZ47ptTwNrreOQYvhFQLbLv5LyBozYdopDefmq9tS1zKWD4tS\nJrvSKfkOKobAAPkFzgZjkS1pDJjQPsjWc/LjQQuN0Qe1VimkgkiJ+N8WlWVrIdDQDimZSahgflf0\nSWQRLZEs+sWjBHpLUx1PXk4oXOQR5eC8Ts52UTdl14bRhaVwn8DmrlaNDMt/lRIlpvx0/0gb3mUQ\nWX3SW+fsF3Wvih8s2acBej+vPjnXoMfFPB+klci3+No7BLRnCn4+qehQRxYxgr9rbWpvYn9PFbnF\ng3Kb/PTnwj887oQQ2N8ybz9H8qabeVlgv2fef975+q2rxSnGCpeg79fit789ePv5jbQZMwgos3yr\nPScy46zpy73ImtBRaohZp12XNJ+WWFOz5rEWoy9aGMDFWpPzcXBdl2zLWRdGwF6V15hTjsYgfS1A\ntEQIntNZZf9tQ0TDMZVBCpG14H5/J2ehCaQ77eSSNTpwMl4p4hmL3aJkEVuyIfc1yK2z7UWHdHHl\nQVg+HFK1tvwxGK4vXsNfwgU5VW67UucNc7GeY2FNkrk1ZDrSux5eC1Wlx2jMFaIqjDYG1mTYySH7\nZF/O2zE6YzWSLeIqBCsuk5TiS/IgPyAnkn4NaeVLUm1vBiFmZ2U/7TautPAl1kSfRfyYAGG5qfDZ\npbklPkB65ll6dxMdj5tIL/PYHCLYGbpgxui00TWL9vltKVoqx/U0i/isPLwGdehsmrRDgRPPufl1\nCiNQ60a9K8mp9+bjGY0S5tBzQ3rmompUdV2qKCnGlpR4lGIgJdm7+xA1L9cs2RxihSwfi00/THP0\nnQH678XTgVv5EbAy53xNvvDRYEyRYkqJDxF68BFT1FiK5xJ2Rpe96vueCGkwHLpmyPE5WK9lNMvD\nvM0YQ8Ypcd8VIhOzs3vGJTf2fOrFpXC6+qUD2QuD58i2bBvL5afKjHXUbeDVpaUXZmCBIwG0oC4c\nj4vjOPU+pUQo0/cr5hW3MSNQEoldOxh0Pukyf3bNeoc0AVAnLOmo7xD1D/V5wN/RQX71Lxaw3eE/\n/edvMlBkY6WGRWNaZI1AvWe+/+XO9TWY4zkW6Jgl+jX47a9f/PwfK/kWmGESut7XRHB8ZCDEzBp6\n8efUQq5dF61pq367afZ6XcMt3DJTjKHZ99UuHl8H7bwcTL/rJe960efUYbpmYisb92131jkyDd0r\n5EAPnePzwdV0UCtSDMYwcq5AYg6l8WihJkflGE0vYRBHO6T0csA9U7tb61xj0Mfg/bt0yTpshBF4\nptprlKpIszE6/eqcjxNWZCtGTplS3FlmrqpxDoxkJfaSgYUYhHlPmRgLKVWqB2Fb8PSaq/H52wf7\ndmOvGyk+uwBow23oa5IR/rOkREVIg4WmWTEm+tSBNtPUXvKpECCSs5abwzQ6CEnKpxB0Sc75ZI0D\nPOFGy+fbJhNWKex5l1kJF+OZA4+iDvNogdkn7eyu3NHzeDxOhi0tdB3ClkOCoXi+nLM+r/24AM2d\nmsPZ0moOjHbKPMJ7JG9aUK/Z2apGVdotCOhWcpaj0y/3NZerWVTh5SLlTomR0S/adRKjlv41F2w2\nB5FNRrsEICtJiq+cCEEI4riMGiO3XDjnRXdVFSG+FDe1VElJLbFiJLAY83pxQZSjqVFmiHIzK7tW\n45mX5vqm8ZVCl7vb2tWBsXzUE58mrcRt36hBY8cRGmc/GG2S04XPFIm4aiUGyWiRH2MsjycMT/7/\n9IWxVFazq/u+vaXnE6F3CO13li2O4+Lr66LsfiGYQFzRhQYWIWQ9A3EL9NY1vokRw5VCS54RuVXh\nGbhipqCT50hLIzMP4vmdnz+mIp9FWgNdYMUAACAASURBVM212PZEX4NrXgw7ZT+2Ck3V3+2nzP/4\nP/+Z89H4+O3ib3895fScg9bg49edvFfK7jmca9JHA48iyznRWtNhd3WMxOfnydfj4uc/vYtxweJx\nfFE9pT7HLAlRlAfh/VvGvr2pMpIGj8/zEAc9JlLefO4mC/fZTvqlBPEtDNIuS3oumXu4sbbCVqpX\n9IF9z+QcsJL501/eX9K3OZW+fVwnX8dJikUwopTIWUuntME5lBFIjgqlzUFyuzW1me9y1ikxZYlf\nHo2SA3Gvekli4GoPWv+RH2jRIKhySxYpocitl6KPk3RRhqSH/WqdPjxI2pS/Odd0GH5kK1p2higN\ndhuLeQ3mOrnZTqyVEjYIUYfglt2R1+jjwmYXSTIYi06gqvJuy2VwyVk6ghTVkAlTS9jhI5oVTUG/\nuFJpwgzaERTnhdsyseyjkYL4K+omFu3UUj3XQr3tvJWM+cu5lcLqQuqOczBKpt4quTraYS15AEan\nAvXtLu2/S13Dtzf6WJSyaQGflKyzbZtfWEGLTse1rqm82JUWZkUHTRRMagVd9m1O2lAISQxd7J8y\nyVmjxD47x/Gg1sItbdL9myrTXLTYzjGK6Ig5WyfyzPqMSQhmvBKWJDd4NqdAb7sHcw9brCh1WgDJ\nMi1w2+7ctgTZsdL4eM2MMFUY5JQoubLXm9yOqXDL2o0EjGSVXjdiGKKidqV0xeja+BVeWvYSFbGW\nYyJEpRk9oVRShwVycBhdKJg92T1KDZrduC44jwdm8HZXTKTyAiSXDFGqplIVCRmW0B8xQA5S4ExE\nRRzzOS6bWtg6LqR40lIIwXHLg6tdv3um/iEH+eiB0aSnznURk3m4Q2AG6Nfg+GisU2klP71XMpNt\nwa1V+nUxrkmwQDsH/UqUqpu8t8E8UUtXIrlKZmSmBJzrGrSHGNtzDubSvDhmHd7mMrMYn3IsnLHh\nWJe1GG29HuQQI3nbCCZL9+M4+Dwegj1N4y0OaigyCcUgOy6JvRZC0AEYU/QD06hbZLrsbbrUTZJB\nXLUQaGuqQs5C9G4lETFWSFiUsxVztKtDmaT79soju8kjuPkkph8SK56SPFhR6IMIpBVIKyjIgkzJ\n0ulbcNfc8oPb+muDX0rm/nYnRTnVhGkdxLCoRe3/mKbA3f6M41JKzTLzue0ztPdO74dGPnERUmbM\nQO+TdRk5R0pWNFsc+AJ6Ql/QtZALOUrLGzKz6aV7LpPWDNjIlFj057ekC4BBBNpqrEvjkADEmGXa\ncomkgWbxfTGOxvW4WM64Ljm/nLORRYnhteg1gWGkw06J3iW3THuCDMMyW6luPnJ1IT4uQpXhHE/b\ndng5Fc3c82ZCxu77XeY7vK03qY1Umfr4bfn47GlI8opawLlTC3OivnfUDUbfFwne8+Tk68881vC9\ngNQ2E816mT7wm8ZsRoyZmIqLSuReflanPGW8Fogm1+wakz5OVofsbupFJMQsy77LbQ0dqE9NEWt5\nJxeISHcu6SYvtMc0IyUFTJdciDkxrJGW8hGWSUe+xvqxRwp6Xln2g92CuWhOmv9g0ePFXGyRIisE\n/0zqqNbUmbgcxjXCJDnOwQjCXlx/Rwf5eXba1ZWoniJp09yZaPRlnMfkb//fST+GpGxpl534Fvn2\n543jq9GvpYNX5jdVt30yzkF/qBq2aMRsbDd9MSlmjnZgc8l9y5OWFtjfbuSkvEiNXYryFQlYmJ6b\nqA49pkguRfLFXIUz7Yt2NUbrfF0PrquzJuSRYKIX+omzDMi9lTKESJ/LiXRLh89zARSNXAvk4tI9\n/bMenw/6XKwaqfv9dWtrDRS42mCOTs1PF543736QqsWNXnklvUilkooOMVuqvGYwZpBrLoyFXYN+\nnYQ0ySi5yAh0V+hoHKUmNMVELJ4ATmZNJcQEFikGtrqzURjDOM6LuBJ0Faeza6yz5kVgct839vsO\nq5Ofea0p8RiD82rMFohbJe5aeuvFELT/aYcfq1NulRzFsnn0g3FOHT7L5AkI0itnl33Org4AFtYX\n1pyTkzM2jdG0xFpoMVVKYPZBuzrn4wKLrBvAUyUVlOtZhG8NKWMWCa6SCiFR8iQgQxtZSpzstvuI\nujSB2wxQFXgcF0+jyIoKcXh1iIiiueVN8DYPXNYawlghKFSB8NxxSjJnEhhILz7dSalD9+YFjz5C\neOniU0oKZQlQQnlJNxdgITDMuIa05Rmhi+elgiOmieXAQDF+07c0EWSO8di9XAvtuGjt1OI6+iI5\nVbZ60+EbfJGKV7PTWUTB5ZQu2zLTs6QRnjpz6QEyW9moWXmpKUg62zvMEBgx0MMQm96d3TZ14ZRY\nnOPnC3BDCilD+6gJKwmNvYL2YlgQHGst6Bq9rWX0OQlhCnlBoLfJbH9HrJXfPj500A29SN/ub+w3\nmXBCn3QgzUjvWjpcj8l2T5Qtst8i7dwoRQvK/V7VsjyZxwGBkEC26CyuSiqFMdFDZw7YipNlnbWi\nw3kUsnCd6g5yVvXcpxZa2RcroWywLRlqTG34MP3dl4D4kgQW6i6SXCzJ29nBNRpzdX9586uKmmMq\nSgvpgpUPqvqhFuFkW2scxwltMWvlfo/kTZmDZmhR2xrn8eD7T9/Y74Vc9O/NIVGDFAdhQZyBvVYh\nAZKq4TWd6YKSaEpKpGAsCTDoR5fFeCXaOdn3O7XstCBtcqCg0lTLwfO8GEOVUI6F9/udrRY3HMkk\nletN6Nij0z++XNmhzqj3C6ZRc+Y6O7kKePXx8cHn18l5TFK8EYsYKaUkrrNxHAcfv/z2Muhe4+L9\n+7uyM2thTwqnXnHS42QtfBENJVbqtqmTSLKyY5Nlg/U88HpnMOkpuLkj8G3PXPPi6+ukXYOyS/Ex\nXOYYXUWyHIObnGzIUgdztk7vk/f7rirZY8FYKEjE/QkhRlKujGGcR+OXvz1kSDOnDlomWiZUdSrS\nN9tLQYLr/1OuEgacF7lkSq4QUFjKJSlo3SsxJeaY5JDIC46rO2tFi8+xhtQXQdb3YEvO3GHO50bx\nhT4Kmr2TbJEtss5JaxdjiacSsgqwuSYpa4TX5qD7IWi34IgLLbXHk3q4FikvQenCszCSL0BZuFmz\n6SB1VGsNo5Fq0QVfEsncqDQmrV+MMIVArlHPqRVqLkybfH59ehi5OutjNO0pQmQronlaWLTRNHIJ\nxufjpJ+dTOL2/qbfqwdxp/AMgRa+92gHc2rPspUboM4qU373TP1DDvI2T1G8gsypwn1m9hTABofB\nvPQ3Edpp1N2NLanx/S8C3Pz2y6Hbz+SwLCkTa/AqSmqBFdwgs55Sq8S0zEomS3EWJzq55nyxXhb3\n1i7B7T25e47iiAsHS6WnKFxYzFQjxEwsi4hmeq8Q3SSjxJiTa3RmgBEWKcrS/zQDgPIVx3RGOhr5\nqEpchC3z8/c35mkUMtEUtjuZL616XJlEJVgB01esPEkn1RE1TrgWlo1YUGW1Br11+tlITw5KSViG\nfjYeHwe//vrJnivzDjlXIpNihpmqRsLT6SZ1RYg/tNg5FS3tCIyhKvYptWrtYh0NgZndqmNoPt6j\nWMxnZwxdxp9fD87WmTMw14OWYO6RUnYpK3rneBwEk0s2x+SWaAgdsiWIhZUTCwHTni1uHwNmYFkn\nZbTDiLrE1/KkoqGD4jEl60wlca8bV+8MM2KpWEgcrfOYFyGbQiCSlm9mgX33ZBwHin08Dkkey/YD\nFbt8AeajPHkKE0ZizM5xdT4+D3LK+l9CoD2atMhkRu+aE7vhzKKe3jk1C46mWXa0p4t4iV2Oio8V\nNcc9r06NRolGssmtbMQonfUT0CZJ6A/GCQiZ28ei9elOaF5LWuZitUV3wFxsg7pXyl4oIYthH0yM\n8OlLyWsqVs0krgkhSf0yFeQgVDIv0xtB3YAcmVk8eZTz2ceghkS9OWjMzUftUmSd1DKQmg5yEVUD\nqy9mV3h0DkLVovQ5Rlis9OQZ+QjZcJVRpI3F13nRJi7jhIwbEMPUzm0GqjljaE2uebFt1bkvv3+m\n/jEJQXmRfVKVSniZVkrZ6GeA0ejHYl6mg/yxmDfDtgl58u3nOznDx8eXiIUjkROkXfq3JwVQCTyS\nHD0PyVrV0vQwneucFBgRVFnPMaWxZSnYdU5aH36QS+ol56BGMrlE6b0jwqvmoJmvOdYU0/Y9Ri3u\nPEKNEDxabSHFoqrwmAJrNvrQoiiYCZpTZYzJtXCrG+NhWAdWol2NvgTxKnUjhUpJAVZidHV/xGcI\nrCRS1hb9kLQt70bYM31KjqiQXyPXRN0KuUaO4+S3ry8+Hhe9QkiFzYq6oNGZGrjroDBJpmJMWtyV\np6kpskxa4NWmW+Clomn9wFr37EZ7OejWUmV4tIOrNUIz0hVU6S9jWWT2zpWN6xSnY67hexHJI0vK\n1K1Qs/gWq0nXnkOGhJtv7MWxDhNG75zXl3jd8U26fNxIEnDVC1znRbdFHpmv7VS1mHShrBh5XBfT\nmub6WR2ieDeJsQRQW3OQQuQ4L1hGKgeWFtUvXMnQpGAwZxStpeDeuX6YcmIU/Y8hLO9KcI6TkIxc\npEaJZC9ShsBhKxApGn25TDHkRKxKohrRaP3i6JMZZXCJwyhv5WW4Imqs2a7h+ms8JET7k7EmZ5Oq\nag4jzPiSmM4mP8IcxhidGBIlaVyJaUSmitspgyZonbkMVix4Y47GNaHk4ZWuxhkhSiZrOWsxj2Gm\n3+P0EYbi3Bw1gNRJvQ9seR7ngHaJbBgMN3A9o/SkRln+ma4po14eg4ned4ue7ZkqhMZxPuj9i1oK\ne3GkBNpbkUT2TJYUBDIaV2gSFiAGzO/9/CEH+U/f37WxJribDcUihcA4J+2hheTs2lh/zKZ5dsz8\n/JYpZdEirA7Xo1Ny4rZtwpECoahltSDi38JEGzS3zU4dyLncIEZt9VkuezJSyOJ95+oktfVahLQ2\nBdWK2qhPi67XfimZwdzJ+JyLusrDgjamkuwFAr5Ndy0pRHd7ZooDSa6jcbSTZXdKiS9myHVOVjNm\nMo7rYKzBvkWPQdNh2ftBbYk3y4Q8lcnooQXzGlyPkzYbtW3s3AFJN0vMPI4vzMSJWSYr8ayR25/f\nybEwY+Ew88OneXSXqqS6RcotK+iYpQWm7zBmazpkOpwPYX7jy3u/nCWiMU+Kgc7gWpN1NcacbKlS\n807ZtPTurSvodw0+H5+MqJds2GS/7+xlZ6+7shq9VZ99eRKMuzZjwVJgBKOmDa5A791NU9CvSbtO\nUkCUy5LIqbBSYAsdGzIGjTXlQC2VRaTbJE6kMimFUISnTcnDdM2XvUMKkxkixMXXdWFxsM2k/YR3\nM6lUxjkIYZGrAkre33di+jN7qtSUtRxbvqyek3ZdKkw88BcfeXx9nqSZ2FNhrxuP8+LxePA4T+p9\n5/b+xm6RFiZ9LbCEzUDri/Y4CCOwb51QArlm2tn5+OWTnKpzYyQQWMkYcfF1HrTesQl73ilBS+3z\n0B4DAiVX5jX56g/u910LyDXp5+kqN0HxUimQIq35/mJO+vmg5sReK7f7nfY4NEYNgdv9BluAGrmO\nQW9ixsSYsRVo5wUzu7M5EXPSAMMEyhtjCGE7n1wZFZ0L+DpPfv34RSajqE7zuE6IRhsX5X6j3jah\nbFPSbi0ll98KNxAQniHlQg6RYQrkPk55REJJ6liHOubf+/lDDvJSCml73jBuzlgwrsH52Tg/u/77\nZcJ1rsC4jNUT7/tPzL44vy7Oz05gULLRb57ikaVYGNZlgAkRIV1V9YPrbF1Xnny58WRxa5Hks7E5\ntHhwd96YPzSolpzv7fba6MJRc6me5qFabuIqAMLS7RtVwasETNq4u5p4ToGhtqoFx4iTYTJN6EX3\nBaJ/3tYPQK3cHErHWQbX6J5sD3Nu3L9V8iZt8exdcszWmMdiG4p1y6Uw+uQ8lCofc2KuxXV2jtmY\n0VhZrf6yoUVOfyJmJxah1MR7vEHWClCNxpPMh7fBi+PR+PhVGYU/fX+nboWwBax6hiWiU6aUsLRY\nBViiinfToulW7twI7rS9tO9IbmVOi/pWuW93trzRR+fqF2NM+qVUm+SW//pTpZYk6NMc2DCmv2ij\nTZfVDbaq766N7hrxSkhSVj2jy+YctNkY/SQAJUVu+8YeK7VUpumwN1PM4HV2rut6NmSEAH1dQtcu\nU7qR+VJ0Tc6H3J+Crg1yMu57poTgqe4wriniXpARDp6KCu0LwtJo6fk77k0L2uvQ+GxOSLGybTdS\n0eK3boUwtSvIN40p5jTHIpjLeyeXNUAxd8FDpsOW2MqNHCtrLGqsBJ/7d095Mgvcwu5KlcUVtdBe\na9HOTg54PJsjY0OUvd3E6p9tMsdiWmClwTgV4bjWIiOiZ44y0/VLe6bg3UPv+h0lF08crQnA55iO\n5X+N6bzwJErhk9pfbhWCa/jdNTvX4uqdeWmH4mgcLBhlqxpdEgjJz44geF27DlaMzACh+FI2Gm11\nAouQ/o505Jhp+ZASj3YQSESTjfzx68XxIc3qk0sSQ2R1sBYpduM4Pjg+LtpXJ8ZFqcZxh1ChenTU\naKIblpL8S5F7alyLmBQOa2bYirqZE24bBqanyFsgTpEGx7VofbAiRM8ffMKVZNZBt9FaDoUv5Fhe\nM7rlrZxUA/q/g1fnmAxL5kakHHVzs+RkHPHJ+JCkEkdqWhYXPWa1hu1qrKVcw2tMzn6J/rg6sbzz\nlndSyZ47KJZHG53Yk2a1IblhqrGiPvdED3Zn+thELlqzRVyKZeutc56XdMd500U4TFHVIWDWX6HH\nROFQz+Pk8+NBzoXvP7/LOp0La0oCuIZs64qFS1A8mafDtYai2uom/EKA89D3Q9VYTB1fJG16zo52\n8jgPzvPiuga3qjzJ0Rrf376Tc4E1hRhuCiEZtmizc83OvkkZkVfkqz2IlrlVGEjWmHLmaTOfYdHp\nRBR4EONGTZlbqrTe/XKQMe16CA0QciBXsfP7aGyWyMhkkk3zZBud87jcJBIZeRCiIGU2hYSYC9o5\nmFMKnFBk0JlrEqaW3AwxVqLDEGaXKiWswOo+shsQhuz4JQV3/A4pTErAhtglbWlH0NtizsC4hs+w\ndeiUW2EPN7799EaoUrjEFRit0ZY+v7GcD1ME+JqTnqITCjXKSKVQcmQyRVwMImCOuXB2lUaHfUqC\nPJzJs4w1NK57/m/XcXF8PhRWEY3Ygy9sE2saH48HIUZqrRRPYloOuorPQzzxsvfveaePi2WTmiTl\nZQBNocr0QBrJR2FLQddIRKAoPC3Azzm42oCcpcK6ecG5Fs26iI4p/u6R+sfIDx8n7KiSPge41O18\ndD5/PTkeXQuKaL5FNuaC87fJX//5i9Yb/XQ4VYfrDHx+LVq42Nfi9h4YyJlX75tMKEEBs0f7IqTE\nvt8IRQ9RG4NtK3IoOt0vTjxCsyjhvXfO0dned+q9sqIq0JD0yw1rAhOiCI01bmxxo5lStq/ZOfql\nQNk1FL1VE1sRnGlOT2BfWuQI0OQExSC1Sog3SihAJBepBFJK7kCdPNrJ7MaYi2t02YYX/Pr5pUg4\nW3z70zcFHJdMqom3b5n7dmMvVfxvg23buOhChyaZI4ItMmA+7w4rQofrks09WODb+xvf//RG3hIT\npd2vJSRAWHIeLrdIL6RgCPCqvNMWWStBSLR+8OtxUPaqhWvVBW1RBqdYArYFZtYYpEVjlcAqGlOt\nFfj8PKVfL4tug3NcHP1E6BQ59IYNjvMgr66uq01WXy8A2tU6X+dJ3d4FNcP8eZ0c49RF6LPiklW5\n3t+UE5kMCoFb2ngrG2kFHr8++PXjg8/zYqXAJBBzom47sUSGDVo76Bg5PU39gWh4rNtidbHny1tk\nuxfqlulX5zgOvj4O+rmwoGiy/acqXn1rWqqSCSNyfF7s9c7tdifHTN4iaQXa4+Lb7c77fpd3YASY\nMEfXSCgIQfG4Ote4xE4PQbJF036qX0tL6tXZeiXnytqXGCYh02ejnSfXebLfFOJt7og9Hw9GGwT7\ngQQoRek/KQbm6NojZAVfn1djBZdJgsOtLuYyUhHgqm47y4yPr08eHw/OQ2OXEPX8tAvaGPShMVcb\nF/W2EaqzgsLC4vK4wQgl0ufwUa4RM4zRWAy2fQcUTbjdN4W0O6RvdcdzmGSUJSsFqJ8n17xgDGnL\ng2r9eM/q9mbn67cvJggy9zs/f8hB/nh4buF/RZeLFmF6mMFwXGPwccdahBW4jsFf/58vUjFm0wEg\naLvmogI/iQ89lrf+AYJbjpnGWEMaYyCv5BhQIxxdo4fsxD2DSCLHrMO3T3KIFCIZzYOnokt4xoeZ\nCa3abTAvOI/OjIuVwFJw9UQmOuRJqSPO4fAqO6RAIipVJwdClY5WipTAbINQzDnNzocwaZJDyGqT\n2yB2gaGeJhLz9vdxnPodLFU5+6bcwTY6x6EXIKSk9q+WH2TGNYXk9YxCsU4i+Sb79hiD2/tNCfHJ\nx2JIYhctOSeje2J8oG5JM/8gPf98kuqIdINuMCywxlTqUkrUGJQKVRIWp7g5Nhim/7yYMOSALXsi\nIflkCEbIULYM8UaMxTGlkS0U8pZkkiLANaXtBVLZKXZj63e2Pam63yLbBpZQJb6JWJiS5ulmws2W\nnCghUlcidTE/sumwnFenPR6skrmGMWNis8B2z1BMHWUY9IWWcALYMIfGBeMcmufmnZ6m2/0H16nv\nvl2KEcy22G0jh0yIfqkejXkushVsTK7zVDrOWoptczQTc9G+Dng4JyUGLGuxOJwWuBCyYVyT0VVt\ntt5ovTOXm+pCwgY8fjtoRcv11k5Gb252k4qpt87H54MI3L+9cdtvQj24XV07iMHVpHhLo0g2O30v\nFVS8LYlcWPGZ3WocTVm8bXSu85ThL0VfRiqsZhKwGFgpMpa6WGtGTgmTQ09FU4IS7BUKInNYYEbt\nnjpdstGY2PPO6B5QEoUzTqYRa0yGxUG3xTEO5myKVLzfWTFyrYbN8VLGpT3CeI6u/v3PH2PRN93g\nwxahJIhiq/QBUzsNnmwQQ3ZyIzD65ONvJ+8/FUmn0Is+uzbbJRWlha/2Iv6dZ2Pbs2cdOraya4Ne\n5ka2TCbSHJ8a96KRiflsfi6142djK5W4DFqXX6e419jBTDbkVDOTEmGsSdwy+VYUDBwL2SJz+Qwx\nuBSNQDQ1ujGqAlrDk09iIhbY8kZrndZ188ccsOSJKoipkXOlt87WB2+r+F5fChCiDpnH46EItwBb\nTJRdSo7runi0hgE1w33fyVuFGFiO3MWMnOprV6DRj+R905IUIzZfLlIpkZSBNvrgODp7KYIj7ca+\nX86OUDvNkLN3LmNghJJY0fxCEkOleHrK0Q+6LcIcrgzSsrjPoQumKN8whUA0I2VFgZUtUOtNQKwI\nse6s5CClOQl5EbMKiXrfIFduhlRBGWLN1Ph0+vol52ja0Z9BCki54Qqd/iXjW41ZeZCmxe5aneMc\ntBnoIRDqjW1LlD1DEKM7Lo3NzAJ5errPNAUiDOin3LRiCUli+wylWHMJsJYSMVZIohPOs7Pvu/sO\nBpbk/B3mBwfaEfRhUsnEqH3JFNCpN/FlSIpC630oQnF5ZJy7OQUgq5gFHl+HoxlgjA5ByVATczOb\n8fl48Ha7OclTiUhziShqazBX10x9TWLqzigKQlBXRROuEBxfoNzRMRth6HfTRsdQNxeTsAkr6YCN\nsRBcJTTShCBc8fAzw9b0Cy+5jNKFFQHiCoJjues6BXH7S87EnHxs5Oqx4BkAfrYt63QaM0xWitSb\nTIKzNy3+TXkJ5V5ZrbP6+N0z9Q85yP/0p2+sYIyoOfZiMtrglw8lr9tcmtGiWzW5VtxA0WAhKs8x\nR9pozH7BeCMuVadzQrbAdTWO8+L9p539rgpsurGDZcwe5IpMkdWXqvjxtNFHVh/89vXF5y9f2ID3\nv9yhK207pEC6b4SSXnNiwaxUac8IbRm3moS4vFVWmGKGdFWzP9jUjWj67HSNm/qlLyzlwL4VbrdM\njcYRGl/toONdzIJl0e3i4oLst8T3+zs1yg041uRck8/r4NePD7pXF2nfvZVDc2+xuwg7lHti2yoh\nZnprTk6MRMvMedIfQupGX2YOJrEnUo3SS5fEtm9stTKbNLv9uHivu2LnBmxblAY3iRdtY+mBntL/\n1zctYNNTJgrsScafzmD1xpxyPpZtI4REswtY9HHBZcS0E6OSzSFjJGrVGMmSUfdKs8l1Nj4evxHa\nFLmQohbXAVyzifGeQgJPUMqxsKedNSU7S8G7LB/XPb4ejF8v7KPzi2VuWVLOkAPlVmQ6WYO+AplB\n3hK3950VIrYumIM1IlfT/DfnnZIKYSsy79ikXzKibftOyT5aqi7Ny1mKDNvYaiGFQgli1QSTPDQm\nfR6CZr/i1CS1/ykRY8FC5Frds3OnW8lVqMiSH53/Unn/VvyiH6pAPWv0ah2zQcoQc3CMb6fbcocw\nHhAzaL1JO+9deUryehiRWMTfWdEIm5usTFTF5RdprFk2/tbp58ledzF2mNStKM0+iFCaS6XuOyFV\ndTFzUmYiBAG6iGivMYb2ILgT257hIPYa/6SYaHNQ3Guy4lLnFnR59zE4Do2USn7znN/AtmWaY4Zb\nMGIJGrXNKXTD08y0CaHwez9/yEH+9r7TzTjX4PM8XZWy+DoabYgcqJ/g0Z4qb4TcFHym1Mz3Nvnb\n30QRG+NkdMjNoPjM1WSsiTm6Xlwvyr5vRCKjCVQzurmxCBkwpoxBs0+Ox0kfw9NUXEI3Ozkkgi2e\n6+g5pHBJ2W3Ba3L1i3CiENkCfV6MpbZTo67oqpglUL5FGLC6nGytdfb7xq1Wuf+WTPg/Eu9xBCfa\noCeX1LnBqUSfsRr0aZRVuN/vmpFH1wALNUKKhdsesahKq88OPZLCdLY2BJIuhwWFSClabC7TOKsk\nhTlYMrlao/jwhlFI3LcbOShmr6RIKaqaWrsISTLAUhL5tmMopzAkfW8xRbVrwfGmJnJiNDGzFayR\nX4hewwgJrtkZY1FSddbLYlwydpfyQQAAIABJREFUiwWDdU4sirFOFECLKDdgm43VtOIbJvdiaKfn\nblYBpFz/3Lv41uLOyGX8jDcbvZOTsWLUksxdDSkWtiVnYioTrMNI5Awa4hVirMStUCxTQuSIvkdB\nXd9ES1m7TneULd7f7pJZBgRQi8GVQiJvkiPk6IRJjS371MgxORdl2ZSF3oKjgZ8AVSOaCfxk2t3s\nm+MtUAhGn9of5aCOdbFe44RUAqU6+3+pAyLqmf720645epYa6ykfvL/fqXslWaKtyepanm/33dOX\nDNqgRKVg5VKY16UFZ5jELVJzoVgBB1oll1CZCfa2PJbOzMgJthrl7i2JMoaMXnMyu3I4pWTTvDuW\n8Pqur+tiq4X9trGbjz6DK12iJKNhcw1+UOrQ/X5nwxi2iFvBkmSPOSfXsj15NeGlvPtvf/6Qg3y6\nHpTgbOwJY4rDMOxJ3XsG26pVDTj8aUIqibefNiiBs31wnIPeD/oVqDctnMiJUDWmKJsWeykl9vsm\nAA+Rx+chA0cwct1eIcv6jGIdtN4hBg9f1l9P+35MMvUs14+HZ8J5kMxprSnITTJiWvR1+Uz8+eWo\nMkpqNfRZOsyrM1vnOk7Z+qcx+xBgaSnZfUUp1yVjDK/RQ4x4KIEck08Jmx6MzNvbO6lUPRgTZhOF\nsoRCShlLk5kUezZN89O1TJr+CRYl9QrLqFmgo+dC51Yq21aZYZGSGOSrTdal0dcWixRBc2Br4MpM\nHa5jkHqkPLMXX4QoT1BCi8a1NAYJc5BsklCdHXEHX9SuAtNcfPqYgYCs3m0QOF1xBOcICGWxiLlA\nMR+7ZIYfymMZ0Z19a0objgdWzKHQkdYvIpURNKKIJn9kzAmqsiDjHohbIK1C8flLuG/c1iQkjbTS\nMipagCtMuHArN1IIrHFx2PRL5ZlMpcNnLo20mMZeNoVop4AMCXI+Xr1jGKkWyC559ZQjzZlNyyGk\nSup9QhxQkvZMy8AmyRYhyaK/ZiPGnRSNsTrH+XAVTpTMOLtnIi1y0W6k7o6PNb3jweWz93fprcVo\ndyVLFOI6VZFG92XEofHZdrtBEG56AcvdlLlmonWFOFBIu1RgJRh9iAFfiqrr2Y12eUjyUpi0sjud\nkFg8uctMxp8hXLI5WiGCnlc0BmLp0hx9MEpXcHnSWVFKAMtECjkkmaiaDI6hBPrsrBxF93Sy5DOx\nUpmg5u/7v//5Qw7y/+uf/0VqhL2KfZyDoDhZBgKP8uOZZ6jlk/4zIWB5sf+U+P4f/8THR6P/64Or\nfdF64D184/39DSuTEQrV1JqHJDJgrIr+CoaWho6X/fbtjbptEALndaplnQq5qFtlqxvDJqHoC97v\nGzMafek2z0Xo2FSykk1WpNZMm53znBDVGQQPPRhe5deYqKXS3VLeHrJ/z7lYfTCvTj8veorAUCze\ntmmWjD7/045EVJJODIFrTc6z6Z+xJvV+J9edmgoL7ShsmVa3LhAac2hRV4KgUnNgwYipMK7J+Xlx\nzAdxCHpUiKzZYQ3ue+Gtbmyl0lYHErMbX8eXGN5TL7J17RB6U5xWzYX4THDqk26d2bWM4sk9zwGK\nghWGZ7myBpubpyRRmwwC8+kqDepa8k2H4hpwjAfHcYDP75/y1lRFzdv2SrlXskXySqRYCRPog32v\npAhrddalSLGwVFX20V1rrpEgQckuOWg5u//8LtJkCeQ981Z2tpi4FrpE4lL47wpCBacdWsZ6ZJ1w\n+/ZGjlPfybho1kl5Z9s3IoKN5RSkjz4Pfrk6t9sb79+/UW+7FobXxX7fJcX1kIsclA3Ze+P29kZM\nka/PL8kQ11T8me8BMpuAa2uSwyLHwIwGNsC6g8cGY1xcl5zZybupWhNv3zZyrWy3xH4vpCLt9nme\nnkeriySnQM2ZfSuAvkNSdM585O3nb7wn5cHaWpztwcKoW5Yhi0XKKGSlRtbawDwLVmpwdYtbIZKw\nHNmz0fLClmiG2+5+lIiUOH0xzkY7Lj+41bmmLPnrdhdcC3Q+KcRCY0dFU2mPs2+R27aRU+G3vz74\n+O3gugb3e6DmQGBiczg0MXinogt3KpDh76sij0ltb0pyaZmBbUoJGocSegKqoggik2EO5AmRsifq\nLZK2xZ//6Y5l+PXjerFM5pIOfMbl7k6NICIKRciuQc3xzRcR0c0SE9zEknMm3dWSDTcbPK5TsXI5\ny92JmCsyk+jzxaRE9Fwy2317Ja+EaC8noUIjFDIcTazlWCDcFiVk2iXDToi7HyBqwQgLS1rqWdDh\ndbUm1cuzvHWVzzgvQhswtHHPnkYy5+JsiqnDgtLtJ3KpMkmmasSWmNa2RG7sa3GNgbXJHgulZPpq\nGlEkN0ChQjUu15u3yfHbw1VIwIoMOs+D9Ha7E8tGLpsUM2Mym9u4k0I0hBc10lJVRkisIQPLFgtb\nSXSP24sxktFupDWplvYCNVW9oLXqkXKlkVJ/5msObpa0qCMSS+Fqg8uZHGWFl1N3DF2ux3E4jwR1\naVXGsmVLqqGhsd2t7Gw1U/ZE3CLNlSFbSuSqnMdxBura+JZ/5j/89J/4+ds/kqzy+csncV1c1y/8\nLXU+1yd9LEZUFRmDpG5T2h9ZV6bY4GMMcndUwYK3202GqDZYsxPrpos0BbbbBjFwnqcW/VGz7IGn\n6eTtFYjQ2yX1VYLtJtBVm43Px8Npok8bu8EctKEM3UGnzcxg4/4mSJ2EBcrafd9v5Jg84Lwwlox5\nQhfjo4X46uZDisSV9OdekxCdC78V8ibkRH/SK6e08qkUqo9mw4oq8EJgHBpNlSjTXPClZG+XRk5R\nLJ2Ssn5nT1loFDv/ak2s81IJQeE4KQdfpOo76qhI0bNz6ZwL6gwUtp7AphchyZOt1qvDBHx89u9/\n/pCD/HYTjTCWQihqS8IMfPtWGQ+YrSvaC3OJklCsCQXFlj1CXnQ7+ekvG7Fm4l8PGQtMnIRFh6Iv\nTinewJLzKuekMNisW32ZqtPlzgJbChJIKVFvhZPGZVLCkAIrQJvdt9OqCjuqpM/r8pSVzFYKsT5N\nMAMz5X2mlBhRVWqYJh36pgtmVeM6Lq4rQwyUokNTE5QkDTOO7VKL8sOYFPWQz7U4xyDZkmrDecZr\naRnauyh7GgsHrzrFkEkkVtTvJiRt+/u8ODyFKKzFVguUSGuy0+es9nOMCXaJce0KDevTHa4yQPSh\nQNlSnTBYdmLMHHYxfYEWs6t4xCJ2s5TGJotFvwbjGuRUhDsIUkKtJcRAnEF8Dpzy6Fr/5ZRIkirS\np2pjTu+Azo55h5hj4OqNsyksOnVYlohmSm26utjVWyVvhVyFALCgf39CSp5gUPMb+75T9sRMExsH\nczUtzGMix0KMkX+4/yN/3v6Rn/Jf+C///f/C9+9/4evjg3/+P/9X/vlfOr9dieQ5sguN4lLQAjCk\nQKFoP9PNSYaB0TpjCAJ1e3/XDmcZM8BWC7d9Fz8+KxJO8leFqsSaYE7tTbJ2R5PB1Q8sbYScuL/f\nxMg/xRZJNbGliGXRO3ufXGOSrg5pYXFC9kzZJDuMRlmFUnaerHRxUXxCHLXsxUdnOsX13KcirvFa\nkxp0SZeSSAHiiuLOWBBbH9yCH9VpRjHODWBOZ/hGhn/GFTUmMzPN1EMSQTRl6rYhALHY79dcwuXm\nQkIdRMpROQs4o2ktLwKkWV/P2bfzXkrKklQHjYksPM/t8Bqp/H49/gcd5N/f3iAVLEp+FJMIZPFP\nwJWxfvDx6wlBy8P9Lrt7yYnbrRD3yDUbvZ389P4P3L/9xLc/f+df/t9/VSX+/zP3bj2SZdt13jfX\nbe+IzKo+N0sURR5BkKgbYRj2g/37/TMMw4Ys0hTJ07eqzIjYe92mH8aKbMlsAn6w0CeBc/qlK6sz\ncu+15mWMbzRt8nPKXMuVtEwzbdSl+U5SvvhTY64KWTl9k9GaKuwh9GoMkZeXq9yUjGUtPvV94orz\nWkaEH394Y8uZ7bJzeb1Q0qrCTURFVR9BPXVwLSbRMmQrBhO2y0ZrnbCixszBmubQI8ADLd5yLuzb\nroWS1gFiOU+HqCCCiJGRi3RdU1gOMIUPDQRV5a4upp2dSOf6+Uow6P3k6/s7j6Mzu7IbwxYhCw4W\n4pOQGLkfVSyVOfn0WW7JrWzk7ULKBaPzfp/02Yi5QIjKqjwPbm932inFycunizjqMYiTM6RqqT44\n2sH5eFDapGwwt8i271SGiJHu7KGw71IRPCWcOUV8RKFxx1hvhHCwtd44jjutHlyvF66XC2A86sHR\nKhqwT87qMAdxBobp+8whlnS0vCibRuiRjY2QFZhwyS+KkgPq8UZvp3jyQYEFqeyknvj97/6C3+R/\nxl//73/Hp3/7W/7iz/895VL4X99/5P/6m/+N98edc8ib4CtS7RkCsl+kEIohrp1wwCY83m7c32/0\nVrmUjW0r7C8X4Va3nW3b8GgagZ0PNaRBkse4JcJYh3hyznpS652zPRjZuexXXl5fxL8PzsWEZnWM\nF5dE8v128vXLO3PJz9TpNNJRV/WaFgo2cT8q9VRntl+va7xmhKKuJwQj5bQ8KAYM4S5K/Hi/xnxG\nHK7Ze5BseUztproLX9D65PpcVp+DUU/hJs4qVPMeSBeNSkNUkVDPkzYPZtEOIhZV5q02em3U1lSl\n52fghJgIAu+di/EUmBYIeV2ordN6ZY6NUpL0FqMzRvs4W2JIAu19MJn+4dcvM1oZqzGxSF7289EH\nly3yu9++kIJYy63LNBBD4vXzxvW1cLkWXn61YaFz+3rnqJWcdBBeXxKjG8YkumnT7wk/tW3uvVMu\nQtGCtNq2APBnVcagBbk5azuptTJ8sO075WlvX4uHLW9qofo6/FeOYS6Zy36lbGXd4isUev2Mw7QM\n8aXlTostTZCT836/03r/UIls28aWFAs3p3gPdTbi+v7trNJZx4AVaZotRMq2KYxi5UPmKKv7mKsB\ntyl1TzCSKRr5/n7XktCTkAhDKqEwIteciXsmmkwg7lOytWgMJsfQBVqPyvk4qM15ub4o4zTr5+vT\nyZdtsUOUED861FOJEjlnti2xX4qQsUxSTtJAW6bSBBEKEctC0FYms1faUPjuGNLYp6fGP4jE19bL\n1EcnxI2UxBu3NWa77DvuTYzw6by9H3x5e9C9c7kU8raTo0lrPpw0IxsQt41UNmLKy2k8yHPjn/7q\nz/lXf/7v+Bf//F/x6eVXxBCo4+THtx/42+/+mr/59q/422//b81dx4U//e2f8Rd/9pf8Kv6a4z/d\n+Zv/4z8RPfBv/vLf0cbkPjo/HjfmDiVvWiAOWe1TMGJKaxQV9buJOowbk3utHI+Dy+MBwbjuG9cX\nhTAMn3z5+sbX+ztnPckp05YPorlm/hZltvEcCJdC8SueAgeDet6VupMjr/v2XyypA304+WXj5ZsL\nITgxOSmBofHB43FQykaMkjHWJRAgQPeG+TINTs2Gg7EO8vCRCau/f1KKUouePP3e+wrOeC4KbV0a\n4pmftSkwekR4MuExQtC5wxTOyt2ltIrG9XLhfBycZ+V+v/ESXohR9NGSVPD1LtSy9UDysvg5Rom7\nlu/JCakzrdKnQHC1V44TzKKe1do46klMmX3fiUWL5vlcSv/M1y/DI3/vhASpBOIWYMoJVEImvWaM\nxO2oPO46lPdr4uVT4fWbwnaRa6+1SevwOA56mmpRk2SHwQLDjKAQT2abC5s6CWVJ/IZYDOAMHxzH\noY1+ipStLDRAlykDVhs1n8ZzUlKOYVtI2uFq77dtp2w7JYu9EYKyROeaTz/T2J8PJjGQUlae4ejc\nzjtzCihvJheZLz6JgidWHFXROKjVqvl0yMjK4R8z/mwRxkp7McVPDZ8ayST9TDHHD5NN7YnZZIf1\nJodhsYI8ClGHqonYFswJmxaLjimMoHeBoGpj+h0Iy7Ks/67BJJSCIbMLrvCEFBNW1CqXPX0sAA25\nQMOTrUFiY9PP5xqPVFc1NOeQJBFf6fW2knhg+liZkQKpZZMGPKVCsEhJCdgBxXg97g/OQzya56zY\nkhQoIQdsSGURYlRgc1gKiDEJHnjdPvGvf//v+R//w//Cv/mXf8nL5TNzTh7ng6/vX/jDD3/H3377\nV/zVf/6PfPv9H3B3fv/f/Qv+2W/+Oa9c+N3nT5z3O1+//Z7jcdANejQOhgJKthUnFnyNG8NPEs2F\nmnWXxfExGsccnHNwq4c4HyVB1CKx1cbtcRdB0ycpbcILB19u5ARJCA2Phm2JHC5MkxqqeSeGrPCG\nIsiaPxVUE0qKCtgwBRfDIMey8BNSYWgkqkCIkEUgfJIiFS63dO9uH13tGJ1WT87zXONRVatzTPqT\nEPiU6zmARqI2Fh/9PFeRlSnzp/QuNxWWoAKA5WVJMZH3jVG7vCtzLDMfHOcpwmbKzLsQyrODd9fv\na33vOaV6S5bJw+SiDoixE4Um5pnoNRElNA08CZmM/yRd/X9//SIH+Q/f3pdKoFIuCTfBc2LWPHe/\nJv7Jn/2Ktx8Ds1W++bSxXwK5QNq0ga690YYzjoNo5xLOZ0pWi9mnlhvn7SRYVPs7ZZP27pyjwTrI\n+5R5Y/rUzA052Mwnozbez8rtdqfsG2FbDsZhtONkziHdquuw27d9mSAMfMp8EMTmwJYTdQ62LWuG\nOaf+W+eCKE21VKVkYhGhb8zO/Xgw+zqQk+A+c3Z67WyXskKkVbXPOVco9KIkrmxGnzpMWTpaC8o0\nlcY1sPuFdu+MU/PzLWxyQ1b9nGZKYY9BS143zfnOPjkeN86mEOhQlM951JP7407e5NIctiLNYiQG\niCTiFgl71Cx3dCadvvgLIZm44lPmqJAC1+uFl09XqVTOk/M4oDW2GLmUguUk3XMKhC3TZlsJRXLx\nPYMmHIUeWFDiUIyO+8nxGByuQ+f1ugvTutkH8mEvhZS0oI5Jv/felZtpblzKhX/6mz/hf/rv/2f+\n4vd/ya9f/wm4FBVbzPzqeuF3v/pT/sO//h+4Pd74j3/1f/Ld93/PnjKXtBH74He//sykcP38WRd1\njqSXnXDdCJvmt0zp04MFUgw/HRYG1TuPo1GPxlEfVJvMHLjPSuqJNBKlPrTb6c/ZdnqSlMUunwZn\nEw4hwjmr2N0B8q4sywCa06PF3ON8UIekgLZGYz7luygpqJvDCUlS32AKPx4rrzHHtJ7bSC6Fo56c\nvRKR0ozuPB4PDJN5rLVlFFR6vbsIoO04+fz6iZSzsmRbpQ/JSPvo1HrSzoPuDY8bOV65XjaiJWm5\ne5D6aDH8CdpLRCIpJeaclH3DzThbU2eRNkpWcWDepZCzwLYQyjjc60EbyvXMeQVqB+dl34lh7XVM\nc3xMrnJzeU0+lp3/yNcvcpCfj75y7AbEIe1ulARnLlB+YPL5NZFi4eV6weOAKFXKVjLX6ys57tT6\noJ4H9XjIIJIjL3uiz8SYMBZFr3VJ6Z4hqbMPxjnUCg2lqmOq1G/vd7aY2cuFzEbtqsMlcJPDrK1Z\nqztEFiktJemum9jfKQeCIwTteUgf3rU8qXOuii5SaasbNa6fXhYqU9S+6XLR1Vql346Ry/ZKTGlV\nTMtS3Rrn4yZlSTDmVkgxUGIibxqp1K6HuGxFoxYLjNE5WsMnlLhzeblim7jwrUr50EYjh8iepX+1\nMJc8RW10iMZ42QAZq2IoFNOyN2+FuLT1IUJ3RXZpVjTW+KZjTTRFtyFt+WqRY15JTEGJRHHJBidD\ni7M4FdfmE++TOTVishDx86dxixbCppQiS9L51kkOiZIuSxetscx2LbyGV9ZtAyYkKSHIZuxOTJnL\ntjOB1ge1dkaFz59+xZ/+yZ/z20+/Yw8bs2lx3hcDZ8zJVgp7yVxS4fIvd+5/8nvq/Z1++5Ev3/2B\n++17JoWZnO37jfvtK8d55/180PtD0jdT6pGlvGBaA19UxePUwpkE+ZplWBmTEIxZjB6ch5CAmieX\nSEbGGnentip4VO3ktEsHvQifjit8AjlH87YRXYdQ6kaser5DCFh0dWyeMFuaYhysf/BbsMVECfIQ\nGAHHOc4H1Tsepzwhy8DTpt7V6YNjvbuOkwKUmMkhEZByZg01lVi1VGsRyBaIKVMsEKf2QM2mGEHB\neHnZmEVL8dobvfXFatLFd5wn9+9O8pb0rFql+0mbjoe2zjBbg4a1gB5jJWcN6e+nckRjCIzRwCNx\n/ewxiOw53D9Qu+fRFJjzj3z9Igd5SkKSWkAqkOB0c2w2bE76VEJLskhJmct1p1HpKImDpV6JJTGn\nZlIhQo7GVgLbFgU/ap12VqXJDOFGx9TSoNdBP6pUCwy1m0E2YWw5H0m4DQX+2sCj/h4zuUznatvG\ndC4xUZIY0nPo5c0lStrnExuD9HRlanBLDIkUCs9MRWGt49Kaa48w+/xgh/ezkWJaeuCIJbV7Pju0\nSXQnmzGDrfDdZVIybUPD0rQKLqYDcUwpfYZDMVvSPPFg5pBZqveuQzRquYNJMaPYqUkwuOyJEC6M\nsWm85cogTDEtnOhSAvhCD5iiu+bpzMcgm14Kj6hF9UBtJ7MNRjuY1tjMsRXq6+ul8OAf3Gaxyoci\n3ZaueQy5NhVsXNaBFDjvD9qjky1RopLfJ03dSknskRXPpSrO1gU8fOr7m7jpCgpe8tJgXC9Xfv35\nt+xxw7rTZyWYigepnBQbl1CAwf5p57Vc+LFV/u79jR+/+zve33/gXgf2/j1fzi/8/Xd/zdcvP3Kc\nBzUeWIQtlcVDl7nmw7W6zFkRk828ROJKJXLX51sZhK40dvf58bwlM4WZr+dmmmE5YTGRFtzOQ6Cb\na5FbVIiYm2ihMZBda8icororF8BufkiJ44dF3tHOR5JgHUWORpBnq3LZJmMsCeSY4M3lFp7KDpiL\nAx4WxyUQsVA+Chx7njfr4TqfMZBB7JscA3EEfXYMmc/CTzvFBahmTu3R2mhCXrRKIlKy0skGlTYn\n3VRIQVgjva73ZO3fDGfMpoDqFMk5q2Caq7ta72Vg0T6Rcmz2AZN1of3Mmfr/6wn9//Hrm19fPuy6\n8Vo4TDD++/1BiQWbxuO4Y103/Te/+bz0lsCSAfbe6NWp9cB9crlsvOw7r/tOycbjdnB/v/H1/U6f\njq+UlbbgWO1stEPg+aeOOMWA5cB+uWLDGI/OeRxYRrrTTbNSKUQCIWXmEAdbL0KgtxO6zBBbLJqD\nzeWKvoiUB5F+Qko72/VFtb4rrk2z3sWAmk5vQy3yWRlnZVgllUg2J8ULcZMjM0djT1GOz2j0JLlm\n8CkZnzl5Gj3LeDCGDnALtoJnBQyqZ6PfJFHU5eKSK06jxYJ51Gwx6FCevWPubNuFl9dXLBWmG+dD\nckIs0k+hCVIOmn2bUidba9Rbo345+fz6WdW7Ga/7FTPnOBNf7u8cx8E5tMCafmFjk5tvSCOdclpp\nPjrAebpFW9VCOUYm4oZYFOf9OO/cvt41bgsDZyOVia9RWDIlxfcpWdkcYlvjjsW8Ogmn1YM6G4pl\n3sipsOeNWRv18SDaICZp5bd9J2ZZ2b0PFQJz0m433r7/e378w9/w43d/y9v7F/7zH77l7TiJ2873\n4yvfj2+lyola5G7lwrZfBGYKpmrPjBAzL1uWUuI86K5j0E1FgrtCR8ZUUpGtlr6kzJYKecsLRhU4\neuc05GwdnZKNwGQMHeIzOs0VCvGEgvmEYoHLVjjbwa12au94gJgTJRdyzoJYtcZRq1AR2ApokX2/\nta45vWwy+v9h9DoooSgJ6ckhiVByIMoGQEz5WfuTsCVrlJN51oeKQZ+UELiUnRwKj9ux0rV0YIcZ\nSKGwJ7FmRp886sFgYMUII61O0QgpMKxr1Dk7Je+iQnahLqY5KSVhDOZkHIOtbFy3nS1vfHv/nvvj\nwRiDy1YWiC5gxLXAHaKdpkSOf0Thy7YrJDYG8NYISTbXaWrXggfZbIOE/7f7Dc9h0f4kg/LqtDoI\n2aQqyRpFnGPw+PELj/cHx5rtjiEAUMkZXIuO++2OV7UuMUTmgG0vlH0jJSlbRjLaYiQEU0kRo9J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sKGrNP7NRNngryQnFUskJBgBmP45HY+8EfH700hDkNjgCee04PIamev1NkpPjFv+DTNf4Ne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3D8/iD/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\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ykiV5fQ0B0VZy3cEVyfWOV9tKJvxvE1nzQi9BKZPiN+d58DgPBVqbsXDuKcdyFQvhmwf0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0MjzjTa98IED2+/DLYg+hCwZXJmnfS2Pvj3MXQBOKSSlAgBwPhoqKL0O0fh8oHLO95fNOq5xkDE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\nn8B48mPxaJEvvqCmUEVomZiDcIOt6kMTm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Vk2s0SKoMiG0thBSMglRkgtjkxqA8yIrwJk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\ni0aW/LvL0gAYM0MXlDinifrIi3LRCQkHsPwuS3Ci88EGOYVlp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ovFuqR0uKWI4aVShFKRqzYvP3jt1+alWWGmYXN7OkYSFzsCWPZlIRafnqRYTt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\nGErFecga8zl95UV6xmPX7tRDrFt08eJ51TB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5dNPxE08+yrwZEsjzcOoR55z+78Pw/+Vi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jhWge/XVZv3I4163ajnDrCeQNF2AqwBeJiVjp2eGXVqWcA+gcNDnz7dCsSPdP41h+PwgWN4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\nH3UUUI++wiIfWUWtO/xaXbM02ec8H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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "test_net = solver.test_nets[0]\n", + "for image_index in range(5):\n", + " plt.figure()\n", + " plt.imshow(transformer.deprocess(copy(test_net.blobs['data'].data[image_index, ...])))\n", + " gtlist = test_net.blobs['label'].data[image_index, ...].astype(np.int)\n", + " estlist = test_net.blobs['score'].data[image_index, ...] > 0\n", + " plt.title('GT: {} \\n EST: {}'.format(classes[np.where(gtlist)], classes[np.where(estlist)]))\n", + " plt.axis('off')" + ] + } + ], + "metadata": { + "description": "Multilabel classification on PASCAL VOC using a Python data layer.", + "example_name": "Multilabel Classification with Python Data Layer", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + }, + "priority": 5 + }, + "nbformat": 4, + "nbformat_minor": 0 +} + + +
    +
    + + diff --git a/gathered/examples/siamese.html b/gathered/examples/siamese.html new file mode 100644 index 00000000000..d28447d9e32 --- /dev/null +++ b/gathered/examples/siamese.html @@ -0,0 +1,258 @@ + + + + + + + + + Caffe | Siamese Network Tutorial + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + +

    Siamese Network Training with Caffe

    +

    This example shows how you can use weight sharing and a contrastive loss +function to learn a model using a siamese network in Caffe.

    + +

    We will assume that you have caffe successfully compiled. If not, please refer +to the Installation page. This example builds on the +MNIST tutorial so it would be a good idea to read that before +continuing.

    + +

    The guide specifies all paths and assumes all commands are executed from the +root caffe directory

    + +

    Prepare Datasets

    + +

    You will first need to download and convert the data from the MNIST +website. To do this, simply run the following commands:

    + +
    ./data/mnist/get_mnist.sh
+./examples/siamese/create_mnist_siamese.sh
+
    +
    + +

    After running the script there should be two datasets, +./examples/siamese/mnist_siamese_train_leveldb, and +./examples/siamese/mnist_siamese_test_leveldb.

    + +

    The Model

    +

    First, we will define the model that we want to train using the siamese network. +We will use the convolutional net defined in +./examples/siamese/mnist_siamese.prototxt. This model is almost +exactly the same as the LeNet model, the only difference is that +we have replaced the top layers that produced probabilities over the 10 digit +classes with a linear “feature” layer that produces a 2 dimensional vector.

    + +
    layer {
+  name: "feat"
+  type: "InnerProduct"
+  bottom: "ip2"
+  top: "feat"
+  param {
+    name: "feat_w"
+    lr_mult: 1
+  }
+  param {
+    name: "feat_b"
+    lr_mult: 2
+  }
+  inner_product_param {
+    num_output: 2
+  }
+}
+
    +
    + +

    Define the Siamese Network

    + +

    In this section we will define the siamese network used for training. The +resulting network is defined in +./examples/siamese/mnist_siamese_train_test.prototxt.

    + +

    Reading in the Pair Data

    + +

    We start with a data layer that reads from the LevelDB database we created +earlier. Each entry in this database contains the image data for a pair of +images (pair_data) and a binary label saying if they belong to the same class +or different classes (sim).

    + +
    layer {
+  name: "pair_data"
+  type: "Data"
+  top: "pair_data"
+  top: "sim"
+  include { phase: TRAIN }
+  transform_param {
+    scale: 0.00390625
+  }
+  data_param {
+    source: "examples/siamese/mnist_siamese_train_leveldb"
+    batch_size: 64
+  }
+}
+
    +
    + +

    In order to pack a pair of images into the same blob in the database we pack one +image per channel. We want to be able to work with these two images separately, +so we add a slice layer after the data layer. This takes the pair_data and +slices it along the channel dimension so that we have a single image in data +and its paired image in data_p.

    + +
    layer {
+  name: "slice_pair"
+  type: "Slice"
+  bottom: "pair_data"
+  top: "data"
+  top: "data_p"
+  slice_param {
+    slice_dim: 1
+    slice_point: 1
+  }
+}
+
    +
    + +

    Building the First Side of the Siamese Net

    + +

    Now we can specify the first side of the siamese net. This side operates on +data and produces feat. Starting from the net in +./examples/siamese/mnist_siamese.prototxt we add default weight fillers. Then +we name the parameters of the convolutional and inner product layers. Naming the +parameters allows Caffe to share the parameters between layers on both sides of +the siamese net. In the definition this looks like:

    + +
    ...
+param { name: "conv1_w" ...  }
+param { name: "conv1_b" ...  }
+...
+param { name: "conv2_w" ...  }
+param { name: "conv2_b" ...  }
+...
+param { name: "ip1_w" ...  }
+param { name: "ip1_b" ...  }
+...
+param { name: "ip2_w" ...  }
+param { name: "ip2_b" ...  }
+...
+
    +
    + +

    Building the Second Side of the Siamese Net

    + +

    Now we need to create the second path that operates on data_p and produces +feat_p. This path is exactly the same as the first. So we can just copy and +paste it. Then we change the name of each layer, input, and output by appending +_p to differentiate the “paired” layers from the originals.

    + +

    Adding the Contrastive Loss Function

    + +

    To train the network we will optimize a contrastive loss function proposed in: +Raia Hadsell, Sumit Chopra, and Yann LeCun “Dimensionality Reduction by Learning +an Invariant Mapping”. This loss function encourages matching pairs to be close +together in feature space while pushing non-matching pairs apart. This cost +function is implemented with the CONTRASTIVE_LOSS layer:

    + +
    layer {
+    name: "loss"
+    type: "ContrastiveLoss"
+    contrastive_loss_param {
+        margin: 1.0
+    }
+    bottom: "feat"
+    bottom: "feat_p"
+    bottom: "sim"
+    top: "loss"
+}
+
    +
    + +

    Define the Solver

    + +

    Nothing special needs to be done to the solver besides pointing it at the +correct model file. The solver is defined in +./examples/siamese/mnist_siamese_solver.prototxt.

    + +

    Training and Testing the Model

    + +

    Training the model is simple after you have written the network definition +protobuf and solver protobuf files. Simply run +./examples/siamese/train_mnist_siamese.sh:

    + +
    ./examples/siamese/train_mnist_siamese.sh
+
    +
    + +

    Plotting the results

    + +

    First, we can draw the model and siamese networks by running the following +commands that draw the DAGs defined in the .prototxt files:

    + +
    ./python/draw_net.py \
+    ./examples/siamese/mnist_siamese.prototxt \
+    ./examples/siamese/mnist_siamese.png
+
+./python/draw_net.py \
+    ./examples/siamese/mnist_siamese_train_test.prototxt \
+    ./examples/siamese/mnist_siamese_train_test.png
+
    +
    + +

    Second, we can load the learned model and plot the features using the iPython +notebook:

    + +
    ipython notebook ./examples/siamese/mnist_siamese.ipynb
+
    +
    + + + +
    +
    + + diff --git a/gathered/examples/siamese/mnist_siamese.ipynb b/gathered/examples/siamese/mnist_siamese.ipynb new file mode 100644 index 00000000000..ffeda21ade3 --- /dev/null +++ b/gathered/examples/siamese/mnist_siamese.ipynb @@ -0,0 +1,1971 @@ + + + + + + + + + Caffe | Siamese network embedding + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup\n", + "\n", + "Import Caffe and the usual modules." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "# Make sure that caffe is on the python path:\n", + "caffe_root = '../../' # this file is expected to be in {caffe_root}/examples/siamese\n", + "import sys\n", + "sys.path.insert(0, caffe_root + 'python')\n", + "\n", + "import caffe" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load the trained net\n", + "\n", + "Load the model definition and weights and set to CPU mode TEST phase computation with input scaling." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "MODEL_FILE = 'mnist_siamese.prototxt'\n", + "# decrease if you want to preview during training\n", + "PRETRAINED_FILE = 'mnist_siamese_iter_50000.caffemodel' \n", + "caffe.set_mode_cpu()\n", + "net = caffe.Net(MODEL_FILE, PRETRAINED_FILE, caffe.TEST)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load some MNIST test data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "TEST_DATA_FILE = '../../data/mnist/t10k-images-idx3-ubyte'\n", + "TEST_LABEL_FILE = '../../data/mnist/t10k-labels-idx1-ubyte'\n", + "n = 10000\n", + "\n", + "with open(TEST_DATA_FILE, 'rb') as f:\n", + " f.read(16) # skip the header\n", + " raw_data = np.fromstring(f.read(n * 28*28), dtype=np.uint8)\n", + "\n", + "with open(TEST_LABEL_FILE, 'rb') as f:\n", + " f.read(8) # skip the header\n", + " labels = np.fromstring(f.read(n), dtype=np.uint8)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate the Siamese features" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# reshape and preprocess\n", + "caffe_in = raw_data.reshape(n, 1, 28, 28) * 0.00390625 # manually scale data instead of using `caffe.io.Transformer`\n", + "out = net.forward_all(data=caffe_in)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize the learned Siamese embedding" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAA54AAAIXCAYAAAD0R4FDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXtwXOWZr/usvurWUktqGdmxaawEHEMuthGXITiIyMaJ\n", + "wbEMFmCTDMkkoyqSyTnZMwdqpmYyzEyS2ruKue2ZqSTHO/vYGQbhCxdjwI637ViWMEEEMJhgB4MB\n", + "gSRLsizJkiypuyX1+WP1Wlp971YvSd3y+1S5rF69Lt/6+lOrf/2+v/dVgsEggiAIgiAIgiAIgjBT\n", + 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'#00ff00', '#00ffff', '#0000ff', \n", + " '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n", + "for i in range(10):\n", + " plt.plot(feat[labels==i,0].flatten(), feat[labels==i,1].flatten(), '.', c=c[i])\n", + "plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])\n", + "plt.grid()\n", + "plt.show()" + ] + } + ], + "metadata": { + "description": "Extracting features and plotting the Siamese network embedding.", + "example_name": "Siamese network embedding", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + }, + "priority": 7 + }, + "nbformat": 4, + "nbformat_minor": 0 +} + + +
    +
    + + diff --git a/gathered/examples/web_demo.html b/gathered/examples/web_demo.html new file mode 100644 index 00000000000..fbec5daa457 --- /dev/null +++ b/gathered/examples/web_demo.html @@ -0,0 +1,99 @@ + + + + + + + + + Caffe | Web demo + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + +

    Web Demo

    + +

    Requirements

    + +

    The demo server requires Python with some dependencies. +To make sure you have the dependencies, please run pip install -r examples/web_demo/requirements.txt, and also make sure that you’ve compiled the Python Caffe interface and that it is on your PYTHONPATH (see installation instructions).

    + +

    Make sure that you have obtained the Reference CaffeNet Model and the ImageNet Auxiliary Data:

    + +
    ./scripts/download_model_binary.py models/bvlc_reference_caffenet
+./data/ilsvrc12/get_ilsvrc_aux.sh
+
    +
    + +

    NOTE: if you run into trouble, try re-downloading the auxiliary files.

    + +

    Run

    + +

    Running python examples/web_demo/app.py will bring up the demo server, accessible at http://0.0.0.0:5000. +You can enable debug mode of the web server, or switch to a different port:

    + +
    % python examples/web_demo/app.py -h
+Usage: app.py [options]
+
+Options:
+  -h, --help            show this help message and exit
+  -d, --debug           enable debug mode
+  -p PORT, --port=PORT  which port to serve content on
+
    +
    + +

    How are the “maximally accurate” results generated?

    + +

    In a nutshell: ImageNet predictions are made at the leaf nodes, but the organization of the project allows leaf nodes to be united via more general parent nodes, with ‘entity’ at the very top. +To give “maximally accurate” results, we “back off” from maximally specific predictions to maintain a high accuracy. +The bet_file that is loaded in the demo provides the graph structure and names of all relevant ImageNet nodes as well as measures of information gain between them. +Please see the “Hedging your bets” paper from CVPR 2012 for further information.

    + + +
    +
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    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + +

    Caffe

    + +

    Caffe is a deep learning framework made with expression, speed, and modularity in mind. +It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. +Yangqing Jia created the project during his PhD at UC Berkeley. +Caffe is released under the BSD 2-Clause license.

    + +

    Check out our web image classification demo!

    + +

    Why Caffe?

    + +

    Expressive architecture encourages application and innovation. +Models and optimization are defined by configuration without hard-coding. +Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.

    + +

    Extensible code fosters active development. +In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. +Thanks to these contributors the framework tracks the state-of-the-art in both code and models.

    + +

    Speed makes Caffe perfect for research experiments and industry deployment. +Caffe can process over 60M images per day with a single NVIDIA K40 GPU*. +That’s 1 ms/image for inference and 4 ms/image for learning. +We believe that Caffe is the fastest convnet implementation available.

    + +

    Community: Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. +Join our community of brewers on the caffe-users group and Github.

    + +

    +* With the ILSVRC2012-winning SuperVision model and caching IO. +Consult performance details. +

    + +

    Documentation

    + + + +

    Examples

    + + + +

    Notebook Examples

    + + + +

    Citing Caffe

    + +

    Please cite Caffe in your publications if it helps your research:

    + +
    @article{jia2014caffe,
+  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
+  Journal = {arXiv preprint arXiv:1408.5093},
+  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
+  Year = {2014}
+}
+
    +
    + +

    If you do publish a paper where Caffe helped your research, we encourage you to update the publications wiki. +Citations are also tracked automatically by Google Scholar.

    + +

    Contacting Us

    + +

    Join the caffe-users group to ask questions and discuss methods and models. This is where we talk about usage, installation, and applications.

    + +

    Framework development discussions and thorough bug reports are collected on Issues.

    + +

    Contact caffe-dev if you have a confidential proposal for the framework and the ability to act on it. +Requests for features, explanations, or personal help will be ignored; post to caffe-users instead.

    + +

    The core Caffe developers offer consulting services for appropriate projects.

    + +

    Acknowledgements

    + +

    The BVLC Caffe developers would like to thank NVIDIA for GPU donation, A9 and Amazon Web Services for a research grant in support of Caffe development and reproducible research in deep learning, and BVLC PI Trevor Darrell for guidance.

    + +

    The BVLC members who have contributed to Caffe are (alphabetical by first name): +Eric Tzeng, Evan Shelhamer, Jeff Donahue, Jon Long, Ross Girshick, Sergey Karayev, Sergio Guadarrama, and Yangqing Jia.

    + +

    The open-source community plays an important and growing role in Caffe’s development. +Check out the Github project pulse for recent activity and the contributors for the full list.

    + +

    We sincerely appreciate your interest and contributions! +If you’d like to contribute, please read the developing & contributing guide.

    + +

    Yangqing would like to give a personal thanks to the NVIDIA Academic program for providing GPUs, Oriol Vinyals for discussions along the journey, and BVLC PI Trevor Darrell for advice.

    + + +
    +
    + + diff --git a/install_apt.html b/install_apt.html new file mode 100644 index 00000000000..ed4601d1d74 --- /dev/null +++ b/install_apt.html @@ -0,0 +1,119 @@ + + + + + + + + + Caffe | Installation: Ubuntu + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + +

    Ubuntu Installation

    + +

    General dependencies

    + +
    sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
+sudo apt-get install --no-install-recommends libboost-all-dev
+
    +
    + +

    CUDA: Install by apt-get or the NVIDIA .run package. +The NVIDIA package tends to follow more recent library and driver versions, but the installation is more manual. +If installing from packages, install the library and latest driver separately; the driver bundled with the library is usually out-of-date. +This can be skipped for CPU-only installation.

    + +

    BLAS: install ATLAS by sudo apt-get install libatlas-base-dev or install OpenBLAS or MKL for better CPU performance.

    + +

    Python (optional): if you use the default Python you will need to sudo apt-get install the python-dev package to have the Python headers for building the pycaffe interface.

    + +

    Compatibility notes, 16.04

    + +

    CUDA 8 is required on Ubuntu 16.04.

    + +

    Remaining dependencies, 14.04

    + +

    Everything is packaged in 14.04.

    + +
    sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
+
    +
    + +

    Remaining dependencies, 12.04

    + +

    These dependencies need manual installation in 12.04.

    + +
    # glog
+wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
+tar zxvf glog-0.3.3.tar.gz
+cd glog-0.3.3
+./configure
+make && make install
+# gflags
+wget https://github.com/schuhschuh/gflags/archive/master.zip
+unzip master.zip
+cd gflags-master
+mkdir build && cd build
+export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
+make && make install
+# lmdb
+git clone https://github.com/LMDB/lmdb
+cd lmdb/libraries/liblmdb
+make && make install
+
    +
    + +

    Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.

    + +

    Continue with compilation.

    + + +
    +
    + + diff --git a/install_osx.html b/install_osx.html new file mode 100644 index 00000000000..063b1670dd0 --- /dev/null +++ b/install_osx.html @@ -0,0 +1,206 @@ + + + + + + + + + Caffe | Installation: OS X + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + +

    OS X Installation

    + +

    We highly recommend using the Homebrew package manager. +Ideally you could start from a clean /usr/local to avoid conflicts. +In the following, we assume that you’re using Anaconda Python and Homebrew.

    + +

    CUDA: Install via the NVIDIA package that includes both CUDA and the bundled driver. CUDA 7 is strongly suggested. Older CUDA require libstdc++ while clang++ is the default compiler and libc++ the default standard library on OS X 10.9+. This disagreement makes it necessary to change the compilation settings for each of the dependencies. This is prone to error.

    + +

    Library Path: We find that everything compiles successfully if $LD_LIBRARY_PATH is not set at all, and $DYLD_FALLBACK_LIBRARY_PATH is set to provide CUDA, Python, and other relevant libraries (e.g. /usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib). +In other ENV settings, things may not work as expected.

    + +

    General dependencies

    + +
    brew install -vd snappy leveldb gflags glog szip lmdb
+# need the homebrew science source for OpenCV and hdf5
+brew tap homebrew/science
+brew install hdf5 opencv
+
    +
    + +

    If using Anaconda Python, a modification to the OpenCV formula might be needed +Do brew edit opencv and change the lines that look like the two lines below to exactly the two lines below.

    + +
      -DPYTHON_LIBRARY=#{py_prefix}/lib/libpython2.7.dylib
+  -DPYTHON_INCLUDE_DIR=#{py_prefix}/include/python2.7
+
    +
    + +

    If using Anaconda Python, HDF5 is bundled and the hdf5 formula can be skipped.

    + +

    Remaining dependencies, with / without Python

    + +
    # with Python pycaffe needs dependencies built from source
+brew install --build-from-source --with-python -vd protobuf
+brew install --build-from-source -vd boost boost-python
+# without Python the usual installation suffices
+brew install protobuf boost
+
    +
    + +

    BLAS: already installed as the Accelerate / vecLib Framework. OpenBLAS and MKL are alternatives for faster CPU computation.

    + +

    Python (optional): Anaconda is the preferred Python. +If you decide against it, please use Homebrew. +Check that Caffe and dependencies are linking against the same, desired Python.

    + +

    Continue with compilation.

    + +

    libstdc++ installation

    + +

    This route is not for the faint of heart. +For OS X 10.10 and 10.9 you should install CUDA 7 and follow the instructions above. +If that is not an option, take a deep breath and carry on.

    + +

    In OS X 10.9+, clang++ is the default C++ compiler and uses libc++ as the standard library. +However, NVIDIA CUDA (even version 6.0) currently links only with libstdc++. +This makes it necessary to change the compilation settings for each of the dependencies.

    + +

    We do this by modifying the Homebrew formulae before installing any packages. +Make sure that Homebrew doesn’t install any software dependencies in the background; all packages must be linked to libstdc++.

    + +

    The prerequisite Homebrew formulae are

    + +
    boost snappy leveldb protobuf gflags glog szip lmdb homebrew/science/opencv
+
    +
    + +

    For each of these formulas, brew edit FORMULA, and add the ENV definitions as shown:

    + +
      def install
+      # ADD THE FOLLOWING:
+      ENV.append "CXXFLAGS", "-stdlib=libstdc++"
+      ENV.append "CFLAGS", "-stdlib=libstdc++"
+      ENV.append "LDFLAGS", "-stdlib=libstdc++ -lstdc++"
+      # The following is necessary because libtool likes to strip LDFLAGS:
+      ENV["CXX"] = "/usr/bin/clang++ -stdlib=libstdc++"
+      ...
+
    +
    + +

    To edit the formulae in turn, run

    + +
    for x in snappy leveldb protobuf gflags glog szip boost boost-python lmdb homebrew/science/opencv; do brew edit $x; done
+
    +
    + +

    After this, run

    + +
    for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source -vd $x; done
+brew uninstall protobuf; brew install --build-from-source --with-python -vd protobuf
+brew install --build-from-source -vd boost boost-python
+
    +
    + +

    If this is not done exactly right then linking errors will trouble you.

    + +

    Homebrew versioning that Homebrew maintains itself as a separate git repository and making the above brew edit FORMULA changes will change files in your local copy of homebrew’s master branch. By default, this will prevent you from updating Homebrew using brew update, as you will get an error message like the following:

    + +
    $ brew update
+error: Your local changes to the following files would be overwritten by merge:
+  Library/Formula/lmdb.rb
+Please, commit your changes or stash them before you can merge.
+Aborting
+Error: Failure while executing: git pull -q origin refs/heads/master:refs/remotes/origin/master
+
    +
    + +

    One solution is to commit your changes to a separate Homebrew branch, run brew update, and rebase your changes onto the updated master. You’ll have to do this both for the main Homebrew repository in /usr/local/ and the Homebrew science repository that contains OpenCV in /usr/local/Library/Taps/homebrew/homebrew-science, as follows:

    + +
    cd /usr/local
+git checkout -b caffe
+git add .
+git commit -m "Update Caffe dependencies to use libstdc++"
+cd /usr/local/Library/Taps/homebrew/homebrew-science
+git checkout -b caffe
+git add .
+git commit -m "Update Caffe dependencies"
+
    +
    + +

    Then, whenever you want to update homebrew, switch back to the master branches, do the update, rebase the caffe branches onto master and fix any conflicts:

    + +
    # Switch batch to homebrew master branches
+cd /usr/local
+git checkout master
+cd /usr/local/Library/Taps/homebrew/homebrew-science
+git checkout master
+
+# Update homebrew; hopefully this works without errors!
+brew update
+
+# Switch back to the caffe branches with the formulae that you modified earlier
+cd /usr/local
+git rebase master caffe
+# Fix any merge conflicts and commit to caffe branch
+cd /usr/local/Library/Taps/homebrew/homebrew-science
+git rebase master caffe
+# Fix any merge conflicts and commit to caffe branch
+
+# Done!
+
    +
    + +

    At this point, you should be running the latest Homebrew packages and your Caffe-related modifications will remain in place.

    + + +
    +
    + + diff --git a/install_yum.html b/install_yum.html new file mode 100644 index 00000000000..4abb76c8e8c --- /dev/null +++ b/install_yum.html @@ -0,0 +1,109 @@ + + + + + + + + + Caffe | Installation: RHEL / Fedora / CentOS + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + +

    RHEL / Fedora / CentOS Installation

    + +

    General dependencies

    + +
    sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel
+
    +
    + +

    Remaining dependencies, recent OS

    + +
    sudo yum install gflags-devel glog-devel lmdb-devel
+
    +
    + +

    Remaining dependencies, if not found

    + +
    # glog
+wget https://storage.googleapis.com/google-code-archive-downloads/v2/code.google.com/google-glog/glog-0.3.3.tar.gz
+tar zxvf glog-0.3.3.tar.gz
+cd glog-0.3.3
+./configure
+make && make install
+# gflags
+wget https://github.com/schuhschuh/gflags/archive/master.zip
+unzip master.zip
+cd gflags-master
+mkdir build && cd build
+export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
+make && make install
+# lmdb
+git clone https://github.com/LMDB/lmdb
+cd lmdb/libraries/liblmdb
+make && make install
+
    +
    + +

    Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.

    + +

    CUDA: Install via the NVIDIA package instead of yum to be certain of the library and driver versions. +Install the library and latest driver separately; the driver bundled with the library is usually out-of-date. + + CentOS/RHEL/Fedora:

    + +

    BLAS: install ATLAS by sudo yum install atlas-devel or install OpenBLAS or MKL for better CPU performance. For the Makefile build, uncomment and set BLAS_LIB accordingly as ATLAS is usually installed under /usr/lib[64]/atlas).

    + +

    Python (optional): if you use the default Python you will need to sudo yum install the python-devel package to have the Python headers for building the pycaffe wrapper.

    + +

    Continue with compilation.

    + + +
    +
    + + diff --git a/installation.html b/installation.html new file mode 100644 index 00000000000..3758e3f4825 --- /dev/null +++ b/installation.html @@ -0,0 +1,232 @@ + + + + + + + + + Caffe | Installation + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + +

    Installation

    + +

    Prior to installing, have a glance through this guide and take note of the details for your platform. +We install and run Caffe on Ubuntu 16.04–12.04, OS X 10.11–10.8, and through Docker and AWS. +The official Makefile and Makefile.config build are complemented by a community CMake build.

    + +

    Step-by-step Instructions:

    + + + +

    Overview:

    + + + +

    When updating Caffe, it’s best to make clean before re-compiling.

    + +

    Prerequisites

    + +

    Caffe has several dependencies:

    + +
      +
    • CUDA is required for GPU mode. +
        +
      • library version 7+ and the latest driver version are recommended, but 6.* is fine too
        • +
      • 5.5, and 5.0 are compatible but considered legacy
        • +
      +
      • +
    • BLAS via ATLAS, MKL, or OpenBLAS.
      • +
    • Boost >= 1.55
      • +
    • protobuf, glog, gflags, hdf5
      • +
    + +

    Optional dependencies:

    + +
      +
    • OpenCV >= 2.4 including 3.0
      • +
    • IO libraries: lmdb, leveldb (note: leveldb requires snappy)
      • +
    • cuDNN for GPU acceleration (v5)
      • +
    + +

    Pycaffe and Matcaffe interfaces have their own natural needs.

    + +
      +
    • For Python Caffe: Python 2.7 or Python 3.3+, numpy (>= 1.7), boost-provided boost.python
      • +
    • For MATLAB Caffe: MATLAB with the mex compiler.
      • +
    + +

    cuDNN Caffe: for fastest operation Caffe is accelerated by drop-in integration of NVIDIA cuDNN. To speed up your Caffe models, install cuDNN then uncomment the USE_CUDNN := 1 flag in Makefile.config when installing Caffe. Acceleration is automatic. The current version is cuDNN v5; older versions are supported in older Caffe.

    + +

    CPU-only Caffe: for cold-brewed CPU-only Caffe uncomment the CPU_ONLY := 1 flag in Makefile.config to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.

    + +

    CUDA and BLAS

    + +

    Caffe requires the CUDA nvcc compiler to compile its GPU code and CUDA driver for GPU operation. +To install CUDA, go to the NVIDIA CUDA website and follow installation instructions there. Install the library and the latest standalone driver separately; the driver bundled with the library is usually out-of-date. Warning! The 331.* CUDA driver series has a critical performance issue: do not use it.

    + +

    For best performance, Caffe can be accelerated by NVIDIA cuDNN. Register for free at the cuDNN site, install it, then continue with these installation instructions. To compile with cuDNN set the USE_CUDNN := 1 flag set in your Makefile.config.

    + +

    Caffe requires BLAS as the backend of its matrix and vector computations. +There are several implementations of this library. The choice is yours:

    + +
      +
    • ATLAS: free, open source, and so the default for Caffe.
      • +
    • Intel MKL: commercial and optimized for Intel CPUs, with a free trial and student licenses. +
        +
      1. Install MKL.
        2. +
      2. Set up MKL environment (Details: Linux, OS X). Example: source /opt/intel/mkl/bin/mklvars.sh intel64
        3. +
      3. Set BLAS := mkl in Makefile.config
        4. +
      +
      • +
    • OpenBLAS: free and open source; this optimized and parallel BLAS could require more effort to install, although it might offer a speedup. +
        +
      1. Install OpenBLAS
        2. +
      2. Set BLAS := open in Makefile.config
        3. +
      +
      • +
    + +

    Python and/or MATLAB Caffe (optional)

    + +

    Python

    + +

    The main requirements are numpy and boost.python (provided by boost). pandas is useful too and needed for some examples.

    + +

    You can install the dependencies with

    + +
    for req in $(cat requirements.txt); do pip install $req; done
+
    +
    + +

    but we suggest first installing the Anaconda Python distribution, which provides most of the necessary packages, as well as the hdf5 library dependency.

    + +

    To import the caffe Python module after completing the installation, add the module directory to your $PYTHONPATH by export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH or the like. You should not import the module in the caffe/python/caffe directory!

    + +

    Caffe’s Python interface works with Python 2.7. Python 3.3+ should work out of the box without protobuf support. For protobuf support please install protobuf 3.0 alpha (https://developers.google.com/protocol-buffers/). Earlier Pythons are your own adventure.

    + +

    MATLAB

    + +

    Install MATLAB, and make sure that its mex is in your $PATH.

    + +

    Caffe’s MATLAB interface works with versions 2015a, 2014a/b, 2013a/b, and 2012b.

    + +

    Compilation

    + +

    Caffe can be compiled with either Make or CMake. Make is officially supported while CMake is supported by the community.

    + +

    Compilation with Make

    + +

    Configure the build by copying and modifying the example Makefile.config for your setup. The defaults should work, but uncomment the relevant lines if using Anaconda Python.

    + +
    cp Makefile.config.example Makefile.config
+# Adjust Makefile.config (for example, if using Anaconda Python, or if cuDNN is desired)
+make all
+make test
+make runtest
+
    +
    + +
      +
    • For CPU & GPU accelerated Caffe, no changes are needed.
      • +
    • For cuDNN acceleration using NVIDIA’s proprietary cuDNN software, uncomment the USE_CUDNN := 1 switch in Makefile.config. cuDNN is sometimes but not always faster than Caffe’s GPU acceleration.
      • +
    • For CPU-only Caffe, uncomment CPU_ONLY := 1 in Makefile.config.
      • +
    + +

    To compile the Python and MATLAB wrappers do make pycaffe and make matcaffe respectively. +Be sure to set your MATLAB and Python paths in Makefile.config first!

    + +

    Distribution: run make distribute to create a distribute directory with all the Caffe headers, compiled libraries, binaries, etc. needed for distribution to other machines.

    + +

    Speed: for a faster build, compile in parallel by doing make all -j8 where 8 is the number of parallel threads for compilation (a good choice for the number of threads is the number of cores in your machine).

    + +

    Now that you have installed Caffe, check out the MNIST tutorial and the reference ImageNet model tutorial.

    + +

    CMake Build

    + +

    In lieu of manually editing Makefile.config to configure the build, Caffe offers an unofficial CMake build thanks to @Nerei, @akosiorek, and other members of the community. It requires CMake version >= 2.8.7. +The basic steps are as follows:

    + +
    mkdir build
+cd build
+cmake ..
+make all
+make install
+make runtest
+
    +
    + +

    See PR #1667 for options and details.

    + +

    Hardware

    + +

    Laboratory Tested Hardware: Berkeley Vision runs Caffe with Titan Xs, K80s, GTX 980s, K40s, K20s, Titans, and GTX 770s including models at ImageNet/ILSVRC scale. We have not encountered any trouble in-house with devices with CUDA capability >= 3.0. All reported hardware issues thus-far have been due to GPU configuration, overheating, and the like.

    + +

    CUDA compute capability: devices with compute capability <= 2.0 may have to reduce CUDA thread numbers and batch sizes due to hardware constraints. Brew with caution; we recommend compute capability >= 3.0.

    + +

    Once installed, check your times against our reference performance numbers to make sure everything is configured properly.

    + +

    Ask hardware questions on the caffe-users group.

    + + +
    +
    + + diff --git a/model_zoo.html b/model_zoo.html new file mode 100644 index 00000000000..9bc145287df --- /dev/null +++ b/model_zoo.html @@ -0,0 +1,141 @@ + + + + + + + + + Caffe | Model Zoo + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + +

    Caffe Model Zoo

    + +

    Lots of researchers and engineers have made Caffe models for different tasks with all kinds of architectures and data. +These models are learned and applied for problems ranging from simple regression, to large-scale visual classification, to Siamese networks for image similarity, to speech and robotics applications.

    + +

    To help share these models, we introduce the model zoo framework:

    + +
      +
    • A standard format for packaging Caffe model info.
      • +
    • Tools to upload/download model info to/from Github Gists, and to download trained .caffemodel binaries.
      • +
    • A central wiki page for sharing model info Gists.
      • +
    + +

    Where to get trained models

    + +

    First of all, we bundle BVLC-trained models for unrestricted, out of the box use. +
    +See the BVLC model license for details. +Each one of these can be downloaded by running scripts/download_model_binary.py <dirname> where <dirname> is specified below:

    + +
      +
    • BVLC Reference CaffeNet in models/bvlc_reference_caffenet: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in ImageNet classification with deep convolutional neural networks by Krizhevsky et al. in NIPS 2012. (Trained by Jeff Donahue @jeffdonahue)
      • +
    • BVLC AlexNet in models/bvlc_alexnet: AlexNet trained on ILSVRC 2012, almost exactly as described in ImageNet classification with deep convolutional neural networks by Krizhevsky et al. in NIPS 2012. (Trained by Evan Shelhamer @shelhamer)
      • +
    • BVLC Reference R-CNN ILSVRC-2013 in models/bvlc_reference_rcnn_ilsvrc13: pure Caffe implementation of R-CNN as described by Girshick et al. in CVPR 2014. (Trained by Ross Girshick @rbgirshick)
      • +
    • BVLC GoogLeNet in models/bvlc_googlenet: GoogLeNet trained on ILSVRC 2012, almost exactly as described in Going Deeper with Convolutions by Szegedy et al. in ILSVRC 2014. (Trained by Sergio Guadarrama @sguada)
      • +
    + +

    Community models made by Caffe users are posted to a publicly editable wiki page. +These models are subject to conditions of their respective authors such as citation and license. +Thank you for sharing your models!

    + +

    Model info format

    + +

    A caffe model is distributed as a directory containing:

    + +
      +
    • Solver/model prototxt(s)
      • +
    • readme.md containing +
        +
      • YAML frontmatter +
          +
        • Caffe version used to train this model (tagged release or commit hash).
          • +
        • [optional] file URL and SHA1 of the trained .caffemodel.
          • +
        • [optional] github gist id.
          • +
        +
        • +
      • Information about what data the model was trained on, modeling choices, etc.
        • +
      • License information.
        • +
      +
      • +
    • [optional] Other helpful scripts.
      • +
    + +

    Hosting model info

    + +

    Github Gist is a good format for model info distribution because it can contain multiple files, is versionable, and has in-browser syntax highlighting and markdown rendering.

    + +

    scripts/upload_model_to_gist.sh <dirname> uploads non-binary files in the model directory as a Github Gist and prints the Gist ID. If gist_id is already part of the <dirname>/readme.md frontmatter, then updates existing Gist.

    + +

    Try doing scripts/upload_model_to_gist.sh models/bvlc_alexnet to test the uploading (don’t forget to delete the uploaded gist afterward).

    + +

    Downloading model info is done just as easily with scripts/download_model_from_gist.sh <gist_id> <dirname>.

    + +

    Hosting trained models

    + +

    It is up to the user where to host the .caffemodel file. +We host our BVLC-provided models on our own server. +Dropbox also works fine (tip: make sure that ?dl=1 is appended to the end of the URL).

    + +

    scripts/download_model_binary.py <dirname> downloads the .caffemodel from the URL specified in the <dirname>/readme.md frontmatter and confirms SHA1.

    + +

    BVLC model license

    + +

    The Caffe models bundled by the BVLC are released for unrestricted use.

    + +

    These models are trained on data from the ImageNet project and training data includes internet photos that may be subject to copyright.

    + +

    Our present understanding as researchers is that there is no restriction placed on the open release of these learned model weights, since none of the original images are distributed in whole or in part. +To the extent that the interpretation arises that weights are derivative works of the original copyright holder and they assert such a copyright, UC Berkeley makes no representations as to what use is allowed other than to consider our present release in the spirit of fair use in the academic mission of the university to disseminate knowledge and tools as broadly as possible without restriction.

    + + +
    +
    + + diff --git a/multigpu.html b/multigpu.html new file mode 100644 index 00000000000..8dea5a3e466 --- /dev/null +++ b/multigpu.html @@ -0,0 +1,84 @@ + + + + + + + + + Caffe | Multi-GPU Usage, Hardware Configuration Assumptions, and Performance + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + +

    Multi-GPU Usage

    + +

    Currently Multi-GPU is only supported via the C/C++ paths and only for training.

    + +

    The GPUs to be used for training can be set with the “-gpu” flag on the command line to the ‘caffe’ tool. e.g. “build/tools/caffe train –solver=models/bvlc_alexnet/solver.prototxt –gpu=0,1” will train on GPUs 0 and 1.

    + +

    NOTE: each GPU runs the batchsize specified in your train_val.prototxt. So if you go from 1 GPU to 2 GPU, your effective batchsize will double. e.g. if your train_val.prototxt specified a batchsize of 256, if you run 2 GPUs your effective batch size is now 512. So you need to adjust the batchsize when running multiple GPUs and/or adjust your solver params, specifically learning rate.

    + +

    Hardware Configuration Assumptions

    + +

    The current implementation uses a tree reduction strategy. e.g. if there are 4 GPUs in the system, 0:1, 2:3 will exchange gradients, then 0:2 (top of the tree) will exchange gradients, 0 will calculate +updated model, 0->2, and then 0->1, 2->3.

    + +

    For best performance, P2P DMA access between devices is needed. Without P2P access, for example crossing PCIe root complex, data is copied through host and effective exchange bandwidth is greatly reduced.

    + +

    Current implementation has a “soft” assumption that the devices being used are homogeneous. In practice, any devices of the same general class should work together, but performance and total size is limited by the smallest device being used. e.g. if you combine a TitanX and a GTX980, performance will be limited by the 980. Mixing vastly different levels of boards, e.g. Kepler and Fermi, is not supported.

    + +

    “nvidia-smi topo -m” will show you the connectivity matrix. You can do P2P through PCIe bridges, but not across socket level links at this time, e.g. across CPU sockets on a multi-socket motherboard.

    + +

    Scaling Performance

    + +

    Performance is heavily dependent on the PCIe topology of the system, the configuration of the neural network you are training, and the speed of each of the layers. Systems like the DIGITS DevBox have an optimized PCIe topology (X99-E WS chipset). In general, scaling on 2 GPUs tends to be ~1.8X on average for networks like AlexNet, CaffeNet, VGG, GoogleNet. 4 GPUs begins to have falloff in scaling. Generally with “weak scaling” where the batchsize increases with the number of GPUs you will see 3.5x scaling or so. With “strong scaling”, the system can become communication bound, especially with layer performance optimizations like those in cuDNNv3, and you will likely see closer to mid 2.x scaling in performance. Networks that have heavy computation compared to the number of parameters tend to have the best scaling performance.

    + + +
    +
    + + diff --git a/performance_hardware.html b/performance_hardware.html new file mode 100644 index 00000000000..a9119d2d42e --- /dev/null +++ b/performance_hardware.html @@ -0,0 +1,142 @@ + + + + + + + + + Caffe | Performance and Hardware Configuration + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + +

    Performance and Hardware Configuration

    + +

    To measure performance on different NVIDIA GPUs we use CaffeNet, the Caffe reference ImageNet model.

    + +

    For training, each time point is 20 iterations/minibatches of 256 images for 5,120 images total. For testing, a 50,000 image validation set is classified.

    + +

    Acknowledgements: BVLC members are very grateful to NVIDIA for providing several GPUs to conduct this research.

    + +

    NVIDIA K40

    + +

    Performance is best with ECC off and boost clock enabled. While ECC makes a negligible difference in speed, disabling it frees ~1 GB of GPU memory.

    + +

    Best settings with ECC off and maximum clock speed in standard Caffe:

    + +
      +
    • Training is 26.5 secs / 20 iterations (5,120 images)
      • +
    • Testing is 100 secs / validation set (50,000 images)
      • +
    + +

    Best settings with Caffe + cuDNN acceleration:

    + +
      +
    • Training is 19.2 secs / 20 iterations (5,120 images)
      • +
    • Testing is 60.7 secs / validation set (50,000 images)
      • +
    + +

    Other settings:

    + +
      +
    • ECC on, max speed: training 26.7 secs / 20 iterations, test 101 secs / validation set
      • +
    • ECC on, default speed: training 31 secs / 20 iterations, test 117 secs / validation set
      • +
    • ECC off, default speed: training 31 secs / 20 iterations, test 118 secs / validation set
      • +
    + +

    K40 configuration tips

    + +

    For maximum K40 performance, turn off ECC and boost the clock speed (at your own risk).

    + +

    To turn off ECC, do

    + +
    sudo nvidia-smi -i 0 --ecc-config=0    # repeat with -i x for each GPU ID
+
    +
    + +

    then reboot.

    + +

    Set the “persistence” mode of the GPU settings by

    + +
    sudo nvidia-smi -pm 1
+
    +
    + +

    and then set the clock speed with

    + +
    sudo nvidia-smi -i 0 -ac 3004,875    # repeat with -i x for each GPU ID
+
    +
    + +

    but note that this configuration resets across driver reloading / rebooting. Include these commands in a boot script to initialize these settings. For a simple fix, add these commands to /etc/rc.local (on Ubuntu).

    + +

    NVIDIA Titan

    + +

    Training: 26.26 secs / 20 iterations (5,120 images). +Testing: 100 secs / validation set (50,000 images).

    + +

    cuDNN Training: 20.25 secs / 20 iterations (5,120 images). +cuDNN Testing: 66.3 secs / validation set (50,000 images).

    + +

    NVIDIA K20

    + +

    Training: 36.0 secs / 20 iterations (5,120 images). +Testing: 133 secs / validation set (50,000 images).

    + +

    NVIDIA GTX 770

    + +

    Training: 33.0 secs / 20 iterations (5,120 images). +Testing: 129 secs / validation set (50,000 images).

    + +

    cuDNN Training: 24.3 secs / 20 iterations (5,120 images). +cuDNN Testing: 104 secs / validation set (50,000 images).

    + + +
    +
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+ } + + header ul, header p.view { + position:static; + } +} + +@media print, screen and (max-width: 480px) { + + header ul li.download { + display:none; + } + + footer { + margin: 0 0 0 20px; + } + + footer a{ + display:block; + } + +} + +@media print { + body { + padding:0.4in; + font-size:12pt; + color:#444; + } +} diff --git a/tutorial/convolution.html b/tutorial/convolution.html new file mode 100644 index 00000000000..75abe25e561 --- /dev/null +++ b/tutorial/convolution.html @@ -0,0 +1,74 @@ + + + + + + + + + Caffe | Convolution + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + +

    Caffeinated Convolution

    + +

    The Caffe strategy for convolution is to reduce the problem to matrix-matrix multiplication. +This linear algebra computation is highly-tuned in BLAS libraries and efficiently computed on GPU devices.

    + +

    For more details read Yangqing’s Convolution in Caffe: a memo.

    + +

    As it turns out, this same reduction was independently explored in the context of conv. nets by

    + +
    +

    K. Chellapilla, S. Puri, P. Simard, et al. High performance convolutional neural networks for document processing. In Tenth International Workshop on Frontiers in Handwriting Recognition, 2006.

    +
    + + +
    +
    + + diff --git a/tutorial/data.html b/tutorial/data.html new file mode 100644 index 00000000000..5fd01ba86d1 --- /dev/null +++ b/tutorial/data.html @@ -0,0 +1,141 @@ + + + + + + + + + Caffe | Data + + + + + + + + + + + + + +
    +
    +

    Caffe

    +

    + Deep learning framework by the BVLC +

    +

    + Created by +
    + Yangqing Jia +
    + Lead Developer +
    + Evan Shelhamer +

    +
    +
    + +

    Data: Ins and Outs

    + +

    Data flows through Caffe as Blobs. +Data layers load input and save output by converting to and from Blob to other formats. +Common transformations like mean-subtraction and feature-scaling are done by data layer configuration. +New input types are supported by developing a new data layer – the rest of the Net follows by the modularity of the Caffe layer catalogue.

    + +

    This data layer definition

    + +
    layer {
+  name: "mnist"
+  # Data layer loads leveldb or lmdb storage DBs for high-throughput.
+  type: "Data"
+  # the 1st top is the data itself: the name is only convention
+  top: "data"
+  # the 2nd top is the ground truth: the name is only convention
+  top: "label"
+  # the Data layer configuration
+  data_param {
+    # path to the DB
+    source: "examples/mnist/mnist_train_lmdb"
+    # type of DB: LEVELDB or LMDB (LMDB supports concurrent reads)
+    backend: LMDB
+    # batch processing improves efficiency.
+    batch_size: 64
+  }
+  # common data transformations
+  transform_param {
+    # feature scaling coefficient: this maps the [0, 255] MNIST data to [0, 1]
+    scale: 0.00390625
+  }
+}
+
    +
    + +

    loads the MNIST digits.

    + +

    Tops and Bottoms: A data layer makes top blobs to output data to the model. +It does not have bottom blobs since it takes no input.

    + +

    Data and Label: a data layer has at least one top canonically named data. +For ground truth a second top can be defined that is canonically named label. +Both tops simply produce blobs and there is nothing inherently special about these names. +The (data, label) pairing is a convenience for classification models.

    + +

    Transformations: data preprocessing is parametrized by transformation messages within the data layer definition.

    + +
    layer {
+  name: "data"
+  type: "Data"
+  [...]
+  transform_param {
+    scale: 0.1
+    mean_file_size: mean.binaryproto
+    # for images in particular horizontal mirroring and random cropping
+    # can be done as simple data augmentations.
+    mirror: 1  # 1 = on, 0 = off
+    # crop a `crop_size` x `crop_size` patch:
+    # - at random during training
+    # - from the center during testing
+    crop_size: 227
+  }
+}
+
    +
    + +

    Prefetching: for throughput data layers fetch the next batch of data and prepare it in the background while the Net computes the current batch.

    + +

    Multiple Inputs: a Net can have multiple inputs of any number and type. Define as many data layers as needed giving each a unique name and top. Multiple inputs are useful for non-trivial ground truth: one data layer loads the actual data and the other data layer loads the ground truth in lock-step. In this arrangement both data and label can be any 4D array. Further applications of multiple inputs are found in multi-modal and sequence models. In these cases you may need to implement your own data preparation routines or a special data layer.

    + +

    Improvements to data processing to add formats, generality, or helper utilities are welcome!

    + +

    Formats

    + +

    Refer to the layer catalogue of data layers for close-ups on each type of data Caffe understands.

    + +

    Deployment Input

    + +

    For on-the-fly computation deployment Nets define their inputs by input fields: these Nets then accept direct assignment of data for online or interactive computation.

    + + +
    +
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@@ -0,0 +1,98 @@ + + + + + + + + + Caffe | Forward and Backward for Inference and Learning + + + + + + + + + + + + + + + + diff --git a/tutorial/index.html b/tutorial/index.html new file mode 100644 index 00000000000..99b0cf4f319 --- /dev/null +++ b/tutorial/index.html @@ -0,0 +1,118 @@ + + + + + + + + + Caffe | Caffe Tutorial + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Caffe Tutorial

      + +

      Caffe is a deep learning framework and this tutorial explains its philosophy, architecture, and usage. +This is a practical guide and framework introduction, so the full frontier, context, and history of deep learning cannot be covered here. +While explanations will be given where possible, a background in machine learning and neural networks is helpful.

      + +

      Philosophy

      + +

      In one sip, Caffe is brewed for

      + +
        +
      • Expression: models and optimizations are defined as plaintext schemas instead of code.
        • +
      • Speed: for research and industry alike speed is crucial for state-of-the-art models and massive data.
        • +
      • Modularity: new tasks and settings require flexibility and extension.
        • +
      • Openness: scientific and applied progress call for common code, reference models, and reproducibility.
        • +
      • Community: academic research, startup prototypes, and industrial applications all share strength by joint discussion and development in a BSD-2 project.
        • +
      + +

      and these principles direct the project.

      + +

      Tour

      + +
        +
      • Nets, Layers, and Blobs: the anatomy of a Caffe model.
        • +
      • Forward / Backward: the essential computations of layered compositional models.
        • +
      • Loss: the task to be learned is defined by the loss.
        • +
      • Solver: the solver coordinates model optimization.
        • +
      • Layer Catalogue: the layer is the fundamental unit of modeling and computation – Caffe’s catalogue includes layers for state-of-the-art models.
        • +
      • Interfaces: command line, Python, and MATLAB Caffe.
        • +
      • Data: how to caffeinate data for model input.
        • +
      + +

      For a closer look at a few details:

      + + + +

      Deeper Learning

      + +

      There are helpful references freely online for deep learning that complement our hands-on tutorial. +These cover introductory and advanced material, background and history, and the latest advances.

      + +

      The Tutorial on Deep Learning for Vision from CVPR ‘14 is a good companion tutorial for researchers. +Once you have the framework and practice foundations from the Caffe tutorial, explore the fundamental ideas and advanced research directions in the CVPR ‘14 tutorial.

      + +

      A broad introduction is given in the free online draft of Neural Networks and Deep Learning by Michael Nielsen. In particular the chapters on using neural nets and how backpropagation works are helpful if you are new to the subject.

      + +

      These recent academic tutorials cover deep learning for researchers in machine learning and vision:

      + + + +

      For an exposition of neural networks in circuits and code, check out Understanding Neural Networks from a Programmer’s Perspective by Andrej Karpathy (Stanford).

      + + +
      +
      + + diff --git a/tutorial/interfaces.html b/tutorial/interfaces.html new file mode 100644 index 00000000000..49542d7bcfe --- /dev/null +++ b/tutorial/interfaces.html @@ -0,0 +1,423 @@ + + + + + + + + + Caffe | Interfaces + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Interfaces

      + +

      Caffe has command line, Python, and MATLAB interfaces for day-to-day usage, interfacing with research code, and rapid prototyping. While Caffe is a C++ library at heart and it exposes a modular interface for development, not every occasion calls for custom compilation. The cmdcaffe, pycaffe, and matcaffe interfaces are here for you.

      + +

      Command Line

      + +

      The command line interface – cmdcaffe – is the caffe tool for model training, scoring, and diagnostics. Run caffe without any arguments for help. This tool and others are found in caffe/build/tools. (The following example calls require completing the LeNet / MNIST example first.)

      + +

      Training: caffe train learns models from scratch, resumes learning from saved snapshots, and fine-tunes models to new data and tasks:

      + +
        +
      • All training requires a solver configuration through the -solver solver.prototxt argument.
        • +
      • Resuming requires the -snapshot model_iter_1000.solverstate argument to load the solver snapshot.
        • +
      • Fine-tuning requires the -weights model.caffemodel argument for the model initialization.
        • +
      + +

      For example, you can run:

      + +
      # train LeNet
+caffe train -solver examples/mnist/lenet_solver.prototxt
+# train on GPU 2
+caffe train -solver examples/mnist/lenet_solver.prototxt -gpu 2
+# resume training from the half-way point snapshot
+caffe train -solver examples/mnist/lenet_solver.prototxt -snapshot examples/mnist/lenet_iter_5000.solverstate
+
      +
      + +

      For a full example of fine-tuning, see examples/finetuning_on_flickr_style, but the training call alone is

      + +
      # fine-tune CaffeNet model weights for style recognition
+caffe train -solver examples/finetuning_on_flickr_style/solver.prototxt -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel
+
      +
      + +

      Testing: caffe test scores models by running them in the test phase and reports the net output as its score. The net architecture must be properly defined to output an accuracy measure or loss as its output. The per-batch score is reported and then the grand average is reported last.

      + +
      # score the learned LeNet model on the validation set as defined in the
+# model architeture lenet_train_test.prototxt
+caffe test -model examples/mnist/lenet_train_test.prototxt -weights examples/mnist/lenet_iter_10000.caffemodel -gpu 0 -iterations 100
+
      +
      + +

      Benchmarking: caffe time benchmarks model execution layer-by-layer through timing and synchronization. This is useful to check system performance and measure relative execution times for models.

      + +
      # (These example calls require you complete the LeNet / MNIST example first.)
+# time LeNet training on CPU for 10 iterations
+caffe time -model examples/mnist/lenet_train_test.prototxt -iterations 10
+# time LeNet training on GPU for the default 50 iterations
+caffe time -model examples/mnist/lenet_train_test.prototxt -gpu 0
+# time a model architecture with the given weights on the first GPU for 10 iterations
+caffe time -model examples/mnist/lenet_train_test.prototxt -weights examples/mnist/lenet_iter_10000.caffemodel -gpu 0 -iterations 10
+
      +
      + +

      Diagnostics: caffe device_query reports GPU details for reference and checking device ordinals for running on a given device in multi-GPU machines.

      + +
      # query the first device
+caffe device_query -gpu 0
+
      +
      + +

      Parallelism: the -gpu flag to the caffe tool can take a comma separated list of IDs to run on multiple GPUs. A solver and net will be instantiated for each GPU so the batch size is effectively multiplied by the number of GPUs. To reproduce single GPU training, reduce the batch size in the network definition accordingly.

      + +
      # train on GPUs 0 & 1 (doubling the batch size)
+caffe train -solver examples/mnist/lenet_solver.prototxt -gpu 0,1
+# train on all GPUs (multiplying batch size by number of devices)
+caffe train -solver examples/mnist/lenet_solver.prototxt -gpu all
+
      +
      + +

      Python

      + +

      The Python interface – pycaffe – is the caffe module and its scripts in caffe/python. import caffe to load models, do forward and backward, handle IO, visualize networks, and even instrument model solving. All model data, derivatives, and parameters are exposed for reading and writing.

      + +
        +
      • caffe.Net is the central interface for loading, configuring, and running models. caffe.Classifier and caffe.Detector provide convenience interfaces for common tasks.
        • +
      • caffe.SGDSolver exposes the solving interface.
        • +
      • caffe.io handles input / output with preprocessing and protocol buffers.
        • +
      • caffe.draw visualizes network architectures.
        • +
      • Caffe blobs are exposed as numpy ndarrays for ease-of-use and efficiency.
        • +
      + +

      Tutorial IPython notebooks are found in caffe/examples: do ipython notebook caffe/examples to try them. For developer reference docstrings can be found throughout the code.

      + +

      Compile pycaffe by make pycaffe. +Add the module directory to your $PYTHONPATH by export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH or the like for import caffe.

      + +

      MATLAB

      + +

      The MATLAB interface – matcaffe – is the caffe package in caffe/matlab in which you can integrate Caffe in your Matlab code.

      + +

      In MatCaffe, you can

      + +
        +
      • Creating multiple Nets in Matlab
        • +
      • Do forward and backward computation
        • +
      • Access any layer within a network, and any parameter blob in a layer
        • +
      • Get and set data or diff to any blob within a network, not restricting to input blobs or output blobs
        • +
      • Save a network’s parameters to file, and load parameters from file
        • +
      • Reshape a blob and reshape a network
        • +
      • Edit network parameter and do network surgery
        • +
      • Create multiple Solvers in Matlab for training
        • +
      • Resume training from solver snapshots
        • +
      • Access train net and test nets in a solver
        • +
      • Run for a certain number of iterations and give back control to Matlab
        • +
      • Intermingle arbitrary Matlab code with gradient steps
        • +
      + +

      An ILSVRC image classification demo is in caffe/matlab/demo/classification_demo.m (you need to download BVLC CaffeNet from Model Zoo to run it).

      + +

      Build MatCaffe

      + +

      Build MatCaffe with make all matcaffe. After that, you may test it using make mattest.

      + +

      Common issue: if you run into error messages like libstdc++.so.6:version 'GLIBCXX_3.4.15' not found during make mattest, then it usually means that your Matlab’s runtime libraries do not match your compile-time libraries. You may need to do the following before you start Matlab:

      + +
      export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
+export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
+
      +
      + +

      Or the equivalent based on where things are installed on your system, and do make mattest again to see if the issue is fixed. Note: this issue is sometimes more complicated since during its startup Matlab may overwrite your LD_LIBRARY_PATH environment variable. You can run !ldd ./matlab/+caffe/private/caffe_.mexa64 (the mex extension may differ on your system) in Matlab to see its runtime libraries, and preload your compile-time libraries by exporting them to your LD_PRELOAD environment variable.

      + +

      After successful building and testing, add this package to Matlab search PATH by starting matlab from caffe root folder and running the following commands in Matlab command window.

      + +
      addpath ./matlab
+
      +
      + +

      You can save your Matlab search PATH by running savepath so that you don’t have to run the command above again every time you use MatCaffe.

      + +

      Use MatCaffe

      + +

      MatCaffe is very similar to PyCaffe in usage.

      + +

      Examples below shows detailed usages and assumes you have downloaded BVLC CaffeNet from Model Zoo and started matlab from caffe root folder.

      + +
      model = './models/bvlc_reference_caffenet/deploy.prototxt';
+weights = './models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel';
+
      +
      + +

      Set mode and device

      + +

      Mode and device should always be set BEFORE you create a net or a solver.

      + +

      Use CPU:

      + +
      caffe.set_mode_cpu();
+
      +
      + +

      Use GPU and specify its gpu_id:

      + +
      caffe.set_mode_gpu();
+caffe.set_device(gpu_id);
+
      +
      + +

      Create a network and access its layers and blobs

      + +

      Create a network:

      + +
      net = caffe.Net(model, weights, 'test'); % create net and load weights
+
      +
      + +

      Or

      + +
      net = caffe.Net(model, 'test'); % create net but not load weights
+net.copy_from(weights); % load weights
+
      +
      + +

      which creates net object as

      + +
        Net with properties:
+
+           layer_vec: [1x23 caffe.Layer]
+            blob_vec: [1x15 caffe.Blob]
+              inputs: {'data'}
+             outputs: {'prob'}
+    name2layer_index: [23x1 containers.Map]
+     name2blob_index: [15x1 containers.Map]
+         layer_names: {23x1 cell}
+          blob_names: {15x1 cell}
+
      +
      + +

      The two containers.Map objects are useful to find the index of a layer or a blob by its name.

      + +

      You have access to every blob in this network. To fill blob ‘data’ with all ones:

      + +
      net.blobs('data').set_data(ones(net.blobs('data').shape));
+
      +
      + +

      To multiply all values in blob ‘data’ by 10:

      + +
      net.blobs('data').set_data(net.blobs('data').get_data() * 10);
+
      +
      + +

      Be aware that since Matlab is 1-indexed and column-major, the usual 4 blob dimensions in Matlab are [width, height, channels, num], and width is the fastest dimension. Also be aware that images are in BGR channels. Also, Caffe uses single-precision float data. If your data is not single, set_data will automatically convert it to single.

      + +

      You also have access to every layer, so you can do network surgery. For example, to multiply conv1 parameters by 10:

      + +
      net.params('conv1', 1).set_data(net.params('conv1', 1).get_data() * 10); % set weights
+net.params('conv1', 2).set_data(net.params('conv1', 2).get_data() * 10); % set bias
+
      +
      + +

      Alternatively, you can use

      + +
      net.layers('conv1').params(1).set_data(net.layers('conv1').params(1).get_data() * 10);
+net.layers('conv1').params(2).set_data(net.layers('conv1').params(2).get_data() * 10);
+
      +
      + +

      To save the network you just modified:

      + +
      net.save('my_net.caffemodel');
+
      +
      + +

      To get a layer’s type (string):

      + +
      layer_type = net.layers('conv1').type;
+
      +
      + +

      Forward and backward

      + +

      Forward pass can be done using net.forward or net.forward_prefilled. Function net.forward takes in a cell array of N-D arrays containing data of input blob(s) and outputs a cell array containing data from output blob(s). Function net.forward_prefilled uses existing data in input blob(s) during forward pass, takes no input and produces no output. After creating some data for input blobs like data = rand(net.blobs('data').shape); you can run

      + +
      res = net.forward({data});
+prob = res{1};
+
      +
      + +

      Or

      + +
      net.blobs('data').set_data(data);
+net.forward_prefilled();
+prob = net.blobs('prob').get_data();
+
      +
      + +

      Backward is similar using net.backward or net.backward_prefilled and replacing get_data and set_data with get_diff and set_diff. After creating some gradients for output blobs like prob_diff = rand(net.blobs('prob').shape); you can run

      + +
      res = net.backward({prob_diff});
+data_diff = res{1};
+
      +
      + +

      Or

      + +
      net.blobs('prob').set_diff(prob_diff);
+net.backward_prefilled();
+data_diff = net.blobs('data').get_diff();
+
      +
      + +

      However, the backward computation above doesn’t get correct results, because Caffe decides that the network does not need backward computation. To get correct backward results, you need to set 'force_backward: true' in your network prototxt.

      + +

      After performing forward or backward pass, you can also get the data or diff in internal blobs. For example, to extract pool5 features after forward pass:

      + +
      pool5_feat = net.blobs('pool5').get_data();
+
      +
      + +

      Reshape

      + +

      Assume you want to run 1 image at a time instead of 10:

      + +
      net.blobs('data').reshape([227 227 3 1]); % reshape blob 'data'
+net.reshape();
+
      +
      + +

      Then the whole network is reshaped, and now net.blobs('prob').shape should be [1000 1];

      + +

      Training

      + +

      Assume you have created training and validation lmdbs following our ImageNET Tutorial, to create a solver and train on ILSVRC 2012 classification dataset:

      + +
      solver = caffe.Solver('./models/bvlc_reference_caffenet/solver.prototxt');
+
      +
      + +

      which creates solver object as

      + +
        Solver with properties:
+
+          net: [1x1 caffe.Net]
+    test_nets: [1x1 caffe.Net]
+
      +
      + +

      To train:

      + +
      solver.solve();
+
      +
      + +

      Or train for only 1000 iterations (so that you can do something to its net before training more iterations)

      + +
      solver.step(1000);
+
      +
      + +

      To get iteration number:

      + +
      iter = solver.iter();
+
      +
      + +

      To get its network:

      + +
      train_net = solver.net;
+test_net = solver.test_nets(1);
+
      +
      + +

      To resume from a snapshot “your_snapshot.solverstate”:

      + +
      solver.restore('your_snapshot.solverstate');
+
      +
      + +

      Input and output

      + +

      caffe.io class provides basic input functions load_image and read_mean. For example, to read ILSVRC 2012 mean file (assume you have downloaded imagenet example auxiliary files by running ./data/ilsvrc12/get_ilsvrc_aux.sh):

      + +
      mean_data = caffe.io.read_mean('./data/ilsvrc12/imagenet_mean.binaryproto');
+
      +
      + +

      To read Caffe’s example image and resize to [width, height] and suppose we want width = 256; height = 256;

      + +
      im_data = caffe.io.load_image('./examples/images/cat.jpg');
+im_data = imresize(im_data, [width, height]); % resize using Matlab's imresize
+
      +
      + +

      Keep in mind that width is the fastest dimension and channels are BGR, which is different from the usual way that Matlab stores an image. If you don’t want to use caffe.io.load_image and prefer to load an image by yourself, you can do

      + +
      im_data = imread('./examples/images/cat.jpg'); % read image
+im_data = im_data(:, :, [3, 2, 1]); % convert from RGB to BGR
+im_data = permute(im_data, [2, 1, 3]); % permute width and height
+im_data = single(im_data); % convert to single precision
+
      +
      + +

      Also, you may take a look at caffe/matlab/demo/classification_demo.m to see how to prepare input by taking crops from an image.

      + +

      We show in caffe/matlab/hdf5creation how to read and write HDF5 data with Matlab. We do not provide extra functions for data output as Matlab itself is already quite powerful in output.

      + +

      Clear nets and solvers

      + +

      Call caffe.reset_all() to clear all solvers and stand-alone nets you have created.

      + + +
      +
      + + diff --git a/tutorial/layers.html b/tutorial/layers.html new file mode 100644 index 00000000000..6e4caf2bf50 --- /dev/null +++ b/tutorial/layers.html @@ -0,0 +1,824 @@ + + + + + + + + + Caffe | Layer Catalogue + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Layers

      + +

      To create a Caffe model you need to define the model architecture in a protocol buffer definition file (prototxt).

      + +

      Caffe layers and their parameters are defined in the protocol buffer definitions for the project in caffe.proto.

      + +

      Vision Layers

      + +
        +
      • Header: ./include/caffe/vision_layers.hpp
        • +
      + +

      Vision layers usually take images as input and produce other images as output. +A typical “image” in the real-world may have one color channel (), as in a grayscale image, or three color channels () as in an RGB (red, green, blue) image. +But in this context, the distinguishing characteristic of an image is its spatial structure: usually an image has some non-trivial height and width . +This 2D geometry naturally lends itself to certain decisions about how to process the input. +In particular, most of the vision layers work by applying a particular operation to some region of the input to produce a corresponding region of the output. +In contrast, other layers (with few exceptions) ignore the spatial structure of the input, effectively treating it as “one big vector” with dimension .

      + +

      Convolution

      + +
        +
      • Layer type: Convolution
        • +
      • CPU implementation: ./src/caffe/layers/convolution_layer.cpp
        • +
      • CUDA GPU implementation: ./src/caffe/layers/convolution_layer.cu
        • +
      • Parameters (ConvolutionParameter convolution_param) +
          +
        • Required +
            +
          • num_output (c_o): the number of filters
            • +
          • kernel_size (or kernel_h and kernel_w): specifies height and width of each filter
            • +
          +
          • +
        • Strongly Recommended +
            +
          • weight_filler [default type: 'constant' value: 0]
            • +
          +
          • +
        • Optional +
            +
          • bias_term [default true]: specifies whether to learn and apply a set of additive biases to the filter outputs
            • +
          • pad (or pad_h and pad_w) [default 0]: specifies the number of pixels to (implicitly) add to each side of the input
            • +
          • stride (or stride_h and stride_w) [default 1]: specifies the intervals at which to apply the filters to the input
            • +
          • group (g) [default 1]: If g > 1, we restrict the connectivity of each filter to a subset of the input. Specifically, the input and output channels are separated into g groups, and the th output group channels will be only connected to the th input group channels.
            • +
          +
          • +
        +
        • +
      • Input +
          +
        • n * c_i * h_i * w_i
          • +
        +
        • +
      • Output +
          +
        • n * c_o * h_o * w_o, where h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1 and w_o likewise.
          • +
        +
        • +
      • +

        Sample (as seen in ./models/bvlc_reference_caffenet/train_val.prototxt)

        + + 
        layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  # learning rate and decay multipliers for the filters
+  param { lr_mult: 1 decay_mult: 1 }
+  # learning rate and decay multipliers for the biases
+  param { lr_mult: 2 decay_mult: 0 }
+  convolution_param {
+    num_output: 96     # learn 96 filters
+    kernel_size: 11    # each filter is 11x11
+    stride: 4          # step 4 pixels between each filter application
+    weight_filler {
+      type: "gaussian" # initialize the filters from a Gaussian
+      std: 0.01        # distribution with stdev 0.01 (default mean: 0)
+    }
+    bias_filler {
+      type: "constant" # initialize the biases to zero (0)
+      value: 0
+    }
+  }
+}
+
        +
        +
        • +
      + +

      The Convolution layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.

      + +

      Pooling

      + +
        +
      • Layer type: Pooling
        • +
      • CPU implementation: ./src/caffe/layers/pooling_layer.cpp
        • +
      • CUDA GPU implementation: ./src/caffe/layers/pooling_layer.cu
        • +
      • Parameters (PoolingParameter pooling_param) +
          +
        • Required +
            +
          • kernel_size (or kernel_h and kernel_w): specifies height and width of each filter
            • +
          +
          • +
        • Optional +
            +
          • pool [default MAX]: the pooling method. Currently MAX, AVE, or STOCHASTIC
            • +
          • pad (or pad_h and pad_w) [default 0]: specifies the number of pixels to (implicitly) add to each side of the input
            • +
          • stride (or stride_h and stride_w) [default 1]: specifies the intervals at which to apply the filters to the input
            • +
          +
          • +
        +
        • +
      • Input +
          +
        • n * c * h_i * w_i
          • +
        +
        • +
      • Output +
          +
        • n * c * h_o * w_o, where h_o and w_o are computed in the same way as convolution.
          • +
        +
        • +
      • +

        Sample (as seen in ./models/bvlc_reference_caffenet/train_val.prototxt)

        + + 
        layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3 # pool over a 3x3 region
+    stride: 2      # step two pixels (in the bottom blob) between pooling regions
+  }
+}
+
        +
        +
        • +
      + +

      Local Response Normalization (LRN)

      + +
        +
      • Layer type: LRN
        • +
      • CPU Implementation: ./src/caffe/layers/lrn_layer.cpp
        • +
      • CUDA GPU Implementation: ./src/caffe/layers/lrn_layer.cu
        • +
      • Parameters (LRNParameter lrn_param) +
          +
        • Optional +
            +
          • local_size [default 5]: the number of channels to sum over (for cross channel LRN) or the side length of the square region to sum over (for within channel LRN)
            • +
          • alpha [default 1]: the scaling parameter (see below)
            • +
          • beta [default 5]: the exponent (see below)
            • +
          • norm_region [default ACROSS_CHANNELS]: whether to sum over adjacent channels (ACROSS_CHANNELS) or nearby spatial locaitons (WITHIN_CHANNEL)
            • +
          +
          • +
        +
        • +
      + +

      The local response normalization layer performs a kind of “lateral inhibition” by normalizing over local input regions. In ACROSS_CHANNELS mode, the local regions extend across nearby channels, but have no spatial extent (i.e., they have shape local_size x 1 x 1). In WITHIN_CHANNEL mode, the local regions extend spatially, but are in separate channels (i.e., they have shape 1 x local_size x local_size). Each input value is divided by , where is the size of each local region, and the sum is taken over the region centered at that value (zero padding is added where necessary).

      + +

      im2col

      + +

      Im2col is a helper for doing the image-to-column transformation that you most likely do not need to know about. This is used in Caffe’s original convolution to do matrix multiplication by laying out all patches into a matrix.

      + +

      Loss Layers

      + +

      Loss drives learning by comparing an output to a target and assigning cost to minimize. The loss itself is computed by the forward pass and the gradient w.r.t. to the loss is computed by the backward pass.

      + +

      Softmax

      + +
        +
      • Layer type: SoftmaxWithLoss
        • +
      + +

      The softmax loss layer computes the multinomial logistic loss of the softmax of its inputs. It’s conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.

      + +

      Sum-of-Squares / Euclidean

      + +
        +
      • Layer type: EuclideanLoss
        • +
      + +

      The Euclidean loss layer computes the sum of squares of differences of its two inputs, .

      + +

      Hinge / Margin

      + +
        +
      • Layer type: HingeLoss
        • +
      • CPU implementation: ./src/caffe/layers/hinge_loss_layer.cpp
        • +
      • CUDA GPU implementation: none yet
        • +
      • Parameters (HingeLossParameter hinge_loss_param) +
          +
        • Optional +
            +
          • norm [default L1]: the norm used. Currently L1, L2
            • +
          +
          • +
        +
        • +
      • Inputs +
          +
        • n * c * h * w Predictions
          • +
        • n * 1 * 1 * 1 Labels
          • +
        +
        • +
      • Output +
          +
        • 1 * 1 * 1 * 1 Computed Loss
          • +
        +
        • +
      • +

        Samples

        + + 
        # L1 Norm
+layer {
+  name: "loss"
+  type: "HingeLoss"
+  bottom: "pred"
+  bottom: "label"
+}
+
+# L2 Norm
+layer {
+  name: "loss"
+  type: "HingeLoss"
+  bottom: "pred"
+  bottom: "label"
+  top: "loss"
+  hinge_loss_param {
+    norm: L2
+  }
+}
+
        +
        +
        • +
      + +

      The hinge loss layer computes a one-vs-all hinge or squared hinge loss.

      + +

      Sigmoid Cross-Entropy

      + +

      SigmoidCrossEntropyLoss

      + +

      Infogain

      + +

      InfogainLoss

      + +

      Accuracy and Top-k

      + +

      Accuracy scores the output as the accuracy of output with respect to target – it is not actually a loss and has no backward step.

      + +

      Activation / Neuron Layers

      + +

      In general, activation / Neuron layers are element-wise operators, taking one bottom blob and producing one top blob of the same size. In the layers below, we will ignore the input and out sizes as they are identical:

      + +
        +
      • Input +
          +
        • n * c * h * w
          • +
        +
        • +
      • Output +
          +
        • n * c * h * w
          • +
        +
        • +
      + +

      ReLU / Rectified-Linear and Leaky-ReLU

      + +
        +
      • Layer type: ReLU
        • +
      • CPU implementation: ./src/caffe/layers/relu_layer.cpp
        • +
      • CUDA GPU implementation: ./src/caffe/layers/relu_layer.cu
        • +
      • Parameters (ReLUParameter relu_param) +
          +
        • Optional +
            +
          • negative_slope [default 0]: specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.
            • +
          +
          • +
        +
        • +
      • +

        Sample (as seen in ./models/bvlc_reference_caffenet/train_val.prototxt)

        + + 
        layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+
        +
        +
        • +
      + +

      Given an input value x, The ReLU layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.

      + +

      Sigmoid

      + +
        +
      • Layer type: Sigmoid
        • +
      • CPU implementation: ./src/caffe/layers/sigmoid_layer.cpp
        • +
      • CUDA GPU implementation: ./src/caffe/layers/sigmoid_layer.cu
        • +
      • +

        Sample (as seen in ./examples/mnist/mnist_autoencoder.prototxt)

        + + 
        layer {
+  name: "encode1neuron"
+  bottom: "encode1"
+  top: "encode1neuron"
+  type: "Sigmoid"
+}
+
        +
        +
        • +
      + +

      The Sigmoid layer computes the output as sigmoid(x) for each input element x.

      + +

      TanH / Hyperbolic Tangent

      + +
        +
      • Layer type: TanH
        • +
      • CPU implementation: ./src/caffe/layers/tanh_layer.cpp
        • +
      • CUDA GPU implementation: ./src/caffe/layers/tanh_layer.cu
        • +
      • +

        Sample

        + + 
        layer {
+  name: "layer"
+  bottom: "in"
+  top: "out"
+  type: "TanH"
+}
+
        +
        +
        • +
      + +

      The TanH layer computes the output as tanh(x) for each input element x.

      + +

      Absolute Value

      + +
        +
      • Layer type: AbsVal
        • +
      • CPU implementation: ./src/caffe/layers/absval_layer.cpp
        • +
      • CUDA GPU implementation: ./src/caffe/layers/absval_layer.cu
        • +
      • +

        Sample

        + + 
        layer {
+  name: "layer"
+  bottom: "in"
+  top: "out"
+  type: "AbsVal"
+}
+
        +
        +
        • +
      + +

      The AbsVal layer computes the output as abs(x) for each input element x.

      + +

      Power

      + +
        +
      • Layer type: Power
        • +
      • CPU implementation: ./src/caffe/layers/power_layer.cpp
        • +
      • CUDA GPU implementation: ./src/caffe/layers/power_layer.cu
        • +
      • Parameters (PowerParameter power_param) +
          +
        • Optional +
            +
          • power [default 1]
            • +
          • scale [default 1]
            • +
          • shift [default 0]
            • +
          +
          • +
        +
        • +
      • +

        Sample

        + + 
        layer {
+  name: "layer"
+  bottom: "in"
+  top: "out"
+  type: "Power"
+  power_param {
+    power: 1
+    scale: 1
+    shift: 0
+  }
+}
+
        +
        +
        • +
      + +

      The Power layer computes the output as (shift + scale * x) ^ power for each input element x.

      + +

      BNLL

      + +
        +
      • Layer type: BNLL
        • +
      • CPU implementation: ./src/caffe/layers/bnll_layer.cpp
        • +
      • CUDA GPU implementation: ./src/caffe/layers/bnll_layer.cu
        • +
      • +

        Sample

        + + 
        layer {
+  name: "layer"
+  bottom: "in"
+  top: "out"
+  type: BNLL
+}
+
        +
        +
        • +
      + +

      The BNLL (binomial normal log likelihood) layer computes the output as log(1 + exp(x)) for each input element x.

      + +

      Data Layers

      + +

      Data enters Caffe through data layers: they lie at the bottom of nets. Data can come from efficient databases (LevelDB or LMDB), directly from memory, or, when efficiency is not critical, from files on disk in HDF5 or common image formats.

      + +

      Common input preprocessing (mean subtraction, scaling, random cropping, and mirroring) is available by specifying TransformationParameters.

      + +

      Database

      + +
        +
      • Layer type: Data
        • +
      • Parameters +
          +
        • Required +
            +
          • source: the name of the directory containing the database
            • +
          • batch_size: the number of inputs to process at one time
            • +
          +
          • +
        • Optional +
            +
          • rand_skip: skip up to this number of inputs at the beginning; useful for asynchronous sgd
            • +
          • backend [default LEVELDB]: choose whether to use a LEVELDB or LMDB
            • +
          +
          • +
        +
        • +
      + +

      In-Memory

      + +
        +
      • Layer type: MemoryData
        • +
      • Parameters +
          +
        • Required +
            +
          • batch_size, channels, height, width: specify the size of input chunks to read from memory
            • +
          +
          • +
        +
        • +
      + +

      The memory data layer reads data directly from memory, without copying it. In order to use it, one must call MemoryDataLayer::Reset (from C++) or Net.set_input_arrays (from Python) in order to specify a source of contiguous data (as 4D row major array), which is read one batch-sized chunk at a time.

      + +

      HDF5 Input

      + +
        +
      • Layer type: HDF5Data
        • +
      • Parameters +
          +
        • Required +
            +
          • source: the name of the file to read from
            • +
          • batch_size
            • +
          +
          • +
        +
        • +
      + +

      HDF5 Output

      + +
        +
      • Layer type: HDF5Output
        • +
      • Parameters +
          +
        • Required +
            +
          • file_name: name of file to write to
            • +
          +
          • +
        +
        • +
      + +

      The HDF5 output layer performs the opposite function of the other layers in this section: it writes its input blobs to disk.

      + +

      Images

      + +
        +
      • Layer type: ImageData
        • +
      • Parameters +
          +
        • Required +
            +
          • source: name of a text file, with each line giving an image filename and label
            • +
          • batch_size: number of images to batch together
            • +
          +
          • +
        • Optional +
            +
          • rand_skip
            • +
          • shuffle [default false]
            • +
          • new_height, new_width: if provided, resize all images to this size
            • +
          +
          • +
        +
        • +
      + +

      Windows

      + +

      WindowData

      + +

      Dummy

      + +

      DummyData is for development and debugging. See DummyDataParameter.

      + +

      Common Layers

      + +

      Inner Product

      + +
        +
      • Layer type: InnerProduct
        • +
      • CPU implementation: ./src/caffe/layers/inner_product_layer.cpp
        • +
      • CUDA GPU implementation: ./src/caffe/layers/inner_product_layer.cu
        • +
      • Parameters (InnerProductParameter inner_product_param) +
          +
        • Required +
            +
          • num_output (c_o): the number of filters
            • +
          +
          • +
        • Strongly recommended +
            +
          • weight_filler [default type: 'constant' value: 0]
            • +
          +
          • +
        • Optional +
            +
          • bias_filler [default type: 'constant' value: 0]
            • +
          • bias_term [default true]: specifies whether to learn and apply a set of additive biases to the filter outputs
            • +
          +
          • +
        +
        • +
      • Input +
          +
        • n * c_i * h_i * w_i
          • +
        +
        • +
      • Output +
          +
        • n * c_o * 1 * 1
          • +
        +
        • +
      • +

        Sample

        + + 
        layer {
+  name: "fc8"
+  type: "InnerProduct"
+  # learning rate and decay multipliers for the weights
+  param { lr_mult: 1 decay_mult: 1 }
+  # learning rate and decay multipliers for the biases
+  param { lr_mult: 2 decay_mult: 0 }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  bottom: "fc7"
+  top: "fc8"
+}
+
        +
        +
        • +
      + +

      The InnerProduct layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob’s height and width set to 1).

      + +

      Splitting

      + +

      The Split layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers.

      + +

      Flattening

      + +

      The Flatten layer is a utility layer that flattens an input of shape n * c * h * w to a simple vector output of shape n * (c*h*w)

      + +

      Reshape

      + +
        +
      • Layer type: Reshape
        • +
      • Implementation: ./src/caffe/layers/reshape_layer.cpp
        • +
      • Parameters (ReshapeParameter reshape_param) +
          +
        • Optional: (also see detailed description below) +
            +
          • shape
            • +
          +
          • +
        +
        • +
      • Input +
          +
        • a single blob with arbitrary dimensions
          • +
        +
        • +
      • Output +
          +
        • the same blob, with modified dimensions, as specified by reshape_param
          • +
        +
        • +
      • +

        Sample

        + + 
          layer {
+    name: "reshape"
+    type: "Reshape"
+    bottom: "input"
+    top: "output"
+    reshape_param {
+      shape {
+        dim: 0  # copy the dimension from below
+        dim: 2
+        dim: 3
+        dim: -1 # infer it from the other dimensions
+      }
+    }
+  }
+
        +
        +
        • +
      + +

      The Reshape layer can be used to change the dimensions of its input, without changing its data. Just like the Flatten layer, only the dimensions are changed; no data is copied in the process.

      + +

      Output dimensions are specified by the ReshapeParam proto. Positive numbers are used directly, setting the corresponding dimension of the output blob. In addition, two special values are accepted for any of the target dimension values:

      + +
        +
      • 0 means “copy the respective dimension of the bottom layer”. That is, if the bottom has 2 as its 1st dimension, the top will have 2 as its 1st dimension as well, given dim: 0 as the 1st target dimension.
        • +
      • -1 stands for “infer this from the other dimensions”. This behavior is similar to that of -1 in numpy’s or [] for MATLAB’s reshape: this dimension is calculated to keep the overall element count the same as in the bottom layer. At most one -1 can be used in a reshape operation.
        • +
      + +

      As another example, specifying reshape_param { shape { dim: 0 dim: -1 } } makes the layer behave in exactly the same way as the Flatten layer.

      + +

      Concatenation

      + +
        +
      • Layer type: Concat
        • +
      • CPU implementation: ./src/caffe/layers/concat_layer.cpp
        • +
      • CUDA GPU implementation: ./src/caffe/layers/concat_layer.cu
        • +
      • Parameters (ConcatParameter concat_param) +
          +
        • Optional +
            +
          • axis [default 1]: 0 for concatenation along num and 1 for channels.
            • +
          +
          • +
        +
        • +
      • Input +
          +
        • n_i * c_i * h * w for each input blob i from 1 to K.
          • +
        +
        • +
      • Output +
          +
        • if axis = 0: (n_1 + n_2 + ... + n_K) * c_1 * h * w, and all input c_i should be the same.
          • +
        • if axis = 1: n_1 * (c_1 + c_2 + ... + c_K) * h * w, and all input n_i should be the same.
          • +
        +
        • +
      • +

        Sample

        + + 
        layer {
+  name: "concat"
+  bottom: "in1"
+  bottom: "in2"
+  top: "out"
+  type: "Concat"
+  concat_param {
+    axis: 1
+  }
+}
+
        +
        +
        • +
      + +

      The Concat layer is a utility layer that concatenates its multiple input blobs to one single output blob.

      + +

      Slicing

      + +

      The Slice layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.

      + +
        +
      • +

        Sample

        + + 
        layer {
+  name: "slicer_label"
+  type: "Slice"
+  bottom: "label"
+  ## Example of label with a shape N x 3 x 1 x 1
+  top: "label1"
+  top: "label2"
+  top: "label3"
+  slice_param {
+    axis: 1
+    slice_point: 1
+    slice_point: 2
+  }
+}
+
        +
        +
        • +
      + +

      axis indicates the target axis; slice_point indicates indexes in the selected dimension (the number of indices must be equal to the number of top blobs minus one).

      + +

      Elementwise Operations

      + +

      Eltwise

      + +

      Argmax

      + +

      ArgMax

      + +

      Softmax

      + +

      Softmax

      + +

      Mean-Variance Normalization

      + +

      MVN

      + + +
      +
      + + diff --git a/tutorial/loss.html b/tutorial/loss.html new file mode 100644 index 00000000000..674171c6d9c --- /dev/null +++ b/tutorial/loss.html @@ -0,0 +1,116 @@ + + + + + + + + + Caffe | Loss + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Loss

      + +

      In Caffe, as in most of machine learning, learning is driven by a loss function (also known as an error, cost, or objective function). +A loss function specifies the goal of learning by mapping parameter settings (i.e., the current network weights) to a scalar value specifying the “badness” of these parameter settings. +Hence, the goal of learning is to find a setting of the weights that minimizes the loss function.

      + +

      The loss in Caffe is computed by the Forward pass of the network. +Each layer takes a set of input (bottom) blobs and produces a set of output (top) blobs. +Some of these layers’ outputs may be used in the loss function. +A typical choice of loss function for one-versus-all classification tasks is the SoftmaxWithLoss function, used in a network definition as follows, for example:

      + +
      layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "pred"
+  bottom: "label"
+  top: "loss"
+}
+
      +
      + +

      In a SoftmaxWithLoss function, the top blob is a scalar (empty shape) which averages the loss (computed from predicted labels pred and actuals labels label) over the entire mini-batch.

      + +

      Loss weights

      + +

      For nets with multiple layers producing a loss (e.g., a network that both classifies the input using a SoftmaxWithLoss layer and reconstructs it using a EuclideanLoss layer), loss weights can be used to specify their relative importance.

      + +

      By convention, Caffe layer types with the suffix Loss contribute to the loss function, but other layers are assumed to be purely used for intermediate computations. +However, any layer can be used as a loss by adding a field loss_weight: <float> to a layer definition for each top blob produced by the layer. +Layers with the suffix Loss have an implicit loss_weight: 1 for the first top blob (and loss_weight: 0 for any additional tops); other layers have an implicit loss_weight: 0 for all tops. +So, the above SoftmaxWithLoss layer could be equivalently written as:

      + +
      layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "pred"
+  bottom: "label"
+  top: "loss"
+  loss_weight: 1
+}
+
      +
      + +

      However, any layer able to backpropagate may be given a non-zero loss_weight, allowing one to, for example, regularize the activations produced by some intermediate layer(s) of the network if desired. +For non-singleton outputs with an associated non-zero loss, the loss is computed simply by summing over all entries of the blob.

      + +

      The final loss in Caffe, then, is computed by summing the total weighted loss over the network, as in the following pseudo-code:

      + +
      loss := 0
+for layer in layers:
+  for top, loss_weight in layer.tops, layer.loss_weights:
+    loss += loss_weight * sum(top)
+
      +
      + + +
      +
      + + diff --git a/tutorial/net_layer_blob.html b/tutorial/net_layer_blob.html new file mode 100644 index 00000000000..0171d45d00e --- /dev/null +++ b/tutorial/net_layer_blob.html @@ -0,0 +1,239 @@ + + + + + + + + + Caffe | Blobs, Layers, and Nets + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Blobs, Layers, and Nets: anatomy of a Caffe model

      + +

      Deep networks are compositional models that are naturally represented as a collection of inter-connected layers that work on chunks of data. Caffe defines a net layer-by-layer in its own model schema. The network defines the entire model bottom-to-top from input data to loss. As data and derivatives flow through the network in the forward and backward passes Caffe stores, communicates, and manipulates the information as blobs: the blob is the standard array and unified memory interface for the framework. The layer comes next as the foundation of both model and computation. The net follows as the collection and connection of layers. The details of blob describe how information is stored and communicated in and across layers and nets.

      + +

      Solving is configured separately to decouple modeling and optimization.

      + +

      We will go over the details of these components in more detail.

      + +

      Blob storage and communication

      + +

      A Blob is a wrapper over the actual data being processed and passed along by Caffe, and also under the hood provides synchronization capability between the CPU and the GPU. Mathematically, a blob is an N-dimensional array stored in a C-contiguous fashion.

      + +

      Caffe stores and communicates data using blobs. Blobs provide a unified memory interface holding data; e.g., batches of images, model parameters, and derivatives for optimization.

      + +

      Blobs conceal the computational and mental overhead of mixed CPU/GPU operation by synchronizing from the CPU host to the GPU device as needed. Memory on the host and device is allocated on demand (lazily) for efficient memory usage.

      + +

      The conventional blob dimensions for batches of image data are number N x channel K x height H x width W. Blob memory is row-major in layout, so the last / rightmost dimension changes fastest. For example, in a 4D blob, the value at index (n, k, h, w) is physically located at index ((n * K + k) * H + h) * W + w.

      + +
        +
      • Number / N is the batch size of the data. Batch processing achieves better throughput for communication and device processing. For an ImageNet training batch of 256 images N = 256.
        • +
      • Channel / K is the feature dimension e.g. for RGB images K = 3.
        • +
      + +

      Note that although many blobs in Caffe examples are 4D with axes for image applications, it is totally valid to use blobs for non-image applications. For example, if you simply need fully-connected networks like the conventional multi-layer perceptron, use 2D blobs (shape (N, D)) and call the InnerProductLayer (which we will cover soon).

      + +

      Parameter blob dimensions vary according to the type and configuration of the layer. For a convolution layer with 96 filters of 11 x 11 spatial dimension and 3 inputs the blob is 96 x 3 x 11 x 11. For an inner product / fully-connected layer with 1000 output channels and 1024 input channels the parameter blob is 1000 x 1024.

      + +

      For custom data it may be necessary to hack your own input preparation tool or data layer. However once your data is in your job is done. The modularity of layers accomplishes the rest of the work for you.

      + +

      Implementation Details

      + +

      As we are often interested in the values as well as the gradients of the blob, a Blob stores two chunks of memories, data and diff. The former is the normal data that we pass along, and the latter is the gradient computed by the network.

      + +

      Further, as the actual values could be stored either on the CPU and on the GPU, there are two different ways to access them: the const way, which does not change the values, and the mutable way, which changes the values:

      + +
      const Dtype* cpu_data() const;
+Dtype* mutable_cpu_data();
+
      +
      + +

      (similarly for gpu and diff).

      + +

      The reason for such design is that, a Blob uses a SyncedMem class to synchronize values between the CPU and GPU in order to hide the synchronization details and to minimize data transfer. A rule of thumb is, always use the const call if you do not want to change the values, and never store the pointers in your own object. Every time you work on a blob, call the functions to get the pointers, as the SyncedMem will need this to figure out when to copy data.

      + +

      In practice when GPUs are present, one loads data from the disk to a blob in CPU code, calls a device kernel to do GPU computation, and ferries the blob off to the next layer, ignoring low-level details while maintaining a high level of performance. As long as all layers have GPU implementations, all the intermediate data and gradients will remain in the GPU.

      + +

      If you want to check out when a Blob will copy data, here is an illustrative example:

      + +
      // Assuming that data are on the CPU initially, and we have a blob.
+const Dtype* foo;
+Dtype* bar;
+foo = blob.gpu_data(); // data copied cpu->gpu.
+foo = blob.cpu_data(); // no data copied since both have up-to-date contents.
+bar = blob.mutable_gpu_data(); // no data copied.
+// ... some operations ...
+bar = blob.mutable_gpu_data(); // no data copied when we are still on GPU.
+foo = blob.cpu_data(); // data copied gpu->cpu, since the gpu side has modified the data
+foo = blob.gpu_data(); // no data copied since both have up-to-date contents
+bar = blob.mutable_cpu_data(); // still no data copied.
+bar = blob.mutable_gpu_data(); // data copied cpu->gpu.
+bar = blob.mutable_cpu_data(); // data copied gpu->cpu.
+
      +
      + +

      Layer computation and connections

      + +

      The layer is the essence of a model and the fundamental unit of computation. Layers convolve filters, pool, take inner products, apply nonlinearities like rectified-linear and sigmoid and other elementwise transformations, normalize, load data, and compute losses like softmax and hinge. See the layer catalogue for all operations. Most of the types needed for state-of-the-art deep learning tasks are there.

      + +

      A layer with bottom and top blob.

      + +

      A layer takes input through bottom connections and makes output through top connections.

      + +

      Each layer type defines three critical computations: setup, forward, and backward.

      + +
        +
      • Setup: initialize the layer and its connections once at model initialization.
        • +
      • Forward: given input from bottom compute the output and send to the top.
        • +
      • Backward: given the gradient w.r.t. the top output compute the gradient w.r.t. to the input and send to the bottom. A layer with parameters computes the gradient w.r.t. to its parameters and stores it internally.
        • +
      + +

      More specifically, there will be two Forward and Backward functions implemented, one for CPU and one for GPU. If you do not implement a GPU version, the layer will fall back to the CPU functions as a backup option. This may come handy if you would like to do quick experiments, although it may come with additional data transfer cost (its inputs will be copied from GPU to CPU, and its outputs will be copied back from CPU to GPU).

      + +

      Layers have two key responsibilities for the operation of the network as a whole: a forward pass that takes the inputs and produces the outputs, and a backward pass that takes the gradient with respect to the output, and computes the gradients with respect to the parameters and to the inputs, which are in turn back-propagated to earlier layers. These passes are simply the composition of each layer’s forward and backward.

      + +

      Developing custom layers requires minimal effort by the compositionality of the network and modularity of the code. Define the setup, forward, and backward for the layer and it is ready for inclusion in a net.

      + +

      Net definition and operation

      + +

      The net jointly defines a function and its gradient by composition and auto-differentiation. The composition of every layer’s output computes the function to do a given task, and the composition of every layer’s backward computes the gradient from the loss to learn the task. Caffe models are end-to-end machine learning engines.

      + +

      The net is a set of layers connected in a computation graph – a directed acyclic graph (DAG) to be exact. Caffe does all the bookkeeping for any DAG of layers to ensure correctness of the forward and backward passes. A typical net begins with a data layer that loads from disk and ends with a loss layer that computes the objective for a task such as classification or reconstruction.

      + +

      The net is defined as a set of layers and their connections in a plaintext modeling language. +A simple logistic regression classifier

      + +

      Softmax Regression

      + +

      is defined by

      + +
      name: "LogReg"
+layer {
+  name: "mnist"
+  type: "Data"
+  top: "data"
+  top: "label"
+  data_param {
+    source: "input_leveldb"
+    batch_size: 64
+  }
+}
+layer {
+  name: "ip"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "ip"
+  inner_product_param {
+    num_output: 2
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip"
+  bottom: "label"
+  top: "loss"
+}
+
      +
      + +

      Model initialization is handled by Net::Init(). The initialization mainly does two things: scaffolding the overall DAG by creating the blobs and layers (for C++ geeks: the network will retain ownership of the blobs and layers during its lifetime), and calls the layers’ SetUp() function. It also does a set of other bookkeeping things, such as validating the correctness of the overall network architecture. Also, during initialization the Net explains its initialization by logging to INFO as it goes:

      + +
      I0902 22:52:17.931977 2079114000 net.cpp:39] Initializing net from parameters:
+name: "LogReg"
+[...model prototxt printout...]
+# construct the network layer-by-layer
+I0902 22:52:17.932152 2079114000 net.cpp:67] Creating Layer mnist
+I0902 22:52:17.932165 2079114000 net.cpp:356] mnist -> data
+I0902 22:52:17.932188 2079114000 net.cpp:356] mnist -> label
+I0902 22:52:17.932200 2079114000 net.cpp:96] Setting up mnist
+I0902 22:52:17.935807 2079114000 data_layer.cpp:135] Opening leveldb input_leveldb
+I0902 22:52:17.937155 2079114000 data_layer.cpp:195] output data size: 64,1,28,28
+I0902 22:52:17.938570 2079114000 net.cpp:103] Top shape: 64 1 28 28 (50176)
+I0902 22:52:17.938593 2079114000 net.cpp:103] Top shape: 64 (64)
+I0902 22:52:17.938611 2079114000 net.cpp:67] Creating Layer ip
+I0902 22:52:17.938617 2079114000 net.cpp:394] ip <- data
+I0902 22:52:17.939177 2079114000 net.cpp:356] ip -> ip
+I0902 22:52:17.939196 2079114000 net.cpp:96] Setting up ip
+I0902 22:52:17.940289 2079114000 net.cpp:103] Top shape: 64 2 (128)
+I0902 22:52:17.941270 2079114000 net.cpp:67] Creating Layer loss
+I0902 22:52:17.941305 2079114000 net.cpp:394] loss <- ip
+I0902 22:52:17.941314 2079114000 net.cpp:394] loss <- label
+I0902 22:52:17.941323 2079114000 net.cpp:356] loss -> loss
+# set up the loss and configure the backward pass
+I0902 22:52:17.941328 2079114000 net.cpp:96] Setting up loss
+I0902 22:52:17.941328 2079114000 net.cpp:103] Top shape: (1)
+I0902 22:52:17.941329 2079114000 net.cpp:109]     with loss weight 1
+I0902 22:52:17.941779 2079114000 net.cpp:170] loss needs backward computation.
+I0902 22:52:17.941787 2079114000 net.cpp:170] ip needs backward computation.
+I0902 22:52:17.941794 2079114000 net.cpp:172] mnist does not need backward computation.
+# determine outputs
+I0902 22:52:17.941800 2079114000 net.cpp:208] This network produces output loss
+# finish initialization and report memory usage
+I0902 22:52:17.941810 2079114000 net.cpp:467] Collecting Learning Rate and Weight Decay.
+I0902 22:52:17.941818 2079114000 net.cpp:219] Network initialization done.
+I0902 22:52:17.941824 2079114000 net.cpp:220] Memory required for data: 201476
+
      +
      + +

      Note that the construction of the network is device agnostic - recall our earlier explanation that blobs and layers hide implementation details from the model definition. After construction, the network is run on either CPU or GPU by setting a single switch defined in Caffe::mode() and set by Caffe::set_mode(). Layers come with corresponding CPU and GPU routines that produce identical results (up to numerical errors, and with tests to guard it). The CPU / GPU switch is seamless and independent of the model definition. For research and deployment alike it is best to divide model and implementation.

      + +

      Model format

      + +

      The models are defined in plaintext protocol buffer schema (prototxt) while the learned models are serialized as binary protocol buffer (binaryproto) .caffemodel files.

      + +

      The model format is defined by the protobuf schema in caffe.proto. The source file is mostly self-explanatory so one is encouraged to check it out.

      + +

      Caffe speaks Google Protocol Buffer for the following strengths: minimal-size binary strings when serialized, efficient serialization, a human-readable text format compatible with the binary version, and efficient interface implementations in multiple languages, most notably C++ and Python. This all contributes to the flexibility and extensibility of modeling in Caffe.

      + + +
      +
      + + diff --git a/tutorial/solver.html b/tutorial/solver.html new file mode 100644 index 00000000000..3aef87463d8 --- /dev/null +++ b/tutorial/solver.html @@ -0,0 +1,399 @@ + + + + + + + + + Caffe | Solver / Model Optimization + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Solver

      + +

      The solver orchestrates model optimization by coordinating the network’s forward inference and backward gradients to form parameter updates that attempt to improve the loss. +The responsibilities of learning are divided between the Solver for overseeing the optimization and generating parameter updates and the Net for yielding loss and gradients.

      + +

      The Caffe solvers are:

      + +
        +
      • Stochastic Gradient Descent (type: "SGD"),
        • +
      • AdaDelta (type: "AdaDelta"),
        • +
      • Adaptive Gradient (type: "AdaGrad"),
        • +
      • Adam (type: "Adam"),
        • +
      • Nesterov’s Accelerated Gradient (type: "Nesterov") and
        • +
      • RMSprop (type: "RMSProp")
        • +
      + +

      The solver

      + +
        +
      1. scaffolds the optimization bookkeeping and creates the training network for learning and test network(s) for evaluation.
        2. +
      2. iteratively optimizes by calling forward / backward and updating parameters
        3. +
      3. (periodically) evaluates the test networks
        4. +
      4. snapshots the model and solver state throughout the optimization
        5. +
      + +

      where each iteration

      + +
        +
      1. calls network forward to compute the output and loss
        2. +
      2. calls network backward to compute the gradients
        3. +
      3. incorporates the gradients into parameter updates according to the solver method
        4. +
      4. updates the solver state according to learning rate, history, and method
        5. +
      + +

      to take the weights all the way from initialization to learned model.

      + +

      Like Caffe models, Caffe solvers run in CPU / GPU modes.

      + +

      Methods

      + +

      The solver methods address the general optimization problem of loss minimization. +For dataset , the optimization objective is the average loss over all data instances throughout the dataset

      + + + +

      where is the loss on data instance and is a regularization term with weight . + can be very large, so in practice, in each solver iteration we use a stochastic approximation of this objective, drawing a mini-batch of instances:

      + + + +

      The model computes in the forward pass and the gradient in the backward pass.

      + +

      The parameter update is formed by the solver from the error gradient , the regularization gradient , and other particulars to each method.

      + +

      SGD

      + +

      Stochastic gradient descent (type: "SGD") updates the weights by a linear combination of the negative gradient and the previous weight update . +The learning rate is the weight of the negative gradient. +The momentum is the weight of the previous update.

      + +

      Formally, we have the following formulas to compute the update value and the updated weights at iteration , given the previous weight update and current weights :

      + + + + + +

      The learning “hyperparameters” ( and ) might require a bit of tuning for best results. +If you’re not sure where to start, take a look at the “Rules of thumb” below, and for further information you might refer to Leon Bottou’s Stochastic Gradient Descent Tricks [1].

      + +

      [1] L. Bottou. + Stochastic Gradient Descent Tricks. + Neural Networks: Tricks of the Trade: Springer, 2012.

      + +

      Rules of thumb for setting the learning rate and momentum

      + +

      A good strategy for deep learning with SGD is to initialize the learning rate to a value around , and dropping it by a constant factor (e.g., 10) throughout training when the loss begins to reach an apparent “plateau”, repeating this several times. +Generally, you probably want to use a momentum or similar value. +By smoothing the weight updates across iterations, momentum tends to make deep learning with SGD both stabler and faster.

      + +

      This was the strategy used by Krizhevsky et al. [1] in their famously winning CNN entry to the ILSVRC-2012 competition, and Caffe makes this strategy easy to implement in a SolverParameter, as in our reproduction of [1] at ./examples/imagenet/alexnet_solver.prototxt.

      + +

      To use a learning rate policy like this, you can put the following lines somewhere in your solver prototxt file:

      + +
      base_lr: 0.01     # begin training at a learning rate of 0.01 = 1e-2
+
+lr_policy: "step" # learning rate policy: drop the learning rate in "steps"
+                  # by a factor of gamma every stepsize iterations
+
+gamma: 0.1        # drop the learning rate by a factor of 10
+                  # (i.e., multiply it by a factor of gamma = 0.1)
+
+stepsize: 100000  # drop the learning rate every 100K iterations
+
+max_iter: 350000  # train for 350K iterations total
+
+momentum: 0.9
+
      +
      + +

      Under the above settings, we’ll always use momentum . +We’ll begin training at a base_lr of for the first 100,000 iterations, then multiply the learning rate by gamma () and train at for iterations 100K-200K, then at for iterations 200K-300K, and finally train until iteration 350K (since we have max_iter: 350000) at .

      + +

      Note that the momentum setting effectively multiplies the size of your updates by a factor of after many iterations of training, so if you increase , it may be a good idea to decrease accordingly (and vice versa).

      + +

      For example, with , we have an effective update size multiplier of . +If we increased the momentum to , we’ve increased our update size multiplier to 100, so we should drop (base_lr) by a factor of 10.

      + +

      Note also that the above settings are merely guidelines, and they’re definitely not guaranteed to be optimal (or even work at all!) in every situation. +If learning diverges (e.g., you start to see very large or NaN or inf loss values or outputs), try dropping the base_lr (e.g., base_lr: 0.001) and re-training, repeating this until you find a base_lr value that works.

      + +

      [1] A. Krizhevsky, I. Sutskever, and G. Hinton. + ImageNet Classification with Deep Convolutional Neural Networks. + Advances in Neural Information Processing Systems, 2012.

      + +

      AdaDelta

      + +

      The AdaDelta (type: "AdaDelta") method (M. Zeiler [1]) is a “robust learning rate method”. It is a gradient-based optimization method (like SGD). The update formulas are

      + + + +

      and

      + + + +

      [1] M. Zeiler + ADADELTA: AN ADAPTIVE LEARNING RATE METHOD. + arXiv preprint, 2012.

      + +

      AdaGrad

      + +

      The adaptive gradient (type: "AdaGrad") method (Duchi et al. [1]) is a gradient-based optimization method (like SGD) that attempts to “find needles in haystacks in the form of very predictive but rarely seen features,” in Duchi et al.’s words. +Given the update information from all previous iterations for , +the update formulas proposed by [1] are as follows, specified for each component of the weights :

      + + + +

      Note that in practice, for weights , AdaGrad implementations (including the one in Caffe) use only extra storage for the historical gradient information (rather than the storage that would be necessary to store each historical gradient individually).

      + +

      [1] J. Duchi, E. Hazan, and Y. Singer. + Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. + The Journal of Machine Learning Research, 2011.

      + +

      Adam

      + +

      The Adam (type: "Adam"), proposed in Kingma et al. [1], is a gradient-based optimization method (like SGD). This includes an “adaptive moment estimation” () and can be regarded as a generalization of AdaGrad. The update formulas are

      + + + +

      and

      + + + +

      Kingma et al. [1] proposed to use as default values. Caffe uses the values of momemtum, momentum2, delta for , respectively.

      + +

      [1] D. Kingma, J. Ba. + Adam: A Method for Stochastic Optimization. + International Conference for Learning Representations, 2015.

      + +

      NAG

      + +

      Nesterov’s accelerated gradient (type: "Nesterov") was proposed by Nesterov [1] as an “optimal” method of convex optimization, achieving a convergence rate of rather than the . +Though the required assumptions to achieve the convergence typically will not hold for deep networks trained with Caffe (e.g., due to non-smoothness and non-convexity), in practice NAG can be a very effective method for optimizing certain types of deep learning architectures, as demonstrated for deep MNIST autoencoders by Sutskever et al. [2].

      + +

      The weight update formulas look very similar to the SGD updates given above:

      + + + + + +

      What distinguishes the method from SGD is the weight setting on which we compute the error gradient – in NAG we take the gradient on weights with added momentum ; in SGD we simply take the gradient on the current weights themselves.

      + +

      [1] Y. Nesterov. + A Method of Solving a Convex Programming Problem with Convergence Rate . + Soviet Mathematics Doklady, 1983.

      + +

      [2] I. Sutskever, J. Martens, G. Dahl, and G. Hinton. + On the Importance of Initialization and Momentum in Deep Learning. + Proceedings of the 30th International Conference on Machine Learning, 2013.

      + +

      RMSprop

      + +

      The RMSprop (type: "RMSProp"), suggested by Tieleman in a Coursera course lecture, is a gradient-based optimization method (like SGD). The update formulas are

      + + + +

      The default value of (rms_decay) is set to .

      + +

      [1] T. Tieleman, and G. Hinton. + RMSProp: Divide the gradient by a running average of its recent magnitude. + COURSERA: Neural Networks for Machine Learning.Technical report, 2012.

      + +

      Scaffolding

      + +

      The solver scaffolding prepares the optimization method and initializes the model to be learned in Solver::Presolve().

      + +
      > caffe train -solver examples/mnist/lenet_solver.prototxt
+I0902 13:35:56.474978 16020 caffe.cpp:90] Starting Optimization
+I0902 13:35:56.475190 16020 solver.cpp:32] Initializing solver from parameters:
+test_iter: 100
+test_interval: 500
+base_lr: 0.01
+display: 100
+max_iter: 10000
+lr_policy: "inv"
+gamma: 0.0001
+power: 0.75
+momentum: 0.9
+weight_decay: 0.0005
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet"
+solver_mode: GPU
+net: "examples/mnist/lenet_train_test.prototxt"
+
      +
      + +

      Net initialization

      + +
      I0902 13:35:56.655681 16020 solver.cpp:72] Creating training net from net file: examples/mnist/lenet_train_test.prototxt
+[...]
+I0902 13:35:56.656740 16020 net.cpp:56] Memory required for data: 0
+I0902 13:35:56.656791 16020 net.cpp:67] Creating Layer mnist
+I0902 13:35:56.656811 16020 net.cpp:356] mnist -> data
+I0902 13:35:56.656846 16020 net.cpp:356] mnist -> label
+I0902 13:35:56.656874 16020 net.cpp:96] Setting up mnist
+I0902 13:35:56.694052 16020 data_layer.cpp:135] Opening lmdb examples/mnist/mnist_train_lmdb
+I0902 13:35:56.701062 16020 data_layer.cpp:195] output data size: 64,1,28,28
+I0902 13:35:56.701146 16020 data_layer.cpp:236] Initializing prefetch
+I0902 13:35:56.701196 16020 data_layer.cpp:238] Prefetch initialized.
+I0902 13:35:56.701212 16020 net.cpp:103] Top shape: 64 1 28 28 (50176)
+I0902 13:35:56.701230 16020 net.cpp:103] Top shape: 64 1 1 1 (64)
+[...]
+I0902 13:35:56.703737 16020 net.cpp:67] Creating Layer ip1
+I0902 13:35:56.703753 16020 net.cpp:394] ip1 <- pool2
+I0902 13:35:56.703778 16020 net.cpp:356] ip1 -> ip1
+I0902 13:35:56.703797 16020 net.cpp:96] Setting up ip1
+I0902 13:35:56.728127 16020 net.cpp:103] Top shape: 64 500 1 1 (32000)
+I0902 13:35:56.728142 16020 net.cpp:113] Memory required for data: 5039360
+I0902 13:35:56.728175 16020 net.cpp:67] Creating Layer relu1
+I0902 13:35:56.728194 16020 net.cpp:394] relu1 <- ip1
+I0902 13:35:56.728219 16020 net.cpp:345] relu1 -> ip1 (in-place)
+I0902 13:35:56.728240 16020 net.cpp:96] Setting up relu1
+I0902 13:35:56.728256 16020 net.cpp:103] Top shape: 64 500 1 1 (32000)
+I0902 13:35:56.728270 16020 net.cpp:113] Memory required for data: 5167360
+I0902 13:35:56.728287 16020 net.cpp:67] Creating Layer ip2
+I0902 13:35:56.728304 16020 net.cpp:394] ip2 <- ip1
+I0902 13:35:56.728333 16020 net.cpp:356] ip2 -> ip2
+I0902 13:35:56.728356 16020 net.cpp:96] Setting up ip2
+I0902 13:35:56.728690 16020 net.cpp:103] Top shape: 64 10 1 1 (640)
+I0902 13:35:56.728705 16020 net.cpp:113] Memory required for data: 5169920
+I0902 13:35:56.728734 16020 net.cpp:67] Creating Layer loss
+I0902 13:35:56.728747 16020 net.cpp:394] loss <- ip2
+I0902 13:35:56.728767 16020 net.cpp:394] loss <- label
+I0902 13:35:56.728786 16020 net.cpp:356] loss -> loss
+I0902 13:35:56.728811 16020 net.cpp:96] Setting up loss
+I0902 13:35:56.728837 16020 net.cpp:103] Top shape: 1 1 1 1 (1)
+I0902 13:35:56.728849 16020 net.cpp:109]     with loss weight 1
+I0902 13:35:56.728878 16020 net.cpp:113] Memory required for data: 5169924
+
      +
      + +

      Loss

      + +
      I0902 13:35:56.728893 16020 net.cpp:170] loss needs backward computation.
+I0902 13:35:56.728909 16020 net.cpp:170] ip2 needs backward computation.
+I0902 13:35:56.728924 16020 net.cpp:170] relu1 needs backward computation.
+I0902 13:35:56.728938 16020 net.cpp:170] ip1 needs backward computation.
+I0902 13:35:56.728953 16020 net.cpp:170] pool2 needs backward computation.
+I0902 13:35:56.728970 16020 net.cpp:170] conv2 needs backward computation.
+I0902 13:35:56.728984 16020 net.cpp:170] pool1 needs backward computation.
+I0902 13:35:56.728998 16020 net.cpp:170] conv1 needs backward computation.
+I0902 13:35:56.729014 16020 net.cpp:172] mnist does not need backward computation.
+I0902 13:35:56.729027 16020 net.cpp:208] This network produces output loss
+I0902 13:35:56.729053 16020 net.cpp:467] Collecting Learning Rate and Weight Decay.
+I0902 13:35:56.729071 16020 net.cpp:219] Network initialization done.
+I0902 13:35:56.729085 16020 net.cpp:220] Memory required for data: 5169924
+I0902 13:35:56.729277 16020 solver.cpp:156] Creating test net (#0) specified by net file: examples/mnist/lenet_train_test.prototxt
+
      +
      + +

      Completion

      + +
      I0902 13:35:56.806970 16020 solver.cpp:46] Solver scaffolding done.
+I0902 13:35:56.806984 16020 solver.cpp:165] Solving LeNet
+
      +
      + +

      Updating Parameters

      + +

      The actual weight update is made by the solver then applied to the net parameters in Solver::ComputeUpdateValue(). +The ComputeUpdateValue method incorporates any weight decay into the weight gradients (which currently just contain the error gradients) to get the final gradient with respect to each network weight. +Then these gradients are scaled by the learning rate and the update to subtract is stored in each parameter Blob’s diff field. +Finally, the Blob::Update method is called on each parameter blob, which performs the final update (subtracting the Blob’s diff from its data).

      + +

      Snapshotting and Resuming

      + +

      The solver snapshots the weights and its own state during training in Solver::Snapshot() and Solver::SnapshotSolverState(). +The weight snapshots export the learned model while the solver snapshots allow training to be resumed from a given point. +Training is resumed by Solver::Restore() and Solver::RestoreSolverState().

      + +

      Weights are saved without extension while solver states are saved with .solverstate extension. +Both files will have an _iter_N suffix for the snapshot iteration number.

      + +

      Snapshotting is configured by:

      + +
      # The snapshot interval in iterations.
+snapshot: 5000
+# File path prefix for snapshotting model weights and solver state.
+# Note: this is relative to the invocation of the `caffe` utility, not the
+# solver definition file.
+snapshot_prefix: "/path/to/model"
+# Snapshot the diff along with the weights. This can help debugging training
+# but takes more storage.
+snapshot_diff: false
+# A final snapshot is saved at the end of training unless
+# this flag is set to false. The default is true.
+snapshot_after_train: true
+
      +
      + +

      in the solver definition prototxt.

      + + +
      +
      + + From 21b277eaeaadba1a20632271f0f69e6b40a8092f Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Wed, 18 Jan 2017 12:10:03 -0800 Subject: [PATCH 2/4] 83143638 Merge pull request #5111 from CDLuminate/add-debian-install-guide --- doxygen/absval__layer_8hpp_source.html | 2 +- doxygen/accuracy__layer_8hpp_source.html | 2 +- doxygen/annotated.html | 2 +- doxygen/argmax__layer_8hpp_source.html | 2 +- doxygen/base__conv__layer_8hpp_source.html | 2 +- doxygen/base__data__layer_8hpp_source.html | 2 +- doxygen/batch__norm__layer_8hpp_source.html | 2 +- doxygen/batch__reindex__layer_8hpp_source.html | 2 +- doxygen/benchmark_8hpp_source.html | 2 +- doxygen/bias__layer_8hpp_source.html | 2 +- doxygen/blob_8hpp_source.html | 2 +- doxygen/blocking__queue_8hpp_source.html | 2 +- doxygen/bnll__layer_8hpp_source.html | 2 +- doxygen/caffe_8hpp_source.html | 2 +- doxygen/classcaffe_1_1AbsValLayer-members.html | 2 +- doxygen/classcaffe_1_1AbsValLayer.html | 2 +- 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      1 // caffe.hpp is the header file that you need to include in your code. It wraps
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      4 #ifndef CAFFE_CAFFE_HPP_
      5 #define CAFFE_CAFFE_HPP_
      6 
      7 #include "caffe/blob.hpp"
      8 #include "caffe/common.hpp"
      9 #include "caffe/filler.hpp"
      10 #include "caffe/layer.hpp"
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      12 #include "caffe/net.hpp"
      13 #include "caffe/parallel.hpp"
      14 #include "caffe/proto/caffe.pb.h"
      15 #include "caffe/solver.hpp"
      16 #include "caffe/solver_factory.hpp"
      17 #include "caffe/util/benchmark.hpp"
      18 #include "caffe/util/io.hpp"
      19 #include "caffe/util/upgrade_proto.hpp"
      20 
      21 #endif // CAFFE_CAFFE_HPP_
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      -Generated on Mon Nov 21 2016 11:23:49 for Caffe by   +Generated on Fri Jan 13 2017 13:56:52 for Caffe by   doxygen 1.8.12
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      -Generated on Mon Nov 21 2016 11:23:49 for Caffe by   +Generated on Fri Jan 13 2017 13:56:52 for Caffe by   doxygen 1.8.12
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      1 #ifndef CAFFE_UTIL_MKL_ALTERNATE_H_
      2 #define CAFFE_UTIL_MKL_ALTERNATE_H_
      3 
      4 #ifdef USE_MKL
      5 
      6 #include <mkl.h>
      7 
      8 #else // If use MKL, simply include the MKL header
      9 
      10 #ifdef USE_ACCELERATE
      11 #include <Accelerate/Accelerate.h>
      12 #else
      13 extern "C" {
      14 #include <cblas.h>
      15 }
      16 #endif // USE_ACCELERATE
      17 
      18 #include <math.h>
      19 
      20 // Functions that caffe uses but are not present if MKL is not linked.
      21 
      22 // A simple way to define the vsl unary functions. The operation should
      23 // be in the form e.g. y[i] = sqrt(a[i])
      24 #define DEFINE_VSL_UNARY_FUNC(name, operation) \
      25  template<typename Dtype> \
      26  void v##name(const int n, const Dtype* a, Dtype* y) { \
      27  CHECK_GT(n, 0); CHECK(a); CHECK(y); \
      28  for (int i = 0; i < n; ++i) { operation; } \
      29  } \
      30  inline void vs##name( \
      31  const int n, const float* a, float* y) { \
      32  v##name<float>(n, a, y); \
      33  } \
      34  inline void vd##name( \
      35  const int n, const double* a, double* y) { \
      36  v##name<double>(n, a, y); \
      37  }
      38 
      39 DEFINE_VSL_UNARY_FUNC(Sqr, y[i] = a[i] * a[i]);
      40 DEFINE_VSL_UNARY_FUNC(Exp, y[i] = exp(a[i]));
      41 DEFINE_VSL_UNARY_FUNC(Ln, y[i] = log(a[i]));
      42 DEFINE_VSL_UNARY_FUNC(Abs, y[i] = fabs(a[i]));
      43 
      44 // A simple way to define the vsl unary functions with singular parameter b.
      45 // The operation should be in the form e.g. y[i] = pow(a[i], b)
      46 #define DEFINE_VSL_UNARY_FUNC_WITH_PARAM(name, operation) \
      47  template<typename Dtype> \
      48  void v##name(const int n, const Dtype* a, const Dtype b, Dtype* y) { \
      49  CHECK_GT(n, 0); CHECK(a); CHECK(y); \
      50  for (int i = 0; i < n; ++i) { operation; } \
      51  } \
      52  inline void vs##name( \
      53  const int n, const float* a, const float b, float* y) { \
      54  v##name<float>(n, a, b, y); \
      55  } \
      56  inline void vd##name( \
      57  const int n, const double* a, const float b, double* y) { \
      58  v##name<double>(n, a, b, y); \
      59  }
      60 
      61 DEFINE_VSL_UNARY_FUNC_WITH_PARAM(Powx, y[i] = pow(a[i], b));
      62 
      63 // A simple way to define the vsl binary functions. The operation should
      64 // be in the form e.g. y[i] = a[i] + b[i]
      65 #define DEFINE_VSL_BINARY_FUNC(name, operation) \
      66  template<typename Dtype> \
      67  void v##name(const int n, const Dtype* a, const Dtype* b, Dtype* y) { \
      68  CHECK_GT(n, 0); CHECK(a); CHECK(b); CHECK(y); \
      69  for (int i = 0; i < n; ++i) { operation; } \
      70  } \
      71  inline void vs##name( \
      72  const int n, const float* a, const float* b, float* y) { \
      73  v##name<float>(n, a, b, y); \
      74  } \
      75  inline void vd##name( \
      76  const int n, const double* a, const double* b, double* y) { \
      77  v##name<double>(n, a, b, y); \
      78  }
      79 
      80 DEFINE_VSL_BINARY_FUNC(Add, y[i] = a[i] + b[i]);
      81 DEFINE_VSL_BINARY_FUNC(Sub, y[i] = a[i] - b[i]);
      82 DEFINE_VSL_BINARY_FUNC(Mul, y[i] = a[i] * b[i]);
      83 DEFINE_VSL_BINARY_FUNC(Div, y[i] = a[i] / b[i]);
      84 
      85 // In addition, MKL comes with an additional function axpby that is not present
      86 // in standard blas. We will simply use a two-step (inefficient, of course) way
      87 // to mimic that.
      88 inline void cblas_saxpby(const int N, const float alpha, const float* X,
      89  const int incX, const float beta, float* Y,
      90  const int incY) {
      91  cblas_sscal(N, beta, Y, incY);
      92  cblas_saxpy(N, alpha, X, incX, Y, incY);
      93 }
      94 inline void cblas_daxpby(const int N, const double alpha, const double* X,
      95  const int incX, const double beta, double* Y,
      96  const int incY) {
      97  cblas_dscal(N, beta, Y, incY);
      98  cblas_daxpy(N, alpha, X, incX, Y, incY);
      99 }
      100 
      101 #endif // USE_MKL
      102 #endif // CAFFE_UTIL_MKL_ALTERNATE_H_
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      diff --git a/doxygen/window__data__layer_8hpp_source.html b/doxygen/window__data__layer_8hpp_source.html index 915a7062295..d46a1e0ae4e 100644 --- a/doxygen/window__data__layer_8hpp_source.html +++ b/doxygen/window__data__layer_8hpp_source.html @@ -77,7 +77,7 @@
      -Generated on Mon Nov 21 2016 11:23:36 for Caffe by   +Generated on Fri Jan 13 2017 13:56:34 for Caffe by   doxygen 1.8.12
      diff --git a/gathered/examples/02-fine-tuning.ipynb b/gathered/examples/02-fine-tuning.ipynb index e3654d675cf..beed35a5a9e 100644 --- a/gathered/examples/02-fine-tuning.ipynb +++ b/gathered/examples/02-fine-tuning.ipynb @@ -126,7 +126,7 @@ "\n", "- `get_ilsvrc_aux.sh` to download the ImageNet data mean, labels, etc.\n", "- `download_model_binary.py` to download the pretrained reference model\n", - "- `finetune_flickr_style/assemble_data.py` downloadsd the style training and testing data\n", + "- `finetune_flickr_style/assemble_data.py` downloads the style training and testing data\n", "\n", "We'll download just a small subset of the full dataset for this exercise: just 2000 of the 80K images, from 5 of the 20 style categories. (To download the full dataset, set `full_dataset = True` in the cell below.)" ] @@ -1197,7 +1197,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n", + "So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occasionally down because it is run on a research machine.\n", "\n", "http://demo.vislab.berkeleyvision.org/" ] diff --git a/install_apt_debian.html b/install_apt_debian.html new file mode 100644 index 00000000000..b0f7319d283 --- /dev/null +++ b/install_apt_debian.html @@ -0,0 +1,235 @@ + + + + + + + + + Caffe | Installation: Debian + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Debian Installation

      + +

      Caffe packages are available for several Debian versions, as shown in the +following chart

      + +
      Your Distro     |  CPU_ONLY  |  CUDA  |     Alias
+----------------+------------+--------+-------------------
+Debian/stable   |     ✘      |   ✘    | Debian Jessie
+Debian/testing  |     ✔      |   ☐    | Debian Stretch/Sid
+Debian/unstable |     ✔      |   ✔    | Debian Sid
+
      +
      + +
        +
      • +

        ✘ You should take a look at Ubuntu installation instruction.

        +
        • +
      • +

        ✔ You can install caffe with a single command line following this guide.

        +
        • +
      • +

        ☐ The same with ✔ . However it will not work any more when Debian/Stretch becomes the stable branch.

        +
        • +
      + +

      Last update: 2017-01-05

      + +

      Binary installation with APT

      + +

      Apart from the installation methods based on source, Debian/unstable +and Debian/testing users can install pre-compiled Caffe packages via the official archive.

      + +

      Make sure that there is something like the follows in your /etc/apt/sources.list: + +deb http://MIRROR/debian CODENAME main contrib non-free + +where MIRROR is your favorate Debian mirror, and CODENAME ∈ {testing,stretch,sid}.

      + +

      Then we update APT cache and directly install Caffe. Note, the cpu version and +the cuda version cannot be installed at the same time. + +# apt update +# apt install [ caffe-cpu | caffe-cuda ] +# caffe # command line interface working +# python3 -c 'import caffe; print(caffe.__path__)' # python3 interface working + +It should work out of box.

      + +

      Customizing caffe packages

      + +

      Some users may need to customize the Caffe package. The way to customize +the package is beyond this guide. Here is only a brief guide of producing +the customized .deb packages.

      + +

      Make sure that there is something like this in your /etc/apt/sources.list: + +deb http://ftp2.cn.debian.org/debian sid main contrib non-free +deb-src http://ftp2.cn.debian.org/debian sid main contrib non-free +

      + +

      Then we build caffe deb files with the following commands: + +$ sudo apt update +$ sudo apt install build-essential debhelper devscripts # standard package building tools +$ sudo apt build-dep [ caffe-cpu | caffe-cuda ] # the most elegant way to pull caffe build dependencies +$ apt source [ caffe-cpu | caffe-cuda ] # download the source tarball and extract +$ cd caffe-XXXX +[ ... optional, customize caffe code/build ... ] +$ dch -llocal "Modified XXX in order to XXX" # write your one-line changelog +$ debuild -B -j4 # build caffe with 4 parallel jobs (similar to make -j4) +[ ... building ...] +$ debc # optional, if you want to check the package contents +$ sudo debi # optional, install the generated packages + +The resulting deb packages can be found under the parent directory of the source tree.

      + +

      Note, the dch ... command line above is for bumping the package version number +and adding an entry to the package changelog. If you would like to write +more than one changelog entry, use subsequent dch command (see man 1 dch) +instead of manually modifing debian/changelog unless you know how to keep its format correct. +The changelog will be installed at e.g. /usr/share/doc/caffe-cpu/changelog.Debian.gz.

      + +

      Source installation

      + +

      Source installation under Debian/unstable is similar to that of Ubuntu, but +here is a more elegant way to pull caffe build dependencies: + +$ sudo apt build-dep [ caffe-cpu | caffe-cuda ] + +Note, this requires a deb-src entry in your /etc/apt/sources.list.

      + +

      Compiler Combinations

      + +

      Some users may find their favorate compiler doesn’t work well with CUDA. + +CXX compiler | CUDA 7.5 | CUDA 8.0 | +-------------+------------+------------+- +GCC-7 | ? | ? | +GCC-6 | ✘ | ✘ | +GCC-5 | ✔ [1] | ✔ | +CLANG-4.0 | ? | ? | +CLANG-3.9 | ✘ | ✘ | +CLANG-3.8 | ? | ✔ | +

      + +

      [1] CUDA 7.5 ‘s host_config.h must be patched before working with GCC-5.

      + +

      BTW, please forget the GCC-4.X series, since its libstdc++ ABI is not compatible with GCC-5’s. +You may encounter failure linking GCC-4.X object files against GCC-5 libraries. +(See https://wiki.debian.org/GCC5 )

      + +

      Notes

      + +
        +
      • +

        Consider re-compiling OpenBLAS locally with optimization flags for sake of +performance. This is highly recommended for any kind of production use, including +academic research.

        +
        • +
      • +

        If you are installing caffe-cuda, APT will automatically pull some of the +CUDA packages and the nvidia driver packages. Please be careful if you have +manually installed or hacked nvidia driver or CUDA toolkit or any other +related stuff, because in this case APT may fail.

        +
        • +
      • +

        Additionally, a manpage (man caffe) and a bash complementation script +(caffe <TAB><TAB>, caffe train <TAB><TAB>) are provided. +Both of the two files are still not merged into caffe master.

        +
        • +
      • +

        The python interface is Python 3 version: python3-caffe-{cpu,cuda}. +No plan to support python2.

        +
        • +
      • +

        If you encountered any problem related to the packaging system (e.g. failed to install caffe-*), +please report bug to Debian via Debian’s bug tracking system. See https://www.debian.org/Bugs/ . +Patches and suggestions are also welcome.

        +
        • +
      + +

      FAQ

      + +
        +
      • where is caffe-cudnn?
        • +
      + +

      CUDNN library seems not redistributable currently. If you really want the +caffe-cudnn deb packages, the workaround is to install cudnn by yourself, +and hack the packaging scripts, then build your customized package.

      + +
        +
      • I installed the CPU version. How can I switch to the CUDA version?
        • +
      + +

      sudo apt install caffe-cuda, apt’s dependency resolver is smart enough to deal with this.

      + +
        +
      • Where are the examples, the models and other documentation stuff?
        • +
      + +
      sudo apt install caffe-doc
+dpkg -L caffe-doc
+
      +
      + +
        +
      • Where can I find the Debian package status?
        • +
      + +

      https://tracker.debian.org/pkg/caffe (for the CPU_ONLY version)

      + +

      https://tracker.debian.org/pkg/caffe-contrib (for the CUDA version)

      + + +
      +
      + + diff --git a/installation.html b/installation.html index 3758e3f4825..dd5d3f24573 100644 --- a/installation.html +++ b/installation.html @@ -65,6 +65,7 @@

      Installation

      -

      ReLU / Rectified-Linear and Leaky-ReLU

      +

      Layers:

        -
      • Layer type: ReLU
        • -
      • CPU implementation: ./src/caffe/layers/relu_layer.cpp
        • -
      • CUDA GPU implementation: ./src/caffe/layers/relu_layer.cu
        • -
      • Parameters (ReLUParameter relu_param) -
          -
        • Optional -
            -
          • negative_slope [default 0]: specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.
            • -
          -
          • -
        -
        • -
      • -

        Sample (as seen in ./models/bvlc_reference_caffenet/train_val.prototxt)

        - - 
        layer {
-  name: "relu1"
-  type: "ReLU"
-  bottom: "conv1"
-  top: "conv1"
-}
-
        -
        -
        • +
      • ReLU / Rectified-Linear and Leaky-ReLU - ReLU and Leaky-ReLU rectification.
        • +
      • PReLU - parametric ReLU.
        • +
      • ELU - exponential linear rectification.
        • +
      • Sigmoid
        • +
      • TanH
        • +
      • Absolute Value
        • +
      • Power - f(x) = (shift + scale * x) ^ power.
        • +
      • Exp - f(x) = base ^ (shift + scale * x).
        • +
      • Log - f(x) = log(x).
        • +
      • BNLL - f(x) = log(1 + exp(x)).
        • +
      • Threshold - performs step function at user defined threshold.
        • +
      • Bias - adds a bias to a blob that can either be learned or fixed.
        • +
      • Scale - scales a blob by an amount that can either be learned or fixed.
      -

      Given an input value x, The ReLU layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.

      +

      Utility Layers

      -

      Sigmoid

      +

      Layers:

        -
      • Layer type: Sigmoid
        • -
      • CPU implementation: ./src/caffe/layers/sigmoid_layer.cpp
        • -
      • CUDA GPU implementation: ./src/caffe/layers/sigmoid_layer.cu
        • +
      • Flatten
        • +
      • Reshape
      • -

        Sample (as seen in ./examples/mnist/mnist_autoencoder.prototxt)

        - - 
        layer {
-  name: "encode1neuron"
-  bottom: "encode1"
-  top: "encode1neuron"
-  type: "Sigmoid"
-}
-
        -
        -
        • -
      - -

      The Sigmoid layer computes the output as sigmoid(x) for each input element x.

      - -

      TanH / Hyperbolic Tangent

      - -
        -
      • Layer type: TanH
        • -
      • CPU implementation: ./src/caffe/layers/tanh_layer.cpp
        • -
      • CUDA GPU implementation: ./src/caffe/layers/tanh_layer.cu
        • +

        Batch Reindex

        + +
      • Split
        • +
      • Concat
        • +
      • Slicing
        • +
      • Eltwise - element-wise operations such as product or sum between two blobs.
        • +
      • Filter / Mask - mask or select output using last blob.
        • +
      • Parameter - enable parameters to be shared between layers.
        • +
      • Reduction - reduce input blob to scalar blob using operations such as sum or mean.
      • -

        Sample

        - - 
        layer {
-  name: "layer"
-  bottom: "in"
-  top: "out"
-  type: "TanH"
-}
-
        -
        +

        Silence - prevent top-level blobs from being printed during training.

        • -
      - -

      The TanH layer computes the output as tanh(x) for each input element x.

      - -

      Absolute Value

      - -
        -
      • Layer type: AbsVal
        • -
      • CPU implementation: ./src/caffe/layers/absval_layer.cpp
        • -
      • CUDA GPU implementation: ./src/caffe/layers/absval_layer.cu
        • +
      • ArgMax
      • -

        Sample

        - - 
        layer {
-  name: "layer"
-  bottom: "in"
-  top: "out"
-  type: "AbsVal"
-}
-
        -
        +

        Softmax

        • +
      • Python - allows custom Python layers.
      -

      The AbsVal layer computes the output as abs(x) for each input element x.

      - -

      Power

      +

      Loss Layers

      -
        -
      • Layer type: Power
        • -
      • CPU implementation: ./src/caffe/layers/power_layer.cpp
        • -
      • CUDA GPU implementation: ./src/caffe/layers/power_layer.cu
        • -
      • Parameters (PowerParameter power_param) -
          -
        • Optional -
            -
          • power [default 1]
            • -
          • scale [default 1]
            • -
          • shift [default 0]
            • -
          -
          • -
        -
        • -
      • -

        Sample

        - - 
        layer {
-  name: "layer"
-  bottom: "in"
-  top: "out"
-  type: "Power"
-  power_param {
-    power: 1
-    scale: 1
-    shift: 0
-  }
-}
-
        -
        -
        • -
      - -

      The Power layer computes the output as (shift + scale * x) ^ power for each input element x.

      - -

      BNLL

      - -
        -
      • Layer type: BNLL
        • -
      • CPU implementation: ./src/caffe/layers/bnll_layer.cpp
        • -
      • CUDA GPU implementation: ./src/caffe/layers/bnll_layer.cu
        • -
      • -

        Sample

        - - 
        layer {
-  name: "layer"
-  bottom: "in"
-  top: "out"
-  type: BNLL
-}
-
        -
        -
        • -
      - -

      The BNLL (binomial normal log likelihood) layer computes the output as log(1 + exp(x)) for each input element x.

      - -

      Data Layers

      - -

      Data enters Caffe through data layers: they lie at the bottom of nets. Data can come from efficient databases (LevelDB or LMDB), directly from memory, or, when efficiency is not critical, from files on disk in HDF5 or common image formats.

      - -

      Common input preprocessing (mean subtraction, scaling, random cropping, and mirroring) is available by specifying TransformationParameters.

      - -

      Database

      - -
        -
      • Layer type: Data
        • -
      • Parameters -
          -
        • Required -
            -
          • source: the name of the directory containing the database
            • -
          • batch_size: the number of inputs to process at one time
            • -
          -
          • -
        • Optional -
            -
          • rand_skip: skip up to this number of inputs at the beginning; useful for asynchronous sgd
            • -
          • backend [default LEVELDB]: choose whether to use a LEVELDB or LMDB
            • -
          -
          • -
        -
        • -
      - -

      In-Memory

      - -
        -
      • Layer type: MemoryData
        • -
      • Parameters -
          -
        • Required -
            -
          • batch_size, channels, height, width: specify the size of input chunks to read from memory
            • -
          -
          • -
        -
        • -
      - -

      The memory data layer reads data directly from memory, without copying it. In order to use it, one must call MemoryDataLayer::Reset (from C++) or Net.set_input_arrays (from Python) in order to specify a source of contiguous data (as 4D row major array), which is read one batch-sized chunk at a time.

      - -

      HDF5 Input

      - -
        -
      • Layer type: HDF5Data
        • -
      • Parameters -
          -
        • Required -
            -
          • source: the name of the file to read from
            • -
          • batch_size
            • -
          -
          • -
        -
        • -
      - -

      HDF5 Output

      - -
        -
      • Layer type: HDF5Output
        • -
      • Parameters -
          -
        • Required -
            -
          • file_name: name of file to write to
            • -
          -
          • -
        -
        • -
      - -

      The HDF5 output layer performs the opposite function of the other layers in this section: it writes its input blobs to disk.

      - -

      Images

      - -
        -
      • Layer type: ImageData
        • -
      • Parameters -
          -
        • Required -
            -
          • source: name of a text file, with each line giving an image filename and label
            • -
          • batch_size: number of images to batch together
            • -
          -
          • -
        • Optional -
            -
          • rand_skip
            • -
          • shuffle [default false]
            • -
          • new_height, new_width: if provided, resize all images to this size
            • -
          -
          • -
        -
        • -
      - -

      Windows

      - -

      WindowData

      - -

      Dummy

      - -

      DummyData is for development and debugging. See DummyDataParameter.

      - -

      Common Layers

      - -

      Inner Product

      - -
        -
      • Layer type: InnerProduct
        • -
      • CPU implementation: ./src/caffe/layers/inner_product_layer.cpp
        • -
      • CUDA GPU implementation: ./src/caffe/layers/inner_product_layer.cu
        • -
      • Parameters (InnerProductParameter inner_product_param) -
          -
        • Required -
            -
          • num_output (c_o): the number of filters
            • -
          -
          • -
        • Strongly recommended -
            -
          • weight_filler [default type: 'constant' value: 0]
            • -
          -
          • -
        • Optional -
            -
          • bias_filler [default type: 'constant' value: 0]
            • -
          • bias_term [default true]: specifies whether to learn and apply a set of additive biases to the filter outputs
            • -
          -
          • -
        -
        • -
      • Input -
          -
        • n * c_i * h_i * w_i
          • -
        -
        • -
      • Output -
          -
        • n * c_o * 1 * 1
          • -
        -
        • -
      • -

        Sample

        - - 
        layer {
-  name: "fc8"
-  type: "InnerProduct"
-  # learning rate and decay multipliers for the weights
-  param { lr_mult: 1 decay_mult: 1 }
-  # learning rate and decay multipliers for the biases
-  param { lr_mult: 2 decay_mult: 0 }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-  bottom: "fc7"
-  top: "fc8"
-}
-
        -
        -
        • -
      - -

      The InnerProduct layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob’s height and width set to 1).

      - -

      Splitting

      - -

      The Split layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers.

      - -

      Flattening

      - -

      The Flatten layer is a utility layer that flattens an input of shape n * c * h * w to a simple vector output of shape n * (c*h*w)

      - -

      Reshape

      - -
        -
      • Layer type: Reshape
        • -
      • Implementation: ./src/caffe/layers/reshape_layer.cpp
        • -
      • Parameters (ReshapeParameter reshape_param) -
          -
        • Optional: (also see detailed description below) -
            -
          • shape
            • -
          -
          • -
        -
        • -
      • Input -
          -
        • a single blob with arbitrary dimensions
          • -
        -
        • -
      • Output -
          -
        • the same blob, with modified dimensions, as specified by reshape_param
          • -
        -
        • -
      • -

        Sample

        - - 
          layer {
-    name: "reshape"
-    type: "Reshape"
-    bottom: "input"
-    top: "output"
-    reshape_param {
-      shape {
-        dim: 0  # copy the dimension from below
-        dim: 2
-        dim: 3
-        dim: -1 # infer it from the other dimensions
-      }
-    }
-  }
-
        -
        -
        • -
      - -

      The Reshape layer can be used to change the dimensions of its input, without changing its data. Just like the Flatten layer, only the dimensions are changed; no data is copied in the process.

      - -

      Output dimensions are specified by the ReshapeParam proto. Positive numbers are used directly, setting the corresponding dimension of the output blob. In addition, two special values are accepted for any of the target dimension values:

      - -
        -
      • 0 means “copy the respective dimension of the bottom layer”. That is, if the bottom has 2 as its 1st dimension, the top will have 2 as its 1st dimension as well, given dim: 0 as the 1st target dimension.
        • -
      • -1 stands for “infer this from the other dimensions”. This behavior is similar to that of -1 in numpy’s or [] for MATLAB’s reshape: this dimension is calculated to keep the overall element count the same as in the bottom layer. At most one -1 can be used in a reshape operation.
        • -
      - -

      As another example, specifying reshape_param { shape { dim: 0 dim: -1 } } makes the layer behave in exactly the same way as the Flatten layer.

      - -

      Concatenation

      - -
        -
      • Layer type: Concat
        • -
      • CPU implementation: ./src/caffe/layers/concat_layer.cpp
        • -
      • CUDA GPU implementation: ./src/caffe/layers/concat_layer.cu
        • -
      • Parameters (ConcatParameter concat_param) -
          -
        • Optional -
            -
          • axis [default 1]: 0 for concatenation along num and 1 for channels.
            • -
          -
          • -
        -
        • -
      • Input -
          -
        • n_i * c_i * h * w for each input blob i from 1 to K.
          • -
        -
        • -
      • Output -
          -
        • if axis = 0: (n_1 + n_2 + ... + n_K) * c_1 * h * w, and all input c_i should be the same.
          • -
        • if axis = 1: n_1 * (c_1 + c_2 + ... + c_K) * h * w, and all input n_i should be the same.
          • -
        -
        • -
      • -

        Sample

        - - 
        layer {
-  name: "concat"
-  bottom: "in1"
-  bottom: "in2"
-  top: "out"
-  type: "Concat"
-  concat_param {
-    axis: 1
-  }
-}
-
        -
        -
        • -
      - -

      The Concat layer is a utility layer that concatenates its multiple input blobs to one single output blob.

      - -

      Slicing

      +

      Loss drives learning by comparing an output to a target and assigning cost to minimize. The loss itself is computed by the forward pass and the gradient w.r.t. to the loss is computed by the backward pass.

      -

      The Slice layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.

      +

      Layers:

        -
      • -

        Sample

        - - 
        layer {
-  name: "slicer_label"
-  type: "Slice"
-  bottom: "label"
-  ## Example of label with a shape N x 3 x 1 x 1
-  top: "label1"
-  top: "label2"
-  top: "label3"
-  slice_param {
-    axis: 1
-    slice_point: 1
-    slice_point: 2
-  }
-}
-
        -
        -
        • +
      • Multinomial Logistic Loss
        • +
      • Infogain Loss - a generalization of MultinomialLogisticLossLayer.
        • +
      • Softmax with Loss - computes the multinomial logistic loss of the softmax of its inputs. It’s conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.
        • +
      • Sum-of-Squares / Euclidean - computes the sum of squares of differences of its two inputs, .
        • +
      • Hinge / Margin - The hinge loss layer computes a one-vs-all hinge (L1) or squared hinge loss (L2).
        • +
      • Sigmoid Cross-Entropy Loss - computes the cross-entropy (logistic) loss, often used for predicting targets interpreted as probabilities.
        • +
      • Accuracy / Top-k layer - scores the output as an accuracy with respect to target – it is not actually a loss and has no backward step.
        • +
      • Contrastive Loss
      -

      axis indicates the target axis; slice_point indicates indexes in the selected dimension (the number of indices must be equal to the number of top blobs minus one).

      - -

      Elementwise Operations

      - -

      Eltwise

      - -

      Argmax

      - -

      ArgMax

      - -

      Softmax

      - -

      Softmax

      - -

      Mean-Variance Normalization

      - -

      MVN

      diff --git a/tutorial/layers/absval.html b/tutorial/layers/absval.html new file mode 100644 index 00000000000..563074c3cfa --- /dev/null +++ b/tutorial/layers/absval.html @@ -0,0 +1,87 @@ + + + + + + + + + Caffe | Absolute Value Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Absolute Value Layer

      + + + +

      The AbsVal layer computes the output as abs(x) for each input element x.

      + + +
      +
      + + diff --git a/tutorial/layers/accuracy.html b/tutorial/layers/accuracy.html new file mode 100644 index 00000000000..bf45619e352 --- /dev/null +++ b/tutorial/layers/accuracy.html @@ -0,0 +1,96 @@ + + + + + + + + + Caffe | Accuracy and Top-k + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Accuracy and Top-k

      + +

      Accuracy scores the output as the accuracy of output with respect to target – it is not actually a loss and has no backward step.

      + + + +

      Parameters

      + + +
      message AccuracyParameter {
+  // When computing accuracy, count as correct by comparing the true label to
+  // the top k scoring classes.  By default, only compare to the top scoring
+  // class (i.e. argmax).
+  optional uint32 top_k = 1 [default = 1];
+
+  // The "label" axis of the prediction blob, whose argmax corresponds to the
+  // predicted label -- may be negative to index from the end (e.g., -1 for the
+  // last axis).  For example, if axis == 1 and the predictions are
+  // (N x C x H x W), the label blob is expected to contain N*H*W ground truth
+  // labels with integer values in {0, 1, ..., C-1}.
+  optional int32 axis = 2 [default = 1];
+
+  // If specified, ignore instances with the given label.
+  optional int32 ignore_label = 3;
+}
      + + +
      +
      + + diff --git a/tutorial/layers/argmax.html b/tutorial/layers/argmax.html new file mode 100644 index 00000000000..57cc8b0ea6b --- /dev/null +++ b/tutorial/layers/argmax.html @@ -0,0 +1,88 @@ + + + + + + + + + Caffe | ArgMax Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      ArgMax Layer

      + + + +

      Parameters

      + + +
      message ArgMaxParameter {
+  // If true produce pairs (argmax, maxval)
+  optional bool out_max_val = 1 [default = false];
+  optional uint32 top_k = 2 [default = 1];
+  // The axis along which to maximise -- may be negative to index from the
+  // end (e.g., -1 for the last axis).
+  // By default ArgMaxLayer maximizes over the flattened trailing dimensions
+  // for each index of the first / num dimension.
+  optional int32 axis = 3;
+}
      + + +
      +
      + + diff --git a/tutorial/layers/batchnorm.html b/tutorial/layers/batchnorm.html new file mode 100644 index 00000000000..e09a24f9bc7 --- /dev/null +++ b/tutorial/layers/batchnorm.html @@ -0,0 +1,91 @@ + + + + + + + + + Caffe | Batch Norm Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Batch Norm Layer

      + + + +

      Parameters

      + + + +
      message BatchNormParameter {
+  // If false, accumulate global mean/variance values via a moving average. If
+  // true, use those accumulated values instead of computing mean/variance
+  // across the batch.
+  optional bool use_global_stats = 1;
+  // How much does the moving average decay each iteration?
+  optional float moving_average_fraction = 2 [default = .999];
+  // Small value to add to the variance estimate so that we don't divide by
+  // zero.
+  optional float eps = 3 [default = 1e-5];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/batchreindex.html b/tutorial/layers/batchreindex.html new file mode 100644 index 00000000000..4d549719e6a --- /dev/null +++ b/tutorial/layers/batchreindex.html @@ -0,0 +1,75 @@ + + + + + + + + + Caffe | Batch Reindex Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Batch Reindex Layer

      + + + +

      Parameters

      + +

      No parameters.

      + + +
      +
      + + diff --git a/tutorial/layers/bias.html b/tutorial/layers/bias.html new file mode 100644 index 00000000000..cdb5fc1a9b3 --- /dev/null +++ b/tutorial/layers/bias.html @@ -0,0 +1,110 @@ + + + + + + + + + Caffe | Bias Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Bias Layer

      + + + +

      Parameters

      + + +
      message BiasParameter {
+  // The first axis of bottom[0] (the first input Blob) along which to apply
+  // bottom[1] (the second input Blob).  May be negative to index from the end
+  // (e.g., -1 for the last axis).
+  //
+  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
+  // top[0] will have the same shape, and bottom[1] may have any of the
+  // following shapes (for the given value of axis):
+  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
+  //    (axis == 1 == -3)          3;     3x40;     3x40x60
+  //    (axis == 2 == -2)                   40;       40x60
+  //    (axis == 3 == -1)                                60
+  // Furthermore, bottom[1] may have the empty shape (regardless of the value of
+  // "axis") -- a scalar bias.
+  optional int32 axis = 1 [default = 1];
+
+  // (num_axes is ignored unless just one bottom is given and the bias is
+  // a learned parameter of the layer.  Otherwise, num_axes is determined by the
+  // number of axes by the second bottom.)
+  // The number of axes of the input (bottom[0]) covered by the bias
+  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
+  // Set num_axes := 0, to add a zero-axis Blob: a scalar.
+  optional int32 num_axes = 2 [default = 1];
+
+  // (filler is ignored unless just one bottom is given and the bias is
+  // a learned parameter of the layer.)
+  // The initialization for the learned bias parameter.
+  // Default is the zero (0) initialization, resulting in the BiasLayer
+  // initially performing the identity operation.
+  optional FillerParameter filler = 3;
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/bnll.html b/tutorial/layers/bnll.html new file mode 100644 index 00000000000..505ce3837a5 --- /dev/null +++ b/tutorial/layers/bnll.html @@ -0,0 +1,87 @@ + + + + + + + + + Caffe | BNLL Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      BNLL Layer

      + + + +

      The BNLL (binomial normal log likelihood) layer computes the output as log(1 + exp(x)) for each input element x.

      + +

      Parameters

      +

      No parameters.

      + +

      Sample

      + +
        layer {
+    name: "layer"
+    bottom: "in"
+    top: "out"
+    type: BNLL
+  }
+
      +
      + + +
      +
      + + diff --git a/tutorial/layers/concat.html b/tutorial/layers/concat.html new file mode 100644 index 00000000000..7c52c655312 --- /dev/null +++ b/tutorial/layers/concat.html @@ -0,0 +1,126 @@ + + + + + + + + + Caffe | Concat Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Concat Layer

      + + + +

      The Concat layer is a utility layer that concatenates its multiple input blobs to one single output blob.

      + +

      Parameters

      +
        +
      • Parameters (ConcatParameter concat_param) +
          +
        • Optional +
            +
          • axis [default 1]: 0 for concatenation along num and 1 for channels.
            • +
          +
          • +
        +
        • +
      • From ./src/caffe/proto/caffe.proto):
        • +
      + +
      message ConcatParameter {
+  // The axis along which to concatenate -- may be negative to index from the
+  // end (e.g., -1 for the last axis).  Other axes must have the
+  // same dimension for all the bottom blobs.
+  // By default, ConcatLayer concatenates blobs along the "channels" axis (1).
+  optional int32 axis = 2 [default = 1];
+
+  // DEPRECATED: alias for "axis" -- does not support negative indexing.
+  optional uint32 concat_dim = 1 [default = 1];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/contrastiveloss.html b/tutorial/layers/contrastiveloss.html new file mode 100644 index 00000000000..59876042856 --- /dev/null +++ b/tutorial/layers/contrastiveloss.html @@ -0,0 +1,91 @@ + + + + + + + + + Caffe | Contrastive Loss Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Contrastive Loss Layer

      + + + +

      Parameters

      + + + +
      message ContrastiveLossParameter {
+  // margin for dissimilar pair
+  optional float margin = 1 [default = 1.0];
+  // The first implementation of this cost did not exactly match the cost of
+  // Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2.
+  // legacy_version = false (the default) uses (margin - d)^2 as proposed in the
+  // Hadsell paper. New models should probably use this version.
+  // legacy_version = true uses (margin - d^2). This is kept to support /
+  // reproduce existing models and results
+  optional bool legacy_version = 2 [default = false];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/convolution.html b/tutorial/layers/convolution.html new file mode 100644 index 00000000000..a1dafe34ada --- /dev/null +++ b/tutorial/layers/convolution.html @@ -0,0 +1,195 @@ + + + + + + + + + Caffe | Convolution Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Convolution Layer

      + + + +

      The Convolution layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.

      + +

      Sample

      + +

      Sample (as seen in ./models/bvlc_reference_caffenet/train_val.prototxt):

      + +
        layer {
+    name: "conv1"
+    type: "Convolution"
+    bottom: "data"
+    top: "conv1"
+    # learning rate and decay multipliers for the filters
+    param { lr_mult: 1 decay_mult: 1 }
+    # learning rate and decay multipliers for the biases
+    param { lr_mult: 2 decay_mult: 0 }
+    convolution_param {
+      num_output: 96     # learn 96 filters
+      kernel_size: 11    # each filter is 11x11
+      stride: 4          # step 4 pixels between each filter application
+      weight_filler {
+        type: "gaussian" # initialize the filters from a Gaussian
+        std: 0.01        # distribution with stdev 0.01 (default mean: 0)
+      }
+      bias_filler {
+        type: "constant" # initialize the biases to zero (0)
+        value: 0
+      }
+    }
+  }
+
      +
      + +

      Parameters

      +
        +
      • Parameters (ConvolutionParameter convolution_param) +
          +
        • Required +
            +
          • num_output (c_o): the number of filters
            • +
          • kernel_size (or kernel_h and kernel_w): specifies height and width of each filter
            • +
          +
          • +
        • Strongly Recommended +
            +
          • weight_filler [default type: 'constant' value: 0]
            • +
          +
          • +
        • Optional +
            +
          • bias_term [default true]: specifies whether to learn and apply a set of additive biases to the filter outputs
            • +
          • pad (or pad_h and pad_w) [default 0]: specifies the number of pixels to (implicitly) add to each side of the input
            • +
          • stride (or stride_h and stride_w) [default 1]: specifies the intervals at which to apply the filters to the input
            • +
          • group (g) [default 1]: If g > 1, we restrict the connectivity of each filter to a subset of the input. Specifically, the input and output channels are separated into g groups, and the th output group channels will be only connected to the th input group channels.
            • +
          +
          • +
        +
        • +
      • From ./src/caffe/proto/caffe.proto):
        • +
      + +
      message ConvolutionParameter {
+  optional uint32 num_output = 1; // The number of outputs for the layer
+  optional bool bias_term = 2 [default = true]; // whether to have bias terms
+
+  // Pad, kernel size, and stride are all given as a single value for equal
+  // dimensions in all spatial dimensions, or once per spatial dimension.
+  repeated uint32 pad = 3; // The padding size; defaults to 0
+  repeated uint32 kernel_size = 4; // The kernel size
+  repeated uint32 stride = 6; // The stride; defaults to 1
+  // Factor used to dilate the kernel, (implicitly) zero-filling the resulting
+  // holes. (Kernel dilation is sometimes referred to by its use in the
+  // algorithme à trous from Holschneider et al. 1987.)
+  repeated uint32 dilation = 18; // The dilation; defaults to 1
+
+  // For 2D convolution only, the *_h and *_w versions may also be used to
+  // specify both spatial dimensions.
+  optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
+  optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
+  optional uint32 kernel_h = 11; // The kernel height (2D only)
+  optional uint32 kernel_w = 12; // The kernel width (2D only)
+  optional uint32 stride_h = 13; // The stride height (2D only)
+  optional uint32 stride_w = 14; // The stride width (2D only)
+
+  optional uint32 group = 5 [default = 1]; // The group size for group conv
+
+  optional FillerParameter weight_filler = 7; // The filler for the weight
+  optional FillerParameter bias_filler = 8; // The filler for the bias
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 15 [default = DEFAULT];
+
+  // The axis to interpret as "channels" when performing convolution.
+  // Preceding dimensions are treated as independent inputs;
+  // succeeding dimensions are treated as "spatial".
+  // With (N, C, H, W) inputs, and axis == 1 (the default), we perform
+  // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
+  // groups g>1) filters across the spatial axes (H, W) of the input.
+  // With (N, C, D, H, W) inputs, and axis == 1, we perform
+  // N independent 3D convolutions, sliding (C/g)-channels
+  // filters across the spatial axes (D, H, W) of the input.
+  optional int32 axis = 16 [default = 1];
+
+  // Whether to force use of the general ND convolution, even if a specific
+  // implementation for blobs of the appropriate number of spatial dimensions
+  // is available. (Currently, there is only a 2D-specific convolution
+  // implementation; for input blobs with num_axes != 2, this option is
+  // ignored and the ND implementation will be used.)
+  optional bool force_nd_im2col = 17 [default = false];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/crop.html b/tutorial/layers/crop.html new file mode 100644 index 00000000000..360d83bc3b3 --- /dev/null +++ b/tutorial/layers/crop.html @@ -0,0 +1,97 @@ + + + + + + + + + Caffe | Crop Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Crop Layer

      + + + +

      Parameters

      + + + +
      message CropParameter {
+  // To crop, elements of the first bottom are selected to fit the dimensions
+  // of the second, reference bottom. The crop is configured by
+  // - the crop `axis` to pick the dimensions for cropping
+  // - the crop `offset` to set the shift for all/each dimension
+  // to align the cropped bottom with the reference bottom.
+  // All dimensions up to but excluding `axis` are preserved, while
+  // the dimensions including and trailing `axis` are cropped.
+  // If only one `offset` is set, then all dimensions are offset by this amount.
+  // Otherwise, the number of offsets must equal the number of cropped axes to
+  // shift the crop in each dimension accordingly.
+  // Note: standard dimensions are N,C,H,W so the default is a spatial crop,
+  // and `axis` may be negative to index from the end (e.g., -1 for the last
+  // axis).
+  optional int32 axis = 1 [default = 2];
+  repeated uint32 offset = 2;
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/data.html b/tutorial/layers/data.html new file mode 100644 index 00000000000..ab851d8799c --- /dev/null +++ b/tutorial/layers/data.html @@ -0,0 +1,131 @@ + + + + + + + + + Caffe | Database Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Database Layer

      + + + +

      Parameters

      + + + +
      message DataParameter {
+  enum DB {
+    LEVELDB = 0;
+    LMDB = 1;
+  }
+  // Specify the data source.
+  optional string source = 1;
+  // Specify the batch size.
+  optional uint32 batch_size = 4;
+  // The rand_skip variable is for the data layer to skip a few data points
+  // to avoid all asynchronous sgd clients to start at the same point. The skip
+  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
+  // be larger than the number of keys in the database.
+  // DEPRECATED. Each solver accesses a different subset of the database.
+  optional uint32 rand_skip = 7 [default = 0];
+  optional DB backend = 8 [default = LEVELDB];
+  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
+  // simple scaling and subtracting the data mean, if provided. Note that the
+  // mean subtraction is always carried out before scaling.
+  optional float scale = 2 [default = 1];
+  optional string mean_file = 3;
+  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
+  // crop an image.
+  optional uint32 crop_size = 5 [default = 0];
+  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
+  // data.
+  optional bool mirror = 6 [default = false];
+  // Force the encoded image to have 3 color channels
+  optional bool force_encoded_color = 9 [default = false];
+  // Prefetch queue (Increase if data feeding bandwidth varies, within the
+  // limit of device memory for GPU training)
+  optional uint32 prefetch = 10 [default = 4];
+}
      + +
        +
      • Parameters +
          +
        • Required +
            +
          • source: the name of the directory containing the database
            • +
          • batch_size: the number of inputs to process at one time
            • +
          +
          • +
        • Optional +
            +
          • rand_skip: skip up to this number of inputs at the beginning; useful for asynchronous sgd
            • +
          • backend [default LEVELDB]: choose whether to use a LEVELDB or LMDB
            • +
          +
          • +
        +
        • +
      + + + +
      +
      + + diff --git a/tutorial/layers/deconvolution.html b/tutorial/layers/deconvolution.html new file mode 100644 index 00000000000..94cc26ebd4f --- /dev/null +++ b/tutorial/layers/deconvolution.html @@ -0,0 +1,134 @@ + + + + + + + + + Caffe | Deconvolution Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Deconvolution Layer

      + + + +

      Parameters

      + +

      Uses the same parameters as the Convolution layer.

      + + + +
      message ConvolutionParameter {
+  optional uint32 num_output = 1; // The number of outputs for the layer
+  optional bool bias_term = 2 [default = true]; // whether to have bias terms
+
+  // Pad, kernel size, and stride are all given as a single value for equal
+  // dimensions in all spatial dimensions, or once per spatial dimension.
+  repeated uint32 pad = 3; // The padding size; defaults to 0
+  repeated uint32 kernel_size = 4; // The kernel size
+  repeated uint32 stride = 6; // The stride; defaults to 1
+  // Factor used to dilate the kernel, (implicitly) zero-filling the resulting
+  // holes. (Kernel dilation is sometimes referred to by its use in the
+  // algorithme à trous from Holschneider et al. 1987.)
+  repeated uint32 dilation = 18; // The dilation; defaults to 1
+
+  // For 2D convolution only, the *_h and *_w versions may also be used to
+  // specify both spatial dimensions.
+  optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
+  optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
+  optional uint32 kernel_h = 11; // The kernel height (2D only)
+  optional uint32 kernel_w = 12; // The kernel width (2D only)
+  optional uint32 stride_h = 13; // The stride height (2D only)
+  optional uint32 stride_w = 14; // The stride width (2D only)
+
+  optional uint32 group = 5 [default = 1]; // The group size for group conv
+
+  optional FillerParameter weight_filler = 7; // The filler for the weight
+  optional FillerParameter bias_filler = 8; // The filler for the bias
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 15 [default = DEFAULT];
+
+  // The axis to interpret as "channels" when performing convolution.
+  // Preceding dimensions are treated as independent inputs;
+  // succeeding dimensions are treated as "spatial".
+  // With (N, C, H, W) inputs, and axis == 1 (the default), we perform
+  // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
+  // groups g>1) filters across the spatial axes (H, W) of the input.
+  // With (N, C, D, H, W) inputs, and axis == 1, we perform
+  // N independent 3D convolutions, sliding (C/g)-channels
+  // filters across the spatial axes (D, H, W) of the input.
+  optional int32 axis = 16 [default = 1];
+
+  // Whether to force use of the general ND convolution, even if a specific
+  // implementation for blobs of the appropriate number of spatial dimensions
+  // is available. (Currently, there is only a 2D-specific convolution
+  // implementation; for input blobs with num_axes != 2, this option is
+  // ignored and the ND implementation will be used.)
+  optional bool force_nd_im2col = 17 [default = false];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/dropout.html b/tutorial/layers/dropout.html new file mode 100644 index 00000000000..8118ed725fb --- /dev/null +++ b/tutorial/layers/dropout.html @@ -0,0 +1,83 @@ + + + + + + + + + Caffe | Dropout Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Dropout Layer

      + + + +

      Parameters

      + + + +
      message DropoutParameter {
+  optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/dummydata.html b/tutorial/layers/dummydata.html new file mode 100644 index 00000000000..28ff17d0ef3 --- /dev/null +++ b/tutorial/layers/dummydata.html @@ -0,0 +1,97 @@ + + + + + + + + + Caffe | Dummy Data Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Dummy Data Layer

      + + + +

      Parameters

      + + + +
      // DummyDataLayer fills any number of arbitrarily shaped blobs with random
+// (or constant) data generated by "Fillers" (see "message FillerParameter").
+message DummyDataParameter {
+  // This layer produces N >= 1 top blobs.  DummyDataParameter must specify 1 or N
+  // shape fields, and 0, 1 or N data_fillers.
+  //
+  // If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.
+  // If 1 data_filler is specified, it is applied to all top blobs.  If N are
+  // specified, the ith is applied to the ith top blob.
+  repeated FillerParameter data_filler = 1;
+  repeated BlobShape shape = 6;
+
+  // 4D dimensions -- deprecated.  Use "shape" instead.
+  repeated uint32 num = 2;
+  repeated uint32 channels = 3;
+  repeated uint32 height = 4;
+  repeated uint32 width = 5;
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/eltwise.html b/tutorial/layers/eltwise.html new file mode 100644 index 00000000000..9a3572eb36e --- /dev/null +++ b/tutorial/layers/eltwise.html @@ -0,0 +1,93 @@ + + + + + + + + + Caffe | Eltwise Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Eltwise Layer

      + + + +

      Parameters

      + + + +
      message EltwiseParameter {
+  enum EltwiseOp {
+    PROD = 0;
+    SUM = 1;
+    MAX = 2;
+  }
+  optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
+  repeated float coeff = 2; // blob-wise coefficient for SUM operation
+
+  // Whether to use an asymptotically slower (for >2 inputs) but stabler method
+  // of computing the gradient for the PROD operation. (No effect for SUM op.)
+  optional bool stable_prod_grad = 3 [default = true];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/elu.html b/tutorial/layers/elu.html new file mode 100644 index 00000000000..7553469bc34 --- /dev/null +++ b/tutorial/layers/elu.html @@ -0,0 +1,94 @@ + + + + + + + + + Caffe | ELU Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      ELU Layer

      + + + +

      References

      + +
        +
      • Clevert, Djork-Arne, Thomas Unterthiner, and Sepp Hochreiter. +“Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)” arXiv:1511.07289. (2015).
        • +
      + +

      Parameters

      + + + +
      // Message that stores parameters used by ELULayer
+message ELUParameter {
+  // Described in:
+  // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate
+  // Deep Network Learning by Exponential Linear Units (ELUs). arXiv
+  optional float alpha = 1 [default = 1];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/embed.html b/tutorial/layers/embed.html new file mode 100644 index 00000000000..9d99ac2441a --- /dev/null +++ b/tutorial/layers/embed.html @@ -0,0 +1,93 @@ + + + + + + + + + Caffe | Embed Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Embed Layer

      + + + +

      Parameters

      + + + +
      // Message that stores parameters used by EmbedLayer
+message EmbedParameter {
+  optional uint32 num_output = 1; // The number of outputs for the layer
+  // The input is given as integers to be interpreted as one-hot
+  // vector indices with dimension num_input.  Hence num_input should be
+  // 1 greater than the maximum possible input value.
+  optional uint32 input_dim = 2;
+
+  optional bool bias_term = 3 [default = true]; // Whether to use a bias term
+  optional FillerParameter weight_filler = 4; // The filler for the weight
+  optional FillerParameter bias_filler = 5; // The filler for the bias
+
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/euclideanloss.html b/tutorial/layers/euclideanloss.html new file mode 100644 index 00000000000..16a8981522a --- /dev/null +++ b/tutorial/layers/euclideanloss.html @@ -0,0 +1,77 @@ + + + + + + + + + Caffe | Euclidean Loss Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Sum-of-Squares / Euclidean Loss Layer

      + + + +

      The Euclidean loss layer computes the sum of squares of differences of its two inputs, .

      + +

      Parameters

      + +

      Does not take any parameters.

      + + +
      +
      + + diff --git a/tutorial/layers/exp.html b/tutorial/layers/exp.html new file mode 100644 index 00000000000..b60d0f1e0e9 --- /dev/null +++ b/tutorial/layers/exp.html @@ -0,0 +1,94 @@ + + + + + + + + + Caffe | Exponential Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Exponential Layer

      + + + +

      Parameters

      + + + +
      // Message that stores parameters used by ExpLayer
+message ExpParameter {
+  // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.
+  // Or if base is set to the default (-1), base is set to e,
+  // so y = exp(shift + scale * x).
+  optional float base = 1 [default = -1.0];
+  optional float scale = 2 [default = 1.0];
+  optional float shift = 3 [default = 0.0];
+}
      + +

      See also

      + + + + +
      +
      + + diff --git a/tutorial/layers/filter.html b/tutorial/layers/filter.html new file mode 100644 index 00000000000..580e874d9e1 --- /dev/null +++ b/tutorial/layers/filter.html @@ -0,0 +1,75 @@ + + + + + + + + + Caffe | Filter Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Filter Layer

      + + + +

      Parameters

      + +

      Does not take any parameters.

      + + +
      +
      + + diff --git a/tutorial/layers/flatten.html b/tutorial/layers/flatten.html new file mode 100644 index 00000000000..4433e131134 --- /dev/null +++ b/tutorial/layers/flatten.html @@ -0,0 +1,92 @@ + + + + + + + + + Caffe | Flatten Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Flatten Layer

      + + + +

      The Flatten layer is a utility layer that flattens an input of shape n * c * h * w to a simple vector output of shape n * (c*h*w).

      + +

      Parameters

      + + + +
      /// Message that stores parameters used by FlattenLayer
+message FlattenParameter {
+  // The first axis to flatten: all preceding axes are retained in the output.
+  // May be negative to index from the end (e.g., -1 for the last axis).
+  optional int32 axis = 1 [default = 1];
+
+  // The last axis to flatten: all following axes are retained in the output.
+  // May be negative to index from the end (e.g., the default -1 for the last
+  // axis).
+  optional int32 end_axis = 2 [default = -1];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/hdf5data.html b/tutorial/layers/hdf5data.html new file mode 100644 index 00000000000..1f94f1cb372 --- /dev/null +++ b/tutorial/layers/hdf5data.html @@ -0,0 +1,94 @@ + + + + + + + + + Caffe | HDF5 Data Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      HDF5 Data Layer

      + + + +

      Parameters

      + + + +
      // Message that stores parameters used by HDF5DataLayer
+message HDF5DataParameter {
+  // Specify the data source.
+  optional string source = 1;
+  // Specify the batch size.
+  optional uint32 batch_size = 2;
+
+  // Specify whether to shuffle the data.
+  // If shuffle == true, the ordering of the HDF5 files is shuffled,
+  // and the ordering of data within any given HDF5 file is shuffled,
+  // but data between different files are not interleaved; all of a file's
+  // data are output (in a random order) before moving onto another file.
+  optional bool shuffle = 3 [default = false];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/hdf5output.html b/tutorial/layers/hdf5output.html new file mode 100644 index 00000000000..26ab5e2e9ad --- /dev/null +++ b/tutorial/layers/hdf5output.html @@ -0,0 +1,93 @@ + + + + + + + + + Caffe | HDF5 Output Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      HDF5 Output Layer

      + + + +

      The HDF5 output layer performs the opposite function of the other layers in this section: it writes its input blobs to disk.

      + +

      Parameters

      + +
        +
      • Parameters (HDF5OutputParameter hdf5_output_param) +
          +
        • Required +
            +
          • file_name: name of file to write to
            • +
          +
          • +
        +
        • +
      • From ./src/caffe/proto/caffe.proto:
        • +
      + +
      message HDF5OutputParameter {
+  optional string file_name = 1;
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/hingeloss.html b/tutorial/layers/hingeloss.html new file mode 100644 index 00000000000..614af426659 --- /dev/null +++ b/tutorial/layers/hingeloss.html @@ -0,0 +1,87 @@ + + + + + + + + + Caffe | Hinge Loss Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Hinge (L1, L2) Loss Layer

      + + + +

      Parameters

      + + + +
      message HingeLossParameter {
+  enum Norm {
+    L1 = 1;
+    L2 = 2;
+  }
+  // Specify the Norm to use L1 or L2
+  optional Norm norm = 1 [default = L1];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/im2col.html b/tutorial/layers/im2col.html new file mode 100644 index 00000000000..64bc2aa4ee0 --- /dev/null +++ b/tutorial/layers/im2col.html @@ -0,0 +1,75 @@ + + + + + + + + + Caffe | Im2col Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      im2col

      + + + +

      Im2col is a helper for doing the image-to-column transformation that you most +likely do not need to know about. This is used in Caffe’s original convolution +to do matrix multiplication by laying out all patches into a matrix.

      + + + +
      +
      + + diff --git a/tutorial/layers/imagedata.html b/tutorial/layers/imagedata.html new file mode 100644 index 00000000000..2cfe013d255 --- /dev/null +++ b/tutorial/layers/imagedata.html @@ -0,0 +1,125 @@ + + + + + + + + + Caffe | ImageData Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      ImageData Layer

      + + + +

      Parameters

      + +
        +
      • Parameters (ImageDataParameter image_data_parameter) +
          +
        • Required +
            +
          • source: name of a text file, with each line giving an image filename and label
            • +
          • batch_size: number of images to batch together
            • +
          +
          • +
        • Optional +
            +
          • rand_skip
            • +
          • shuffle [default false]
            • +
          • new_height, new_width: if provided, resize all images to this size
            • +
          +
          • +
        +
        • +
      • From ./src/caffe/proto/caffe.proto:
        • +
      + +
      message ImageDataParameter {
+  // Specify the data source.
+  optional string source = 1;
+  // Specify the batch size.
+  optional uint32 batch_size = 4 [default = 1];
+  // The rand_skip variable is for the data layer to skip a few data points
+  // to avoid all asynchronous sgd clients to start at the same point. The skip
+  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
+  // be larger than the number of keys in the database.
+  optional uint32 rand_skip = 7 [default = 0];
+  // Whether or not ImageLayer should shuffle the list of files at every epoch.
+  optional bool shuffle = 8 [default = false];
+  // It will also resize images if new_height or new_width are not zero.
+  optional uint32 new_height = 9 [default = 0];
+  optional uint32 new_width = 10 [default = 0];
+  // Specify if the images are color or gray
+  optional bool is_color = 11 [default = true];
+  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
+  // simple scaling and subtracting the data mean, if provided. Note that the
+  // mean subtraction is always carried out before scaling.
+  optional float scale = 2 [default = 1];
+  optional string mean_file = 3;
+  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
+  // crop an image.
+  optional uint32 crop_size = 5 [default = 0];
+  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
+  // data.
+  optional bool mirror = 6 [default = false];
+  optional string root_folder = 12 [default = ""];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/infogainloss.html b/tutorial/layers/infogainloss.html new file mode 100644 index 00000000000..3d243dd4968 --- /dev/null +++ b/tutorial/layers/infogainloss.html @@ -0,0 +1,88 @@ + + + + + + + + + Caffe | Infogain Loss Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Infogain Loss Layer

      + + + +

      A generalization of MultinomialLogisticLossLayer that takes an “information gain” (infogain) matrix specifying the “value” of all label pairs.

      + +

      Equivalent to the MultinomialLogisticLossLayer if the infogain matrix is the identity.

      + +

      Parameters

      + + + +
      message InfogainLossParameter {
+  // Specify the infogain matrix source.
+  optional string source = 1;
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/innerproduct.html b/tutorial/layers/innerproduct.html new file mode 100644 index 00000000000..764326e545d --- /dev/null +++ b/tutorial/layers/innerproduct.html @@ -0,0 +1,156 @@ + + + + + + + + + Caffe | Inner Product / Fully Connected Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Inner Product / Fully Connected Layer

      + +
        +
      • Layer type: InnerProduct
        • +
      • Doxygen Documentation
        • +
      • Header: ./include/caffe/layers/inner_product_layer.hpp
        • +
      • CPU implementation: ./src/caffe/layers/inner_product_layer.cpp
        • +
      • +

        CUDA GPU implementation: ./src/caffe/layers/inner_product_layer.cu

        +
        • +
      • Input +
          +
        • n * c_i * h_i * w_i
          • +
        +
        • +
      • Output +
          +
        • n * c_o * 1 * 1
          • +
        +
        • +
      • +

        Sample

        + + 
        layer {
+  name: "fc8"
+  type: "InnerProduct"
+  # learning rate and decay multipliers for the weights
+  param { lr_mult: 1 decay_mult: 1 }
+  # learning rate and decay multipliers for the biases
+  param { lr_mult: 2 decay_mult: 0 }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  bottom: "fc7"
+  top: "fc8"
+}
+
        +
        +
        • +
      + +

      The InnerProduct layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob’s height and width set to 1).

      + +

      Parameters

      + +
        +
      • Parameters (InnerProductParameter inner_product_param) +
          +
        • Required +
            +
          • num_output (c_o): the number of filters
            • +
          +
          • +
        • Strongly recommended +
            +
          • weight_filler [default type: 'constant' value: 0]
            • +
          +
          • +
        • Optional +
            +
          • bias_filler [default type: 'constant' value: 0]
            • +
          • bias_term [default true]: specifies whether to learn and apply a set of additive biases to the filter outputs
            • +
          +
          • +
        +
        • +
      • From ./src/caffe/proto/caffe.proto:
        • +
      + +
      message InnerProductParameter {
+  optional uint32 num_output = 1; // The number of outputs for the layer
+  optional bool bias_term = 2 [default = true]; // whether to have bias terms
+  optional FillerParameter weight_filler = 3; // The filler for the weight
+  optional FillerParameter bias_filler = 4; // The filler for the bias
+
+  // The first axis to be lumped into a single inner product computation;
+  // all preceding axes are retained in the output.
+  // May be negative to index from the end (e.g., -1 for the last axis).
+  optional int32 axis = 5 [default = 1];
+  // Specify whether to transpose the weight matrix or not.
+  // If transpose == true, any operations will be performed on the transpose
+  // of the weight matrix. The weight matrix itself is not going to be transposed
+  // but rather the transfer flag of operations will be toggled accordingly.
+  optional bool transpose = 6 [default = false];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/input.html b/tutorial/layers/input.html new file mode 100644 index 00000000000..73f3501046b --- /dev/null +++ b/tutorial/layers/input.html @@ -0,0 +1,86 @@ + + + + + + + + + Caffe | Input Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Input Layer

      + + + +

      Parameters

      + + + +
      message InputParameter {
+  // This layer produces N >= 1 top blob(s) to be assigned manually.
+  // Define N shapes to set a shape for each top.
+  // Define 1 shape to set the same shape for every top.
+  // Define no shape to defer to reshaping manually.
+  repeated BlobShape shape = 1;
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/log.html b/tutorial/layers/log.html new file mode 100644 index 00000000000..07d3e1ac041 --- /dev/null +++ b/tutorial/layers/log.html @@ -0,0 +1,89 @@ + + + + + + + + + Caffe | Log Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Log Layer

      + + + +

      Parameters

      + + + +
      // Message that stores parameters used by LogLayer
+message LogParameter {
+  // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.
+  // Or if base is set to the default (-1), base is set to e,
+  // so y = ln(shift + scale * x) = log_e(shift + scale * x)
+  optional float base = 1 [default = -1.0];
+  optional float scale = 2 [default = 1.0];
+  optional float shift = 3 [default = 0.0];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/lrn.html b/tutorial/layers/lrn.html new file mode 100644 index 00000000000..5a70fc56b30 --- /dev/null +++ b/tutorial/layers/lrn.html @@ -0,0 +1,105 @@ + + + + + + + + + Caffe | Local Response Normalization (LRN) + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Local Response Normalization (LRN)

      + +
        +
      • Layer type: LRN
        • +
      • Doxygen Documentation
        • +
      • Header: ./include/caffe/layers/lrn_layer.hpp
        • +
      • CPU Implementation: ./src/caffe/layers/lrn_layer.cpp
        • +
      • CUDA GPU Implementation: ./src/caffe/layers/lrn_layer.cu
        • +
      • Parameters (LRNParameter lrn_param) +
          +
        • Optional +
            +
          • local_size [default 5]: the number of channels to sum over (for cross channel LRN) or the side length of the square region to sum over (for within channel LRN)
            • +
          • alpha [default 1]: the scaling parameter (see below)
            • +
          • beta [default 5]: the exponent (see below)
            • +
          • norm_region [default ACROSS_CHANNELS]: whether to sum over adjacent channels (ACROSS_CHANNELS) or nearby spatial locaitons (WITHIN_CHANNEL)
            • +
          +
          • +
        +
        • +
      + +

      The local response normalization layer performs a kind of “lateral inhibition” by normalizing over local input regions. In ACROSS_CHANNELS mode, the local regions extend across nearby channels, but have no spatial extent (i.e., they have shape local_size x 1 x 1). In WITHIN_CHANNEL mode, the local regions extend spatially, but are in separate channels (i.e., they have shape 1 x local_size x local_size). Each input value is divided by , where is the size of each local region, and the sum is taken over the region centered at that value (zero padding is added where necessary).

      + +

      Parameters

      + + + +
      message BatchNormParameter {
+  // If false, accumulate global mean/variance values via a moving average. If
+  // true, use those accumulated values instead of computing mean/variance
+  // across the batch.
+  optional bool use_global_stats = 1;
+  // How much does the moving average decay each iteration?
+  optional float moving_average_fraction = 2 [default = .999];
+  // Small value to add to the variance estimate so that we don't divide by
+  // zero.
+  optional float eps = 3 [default = 1e-5];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/lstm.html b/tutorial/layers/lstm.html new file mode 100644 index 00000000000..18cb0bbca95 --- /dev/null +++ b/tutorial/layers/lstm.html @@ -0,0 +1,99 @@ + + + + + + + + + Caffe | LSTM Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      LSTM Layer

      + + + +

      Parameters

      + + + +
      // Message that stores parameters used by RecurrentLayer
+message RecurrentParameter {
+  // The dimension of the output (and usually hidden state) representation --
+  // must be explicitly set to non-zero.
+  optional uint32 num_output = 1 [default = 0];
+
+  optional FillerParameter weight_filler = 2; // The filler for the weight
+  optional FillerParameter bias_filler = 3; // The filler for the bias
+
+  // Whether to enable displaying debug_info in the unrolled recurrent net.
+  optional bool debug_info = 4 [default = false];
+
+  // Whether to add as additional inputs (bottoms) the initial hidden state
+  // blobs, and add as additional outputs (tops) the final timestep hidden state
+  // blobs.  The number of additional bottom/top blobs required depends on the
+  // recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.
+  optional bool expose_hidden = 5 [default = false];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/memorydata.html b/tutorial/layers/memorydata.html new file mode 100644 index 00000000000..75360f8a158 --- /dev/null +++ b/tutorial/layers/memorydata.html @@ -0,0 +1,98 @@ + + + + + + + + + Caffe | Memory Data Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Memory Data Layer

      + + + +

      The memory data layer reads data directly from memory, without copying it. In order to use it, one must call MemoryDataLayer::Reset (from C++) or Net.set_input_arrays (from Python) in order to specify a source of contiguous data (as 4D row major array), which is read one batch-sized chunk at a time.

      + +

      Parameters

      + + + +
      message MemoryDataParameter {
+  optional uint32 batch_size = 1;
+  optional uint32 channels = 2;
+  optional uint32 height = 3;
+  optional uint32 width = 4;
+}
      + +
        +
      • Parameters +
          +
        • Required +
            +
          • batch_size, channels, height, width: specify the size of input chunks to read from memory
            • +
          +
          • +
        +
        • +
      + + +
      +
      + + diff --git a/tutorial/layers/multinomiallogisticloss.html b/tutorial/layers/multinomiallogisticloss.html new file mode 100644 index 00000000000..3d3acedda80 --- /dev/null +++ b/tutorial/layers/multinomiallogisticloss.html @@ -0,0 +1,107 @@ + + + + + + + + + Caffe | Multinomial Logistic Loss Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Multinomial Logistic Loss Layer

      + + + +

      Parameters

      + + + +
      // Message that stores parameters shared by loss layers
+message LossParameter {
+  // If specified, ignore instances with the given label.
+  optional int32 ignore_label = 1;
+  // How to normalize the loss for loss layers that aggregate across batches,
+  // spatial dimensions, or other dimensions.  Currently only implemented in
+  // SoftmaxWithLoss and SigmoidCrossEntropyLoss layers.
+  enum NormalizationMode {
+    // Divide by the number of examples in the batch times spatial dimensions.
+    // Outputs that receive the ignore label will NOT be ignored in computing
+    // the normalization factor.
+    FULL = 0;
+    // Divide by the total number of output locations that do not take the
+    // ignore_label.  If ignore_label is not set, this behaves like FULL.
+    VALID = 1;
+    // Divide by the batch size.
+    BATCH_SIZE = 2;
+    // Do not normalize the loss.
+    NONE = 3;
+  }
+  // For historical reasons, the default normalization for
+  // SigmoidCrossEntropyLoss is BATCH_SIZE and *not* VALID.
+  optional NormalizationMode normalization = 3 [default = VALID];
+  // Deprecated.  Ignored if normalization is specified.  If normalization
+  // is not specified, then setting this to false will be equivalent to
+  // normalization = BATCH_SIZE to be consistent with previous behavior.
+  optional bool normalize = 2;
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/mvn.html b/tutorial/layers/mvn.html new file mode 100644 index 00000000000..5aa125a33a1 --- /dev/null +++ b/tutorial/layers/mvn.html @@ -0,0 +1,90 @@ + + + + + + + + + Caffe | Mean-Variance Normalization (MVN) Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Mean-Variance Normalization (MVN) Layer

      + + + +

      Parameters

      + + + +
      message MVNParameter {
+  // This parameter can be set to false to normalize mean only
+  optional bool normalize_variance = 1 [default = true];
+
+  // This parameter can be set to true to perform DNN-like MVN
+  optional bool across_channels = 2 [default = false];
+
+  // Epsilon for not dividing by zero while normalizing variance
+  optional float eps = 3 [default = 1e-9];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/parameter.html b/tutorial/layers/parameter.html new file mode 100644 index 00000000000..8da63b86525 --- /dev/null +++ b/tutorial/layers/parameter.html @@ -0,0 +1,84 @@ + + + + + + + + + Caffe | Parameter Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Parameter Layer

      + + + +

      See https://github.com/BVLC/caffe/pull/2079.

      + +

      Parameters

      + + + +
      message ParameterParameter {
+  optional BlobShape shape = 1;
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/pooling.html b/tutorial/layers/pooling.html new file mode 100644 index 00000000000..07e6ff0d9b3 --- /dev/null +++ b/tutorial/layers/pooling.html @@ -0,0 +1,155 @@ + + + + + + + + + Caffe | Pooling Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Pooling

      + + + +

      Parameters

      + +
        +
      • Parameters (PoolingParameter pooling_param) +
          +
        • Required +
            +
          • kernel_size (or kernel_h and kernel_w): specifies height and width of each filter
            • +
          +
          • +
        • Optional +
            +
          • pool [default MAX]: the pooling method. Currently MAX, AVE, or STOCHASTIC
            • +
          • pad (or pad_h and pad_w) [default 0]: specifies the number of pixels to (implicitly) add to each side of the input
            • +
          • stride (or stride_h and stride_w) [default 1]: specifies the intervals at which to apply the filters to the input
            • +
          +
          • +
        +
        • +
      • From ./src/caffe/proto/caffe.proto:
        • +
      + +
      message PoolingParameter {
+  enum PoolMethod {
+    MAX = 0;
+    AVE = 1;
+    STOCHASTIC = 2;
+  }
+  optional PoolMethod pool = 1 [default = MAX]; // The pooling method
+  // Pad, kernel size, and stride are all given as a single value for equal
+  // dimensions in height and width or as Y, X pairs.
+  optional uint32 pad = 4 [default = 0]; // The padding size (equal in Y, X)
+  optional uint32 pad_h = 9 [default = 0]; // The padding height
+  optional uint32 pad_w = 10 [default = 0]; // The padding width
+  optional uint32 kernel_size = 2; // The kernel size (square)
+  optional uint32 kernel_h = 5; // The kernel height
+  optional uint32 kernel_w = 6; // The kernel width
+  optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X)
+  optional uint32 stride_h = 7; // The stride height
+  optional uint32 stride_w = 8; // The stride width
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 11 [default = DEFAULT];
+  // If global_pooling then it will pool over the size of the bottom by doing
+  // kernel_h = bottom->height and kernel_w = bottom->width
+  optional bool global_pooling = 12 [default = false];
+}
      + +

      Sample

      +
        +
      • +

        Sample (as seen in ./models/bvlc_reference_caffenet/train_val.prototxt)

        + + 
        layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3 # pool over a 3x3 region
+    stride: 2      # step two pixels (in the bottom blob) between pooling regions
+  }
+}
+
        +
        +
        • +
      + + +
      +
      + + diff --git a/tutorial/layers/power.html b/tutorial/layers/power.html new file mode 100644 index 00000000000..11f59141c19 --- /dev/null +++ b/tutorial/layers/power.html @@ -0,0 +1,118 @@ + + + + + + + + + Caffe | Power Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Power Layer

      + + + +

      The Power layer computes the output as (shift + scale * x) ^ power for each input element x.

      + +

      Parameters

      +
        +
      • Parameters (PowerParameter power_param) +
          +
        • Optional +
            +
          • power [default 1]
            • +
          • scale [default 1]
            • +
          • shift [default 0]
            • +
          +
          • +
        +
        • +
      • From ./src/caffe/proto/caffe.proto:
        • +
      + +
      message PowerParameter {
+  // PowerLayer computes outputs y = (shift + scale * x) ^ power.
+  optional float power = 1 [default = 1.0];
+  optional float scale = 2 [default = 1.0];
+  optional float shift = 3 [default = 0.0];
+}
      + +

      Sample

      + +
        layer {
+    name: "layer"
+    bottom: "in"
+    top: "out"
+    type: "Power"
+    power_param {
+      power: 1
+      scale: 1
+      shift: 0
+    }
+  }
+
      +
      + +

      See also

      + + + + +
      +
      + + diff --git a/tutorial/layers/prelu.html b/tutorial/layers/prelu.html new file mode 100644 index 00000000000..0afe36d323e --- /dev/null +++ b/tutorial/layers/prelu.html @@ -0,0 +1,89 @@ + + + + + + + + + Caffe | PReLU Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      PReLU Layer

      + + + +

      Parameters

      + + + +
      message PReLUParameter {
+  // Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:
+  // Surpassing Human-Level Performance on ImageNet Classification, 2015.
+
+  // Initial value of a_i. Default is a_i=0.25 for all i.
+  optional FillerParameter filler = 1;
+  // Whether or not slope parameters are shared across channels.
+  optional bool channel_shared = 2 [default = false];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/python.html b/tutorial/layers/python.html new file mode 100644 index 00000000000..162c1cafe8c --- /dev/null +++ b/tutorial/layers/python.html @@ -0,0 +1,101 @@ + + + + + + + + + Caffe | Python Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Python Layer

      + + + +

      The Python layer allows users to add customized layers without modifying the Caffe core code.

      + +

      Parameters

      + + + +
      message PythonParameter {
+  optional string module = 1;
+  optional string layer = 2;
+  // This value is set to the attribute `param_str` of the `PythonLayer` object
+  // in Python before calling the `setup()` method. This could be a number,
+  // string, dictionary in Python dict format, JSON, etc. You may parse this
+  // string in `setup` method and use it in `forward` and `backward`.
+  optional string param_str = 3 [default = ''];
+  // Whether this PythonLayer is shared among worker solvers during data parallelism.
+  // If true, each worker solver sequentially run forward from this layer.
+  // This value should be set true if you are using it as a data layer.
+  optional bool share_in_parallel = 4 [default = false];
+}
      + +

      Examples and tutorials

      + + + + +
      +
      + + diff --git a/tutorial/layers/recurrent.html b/tutorial/layers/recurrent.html new file mode 100644 index 00000000000..c81774e95bc --- /dev/null +++ b/tutorial/layers/recurrent.html @@ -0,0 +1,98 @@ + + + + + + + + + Caffe | Recurrent Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Recurrent Layer

      + + + +

      Parameters

      + + + +
      // Message that stores parameters used by RecurrentLayer
+message RecurrentParameter {
+  // The dimension of the output (and usually hidden state) representation --
+  // must be explicitly set to non-zero.
+  optional uint32 num_output = 1 [default = 0];
+
+  optional FillerParameter weight_filler = 2; // The filler for the weight
+  optional FillerParameter bias_filler = 3; // The filler for the bias
+
+  // Whether to enable displaying debug_info in the unrolled recurrent net.
+  optional bool debug_info = 4 [default = false];
+
+  // Whether to add as additional inputs (bottoms) the initial hidden state
+  // blobs, and add as additional outputs (tops) the final timestep hidden state
+  // blobs.  The number of additional bottom/top blobs required depends on the
+  // recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.
+  optional bool expose_hidden = 5 [default = false];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/reduction.html b/tutorial/layers/reduction.html new file mode 100644 index 00000000000..ead0f1fff7a --- /dev/null +++ b/tutorial/layers/reduction.html @@ -0,0 +1,108 @@ + + + + + + + + + Caffe | Reduction Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Reduction Layer

      + + + +

      Parameters

      + + + +
      // Message that stores parameters used by ReductionLayer
+message ReductionParameter {
+  enum ReductionOp {
+    SUM = 1;
+    ASUM = 2;
+    SUMSQ = 3;
+    MEAN = 4;
+  }
+
+  optional ReductionOp operation = 1 [default = SUM]; // reduction operation
+
+  // The first axis to reduce to a scalar -- may be negative to index from the
+  // end (e.g., -1 for the last axis).
+  // (Currently, only reduction along ALL "tail" axes is supported; reduction
+  // of axis M through N, where N < num_axes - 1, is unsupported.)
+  // Suppose we have an n-axis bottom Blob with shape:
+  //     (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)).
+  // If axis == m, the output Blob will have shape
+  //     (d0, d1, d2, ..., d(m-1)),
+  // and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1))
+  // times, each including (dm * d(m+1) * ... * d(n-1)) individual data.
+  // If axis == 0 (the default), the output Blob always has the empty shape
+  // (count 1), performing reduction across the entire input --
+  // often useful for creating new loss functions.
+  optional int32 axis = 2 [default = 0];
+
+  optional float coeff = 3 [default = 1.0]; // coefficient for output
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/relu.html b/tutorial/layers/relu.html new file mode 100644 index 00000000000..1fdbfbbed78 --- /dev/null +++ b/tutorial/layers/relu.html @@ -0,0 +1,117 @@ + + + + + + + + + Caffe | ReLU / Rectified-Linear and Leaky-ReLU Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      ReLU / Rectified-Linear and Leaky-ReLU Layer

      + + + +

      Given an input value x, The ReLU layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.

      + +

      Parameters

      + +
        +
      • Parameters (ReLUParameter relu_param) +
          +
        • Optional +
            +
          • negative_slope [default 0]: specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.
            • +
          +
          • +
        +
        • +
      • From ./src/caffe/proto/caffe.proto:
        • +
      + +
      // Message that stores parameters used by ReLULayer
+message ReLUParameter {
+  // Allow non-zero slope for negative inputs to speed up optimization
+  // Described in:
+  // Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities
+  // improve neural network acoustic models. In ICML Workshop on Deep Learning
+  // for Audio, Speech, and Language Processing.
+  optional float negative_slope = 1 [default = 0];
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 2 [default = DEFAULT];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/reshape.html b/tutorial/layers/reshape.html new file mode 100644 index 00000000000..90bee0588fc --- /dev/null +++ b/tutorial/layers/reshape.html @@ -0,0 +1,192 @@ + + + + + + + + + Caffe | Reshape Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Reshape Layer

      +
        +
      • Layer type: Reshape
        • +
      • Doxygen Documentation
        • +
      • Header: ./include/caffe/layers/reshape_layer.hpp
        • +
      • +

        Implementation: ./src/caffe/layers/reshape_layer.cpp

        +
        • +
      • Input +
          +
        • a single blob with arbitrary dimensions
          • +
        +
        • +
      • Output +
          +
        • the same blob, with modified dimensions, as specified by reshape_param
          • +
        +
        • +
      • +

        Sample

        + + 
          layer {
+    name: "reshape"
+    type: "Reshape"
+    bottom: "input"
+    top: "output"
+    reshape_param {
+      shape {
+        dim: 0  # copy the dimension from below
+        dim: 2
+        dim: 3
+        dim: -1 # infer it from the other dimensions
+      }
+    }
+  }
+
        +
        +
        • +
      + +

      The Reshape layer can be used to change the dimensions of its input, without changing its data. Just like the Flatten layer, only the dimensions are changed; no data is copied in the process.

      + +

      Output dimensions are specified by the ReshapeParam proto. Positive numbers are used directly, setting the corresponding dimension of the output blob. In addition, two special values are accepted for any of the target dimension values:

      + +
        +
      • 0 means “copy the respective dimension of the bottom layer”. That is, if the bottom has 2 as its 1st dimension, the top will have 2 as its 1st dimension as well, given dim: 0 as the 1st target dimension.
        • +
      • -1 stands for “infer this from the other dimensions”. This behavior is similar to that of -1 in numpy’s or [] for MATLAB’s reshape: this dimension is calculated to keep the overall element count the same as in the bottom layer. At most one -1 can be used in a reshape operation.
        • +
      + +

      As another example, specifying reshape_param { shape { dim: 0 dim: -1 } } makes the layer behave in exactly the same way as the Flatten layer.

      + +

      Parameters

      + +
        +
      • Parameters (ReshapeParameter reshape_param) +
          +
        • Optional: (also see detailed description below) +
            +
          • shape
            • +
          +
          • +
        +
        • +
      • From ./src/caffe/proto/caffe.proto:
        • +
      + +
      message ReshapeParameter {
+  // Specify the output dimensions. If some of the dimensions are set to 0,
+  // the corresponding dimension from the bottom layer is used (unchanged).
+  // Exactly one dimension may be set to -1, in which case its value is
+  // inferred from the count of the bottom blob and the remaining dimensions.
+  // For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8:
+  //
+  //   layer {
+  //     type: "Reshape" bottom: "input" top: "output"
+  //     reshape_param { ... }
+  //   }
+  //
+  // If "input" is 2D with shape 2 x 8, then the following reshape_param
+  // specifications are all equivalent, producing a 3D blob "output" with shape
+  // 2 x 2 x 4:
+  //
+  //   reshape_param { shape { dim:  2  dim: 2  dim:  4 } }
+  //   reshape_param { shape { dim:  0  dim: 2  dim:  4 } }
+  //   reshape_param { shape { dim:  0  dim: 2  dim: -1 } }
+  //   reshape_param { shape { dim:  0  dim:-1  dim:  4 } }
+  //
+  optional BlobShape shape = 1;
+
+  // axis and num_axes control the portion of the bottom blob's shape that are
+  // replaced by (included in) the reshape. By default (axis == 0 and
+  // num_axes == -1), the entire bottom blob shape is included in the reshape,
+  // and hence the shape field must specify the entire output shape.
+  //
+  // axis may be non-zero to retain some portion of the beginning of the input
+  // shape (and may be negative to index from the end; e.g., -1 to begin the
+  // reshape after the last axis, including nothing in the reshape,
+  // -2 to include only the last axis, etc.).
+  //
+  // For example, suppose "input" is a 2D blob with shape 2 x 8.
+  // Then the following ReshapeLayer specifications are all equivalent,
+  // producing a blob "output" with shape 2 x 2 x 4:
+  //
+  //   reshape_param { shape { dim: 2  dim: 2  dim: 4 } }
+  //   reshape_param { shape { dim: 2  dim: 4 } axis:  1 }
+  //   reshape_param { shape { dim: 2  dim: 4 } axis: -3 }
+  //
+  // num_axes specifies the extent of the reshape.
+  // If num_axes >= 0 (and axis >= 0), the reshape will be performed only on
+  // input axes in the range [axis, axis+num_axes].
+  // num_axes may also be -1, the default, to include all remaining axes
+  // (starting from axis).
+  //
+  // For example, suppose "input" is a 2D blob with shape 2 x 8.
+  // Then the following ReshapeLayer specifications are equivalent,
+  // producing a blob "output" with shape 1 x 2 x 8.
+  //
+  //   reshape_param { shape { dim:  1  dim: 2  dim:  8 } }
+  //   reshape_param { shape { dim:  1  dim: 2  }  num_axes: 1 }
+  //   reshape_param { shape { dim:  1  }  num_axes: 0 }
+  //
+  // On the other hand, these would produce output blob shape 2 x 1 x 8:
+  //
+  //   reshape_param { shape { dim: 2  dim: 1  dim: 8  }  }
+  //   reshape_param { shape { dim: 1 }  axis: 1  num_axes: 0 }
+  //
+  optional int32 axis = 2 [default = 0];
+  optional int32 num_axes = 3 [default = -1];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/rnn.html b/tutorial/layers/rnn.html new file mode 100644 index 00000000000..1e82e0870ae --- /dev/null +++ b/tutorial/layers/rnn.html @@ -0,0 +1,97 @@ + + + + + + + + + Caffe | RNN Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      RNN Layer

      + + + +

      Parameters

      + + + +
      // Message that stores parameters used by RecurrentLayer
+message RecurrentParameter {
+  // The dimension of the output (and usually hidden state) representation --
+  // must be explicitly set to non-zero.
+  optional uint32 num_output = 1 [default = 0];
+
+  optional FillerParameter weight_filler = 2; // The filler for the weight
+  optional FillerParameter bias_filler = 3; // The filler for the bias
+
+  // Whether to enable displaying debug_info in the unrolled recurrent net.
+  optional bool debug_info = 4 [default = false];
+
+  // Whether to add as additional inputs (bottoms) the initial hidden state
+  // blobs, and add as additional outputs (tops) the final timestep hidden state
+  // blobs.  The number of additional bottom/top blobs required depends on the
+  // recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.
+  optional bool expose_hidden = 5 [default = false];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/scale.html b/tutorial/layers/scale.html new file mode 100644 index 00000000000..9ab2970015f --- /dev/null +++ b/tutorial/layers/scale.html @@ -0,0 +1,116 @@ + + + + + + + + + Caffe | Scale Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Scale Layer

      + + + +

      Parameters

      + + + +
      message ScaleParameter {
+  // The first axis of bottom[0] (the first input Blob) along which to apply
+  // bottom[1] (the second input Blob).  May be negative to index from the end
+  // (e.g., -1 for the last axis).
+  //
+  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
+  // top[0] will have the same shape, and bottom[1] may have any of the
+  // following shapes (for the given value of axis):
+  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
+  //    (axis == 1 == -3)          3;     3x40;     3x40x60
+  //    (axis == 2 == -2)                   40;       40x60
+  //    (axis == 3 == -1)                                60
+  // Furthermore, bottom[1] may have the empty shape (regardless of the value of
+  // "axis") -- a scalar multiplier.
+  optional int32 axis = 1 [default = 1];
+
+  // (num_axes is ignored unless just one bottom is given and the scale is
+  // a learned parameter of the layer.  Otherwise, num_axes is determined by the
+  // number of axes by the second bottom.)
+  // The number of axes of the input (bottom[0]) covered by the scale
+  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
+  // Set num_axes := 0, to multiply with a zero-axis Blob: a scalar.
+  optional int32 num_axes = 2 [default = 1];
+
+  // (filler is ignored unless just one bottom is given and the scale is
+  // a learned parameter of the layer.)
+  // The initialization for the learned scale parameter.
+  // Default is the unit (1) initialization, resulting in the ScaleLayer
+  // initially performing the identity operation.
+  optional FillerParameter filler = 3;
+
+  // Whether to also learn a bias (equivalent to a ScaleLayer+BiasLayer, but
+  // may be more efficient).  Initialized with bias_filler (defaults to 0).
+  optional bool bias_term = 4 [default = false];
+  optional FillerParameter bias_filler = 5;
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/sigmoid.html b/tutorial/layers/sigmoid.html new file mode 100644 index 00000000000..995f8662221 --- /dev/null +++ b/tutorial/layers/sigmoid.html @@ -0,0 +1,88 @@ + + + + + + + + + Caffe | Sigmoid Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Sigmoid Layer

      + + + +

      Parameters

      + + + +
      message SigmoidParameter {
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 1 [default = DEFAULT];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/sigmoidcrossentropyloss.html b/tutorial/layers/sigmoidcrossentropyloss.html new file mode 100644 index 00000000000..feed41fb1f1 --- /dev/null +++ b/tutorial/layers/sigmoidcrossentropyloss.html @@ -0,0 +1,73 @@ + + + + + + + + + Caffe | Sigmoid Cross-Entropy Loss Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Sigmoid Cross-Entropy Loss Layer

      + + + +

      To-do.

      + + +
      +
      + + diff --git a/tutorial/layers/silence.html b/tutorial/layers/silence.html new file mode 100644 index 00000000000..b109c64b424 --- /dev/null +++ b/tutorial/layers/silence.html @@ -0,0 +1,93 @@ + + + + + + + + + Caffe | Silence Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Silence Layer

      + + + +

      Silences a blob, so that it is not printed.

      + +

      Parameters

      + + + +
      message BatchNormParameter {
+  // If false, accumulate global mean/variance values via a moving average. If
+  // true, use those accumulated values instead of computing mean/variance
+  // across the batch.
+  optional bool use_global_stats = 1;
+  // How much does the moving average decay each iteration?
+  optional float moving_average_fraction = 2 [default = .999];
+  // Small value to add to the variance estimate so that we don't divide by
+  // zero.
+  optional float eps = 3 [default = 1e-5];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/slice.html b/tutorial/layers/slice.html new file mode 100644 index 00000000000..d9f2ceab083 --- /dev/null +++ b/tutorial/layers/slice.html @@ -0,0 +1,117 @@ + + + + + + + + + Caffe | Slice Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Slice Layer

      + + + +

      The Slice layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.

      + +
        +
      • +

        Sample

        + + 
        layer {
+  name: "slicer_label"
+  type: "Slice"
+  bottom: "label"
+  ## Example of label with a shape N x 3 x 1 x 1
+  top: "label1"
+  top: "label2"
+  top: "label3"
+  slice_param {
+    axis: 1
+    slice_point: 1
+    slice_point: 2
+  }
+}
+
        +
        +
        • +
      + +

      axis indicates the target axis; slice_point indicates indexes in the selected dimension (the number of indices must be equal to the number of top blobs minus one).

      + +

      Parameters

      + + + +
      message SliceParameter {
+  // The axis along which to slice -- may be negative to index from the end
+  // (e.g., -1 for the last axis).
+  // By default, SliceLayer concatenates blobs along the "channels" axis (1).
+  optional int32 axis = 3 [default = 1];
+  repeated uint32 slice_point = 2;
+
+  // DEPRECATED: alias for "axis" -- does not support negative indexing.
+  optional uint32 slice_dim = 1 [default = 1];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/softmax.html b/tutorial/layers/softmax.html new file mode 100644 index 00000000000..8f78f711eb5 --- /dev/null +++ b/tutorial/layers/softmax.html @@ -0,0 +1,99 @@ + + + + + + + + + Caffe | Softmax Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Softmax Layer

      + + + +

      Parameters

      + + + +
      // Message that stores parameters used by SoftmaxLayer, SoftmaxWithLossLayer
+message SoftmaxParameter {
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 1 [default = DEFAULT];
+
+  // The axis along which to perform the softmax -- may be negative to index
+  // from the end (e.g., -1 for the last axis).
+  // Any other axes will be evaluated as independent softmaxes.
+  optional int32 axis = 2 [default = 1];
+}
      + +

      See also

      + + + + +
      +
      + + diff --git a/tutorial/layers/softmaxwithloss.html b/tutorial/layers/softmaxwithloss.html new file mode 100644 index 00000000000..5bee89450de --- /dev/null +++ b/tutorial/layers/softmaxwithloss.html @@ -0,0 +1,135 @@ + + + + + + + + + Caffe | Softmax with Loss Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Softmax with Loss Layer

      + + + +

      The softmax loss layer computes the multinomial logistic loss of the softmax of its inputs. It’s conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.

      + +

      Parameters

      + + + +
      // Message that stores parameters used by SoftmaxLayer, SoftmaxWithLossLayer
+message SoftmaxParameter {
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 1 [default = DEFAULT];
+
+  // The axis along which to perform the softmax -- may be negative to index
+  // from the end (e.g., -1 for the last axis).
+  // Any other axes will be evaluated as independent softmaxes.
+  optional int32 axis = 2 [default = 1];
+}
      + + + +
      // Message that stores parameters shared by loss layers
+message LossParameter {
+  // If specified, ignore instances with the given label.
+  optional int32 ignore_label = 1;
+  // How to normalize the loss for loss layers that aggregate across batches,
+  // spatial dimensions, or other dimensions.  Currently only implemented in
+  // SoftmaxWithLoss and SigmoidCrossEntropyLoss layers.
+  enum NormalizationMode {
+    // Divide by the number of examples in the batch times spatial dimensions.
+    // Outputs that receive the ignore label will NOT be ignored in computing
+    // the normalization factor.
+    FULL = 0;
+    // Divide by the total number of output locations that do not take the
+    // ignore_label.  If ignore_label is not set, this behaves like FULL.
+    VALID = 1;
+    // Divide by the batch size.
+    BATCH_SIZE = 2;
+    // Do not normalize the loss.
+    NONE = 3;
+  }
+  // For historical reasons, the default normalization for
+  // SigmoidCrossEntropyLoss is BATCH_SIZE and *not* VALID.
+  optional NormalizationMode normalization = 3 [default = VALID];
+  // Deprecated.  Ignored if normalization is specified.  If normalization
+  // is not specified, then setting this to false will be equivalent to
+  // normalization = BATCH_SIZE to be consistent with previous behavior.
+  optional bool normalize = 2;
+}
      + +

      See also

      + + + + +
      +
      + + diff --git a/tutorial/layers/split.html b/tutorial/layers/split.html new file mode 100644 index 00000000000..0d2ba9c0adb --- /dev/null +++ b/tutorial/layers/split.html @@ -0,0 +1,77 @@ + + + + + + + + + Caffe | Split Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Split Layer

      + + + +

      The Split layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers.

      + +

      Parameters

      + +

      Does not take any parameters.

      + + +
      +
      + + diff --git a/tutorial/layers/spp.html b/tutorial/layers/spp.html new file mode 100644 index 00000000000..f46cbd87c6f --- /dev/null +++ b/tutorial/layers/spp.html @@ -0,0 +1,94 @@ + + + + + + + + + Caffe | Spatial Pyramid Pooling Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Spatial Pyramid Pooling Layer

      + + + +

      Parameters

      + + + +
      message SPPParameter {
+  enum PoolMethod {
+    MAX = 0;
+    AVE = 1;
+    STOCHASTIC = 2;
+  }
+  optional uint32 pyramid_height = 1;
+  optional PoolMethod pool = 2 [default = MAX]; // The pooling method
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 6 [default = DEFAULT];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/tanh.html b/tutorial/layers/tanh.html new file mode 100644 index 00000000000..442331700d3 --- /dev/null +++ b/tutorial/layers/tanh.html @@ -0,0 +1,86 @@ + + + + + + + + + Caffe | TanH Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      TanH Layer

      + + + +

      Parameters

      + + + +
      message TanHParameter {
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 1 [default = DEFAULT];
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/threshold.html b/tutorial/layers/threshold.html new file mode 100644 index 00000000000..00c91c9b7a2 --- /dev/null +++ b/tutorial/layers/threshold.html @@ -0,0 +1,82 @@ + + + + + + + + + Caffe | Threshold Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Threshold Layer

      + + + +

      Parameters

      + + + +
      // Message that stores parameters used by ThresholdLayer
+message ThresholdParameter {
+  optional float threshold = 1 [default = 0]; // Strictly positive values
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/tile.html b/tutorial/layers/tile.html new file mode 100644 index 00000000000..da508757d34 --- /dev/null +++ b/tutorial/layers/tile.html @@ -0,0 +1,88 @@ + + + + + + + + + Caffe | Tile Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Tile Layer

      + + + +

      Parameters

      + + + +
      // Message that stores parameters used by TileLayer
+message TileParameter {
+  // The index of the axis to tile.
+  optional int32 axis = 1 [default = 1];
+
+  // The number of copies (tiles) of the blob to output.
+  optional int32 tiles = 2;
+}
      + + + +
      +
      + + diff --git a/tutorial/layers/windowdata.html b/tutorial/layers/windowdata.html new file mode 100644 index 00000000000..6967e82f899 --- /dev/null +++ b/tutorial/layers/windowdata.html @@ -0,0 +1,111 @@ + + + + + + + + + Caffe | WindowData Layer + + + + + + + + + + + + + +
      +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      WindowData Layer

      + + + +

      Parameters

      + + + +
      message WindowDataParameter {
+  // Specify the data source.
+  optional string source = 1;
+  // For data pre-processing, we can do simple scaling and subtracting the
+  // data mean, if provided. Note that the mean subtraction is always carried
+  // out before scaling.
+  optional float scale = 2 [default = 1];
+  optional string mean_file = 3;
+  // Specify the batch size.
+  optional uint32 batch_size = 4;
+  // Specify if we would like to randomly crop an image.
+  optional uint32 crop_size = 5 [default = 0];
+  // Specify if we want to randomly mirror data.
+  optional bool mirror = 6 [default = false];
+  // Foreground (object) overlap threshold
+  optional float fg_threshold = 7 [default = 0.5];
+  // Background (non-object) overlap threshold
+  optional float bg_threshold = 8 [default = 0.5];
+  // Fraction of batch that should be foreground objects
+  optional float fg_fraction = 9 [default = 0.25];
+  // Amount of contextual padding to add around a window
+  // (used only by the window_data_layer)
+  optional uint32 context_pad = 10 [default = 0];
+  // Mode for cropping out a detection window
+  // warp: cropped window is warped to a fixed size and aspect ratio
+  // square: the tightest square around the window is cropped
+  optional string crop_mode = 11 [default = "warp"];
+  // cache_images: will load all images in memory for faster access
+  optional bool cache_images = 12 [default = false];
+  // append root_folder to locate images
+  optional string root_folder = 13 [default = ""];
+}
      + + + +
      +
      + + From d219d6ec94492446cdd5d8787414786a30e56db3 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Fri, 14 Apr 2017 17:13:04 -0700 Subject: [PATCH 4/4] 69cf20ad Merge pull request #5539 from shelhamer/caffe-1.0 --- development.html | 8 +- doxygen/absval__layer_8hpp_source.html | 8 +- doxygen/accuracy__layer_8hpp_source.html | 8 +- doxygen/annotated.html | 52 +- doxygen/argmax__layer_8hpp_source.html | 8 +- doxygen/arrowdown.png | Bin 246 -> 0 bytes doxygen/arrowright.png | Bin 229 -> 0 bytes doxygen/base__conv__layer_8hpp_source.html | 10 +- doxygen/base__data__layer_8hpp_source.html | 18 +- doxygen/batch__norm__layer_8hpp_source.html | 8 +- doxygen/batch__reindex__layer_8hpp_source.html | 8 +- doxygen/benchmark_8hpp_source.html | 8 +- doxygen/bias__layer_8hpp_source.html | 8 +- doxygen/blob_8hpp_source.html | 8 +- doxygen/blocking__queue_8hpp_source.html | 8 +- doxygen/bnll__layer_8hpp_source.html | 8 +- doxygen/caffe_8hpp_source.html | 8 +- 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doxygen/form_177.png delete mode 100644 doxygen/form_178.png delete mode 100644 doxygen/form_179.png delete mode 100644 doxygen/loss__layers_8hpp_source.html delete mode 100644 doxygen/neuron__layers_8hpp_source.html delete mode 100644 doxygen/search/all_15.html delete mode 100644 doxygen/search/all_15.js delete mode 100644 doxygen/search/classes_13.html delete mode 100644 doxygen/search/classes_13.js delete mode 100644 doxygen/vision__layers_8hpp_source.html delete mode 100644 performance_hardware.html diff --git a/development.html b/development.html index 89eb2249b35..d0bd7a71526 100644 --- a/development.html +++ b/development.html @@ -36,7 +36,7 @@

      Caffe

      - Deep learning framework by the BVLC + Deep learning framework by BAIR

      Created by @@ -57,7 +57,7 @@

      Caffe

      Development and Contributing

      Caffe is developed with active participation of the community.
      -The BVLC brewers welcome all contributions!

      +The BAIR/BVLC brewers welcome all contributions!

      The exact details of contributions are recorded by versioning and cited in our acknowledgements. This method is impartial and always up-to-date.

      @@ -90,7 +90,7 @@

      Versioning

      The master branch receives all new development including community contributions. We try to keep it in a reliable state, but it is the bleeding edge, and things do get broken every now and then. -BVLC maintainers will periodically make releases by marking stable checkpoints as tags and maintenance branches. Past releases are catalogued online.

      +BAIR maintainers will periodically make releases by marking stable checkpoints as tags and maintenance branches. Past releases are catalogued online.

      Issues & Pull Request Protocol

      @@ -117,7 +117,7 @@

      Issues & Pull Request Protocol

      The following is a poetic presentation of the protocol in code form.

      -

      Shelhamer’s “life of a branch in four acts”

      +

      Shelhamer’s “life of a branch in four acts”

      Make the feature branch off of the latest bvlc/master

      diff --git a/doxygen/absval__layer_8hpp_source.html b/doxygen/absval__layer_8hpp_source.html index 700dd5cb9a3..236a9fe6454 100644 --- a/doxygen/absval__layer_8hpp_source.html +++ b/doxygen/absval__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/absval_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -81,9 +81,9 @@
      -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/accuracy__layer_8hpp_source.html b/doxygen/accuracy__layer_8hpp_source.html index 0cb16a293ea..57d5746571c 100644 --- a/doxygen/accuracy__layer_8hpp_source.html +++ b/doxygen/accuracy__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/accuracy_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -85,9 +85,9 @@
      -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/annotated.html b/doxygen/annotated.html index ccab8d355cd..94b51cc8e47 100644 --- a/doxygen/annotated.html +++ b/doxygen/annotated.html @@ -3,7 +3,7 @@ - + Caffe: Class List @@ -29,7 +29,7 @@ - + @@ -69,12 +69,12 @@  CCursor  CDB  CTransaction - CAbsValLayerComputes $ y = |x| $ + CAbsValLayerComputes $ y = |x| $  CAccuracyLayerComputes the classification accuracy for a one-of-many classification task  CAdaDeltaSolver  CAdaGradSolver  CAdamSolverAdamSolver, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. Described in [1] - CArgMaxLayerCompute the index of the $ K $ max values for each datum across all dimensions $ (C \times H \times W) $ + CArgMaxLayerCompute the index of the $ K $ max values for each datum across all dimensions $ (C \times H \times W) $  CBaseConvolutionLayerAbstract base class that factors out the BLAS code common to ConvolutionLayer and DeconvolutionLayer  CBaseDataLayerProvides base for data layers that feed blobs to the Net  CBasePrefetchingDataLayer @@ -86,30 +86,30 @@  CBlobA wrapper around SyncedMemory holders serving as the basic computational unit through which Layers, Nets, and Solvers interact  ►CBlockingQueue  Csync - CBNLLLayerComputes $ y = x + \log(1 + \exp(-x)) $ if $ x > 0 $; $ y = \log(1 + \exp(x)) $ otherwise + CBNLLLayerComputes $ y = x + \log(1 + \exp(-x)) $ if $ x > 0 $; $ y = \log(1 + \exp(x)) $ otherwise  ►CCaffe  ►CRNG  CGenerator  CConcatLayerTakes at least two Blobs and concatenates them along either the num or channel dimension, outputting the result - CConstantFillerFills a Blob with constant values $ x = 0 $ - CContrastiveLossLayerComputes the contrastive loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks + CConstantFillerFills a Blob with constant values $ x = 0 $ + CContrastiveLossLayerComputes the contrastive loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks  CConvolutionLayerConvolves the input image with a bank of learned filters, and (optionally) adds biases  CCPUTimer  CCropLayerTakes a Blob and crop it, to the shape specified by the second input Blob, across all dimensions after the specified axis  CDataLayer  CDataTransformerApplies common transformations to the input data, such as scaling, mirroring, substracting the image mean..  CDeconvolutionLayerConvolve the input with a bank of learned filters, and (optionally) add biases, treating filters and convolution parameters in the opposite sense as ConvolutionLayer - CDropoutLayerDuring training only, sets a random portion of $x$ to 0, adjusting the rest of the vector magnitude accordingly + CDropoutLayerDuring training only, sets a random portion of $x$ to 0, adjusting the rest of the vector magnitude accordingly  CDummyDataLayerProvides data to the Net generated by a Filler  CEltwiseLayerCompute elementwise operations, such as product and sum, along multiple input Blobs - CELULayerExponential Linear Unit non-linearity $ y = \left\{ \begin{array}{lr} x & \mathrm{if} \; x > 0 \\ \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 \end{array} \right. $ + CELULayerExponential Linear Unit non-linearity $ y = \left\{ \begin{array}{lr} x & \mathrm{if} \; x > 0 \\ \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 \end{array} \right. $  CEmbedLayerA layer for learning "embeddings" of one-hot vector input. Equivalent to an InnerProductLayer with one-hot vectors as input, but for efficiency the input is the "hot" index of each column itself - CEuclideanLossLayerComputes the Euclidean (L2) loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ for real-valued regression tasks - CExpLayerComputes $ y = \gamma ^ {\alpha x + \beta} $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $ + CEuclideanLossLayerComputes the Euclidean (L2) loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ for real-valued regression tasks + CExpLayerComputes $ y = \gamma ^ {\alpha x + \beta} $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $  CFillerFills a Blob with constant or randomly-generated data  CFilterLayerTakes two+ Blobs, interprets last Blob as a selector and filter remaining Blobs accordingly with selector data (0 means that the corresponding item has to be filtered, non-zero means that corresponding item needs to stay)  CFlattenLayerReshapes the input Blob into flat vectors - CGaussianFillerFills a Blob with Gaussian-distributed values $ x = a $ + CGaussianFillerFills a Blob with Gaussian-distributed values $ x = a $  CHDF5DataLayerProvides data to the Net from HDF5 files  CHDF5OutputLayerWrite blobs to disk as HDF5 files  CHingeLossLayerComputes the hinge loss for a one-of-many classification task @@ -122,35 +122,35 @@  CLayerAn interface for the units of computation which can be composed into a Net  CLayerRegisterer  CLayerRegistry - CLogLayerComputes $ y = log_{\gamma}(\alpha x + \beta) $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $ + CLogLayerComputes $ y = log_{\gamma}(\alpha x + \beta) $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $  CLossLayerAn interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth labels – and output a singleton Blob representing the loss  CLRNLayerNormalize the input in a local region across or within feature maps  CLSTMLayerProcesses sequential inputs using a "Long Short-Term Memory" (LSTM) [1] style recurrent neural network (RNN). Implemented by unrolling the LSTM computation through time  CLSTMUnitLayerA helper for LSTMLayer: computes a single timestep of the non-linearity of the LSTM, producing the updated cell and hidden states  CMemoryDataLayerProvides data to the Net from memory - CMSRAFillerFills a Blob with values $ x \sim N(0, \sigma^2) $ where $ \sigma^2 $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average + CMSRAFillerFills a Blob with values $ x \sim N(0, \sigma^2) $ where $ \sigma^2 $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average  CMultinomialLogisticLossLayerComputes the multinomial logistic loss for a one-of-many classification task, directly taking a predicted probability distribution as input  CMVNLayerNormalizes the input to have 0-mean and/or unit (1) variance  CNesterovSolver  ►CNetConnects Layers together into a directed acyclic graph (DAG) specified by a NetParameter  CCallback - CNeuronLayerAn interface for layers that take one blob as input ( $ x $) and produce one equally-sized blob as output ( $ y $), where each element of the output depends only on the corresponding input element + CNeuronLayerAn interface for layers that take one blob as input ( $ x $) and produce one equally-sized blob as output ( $ y $), where each element of the output depends only on the corresponding input element  CParameterLayer  CPoolingLayerPools the input image by taking the max, average, etc. within regions - CPositiveUnitballFillerFills a Blob with values $ x \in [0, 1] $ such that $ \forall i \sum_j x_{ij} = 1 $ - CPowerLayerComputes $ y = (\alpha x + \beta) ^ \gamma $, as specified by the scale $ \alpha $, shift $ \beta $, and power $ \gamma $ - CPReLULayerParameterized Rectified Linear Unit non-linearity $ y_i = \max(0, x_i) + a_i \min(0, x_i) $. The differences from ReLULayer are 1) negative slopes are learnable though backprop and 2) negative slopes can vary across channels. The number of axes of input blob should be greater than or equal to 2. The 1st axis (0-based) is seen as channels + CPositiveUnitballFillerFills a Blob with values $ x \in [0, 1] $ such that $ \forall i \sum_j x_{ij} = 1 $ + CPowerLayerComputes $ y = (\alpha x + \beta) ^ \gamma $, as specified by the scale $ \alpha $, shift $ \beta $, and power $ \gamma $ + CPReLULayerParameterized Rectified Linear Unit non-linearity $ y_i = \max(0, x_i) + a_i \min(0, x_i) $. The differences from ReLULayer are 1) negative slopes are learnable though backprop and 2) negative slopes can vary across channels. The number of axes of input blob should be greater than or equal to 2. The 1st axis (0-based) is seen as channels  CPythonLayer  CRecurrentLayerAn abstract class for implementing recurrent behavior inside of an unrolled network. This Layer type cannot be instantiated – instead, you should use one of its implementations which defines the recurrent architecture, such as RNNLayer or LSTMLayer  CReductionLayerCompute "reductions" – operations that return a scalar output Blob for an input Blob of arbitrary size, such as the sum, absolute sum, and sum of squares - CReLULayerRectified Linear Unit non-linearity $ y = \max(0, x) $. The simple max is fast to compute, and the function does not saturate + CReLULayerRectified Linear Unit non-linearity $ y = \max(0, x) $. The simple max is fast to compute, and the function does not saturate  CReshapeLayer  CRMSPropSolver  CRNNLayerProcesses time-varying inputs using a simple recurrent neural network (RNN). Implemented as a network unrolling the RNN computation in time  CScaleLayerComputes the elementwise product of two input Blobs, with the shape of the latter Blob "broadcast" to match the shape of the former. Equivalent to tiling the latter Blob, then computing the elementwise product. Note: for efficiency and convenience, this layer can additionally perform a "broadcast" sum too when bias_term: true is set  CSGDSolverOptimizes the parameters of a Net using stochastic gradient descent (SGD) with momentum - CSigmoidCrossEntropyLossLayerComputes the cross-entropy (logistic) loss $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $, often used for predicting targets interpreted as probabilities - CSigmoidLayerSigmoid function non-linearity $ y = (1 + \exp(-x))^{-1} $, a classic choice in neural networks + CSigmoidCrossEntropyLossLayerComputes the cross-entropy (logistic) loss $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $, often used for predicting targets interpreted as probabilities + CSigmoidLayerSigmoid function non-linearity $ y = (1 + \exp(-x))^{-1} $, a classic choice in neural networks  CSignalHandler  CSilenceLayerIgnores bottom blobs while producing no top blobs. (This is useful to suppress outputs during testing.)  CSliceLayerTakes a Blob and slices it along either the num or channel dimension, outputting multiple sliced Blob results @@ -163,21 +163,21 @@  CSplitLayerCreates a "split" path in the network by copying the bottom Blob into multiple top Blobs to be used by multiple consuming layers  CSPPLayerDoes spatial pyramid pooling on the input image by taking the max, average, etc. within regions so that the result vector of different sized images are of the same size  CSyncedMemoryManages memory allocation and synchronization between the host (CPU) and device (GPU) - CTanHLayerTanH hyperbolic tangent non-linearity $ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $, popular in auto-encoders + CTanHLayerTanH hyperbolic tangent non-linearity $ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $, popular in auto-encoders  CThresholdLayerTests whether the input exceeds a threshold: outputs 1 for inputs above threshold; 0 otherwise  CTileLayerCopy a Blob along specified dimensions  CTimer - CUniformFillerFills a Blob with uniformly distributed values $ x\sim U(a, b) $ + CUniformFillerFills a Blob with uniformly distributed values $ x\sim U(a, b) $  CWindowDataLayerProvides data to the Net from windows of images files, specified by a window data file - CXavierFillerFills a Blob with values $ x \sim U(-a, +a) $ where $ a $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average + CXavierFillerFills a Blob with values $ x \sim U(-a, +a) $ where $ a $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average
      -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/argmax__layer_8hpp_source.html b/doxygen/argmax__layer_8hpp_source.html index 06669360595..63d59facc58 100644 --- a/doxygen/argmax__layer_8hpp_source.html +++ b/doxygen/argmax__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/argmax_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -81,9 +81,9 @@
      -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/arrowdown.png b/doxygen/arrowdown.png deleted file mode 100644 index 0b63f6d38c4b9ec907b820192ebe9724ed6eca22..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 246 zcmVkw!R34#Lv2LOS^S2tZA31X++9RY}n zChwn@Z)Wz*WWHH{)HDtJnq&A2hk$b-y(>?@z0iHr41EKCGp#T5?07*qoM6N<$f(V3Pvj6}9 diff --git a/doxygen/arrowright.png b/doxygen/arrowright.png deleted file mode 100644 index c6ee22f937a07d1dbfc27c669d11f8ed13e2f152..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 229 zcmV^P)R?RzRoKvklcaQ%HF6%rK2&ZgO(-ihJ_C zzrKgp4jgO( fd_(yg|3PpEQb#9`a?Pz_00000NkvXXu0mjftR`5K diff --git a/doxygen/base__conv__layer_8hpp_source.html b/doxygen/base__conv__layer_8hpp_source.html index 92e3cea3125..c1d57cf8c96 100644 --- a/doxygen/base__conv__layer_8hpp_source.html +++ b/doxygen/base__conv__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/base_conv_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -78,7 +78,7 @@
      caffe::BaseConvolutionLayer::MinTopBlobs
      virtual int MinTopBlobs() const
      Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
      Definition: base_conv_layer.hpp:28
      caffe::BaseConvolutionLayer::col_buffer_shape_
      vector< int > col_buffer_shape_
      The spatial dimensions of the col_buffer.
      Definition: base_conv_layer.hpp:76
      caffe::BaseConvolutionLayer::output_shape_
      vector< int > output_shape_
      The spatial dimensions of the output.
      Definition: base_conv_layer.hpp:78
      -
      caffe::BaseConvolutionLayer::Reshape
      virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
      Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
      Definition: base_conv_layer.cpp:186
      +
      caffe::BaseConvolutionLayer::Reshape
      virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
      Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
      Definition: base_conv_layer.cpp:185
      caffe::BaseConvolutionLayer::conv_input_shape_
      Blob< int > conv_input_shape_
      The spatial dimensions of the convolution input.
      Definition: base_conv_layer.hpp:74
      caffe::BaseConvolutionLayer::stride_
      Blob< int > stride_
      The spatial dimensions of the stride.
      Definition: base_conv_layer.hpp:68
      caffe::Blob
      A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
      Definition: blob.hpp:24
      @@ -86,9 +86,9 @@
      -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/base__data__layer_8hpp_source.html b/doxygen/base__data__layer_8hpp_source.html index 2bc2784907c..73785aad8a5 100644 --- a/doxygen/base__data__layer_8hpp_source.html +++ b/doxygen/base__data__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/base_data_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -66,26 +66,26 @@
      base_data_layer.hpp
      -
      1 #ifndef CAFFE_DATA_LAYERS_HPP_
      2 #define CAFFE_DATA_LAYERS_HPP_
      3 
      4 #include <vector>
      5 
      6 #include "caffe/blob.hpp"
      7 #include "caffe/data_transformer.hpp"
      8 #include "caffe/internal_thread.hpp"
      9 #include "caffe/layer.hpp"
      10 #include "caffe/proto/caffe.pb.h"
      11 #include "caffe/util/blocking_queue.hpp"
      12 
      13 namespace caffe {
      14 
      20 template <typename Dtype>
      21 class BaseDataLayer : public Layer<Dtype> {
      22  public:
      23  explicit BaseDataLayer(const LayerParameter& param);
      24  // LayerSetUp: implements common data layer setup functionality, and calls
      25  // DataLayerSetUp to do special data layer setup for individual layer types.
      26  // This method may not be overridden except by the BasePrefetchingDataLayer.
      27  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      28  const vector<Blob<Dtype>*>& top);
      29  // Data layers should be shared by multiple solvers in parallel
      30  virtual inline bool ShareInParallel() const { return true; }
      31  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
      32  const vector<Blob<Dtype>*>& top) {}
      33  // Data layers have no bottoms, so reshaping is trivial.
      34  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
      35  const vector<Blob<Dtype>*>& top) {}
      36 
      37  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
      38  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
      39  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
      40  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
      41 
      42  protected:
      43  TransformationParameter transform_param_;
      44  shared_ptr<DataTransformer<Dtype> > data_transformer_;
      45  bool output_labels_;
      46 };
      47 
      48 template <typename Dtype>
      49 class Batch {
      50  public:
      51  Blob<Dtype> data_, label_;
      52 };
      53 
      54 template <typename Dtype>
      55 class BasePrefetchingDataLayer :
      56  public BaseDataLayer<Dtype>, public InternalThread {
      57  public:
      58  explicit BasePrefetchingDataLayer(const LayerParameter& param);
      59  // LayerSetUp: implements common data layer setup functionality, and calls
      60  // DataLayerSetUp to do special data layer setup for individual layer types.
      61  // This method may not be overridden.
      62  void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      63  const vector<Blob<Dtype>*>& top);
      64 
      65  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
      66  const vector<Blob<Dtype>*>& top);
      67  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
      68  const vector<Blob<Dtype>*>& top);
      69 
      70  protected:
      71  virtual void InternalThreadEntry();
      72  virtual void load_batch(Batch<Dtype>* batch) = 0;
      73 
      74  vector<shared_ptr<Batch<Dtype> > > prefetch_;
      75  BlockingQueue<Batch<Dtype>*> prefetch_free_;
      76  BlockingQueue<Batch<Dtype>*> prefetch_full_;
      77  Batch<Dtype>* prefetch_current_;
      78 
      79  Blob<Dtype> transformed_data_;
      80 };
      81 
      82 } // namespace caffe
      83 
      84 #endif // CAFFE_DATA_LAYERS_HPP_
      caffe::Batch
      Definition: base_data_layer.hpp:49
      -
      caffe::BasePrefetchingDataLayer
      Definition: base_data_layer.hpp:55
      +
      1 #ifndef CAFFE_DATA_LAYERS_HPP_
      2 #define CAFFE_DATA_LAYERS_HPP_
      3 
      4 #include <vector>
      5 
      6 #include "caffe/blob.hpp"
      7 #include "caffe/data_transformer.hpp"
      8 #include "caffe/internal_thread.hpp"
      9 #include "caffe/layer.hpp"
      10 #include "caffe/proto/caffe.pb.h"
      11 #include "caffe/util/blocking_queue.hpp"
      12 
      13 namespace caffe {
      14 
      20 template <typename Dtype>
      21 class BaseDataLayer : public Layer<Dtype> {
      22  public:
      23  explicit BaseDataLayer(const LayerParameter& param);
      24  // LayerSetUp: implements common data layer setup functionality, and calls
      25  // DataLayerSetUp to do special data layer setup for individual layer types.
      26  // This method may not be overridden except by the BasePrefetchingDataLayer.
      27  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      28  const vector<Blob<Dtype>*>& top);
      29  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
      30  const vector<Blob<Dtype>*>& top) {}
      31  // Data layers have no bottoms, so reshaping is trivial.
      32  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
      33  const vector<Blob<Dtype>*>& top) {}
      34 
      35  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
      36  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
      37  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
      38  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
      39 
      40  protected:
      41  TransformationParameter transform_param_;
      42  shared_ptr<DataTransformer<Dtype> > data_transformer_;
      43  bool output_labels_;
      44 };
      45 
      46 template <typename Dtype>
      47 class Batch {
      48  public:
      49  Blob<Dtype> data_, label_;
      50 };
      51 
      52 template <typename Dtype>
      53 class BasePrefetchingDataLayer :
      54  public BaseDataLayer<Dtype>, public InternalThread {
      55  public:
      56  explicit BasePrefetchingDataLayer(const LayerParameter& param);
      57  // LayerSetUp: implements common data layer setup functionality, and calls
      58  // DataLayerSetUp to do special data layer setup for individual layer types.
      59  // This method may not be overridden.
      60  void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      61  const vector<Blob<Dtype>*>& top);
      62 
      63  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
      64  const vector<Blob<Dtype>*>& top);
      65  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
      66  const vector<Blob<Dtype>*>& top);
      67 
      68  protected:
      69  virtual void InternalThreadEntry();
      70  virtual void load_batch(Batch<Dtype>* batch) = 0;
      71 
      72  vector<shared_ptr<Batch<Dtype> > > prefetch_;
      73  BlockingQueue<Batch<Dtype>*> prefetch_free_;
      74  BlockingQueue<Batch<Dtype>*> prefetch_full_;
      75  Batch<Dtype>* prefetch_current_;
      76 
      77  Blob<Dtype> transformed_data_;
      78 };
      79 
      80 } // namespace caffe
      81 
      82 #endif // CAFFE_DATA_LAYERS_HPP_
      caffe::Batch
      Definition: base_data_layer.hpp:47
      +
      caffe::BasePrefetchingDataLayer
      Definition: base_data_layer.hpp:53
      caffe::Layer::Forward_gpu
      virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
      Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
      Definition: layer.hpp:316
      caffe::Layer
      An interface for the units of computation which can be composed into a Net.
      Definition: layer.hpp:33
      caffe
      A layer factory that allows one to register layers. During runtime, registered layers can be called b...
      Definition: blob.hpp:14
      -
      caffe::BaseDataLayer::Reshape
      virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
      Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
      Definition: base_data_layer.hpp:34
      -
      caffe::BaseDataLayer::Backward_cpu
      virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
      Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
      Definition: base_data_layer.hpp:37
      +
      caffe::BaseDataLayer::Reshape
      virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
      Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
      Definition: base_data_layer.hpp:32
      +
      caffe::BaseDataLayer::Backward_cpu
      virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
      Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
      Definition: base_data_layer.hpp:35
      caffe::BaseDataLayer
      Provides base for data layers that feed blobs to the Net.
      Definition: base_data_layer.hpp:21
      caffe::Layer::Forward_cpu
      virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)=0
      Using the CPU device, compute the layer output.
      caffe::InternalThread
      Definition: internal_thread.hpp:19
      -
      caffe::BaseDataLayer::Backward_gpu
      virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
      Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
      Definition: base_data_layer.hpp:39
      +
      caffe::BaseDataLayer::Backward_gpu
      virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
      Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
      Definition: base_data_layer.hpp:37
      caffe::BlockingQueue
      Definition: blocking_queue.hpp:10
      caffe::Blob
      A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
      Definition: blob.hpp:24
      caffe::BaseDataLayer::LayerSetUp
      virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
      Does layer-specific setup: your layer should implement this function as well as Reshape.
      Definition: base_data_layer.cpp:21
      -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/batch__norm__layer_8hpp_source.html b/doxygen/batch__norm__layer_8hpp_source.html index cebdf12dd6c..2b1f1f380c6 100644 --- a/doxygen/batch__norm__layer_8hpp_source.html +++ b/doxygen/batch__norm__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/batch_norm_layer.hpp Source File @@ -29,7 +29,7 @@
      - + @@ -82,9 +82,9 @@
      -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/batch__reindex__layer_8hpp_source.html b/doxygen/batch__reindex__layer_8hpp_source.html index efe1a324240..d8c979a8bf7 100644 --- a/doxygen/batch__reindex__layer_8hpp_source.html +++ b/doxygen/batch__reindex__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/batch_reindex_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -81,9 +81,9 @@
      -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/benchmark_8hpp_source.html b/doxygen/benchmark_8hpp_source.html index dbb3b5492d2..3e007c0808f 100644 --- a/doxygen/benchmark_8hpp_source.html +++ b/doxygen/benchmark_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/util/benchmark.hpp Source File @@ -29,7 +29,7 @@ - + @@ -72,9 +72,9 @@
      -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/bias__layer_8hpp_source.html b/doxygen/bias__layer_8hpp_source.html index bfd94f4554b..f7dd3d2e16a 100644 --- a/doxygen/bias__layer_8hpp_source.html +++ b/doxygen/bias__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/bias_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -83,9 +83,9 @@
      -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/blob_8hpp_source.html b/doxygen/blob_8hpp_source.html index 9acb2e55a7c..5cb5f991cb2 100644 --- a/doxygen/blob_8hpp_source.html +++ b/doxygen/blob_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/blob.hpp Source File @@ -29,7 +29,7 @@ - + @@ -89,9 +89,9 @@
      -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/blocking__queue_8hpp_source.html b/doxygen/blocking__queue_8hpp_source.html index aed8f44d1d1..4a3f03e0e01 100644 --- a/doxygen/blocking__queue_8hpp_source.html +++ b/doxygen/blocking__queue_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/util/blocking_queue.hpp Source File @@ -29,7 +29,7 @@ - + @@ -72,9 +72,9 @@
      -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/bnll__layer_8hpp_source.html b/doxygen/bnll__layer_8hpp_source.html index fdc94493ed5..40fc6446f1a 100644 --- a/doxygen/bnll__layer_8hpp_source.html +++ b/doxygen/bnll__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/bnll_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -78,9 +78,9 @@
      -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/caffe_8hpp_source.html b/doxygen/caffe_8hpp_source.html index c47c0b8e2b5..209e4f39b6f 100644 --- a/doxygen/caffe_8hpp_source.html +++ b/doxygen/caffe_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/caffe.hpp Source File @@ -29,7 +29,7 @@ - + @@ -69,9 +69,9 @@
      1 // caffe.hpp is the header file that you need to include in your code. It wraps
      2 // all the internal caffe header files into one for simpler inclusion.
      3 
      4 #ifndef CAFFE_CAFFE_HPP_
      5 #define CAFFE_CAFFE_HPP_
      6 
      7 #include "caffe/blob.hpp"
      8 #include "caffe/common.hpp"
      9 #include "caffe/filler.hpp"
      10 #include "caffe/layer.hpp"
      11 #include "caffe/layer_factory.hpp"
      12 #include "caffe/net.hpp"
      13 #include "caffe/parallel.hpp"
      14 #include "caffe/proto/caffe.pb.h"
      15 #include "caffe/solver.hpp"
      16 #include "caffe/solver_factory.hpp"
      17 #include "caffe/util/benchmark.hpp"
      18 #include "caffe/util/io.hpp"
      19 #include "caffe/util/upgrade_proto.hpp"
      20 
      21 #endif // CAFFE_CAFFE_HPP_
      -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1AbsValLayer-members.html b/doxygen/classcaffe_1_1AbsValLayer-members.html index 79ba1790f9a..d28649c2940 100644 --- a/doxygen/classcaffe_1_1AbsValLayer-members.html +++ b/doxygen/classcaffe_1_1AbsValLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@ - + @@ -109,9 +109,9 @@
      -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:46 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1AbsValLayer.html b/doxygen/classcaffe_1_1AbsValLayer.html index 514d204bfa4..3314aa7c2cf 100644 --- a/doxygen/classcaffe_1_1AbsValLayer.html +++ b/doxygen/classcaffe_1_1AbsValLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::AbsValLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@ - + @@ -71,7 +71,7 @@
      -

      Computes $ y = |x| $. +

      Computes $ y = |x| $. More...

      #include <absval_layer.hpp>

      @@ -80,7 +80,7 @@
      - + caffe::NeuronLayer< Dtype > caffe::Layer< Dtype > @@ -175,7 +175,7 @@

      Protected Member Functions

      virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) - Computes $ y = |x| $. More...
      + Computes $ y = |x| $. More...
        virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) @@ -212,15 +212,15 @@

      template<typename Dtype>
      class caffe::AbsValLayer< Dtype >

      -

      Computes $ y = |x| $.

      +

      Computes $ y = |x| $.

      Parameters
      bottominput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x $
        2. +
      2. $ (N \times C \times H \times W) $ the inputs $ x $
      topoutput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the computed outputs $ y = |x| $
        2. +
      2. $ (N \times C \times H \times W) $ the computed outputs $ y = |x| $
      @@ -228,7 +228,7 @@

      Member Function Documentation

      -

      § Backward_cpu()

      +

      ◆ Backward_cpu()

      @@ -273,12 +273,12 @@

      Parameters
      topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
        -
      1. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
        2. +
      2. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
      propagate_downsee Layer::Backward.
      bottominput Blob vector (length 2)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \mathrm{sign}(x) \frac{\partial E}{\partial y} $ if propagate_down[0]
        2. +
      2. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \mathrm{sign}(x) \frac{\partial E}{\partial y} $ if propagate_down[0]
      @@ -290,7 +290,7 @@

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -322,7 +322,7 @@

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -354,7 +354,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -389,15 +389,15 @@

      -

      Computes $ y = |x| $.

      +

      Computes $ y = |x| $.

      Parameters
      bottominput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x $
        2. +
      2. $ (N \times C \times H \times W) $ the inputs $ x $
      topoutput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the computed outputs $ y = |x| $
        2. +
      2. $ (N \times C \times H \times W) $ the computed outputs $ y = |x| $
      @@ -409,7 +409,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -465,9 +465,9 @@

      -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:46 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1AccuracyLayer-members.html b/doxygen/classcaffe_1_1AccuracyLayer-members.html index 5062a0ea0f7..353f9432acc 100644 --- a/doxygen/classcaffe_1_1AccuracyLayer-members.html +++ b/doxygen/classcaffe_1_1AccuracyLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -115,9 +115,9 @@
      -Generated on Wed Jan 18 2017 12:26:05 for Caffe by   +Generated on Fri Apr 14 2017 12:50:46 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1AccuracyLayer.html b/doxygen/classcaffe_1_1AccuracyLayer.html index 3447a6ef42d..81d4eae96a7 100644 --- a/doxygen/classcaffe_1_1AccuracyLayer.html +++ b/doxygen/classcaffe_1_1AccuracyLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::AccuracyLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -81,7 +81,7 @@
      - + caffe::Layer< Dtype >
      @@ -234,7 +234,7 @@

      Computes the classification accuracy for a one-of-many classification task.

      Constructor & Destructor Documentation

      -

      § AccuracyLayer()

      +

      ◆ AccuracyLayer()

      @@ -261,7 +261,7 @@

      Parameters
      paramprovides AccuracyParameter accuracy_param, with AccuracyLayer options:
        -
      • top_k (optional, default 1). Sets the maximum rank $ k $ at which a prediction is considered correct. For example, if $ k = 5 $, a prediction is counted correct if the correct label is among the top 5 predicted labels.
        • +
      • top_k (optional, default 1). Sets the maximum rank $ k $ at which a prediction is considered correct. For example, if $ k = 5 $, a prediction is counted correct if the correct label is among the top 5 predicted labels.
      @@ -272,7 +272,7 @@

      Member Function Documentation

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -304,7 +304,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -341,12 +341,12 @@

      Parameters
      bottominput Blob vector (length 2)
        -
      1. $ (N \times C \times H \times W) $ the predictions $ x $, a Blob with values in $ [-\infty, +\infty] $ indicating the predicted score for each of the $ K = CHW $ classes. Each $ x_n $ is mapped to a predicted label $ \hat{l}_n $ given by its maximal index: $ \hat{l}_n = \arg\max\limits_k x_{nk} $
        2. -
      2. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
        3. +
      3. $ (N \times C \times H \times W) $ the predictions $ x $, a Blob with values in $ [-\infty, +\infty] $ indicating the predicted score for each of the $ K = CHW $ classes. Each $ x_n $ is mapped to a predicted label $ \hat{l}_n $ given by its maximal index: $ \hat{l}_n = \arg\max\limits_k x_{nk} $
        4. +
      4. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
      topoutput Blob vector (length 1)
        -
      1. $ (1 \times 1 \times 1 \times 1) $ the computed accuracy: $ \frac{1}{N} \sum\limits_{n=1}^N \delta\{ \hat{l}_n = l_n \} $, where $ \delta\{\mathrm{condition}\} = \left\{ \begin{array}{lr} 1 & \mbox{if condition} \\ 0 & \mbox{otherwise} \end{array} \right. $
        2. +
      2. $ (1 \times 1 \times 1 \times 1) $ the computed accuracy: $ \frac{1}{N} \sum\limits_{n=1}^N \delta\{ \hat{l}_n = l_n \} $, where $ \delta\{\mathrm{condition}\} = \left\{ \begin{array}{lr} 1 & \mbox{if condition} \\ 0 & \mbox{otherwise} \end{array} \right. $
      @@ -358,7 +358,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -408,7 +408,7 @@

      -

      § MaxTopBlobs()

      +

      ◆ MaxTopBlobs()

      @@ -440,7 +440,7 @@

      -

      § MinTopBlobs()

      +

      ◆ MinTopBlobs()

      @@ -472,7 +472,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -528,9 +528,9 @@

      -Generated on Wed Jan 18 2017 12:26:05 for Caffe by   +Generated on Fri Apr 14 2017 12:50:46 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1AdaDeltaSolver-members.html b/doxygen/classcaffe_1_1AdaDeltaSolver-members.html index fdc79869d1f..de6dd6ca19c 100644 --- a/doxygen/classcaffe_1_1AdaDeltaSolver-members.html +++ b/doxygen/classcaffe_1_1AdaDeltaSolver-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -136,9 +136,9 @@
      -Generated on Wed Jan 18 2017 12:26:05 for Caffe by   +Generated on Fri Apr 14 2017 12:50:46 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1AdaDeltaSolver.html b/doxygen/classcaffe_1_1AdaDeltaSolver.html index 6f54b039f73..590ba6ffd45 100644 --- a/doxygen/classcaffe_1_1AdaDeltaSolver.html +++ b/doxygen/classcaffe_1_1AdaDeltaSolver.html @@ -3,7 +3,7 @@ - + Caffe: caffe::AdaDeltaSolver< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -75,7 +75,7 @@
      - + caffe::SGDSolver< Dtype > caffe::Solver< Dtype > @@ -293,9 +293,9 @@
      -Generated on Wed Jan 18 2017 12:26:05 for Caffe by   +Generated on Fri Apr 14 2017 12:50:46 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1AdaGradSolver-members.html b/doxygen/classcaffe_1_1AdaGradSolver-members.html index 6d5d5e39e29..d78389bddef 100644 --- a/doxygen/classcaffe_1_1AdaGradSolver-members.html +++ b/doxygen/classcaffe_1_1AdaGradSolver-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@
      - + @@ -136,9 +136,9 @@
      -Generated on Wed Jan 18 2017 12:26:05 for Caffe by   +Generated on Fri Apr 14 2017 12:50:46 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1AdaGradSolver.html b/doxygen/classcaffe_1_1AdaGradSolver.html index 427a779041e..9e0ff2287ee 100644 --- a/doxygen/classcaffe_1_1AdaGradSolver.html +++ b/doxygen/classcaffe_1_1AdaGradSolver.html @@ -3,7 +3,7 @@ - + Caffe: caffe::AdaGradSolver< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -75,7 +75,7 @@
      - + caffe::SGDSolver< Dtype > caffe::Solver< Dtype > @@ -293,9 +293,9 @@
      -Generated on Wed Jan 18 2017 12:26:05 for Caffe by   +Generated on Fri Apr 14 2017 12:50:46 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1AdamSolver-members.html b/doxygen/classcaffe_1_1AdamSolver-members.html index 4a06760e345..526065e3cc4 100644 --- a/doxygen/classcaffe_1_1AdamSolver-members.html +++ b/doxygen/classcaffe_1_1AdamSolver-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@
      - + @@ -136,9 +136,9 @@
      -Generated on Wed Jan 18 2017 12:26:05 for Caffe by   +Generated on Fri Apr 14 2017 12:50:46 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1AdamSolver.html b/doxygen/classcaffe_1_1AdamSolver.html index 05360ae8fdd..d52804ce408 100644 --- a/doxygen/classcaffe_1_1AdamSolver.html +++ b/doxygen/classcaffe_1_1AdamSolver.html @@ -3,7 +3,7 @@ - + Caffe: caffe::AdamSolver< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -80,7 +80,7 @@
      - + caffe::SGDSolver< Dtype > caffe::Solver< Dtype > @@ -304,9 +304,9 @@
      -Generated on Wed Jan 18 2017 12:26:05 for Caffe by   +Generated on Fri Apr 14 2017 12:50:46 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ArgMaxLayer-members.html b/doxygen/classcaffe_1_1ArgMaxLayer-members.html index a0282d84197..d045c350c11 100644 --- a/doxygen/classcaffe_1_1ArgMaxLayer-members.html +++ b/doxygen/classcaffe_1_1ArgMaxLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@
      - + @@ -112,9 +112,9 @@
      -Generated on Wed Jan 18 2017 12:26:05 for Caffe by   +Generated on Fri Apr 14 2017 12:50:46 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ArgMaxLayer.html b/doxygen/classcaffe_1_1ArgMaxLayer.html index d54a4762d7a..9ee3ace29b3 100644 --- a/doxygen/classcaffe_1_1ArgMaxLayer.html +++ b/doxygen/classcaffe_1_1ArgMaxLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::ArgMaxLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -72,7 +72,7 @@
      -

      Compute the index of the $ K $ max values for each datum across all dimensions $ (C \times H \times W) $. +

      Compute the index of the $ K $ max values for each datum across all dimensions $ (C \times H \times W) $. More...

      #include <argmax_layer.hpp>

      @@ -81,7 +81,7 @@
      - + caffe::Layer< Dtype >
      @@ -219,12 +219,12 @@

      template<typename Dtype>
      class caffe::ArgMaxLayer< Dtype >

      -

      Compute the index of the $ K $ max values for each datum across all dimensions $ (C \times H \times W) $.

      +

      Compute the index of the $ K $ max values for each datum across all dimensions $ (C \times H \times W) $.

      Intended for use after a classification layer to produce a prediction. If parameter out_max_val is set to true, output is a vector of pairs (max_ind, max_val) for each image. The axis parameter specifies an axis along which to maximise.

      NOTE: does not implement Backwards operation.

      Constructor & Destructor Documentation

      -

      § ArgMaxLayer()

      +

      ◆ ArgMaxLayer()

      @@ -251,7 +251,7 @@

      Parameters
      paramprovides ArgMaxParameter argmax_param, with ArgMaxLayer options:
        -
      • top_k (optional uint, default 1). the number $ K $ of maximal items to output.
        • +
      • top_k (optional uint, default 1). the number $ K $ of maximal items to output.
      • out_max_val (optional bool, default false). if set, output a vector of pairs (max_ind, max_val) unless axis is set then output max_val along the specified axis.
      • axis (optional int). if set, maximise along the specified axis else maximise the flattened trailing dimensions for each index of the first / num dimension.
      @@ -264,7 +264,7 @@

      Member Function Documentation

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -296,7 +296,7 @@

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -328,7 +328,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -365,11 +365,11 @@

      Parameters
      bottominput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x $
        2. +
      2. $ (N \times C \times H \times W) $ the inputs $ x $
      topoutput Blob vector (length 1)
        -
      1. $ (N \times 1 \times K) $ or, if out_max_val $ (N \times 2 \times K) $ unless axis set than e.g. $ (N \times K \times H \times W) $ if axis == 1 the computed outputs $ y_n = \arg\max\limits_i x_{ni} $ (for $ K = 1 $).
        2. +
      2. $ (N \times 1 \times K) $ or, if out_max_val $ (N \times 2 \times K) $ unless axis set than e.g. $ (N \times K \times H \times W) $ if axis == 1 the computed outputs $ y_n = \arg\max\limits_i x_{ni} $ (for $ K = 1 $).
      @@ -381,7 +381,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -431,7 +431,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -487,9 +487,9 @@

      -Generated on Wed Jan 18 2017 12:26:05 for Caffe by   +Generated on Fri Apr 14 2017 12:50:46 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BNLLLayer-members.html b/doxygen/classcaffe_1_1BNLLLayer-members.html index b05c49225b5..9a6c28dd47a 100644 --- a/doxygen/classcaffe_1_1BNLLLayer-members.html +++ b/doxygen/classcaffe_1_1BNLLLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -109,9 +109,9 @@
      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BNLLLayer.html b/doxygen/classcaffe_1_1BNLLLayer.html index 43f22d9913e..2d3afc574e8 100644 --- a/doxygen/classcaffe_1_1BNLLLayer.html +++ b/doxygen/classcaffe_1_1BNLLLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::BNLLLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@
      - + @@ -71,7 +71,7 @@

      -

      Computes $ y = x + \log(1 + \exp(-x)) $ if $ x > 0 $; $ y = \log(1 + \exp(x)) $ otherwise. +

      Computes $ y = x + \log(1 + \exp(-x)) $ if $ x > 0 $; $ y = \log(1 + \exp(x)) $ otherwise. More...

      #include <bnll_layer.hpp>

      @@ -80,7 +80,7 @@
      - + caffe::NeuronLayer< Dtype > caffe::Layer< Dtype > @@ -175,7 +175,7 @@

      Protected Member Functions

      virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) - Computes $ y = x + \log(1 + \exp(-x)) $ if $ x > 0 $; $ y = \log(1 + \exp(x)) $ otherwise. More...
      + Computes $ y = x + \log(1 + \exp(-x)) $ if $ x > 0 $; $ y = \log(1 + \exp(x)) $ otherwise. More...
        virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) @@ -212,15 +212,15 @@

      template<typename Dtype>
      class caffe::BNLLLayer< Dtype >

      -

      Computes $ y = x + \log(1 + \exp(-x)) $ if $ x > 0 $; $ y = \log(1 + \exp(x)) $ otherwise.

      +

      Computes $ y = x + \log(1 + \exp(-x)) $ if $ x > 0 $; $ y = \log(1 + \exp(x)) $ otherwise.

      Parameters
      bottominput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x $
        2. +
      2. $ (N \times C \times H \times W) $ the inputs $ x $
      topoutput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the computed outputs $ y = \left\{ \begin{array}{ll} x + \log(1 + \exp(-x)) & \mbox{if } x > 0 \\ \log(1 + \exp(x)) & \mbox{otherwise} \end{array} \right. $
        2. +
      2. $ (N \times C \times H \times W) $ the computed outputs $ y = \left\{ \begin{array}{ll} x + \log(1 + \exp(-x)) & \mbox{if } x > 0 \\ \log(1 + \exp(x)) & \mbox{otherwise} \end{array} \right. $
      @@ -228,7 +228,7 @@

      Member Function Documentation

      -

      § Backward_cpu()

      +

      ◆ Backward_cpu()

      @@ -273,12 +273,12 @@

      Parameters
      topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
        -
      1. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
        2. +
      2. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
      propagate_downsee Layer::Backward.
      bottominput Blob vector (length 2)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} $ if propagate_down[0]
        2. +
      2. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} $ if propagate_down[0]
      @@ -290,7 +290,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -325,15 +325,15 @@

      -

      Computes $ y = x + \log(1 + \exp(-x)) $ if $ x > 0 $; $ y = \log(1 + \exp(x)) $ otherwise.

      +

      Computes $ y = x + \log(1 + \exp(-x)) $ if $ x > 0 $; $ y = \log(1 + \exp(x)) $ otherwise.

      Parameters
      bottominput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x $
        2. +
      2. $ (N \times C \times H \times W) $ the inputs $ x $
      topoutput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the computed outputs $ y = \left\{ \begin{array}{ll} x + \log(1 + \exp(-x)) & \mbox{if } x > 0 \\ \log(1 + \exp(x)) & \mbox{otherwise} \end{array} \right. $
        2. +
      2. $ (N \times C \times H \times W) $ the computed outputs $ y = \left\{ \begin{array}{ll} x + \log(1 + \exp(-x)) & \mbox{if } x > 0 \\ \log(1 + \exp(x)) & \mbox{otherwise} \end{array} \right. $
      @@ -351,9 +351,9 @@

      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BaseConvolutionLayer-members.html b/doxygen/classcaffe_1_1BaseConvolutionLayer-members.html index 1274ab38224..f7514966b92 100644 --- a/doxygen/classcaffe_1_1BaseConvolutionLayer-members.html +++ b/doxygen/classcaffe_1_1BaseConvolutionLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -142,9 +142,9 @@
      -Generated on Wed Jan 18 2017 12:26:05 for Caffe by   +Generated on Fri Apr 14 2017 12:50:46 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BaseConvolutionLayer.html b/doxygen/classcaffe_1_1BaseConvolutionLayer.html index 59818211fed..31acc808cd0 100644 --- a/doxygen/classcaffe_1_1BaseConvolutionLayer.html +++ b/doxygen/classcaffe_1_1BaseConvolutionLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::BaseConvolutionLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -81,7 +81,7 @@
      - + caffe::Layer< Dtype > caffe::ConvolutionLayer< Dtype > caffe::DeconvolutionLayer< Dtype > @@ -325,7 +325,7 @@

      Abstract base class that factors out the BLAS code common to ConvolutionLayer and DeconvolutionLayer.

      Member Function Documentation

      -

      § EqualNumBottomTopBlobs()

      +

      ◆ EqualNumBottomTopBlobs()

      @@ -357,7 +357,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -407,7 +407,7 @@

      -

      § MinBottomBlobs()

      +

      ◆ MinBottomBlobs()

      @@ -439,7 +439,7 @@

      -

      § MinTopBlobs()

      +

      ◆ MinTopBlobs()

      @@ -471,7 +471,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -527,9 +527,9 @@

      -Generated on Wed Jan 18 2017 12:26:05 for Caffe by   +Generated on Fri Apr 14 2017 12:50:46 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BaseDataLayer-members.html b/doxygen/classcaffe_1_1BaseDataLayer-members.html index 565199d53a3..43b925ba1b4 100644 --- a/doxygen/classcaffe_1_1BaseDataLayer-members.html +++ b/doxygen/classcaffe_1_1BaseDataLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -105,17 +105,16 @@ set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline - ShareInParallel() const (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >inlinevirtual - ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual - transform_param_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected - type() constcaffe::Layer< Dtype >inlinevirtual - ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual + ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual + transform_param_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected + type() constcaffe::Layer< Dtype >inlinevirtual + ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
      -Generated on Wed Jan 18 2017 12:26:06 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BaseDataLayer.html b/doxygen/classcaffe_1_1BaseDataLayer.html index 0b1d24f0888..e806c96390e 100644 --- a/doxygen/classcaffe_1_1BaseDataLayer.html +++ b/doxygen/classcaffe_1_1BaseDataLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::BaseDataLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -80,7 +80,7 @@
      - + caffe::Layer< Dtype > caffe::BasePrefetchingDataLayer< Dtype > caffe::MemoryDataLayer< Dtype > @@ -98,9 +98,6 @@ virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)  Does layer-specific setup: your layer should implement this function as well as Reshape. More...
        - -virtual bool ShareInParallel () const -  virtual void DataLayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)   @@ -233,7 +230,7 @@

      TODO(dox): thorough documentation for Forward and proto params.

      Member Function Documentation

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -285,7 +282,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -341,9 +338,9 @@

      -Generated on Wed Jan 18 2017 12:26:06 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BasePrefetchingDataLayer-members.html b/doxygen/classcaffe_1_1BasePrefetchingDataLayer-members.html index 6f68d3636a8..0e3e8628b92 100644 --- a/doxygen/classcaffe_1_1BasePrefetchingDataLayer-members.html +++ b/doxygen/classcaffe_1_1BasePrefetchingDataLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -115,21 +115,20 @@ set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline - ShareInParallel() const (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >inlinevirtual - StartInternalThread()caffe::InternalThread - StopInternalThread()caffe::InternalThread - ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual - transform_param_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected - transformed_data_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected - type() constcaffe::Layer< Dtype >inlinevirtual - ~InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadvirtual - ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual + StartInternalThread()caffe::InternalThread + StopInternalThread()caffe::InternalThread + ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual + transform_param_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected + transformed_data_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected + type() constcaffe::Layer< Dtype >inlinevirtual + ~InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadvirtual + ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
      -Generated on Wed Jan 18 2017 12:26:06 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BasePrefetchingDataLayer.html b/doxygen/classcaffe_1_1BasePrefetchingDataLayer.html index d30c18947b8..b4b17d0ebe6 100644 --- a/doxygen/classcaffe_1_1BasePrefetchingDataLayer.html +++ b/doxygen/classcaffe_1_1BasePrefetchingDataLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::BasePrefetchingDataLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -76,7 +76,7 @@
      - + caffe::BaseDataLayer< Dtype > caffe::InternalThread caffe::Layer< Dtype > @@ -106,9 +106,6 @@  BaseDataLayer (const LayerParameter &param)   - -virtual bool ShareInParallel () const -  virtual void DataLayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)   @@ -261,7 +258,7 @@

      Member Function Documentation

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -317,9 +314,9 @@

      -Generated on Wed Jan 18 2017 12:26:06 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Batch-members.html b/doxygen/classcaffe_1_1Batch-members.html index c559dc03e10..f95ebbf2a1a 100644 --- a/doxygen/classcaffe_1_1Batch-members.html +++ b/doxygen/classcaffe_1_1Batch-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -74,9 +74,9 @@
      -Generated on Wed Jan 18 2017 12:26:06 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Batch.html b/doxygen/classcaffe_1_1Batch.html index 884ecd79fc5..689c809e38d 100644 --- a/doxygen/classcaffe_1_1Batch.html +++ b/doxygen/classcaffe_1_1Batch.html @@ -3,7 +3,7 @@ - + Caffe: caffe::Batch< Dtype > Class Template Reference @@ -29,7 +29,7 @@
      - + @@ -85,9 +85,9 @@
      -Generated on Wed Jan 18 2017 12:26:06 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BatchNormLayer-members.html b/doxygen/classcaffe_1_1BatchNormLayer-members.html index 2e2e8ba3b98..8155498bc78 100644 --- a/doxygen/classcaffe_1_1BatchNormLayer-members.html +++ b/doxygen/classcaffe_1_1BatchNormLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@
      - + @@ -119,9 +119,9 @@
      -Generated on Wed Jan 18 2017 12:26:06 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BatchNormLayer.html b/doxygen/classcaffe_1_1BatchNormLayer.html index 287c6c5650c..dffb5f96d84 100644 --- a/doxygen/classcaffe_1_1BatchNormLayer.html +++ b/doxygen/classcaffe_1_1BatchNormLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::BatchNormLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@
      - + @@ -81,7 +81,7 @@
      - + caffe::Layer< Dtype >
      @@ -252,7 +252,7 @@

      TODO(dox): thorough documentation for Forward, Backward, and proto params.

      Member Function Documentation

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -284,7 +284,7 @@

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -316,7 +316,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -366,7 +366,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -422,9 +422,9 @@

      -Generated on Wed Jan 18 2017 12:26:06 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BatchReindexLayer-members.html b/doxygen/classcaffe_1_1BatchReindexLayer-members.html index 43a9d77712c..abd80a99df3 100644 --- a/doxygen/classcaffe_1_1BatchReindexLayer-members.html +++ b/doxygen/classcaffe_1_1BatchReindexLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -108,9 +108,9 @@
      -Generated on Wed Jan 18 2017 12:26:06 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BatchReindexLayer.html b/doxygen/classcaffe_1_1BatchReindexLayer.html index 15251576ad8..b50ecd4c0c6 100644 --- a/doxygen/classcaffe_1_1BatchReindexLayer.html +++ b/doxygen/classcaffe_1_1BatchReindexLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::BatchReindexLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -81,7 +81,7 @@
      - + caffe::Layer< Dtype >
      @@ -211,7 +211,7 @@

      This layer can be used to select, reorder, and even replicate examples in a batch. The second blob is cast to int and treated as an index into the first axis of the first blob.

      Member Function Documentation

      -

      § Backward_cpu()

      +

      ◆ Backward_cpu()

      @@ -256,12 +256,12 @@

      Parameters
      @@ -274,7 +274,7 @@

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -306,7 +306,7 @@

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -338,7 +338,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -375,12 +375,12 @@

      Parameters

      topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
        -
      1. $ (M \times ...) $: containing error gradients $ \frac{\partial E}{\partial y} $ with respect to concatenated outputs $ y $
        2. +
      2. $ (M \times ...) $: containing error gradients $ \frac{\partial E}{\partial y} $ with respect to concatenated outputs $ y $
      propagate_downsee Layer::Backward.
      bottominput Blob vector (length 2):
        -
      • $ \frac{\partial E}{\partial y} $ is de-indexed (summing where required) back to the input x_1
        • +
      • $ \frac{\partial E}{\partial y} $ is de-indexed (summing where required) back to the input x_1
      • This layer cannot backprop to x_2, i.e. propagate_down[1] must be false.
      bottominput Blob vector (length 2+)
        -
      1. $ (N \times ...) $ the inputs $ x_1 $
        2. -
      2. $ (M) $ the inputs $ x_2 $
        3. +
      3. $ (N \times ...) $ the inputs $ x_1 $
        4. +
      4. $ (M) $ the inputs $ x_2 $
      topoutput Blob vector (length 1)
        -
      1. $ (M \times ...) $: the reindexed array $ y = x_1[x_2] $
        2. +
      2. $ (M \times ...) $: the reindexed array $ y = x_1[x_2] $
      @@ -392,7 +392,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -448,9 +448,9 @@

      -Generated on Wed Jan 18 2017 12:26:06 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BiasLayer-members.html b/doxygen/classcaffe_1_1BiasLayer-members.html index ce7805b5d36..fc5dbbc0ebc 100644 --- a/doxygen/classcaffe_1_1BiasLayer-members.html +++ b/doxygen/classcaffe_1_1BiasLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -108,9 +108,9 @@
      -Generated on Wed Jan 18 2017 12:26:06 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BiasLayer.html b/doxygen/classcaffe_1_1BiasLayer.html index d59af7627ad..fc2657d7f2a 100644 --- a/doxygen/classcaffe_1_1BiasLayer.html +++ b/doxygen/classcaffe_1_1BiasLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::BiasLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -79,7 +79,7 @@
      - + caffe::Layer< Dtype >
      @@ -209,7 +209,7 @@

      The second input may be omitted, in which case it's learned as a parameter of the layer. Note: in case bias and scaling are desired, both operations can be handled by ScaleLayer configured with bias_term: true.

      Member Function Documentation

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -241,7 +241,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -291,7 +291,7 @@

      -

      § MaxBottomBlobs()

      +

      ◆ MaxBottomBlobs()

      @@ -323,7 +323,7 @@

      -

      § MinBottomBlobs()

      +

      ◆ MinBottomBlobs()

      @@ -355,7 +355,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -411,9 +411,9 @@

      -Generated on Wed Jan 18 2017 12:26:06 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BilinearFiller-members.html b/doxygen/classcaffe_1_1BilinearFiller-members.html index 3bb3f900e13..23132b8c681 100644 --- a/doxygen/classcaffe_1_1BilinearFiller-members.html +++ b/doxygen/classcaffe_1_1BilinearFiller-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -77,9 +77,9 @@
      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BilinearFiller.html b/doxygen/classcaffe_1_1BilinearFiller.html index 8623cded75b..8ede49e07df 100644 --- a/doxygen/classcaffe_1_1BilinearFiller.html +++ b/doxygen/classcaffe_1_1BilinearFiller.html @@ -3,7 +3,7 @@ - + Caffe: caffe::BilinearFiller< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -79,7 +79,7 @@
      - + caffe::Filler< Dtype >
      @@ -115,9 +115,9 @@
      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Blob-members.html b/doxygen/classcaffe_1_1Blob-members.html index cf181563b25..3eaf75fac39 100644 --- a/doxygen/classcaffe_1_1Blob-members.html +++ b/doxygen/classcaffe_1_1Blob-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -147,9 +147,9 @@
      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Blob.html b/doxygen/classcaffe_1_1Blob.html index 1bb6cc312f2..52325a6e5dc 100644 --- a/doxygen/classcaffe_1_1Blob.html +++ b/doxygen/classcaffe_1_1Blob.html @@ -3,7 +3,7 @@ - + Caffe: caffe::Blob< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -344,7 +344,7 @@

      TODO(dox): more thorough description.

      Member Function Documentation

      -

      § CanonicalAxisIndex()

      +

      ◆ CanonicalAxisIndex()

      @@ -380,7 +380,7 @@

      -

      § CopyFrom()

      +

      ◆ CopyFrom()

      @@ -426,7 +426,7 @@

      -

      § count() [1/2]

      +

      ◆ count() [1/2]

      @@ -473,7 +473,7 @@

      -

      § count() [2/2]

      +

      ◆ count() [2/2]

      @@ -509,7 +509,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -533,7 +533,7 @@

      -

      § shape()

      +

      ◆ shape()

      @@ -569,7 +569,7 @@

      -

      § ShareData()

      +

      ◆ ShareData()

      @@ -592,7 +592,7 @@

      -

      § ShareDiff()

      +

      ◆ ShareDiff()

      @@ -621,9 +621,9 @@

      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BlockingQueue-members.html b/doxygen/classcaffe_1_1BlockingQueue-members.html index b4a76993359..74ee26f4223 100644 --- a/doxygen/classcaffe_1_1BlockingQueue-members.html +++ b/doxygen/classcaffe_1_1BlockingQueue-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -82,9 +82,9 @@
      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BlockingQueue.html b/doxygen/classcaffe_1_1BlockingQueue.html index 7641955e51c..e7a8a43d668 100644 --- a/doxygen/classcaffe_1_1BlockingQueue.html +++ b/doxygen/classcaffe_1_1BlockingQueue.html @@ -3,7 +3,7 @@ - + Caffe: caffe::BlockingQueue< T > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -121,9 +121,9 @@
      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BlockingQueue_1_1sync-members.html b/doxygen/classcaffe_1_1BlockingQueue_1_1sync-members.html index b079b9caa41..e31497a0ef5 100644 --- a/doxygen/classcaffe_1_1BlockingQueue_1_1sync-members.html +++ b/doxygen/classcaffe_1_1BlockingQueue_1_1sync-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -74,9 +74,9 @@
      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1BlockingQueue_1_1sync.html b/doxygen/classcaffe_1_1BlockingQueue_1_1sync.html index 6b9fd406723..a5a18440e8c 100644 --- a/doxygen/classcaffe_1_1BlockingQueue_1_1sync.html +++ b/doxygen/classcaffe_1_1BlockingQueue_1_1sync.html @@ -3,7 +3,7 @@ - + Caffe: caffe::BlockingQueue< T >::sync Class Reference @@ -29,7 +29,7 @@

      - + @@ -85,9 +85,9 @@
      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
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      - + @@ -94,9 +94,9 @@
      -Generated on Wed Jan 18 2017 12:26:08 for Caffe by   +Generated on Fri Apr 14 2017 12:50:48 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1CPUTimer.html b/doxygen/classcaffe_1_1CPUTimer.html index 0a3583481de..034de03ad97 100644 --- a/doxygen/classcaffe_1_1CPUTimer.html +++ b/doxygen/classcaffe_1_1CPUTimer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::CPUTimer Class Reference @@ -29,7 +29,7 @@

      - + @@ -149,9 +149,9 @@
      -Generated on Wed Jan 18 2017 12:26:08 for Caffe by   +Generated on Fri Apr 14 2017 12:50:48 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Caffe-members.html b/doxygen/classcaffe_1_1Caffe-members.html index fff7ef31a84..70afb703d2c 100644 --- a/doxygen/classcaffe_1_1Caffe-members.html +++ b/doxygen/classcaffe_1_1Caffe-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -101,9 +101,9 @@
      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Caffe.html b/doxygen/classcaffe_1_1Caffe.html index ddd00bc7c3f..9cf3f3e8b4b 100644 --- a/doxygen/classcaffe_1_1Caffe.html +++ b/doxygen/classcaffe_1_1Caffe.html @@ -3,7 +3,7 @@ - + Caffe: caffe::Caffe Class Reference @@ -29,7 +29,7 @@

      - + @@ -173,9 +173,9 @@
      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Caffe_1_1RNG-members.html b/doxygen/classcaffe_1_1Caffe_1_1RNG-members.html index e6731d8c14b..48d3915ccaf 100644 --- a/doxygen/classcaffe_1_1Caffe_1_1RNG-members.html +++ b/doxygen/classcaffe_1_1Caffe_1_1RNG-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -77,9 +77,9 @@
      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Caffe_1_1RNG.html b/doxygen/classcaffe_1_1Caffe_1_1RNG.html index 603723b8e41..f3fbee8fdad 100644 --- a/doxygen/classcaffe_1_1Caffe_1_1RNG.html +++ b/doxygen/classcaffe_1_1Caffe_1_1RNG.html @@ -3,7 +3,7 @@ - + Caffe: caffe::Caffe::RNG Class Reference @@ -29,7 +29,7 @@

      - + @@ -98,9 +98,9 @@
      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Caffe_1_1RNG_1_1Generator-members.html b/doxygen/classcaffe_1_1Caffe_1_1RNG_1_1Generator-members.html index 349dde513f7..336234da7fc 100644 --- a/doxygen/classcaffe_1_1Caffe_1_1RNG_1_1Generator-members.html +++ b/doxygen/classcaffe_1_1Caffe_1_1RNG_1_1Generator-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -75,9 +75,9 @@
      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Caffe_1_1RNG_1_1Generator.html b/doxygen/classcaffe_1_1Caffe_1_1RNG_1_1Generator.html index 4bca53d1fb9..f9417046cec 100644 --- a/doxygen/classcaffe_1_1Caffe_1_1RNG_1_1Generator.html +++ b/doxygen/classcaffe_1_1Caffe_1_1RNG_1_1Generator.html @@ -3,7 +3,7 @@ - + Caffe: caffe::Caffe::RNG::Generator Class Reference @@ -29,7 +29,7 @@
      - + @@ -85,9 +85,9 @@

      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:47 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ConcatLayer-members.html b/doxygen/classcaffe_1_1ConcatLayer-members.html index c9461edc3c5..7a1a4ff7d0b 100644 --- a/doxygen/classcaffe_1_1ConcatLayer-members.html +++ b/doxygen/classcaffe_1_1ConcatLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@
      - + @@ -112,9 +112,9 @@

      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:48 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ConcatLayer.html b/doxygen/classcaffe_1_1ConcatLayer.html index 3ff795e62b2..4a139c48831 100644 --- a/doxygen/classcaffe_1_1ConcatLayer.html +++ b/doxygen/classcaffe_1_1ConcatLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::ConcatLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@
      - + @@ -81,7 +81,7 @@
      - + caffe::Layer< Dtype >
      @@ -222,7 +222,7 @@

      Takes at least two Blobs and concatenates them along either the num or channel dimension, outputting the result.

      Member Function Documentation

      -

      § Backward_cpu()

      +

      ◆ Backward_cpu()

      @@ -267,11 +267,11 @@

      Parameters
      - +
      topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
        -
      1. $ (KN \times C \times H \times W) $ if axis == 0, or $ (N \times KC \times H \times W) $ if axis == 1: containing error gradients $ \frac{\partial E}{\partial y} $ with respect to concatenated outputs $ y $
        2. +
      2. $ (KN \times C \times H \times W) $ if axis == 0, or $ (N \times KC \times H \times W) $ if axis == 1: containing error gradients $ \frac{\partial E}{\partial y} $ with respect to concatenated outputs $ y $
      propagate_downsee Layer::Backward.
      bottominput Blob vector (length K), into which the top gradient $ \frac{\partial E}{\partial y} $ is deconcatenated back to the inputs $ \left[ \begin{array}{cccc} \frac{\partial E}{\partial x_1} & \frac{\partial E}{\partial x_2} & ... & \frac{\partial E}{\partial x_K} \end{array} \right] = \frac{\partial E}{\partial y} $
      bottominput Blob vector (length K), into which the top gradient $ \frac{\partial E}{\partial y} $ is deconcatenated back to the inputs $ \left[ \begin{array}{cccc} \frac{\partial E}{\partial x_1} & \frac{\partial E}{\partial x_2} & ... & \frac{\partial E}{\partial x_K} \end{array} \right] = \frac{\partial E}{\partial y} $
      @@ -281,7 +281,7 @@

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -313,7 +313,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -350,16 +350,16 @@

      Parameters
      bottominput Blob vector (length 2+)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x_1 $
        2. -
      2. $ (N \times C \times H \times W) $ the inputs $ x_2 $
        3. +
      3. $ (N \times C \times H \times W) $ the inputs $ x_1 $
        4. +
      4. $ (N \times C \times H \times W) $ the inputs $ x_2 $
      5. ...
        -
      • K $ (N \times C \times H \times W) $ the inputs $ x_K $
        • +
      • K $ (N \times C \times H \times W) $ the inputs $ x_K $
      topoutput Blob vector (length 1)
        -
      1. $ (KN \times C \times H \times W) $ if axis == 0, or $ (N \times KC \times H \times W) $ if axis == 1: the concatenated output $ y = [\begin{array}{cccc} x_1 & x_2 & ... & x_K \end{array}] $
        2. +
      2. $ (KN \times C \times H \times W) $ if axis == 0, or $ (N \times KC \times H \times W) $ if axis == 1: the concatenated output $ y = [\begin{array}{cccc} x_1 & x_2 & ... & x_K \end{array}] $
      @@ -371,7 +371,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -421,7 +421,7 @@

      -

      § MinBottomBlobs()

      +

      ◆ MinBottomBlobs()

      @@ -453,7 +453,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -509,9 +509,9 @@

      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:48 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ConstantFiller-members.html b/doxygen/classcaffe_1_1ConstantFiller-members.html index 56ab11e887d..f0ca96862fd 100644 --- a/doxygen/classcaffe_1_1ConstantFiller-members.html +++ b/doxygen/classcaffe_1_1ConstantFiller-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -77,9 +77,9 @@
      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:48 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ConstantFiller.html b/doxygen/classcaffe_1_1ConstantFiller.html index 1bda5662e76..136ce8cd674 100644 --- a/doxygen/classcaffe_1_1ConstantFiller.html +++ b/doxygen/classcaffe_1_1ConstantFiller.html @@ -3,7 +3,7 @@ - + Caffe: caffe::ConstantFiller< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -70,7 +70,7 @@
      -

      Fills a Blob with constant values $ x = 0 $. +

      Fills a Blob with constant values $ x = 0 $. More...

      #include <filler.hpp>

      @@ -79,7 +79,7 @@
      - + caffe::Filler< Dtype >
      @@ -108,16 +108,16 @@

      template<typename Dtype>
      class caffe::ConstantFiller< Dtype >

      -

      Fills a Blob with constant values $ x = 0 $.

      +

      Fills a Blob with constant values $ x = 0 $.

      The documentation for this class was generated from the following file:
      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:48 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ContrastiveLossLayer-members.html b/doxygen/classcaffe_1_1ContrastiveLossLayer-members.html index 5b7508aa3cc..6d5ad50489a 100644 --- a/doxygen/classcaffe_1_1ContrastiveLossLayer-members.html +++ b/doxygen/classcaffe_1_1ContrastiveLossLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -113,9 +113,9 @@
      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:48 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ContrastiveLossLayer.html b/doxygen/classcaffe_1_1ContrastiveLossLayer.html index 8f123d49272..dfef10f8a2e 100644 --- a/doxygen/classcaffe_1_1ContrastiveLossLayer.html +++ b/doxygen/classcaffe_1_1ContrastiveLossLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::ContrastiveLossLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -72,7 +72,7 @@
      -

      Computes the contrastive loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks. +

      Computes the contrastive loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks. More...

      #include <contrastive_loss_layer.hpp>

      @@ -81,7 +81,7 @@
      - + caffe::LossLayer< Dtype > caffe::Layer< Dtype > @@ -176,7 +176,7 @@

      Protected Member Functions

      virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) - Computes the contrastive loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks. More...
      + Computes the contrastive loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks. More...
        virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) @@ -225,17 +225,17 @@

      template<typename Dtype>
      class caffe::ContrastiveLossLayer< Dtype >

      -

      Computes the contrastive loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks.

      +

      Computes the contrastive loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks.

      Parameters
      bottominput Blob vector (length 3)
        -
      1. $ (N \times C \times 1 \times 1) $ the features $ a \in [-\infty, +\infty]$
        2. -
      2. $ (N \times C \times 1 \times 1) $ the features $ b \in [-\infty, +\infty]$
        3. -
      3. $ (N \times 1 \times 1 \times 1) $ the binary similarity $ s \in [0, 1]$
        4. +
      4. $ (N \times C \times 1 \times 1) $ the features $ a \in [-\infty, +\infty]$
        5. +
      5. $ (N \times C \times 1 \times 1) $ the features $ b \in [-\infty, +\infty]$
        6. +
      6. $ (N \times 1 \times 1 \times 1) $ the binary similarity $ s \in [0, 1]$
      topoutput Blob vector (length 1)
        -
      1. $ (1 \times 1 \times 1 \times 1) $ the computed contrastive loss: $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks.
        2. +
      2. $ (1 \times 1 \times 1 \times 1) $ the computed contrastive loss: $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks.
      @@ -243,7 +243,7 @@

      Member Function Documentation

      -

      § AllowForceBackward()

      +

      ◆ AllowForceBackward()

      @@ -274,7 +274,7 @@

      -

      § Backward_cpu()

      +

      ◆ Backward_cpu()

      @@ -320,13 +320,13 @@

      Parameters
      topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
        -
      1. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
        2. +
      2. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
      propagate_downsee Layer::Backward.
      bottominput Blob vector (length 2)
        -
      1. $ (N \times C \times 1 \times 1) $ the features $a$; Backward fills their diff with gradients if propagate_down[0]
        2. -
      2. $ (N \times C \times 1 \times 1) $ the features $b$; Backward fills their diff with gradients if propagate_down[1]
        3. +
      3. $ (N \times C \times 1 \times 1) $ the features $a$; Backward fills their diff with gradients if propagate_down[0]
        4. +
      4. $ (N \times C \times 1 \times 1) $ the features $b$; Backward fills their diff with gradients if propagate_down[1]
      @@ -338,7 +338,7 @@

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -370,7 +370,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -405,17 +405,17 @@

      -

      Computes the contrastive loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks.

      +

      Computes the contrastive loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks.

      Parameters
      bottominput Blob vector (length 3)
        -
      1. $ (N \times C \times 1 \times 1) $ the features $ a \in [-\infty, +\infty]$
        2. -
      2. $ (N \times C \times 1 \times 1) $ the features $ b \in [-\infty, +\infty]$
        3. -
      3. $ (N \times 1 \times 1 \times 1) $ the binary similarity $ s \in [0, 1]$
        4. +
      4. $ (N \times C \times 1 \times 1) $ the features $ a \in [-\infty, +\infty]$
        5. +
      5. $ (N \times C \times 1 \times 1) $ the features $ b \in [-\infty, +\infty]$
        6. +
      6. $ (N \times 1 \times 1 \times 1) $ the binary similarity $ s \in [0, 1]$
      topoutput Blob vector (length 1)
        -
      1. $ (1 \times 1 \times 1 \times 1) $ the computed contrastive loss: $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks.
        2. +
      2. $ (1 \times 1 \times 1 \times 1) $ the computed contrastive loss: $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks.
      @@ -427,7 +427,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -483,9 +483,9 @@

      -Generated on Wed Jan 18 2017 12:26:07 for Caffe by   +Generated on Fri Apr 14 2017 12:50:48 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ConvolutionLayer-members.html b/doxygen/classcaffe_1_1ConvolutionLayer-members.html index 3291ecf7336..a87719ec62d 100644 --- a/doxygen/classcaffe_1_1ConvolutionLayer-members.html +++ b/doxygen/classcaffe_1_1ConvolutionLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -143,9 +143,9 @@
      -Generated on Wed Jan 18 2017 12:26:08 for Caffe by   +Generated on Fri Apr 14 2017 12:50:48 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ConvolutionLayer.html b/doxygen/classcaffe_1_1ConvolutionLayer.html index e494fc34f4b..df9fb40828d 100644 --- a/doxygen/classcaffe_1_1ConvolutionLayer.html +++ b/doxygen/classcaffe_1_1ConvolutionLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::ConvolutionLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -80,7 +80,7 @@
      - + caffe::BaseConvolutionLayer< Dtype > caffe::Layer< Dtype > @@ -330,7 +330,7 @@

      The input is "im2col" transformed to a channel K' x H x W data matrix for multiplication with the N x K' x H x W filter matrix to yield a N' x H x W output matrix that is then "col2im" restored. K' is the input channel * kernel height * kernel width dimension of the unrolled inputs so that the im2col matrix has a column for each input region to be filtered. col2im restores the output spatial structure by rolling up the output channel N' columns of the output matrix.

      Constructor & Destructor Documentation

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      § ConvolutionLayer()

      +

      ◆ ConvolutionLayer()

      @@ -362,7 +362,7 @@

      , the convolutional filters' input and output channels are separated s.t. each group takes 1 / group of the input channels and makes 1 / group of the output channels. Concretely 4 input channels, 8 output channels, and 2 groups separate input channels 1-2 and output channels 1-4 into the first group and input channels 3-4 and output channels 5-8 into the second group. +
    1. group (optional, default 1). The number of filter groups. Group convolution is a method for reducing parameterization by selectively connecting input and output channels. The input and output channel dimensions must be divisible by the number of groups. For group $ \geq 1 $, the convolutional filters' input and output channels are separated s.t. each group takes 1 / group of the input channels and makes 1 / group of the output channels. Concretely 4 input channels, 8 output channels, and 2 groups separate input channels 1-2 and output channels 1-4 into the first group and input channels 3-4 and output channels 5-8 into the second group.
    2. bias_term (optional, default true). Whether to have a bias.
    3. engine: convolution has CAFFE (matrix multiplication) and CUDNN (library kernels + stream parallelism) engines.
      4. @@ -380,9 +380,9 @@

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      TODO(dox): thorough documentation for Forward, Backward, and proto params.

      Member Function Documentation

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      § ExactNumBottomBlobs()

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      ◆ ExactNumBottomBlobs()

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      ◆ LayerSetUp()

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      § Reshape()

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      ◆ Reshape()

      @@ -387,9 +387,9 @@

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      - + @@ -120,23 +120,22 @@ set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline - ShareInParallel() const (defined in caffe::DataLayer< Dtype >)caffe::DataLayer< Dtype >inlinevirtual - Skip() (defined in caffe::DataLayer< Dtype >)caffe::DataLayer< Dtype >protected - StartInternalThread()caffe::InternalThread - StopInternalThread()caffe::InternalThread - ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual - transform_param_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected - transformed_data_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected - type() constcaffe::DataLayer< Dtype >inlinevirtual - ~DataLayer() (defined in caffe::DataLayer< Dtype >)caffe::DataLayer< Dtype >virtual - ~InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadvirtual - ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual + Skip() (defined in caffe::DataLayer< Dtype >)caffe::DataLayer< Dtype >protected + StartInternalThread()caffe::InternalThread + StopInternalThread()caffe::InternalThread + ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual + transform_param_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected + transformed_data_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected + type() constcaffe::DataLayer< Dtype >inlinevirtual + ~DataLayer() (defined in caffe::DataLayer< Dtype >)caffe::DataLayer< Dtype >virtual + ~InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadvirtual + ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
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      - + @@ -76,7 +76,7 @@
      - + caffe::BasePrefetchingDataLayer< Dtype > caffe::BaseDataLayer< Dtype > caffe::InternalThread @@ -92,9 +92,6 @@ virtual void DataLayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)   - -virtual bool ShareInParallel () const -  virtual const char * type () const  Returns the layer type.
      @@ -280,7 +277,7 @@

      Member Function Documentation

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      ◆ ExactNumBottomBlobs()

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      ◆ MinTopBlobs()

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      caffe::DataReader Member List
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      This is the complete list of members for caffe::DataReader, including all inherited members.

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      bodies_ (defined in caffe::DataReader)caffe::DataReaderprotectedstatic
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      DISABLE_COPY_AND_ASSIGN(DataReader) (defined in caffe::DataReader)caffe::DataReaderprotected
      free() const (defined in caffe::DataReader)caffe::DataReaderinline
      full() const (defined in caffe::DataReader)caffe::DataReaderinline
      queue_pair_ (defined in caffe::DataReader)caffe::DataReaderprotected
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      caffe::DataReader Class Reference
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      Reads data from a source to queues available to data layers. A single reading thread is created per source, even if multiple solvers are running in parallel, e.g. for multi-GPU training. This makes sure databases are read sequentially, and that each solver accesses a different subset of the database. Data is distributed to solvers in a round-robin way to keep parallel training deterministic. - More...

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      #include <data_reader.hpp>

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      class  Body
       
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      -

      Detailed Description

      -

      Reads data from a source to queues available to data layers. A single reading thread is created per source, even if multiple solvers are running in parallel, e.g. for multi-GPU training. This makes sure databases are read sequentially, and that each solver accesses a different subset of the database. Data is distributed to solvers in a round-robin way to keep parallel training deterministic.

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      Body(const LayerParameter &param) (defined in caffe::DataReader::Body)caffe::DataReader::Bodyexplicit
      DataReader (defined in caffe::DataReader::Body)caffe::DataReader::Bodyfriend
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      InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadinline
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      is_started() const (defined in caffe::InternalThread)caffe::InternalThread
      must_stop() (defined in caffe::InternalThread)caffe::InternalThreadprotected
      new_queue_pairs_ (defined in caffe::DataReader::Body)caffe::DataReader::Bodyprotected
      param_ (defined in caffe::DataReader::Body)caffe::DataReader::Bodyprotected
      read_one(db::Cursor *cursor, QueuePair *qp) (defined in caffe::DataReader::Body)caffe::DataReader::Bodyprotected
      StartInternalThread()caffe::InternalThread
      StopInternalThread()caffe::InternalThread
      ~Body() (defined in caffe::DataReader::Body)caffe::DataReader::Bodyvirtual
      ~InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadvirtual
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      - Body (const LayerParameter &param)
       
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      Applies common transformations to the input data, such as scaling, mirroring, substracting the image mean...

      Member Function Documentation

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      - - diff --git a/doxygen/classcaffe_1_1DevicePair.html b/doxygen/classcaffe_1_1DevicePair.html deleted file mode 100644 index 88beb911be3..00000000000 --- a/doxygen/classcaffe_1_1DevicePair.html +++ /dev/null @@ -1,114 +0,0 @@ - - - - - - - -Caffe: caffe::DevicePair Class Reference - - - - - - - - - -
      -
      - - - - - - -
      -
      Caffe -
      -
      -
      - - - - - - - - -
      -
      - - -
      - -
      - - -
      -
      -
      -Public Member Functions | -Static Public Member Functions | -Protected Attributes | -List of all members
      -
      -
      caffe::DevicePair Class Reference
      -
      -
      - - - - - - - - -

      -Public Member Functions

      - DevicePair (int parent, int device)
       
      -int parent ()
       
      -int device ()
       
      - - - -

      -Static Public Member Functions

      -static void compute (const vector< int > devices, vector< DevicePair > *pairs)
       
      - - - - - -

      -Protected Attributes

      -int parent_
       
      -int device_
       
      -
      The documentation for this class was generated from the following files: -
      - -
      -Generated on Fri Jan 13 2017 13:56:39 for Caffe by   -doxygen - 1.8.12 -
      - - diff --git a/doxygen/classcaffe_1_1DropoutLayer-members.html b/doxygen/classcaffe_1_1DropoutLayer-members.html index d1a8e91b723..496dc245039 100644 --- a/doxygen/classcaffe_1_1DropoutLayer-members.html +++ b/doxygen/classcaffe_1_1DropoutLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@
      - + @@ -113,9 +113,9 @@
      -Generated on Wed Jan 18 2017 12:26:09 for Caffe by   +Generated on Fri Apr 14 2017 12:50:49 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1DropoutLayer.html b/doxygen/classcaffe_1_1DropoutLayer.html index 1311e132b3c..d6f6332ef97 100644 --- a/doxygen/classcaffe_1_1DropoutLayer.html +++ b/doxygen/classcaffe_1_1DropoutLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::DropoutLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -72,7 +72,7 @@
      -

      During training only, sets a random portion of $x$ to 0, adjusting the rest of the vector magnitude accordingly. +

      During training only, sets a random portion of $x$ to 0, adjusting the rest of the vector magnitude accordingly. More...

      #include <dropout_layer.hpp>

      @@ -81,7 +81,7 @@
      - + caffe::NeuronLayer< Dtype > caffe::Layer< Dtype > @@ -198,15 +198,15 @@ Protected Attributes

      Blob< unsigned int > rand_vec_ - when divided by UINT_MAX, the randomly generated values $u\sim U(0,1)$
      + when divided by UINT_MAX, the randomly generated values $u\sim U(0,1)$
        Dtype threshold_ - the probability $ p $ of dropping any input
      + the probability $ p $ of dropping any input
        Dtype scale_ - the scale for undropped inputs at train time $ 1 / (1 - p) $
      + the scale for undropped inputs at train time $ 1 / (1 - p) $
        unsigned int uint_thres_ @@ -227,15 +227,15 @@

      template<typename Dtype>
      class caffe::DropoutLayer< Dtype >

      -

      During training only, sets a random portion of $x$ to 0, adjusting the rest of the vector magnitude accordingly.

      +

      During training only, sets a random portion of $x$ to 0, adjusting the rest of the vector magnitude accordingly.

      Parameters
      bottominput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x $
        2. +
      2. $ (N \times C \times H \times W) $ the inputs $ x $
      topoutput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the computed outputs $ y = |x| $
        2. +
      2. $ (N \times C \times H \times W) $ the computed outputs $ y = |x| $
      @@ -243,7 +243,7 @@

      Constructor & Destructor Documentation

      -

      § DropoutLayer()

      +

      ◆ DropoutLayer()

      @@ -270,7 +270,7 @@

      Parameters
      paramprovides DropoutParameter dropout_param, with DropoutLayer options:
        -
      • dropout_ratio (optional, default 0.5). Sets the probability $ p $ that any given unit is dropped.
        • +
      • dropout_ratio (optional, default 0.5). Sets the probability $ p $ that any given unit is dropped.
      @@ -281,7 +281,7 @@

      Member Function Documentation

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -318,11 +318,11 @@

      Parameters
      bottominput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x $
        2. +
      2. $ (N \times C \times H \times W) $ the inputs $ x $
      topoutput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the computed outputs. At training time, we have $ y_{\mbox{train}} = \left\{ \begin{array}{ll} \frac{x}{1 - p} & \mbox{if } u > p \\ 0 & \mbox{otherwise} \end{array} \right. $, where $ u \sim U(0, 1)$ is generated independently for each input at each iteration. At test time, we simply have $ y_{\mbox{test}} = \mathbb{E}[y_{\mbox{train}}] = x $.
        2. +
      2. $ (N \times C \times H \times W) $ the computed outputs. At training time, we have $ y_{\mbox{train}} = \left\{ \begin{array}{ll} \frac{x}{1 - p} & \mbox{if } u > p \\ 0 & \mbox{otherwise} \end{array} \right. $, where $ u \sim U(0, 1)$ is generated independently for each input at each iteration. At test time, we simply have $ y_{\mbox{test}} = \mathbb{E}[y_{\mbox{train}}] = x $.
      @@ -334,7 +334,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -384,7 +384,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -440,9 +440,9 @@

      -Generated on Wed Jan 18 2017 12:26:09 for Caffe by   +Generated on Fri Apr 14 2017 12:50:49 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1DummyDataLayer-members.html b/doxygen/classcaffe_1_1DummyDataLayer-members.html index 56c8656c7b1..7ef3fa34fb3 100644 --- a/doxygen/classcaffe_1_1DummyDataLayer-members.html +++ b/doxygen/classcaffe_1_1DummyDataLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -104,16 +104,15 @@ set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline - ShareInParallel() const (defined in caffe::DummyDataLayer< Dtype >)caffe::DummyDataLayer< Dtype >inlinevirtual - ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual - type() constcaffe::DummyDataLayer< Dtype >inlinevirtual - ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual + ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual + type() constcaffe::DummyDataLayer< Dtype >inlinevirtual + ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
      -Generated on Wed Jan 18 2017 12:26:09 for Caffe by   +Generated on Fri Apr 14 2017 12:50:49 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1DummyDataLayer.html b/doxygen/classcaffe_1_1DummyDataLayer.html index b4c741c3a69..0505e49081d 100644 --- a/doxygen/classcaffe_1_1DummyDataLayer.html +++ b/doxygen/classcaffe_1_1DummyDataLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::DummyDataLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -81,7 +81,7 @@
      - + caffe::Layer< Dtype >
      @@ -94,9 +94,6 @@ virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)  Does layer-specific setup: your layer should implement this function as well as Reshape. More...
        - -virtual bool ShareInParallel () const -  virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)  Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
        @@ -223,7 +220,7 @@

      TODO(dox): thorough documentation for Forward and proto params.

      Member Function Documentation

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -255,7 +252,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -305,7 +302,7 @@

      -

      § MinTopBlobs()

      +

      ◆ MinTopBlobs()

      @@ -337,7 +334,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -393,9 +390,9 @@

      -Generated on Wed Jan 18 2017 12:26:09 for Caffe by   +Generated on Fri Apr 14 2017 12:50:49 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ELULayer-members.html b/doxygen/classcaffe_1_1ELULayer-members.html index 510f559257d..5c9bccd2a16 100644 --- a/doxygen/classcaffe_1_1ELULayer-members.html +++ b/doxygen/classcaffe_1_1ELULayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -109,9 +109,9 @@
      -Generated on Wed Jan 18 2017 12:26:09 for Caffe by   +Generated on Fri Apr 14 2017 12:50:49 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ELULayer.html b/doxygen/classcaffe_1_1ELULayer.html index 30dfbb14026..5da943ad72a 100644 --- a/doxygen/classcaffe_1_1ELULayer.html +++ b/doxygen/classcaffe_1_1ELULayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::ELULayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -71,7 +71,7 @@
      -

      Exponential Linear Unit non-linearity $ y = \left\{ \begin{array}{lr} x & \mathrm{if} \; x > 0 \\ \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 \end{array} \right. $. +

      Exponential Linear Unit non-linearity $ y = \left\{ \begin{array}{lr} x & \mathrm{if} \; x > 0 \\ \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 \end{array} \right. $. More...

      #include <elu_layer.hpp>

      @@ -80,7 +80,7 @@
      - + caffe::NeuronLayer< Dtype > caffe::Layer< Dtype > @@ -210,10 +210,10 @@

      template<typename Dtype>
      class caffe::ELULayer< Dtype >

      -

      Exponential Linear Unit non-linearity $ y = \left\{ \begin{array}{lr} x & \mathrm{if} \; x > 0 \\ \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 \end{array} \right. $.

      +

      Exponential Linear Unit non-linearity $ y = \left\{ \begin{array}{lr} x & \mathrm{if} \; x > 0 \\ \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 \end{array} \right. $.

      Constructor & Destructor Documentation

      -

      § ELULayer()

      +

      ◆ ELULayer()

      @@ -240,7 +240,7 @@

      Parameters
      paramprovides ELUParameter elu_param, with ELULayer options:
        -
      • alpha (optional, default 1). the value $ \alpha $ by which controls saturation for negative inputs.
        • +
      • alpha (optional, default 1). the value $ \alpha $ by which controls saturation for negative inputs.
      @@ -251,7 +251,7 @@

      Member Function Documentation

      -

      § Backward_cpu()

      +

      ◆ Backward_cpu()

      @@ -296,12 +296,12 @@

      Parameters
      topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
        -
      1. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
        2. +
      2. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
      propagate_downsee Layer::Backward.
      bottominput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} 1 & \mathrm{if} \; x > 0 \\ y + \alpha & \mathrm{if} \; x \le 0 \end{array} \right. $ if propagate_down[0].
        2. +
      2. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} 1 & \mathrm{if} \; x > 0 \\ y + \alpha & \mathrm{if} \; x \le 0 \end{array} \right. $ if propagate_down[0].
      @@ -313,7 +313,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -350,11 +350,11 @@

      Parameters
      bottominput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x $
        2. +
      2. $ (N \times C \times H \times W) $ the inputs $ x $
      topoutput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the computed outputs $ y = \left\{ \begin{array}{lr} x & \mathrm{if} \; x > 0 \\ \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 \end{array} \right. $.
        2. +
      2. $ (N \times C \times H \times W) $ the computed outputs $ y = \left\{ \begin{array}{lr} x & \mathrm{if} \; x > 0 \\ \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 \end{array} \right. $.
      @@ -372,9 +372,9 @@

      -Generated on Wed Jan 18 2017 12:26:09 for Caffe by   +Generated on Fri Apr 14 2017 12:50:49 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1EltwiseLayer-members.html b/doxygen/classcaffe_1_1EltwiseLayer-members.html index 9a9f71d9266..0284ad9d118 100644 --- a/doxygen/classcaffe_1_1EltwiseLayer-members.html +++ b/doxygen/classcaffe_1_1EltwiseLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -112,9 +112,9 @@
      -Generated on Wed Jan 18 2017 12:26:09 for Caffe by   +Generated on Fri Apr 14 2017 12:50:49 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1EltwiseLayer.html b/doxygen/classcaffe_1_1EltwiseLayer.html index df4b823c82c..1eae6aec415 100644 --- a/doxygen/classcaffe_1_1EltwiseLayer.html +++ b/doxygen/classcaffe_1_1EltwiseLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::EltwiseLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -81,7 +81,7 @@
      - + caffe::Layer< Dtype >
      @@ -226,7 +226,7 @@

      TODO(dox): thorough documentation for Forward, Backward, and proto params.

      Member Function Documentation

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -258,7 +258,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -308,7 +308,7 @@

      -

      § MinBottomBlobs()

      +

      ◆ MinBottomBlobs()

      @@ -340,7 +340,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -396,9 +396,9 @@

      -Generated on Wed Jan 18 2017 12:26:09 for Caffe by   +Generated on Fri Apr 14 2017 12:50:49 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1EmbedLayer-members.html b/doxygen/classcaffe_1_1EmbedLayer-members.html index 7bc16347feb..2d06bdff52e 100644 --- a/doxygen/classcaffe_1_1EmbedLayer-members.html +++ b/doxygen/classcaffe_1_1EmbedLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -113,9 +113,9 @@
      -Generated on Wed Jan 18 2017 12:26:09 for Caffe by   +Generated on Fri Apr 14 2017 12:50:49 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1EmbedLayer.html b/doxygen/classcaffe_1_1EmbedLayer.html index 358d6c4dd45..bc7060eeb80 100644 --- a/doxygen/classcaffe_1_1EmbedLayer.html +++ b/doxygen/classcaffe_1_1EmbedLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::EmbedLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -81,7 +81,7 @@
      - + caffe::Layer< Dtype >
      @@ -229,7 +229,7 @@

      TODO(dox): thorough documentation for Forward, Backward, and proto params.

      Member Function Documentation

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -261,7 +261,7 @@

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -293,7 +293,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -343,7 +343,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -399,9 +399,9 @@

      -Generated on Wed Jan 18 2017 12:26:09 for Caffe by   +Generated on Fri Apr 14 2017 12:50:49 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1EuclideanLossLayer-members.html b/doxygen/classcaffe_1_1EuclideanLossLayer-members.html index bee9a869715..78534e9947b 100644 --- a/doxygen/classcaffe_1_1EuclideanLossLayer-members.html +++ b/doxygen/classcaffe_1_1EuclideanLossLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -110,9 +110,9 @@
      -Generated on Wed Jan 18 2017 12:26:10 for Caffe by   +Generated on Fri Apr 14 2017 12:50:49 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1EuclideanLossLayer.html b/doxygen/classcaffe_1_1EuclideanLossLayer.html index b927adec132..0d13d588361 100644 --- a/doxygen/classcaffe_1_1EuclideanLossLayer.html +++ b/doxygen/classcaffe_1_1EuclideanLossLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::EuclideanLossLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -72,7 +72,7 @@
      -

      Computes the Euclidean (L2) loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ for real-valued regression tasks. +

      Computes the Euclidean (L2) loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ for real-valued regression tasks. More...

      #include <euclidean_loss_layer.hpp>

      @@ -81,7 +81,7 @@
      - + caffe::LossLayer< Dtype > caffe::Layer< Dtype > @@ -176,7 +176,7 @@

      Protected Member Functions

      virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) - Computes the Euclidean (L2) loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ for real-valued regression tasks. More...
      + Computes the Euclidean (L2) loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ for real-valued regression tasks. More...
        virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) @@ -216,16 +216,16 @@

      template<typename Dtype>
      class caffe::EuclideanLossLayer< Dtype >

      -

      Computes the Euclidean (L2) loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ for real-valued regression tasks.

      +

      Computes the Euclidean (L2) loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ for real-valued regression tasks.

      Parameters
      bottominput Blob vector (length 2)
        -
      1. $ (N \times C \times H \times W) $ the predictions $ \hat{y} \in [-\infty, +\infty]$
        2. -
      2. $ (N \times C \times H \times W) $ the targets $ y \in [-\infty, +\infty]$
        3. +
      3. $ (N \times C \times H \times W) $ the predictions $ \hat{y} \in [-\infty, +\infty]$
        4. +
      4. $ (N \times C \times H \times W) $ the targets $ y \in [-\infty, +\infty]$
      topoutput Blob vector (length 1)
        -
      1. $ (1 \times 1 \times 1 \times 1) $ the computed Euclidean loss: $ E = \frac{1}{2n} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $
        2. +
      2. $ (1 \times 1 \times 1 \times 1) $ the computed Euclidean loss: $ E = \frac{1}{2n} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $
      @@ -235,7 +235,7 @@

      (Note: Caffe, and SGD in general, is certainly not the best way to solve linear least squares problems! We use it only as an instructive example.)

      Member Function Documentation

      -

      § AllowForceBackward()

      +

      ◆ AllowForceBackward()

      @@ -266,7 +266,7 @@

      -

      § Backward_cpu()

      +

      ◆ Backward_cpu()

      @@ -312,13 +312,13 @@

      Parameters
      topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
        -
      1. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
        2. +
      2. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
      propagate_downsee Layer::Backward.
      bottominput Blob vector (length 2)
        -
      1. $ (N \times C \times H \times W) $ the predictions $\hat{y}$; Backward fills their diff with gradients $ \frac{\partial E}{\partial \hat{y}} = \frac{1}{n} \sum\limits_{n=1}^N (\hat{y}_n - y_n) $ if propagate_down[0]
        2. -
      2. $ (N \times C \times H \times W) $ the targets $y$; Backward fills their diff with gradients $ \frac{\partial E}{\partial y} = \frac{1}{n} \sum\limits_{n=1}^N (y_n - \hat{y}_n) $ if propagate_down[1]
        3. +
      3. $ (N \times C \times H \times W) $ the predictions $\hat{y}$; Backward fills their diff with gradients $ \frac{\partial E}{\partial \hat{y}} = \frac{1}{n} \sum\limits_{n=1}^N (\hat{y}_n - y_n) $ if propagate_down[0]
        4. +
      4. $ (N \times C \times H \times W) $ the targets $y$; Backward fills their diff with gradients $ \frac{\partial E}{\partial y} = \frac{1}{n} \sum\limits_{n=1}^N (y_n - \hat{y}_n) $ if propagate_down[1]
      @@ -330,7 +330,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -365,16 +365,16 @@

      -

      Computes the Euclidean (L2) loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ for real-valued regression tasks.

      +

      Computes the Euclidean (L2) loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ for real-valued regression tasks.

      Parameters
      bottominput Blob vector (length 2)
        -
      1. $ (N \times C \times H \times W) $ the predictions $ \hat{y} \in [-\infty, +\infty]$
        2. -
      2. $ (N \times C \times H \times W) $ the targets $ y \in [-\infty, +\infty]$
        3. +
      3. $ (N \times C \times H \times W) $ the predictions $ \hat{y} \in [-\infty, +\infty]$
        4. +
      4. $ (N \times C \times H \times W) $ the targets $ y \in [-\infty, +\infty]$
      topoutput Blob vector (length 1)
        -
      1. $ (1 \times 1 \times 1 \times 1) $ the computed Euclidean loss: $ E = \frac{1}{2n} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $
        2. +
      2. $ (1 \times 1 \times 1 \times 1) $ the computed Euclidean loss: $ E = \frac{1}{2n} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $
      @@ -388,7 +388,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -444,9 +444,9 @@

      -Generated on Wed Jan 18 2017 12:26:10 for Caffe by   +Generated on Fri Apr 14 2017 12:50:49 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ExpLayer-members.html b/doxygen/classcaffe_1_1ExpLayer-members.html index 453e394be44..46e10870764 100644 --- a/doxygen/classcaffe_1_1ExpLayer-members.html +++ b/doxygen/classcaffe_1_1ExpLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -111,9 +111,9 @@
      -Generated on Wed Jan 18 2017 12:26:10 for Caffe by   +Generated on Fri Apr 14 2017 12:50:50 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ExpLayer.html b/doxygen/classcaffe_1_1ExpLayer.html index 2caf8670492..4c4a6085b5d 100644 --- a/doxygen/classcaffe_1_1ExpLayer.html +++ b/doxygen/classcaffe_1_1ExpLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::ExpLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -72,7 +72,7 @@
      -

      Computes $ y = \gamma ^ {\alpha x + \beta} $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $. +

      Computes $ y = \gamma ^ {\alpha x + \beta} $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $. More...

      #include <exp_layer.hpp>

      @@ -81,7 +81,7 @@
      - + caffe::NeuronLayer< Dtype > caffe::Layer< Dtype > @@ -217,10 +217,10 @@

      template<typename Dtype>
      class caffe::ExpLayer< Dtype >

      -

      Computes $ y = \gamma ^ {\alpha x + \beta} $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $.

      +

      Computes $ y = \gamma ^ {\alpha x + \beta} $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $.

      Constructor & Destructor Documentation

      -

      § ExpLayer()

      +

      ◆ ExpLayer()

      @@ -247,9 +247,9 @@

      Parameters
      paramprovides ExpParameter exp_param, with ExpLayer options:
        -
      • scale (optional, default 1) the scale $ \alpha $
        • -
      • shift (optional, default 0) the shift $ \beta $
        • -
      • base (optional, default -1 for a value of $ e \approx 2.718 $) the base $ \gamma $
        • +
      • scale (optional, default 1) the scale $ \alpha $
        • +
      • shift (optional, default 0) the shift $ \beta $
        • +
      • base (optional, default -1 for a value of $ e \approx 2.718 $) the base $ \gamma $
      @@ -260,7 +260,7 @@

      Member Function Documentation

      -

      § Backward_cpu()

      +

      ◆ Backward_cpu()

      @@ -305,12 +305,12 @@

      Parameters
      topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
        -
      1. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
        2. +
      2. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
      propagate_downsee Layer::Backward.
      bottominput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} y \alpha \log_e(gamma) $ if propagate_down[0]
        2. +
      2. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} y \alpha \log_e(gamma) $ if propagate_down[0]
      @@ -322,7 +322,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -359,11 +359,11 @@

      Parameters
      bottominput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x $
        2. +
      2. $ (N \times C \times H \times W) $ the inputs $ x $
      topoutput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the computed outputs $ y = \gamma ^ {\alpha x + \beta} $
        2. +
      2. $ (N \times C \times H \times W) $ the computed outputs $ y = \gamma ^ {\alpha x + \beta} $
      @@ -375,7 +375,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -431,9 +431,9 @@

      -Generated on Wed Jan 18 2017 12:26:10 for Caffe by   +Generated on Fri Apr 14 2017 12:50:50 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Filler-members.html b/doxygen/classcaffe_1_1Filler-members.html index 1aaed537ccf..3d4aa9fbeac 100644 --- a/doxygen/classcaffe_1_1Filler-members.html +++ b/doxygen/classcaffe_1_1Filler-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -76,9 +76,9 @@
      -Generated on Wed Jan 18 2017 12:26:10 for Caffe by   +Generated on Fri Apr 14 2017 12:50:50 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Filler.html b/doxygen/classcaffe_1_1Filler.html index 72b04059b88..5fc0ff474e3 100644 --- a/doxygen/classcaffe_1_1Filler.html +++ b/doxygen/classcaffe_1_1Filler.html @@ -3,7 +3,7 @@ - + Caffe: caffe::Filler< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -80,7 +80,7 @@
      - + caffe::BilinearFiller< Dtype > caffe::ConstantFiller< Dtype > caffe::GaussianFiller< Dtype > @@ -117,9 +117,9 @@
      -Generated on Wed Jan 18 2017 12:26:10 for Caffe by   +Generated on Fri Apr 14 2017 12:50:50 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1FilterLayer-members.html b/doxygen/classcaffe_1_1FilterLayer-members.html index 1dc7bbcb4b9..f3e9be7a284 100644 --- a/doxygen/classcaffe_1_1FilterLayer-members.html +++ b/doxygen/classcaffe_1_1FilterLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@
      - + @@ -110,9 +110,9 @@
      -Generated on Wed Jan 18 2017 12:26:10 for Caffe by   +Generated on Fri Apr 14 2017 12:50:50 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1FilterLayer.html b/doxygen/classcaffe_1_1FilterLayer.html index a4c8fe638a1..f0316262829 100644 --- a/doxygen/classcaffe_1_1FilterLayer.html +++ b/doxygen/classcaffe_1_1FilterLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::FilterLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -81,7 +81,7 @@
      - + caffe::Layer< Dtype >
      @@ -216,7 +216,7 @@

      Takes two+ Blobs, interprets last Blob as a selector and filter remaining Blobs accordingly with selector data (0 means that the corresponding item has to be filtered, non-zero means that corresponding item needs to stay).

      Member Function Documentation

      -

      § Backward_cpu()

      +

      ◆ Backward_cpu()

      @@ -272,7 +272,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -309,14 +309,14 @@

      Parameters
      bottominput Blob vector (length 2+)
        -
      1. $ (N \times C \times H \times W) $ the inputs to be filtered $ x_1 $
        2. +
      2. $ (N \times C \times H \times W) $ the inputs to be filtered $ x_1 $
      3. ...
        4. -
      4. $ (N \times C \times H \times W) $ the inputs to be filtered $ x_K $
        5. -
      5. $ (N \times 1 \times 1 \times 1) $ the selector blob
        6. +
      6. $ (N \times C \times H \times W) $ the inputs to be filtered $ x_K $
        7. +
      7. $ (N \times 1 \times 1 \times 1) $ the selector blob
      topoutput Blob vector (length 1+)
        -
      1. $ (S \times C \times H \times W) $ () the filtered output $ x_1 $ where S is the number of items that haven't been filtered $ (S \times C \times H \times W) $ the filtered output $ x_K $ where S is the number of items that haven't been filtered
        2. +
      2. $ (S \times C \times H \times W) $ () the filtered output $ x_1 $ where S is the number of items that haven't been filtered $ (S \times C \times H \times W) $ the filtered output $ x_K $ where S is the number of items that haven't been filtered
      @@ -328,7 +328,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -378,7 +378,7 @@

      -

      § MinBottomBlobs()

      +

      ◆ MinBottomBlobs()

      @@ -410,7 +410,7 @@

      -

      § MinTopBlobs()

      +

      ◆ MinTopBlobs()

      @@ -442,7 +442,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -498,9 +498,9 @@

      -Generated on Wed Jan 18 2017 12:26:10 for Caffe by   +Generated on Fri Apr 14 2017 12:50:50 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1FlattenLayer-members.html b/doxygen/classcaffe_1_1FlattenLayer-members.html index b668cd1d646..f391e00ba5b 100644 --- a/doxygen/classcaffe_1_1FlattenLayer-members.html +++ b/doxygen/classcaffe_1_1FlattenLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -108,9 +108,9 @@
      -Generated on Wed Jan 18 2017 12:26:10 for Caffe by   +Generated on Fri Apr 14 2017 12:50:50 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1FlattenLayer.html b/doxygen/classcaffe_1_1FlattenLayer.html index 6334cffff39..37d67cecfe4 100644 --- a/doxygen/classcaffe_1_1FlattenLayer.html +++ b/doxygen/classcaffe_1_1FlattenLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::FlattenLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -80,7 +80,7 @@
      - + caffe::Layer< Dtype >
      @@ -210,7 +210,7 @@

      Note: because this layer does not change the input values – merely the dimensions – it can simply copy the input. The copy happens "virtually" (thus taking effectively 0 real time) by setting, in Forward, the data pointer of the top Blob to that of the bottom Blob (see Blob::ShareData), and in Backward, the diff pointer of the bottom Blob to that of the top Blob (see Blob::ShareDiff).

      Member Function Documentation

      -

      § Backward_cpu()

      +

      ◆ Backward_cpu()

      @@ -266,7 +266,7 @@

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -298,7 +298,7 @@

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -330,7 +330,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -367,11 +367,11 @@

      Parameters
      bottominput Blob vector (length 2+)
        -
      1. $ (N \times C \times H \times W) $ the inputs
        2. +
      2. $ (N \times C \times H \times W) $ the inputs
      topoutput Blob vector (length 1)
        -
      1. $ (N \times CHW \times 1 \times 1) $ the outputs – i.e., the (virtually) copied, flattened inputs
        2. +
      2. $ (N \times CHW \times 1 \times 1) $ the outputs – i.e., the (virtually) copied, flattened inputs
      @@ -383,7 +383,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -439,9 +439,9 @@

      -Generated on Wed Jan 18 2017 12:26:10 for Caffe by   +Generated on Fri Apr 14 2017 12:50:50 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1GPUParams-members.html b/doxygen/classcaffe_1_1GPUParams-members.html deleted file mode 100644 index a8bdcfb38c0..00000000000 --- a/doxygen/classcaffe_1_1GPUParams-members.html +++ /dev/null @@ -1,92 +0,0 @@ - - - - - - - -Caffe: Member List - - - - - - - - - -
      -
      - - - - - - -
      -
      Caffe -
      -
      -
      - - - - - - - - -
      -
      - - -
      - -
      - - -
      -
      -
      -
      caffe::GPUParams< Dtype > Member List
      -
      -
      - -

      This is the complete list of members for caffe::GPUParams< Dtype >, including all inherited members.

      - - - - - - - - - - - - - -
      configure(Solver< Dtype > *solver) const (defined in caffe::GPUParams< Dtype >)caffe::GPUParams< Dtype >
      data() const (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inline
      data_ (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
      diff() const (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inline
      diff_ (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
      DISABLE_COPY_AND_ASSIGN(Params) (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
      GPUParams(shared_ptr< Solver< Dtype > > root_solver, int device) (defined in caffe::GPUParams< Dtype >)caffe::GPUParams< Dtype >
      Params(shared_ptr< Solver< Dtype > > root_solver) (defined in caffe::Params< Dtype >)caffe::Params< Dtype >explicit
      size() const (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inline
      size_ (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
      ~GPUParams() (defined in caffe::GPUParams< Dtype >)caffe::GPUParams< Dtype >virtual
      ~Params() (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inlinevirtual
      - -
      -Generated on Fri Jan 13 2017 13:56:41 for Caffe by   -doxygen - 1.8.12 -
      - - diff --git a/doxygen/classcaffe_1_1GPUParams.html b/doxygen/classcaffe_1_1GPUParams.html deleted file mode 100644 index aed3f79efc6..00000000000 --- a/doxygen/classcaffe_1_1GPUParams.html +++ /dev/null @@ -1,134 +0,0 @@ - - - - - - - -Caffe: caffe::GPUParams< Dtype > Class Template Reference - - - - - - - - - -
      -
      - - - - - - -
      -
      Caffe -
      -
      -
      - - - - - - - - -
      -
      - - -
      - -
      - - -
      -
      -
      -Public Member Functions | -List of all members
      -
      -
      caffe::GPUParams< Dtype > Class Template Reference
      -
      -
      -
      -Inheritance diagram for caffe::GPUParams< Dtype >:
      -
      -
      - - -caffe::Params< Dtype > -caffe::P2PSync< Dtype > - -
      - - - - - - - - - - - - - - - -

      -Public Member Functions

      - GPUParams (shared_ptr< Solver< Dtype > > root_solver, int device)
       
      -void configure (Solver< Dtype > *solver) const
       
      - Public Member Functions inherited from caffe::Params< Dtype >
      - Params (shared_ptr< Solver< Dtype > > root_solver)
       
      -size_t size () const
       
      -Dtype * data () const
       
      -Dtype * diff () const
       
      - - - - - - - - - - - -

      -Additional Inherited Members

      - Protected Member Functions inherited from caffe::Params< Dtype >
      - DISABLE_COPY_AND_ASSIGN (Params)
       
      - Protected Attributes inherited from caffe::Params< Dtype >
      -const size_t size_
       
      -Dtype * data_
       
      -Dtype * diff_
       
      -
      The documentation for this class was generated from the following files: -
      - -
      -Generated on Fri Jan 13 2017 13:56:41 for Caffe by   -doxygen - 1.8.12 -
      - - diff --git a/doxygen/classcaffe_1_1GPUParams.png b/doxygen/classcaffe_1_1GPUParams.png deleted file mode 100644 index 3f8a811f79b50c55947bff4f1256dda3653218a1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1066 zcmeAS@N?(olHy`uVBq!ia0vp^tAV(KgBeJ!=X<>wNJ#|vgt-3y{~ySF@#br3|Dg#$ z78oBmaDcV*jy#adQ4-`A%m7pb0#{Fk7%?y~@APzW45?szJNMzFPYOJ){L>}x|DR~T zRhK1WYw70sr+sAJZOicBQ%w0ksrcOF4js>6jR@6~@dvd$|5+WLb!pMJ-M`%@>8)I| zYu^`{*N5Lf_`SI!b*l2~ZNF6)XU@Itb8SWRqOi+vORWVwOLaaUz2Eg`#*7J*N|wZ4 zdvwh1%$5x^8nh{#~~g?>`Zx9W}}CMfI9z zOx~9`Yd5p0yzFWUo%Ba^&7>#mt+UTro>ye3zssO@(Pho{+cq17H{J_e-Z;0@^cTZ{ ze2oZ3pgM+sTwx6LtlACxn?w)9ALM$lK7sW^_y(pQ+9=YROHDdG7=XbC(PNdgH-6W0 z2IG0t3v*@9A3Z%km#-i$_3F)~>}y(CRxI8iYr}YM^_pGhcI6hGeDU|UZtL35;b)E> zpY`U`uDK!s$Bp8d)}H9^Ok{kcvF7M4yE7%+KlS!=7sdYM$-ge%(|ffoH*Ia@@u;~a zYtpQS&4SwJdeQI!#{nF;Iw?q!6uVMch<=gwY{A<>4hr1~|&z6>zJp23p=){$b z3xHwj`*!{-W`_G$I_3U`M`XJ)zj@uip|4kX!R7fY85_)DAtZO#eCd>B0uB4~7$#>Z zUAvttccU}n{ff&6&K;4eWq4pNLTqRP1AFS6;AVnX?Z}nwx6h+n%+jF5f)4_N`?9)bayS@z&SY-3vPtwfwYT zkLabgd&#+nWiQBW)MHTHw%7cw_v4Lc{S$K!m94q9WA{ASw~Vj-czbT&oT+z=`E_F4 z_g?)cUpK_rr`%nAJ!{j{Utg=X)-GVXyUTI9v2!Jn2ht?5o-0 z|Fr)wy)M17eq&w3uf2P`F0F^>yIq@~&G{_zi~S9w>k*k(%<905&EV - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -78,9 +78,9 @@
      -Generated on Wed Jan 18 2017 12:26:11 for Caffe by   +Generated on Fri Apr 14 2017 12:50:50 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1GaussianFiller.html b/doxygen/classcaffe_1_1GaussianFiller.html index e39fb5f32c2..939b321c5a4 100644 --- a/doxygen/classcaffe_1_1GaussianFiller.html +++ b/doxygen/classcaffe_1_1GaussianFiller.html @@ -3,7 +3,7 @@ - + Caffe: caffe::GaussianFiller< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -71,7 +71,7 @@
      -

      Fills a Blob with Gaussian-distributed values $ x = a $. +

      Fills a Blob with Gaussian-distributed values $ x = a $. More...

      #include <filler.hpp>

      @@ -80,7 +80,7 @@
      - + caffe::Filler< Dtype >
      @@ -112,16 +112,16 @@

      template<typename Dtype>
      class caffe::GaussianFiller< Dtype >

      -

      Fills a Blob with Gaussian-distributed values $ x = a $.

      +

      Fills a Blob with Gaussian-distributed values $ x = a $.

      The documentation for this class was generated from the following file:
      -Generated on Wed Jan 18 2017 12:26:11 for Caffe by   +Generated on Fri Apr 14 2017 12:50:50 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1HDF5DataLayer-members.html b/doxygen/classcaffe_1_1HDF5DataLayer-members.html index edfca11d75d..91186d1981f 100644 --- a/doxygen/classcaffe_1_1HDF5DataLayer-members.html +++ b/doxygen/classcaffe_1_1HDF5DataLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -112,18 +112,17 @@ set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline - ShareInParallel() const (defined in caffe::HDF5DataLayer< Dtype >)caffe::HDF5DataLayer< Dtype >inlinevirtual - Skip() (defined in caffe::HDF5DataLayer< Dtype >)caffe::HDF5DataLayer< Dtype >protected - ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual - type() constcaffe::HDF5DataLayer< Dtype >inlinevirtual - ~HDF5DataLayer() (defined in caffe::HDF5DataLayer< Dtype >)caffe::HDF5DataLayer< Dtype >virtual - ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual + Skip() (defined in caffe::HDF5DataLayer< Dtype >)caffe::HDF5DataLayer< Dtype >protected + ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual + type() constcaffe::HDF5DataLayer< Dtype >inlinevirtual + ~HDF5DataLayer() (defined in caffe::HDF5DataLayer< Dtype >)caffe::HDF5DataLayer< Dtype >virtual + ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
      -Generated on Wed Jan 18 2017 12:26:11 for Caffe by   +Generated on Fri Apr 14 2017 12:50:50 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1HDF5DataLayer.html b/doxygen/classcaffe_1_1HDF5DataLayer.html index 2414c5bbd26..b583827f66a 100644 --- a/doxygen/classcaffe_1_1HDF5DataLayer.html +++ b/doxygen/classcaffe_1_1HDF5DataLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::HDF5DataLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -81,7 +81,7 @@
      - + caffe::Layer< Dtype >
      @@ -94,9 +94,6 @@ virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)  Does layer-specific setup: your layer should implement this function as well as Reshape. More...
        - -virtual bool ShareInParallel () const -  virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)  Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
        @@ -250,7 +247,7 @@

      TODO(dox): thorough documentation for Forward and proto params.

      Member Function Documentation

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -282,7 +279,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -332,7 +329,7 @@

      -

      § MinTopBlobs()

      +

      ◆ MinTopBlobs()

      @@ -364,7 +361,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -420,9 +417,9 @@

      -Generated on Wed Jan 18 2017 12:26:11 for Caffe by   +Generated on Fri Apr 14 2017 12:50:50 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1HDF5OutputLayer-members.html b/doxygen/classcaffe_1_1HDF5OutputLayer-members.html index 9dd754c60ee..106424175b5 100644 --- a/doxygen/classcaffe_1_1HDF5OutputLayer-members.html +++ b/doxygen/classcaffe_1_1HDF5OutputLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -109,17 +109,16 @@ set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline - ShareInParallel() const (defined in caffe::HDF5OutputLayer< Dtype >)caffe::HDF5OutputLayer< Dtype >inlinevirtual - ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual - type() constcaffe::HDF5OutputLayer< Dtype >inlinevirtual - ~HDF5OutputLayer() (defined in caffe::HDF5OutputLayer< Dtype >)caffe::HDF5OutputLayer< Dtype >virtual - ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual + ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual + type() constcaffe::HDF5OutputLayer< Dtype >inlinevirtual + ~HDF5OutputLayer() (defined in caffe::HDF5OutputLayer< Dtype >)caffe::HDF5OutputLayer< Dtype >virtual + ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
      -Generated on Wed Jan 18 2017 12:26:11 for Caffe by   +Generated on Fri Apr 14 2017 12:50:50 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1HDF5OutputLayer.html b/doxygen/classcaffe_1_1HDF5OutputLayer.html index ae123426f27..4663ebd6604 100644 --- a/doxygen/classcaffe_1_1HDF5OutputLayer.html +++ b/doxygen/classcaffe_1_1HDF5OutputLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::HDF5OutputLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -81,7 +81,7 @@
      - + caffe::Layer< Dtype >
      @@ -94,9 +94,6 @@ virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)  Does layer-specific setup: your layer should implement this function as well as Reshape. More...
        - -virtual bool ShareInParallel () const -  virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)  Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
        @@ -238,7 +235,7 @@

      TODO(dox): thorough documentation for Forward and proto params.

      Member Function Documentation

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -270,7 +267,7 @@

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -302,7 +299,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -352,7 +349,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -408,9 +405,9 @@

      -Generated on Wed Jan 18 2017 12:26:11 for Caffe by   +Generated on Fri Apr 14 2017 12:50:50 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1HingeLossLayer-members.html b/doxygen/classcaffe_1_1HingeLossLayer-members.html index 502c13e1bf0..89616bff462 100644 --- a/doxygen/classcaffe_1_1HingeLossLayer-members.html +++ b/doxygen/classcaffe_1_1HingeLossLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -109,9 +109,9 @@
      -Generated on Wed Jan 18 2017 12:26:11 for Caffe by   +Generated on Fri Apr 14 2017 12:50:51 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1HingeLossLayer.html b/doxygen/classcaffe_1_1HingeLossLayer.html index d64d4be0cae..e230e4720c2 100644 --- a/doxygen/classcaffe_1_1HingeLossLayer.html +++ b/doxygen/classcaffe_1_1HingeLossLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::HingeLossLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -80,7 +80,7 @@
      - + caffe::LossLayer< Dtype > caffe::Layer< Dtype > @@ -216,21 +216,21 @@
      Parameters
      bottominput Blob vector (length 2)
        -
      1. $ (N \times C \times H \times W) $ the predictions $ t $, a Blob with values in $ [-\infty, +\infty] $ indicating the predicted score for each of the $ K = CHW $ classes. In an SVM, $ t $ is the result of taking the inner product $ X^T W $ of the D-dimensional features $ X \in \mathcal{R}^{D \times N} $ and the learned hyperplane parameters $ W \in \mathcal{R}^{D \times K} $, so a Net with just an InnerProductLayer (with num_output = D) providing predictions to a HingeLossLayer and no other learnable parameters or losses is equivalent to an SVM.
        2. -
      2. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
        3. +
      3. $ (N \times C \times H \times W) $ the predictions $ t $, a Blob with values in $ [-\infty, +\infty] $ indicating the predicted score for each of the $ K = CHW $ classes. In an SVM, $ t $ is the result of taking the inner product $ X^T W $ of the D-dimensional features $ X \in \mathcal{R}^{D \times N} $ and the learned hyperplane parameters $ W \in \mathcal{R}^{D \times K} $, so a Net with just an InnerProductLayer (with num_output = D) providing predictions to a HingeLossLayer and no other learnable parameters or losses is equivalent to an SVM.
        4. +
      4. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
      topoutput Blob vector (length 1)
        -
      1. $ (1 \times 1 \times 1 \times 1) $ the computed hinge loss: $ E = \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p $, for the $ L^p $ norm (defaults to $ p = 1 $, the L1 norm; L2 norm, as in L2-SVM, is also available), and $ \delta\{\mathrm{condition}\} = \left\{ \begin{array}{lr} 1 & \mbox{if condition} \\ -1 & \mbox{otherwise} \end{array} \right. $
        2. +
      2. $ (1 \times 1 \times 1 \times 1) $ the computed hinge loss: $ E = \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p $, for the $ L^p $ norm (defaults to $ p = 1 $, the L1 norm; L2 norm, as in L2-SVM, is also available), and $ \delta\{\mathrm{condition}\} = \left\{ \begin{array}{lr} 1 & \mbox{if condition} \\ -1 & \mbox{otherwise} \end{array} \right. $
      -

      In an SVM, $ t \in \mathcal{R}^{N \times K} $ is the result of taking the inner product $ X^T W $ of the features $ X \in \mathcal{R}^{D \times N} $ and the learned hyperplane parameters $ W \in \mathcal{R}^{D \times K} $. So, a Net with just an InnerProductLayer (with num_output = $k$) providing predictions to a HingeLossLayer is equivalent to an SVM (assuming it has no other learned outside the InnerProductLayer and no other losses outside the HingeLossLayer).

      +

      In an SVM, $ t \in \mathcal{R}^{N \times K} $ is the result of taking the inner product $ X^T W $ of the features $ X \in \mathcal{R}^{D \times N} $ and the learned hyperplane parameters $ W \in \mathcal{R}^{D \times K} $. So, a Net with just an InnerProductLayer (with num_output = $k$) providing predictions to a HingeLossLayer is equivalent to an SVM (assuming it has no other learned outside the InnerProductLayer and no other losses outside the HingeLossLayer).

      Member Function Documentation

      -

      § Backward_cpu()

      +

      ◆ Backward_cpu()

      @@ -276,13 +276,13 @@

      Parameters
      topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
        -
      1. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
        2. +
      2. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
      propagate_downsee Layer::Backward. propagate_down[1] must be false as we can't compute gradients with respect to the labels.
      bottominput Blob vector (length 2)
        -
      1. $ (N \times C \times H \times W) $ the predictions $t$; Backward computes diff $ \frac{\partial E}{\partial t} $
        2. -
      2. $ (N \times 1 \times 1 \times 1) $ the labels – ignored as we can't compute their error gradients
        3. +
      3. $ (N \times C \times H \times W) $ the predictions $t$; Backward computes diff $ \frac{\partial E}{\partial t} $
        4. +
      4. $ (N \times 1 \times 1 \times 1) $ the labels – ignored as we can't compute their error gradients
      @@ -294,7 +294,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -333,18 +333,18 @@

      Parameters
      bottominput Blob vector (length 2)
        -
      1. $ (N \times C \times H \times W) $ the predictions $ t $, a Blob with values in $ [-\infty, +\infty] $ indicating the predicted score for each of the $ K = CHW $ classes. In an SVM, $ t $ is the result of taking the inner product $ X^T W $ of the D-dimensional features $ X \in \mathcal{R}^{D \times N} $ and the learned hyperplane parameters $ W \in \mathcal{R}^{D \times K} $, so a Net with just an InnerProductLayer (with num_output = D) providing predictions to a HingeLossLayer and no other learnable parameters or losses is equivalent to an SVM.
        2. -
      2. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
        3. +
      3. $ (N \times C \times H \times W) $ the predictions $ t $, a Blob with values in $ [-\infty, +\infty] $ indicating the predicted score for each of the $ K = CHW $ classes. In an SVM, $ t $ is the result of taking the inner product $ X^T W $ of the D-dimensional features $ X \in \mathcal{R}^{D \times N} $ and the learned hyperplane parameters $ W \in \mathcal{R}^{D \times K} $, so a Net with just an InnerProductLayer (with num_output = D) providing predictions to a HingeLossLayer and no other learnable parameters or losses is equivalent to an SVM.
        4. +
      4. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
      topoutput Blob vector (length 1)
        -
      1. $ (1 \times 1 \times 1 \times 1) $ the computed hinge loss: $ E = \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p $, for the $ L^p $ norm (defaults to $ p = 1 $, the L1 norm; L2 norm, as in L2-SVM, is also available), and $ \delta\{\mathrm{condition}\} = \left\{ \begin{array}{lr} 1 & \mbox{if condition} \\ -1 & \mbox{otherwise} \end{array} \right. $
        2. +
      2. $ (1 \times 1 \times 1 \times 1) $ the computed hinge loss: $ E = \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p $, for the $ L^p $ norm (defaults to $ p = 1 $, the L1 norm; L2 norm, as in L2-SVM, is also available), and $ \delta\{\mathrm{condition}\} = \left\{ \begin{array}{lr} 1 & \mbox{if condition} \\ -1 & \mbox{otherwise} \end{array} \right. $
      -

      In an SVM, $ t \in \mathcal{R}^{N \times K} $ is the result of taking the inner product $ X^T W $ of the features $ X \in \mathcal{R}^{D \times N} $ and the learned hyperplane parameters $ W \in \mathcal{R}^{D \times K} $. So, a Net with just an InnerProductLayer (with num_output = $k$) providing predictions to a HingeLossLayer is equivalent to an SVM (assuming it has no other learned outside the InnerProductLayer and no other losses outside the HingeLossLayer).

      +

      In an SVM, $ t \in \mathcal{R}^{N \times K} $ is the result of taking the inner product $ X^T W $ of the features $ X \in \mathcal{R}^{D \times N} $ and the learned hyperplane parameters $ W \in \mathcal{R}^{D \times K} $. So, a Net with just an InnerProductLayer (with num_output = $k$) providing predictions to a HingeLossLayer is equivalent to an SVM (assuming it has no other learned outside the InnerProductLayer and no other losses outside the HingeLossLayer).

      Implements caffe::Layer< Dtype >.

      @@ -357,9 +357,9 @@

      -Generated on Wed Jan 18 2017 12:26:11 for Caffe by   +Generated on Fri Apr 14 2017 12:50:51 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Im2colLayer-members.html b/doxygen/classcaffe_1_1Im2colLayer-members.html index 1e77b6c66d2..1dd8149eb46 100644 --- a/doxygen/classcaffe_1_1Im2colLayer-members.html +++ b/doxygen/classcaffe_1_1Im2colLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -119,9 +119,9 @@
      -Generated on Wed Jan 18 2017 12:26:11 for Caffe by   +Generated on Fri Apr 14 2017 12:50:51 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Im2colLayer.html b/doxygen/classcaffe_1_1Im2colLayer.html index d5b40b15ac4..4501a4aeba1 100644 --- a/doxygen/classcaffe_1_1Im2colLayer.html +++ b/doxygen/classcaffe_1_1Im2colLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::Im2colLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -81,7 +81,7 @@
      - + caffe::Layer< Dtype >
      @@ -251,7 +251,7 @@

      TODO(dox): thorough documentation for Forward, Backward, and proto params.

      Member Function Documentation

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -283,7 +283,7 @@

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -315,7 +315,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -365,7 +365,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -421,9 +421,9 @@

      -Generated on Wed Jan 18 2017 12:26:11 for Caffe by   +Generated on Fri Apr 14 2017 12:50:51 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ImageDataLayer-members.html b/doxygen/classcaffe_1_1ImageDataLayer-members.html index bc581d01ef2..530d2c11fba 100644 --- a/doxygen/classcaffe_1_1ImageDataLayer-members.html +++ b/doxygen/classcaffe_1_1ImageDataLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -119,23 +119,22 @@ set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline - ShareInParallel() const (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >inlinevirtual - ShuffleImages() (defined in caffe::ImageDataLayer< Dtype >)caffe::ImageDataLayer< Dtype >protectedvirtual - StartInternalThread()caffe::InternalThread - StopInternalThread()caffe::InternalThread - ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual - transform_param_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected - transformed_data_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected - type() constcaffe::ImageDataLayer< Dtype >inlinevirtual - ~ImageDataLayer() (defined in caffe::ImageDataLayer< Dtype >)caffe::ImageDataLayer< Dtype >virtual - ~InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadvirtual - ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual + ShuffleImages() (defined in caffe::ImageDataLayer< Dtype >)caffe::ImageDataLayer< Dtype >protectedvirtual + StartInternalThread()caffe::InternalThread + StopInternalThread()caffe::InternalThread + ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual + transform_param_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected + transformed_data_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected + type() constcaffe::ImageDataLayer< Dtype >inlinevirtual + ~ImageDataLayer() (defined in caffe::ImageDataLayer< Dtype >)caffe::ImageDataLayer< Dtype >virtual + ~InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadvirtual + ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
      -Generated on Wed Jan 18 2017 12:26:12 for Caffe by   +Generated on Fri Apr 14 2017 12:50:51 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ImageDataLayer.html b/doxygen/classcaffe_1_1ImageDataLayer.html index b54716a9264..fb739e39599 100644 --- a/doxygen/classcaffe_1_1ImageDataLayer.html +++ b/doxygen/classcaffe_1_1ImageDataLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::ImageDataLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -81,7 +81,7 @@
      - + caffe::BasePrefetchingDataLayer< Dtype > caffe::BaseDataLayer< Dtype > caffe::InternalThread @@ -126,9 +126,6 @@  BaseDataLayer (const LayerParameter &param)   - -virtual bool ShareInParallel () const -  virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)  Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
        @@ -288,7 +285,7 @@

      TODO(dox): thorough documentation for Forward and proto params.

      Member Function Documentation

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -320,7 +317,7 @@

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -357,9 +354,9 @@

      -Generated on Wed Jan 18 2017 12:26:11 for Caffe by   +Generated on Fri Apr 14 2017 12:50:51 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1InfogainLossLayer-members.html b/doxygen/classcaffe_1_1InfogainLossLayer-members.html index 82bd0a8e7bd..bec5f6c56a6 100644 --- a/doxygen/classcaffe_1_1InfogainLossLayer-members.html +++ b/doxygen/classcaffe_1_1InfogainLossLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -79,40 +79,54 @@ CheckBlobCounts(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual EqualNumBottomTopBlobs() constcaffe::Layer< Dtype >inlinevirtual ExactNumBottomBlobs() constcaffe::InfogainLossLayer< Dtype >inlinevirtual - ExactNumTopBlobs() constcaffe::LossLayer< Dtype >inlinevirtual + ExactNumTopBlobs() constcaffe::InfogainLossLayer< Dtype >inlinevirtual Forward(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::InfogainLossLayer< Dtype >protectedvirtual Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotectedvirtual - infogain_ (defined in caffe::InfogainLossLayer< Dtype >)caffe::InfogainLossLayer< Dtype >protected + get_normalizer(LossParameter_NormalizationMode normalization_mode, int valid_count)caffe::InfogainLossLayer< Dtype >protectedvirtual + has_ignore_label_caffe::InfogainLossLayer< Dtype >protected + ignore_label_caffe::InfogainLossLayer< Dtype >protected + infogain_ (defined in caffe::InfogainLossLayer< Dtype >)caffe::InfogainLossLayer< Dtype >protected + infogain_axis_ (defined in caffe::InfogainLossLayer< Dtype >)caffe::InfogainLossLayer< Dtype >protected InfogainLossLayer(const LayerParameter &param) (defined in caffe::InfogainLossLayer< Dtype >)caffe::InfogainLossLayer< Dtype >inlineexplicit - Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit - layer_param() constcaffe::Layer< Dtype >inline - layer_param_caffe::Layer< Dtype >protected - LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::InfogainLossLayer< Dtype >virtual - loss(const int top_index) constcaffe::Layer< Dtype >inline - loss_caffe::Layer< Dtype >protected - LossLayer(const LayerParameter &param) (defined in caffe::LossLayer< Dtype >)caffe::LossLayer< Dtype >inlineexplicit - MaxBottomBlobs() constcaffe::InfogainLossLayer< Dtype >inlinevirtual - MaxTopBlobs() constcaffe::Layer< Dtype >inlinevirtual - MinBottomBlobs() constcaffe::InfogainLossLayer< Dtype >inlinevirtual - MinTopBlobs() constcaffe::Layer< Dtype >inlinevirtual + inner_num_ (defined in caffe::InfogainLossLayer< Dtype >)caffe::InfogainLossLayer< Dtype >protected + Layer(const LayerParameter &param)caffe::Layer< Dtype >inlineexplicit + layer_param() constcaffe::Layer< Dtype >inline + layer_param_caffe::Layer< Dtype >protected + LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::InfogainLossLayer< Dtype >virtual + loss(const int top_index) constcaffe::Layer< Dtype >inline + loss_caffe::Layer< Dtype >protected + LossLayer(const LayerParameter &param) (defined in caffe::LossLayer< Dtype >)caffe::LossLayer< Dtype >inlineexplicit + MaxBottomBlobs() constcaffe::InfogainLossLayer< Dtype >inlinevirtual + MaxTopBlobs() constcaffe::InfogainLossLayer< Dtype >inlinevirtual + MinBottomBlobs() constcaffe::InfogainLossLayer< Dtype >inlinevirtual + MinTopBlobs() constcaffe::InfogainLossLayer< Dtype >inlinevirtual + normalization_caffe::InfogainLossLayer< Dtype >protected + num_labels_ (defined in caffe::InfogainLossLayer< Dtype >)caffe::InfogainLossLayer< Dtype >protected + outer_num_ (defined in caffe::InfogainLossLayer< Dtype >)caffe::InfogainLossLayer< Dtype >protected param_propagate_down(const int param_id)caffe::Layer< Dtype >inline param_propagate_down_caffe::Layer< Dtype >protected phase_caffe::Layer< Dtype >protected - Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::InfogainLossLayer< Dtype >virtual - set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline - set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline - SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected - SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline + prob_caffe::InfogainLossLayer< Dtype >protected + Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::InfogainLossLayer< Dtype >virtual + set_loss(const int top_index, const Dtype value)caffe::Layer< Dtype >inline + set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline + SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected + SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline + softmax_bottom_vec_caffe::InfogainLossLayer< Dtype >protected + softmax_layer_caffe::InfogainLossLayer< Dtype >protected + softmax_top_vec_caffe::InfogainLossLayer< Dtype >protected + sum_rows_H_ (defined in caffe::InfogainLossLayer< Dtype >)caffe::InfogainLossLayer< Dtype >protected + sum_rows_of_H(const Blob< Dtype > *H)caffe::InfogainLossLayer< Dtype >protectedvirtual ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual type() constcaffe::InfogainLossLayer< Dtype >inlinevirtual ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
      -Generated on Wed Jan 18 2017 12:26:12 for Caffe by   +Generated on Fri Apr 14 2017 12:50:51 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1InfogainLossLayer.html b/doxygen/classcaffe_1_1InfogainLossLayer.html index 162bf01abb5..2e2020cf051 100644 --- a/doxygen/classcaffe_1_1InfogainLossLayer.html +++ b/doxygen/classcaffe_1_1InfogainLossLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::InfogainLossLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -81,7 +81,7 @@
      - + caffe::LossLayer< Dtype > caffe::Layer< Dtype > @@ -107,6 +107,15 @@ virtual int MaxBottomBlobs () const  Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
        +virtual int ExactNumTopBlobs () const + Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
      +  +virtual int MinTopBlobs () const + Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
      +  +virtual int MaxTopBlobs () const + Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
      +  virtual const char * type () const  Returns the layer type.
      @@ -119,9 +128,6 @@ virtual bool AutoTopBlobs () const  For convenience and backwards compatibility, instruct the Net to automatically allocate a single top Blob for LossLayers, into which they output their singleton loss, (even if the user didn't specify one in the prototxt, etc.).
        -virtual int ExactNumTopBlobs () const - Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
      -  virtual bool AllowForceBackward (const int bottom_index) const   - Public Member Functions inherited from caffe::Layer< Dtype > @@ -156,12 +162,6 @@ void set_loss (const int top_index, const Dtype value)  Sets the loss associated with a top blob at a given index.
        -virtual int MinTopBlobs () const - Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
      -  -virtual int MaxTopBlobs () const - Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
      -  virtual bool EqualNumBottomTopBlobs () const  Returns true if the layer requires an equal number of bottom and top blobs. More...
        @@ -181,6 +181,12 @@ virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)  Computes the infogain loss error gradient w.r.t. the predictions. More...
        +virtual Dtype get_normalizer (LossParameter_NormalizationMode normalization_mode, int valid_count) +  + +virtual void sum_rows_of_H (const Blob< Dtype > *H) + fill sum_rows_H_ according to matrix H
      +  - Protected Member Functions inherited from caffe::Layer< Dtype > virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) @@ -197,9 +203,52 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -221,13 +270,13 @@
      Parameters

      Protected Attributes

      +shared_ptr< Layer< Dtype > > softmax_layer_
       The internal SoftmaxLayer used to map predictions to a distribution.
       
      +Blob< Dtype > prob_
       prob stores the output probability predictions from the SoftmaxLayer.
       
      +vector< Blob< Dtype > * > softmax_bottom_vec_
       bottom vector holder used in call to the underlying SoftmaxLayer::Forward
       
      +vector< Blob< Dtype > * > softmax_top_vec_
       top vector holder used in call to the underlying SoftmaxLayer::Forward
       
      Blob< Dtype > infogain_
       
      +Blob< Dtype > sum_rows_H_
       
      +bool has_ignore_label_
       Whether to ignore instances with a certain label.
       
      +int ignore_label_
       The label indicating that an instance should be ignored.
       
      +LossParameter_NormalizationMode normalization_
       How to normalize the output loss.
       
      +int infogain_axis_
       
      +int outer_num_
       
      +int inner_num_
       
      +int num_labels_
       
      - Protected Attributes inherited from caffe::Layer< Dtype >
      LayerParameter layer_param_
       
      bottominput Blob vector (length 2-3)
        -
      1. $ (N \times C \times H \times W) $ the predictions $ \hat{p} $, a Blob with values in $ [0, 1] $ indicating the predicted probability of each of the $ K = CHW $ classes. Each prediction vector $ \hat{p}_n $ should sum to 1 as in a probability distribution: $ \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 $.
        2. -
      2. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
        3. -
      3. $ (1 \times 1 \times K \times K) $ (optional) the infogain matrix $ H $. This must be provided as the third bottom blob input if not provided as the infogain_mat in the InfogainLossParameter. If $ H = I $, this layer is equivalent to the MultinomialLogisticLossLayer.
        4. +
      4. $ (N \times C \times H \times W) $ the predictions $ \hat{p} $, a Blob with values in $ [0, 1] $ indicating the predicted probability of each of the $ K = CHW $ classes. Each prediction vector $ \hat{p}_n $ should sum to 1 as in a probability distribution: $ \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 $.
        5. +
      5. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
        6. +
      6. $ (1 \times 1 \times K \times K) $ (optional) the infogain matrix $ H $. This must be provided as the third bottom blob input if not provided as the infogain_mat in the InfogainLossParameter. If $ H = I $, this layer is equivalent to the MultinomialLogisticLossLayer.
      topoutput Blob vector (length 1)
        -
      1. $ (1 \times 1 \times 1 \times 1) $ the computed infogain multinomial logistic loss: $ E = \frac{-1}{N} \sum\limits_{n=1}^N H_{l_n} \log(\hat{p}_n) = \frac{-1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^{K} H_{l_n,k} \log(\hat{p}_{n,k}) $, where $ H_{l_n} $ denotes row $l_n$ of $H$.
        2. +
      2. $ (1 \times 1 \times 1 \times 1) $ the computed infogain multinomial logistic loss: $ E = \frac{-1}{N} \sum\limits_{n=1}^N H_{l_n} \log(\hat{p}_n) = \frac{-1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^{K} H_{l_n,k} \log(\hat{p}_{n,k}) $, where $ H_{l_n} $ denotes row $l_n$ of $H$.
      @@ -235,7 +284,7 @@

      Member Function Documentation

      -

      § Backward_cpu()

      +

      ◆ Backward_cpu()

      @@ -281,14 +330,14 @@

      Parameters
      topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
        -
      1. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
        2. +
      2. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
      propagate_downsee Layer::Backward. propagate_down[1] must be false as we can't compute gradients with respect to the labels (similarly for propagate_down[2] and the infogain matrix, if provided as bottom[2])
      bottominput Blob vector (length 2-3)
        -
      1. $ (N \times C \times H \times W) $ the predictions $ \hat{p} $; Backward computes diff $ \frac{\partial E}{\partial \hat{p}} $
        2. -
      2. $ (N \times 1 \times 1 \times 1) $ the labels – ignored as we can't compute their error gradients
        3. -
      3. $ (1 \times 1 \times K \times K) $ (optional) the information gain matrix – ignored as its error gradient computation is not implemented.
        4. +
      4. $ (N \times C \times H \times W) $ the predictions $ \hat{p} $; Backward computes diff $ \frac{\partial E}{\partial \hat{p}} $
        5. +
      5. $ (N \times 1 \times 1 \times 1) $ the labels – ignored as we can't compute their error gradients
        6. +
      6. $ (1 \times 1 \times K \times K) $ (optional) the information gain matrix – ignored as its error gradient computation is not implemented.
      @@ -300,7 +349,7 @@

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -331,8 +380,40 @@

      +

      ◆ ExactNumTopBlobs()

      + +
      +
      +
      +template<typename Dtype >
      + + + + + +
      + + + + + + + +
      virtual int caffe::InfogainLossLayer< Dtype >::ExactNumTopBlobs () const
      +       		+inlinevirtual
      +
      + +

      Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

      +

      This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

      + +

      Reimplemented from caffe::LossLayer< Dtype >.

      + +
      +
      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -372,13 +453,13 @@

      Parameters
      bottominput Blob vector (length 2-3)
        -
      1. $ (N \times C \times H \times W) $ the predictions $ \hat{p} $, a Blob with values in $ [0, 1] $ indicating the predicted probability of each of the $ K = CHW $ classes. Each prediction vector $ \hat{p}_n $ should sum to 1 as in a probability distribution: $ \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 $.
        2. -
      2. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
        3. -
      3. $ (1 \times 1 \times K \times K) $ (optional) the infogain matrix $ H $. This must be provided as the third bottom blob input if not provided as the infogain_mat in the InfogainLossParameter. If $ H = I $, this layer is equivalent to the MultinomialLogisticLossLayer.
        4. +
      4. $ (N \times C \times H \times W) $ the predictions $ \hat{p} $, a Blob with values in $ [0, 1] $ indicating the predicted probability of each of the $ K = CHW $ classes. Each prediction vector $ \hat{p}_n $ should sum to 1 as in a probability distribution: $ \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 $.
        5. +
      5. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
        6. +
      6. $ (1 \times 1 \times K \times K) $ (optional) the infogain matrix $ H $. This must be provided as the third bottom blob input if not provided as the infogain_mat in the InfogainLossParameter. If $ H = I $, this layer is equivalent to the MultinomialLogisticLossLayer.
      topoutput Blob vector (length 1)
        -
      1. $ (1 \times 1 \times 1 \times 1) $ the computed infogain multinomial logistic loss: $ E = \frac{-1}{N} \sum\limits_{n=1}^N H_{l_n} \log(\hat{p}_n) = \frac{-1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^{K} H_{l_n,k} \log(\hat{p}_{n,k}) $, where $ H_{l_n} $ denotes row $l_n$ of $H$.
        2. +
      2. $ (1 \times 1 \times 1 \times 1) $ the computed infogain multinomial logistic loss: $ E = \frac{-1}{N} \sum\limits_{n=1}^N H_{l_n} \log(\hat{p}_n) = \frac{-1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^{K} H_{l_n,k} \log(\hat{p}_{n,k}) $, where $ H_{l_n} $ denotes row $l_n$ of $H$.
      @@ -389,8 +470,47 @@

      +

      ◆ get_normalizer()

      + +
      +
      +
      +template<typename Dtype >
      + + + + + +
      + + + + + + + + + + + + + + + + + + +
      Dtype caffe::InfogainLossLayer< Dtype >::get_normalizer (LossParameter_NormalizationMode normalization_mode,
      int valid_count 
      )
      +       		+protectedvirtual
      +
      +

      Read the normalization mode parameter and compute the normalizer based on the blob size. If normalization_mode is VALID, the count of valid outputs will be read from valid_count, unless it is -1 in which case all outputs are assumed to be valid.

      + +
      +
      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -440,7 +560,7 @@

      -

      § MaxBottomBlobs()

      +

      ◆ MaxBottomBlobs()

      @@ -471,8 +591,40 @@

      +

      ◆ MaxTopBlobs()

      + +
      +
      +
      +template<typename Dtype >
      + + + + + +
      + + + + + + + +
      virtual int caffe::InfogainLossLayer< Dtype >::MaxTopBlobs () const
      +       		+inlinevirtual
      +
      + +

      Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required.

      +

      This method should be overridden to return a non-negative value if your layer expects some maximum number of top blobs.

      + +

      Reimplemented from caffe::Layer< Dtype >.

      + +
      +
      -

      § MinBottomBlobs()

      +

      ◆ MinBottomBlobs()

      @@ -503,8 +655,40 @@

      +

      ◆ MinTopBlobs()

      + +
      +
      +
      +template<typename Dtype >
      + + + + + +
      + + + + + + + +
      virtual int caffe::InfogainLossLayer< Dtype >::MinTopBlobs () const
      +       		+inlinevirtual
      +
      + +

      Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required.

      +

      This method should be overridden to return a non-negative value if your layer expects some minimum number of top blobs.

      + +

      Reimplemented from caffe::Layer< Dtype >.

      + +
      +
      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -560,9 +744,9 @@

      -Generated on Wed Jan 18 2017 12:26:12 for Caffe by   +Generated on Fri Apr 14 2017 12:50:51 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1InnerProductLayer-members.html b/doxygen/classcaffe_1_1InnerProductLayer-members.html index 0eced590400..d34cc292baa 100644 --- a/doxygen/classcaffe_1_1InnerProductLayer-members.html +++ b/doxygen/classcaffe_1_1InnerProductLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -114,9 +114,9 @@
      -Generated on Wed Jan 18 2017 12:26:12 for Caffe by   +Generated on Fri Apr 14 2017 12:50:51 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1InnerProductLayer.html b/doxygen/classcaffe_1_1InnerProductLayer.html index b869d98d1da..70f839c5cf5 100644 --- a/doxygen/classcaffe_1_1InnerProductLayer.html +++ b/doxygen/classcaffe_1_1InnerProductLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::InnerProductLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -81,7 +81,7 @@
      - + caffe::Layer< Dtype >
      @@ -233,7 +233,7 @@

      TODO(dox): thorough documentation for Forward, Backward, and proto params.

      Member Function Documentation

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -265,7 +265,7 @@

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -297,7 +297,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -347,7 +347,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -403,9 +403,9 @@

      -Generated on Wed Jan 18 2017 12:26:12 for Caffe by   +Generated on Fri Apr 14 2017 12:50:51 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1InputLayer-members.html b/doxygen/classcaffe_1_1InputLayer-members.html index d3d418b408a..df0b894e805 100644 --- a/doxygen/classcaffe_1_1InputLayer-members.html +++ b/doxygen/classcaffe_1_1InputLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -102,16 +102,15 @@ set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline - ShareInParallel() const (defined in caffe::InputLayer< Dtype >)caffe::InputLayer< Dtype >inlinevirtual - ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual - type() constcaffe::InputLayer< Dtype >inlinevirtual - ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual + ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual + type() constcaffe::InputLayer< Dtype >inlinevirtual + ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
      -Generated on Wed Jan 18 2017 12:26:12 for Caffe by   +Generated on Fri Apr 14 2017 12:50:51 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1InputLayer.html b/doxygen/classcaffe_1_1InputLayer.html index 34a41e4432e..a2c16263751 100644 --- a/doxygen/classcaffe_1_1InputLayer.html +++ b/doxygen/classcaffe_1_1InputLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::InputLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -80,7 +80,7 @@
      - + caffe::Layer< Dtype >
      @@ -93,9 +93,6 @@ virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)  Does layer-specific setup: your layer should implement this function as well as Reshape. More...
        - -virtual bool ShareInParallel () const -  virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)  Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
        @@ -216,7 +213,7 @@

      This data layer is a container that merely holds the data assigned to it; forward, backward, and reshape are all no-ops.

      Member Function Documentation

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -248,7 +245,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -298,7 +295,7 @@

      -

      § MinTopBlobs()

      +

      ◆ MinTopBlobs()

      @@ -330,7 +327,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -386,9 +383,9 @@

      -Generated on Wed Jan 18 2017 12:26:12 for Caffe by   +Generated on Fri Apr 14 2017 12:50:51 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1InternalThread-members.html b/doxygen/classcaffe_1_1InternalThread-members.html index b993b461002..ba464c967cc 100644 --- a/doxygen/classcaffe_1_1InternalThread-members.html +++ b/doxygen/classcaffe_1_1InternalThread-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -79,9 +79,9 @@
      -Generated on Wed Jan 18 2017 12:26:12 for Caffe by   +Generated on Fri Apr 14 2017 12:50:51 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1InternalThread.html b/doxygen/classcaffe_1_1InternalThread.html index 19ffebf9328..87172fef38a 100644 --- a/doxygen/classcaffe_1_1InternalThread.html +++ b/doxygen/classcaffe_1_1InternalThread.html @@ -3,7 +3,7 @@ - + Caffe: caffe::InternalThread Class Reference @@ -29,7 +29,7 @@

      - + @@ -108,7 +108,7 @@

      Virtual class encapsulate boost::thread for use in base class The child class will acquire the ability to run a single thread, by reimplementing the virtual function InternalThreadEntry.

      Member Function Documentation

      -

      § StartInternalThread()

      +

      ◆ StartInternalThread()

      @@ -126,7 +126,7 @@

      -

      § StopInternalThread()

      +

      ◆ StopInternalThread()

      @@ -150,9 +150,9 @@

      -Generated on Wed Jan 18 2017 12:26:12 for Caffe by   +Generated on Fri Apr 14 2017 12:50:51 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1LRNLayer-members.html b/doxygen/classcaffe_1_1LRNLayer-members.html index 9a55c9a7b03..d4d6f3501e2 100644 --- a/doxygen/classcaffe_1_1LRNLayer-members.html +++ b/doxygen/classcaffe_1_1LRNLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -140,9 +140,9 @@
      -Generated on Wed Jan 18 2017 12:26:13 for Caffe by   +Generated on Fri Apr 14 2017 12:50:52 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1LRNLayer.html b/doxygen/classcaffe_1_1LRNLayer.html index fafc7e9131b..2fbe84e96cc 100644 --- a/doxygen/classcaffe_1_1LRNLayer.html +++ b/doxygen/classcaffe_1_1LRNLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::LRNLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -81,7 +81,7 @@
      - + caffe::Layer< Dtype >
      @@ -310,7 +310,7 @@

      TODO(dox): thorough documentation for Forward, Backward, and proto params.

      Member Function Documentation

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -342,7 +342,7 @@

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -374,7 +374,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -424,7 +424,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -480,9 +480,9 @@

      -Generated on Wed Jan 18 2017 12:26:13 for Caffe by   +Generated on Fri Apr 14 2017 12:50:52 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1LSTMLayer-members.html b/doxygen/classcaffe_1_1LSTMLayer-members.html index 4b5290c4960..c78fca3ba2b 100644 --- a/doxygen/classcaffe_1_1LSTMLayer-members.html +++ b/doxygen/classcaffe_1_1LSTMLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -127,9 +127,9 @@
      -Generated on Wed Jan 18 2017 12:26:13 for Caffe by   +Generated on Fri Apr 14 2017 12:50:52 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1LSTMLayer.html b/doxygen/classcaffe_1_1LSTMLayer.html index 2ae5169b46d..11feae414c2 100644 --- a/doxygen/classcaffe_1_1LSTMLayer.html +++ b/doxygen/classcaffe_1_1LSTMLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::LSTMLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -80,7 +80,7 @@
      - + caffe::RecurrentLayer< Dtype > caffe::Layer< Dtype > @@ -292,9 +292,9 @@
      -Generated on Wed Jan 18 2017 12:26:13 for Caffe by   +Generated on Fri Apr 14 2017 12:50:52 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1LSTMUnitLayer-members.html b/doxygen/classcaffe_1_1LSTMUnitLayer-members.html index 7dec74ac216..89841e749db 100644 --- a/doxygen/classcaffe_1_1LSTMUnitLayer-members.html +++ b/doxygen/classcaffe_1_1LSTMUnitLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@
      - + @@ -110,9 +110,9 @@
      -Generated on Wed Jan 18 2017 12:26:13 for Caffe by   +Generated on Fri Apr 14 2017 12:50:52 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1LSTMUnitLayer.html b/doxygen/classcaffe_1_1LSTMUnitLayer.html index aba5ef4cb19..5c2b82062ba 100644 --- a/doxygen/classcaffe_1_1LSTMUnitLayer.html +++ b/doxygen/classcaffe_1_1LSTMUnitLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::LSTMUnitLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -81,7 +81,7 @@
      - + caffe::Layer< Dtype >
      @@ -217,7 +217,7 @@

      A helper for LSTMLayer: computes a single timestep of the non-linearity of the LSTM, producing the updated cell and hidden states.

      Member Function Documentation

      -

      § AllowForceBackward()

      +

      ◆ AllowForceBackward()

      @@ -250,7 +250,7 @@

      -

      § Backward_cpu()

      +

      ◆ Backward_cpu()

      @@ -295,15 +295,15 @@

      Parameters
      -
      topoutput Blob vector (length 2), providing the error gradient with respect to the outputs
        -
      1. $ (1 \times N \times D) $: containing error gradients $ \frac{\partial E}{\partial c_t} $ with respect to the updated cell state $ c_t $
        2. -
      2. $ (1 \times N \times D) $: containing error gradients $ \frac{\partial E}{\partial h_t} $ with respect to the updated cell state $ h_t $
        3. +
      3. $ (1 \times N \times D) $: containing error gradients $ \frac{\partial E}{\partial c_t} $ with respect to the updated cell state $ c_t $
        4. +
      4. $ (1 \times N \times D) $: containing error gradients $ \frac{\partial E}{\partial h_t} $ with respect to the updated cell state $ h_t $
      propagate_downsee Layer::Backward.
      bottominput Blob vector (length 3), into which the error gradients with respect to the LSTMUnit inputs $ c_{t-1} $ and the gate inputs are computed. Computatation of the error gradients w.r.t. the sequence indicators is not implemented.
        -
      1. $ (1 \times N \times D) $ the error gradient w.r.t. the previous timestep cell state $ c_{t-1} $
        2. -
      2. $ (1 \times N \times 4D) $ the error gradient w.r.t. the "gate inputs" $ [ \frac{\partial E}{\partial i_t} \frac{\partial E}{\partial f_t} \frac{\partial E}{\partial o_t} \frac{\partial E}{\partial g_t} ] $
        3. -
      3. $ (1 \times 1 \times N) $ the gradient w.r.t. the sequence continuation indicators $ \delta_t $ is currently not computed.
        4. +
      bottominput Blob vector (length 3), into which the error gradients with respect to the LSTMUnit inputs $ c_{t-1} $ and the gate inputs are computed. Computatation of the error gradients w.r.t. the sequence indicators is not implemented.
        +
      1. $ (1 \times N \times D) $ the error gradient w.r.t. the previous timestep cell state $ c_{t-1} $
        2. +
      2. $ (1 \times N \times 4D) $ the error gradient w.r.t. the "gate inputs" $ [ \frac{\partial E}{\partial i_t} \frac{\partial E}{\partial f_t} \frac{\partial E}{\partial o_t} \frac{\partial E}{\partial g_t} ] $
        3. +
      3. $ (1 \times 1 \times N) $ the gradient w.r.t. the sequence continuation indicators $ \delta_t $ is currently not computed.
      @@ -315,7 +315,7 @@

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -347,7 +347,7 @@

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -379,7 +379,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -416,14 +416,14 @@

      Parameters
      bottominput Blob vector (length 3)
        -
      1. $ (1 \times N \times D) $ the previous timestep cell state $ c_{t-1} $
        2. -
      2. $ (1 \times N \times 4D) $ the "gate inputs" $ [i_t', f_t', o_t', g_t'] $
        3. -
      3. $ (1 \times N) $ the sequence continuation indicators $ \delta_t $
        4. +
      4. $ (1 \times N \times D) $ the previous timestep cell state $ c_{t-1} $
        5. +
      5. $ (1 \times N \times 4D) $ the "gate inputs" $ [i_t', f_t', o_t', g_t'] $
        6. +
      6. $ (1 \times N) $ the sequence continuation indicators $ \delta_t $
      topoutput Blob vector (length 2)
        -
      1. $ (1 \times N \times D) $ the updated cell state $ c_t $, computed as: i_t := [i_t'] f_t := [f_t'] o_t := [o_t'] g_t := [g_t'] c_t := cont_t * (f_t .* c_{t-1}) + (i_t .* g_t)
        2. -
      2. $ (1 \times N \times D) $ the updated hidden state $ h_t $, computed as: h_t := o_t .* [c_t]
        3. +
      3. $ (1 \times N \times D) $ the updated cell state $ c_t $, computed as: i_t := [i_t'] f_t := [f_t'] o_t := [o_t'] g_t := [g_t'] c_t := cont_t * (f_t .* c_{t-1}) + (i_t .* g_t)
        4. +
      4. $ (1 \times N \times D) $ the updated hidden state $ h_t $, computed as: h_t := o_t .* [c_t]
      @@ -435,7 +435,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -491,9 +491,9 @@

      -Generated on Wed Jan 18 2017 12:26:13 for Caffe by   +Generated on Fri Apr 14 2017 12:50:52 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Layer-members.html b/doxygen/classcaffe_1_1Layer-members.html index 580ad3d0d7f..aa227e1cb86 100644 --- a/doxygen/classcaffe_1_1Layer-members.html +++ b/doxygen/classcaffe_1_1Layer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -107,9 +107,9 @@
      -Generated on Wed Jan 18 2017 12:26:13 for Caffe by   +Generated on Fri Apr 14 2017 12:50:52 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Layer.html b/doxygen/classcaffe_1_1Layer.html index af899a2b79b..e701d871317 100644 --- a/doxygen/classcaffe_1_1Layer.html +++ b/doxygen/classcaffe_1_1Layer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::Layer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -81,7 +81,7 @@
      - + caffe::AccuracyLayer< Dtype > caffe::ArgMaxLayer< Dtype > caffe::BaseConvolutionLayer< Dtype > @@ -244,7 +244,7 @@

      Layers must implement a Forward function, in which they take their input (bottom) Blobs (if any) and compute their output Blobs (if any). They may also implement a Backward function, in which they compute the error gradients with respect to their input Blobs, given the error gradients with their output Blobs.

      Constructor & Destructor Documentation

      -

      § Layer()

      +

      ◆ Layer()

      @@ -274,7 +274,7 @@

      Member Function Documentation

      -

      § AllowForceBackward()

      +

      ◆ AllowForceBackward()

      @@ -307,7 +307,7 @@

      -

      § AutoTopBlobs()

      +

      ◆ AutoTopBlobs()

      @@ -339,7 +339,7 @@

      -

      § Backward()

      +

      ◆ Backward()

      @@ -395,7 +395,7 @@

      -

      § CheckBlobCounts()

      +

      ◆ CheckBlobCounts()

      @@ -434,7 +434,7 @@

      -

      § EqualNumBottomTopBlobs()

      +

      ◆ EqualNumBottomTopBlobs()

      @@ -466,7 +466,7 @@

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -493,12 +493,12 @@

      caffe::LSTMUnitLayer< Dtype >, caffe::InfogainLossLayer< Dtype >, caffe::BatchNormLayer< Dtype >, caffe::ArgMaxLayer< Dtype >, caffe::ContrastiveLossLayer< Dtype >, caffe::HDF5OutputLayer< Dtype >, caffe::AccuracyLayer< Dtype >, caffe::HDF5DataLayer< Dtype >, caffe::AbsValLayer< Dtype >, caffe::LRNLayer< Dtype >, caffe::WindowDataLayer< Dtype >, caffe::DummyDataLayer< Dtype >, caffe::ImageDataLayer< Dtype >, caffe::InputLayer< Dtype >, caffe::LossLayer< Dtype >, caffe::CropLayer< Dtype >, caffe::FlattenLayer< Dtype >, caffe::EmbedLayer< Dtype >, caffe::Im2colLayer< Dtype >, caffe::ReductionLayer< Dtype >, caffe::BatchReindexLayer< Dtype >, caffe::InnerProductLayer< Dtype >, caffe::ParameterLayer< Dtype >, caffe::ReshapeLayer< Dtype >, caffe::SliceLayer< Dtype >, caffe::SPPLayer< Dtype >, caffe::MemoryDataLayer< Dtype >, caffe::PoolingLayer< Dtype >, caffe::SplitLayer< Dtype >, caffe::DataLayer< Dtype >, caffe::MVNLayer< Dtype >, caffe::NeuronLayer< Dtype >, caffe::SoftmaxLayer< Dtype >, and caffe::TileLayer< Dtype >.

      +

      Reimplemented in caffe::LSTMUnitLayer< Dtype >, caffe::InfogainLossLayer< Dtype >, caffe::BatchNormLayer< Dtype >, caffe::ArgMaxLayer< Dtype >, caffe::ContrastiveLossLayer< Dtype >, caffe::AccuracyLayer< Dtype >, caffe::HDF5OutputLayer< Dtype >, caffe::HDF5DataLayer< Dtype >, caffe::AbsValLayer< Dtype >, caffe::LRNLayer< Dtype >, caffe::WindowDataLayer< Dtype >, caffe::ImageDataLayer< Dtype >, caffe::LossLayer< Dtype >, caffe::CropLayer< Dtype >, caffe::FlattenLayer< Dtype >, caffe::DummyDataLayer< Dtype >, caffe::EmbedLayer< Dtype >, caffe::Im2colLayer< Dtype >, caffe::InputLayer< Dtype >, caffe::ReductionLayer< Dtype >, caffe::BatchReindexLayer< Dtype >, caffe::InnerProductLayer< Dtype >, caffe::ParameterLayer< Dtype >, caffe::ReshapeLayer< Dtype >, caffe::SliceLayer< Dtype >, caffe::SPPLayer< Dtype >, caffe::MemoryDataLayer< Dtype >, caffe::PoolingLayer< Dtype >, caffe::SplitLayer< Dtype >, caffe::MVNLayer< Dtype >, caffe::NeuronLayer< Dtype >, caffe::SoftmaxLayer< Dtype >, caffe::DataLayer< Dtype >, and caffe::TileLayer< Dtype >.

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -525,12 +525,12 @@

      caffe::LSTMUnitLayer< Dtype >, caffe::SoftmaxWithLossLayer< Dtype >, caffe::BatchNormLayer< Dtype >, caffe::ArgMaxLayer< Dtype >, caffe::RecurrentLayer< Dtype >, caffe::LossLayer< Dtype >, caffe::HDF5OutputLayer< Dtype >, caffe::ScaleLayer< Dtype >, caffe::AbsValLayer< Dtype >, caffe::BiasLayer< Dtype >, caffe::LRNLayer< Dtype >, caffe::WindowDataLayer< Dtype >, caffe::ImageDataLayer< Dtype >, caffe::CropLayer< Dtype >, caffe::FlattenLayer< Dtype >, caffe::EmbedLayer< Dtype >, caffe::Im2colLayer< Dtype >, caffe::ReductionLayer< Dtype >, caffe::BatchReindexLayer< Dtype >, caffe::EltwiseLayer< Dtype >, caffe::InnerProductLayer< Dtype >, caffe::ParameterLayer< Dtype >, caffe::ReshapeLayer< Dtype >, caffe::SPPLayer< Dtype >, caffe::MemoryDataLayer< Dtype >, caffe::ConcatLayer< Dtype >, caffe::MVNLayer< Dtype >, caffe::NeuronLayer< Dtype >, caffe::SoftmaxLayer< Dtype >, caffe::SilenceLayer< Dtype >, and caffe::TileLayer< Dtype >.

      +

      Reimplemented in caffe::LSTMUnitLayer< Dtype >, caffe::InfogainLossLayer< Dtype >, caffe::SoftmaxWithLossLayer< Dtype >, caffe::BatchNormLayer< Dtype >, caffe::ArgMaxLayer< Dtype >, caffe::RecurrentLayer< Dtype >, caffe::LossLayer< Dtype >, caffe::ScaleLayer< Dtype >, caffe::HDF5OutputLayer< Dtype >, caffe::AbsValLayer< Dtype >, caffe::BiasLayer< Dtype >, caffe::LRNLayer< Dtype >, caffe::WindowDataLayer< Dtype >, caffe::ImageDataLayer< Dtype >, caffe::CropLayer< Dtype >, caffe::FlattenLayer< Dtype >, caffe::EmbedLayer< Dtype >, caffe::Im2colLayer< Dtype >, caffe::ReductionLayer< Dtype >, caffe::BatchReindexLayer< Dtype >, caffe::EltwiseLayer< Dtype >, caffe::InnerProductLayer< Dtype >, caffe::ParameterLayer< Dtype >, caffe::ReshapeLayer< Dtype >, caffe::SPPLayer< Dtype >, caffe::MemoryDataLayer< Dtype >, caffe::ConcatLayer< Dtype >, caffe::MVNLayer< Dtype >, caffe::NeuronLayer< Dtype >, caffe::SoftmaxLayer< Dtype >, caffe::SilenceLayer< Dtype >, and caffe::TileLayer< Dtype >.

      -

      § Forward()

      +

      ◆ Forward()

      @@ -580,7 +580,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -630,7 +630,7 @@

      -

      § MaxBottomBlobs()

      +

      ◆ MaxBottomBlobs()

      @@ -662,7 +662,7 @@

      -

      § MaxTopBlobs()

      +

      ◆ MaxTopBlobs()

      @@ -689,12 +689,12 @@

      caffe::SoftmaxWithLossLayer< Dtype >, caffe::AccuracyLayer< Dtype >, caffe::PoolingLayer< Dtype >, and caffe::DataLayer< Dtype >.

      +

      Reimplemented in caffe::InfogainLossLayer< Dtype >, caffe::SoftmaxWithLossLayer< Dtype >, caffe::AccuracyLayer< Dtype >, caffe::PoolingLayer< Dtype >, and caffe::DataLayer< Dtype >.

      -

      § MinBottomBlobs()

      +

      ◆ MinBottomBlobs()

      @@ -726,7 +726,7 @@

      -

      § MinTopBlobs()

      +

      ◆ MinTopBlobs()

      @@ -753,12 +753,12 @@

      caffe::SoftmaxWithLossLayer< Dtype >, caffe::AccuracyLayer< Dtype >, caffe::HDF5DataLayer< Dtype >, caffe::DummyDataLayer< Dtype >, caffe::InputLayer< Dtype >, caffe::FilterLayer< Dtype >, caffe::SliceLayer< Dtype >, caffe::PoolingLayer< Dtype >, caffe::BaseConvolutionLayer< Dtype >, caffe::SplitLayer< Dtype >, and caffe::DataLayer< Dtype >.

      +

      Reimplemented in caffe::InfogainLossLayer< Dtype >, caffe::SoftmaxWithLossLayer< Dtype >, caffe::AccuracyLayer< Dtype >, caffe::HDF5DataLayer< Dtype >, caffe::DummyDataLayer< Dtype >, caffe::InputLayer< Dtype >, caffe::FilterLayer< Dtype >, caffe::SliceLayer< Dtype >, caffe::PoolingLayer< Dtype >, caffe::BaseConvolutionLayer< Dtype >, caffe::SplitLayer< Dtype >, and caffe::DataLayer< Dtype >.

      -

      § param_propagate_down()

      +

      ◆ param_propagate_down()

      @@ -789,7 +789,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -834,12 +834,12 @@

      caffe::LSTMUnitLayer< Dtype >, caffe::SoftmaxWithLossLayer< Dtype >, caffe::InfogainLossLayer< Dtype >, caffe::SigmoidCrossEntropyLossLayer< Dtype >, caffe::MultinomialLogisticLossLayer< Dtype >, caffe::BatchNormLayer< Dtype >, caffe::EuclideanLossLayer< Dtype >, caffe::ArgMaxLayer< Dtype >, caffe::PReLULayer< Dtype >, caffe::DropoutLayer< Dtype >, caffe::BaseDataLayer< Dtype >, caffe::HDF5OutputLayer< Dtype >, caffe::AccuracyLayer< Dtype >, caffe::HDF5DataLayer< Dtype >, caffe::PythonLayer< Dtype >, caffe::RecurrentLayer< Dtype >, caffe::ScaleLayer< Dtype >, caffe::LossLayer< Dtype >, caffe::LRNLayer< Dtype >, caffe::BiasLayer< Dtype >, caffe::DummyDataLayer< Dtype >, caffe::InputLayer< Dtype >, caffe::CropLayer< Dtype >, caffe::FlattenLayer< Dtype >, caffe::EmbedLayer< Dtype >, caffe::Im2colLayer< Dtype >, caffe::ParameterLayer< Dtype >, caffe::ReductionLayer< Dtype >, caffe::BatchReindexLayer< Dtype >, caffe::EltwiseLayer< Dtype >, caffe::FilterLayer< Dtype >, caffe::InnerProductLayer< Dtype >, caffe::ReshapeLayer< Dtype >, caffe::SliceLayer< Dtype >, caffe::SPPLayer< Dtype >, caffe::BaseConvolutionLayer< Dtype >, caffe::PoolingLayer< Dtype >, caffe::ConcatLayer< Dtype >, caffe::NeuronLayer< Dtype >, caffe::SplitLayer< Dtype >, caffe::MVNLayer< Dtype >, caffe::SoftmaxLayer< Dtype >, caffe::SilenceLayer< Dtype >, and caffe::TileLayer< Dtype >.

      +

      Implemented in caffe::LSTMUnitLayer< Dtype >, caffe::SoftmaxWithLossLayer< Dtype >, caffe::InfogainLossLayer< Dtype >, caffe::SigmoidCrossEntropyLossLayer< Dtype >, caffe::MultinomialLogisticLossLayer< Dtype >, caffe::BatchNormLayer< Dtype >, caffe::EuclideanLossLayer< Dtype >, caffe::ArgMaxLayer< Dtype >, caffe::PReLULayer< Dtype >, caffe::DropoutLayer< Dtype >, caffe::AccuracyLayer< Dtype >, caffe::BaseDataLayer< Dtype >, caffe::HDF5OutputLayer< Dtype >, caffe::PythonLayer< Dtype >, caffe::RecurrentLayer< Dtype >, caffe::ScaleLayer< Dtype >, caffe::HDF5DataLayer< Dtype >, caffe::LossLayer< Dtype >, caffe::LRNLayer< Dtype >, caffe::BiasLayer< Dtype >, caffe::CropLayer< Dtype >, caffe::FlattenLayer< Dtype >, caffe::DummyDataLayer< Dtype >, caffe::EmbedLayer< Dtype >, caffe::Im2colLayer< Dtype >, caffe::InputLayer< Dtype >, caffe::ParameterLayer< Dtype >, caffe::ReductionLayer< Dtype >, caffe::BatchReindexLayer< Dtype >, caffe::EltwiseLayer< Dtype >, caffe::FilterLayer< Dtype >, caffe::InnerProductLayer< Dtype >, caffe::ReshapeLayer< Dtype >, caffe::SliceLayer< Dtype >, caffe::SPPLayer< Dtype >, caffe::BaseConvolutionLayer< Dtype >, caffe::PoolingLayer< Dtype >, caffe::ConcatLayer< Dtype >, caffe::NeuronLayer< Dtype >, caffe::SplitLayer< Dtype >, caffe::MVNLayer< Dtype >, caffe::SoftmaxLayer< Dtype >, caffe::SilenceLayer< Dtype >, and caffe::TileLayer< Dtype >.

      -

      § SetLossWeights()

      +

      ◆ SetLossWeights()

      @@ -868,7 +868,7 @@

      -

      § SetUp()

      +

      ◆ SetUp()

      @@ -917,7 +917,7 @@

      Member Data Documentation

      -

      § blobs_

      +

      ◆ blobs_

      @@ -942,7 +942,7 @@

      -

      § layer_param_

      +

      ◆ layer_param_

      @@ -967,7 +967,7 @@

      -

      § loss_

      +

      ◆ loss_

      @@ -992,7 +992,7 @@

      -

      § param_propagate_down_

      +

      ◆ param_propagate_down_

      @@ -1017,7 +1017,7 @@

      -

      § phase_

      +

      ◆ phase_

      @@ -1047,9 +1047,9 @@

      -Generated on Wed Jan 18 2017 12:26:13 for Caffe by   +Generated on Fri Apr 14 2017 12:50:52 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1LayerRegisterer-members.html b/doxygen/classcaffe_1_1LayerRegisterer-members.html index 4e2d2832b94..a575dffa1b0 100644 --- a/doxygen/classcaffe_1_1LayerRegisterer-members.html +++ b/doxygen/classcaffe_1_1LayerRegisterer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -73,9 +73,9 @@
      -Generated on Wed Jan 18 2017 12:26:13 for Caffe by   +Generated on Fri Apr 14 2017 12:50:52 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1LayerRegisterer.html b/doxygen/classcaffe_1_1LayerRegisterer.html index 392162ea5ff..69d878cc306 100644 --- a/doxygen/classcaffe_1_1LayerRegisterer.html +++ b/doxygen/classcaffe_1_1LayerRegisterer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::LayerRegisterer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -82,9 +82,9 @@
      -Generated on Wed Jan 18 2017 12:26:13 for Caffe by   +Generated on Fri Apr 14 2017 12:50:52 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1LayerRegistry-members.html b/doxygen/classcaffe_1_1LayerRegistry-members.html index 49cfbe51486..9831aaf9d98 100644 --- a/doxygen/classcaffe_1_1LayerRegistry-members.html +++ b/doxygen/classcaffe_1_1LayerRegistry-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -78,9 +78,9 @@
      -Generated on Wed Jan 18 2017 12:26:13 for Caffe by   +Generated on Fri Apr 14 2017 12:50:52 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1LayerRegistry.html b/doxygen/classcaffe_1_1LayerRegistry.html index e153e16d5d1..ceb13f152f7 100644 --- a/doxygen/classcaffe_1_1LayerRegistry.html +++ b/doxygen/classcaffe_1_1LayerRegistry.html @@ -3,7 +3,7 @@ - + Caffe: caffe::LayerRegistry< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -101,9 +101,9 @@
      -Generated on Wed Jan 18 2017 12:26:13 for Caffe by   +Generated on Fri Apr 14 2017 12:50:52 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1LogLayer-members.html b/doxygen/classcaffe_1_1LogLayer-members.html index 7cc769b8cf8..12b6e5006e2 100644 --- a/doxygen/classcaffe_1_1LogLayer-members.html +++ b/doxygen/classcaffe_1_1LogLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -113,9 +113,9 @@
      -Generated on Wed Jan 18 2017 12:26:13 for Caffe by   +Generated on Fri Apr 14 2017 12:50:52 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1LogLayer.html b/doxygen/classcaffe_1_1LogLayer.html index 55766ab9697..a3af3a5261b 100644 --- a/doxygen/classcaffe_1_1LogLayer.html +++ b/doxygen/classcaffe_1_1LogLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::LogLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@
      - + @@ -72,7 +72,7 @@
      -

      Computes $ y = log_{\gamma}(\alpha x + \beta) $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $. +

      Computes $ y = log_{\gamma}(\alpha x + \beta) $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $. More...

      #include <log_layer.hpp>

      @@ -81,7 +81,7 @@
      - + caffe::NeuronLayer< Dtype > caffe::Layer< Dtype > @@ -223,10 +223,10 @@

      template<typename Dtype>
      class caffe::LogLayer< Dtype >

      -

      Computes $ y = log_{\gamma}(\alpha x + \beta) $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $.

      +

      Computes $ y = log_{\gamma}(\alpha x + \beta) $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $.

      Constructor & Destructor Documentation

      -

      § LogLayer()

      +

      ◆ LogLayer()

      @@ -253,9 +253,9 @@

      Parameters
      paramprovides LogParameter log_param, with LogLayer options:
        -
      • scale (optional, default 1) the scale $ \alpha $
        • -
      • shift (optional, default 0) the shift $ \beta $
        • -
      • base (optional, default -1 for a value of $ e \approx 2.718 $) the base $ \gamma $
        • +
      • scale (optional, default 1) the scale $ \alpha $
        • +
      • shift (optional, default 0) the shift $ \beta $
        • +
      • base (optional, default -1 for a value of $ e \approx 2.718 $) the base $ \gamma $
      @@ -266,7 +266,7 @@

      Member Function Documentation

      -

      § Backward_cpu()

      +

      ◆ Backward_cpu()

      @@ -311,12 +311,12 @@

      Parameters
      topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
        -
      1. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
        2. +
      2. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
      propagate_downsee Layer::Backward.
      bottominput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} y \alpha \log_e(gamma) $ if propagate_down[0]
        2. +
      2. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} y \alpha \log_e(gamma) $ if propagate_down[0]
      @@ -328,7 +328,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -365,11 +365,11 @@

      Parameters
      bottominput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x $
        2. +
      2. $ (N \times C \times H \times W) $ the inputs $ x $
      topoutput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the computed outputs $ y = log_{\gamma}(\alpha x + \beta) $
        2. +
      2. $ (N \times C \times H \times W) $ the computed outputs $ y = log_{\gamma}(\alpha x + \beta) $
      @@ -381,7 +381,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -437,9 +437,9 @@

      -Generated on Wed Jan 18 2017 12:26:13 for Caffe by   +Generated on Fri Apr 14 2017 12:50:52 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1LossLayer-members.html b/doxygen/classcaffe_1_1LossLayer-members.html index acd4d97ef71..285ec70765f 100644 --- a/doxygen/classcaffe_1_1LossLayer-members.html +++ b/doxygen/classcaffe_1_1LossLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -108,9 +108,9 @@
      -Generated on Wed Jan 18 2017 12:26:13 for Caffe by   +Generated on Fri Apr 14 2017 12:50:52 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1LossLayer.html b/doxygen/classcaffe_1_1LossLayer.html index 405d97c8612..a4d76839cab 100644 --- a/doxygen/classcaffe_1_1LossLayer.html +++ b/doxygen/classcaffe_1_1LossLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::LossLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -79,7 +79,7 @@
      - + caffe::Layer< Dtype > caffe::ContrastiveLossLayer< Dtype > caffe::EuclideanLossLayer< Dtype > @@ -216,7 +216,7 @@

      LossLayers are typically only capable of backpropagating to their first input – the predictions.

      Member Function Documentation

      -

      § AllowForceBackward()

      +

      ◆ AllowForceBackward()

      @@ -249,7 +249,7 @@

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -283,7 +283,7 @@

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -312,12 +312,12 @@

      caffe::Layer< Dtype >.

      -

      Reimplemented in caffe::SoftmaxWithLossLayer< Dtype >.

      +

      Reimplemented in caffe::InfogainLossLayer< Dtype >, and caffe::SoftmaxWithLossLayer< Dtype >.

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -369,7 +369,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -427,9 +427,9 @@

      -Generated on Wed Jan 18 2017 12:26:13 for Caffe by   +Generated on Fri Apr 14 2017 12:50:52 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1MSRAFiller-members.html b/doxygen/classcaffe_1_1MSRAFiller-members.html index bcc4a25fbcf..70bff453bae 100644 --- a/doxygen/classcaffe_1_1MSRAFiller-members.html +++ b/doxygen/classcaffe_1_1MSRAFiller-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -77,9 +77,9 @@
      -Generated on Wed Jan 18 2017 12:26:14 for Caffe by   +Generated on Fri Apr 14 2017 12:50:53 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1MSRAFiller.html b/doxygen/classcaffe_1_1MSRAFiller.html index 1c22d27c8a5..4dbe7ae04e7 100644 --- a/doxygen/classcaffe_1_1MSRAFiller.html +++ b/doxygen/classcaffe_1_1MSRAFiller.html @@ -3,7 +3,7 @@ - + Caffe: caffe::MSRAFiller< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -70,7 +70,7 @@
      -

      Fills a Blob with values $ x \sim N(0, \sigma^2) $ where $ \sigma^2 $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average. +

      Fills a Blob with values $ x \sim N(0, \sigma^2) $ where $ \sigma^2 $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average. More...

      #include <filler.hpp>

      @@ -79,7 +79,7 @@
      - + caffe::Filler< Dtype >
      @@ -108,7 +108,7 @@

      template<typename Dtype>
      class caffe::MSRAFiller< Dtype >

      -

      Fills a Blob with values $ x \sim N(0, \sigma^2) $ where $ \sigma^2 $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average.

      +

      Fills a Blob with values $ x \sim N(0, \sigma^2) $ where $ \sigma^2 $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average.

      A Filler based on the paper [He, Zhang, Ren and Sun 2015]: Specifically accounts for ReLU nonlinearities.

      Aside: for another perspective on the scaling factor, see the derivation of [Saxe, McClelland, and Ganguli 2013 (v3)].

      It fills the incoming matrix by randomly sampling Gaussian data with std = sqrt(2 / n) where n is the fan_in, fan_out, or their average, depending on the variance_norm option. You should make sure the input blob has shape (num, a, b, c) where a * b * c = fan_in and num * b * c = fan_out. Note that this is currently not the case for inner product layers.

      @@ -118,9 +118,9 @@
      -Generated on Wed Jan 18 2017 12:26:14 for Caffe by   +Generated on Fri Apr 14 2017 12:50:53 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1MVNLayer-members.html b/doxygen/classcaffe_1_1MVNLayer-members.html index 67ea0fee99d..659a65713e4 100644 --- a/doxygen/classcaffe_1_1MVNLayer-members.html +++ b/doxygen/classcaffe_1_1MVNLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@
      - + @@ -113,9 +113,9 @@

      -Generated on Wed Jan 18 2017 12:26:14 for Caffe by   +Generated on Fri Apr 14 2017 12:50:53 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1MVNLayer.html b/doxygen/classcaffe_1_1MVNLayer.html index f9629176dd6..0a9d6eb89bb 100644 --- a/doxygen/classcaffe_1_1MVNLayer.html +++ b/doxygen/classcaffe_1_1MVNLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::MVNLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@
      - + @@ -81,7 +81,7 @@
      - + caffe::Layer< Dtype >
      @@ -230,7 +230,7 @@

      TODO(dox): thorough documentation for Forward, Backward, and proto params.

      Member Function Documentation

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -262,7 +262,7 @@

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -294,7 +294,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -350,9 +350,9 @@

      -Generated on Wed Jan 18 2017 12:26:14 for Caffe by   +Generated on Fri Apr 14 2017 12:50:53 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1MemoryDataLayer-members.html b/doxygen/classcaffe_1_1MemoryDataLayer-members.html index a71a6138f6f..31e0dc2db58 100644 --- a/doxygen/classcaffe_1_1MemoryDataLayer-members.html +++ b/doxygen/classcaffe_1_1MemoryDataLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -122,20 +122,19 @@ set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline - ShareInParallel() const (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >inlinevirtual - size_ (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >protected - ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual - transform_param_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected - type() constcaffe::MemoryDataLayer< Dtype >inlinevirtual - width() (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >inline - width_ (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >protected - ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual + size_ (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >protected + ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual + transform_param_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected + type() constcaffe::MemoryDataLayer< Dtype >inlinevirtual + width() (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >inline + width_ (defined in caffe::MemoryDataLayer< Dtype >)caffe::MemoryDataLayer< Dtype >protected + ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
      -Generated on Wed Jan 18 2017 12:26:14 for Caffe by   +Generated on Fri Apr 14 2017 12:50:52 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1MemoryDataLayer.html b/doxygen/classcaffe_1_1MemoryDataLayer.html index b4b750b5e9f..868b65660e6 100644 --- a/doxygen/classcaffe_1_1MemoryDataLayer.html +++ b/doxygen/classcaffe_1_1MemoryDataLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::MemoryDataLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -81,7 +81,7 @@
      - + caffe::BaseDataLayer< Dtype > caffe::Layer< Dtype > @@ -133,9 +133,6 @@ virtual void LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)  Does layer-specific setup: your layer should implement this function as well as Reshape. More...
        - -virtual bool ShareInParallel () const -  virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)  Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
        @@ -292,7 +289,7 @@

      TODO(dox): thorough documentation for Forward and proto params.

      Member Function Documentation

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -324,7 +321,7 @@

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -362,9 +359,9 @@

      -Generated on Wed Jan 18 2017 12:26:14 for Caffe by   +Generated on Fri Apr 14 2017 12:50:52 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1MultinomialLogisticLossLayer-members.html b/doxygen/classcaffe_1_1MultinomialLogisticLossLayer-members.html index b029cbc1a0c..e242d6ea1ad 100644 --- a/doxygen/classcaffe_1_1MultinomialLogisticLossLayer-members.html +++ b/doxygen/classcaffe_1_1MultinomialLogisticLossLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -109,9 +109,9 @@
      -Generated on Wed Jan 18 2017 12:26:14 for Caffe by   +Generated on Fri Apr 14 2017 12:50:53 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1MultinomialLogisticLossLayer.html b/doxygen/classcaffe_1_1MultinomialLogisticLossLayer.html index 33fa494bd49..ad15a9f9e4d 100644 --- a/doxygen/classcaffe_1_1MultinomialLogisticLossLayer.html +++ b/doxygen/classcaffe_1_1MultinomialLogisticLossLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::MultinomialLogisticLossLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -80,7 +80,7 @@
      - + caffe::LossLayer< Dtype > caffe::Layer< Dtype > @@ -217,12 +217,12 @@
      Parameters
      bottominput Blob vector (length 2)
        -
      1. $ (N \times C \times H \times W) $ the predictions $ \hat{p} $, a Blob with values in $ [0, 1] $ indicating the predicted probability of each of the $ K = CHW $ classes. Each prediction vector $ \hat{p}_n $ should sum to 1 as in a probability distribution: $ \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 $.
        2. -
      2. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
        3. +
      3. $ (N \times C \times H \times W) $ the predictions $ \hat{p} $, a Blob with values in $ [0, 1] $ indicating the predicted probability of each of the $ K = CHW $ classes. Each prediction vector $ \hat{p}_n $ should sum to 1 as in a probability distribution: $ \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 $.
        4. +
      4. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
      topoutput Blob vector (length 1)
        -
      1. $ (1 \times 1 \times 1 \times 1) $ the computed multinomial logistic loss: $ E = \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n}) $
        2. +
      2. $ (1 \times 1 \times 1 \times 1) $ the computed multinomial logistic loss: $ E = \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n}) $
      @@ -230,7 +230,7 @@

      Member Function Documentation

      -

      § Backward_cpu()

      +

      ◆ Backward_cpu()

      @@ -276,13 +276,13 @@

      Parameters
      topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
        -
      1. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
        2. +
      2. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
      propagate_downsee Layer::Backward. propagate_down[1] must be false as we can't compute gradients with respect to the labels.
      bottominput Blob vector (length 2)
        -
      1. $ (N \times C \times H \times W) $ the predictions $ \hat{p} $; Backward computes diff $ \frac{\partial E}{\partial \hat{p}} $
        2. -
      2. $ (N \times 1 \times 1 \times 1) $ the labels – ignored as we can't compute their error gradients
        3. +
      3. $ (N \times C \times H \times W) $ the predictions $ \hat{p} $; Backward computes diff $ \frac{\partial E}{\partial \hat{p}} $
        4. +
      4. $ (N \times 1 \times 1 \times 1) $ the labels – ignored as we can't compute their error gradients
      @@ -294,7 +294,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -334,12 +334,12 @@

      Parameters
      bottominput Blob vector (length 2)
        -
      1. $ (N \times C \times H \times W) $ the predictions $ \hat{p} $, a Blob with values in $ [0, 1] $ indicating the predicted probability of each of the $ K = CHW $ classes. Each prediction vector $ \hat{p}_n $ should sum to 1 as in a probability distribution: $ \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 $.
        2. -
      2. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
        3. +
      3. $ (N \times C \times H \times W) $ the predictions $ \hat{p} $, a Blob with values in $ [0, 1] $ indicating the predicted probability of each of the $ K = CHW $ classes. Each prediction vector $ \hat{p}_n $ should sum to 1 as in a probability distribution: $ \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 $.
        4. +
      4. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
      topoutput Blob vector (length 1)
        -
      1. $ (1 \times 1 \times 1 \times 1) $ the computed multinomial logistic loss: $ E = \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n}) $
        2. +
      2. $ (1 \times 1 \times 1 \times 1) $ the computed multinomial logistic loss: $ E = \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n}) $
      @@ -351,7 +351,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -407,9 +407,9 @@

      -Generated on Wed Jan 18 2017 12:26:14 for Caffe by   +Generated on Fri Apr 14 2017 12:50:53 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1NesterovSolver-members.html b/doxygen/classcaffe_1_1NesterovSolver-members.html index c0b14d0bd43..850ef38b1f4 100644 --- a/doxygen/classcaffe_1_1NesterovSolver-members.html +++ b/doxygen/classcaffe_1_1NesterovSolver-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -135,9 +135,9 @@
      -Generated on Wed Jan 18 2017 12:26:14 for Caffe by   +Generated on Fri Apr 14 2017 12:50:53 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1NesterovSolver.html b/doxygen/classcaffe_1_1NesterovSolver.html index d60f58227c1..8f53f5d189a 100644 --- a/doxygen/classcaffe_1_1NesterovSolver.html +++ b/doxygen/classcaffe_1_1NesterovSolver.html @@ -3,7 +3,7 @@ - + Caffe: caffe::NesterovSolver< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -75,7 +75,7 @@
      - + caffe::SGDSolver< Dtype > caffe::Solver< Dtype > @@ -290,9 +290,9 @@
      -Generated on Wed Jan 18 2017 12:26:14 for Caffe by   +Generated on Fri Apr 14 2017 12:50:53 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Net-members.html b/doxygen/classcaffe_1_1Net-members.html index fd7f4c45367..9c07c31a1d7 100644 --- a/doxygen/classcaffe_1_1Net-members.html +++ b/doxygen/classcaffe_1_1Net-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@
      - + @@ -186,9 +186,9 @@
      -Generated on Wed Jan 18 2017 12:26:14 for Caffe by   +Generated on Fri Apr 14 2017 12:50:53 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Net.html b/doxygen/classcaffe_1_1Net.html index 43419c8a8ff..d2cea7b6165 100644 --- a/doxygen/classcaffe_1_1Net.html +++ b/doxygen/classcaffe_1_1Net.html @@ -3,7 +3,7 @@ - + Caffe: caffe::Net< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -481,7 +481,7 @@

      TODO(dox): more thorough description.

      Member Function Documentation

      -

      § Backward()

      +

      ◆ Backward()

      @@ -501,7 +501,7 @@

      -

      § ForwardFromTo()

      +

      ◆ ForwardFromTo()

      @@ -532,7 +532,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -554,7 +554,7 @@

      -

      § ShareWeights()

      +

      ◆ ShareWeights()

      @@ -577,7 +577,7 @@

      Member Data Documentation

      -

      § blob_loss_weights_

      +

      ◆ blob_loss_weights_

      @@ -602,7 +602,7 @@

      -

      § bottom_vecs_

      +

      ◆ bottom_vecs_

      @@ -627,7 +627,7 @@

      -

      § learnable_param_ids_

      +

      ◆ learnable_param_ids_

      @@ -658,9 +658,9 @@

      -Generated on Wed Jan 18 2017 12:26:14 for Caffe by   +Generated on Fri Apr 14 2017 12:50:53 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Net_1_1Callback-members.html b/doxygen/classcaffe_1_1Net_1_1Callback-members.html index 2ab7bba4b5d..d4d053a47e5 100644 --- a/doxygen/classcaffe_1_1Net_1_1Callback-members.html +++ b/doxygen/classcaffe_1_1Net_1_1Callback-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -74,9 +74,9 @@
      -Generated on Wed Jan 18 2017 12:26:14 for Caffe by   +Generated on Fri Apr 14 2017 12:50:53 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1Net_1_1Callback.html b/doxygen/classcaffe_1_1Net_1_1Callback.html index be8274d13fa..4582c2aff67 100644 --- a/doxygen/classcaffe_1_1Net_1_1Callback.html +++ b/doxygen/classcaffe_1_1Net_1_1Callback.html @@ -3,7 +3,7 @@ - + Caffe: caffe::Net< Dtype >::Callback Class Reference @@ -29,7 +29,7 @@

      - + @@ -90,9 +90,9 @@
      -Generated on Wed Jan 18 2017 12:26:14 for Caffe by   +Generated on Fri Apr 14 2017 12:50:53 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1NeuronLayer-members.html b/doxygen/classcaffe_1_1NeuronLayer-members.html index 20dbd8119d3..c1b8cb5f195 100644 --- a/doxygen/classcaffe_1_1NeuronLayer-members.html +++ b/doxygen/classcaffe_1_1NeuronLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -108,9 +108,9 @@
      -Generated on Wed Jan 18 2017 12:26:15 for Caffe by   +Generated on Fri Apr 14 2017 12:50:53 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1NeuronLayer.html b/doxygen/classcaffe_1_1NeuronLayer.html index 6ba4f495d8b..b0cac27a41b 100644 --- a/doxygen/classcaffe_1_1NeuronLayer.html +++ b/doxygen/classcaffe_1_1NeuronLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::NeuronLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@
      - + @@ -70,7 +70,7 @@
      -

      An interface for layers that take one blob as input ( $ x $) and produce one equally-sized blob as output ( $ y $), where each element of the output depends only on the corresponding input element. +

      An interface for layers that take one blob as input ( $ x $) and produce one equally-sized blob as output ( $ y $), where each element of the output depends only on the corresponding input element. More...

      #include <neuron_layer.hpp>

      @@ -79,7 +79,7 @@
      - + caffe::Layer< Dtype > caffe::AbsValLayer< Dtype > caffe::BNLLLayer< Dtype > @@ -217,10 +217,10 @@

      template<typename Dtype>
      class caffe::NeuronLayer< Dtype >

      -

      An interface for layers that take one blob as input ( $ x $) and produce one equally-sized blob as output ( $ y $), where each element of the output depends only on the corresponding input element.

      +

      An interface for layers that take one blob as input ( $ x $) and produce one equally-sized blob as output ( $ y $), where each element of the output depends only on the corresponding input element.

      Member Function Documentation

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -254,7 +254,7 @@

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -288,7 +288,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -346,9 +346,9 @@

      -Generated on Wed Jan 18 2017 12:26:15 for Caffe by   +Generated on Fri Apr 14 2017 12:50:53 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1P2PSync-members.html b/doxygen/classcaffe_1_1P2PSync-members.html deleted file mode 100644 index d22f29cbf89..00000000000 --- a/doxygen/classcaffe_1_1P2PSync-members.html +++ /dev/null @@ -1,113 +0,0 @@ - - - - - - - -Caffe: Member List - - - - - - - - - -
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      caffe::P2PSync< Dtype > Member List
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      This is the complete list of members for caffe::P2PSync< Dtype >, including all inherited members.

      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      children_ (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >protected
      configure(Solver< Dtype > *solver) const (defined in caffe::GPUParams< Dtype >)caffe::GPUParams< Dtype >
      data() const (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inline
      data_ (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
      diff() const (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inline
      diff_ (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
      DISABLE_COPY_AND_ASSIGN(Params) (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
      GPUParams(shared_ptr< Solver< Dtype > > root_solver, int device) (defined in caffe::GPUParams< Dtype >)caffe::GPUParams< Dtype >
      initial_iter() const (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >inline
      initial_iter_ (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >protected
      InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadinline
      InternalThreadEntry() (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >protectedvirtual
      is_started() const (defined in caffe::InternalThread)caffe::InternalThread
      must_stop() (defined in caffe::InternalThread)caffe::InternalThreadprotected
      on_gradients_ready() (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >protectedvirtual
      on_start() (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >protectedvirtual
      P2PSync(shared_ptr< Solver< Dtype > > root_solver, P2PSync< Dtype > *parent, const SolverParameter &param) (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >explicit
      Params(shared_ptr< Solver< Dtype > > root_solver) (defined in caffe::Params< Dtype >)caffe::Params< Dtype >explicit
      parent_ (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >protected
      parent_grads_ (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >protected
      Prepare(const vector< int > &gpus, vector< shared_ptr< P2PSync< Dtype > > > *syncs) (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >
      queue_ (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >protected
      Run(const vector< int > &gpus) (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >
      size() const (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inline
      size_ (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
      solver() const (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >inline
      solver_ (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >protected
      StartInternalThread()caffe::InternalThread
      StopInternalThread()caffe::InternalThread
      ~GPUParams() (defined in caffe::GPUParams< Dtype >)caffe::GPUParams< Dtype >virtual
      ~InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadvirtual
      ~P2PSync() (defined in caffe::P2PSync< Dtype >)caffe::P2PSync< Dtype >virtual
      ~Params() (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inlinevirtual
      - -
      -Generated on Fri Jan 13 2017 13:56:46 for Caffe by   -doxygen - 1.8.12 -
      - - diff --git a/doxygen/classcaffe_1_1P2PSync.html b/doxygen/classcaffe_1_1P2PSync.html deleted file mode 100644 index 6e8594c4332..00000000000 --- a/doxygen/classcaffe_1_1P2PSync.html +++ /dev/null @@ -1,196 +0,0 @@ - - - - - - - -Caffe: caffe::P2PSync< Dtype > Class Template Reference - - - - - - - - - -
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      -Public Member Functions | -Protected Member Functions | -Protected Attributes | -List of all members
      -
      -
      caffe::P2PSync< Dtype > Class Template Reference
      -
      -
      -
      -Inheritance diagram for caffe::P2PSync< Dtype >:
      -
      -
      - - -caffe::GPUParams< Dtype > -caffe::Solver< Dtype >::Callback -caffe::InternalThread -caffe::Params< Dtype > - -
      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

      -Public Member Functions

      - P2PSync (shared_ptr< Solver< Dtype > > root_solver, P2PSync< Dtype > *parent, const SolverParameter &param)
       
      -const shared_ptr< Solver< Dtype > > & solver () const
       
      -void Run (const vector< int > &gpus)
       
      -void Prepare (const vector< int > &gpus, vector< shared_ptr< P2PSync< Dtype > > > *syncs)
       
      -const int initial_iter () const
       
      - Public Member Functions inherited from caffe::GPUParams< Dtype >
      - GPUParams (shared_ptr< Solver< Dtype > > root_solver, int device)
       
      -void configure (Solver< Dtype > *solver) const
       
      - Public Member Functions inherited from caffe::Params< Dtype >
      - Params (shared_ptr< Solver< Dtype > > root_solver)
       
      -size_t size () const
       
      -Dtype * data () const
       
      -Dtype * diff () const
       
      - Public Member Functions inherited from caffe::InternalThread
      void StartInternalThread ()
       
      void StopInternalThread ()
       
      -bool is_started () const
       
      - - - - - - - - - - - - - -

      -Protected Member Functions

      -void on_start ()
       
      -void on_gradients_ready ()
       
      -void InternalThreadEntry ()
       
      - Protected Member Functions inherited from caffe::Params< Dtype >
      - DISABLE_COPY_AND_ASSIGN (Params)
       
      - Protected Member Functions inherited from caffe::InternalThread
      -bool must_stop ()
       
      - - - - - - - - - - - - - - - - - - - - -

      -Protected Attributes

      -P2PSync< Dtype > * parent_
       
      -vector< P2PSync< Dtype > * > children_
       
      -BlockingQueue< P2PSync< Dtype > * > queue_
       
      -const int initial_iter_
       
      -Dtype * parent_grads_
       
      -shared_ptr< Solver< Dtype > > solver_
       
      - Protected Attributes inherited from caffe::Params< Dtype >
      -const size_t size_
       
      -Dtype * data_
       
      -Dtype * diff_
       
      -
      The documentation for this class was generated from the following files: -
      - -
      -Generated on Fri Jan 13 2017 13:56:46 for Caffe by   -doxygen - 1.8.12 -
      - - diff --git a/doxygen/classcaffe_1_1P2PSync.png b/doxygen/classcaffe_1_1P2PSync.png deleted file mode 100644 index 956f8b17de0361564d3e7b571c3c03c960f72547..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2025 zcmb`IdsI@{9>+1E6t#zGkk{7|P}A$9hdOH5L(PsUKBrVP>1O7n=^aW@BdwEDsV$u- z%}~ZHi^@RFoA^$eY0L~s$wU#;(LfV)jU);>au40^ANQ_%|C_zm+53C;{_S(t{_XGn z?EFwdfDzIR34_6mfP;SFFxVOrq_67fLNx`?#6k@^G$_(vqtQTeO16+*{NhJQeR(=N zI}!OKR?w^7*>FMx3<}bgf!C2cV6b(WfZx8zOdYv+hWuNb@qzIxw;qpZaB~STqR&Rd zt@5y??AOWH%o)S&BkCEdPi3xmXy=8B8ko)i^DReI?(AEBq0S;~Q_6K7_3Gad~f=NTIw_;{TZF)JbpD&VJ9-+tCi1PM-Wk=sUjFW-G8 z`w2_2=aM3e^xQSE`tAcwKe+Se?&lfAE8#?Ea%8=B_CpdXUt%+0e%wIt0Hb)XasL!k zHLL&r{mYDb-GUO5tHU~k$kT?i-qzD8+PrAdQE1n*FJ$UlklSRwDdk+YQ}HWZkVk*k zUe1mvAzCzSSa9HyVlDNR=_GOL8Ah|uY2I!18;exdw{kjPkj#(fWU;WG2ldz9n-8tS zPV*!Av`Ij_{_XHq%SvE=Hx7O6^+qs`=fEA~p(n2f(T;3`R`8!W+^@eDad!M`vU6!0 z?C)H79t`z;9-FvlcxjDSD7a4tzx25V(yKn&%;)zTAg8{}s)Aoy)1n=Zv*+Hq;QT)} z=uZ#RME6P+Do#XS1z)Nt`^K(&?m;j>TS*;tq6Y#rLqGjetZx{I>n^)V0(O|-qG*Qx zP}gy?U!7ojKTYCwezkRUbwsnfTm+CSzI&(eEv`=R06!kEa#`uO^eBvFW21NMS!pJn z8}H?%adKPoRG#W(`wh=8>4L#(?Co0Mq4y%zvv=3yLQ?DITKDX|btq+1+@!`@_{ZOi z^%}+Lv?r=65+`I~e%nfYc1}y|7@H)J<}fCd3S~a-cI~0%r*9Yjpt{l9vEBHL@cAG( z2T45?)T*eI*GIFW6WmVKuNHz9$i=6#@1$nGkv&+Xa(FMt3fV{<7o1Dm!@UCXhUfvS zLub5mvUNC<&w89=dS-#)qTE`nxS z`19{M=}jNAa56_z*)e!4=;XDMR7@#)#d{&h+p}4=uc-&w7}&48I>I`dTMLTu{iu_Z z$bjmL};?`eY%9U%x2A%vX_WjLWbALQ`3d z$!IF?2)@!>VYOKN%fbg{J{_~8n;JaO=6Sfb{XH{=_CyGDU(}P}%H2fu!0^l?y!-XJ zwu{Q>hSd`^6x@4b>O|K+Bx-b9NuQk3Sf)w7Y=tbvYJL>2*mheS1t3%Aom?!#b}ga$ zuz=M86Io0EZ4#}q#)Zsn2Z|t%0s-vkzr@r@wL$h=T7vzk6HaSbt%r|?Lmm$Ow}-C* zT3i2~0Wu)dyD))n71c*W9(6ODdaFF= znDcw58)i^FCo47VJ%@R|oB6t9{#wNSJfy;G+%gzkG-D-_RNZD4;m~cE4=0!`LcAvU z#3{#DMWhC=cucvo%S$pdoQ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -113,9 +113,9 @@
      -Generated on Wed Jan 18 2017 12:26:16 for Caffe by   +Generated on Fri Apr 14 2017 12:50:54 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1PReLULayer.html b/doxygen/classcaffe_1_1PReLULayer.html index b8f294e86bb..c101e95a60c 100644 --- a/doxygen/classcaffe_1_1PReLULayer.html +++ b/doxygen/classcaffe_1_1PReLULayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::PReLULayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -72,7 +72,7 @@
      -

      Parameterized Rectified Linear Unit non-linearity $ y_i = \max(0, x_i) + a_i \min(0, x_i) $. The differences from ReLULayer are 1) negative slopes are learnable though backprop and 2) negative slopes can vary across channels. The number of axes of input blob should be greater than or equal to 2. The 1st axis (0-based) is seen as channels. +

      Parameterized Rectified Linear Unit non-linearity $ y_i = \max(0, x_i) + a_i \min(0, x_i) $. The differences from ReLULayer are 1) negative slopes are learnable though backprop and 2) negative slopes can vary across channels. The number of axes of input blob should be greater than or equal to 2. The 1st axis (0-based) is seen as channels. More...

      #include <prelu_layer.hpp>

      @@ -81,7 +81,7 @@
      - + caffe::NeuronLayer< Dtype > caffe::Layer< Dtype > @@ -223,10 +223,10 @@

      template<typename Dtype>
      class caffe::PReLULayer< Dtype >

      -

      Parameterized Rectified Linear Unit non-linearity $ y_i = \max(0, x_i) + a_i \min(0, x_i) $. The differences from ReLULayer are 1) negative slopes are learnable though backprop and 2) negative slopes can vary across channels. The number of axes of input blob should be greater than or equal to 2. The 1st axis (0-based) is seen as channels.

      +

      Parameterized Rectified Linear Unit non-linearity $ y_i = \max(0, x_i) + a_i \min(0, x_i) $. The differences from ReLULayer are 1) negative slopes are learnable though backprop and 2) negative slopes can vary across channels. The number of axes of input blob should be greater than or equal to 2. The 1st axis (0-based) is seen as channels.

      Constructor & Destructor Documentation

      -

      § PReLULayer()

      +

      ◆ PReLULayer()

      @@ -265,7 +265,7 @@

      Member Function Documentation

      -

      § Backward_cpu()

      +

      ◆ Backward_cpu()

      @@ -310,12 +310,12 @@

      Parameters
      topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
        -
      1. $ (N \times C \times ...) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
        2. +
      2. $ (N \times C \times ...) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
      propagate_downsee Layer::Backward.
      bottominput Blob vector (length 1)
        -
      1. $ (N \times C \times ...) $ the inputs $ x $; For each channel $i$, backward fills their diff with gradients $ \frac{\partial E}{\partial x_i} = \left\{ \begin{array}{lr} a_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\ \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i > 0 \end{array} \right. $. If param_propagate_down_[0] is true, it fills the diff with gradients $ \frac{\partial E}{\partial a_i} = \left\{ \begin{array}{lr} \sum_{x_i} x_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\ 0 & \mathrm{if} \; x_i > 0 \end{array} \right. $.
        2. +
      2. $ (N \times C \times ...) $ the inputs $ x $; For each channel $i$, backward fills their diff with gradients $ \frac{\partial E}{\partial x_i} = \left\{ \begin{array}{lr} a_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\ \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i > 0 \end{array} \right. $. If param_propagate_down_[0] is true, it fills the diff with gradients $ \frac{\partial E}{\partial a_i} = \left\{ \begin{array}{lr} \sum_{x_i} x_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\ 0 & \mathrm{if} \; x_i > 0 \end{array} \right. $.
      @@ -327,7 +327,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -364,11 +364,11 @@

      Parameters
      bottominput Blob vector (length 1)
        -
      1. $ (N \times C \times ...) $ the inputs $ x $
        2. +
      2. $ (N \times C \times ...) $ the inputs $ x $
      topoutput Blob vector (length 1)
        -
      1. $ (N \times C \times ...) $ the computed outputs for each channel $i$ $ y_i = \max(0, x_i) + a_i \min(0, x_i) $.
        2. +
      2. $ (N \times C \times ...) $ the computed outputs for each channel $i$ $ y_i = \max(0, x_i) + a_i \min(0, x_i) $.
      @@ -380,7 +380,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -430,7 +430,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -486,9 +486,9 @@

      -Generated on Wed Jan 18 2017 12:26:16 for Caffe by   +Generated on Fri Apr 14 2017 12:50:54 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ParameterLayer-members.html b/doxygen/classcaffe_1_1ParameterLayer-members.html index 6828b0ff07d..ecfb758c69e 100644 --- a/doxygen/classcaffe_1_1ParameterLayer-members.html +++ b/doxygen/classcaffe_1_1ParameterLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -108,9 +108,9 @@
      -Generated on Wed Jan 18 2017 12:26:15 for Caffe by   +Generated on Fri Apr 14 2017 12:50:53 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ParameterLayer.html b/doxygen/classcaffe_1_1ParameterLayer.html index c816a10518b..14c82c96966 100644 --- a/doxygen/classcaffe_1_1ParameterLayer.html +++ b/doxygen/classcaffe_1_1ParameterLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::ParameterLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -75,7 +75,7 @@
      - + caffe::Layer< Dtype >
      @@ -202,7 +202,7 @@

      Member Function Documentation

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -234,7 +234,7 @@

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -266,7 +266,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -316,7 +316,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -371,9 +371,9 @@

      -Generated on Wed Jan 18 2017 12:26:15 for Caffe by   +Generated on Fri Apr 14 2017 12:50:53 for Caffe by   doxygen - 1.8.12 + 1.8.13
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      caffe::Params< Dtype > Member List
      -
      -
      - -

      This is the complete list of members for caffe::Params< Dtype >, including all inherited members.

      - - - - - - - - - - -
      data() const (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inline
      data_ (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
      diff() const (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inline
      diff_ (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
      DISABLE_COPY_AND_ASSIGN(Params) (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
      Params(shared_ptr< Solver< Dtype > > root_solver) (defined in caffe::Params< Dtype >)caffe::Params< Dtype >explicit
      size() const (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inline
      size_ (defined in caffe::Params< Dtype >)caffe::Params< Dtype >protected
      ~Params() (defined in caffe::Params< Dtype >)caffe::Params< Dtype >inlinevirtual
      - -
      -Generated on Fri Jan 13 2017 13:56:46 for Caffe by   -doxygen - 1.8.12 -
      - - diff --git a/doxygen/classcaffe_1_1Params.html b/doxygen/classcaffe_1_1Params.html deleted file mode 100644 index e6adda148ea..00000000000 --- a/doxygen/classcaffe_1_1Params.html +++ /dev/null @@ -1,130 +0,0 @@ - - - - - - - -Caffe: caffe::Params< Dtype > Class Template Reference - - - - - - - - - -
      -
      - - - - - - -
      -
      Caffe -
      -
      -
      - - - - - - - - -
      -
      - - -
      - -
      - - -
      -
      -
      -Public Member Functions | -Protected Member Functions | -Protected Attributes | -List of all members
      -
      -
      caffe::Params< Dtype > Class Template Reference
      -
      -
      -
      -Inheritance diagram for caffe::Params< Dtype >:
      -
      -
      - - -caffe::GPUParams< Dtype > -caffe::P2PSync< Dtype > - -
      - - - - - - - - - - -

      -Public Member Functions

      - Params (shared_ptr< Solver< Dtype > > root_solver)
       
      -size_t size () const
       
      -Dtype * data () const
       
      -Dtype * diff () const
       
      - - - -

      -Protected Member Functions

      - DISABLE_COPY_AND_ASSIGN (Params)
       
      - - - - - - - -

      -Protected Attributes

      -const size_t size_
       
      -Dtype * data_
       
      -Dtype * diff_
       
      -
      The documentation for this class was generated from the following files: -
      - -
      -Generated on Fri Jan 13 2017 13:56:46 for Caffe by   -doxygen - 1.8.12 -
      - - diff --git a/doxygen/classcaffe_1_1Params.png b/doxygen/classcaffe_1_1Params.png deleted file mode 100644 index e263d2cb32f118809afea4abfded6e480426f9fd..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1072 zcmeAS@N?(olHy`uVBq!ia0vp^tAV(KgBeJ!=X<>wNJ#|vgt-3y{~ySF@#br3|Dg#$ z78oBmaDcV*jy#adQ4-`A%m7pb0#{Fk7%?y~@Aq_Z45?szJNMzFPYOJ){L>}x|DR~T zRhK1dOX=qMr+sAJZM*2erh^}aG5X(bE$BW^3K&xqfoXJcCW zY>w26AmQ2n%~tQ9BD>ml?Ynhy7xyLEu4w#Q)^KH&<=?`%>lf5cWMA@s_0{I}g1FVI zHNW(~zWS|k*YBdUvu)Od@A_(P%5Wv{cXsTT_F9Gq_Aj#f|0!j!&1ew*CAWI#0S5Ii zXQdc+EJ|Sg5Wa!w$Lot-!CskMj6Ytp8FZ>8?Y;Y~ac;-gJ}cfgK5H(rGt@bQ!T_pP zJA(0#=o*H9Twx6LtlACxVL=3!K53a=ez)k{&Da#rSxyJFJb@}bKV9!IUFtgNjK}x$ zzbfqJ-w%zRbZ+$-E9=q&Q64H+18=bTds?kGdTsT3+ey{MnOk!Yow_|?t4daEa`jz5 zuOQj(y)01^<=YY`rno2S-usq#M)%3?KYAxhpBe9&8*gA1CcSNA)c3xvdY3lNdb=i5 z{#v%MyB8!tU)-G{`&wvQ+6kHV*qZtA)!%2eZhQLc_N|S#lHa_0mGxx}yJ^9WABx$N zuB-$F>hxXPE|xDzf4x<+>3jtH*QnUDkKVrm1?$P8*{e;hKL7i^X~IfI&(bUZva>zuF~@;;^9D~7 z!Pjri_}{p!DZi4Lke<|Q$M8WKXE`u^OPlfU$J=ZT5x@hznJIQ8;{o5M|}?Y@#sf#*zC>6=TF*GyN3Ci*T)%| z*Iq}?-+%w>xu2q|J;Q1xHv1-=Jhy3nTI;Jlb@}GW*R6`~h2Lw~y02_a?B2CAw)#z1 zz7g&r{i|auulWMmjd~2K+xD8@_1>H`d;XzYEpH>%7MI&v-(r02$opn{af04C>uZT| z{d4u7lt!$V-z0Z6yY!~l->SX0>;jte?k?~ST`3%FW<8@;eg*GNtNKgqDleDsx?-;X zC;SKNwbv`+lm9Y&t*)H9Bt8)2yQa`de@fHOT0Xb@!ryQ1>iOcDVJk3uGkCiCxvX - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -122,9 +122,9 @@
      -Generated on Wed Jan 18 2017 12:26:15 for Caffe by   +Generated on Fri Apr 14 2017 12:50:54 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1PoolingLayer.html b/doxygen/classcaffe_1_1PoolingLayer.html index 4e4080b00d6..f5342e892c1 100644 --- a/doxygen/classcaffe_1_1PoolingLayer.html +++ b/doxygen/classcaffe_1_1PoolingLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::PoolingLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -81,7 +81,7 @@
      - + caffe::Layer< Dtype >
      @@ -256,7 +256,7 @@

      TODO(dox): thorough documentation for Forward, Backward, and proto params.

      Member Function Documentation

      -

      § ExactNumBottomBlobs()

      +

      ◆ ExactNumBottomBlobs()

      @@ -288,7 +288,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -338,7 +338,7 @@

      -

      § MaxTopBlobs()

      +

      ◆ MaxTopBlobs()

      @@ -370,7 +370,7 @@

      -

      § MinTopBlobs()

      +

      ◆ MinTopBlobs()

      @@ -402,7 +402,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -458,9 +458,9 @@

      -Generated on Wed Jan 18 2017 12:26:15 for Caffe by   +Generated on Fri Apr 14 2017 12:50:54 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1PositiveUnitballFiller-members.html b/doxygen/classcaffe_1_1PositiveUnitballFiller-members.html index 36917027f6b..53764755a1d 100644 --- a/doxygen/classcaffe_1_1PositiveUnitballFiller-members.html +++ b/doxygen/classcaffe_1_1PositiveUnitballFiller-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -77,9 +77,9 @@
      -Generated on Wed Jan 18 2017 12:26:15 for Caffe by   +Generated on Fri Apr 14 2017 12:50:54 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1PositiveUnitballFiller.html b/doxygen/classcaffe_1_1PositiveUnitballFiller.html index 0a99b1cb5b2..f44703726ba 100644 --- a/doxygen/classcaffe_1_1PositiveUnitballFiller.html +++ b/doxygen/classcaffe_1_1PositiveUnitballFiller.html @@ -3,7 +3,7 @@ - + Caffe: caffe::PositiveUnitballFiller< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -70,7 +70,7 @@
      -

      Fills a Blob with values $ x \in [0, 1] $ such that $ \forall i \sum_j x_{ij} = 1 $. +

      Fills a Blob with values $ x \in [0, 1] $ such that $ \forall i \sum_j x_{ij} = 1 $. More...

      #include <filler.hpp>

      @@ -79,7 +79,7 @@
      - + caffe::Filler< Dtype >
      @@ -108,16 +108,16 @@

      template<typename Dtype>
      class caffe::PositiveUnitballFiller< Dtype >

      -

      Fills a Blob with values $ x \in [0, 1] $ such that $ \forall i \sum_j x_{ij} = 1 $.

      +

      Fills a Blob with values $ x \in [0, 1] $ such that $ \forall i \sum_j x_{ij} = 1 $.

      The documentation for this class was generated from the following file:
      -Generated on Wed Jan 18 2017 12:26:15 for Caffe by   +Generated on Fri Apr 14 2017 12:50:54 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1PowerLayer-members.html b/doxygen/classcaffe_1_1PowerLayer-members.html index e0876380275..705bc3af0f6 100644 --- a/doxygen/classcaffe_1_1PowerLayer-members.html +++ b/doxygen/classcaffe_1_1PowerLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -113,9 +113,9 @@
      -Generated on Wed Jan 18 2017 12:26:15 for Caffe by   +Generated on Fri Apr 14 2017 12:50:54 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1PowerLayer.html b/doxygen/classcaffe_1_1PowerLayer.html index 26473d93fec..7b1194d41ce 100644 --- a/doxygen/classcaffe_1_1PowerLayer.html +++ b/doxygen/classcaffe_1_1PowerLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::PowerLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -72,7 +72,7 @@
      -

      Computes $ y = (\alpha x + \beta) ^ \gamma $, as specified by the scale $ \alpha $, shift $ \beta $, and power $ \gamma $. +

      Computes $ y = (\alpha x + \beta) ^ \gamma $, as specified by the scale $ \alpha $, shift $ \beta $, and power $ \gamma $. More...

      #include <power_layer.hpp>

      @@ -81,7 +81,7 @@
      - + caffe::NeuronLayer< Dtype > caffe::Layer< Dtype > @@ -197,19 +197,19 @@ Protected Attributes

      Dtype power_ - $ \gamma $ from layer_param_.power_param()
      + $ \gamma $ from layer_param_.power_param()
        Dtype scale_ - $ \alpha $ from layer_param_.power_param()
      + $ \alpha $ from layer_param_.power_param()
        Dtype shift_ - $ \beta $ from layer_param_.power_param()
      + $ \beta $ from layer_param_.power_param()
        Dtype diff_scale_ - Result of $ \alpha \gamma $.
      + Result of $ \alpha \gamma $.
        - Protected Attributes inherited from caffe::Layer< Dtype > LayerParameter layer_param_ @@ -227,10 +227,10 @@

      template<typename Dtype>
      class caffe::PowerLayer< Dtype >

      -

      Computes $ y = (\alpha x + \beta) ^ \gamma $, as specified by the scale $ \alpha $, shift $ \beta $, and power $ \gamma $.

      +

      Computes $ y = (\alpha x + \beta) ^ \gamma $, as specified by the scale $ \alpha $, shift $ \beta $, and power $ \gamma $.

      Constructor & Destructor Documentation

      -

      § PowerLayer()

      +

      ◆ PowerLayer()

      @@ -257,9 +257,9 @@

      Parameters
      paramprovides PowerParameter power_param, with PowerLayer options:
        -
      • scale (optional, default 1) the scale $ \alpha $
        • -
      • shift (optional, default 0) the shift $ \beta $
        • -
      • power (optional, default 1) the power $ \gamma $
        • +
      • scale (optional, default 1) the scale $ \alpha $
        • +
      • shift (optional, default 0) the shift $ \beta $
        • +
      • power (optional, default 1) the power $ \gamma $
      @@ -270,7 +270,7 @@

      Member Function Documentation

      -

      § Backward_cpu()

      +

      ◆ Backward_cpu()

      @@ -315,12 +315,12 @@

      Parameters
      topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
        -
      1. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
        2. +
      2. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
      propagate_downsee Layer::Backward.
      bottominput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} \alpha \gamma (\alpha x + \beta) ^ {\gamma - 1} = \frac{\partial E}{\partial y} \frac{\alpha \gamma y}{\alpha x + \beta} $ if propagate_down[0]
        2. +
      2. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} \alpha \gamma (\alpha x + \beta) ^ {\gamma - 1} = \frac{\partial E}{\partial y} \frac{\alpha \gamma y}{\alpha x + \beta} $ if propagate_down[0]
      @@ -332,7 +332,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -369,11 +369,11 @@

      Parameters
      bottominput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x $
        2. +
      2. $ (N \times C \times H \times W) $ the inputs $ x $
      topoutput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the computed outputs $ y = (\alpha x + \beta) ^ \gamma $
        2. +
      2. $ (N \times C \times H \times W) $ the computed outputs $ y = (\alpha x + \beta) ^ \gamma $
      @@ -385,7 +385,7 @@

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -441,9 +441,9 @@

      -Generated on Wed Jan 18 2017 12:26:15 for Caffe by   +Generated on Fri Apr 14 2017 12:50:54 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1PythonLayer-members.html b/doxygen/classcaffe_1_1PythonLayer-members.html index deb9f9190f6..018b3738abc 100644 --- a/doxygen/classcaffe_1_1PythonLayer-members.html +++ b/doxygen/classcaffe_1_1PythonLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -102,16 +102,15 @@ set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline - ShareInParallel() const (defined in caffe::PythonLayer< Dtype >)caffe::PythonLayer< Dtype >inlinevirtual - ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual - type() constcaffe::PythonLayer< Dtype >inlinevirtual - ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual + ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual + type() constcaffe::PythonLayer< Dtype >inlinevirtual + ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual
      -Generated on Wed Jan 18 2017 12:26:16 for Caffe by   +Generated on Fri Apr 14 2017 12:50:54 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1PythonLayer.html b/doxygen/classcaffe_1_1PythonLayer.html index 2f11b5b6cc9..7cfbd996bda 100644 --- a/doxygen/classcaffe_1_1PythonLayer.html +++ b/doxygen/classcaffe_1_1PythonLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::PythonLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -75,7 +75,7 @@
      - + caffe::Layer< Dtype >
      @@ -91,9 +91,6 @@ virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)  Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
        - -virtual bool ShareInParallel () const -  virtual const char * type () const  Returns the layer type.
      @@ -205,7 +202,7 @@

      Member Function Documentation

      -

      § LayerSetUp()

      +

      ◆ LayerSetUp()

      @@ -255,7 +252,7 @@

      -

      § Reshape()

      +

      ◆ Reshape()

      @@ -310,9 +307,9 @@

      -Generated on Wed Jan 18 2017 12:26:16 for Caffe by   +Generated on Fri Apr 14 2017 12:50:54 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1RMSPropSolver-members.html b/doxygen/classcaffe_1_1RMSPropSolver-members.html index a587daba027..3a3dd1396b1 100644 --- a/doxygen/classcaffe_1_1RMSPropSolver-members.html +++ b/doxygen/classcaffe_1_1RMSPropSolver-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -136,9 +136,9 @@
      -Generated on Wed Jan 18 2017 12:26:17 for Caffe by   +Generated on Fri Apr 14 2017 12:50:55 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1RMSPropSolver.html b/doxygen/classcaffe_1_1RMSPropSolver.html index 20b2ed2029a..4cc1e974dc3 100644 --- a/doxygen/classcaffe_1_1RMSPropSolver.html +++ b/doxygen/classcaffe_1_1RMSPropSolver.html @@ -3,7 +3,7 @@ - + Caffe: caffe::RMSPropSolver< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -75,7 +75,7 @@
      - + caffe::SGDSolver< Dtype > caffe::Solver< Dtype > @@ -293,9 +293,9 @@
      -Generated on Wed Jan 18 2017 12:26:17 for Caffe by   +Generated on Fri Apr 14 2017 12:50:55 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1RNNLayer-members.html b/doxygen/classcaffe_1_1RNNLayer-members.html index 04075ab4cb8..4378c16477f 100644 --- a/doxygen/classcaffe_1_1RNNLayer-members.html +++ b/doxygen/classcaffe_1_1RNNLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@
      - + @@ -127,9 +127,9 @@
      -Generated on Wed Jan 18 2017 12:26:17 for Caffe by   +Generated on Fri Apr 14 2017 12:50:55 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1RNNLayer.html b/doxygen/classcaffe_1_1RNNLayer.html index 6ec75071473..d43c0837ebd 100644 --- a/doxygen/classcaffe_1_1RNNLayer.html +++ b/doxygen/classcaffe_1_1RNNLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::RNNLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@
      - + @@ -80,7 +80,7 @@
      - + caffe::RecurrentLayer< Dtype > caffe::Layer< Dtype > @@ -280,7 +280,7 @@ class caffe::RNNLayer< Dtype >

      Processes time-varying inputs using a simple recurrent neural network (RNN). Implemented as a network unrolling the RNN computation in time.

      -

      Given time-varying inputs $ x_t $, computes hidden state $ h_t := \tanh[ W_{hh} h_{t_1} + W_{xh} x_t + b_h ] $, and outputs $ o_t := \tanh[ W_{ho} h_t + b_o ] $.

      +

      Given time-varying inputs $ x_t $, computes hidden state $ h_t := \tanh[ W_{hh} h_{t_1} + W_{xh} x_t + b_h ] $, and outputs $ o_t := \tanh[ W_{ho} h_t + b_o ] $.

      The documentation for this class was generated from the following files:
      • include/caffe/layers/rnn_layer.hpp
      • src/caffe/layers/rnn_layer.cpp
        • @@ -288,9 +288,9 @@
      -Generated on Wed Jan 18 2017 12:26:17 for Caffe by   +Generated on Fri Apr 14 2017 12:50:55 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ReLULayer-members.html b/doxygen/classcaffe_1_1ReLULayer-members.html index 824464b76e8..ab3bc959765 100644 --- a/doxygen/classcaffe_1_1ReLULayer-members.html +++ b/doxygen/classcaffe_1_1ReLULayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -109,9 +109,9 @@
      -Generated on Wed Jan 18 2017 12:26:16 for Caffe by   +Generated on Fri Apr 14 2017 12:50:55 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1ReLULayer.html b/doxygen/classcaffe_1_1ReLULayer.html index 761cc87d1be..6d86acac89e 100644 --- a/doxygen/classcaffe_1_1ReLULayer.html +++ b/doxygen/classcaffe_1_1ReLULayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::ReLULayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@
      - + @@ -71,7 +71,7 @@
      -

      Rectified Linear Unit non-linearity $ y = \max(0, x) $. The simple max is fast to compute, and the function does not saturate. +

      Rectified Linear Unit non-linearity $ y = \max(0, x) $. The simple max is fast to compute, and the function does not saturate. More...

      #include <relu_layer.hpp>

      @@ -80,7 +80,7 @@
      - + caffe::NeuronLayer< Dtype > caffe::Layer< Dtype > @@ -210,10 +210,10 @@

      template<typename Dtype>
      class caffe::ReLULayer< Dtype >

      -

      Rectified Linear Unit non-linearity $ y = \max(0, x) $. The simple max is fast to compute, and the function does not saturate.

      +

      Rectified Linear Unit non-linearity $ y = \max(0, x) $. The simple max is fast to compute, and the function does not saturate.

      Constructor & Destructor Documentation

      -

      § ReLULayer()

      +

      ◆ ReLULayer()

      @@ -240,7 +240,7 @@

      Parameters
      paramprovides ReLUParameter relu_param, with ReLULayer options:
        -
      • negative_slope (optional, default 0). the value $ \nu $ by which negative values are multiplied.
        • +
      • negative_slope (optional, default 0). the value $ \nu $ by which negative values are multiplied.
      @@ -251,7 +251,7 @@

      Member Function Documentation

      -

      § Backward_cpu()

      +

      ◆ Backward_cpu()

      @@ -296,12 +296,12 @@

      Parameters
      topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
        -
      1. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
        2. +
      2. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
      propagate_downsee Layer::Backward.
      bottominput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} 0 & \mathrm{if} \; x \le 0 \\ \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0 \end{array} \right. $ if propagate_down[0], by default. If a non-zero negative_slope $ \nu $ is provided, the computed gradients are $ \frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} \nu \frac{\partial E}{\partial y} & \mathrm{if} \; x \le 0 \\ \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0 \end{array} \right. $.
        2. +
      2. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} 0 & \mathrm{if} \; x \le 0 \\ \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0 \end{array} \right. $ if propagate_down[0], by default. If a non-zero negative_slope $ \nu $ is provided, the computed gradients are $ \frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} \nu \frac{\partial E}{\partial y} & \mathrm{if} \; x \le 0 \\ \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0 \end{array} \right. $.
      @@ -313,7 +313,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -350,11 +350,11 @@

      Parameters
      bottominput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the inputs $ x $
        2. +
      2. $ (N \times C \times H \times W) $ the inputs $ x $
      topoutput Blob vector (length 1)
        -
      1. $ (N \times C \times H \times W) $ the computed outputs $ y = \max(0, x) $ by default. If a non-zero negative_slope $ \nu $ is provided, the computed outputs are $ y = \max(0, x) + \nu \min(0, x) $.
        2. +
      2. $ (N \times C \times H \times W) $ the computed outputs $ y = \max(0, x) $ by default. If a non-zero negative_slope $ \nu $ is provided, the computed outputs are $ y = \max(0, x) + \nu \min(0, x) $.
      @@ -372,9 +372,9 @@

      -Generated on Wed Jan 18 2017 12:26:16 for Caffe by   +Generated on Fri Apr 14 2017 12:50:55 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1RecurrentLayer-members.html b/doxygen/classcaffe_1_1RecurrentLayer-members.html index 64bdfc5f852..a8d243e859e 100644 --- a/doxygen/classcaffe_1_1RecurrentLayer-members.html +++ b/doxygen/classcaffe_1_1RecurrentLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

      - + @@ -126,9 +126,9 @@
      -Generated on Wed Jan 18 2017 12:26:16 for Caffe by   +Generated on Fri Apr 14 2017 12:50:54 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/classcaffe_1_1RecurrentLayer.html b/doxygen/classcaffe_1_1RecurrentLayer.html index 1767e94b7e5..77ae559f828 100644 --- a/doxygen/classcaffe_1_1RecurrentLayer.html +++ b/doxygen/classcaffe_1_1RecurrentLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::RecurrentLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

      - + @@ -81,7 +81,7 @@
      - + caffe::Layer< Dtype > caffe::LSTMLayer< Dtype > caffe::RNNLayer< Dtype > @@ -278,7 +278,7 @@

      An abstract class for implementing recurrent behavior inside of an unrolled network. This Layer type cannot be instantiated – instead, you should use one of its implementations which defines the recurrent architecture, such as RNNLayer or LSTMLayer.

      Member Function Documentation

      -

      § AllowForceBackward()

      +

      ◆ AllowForceBackward()

      @@ -311,7 +311,7 @@

      -

      § ExactNumTopBlobs()

      +

      ◆ ExactNumTopBlobs()

      @@ -343,7 +343,7 @@

      -

      § Forward_cpu()

      +

      ◆ Forward_cpu()

      @@ -384,14 +384,14 @@

      -
    4. $ (T \times N \times ...) $ the time-varying input $ x $. After the first two axes, whose dimensions must correspond to the number of timesteps $ T $ and the number of independent streams $ N $, respectively, its dimensions may be arbitrary. Note that the ordering of dimensions – $ (T \times N \times ...) $, rather than $ (N \times T \times ...) $ – means that the $ N $ independent input streams must be "interleaved".
      5. -
    5. $ (T \times N) $ the sequence continuation indicators $ \delta $. These inputs should be binary (0 or 1) indicators, where $ \delta_{t,n} = 0 $ means that timestep $ t $ of stream $ n $ is the beginning of a new sequence, and hence the previous hidden state $ h_{t-1} $ is multiplied by $ \delta_t = 0 $ and has no effect on the cell's output at timestep $ t $, and a value of $ \delta_{t,n} = 1 $ means that timestep $ t $ of stream $ n $ is a continuation from the previous timestep $ t-1 $, and the previous hidden state $ h_{t-1} $ affects the updated hidden state and output.
      6. -
    6. $ (N \times ...) $ (optional) the static (non-time-varying) input $ x_{static} $. After the first axis, whose dimension must be the number of independent streams, its dimensions may be arbitrary. This is mathematically equivalent to using a time-varying input of $ x'_t = [x_t; x_{static}] $ – i.e., tiling the static input across the $ T $ timesteps and concatenating with the time-varying input. Note that if this input is used, all timesteps in a single batch within a particular one of the $ N $ streams must share the same static input, even if the sequence continuation indicators suggest that difference sequences are ending and beginning within a single batch. This may require padding and/or truncation for uniform length.
      7. +
    7. $ (T \times N \times ...) $ the time-varying input $ x $. After the first two axes, whose dimensions must correspond to the number of timesteps $ T $ and the number of independent streams $ N $, respectively, its dimensions may be arbitrary. Note that the ordering of dimensions – $ (T \times N \times ...) $, rather than $ (N \times T \times ...) $ – means that the $ N $ independent input streams must be "interleaved".
      8. +
    8. $ (T \times N) $ the sequence continuation indicators $ \delta $. These inputs should be binary (0 or 1) indicators, where $ \delta_{t,n} = 0 $ means that timestep $ t $ of stream $ n $ is the beginning of a new sequence, and hence the previous hidden state $ h_{t-1} $ is multiplied by $ \delta_t = 0 $ and has no effect on the cell's output at timestep $ t $, and a value of $ \delta_{t,n} = 1 $ means that timestep $ t $ of stream $ n $ is a continuation from the previous timestep $ t-1 $, and the previous hidden state $ h_{t-1} $ affects the updated hidden state and output.
      9. +
    9. $ (N \times ...) $ (optional) the static (non-time-varying) input $ x_{static} $. After the first axis, whose dimension must be the number of independent streams, its dimensions may be arbitrary. This is mathematically equivalent to using a time-varying input of $ x'_t = [x_t; x_{static}] $ – i.e., tiling the static input across the $ T $ timesteps and concatenating with the time-varying input. Note that if this input is used, all timesteps in a single batch within a particular one of the $ N $ streams must share the same static input, even if the sequence continuation indicators suggest that difference sequences are ending and beginning within a single batch. This may require padding and/or truncation for uniform length.

      10. +
      +

      Caffe

      +

      + Deep learning framework by the BVLC +

      +

      + Created by +
      + Yangqing Jia +
      + Lead Developer +
      + Evan Shelhamer +

      +
      +
      + +

      Forward and Backward

      + +

      The forward and backward passes are the essential computations of a Net.

      + +

      Forward and Backward

      + +

      Let’s consider a simple logistic regression classifier.

      + +

      The forward pass computes the output given the input for inference. +In forward Caffe composes the computation of each layer to compute the “function” represented by the model. +This pass goes from bottom to top.

      + +

      Forward pass

      + +

      The data is passed through an inner product layer for then through a softmax for and softmax loss to give .

      + +

      The backward pass computes the gradient given the loss for learning. +In backward Caffe reverse-composes the gradient of each layer to compute the gradient of the whole model by automatic differentiation. +This is back-propagation. +This pass goes from top to bottom.

      + +

      Backward pass

      + +

      The backward pass begins with the loss and computes the gradient with respect to the output . The gradient with respect to the rest of the model is computed layer-by-layer through the chain rule. Layers with parameters, like the INNER_PRODUCT layer, compute the gradient with respect to their parameters during the backward step.

      + +

      These computations follow immediately from defining the model: Caffe plans and carries out the forward and backward passes for you.

      + +
        +
      • The Net::Forward() and Net::Backward() methods carry out the respective passes while Layer::Forward() and Layer::Backward() compute each step.
        • +
      • Every layer type has forward_{cpu,gpu}() and backward_{cpu,gpu}() methods to compute its steps according to the mode of computation. A layer may only implement CPU or GPU mode due to constraints or convenience.
        • +
      + +

      The Solver optimizes a model by first calling forward to yield the output and loss, then calling backward to generate the gradient of the model, and then incorporating the gradient into a weight update that attempts to minimize the loss. Division of labor between the Solver, Net, and Layer keep Caffe modular and open to development.

      + +

      For the details of the forward and backward steps of Caffe’s layer types, refer to the layer catalogue.

      + + + +
      +
    Parameters
    topoutput Blob vector (length 1)
      -
    1. $ (T \times N \times D) $ the time-varying output $ y $, where $ D $ is recurrent_param.num_output(). Refer to documentation for particular RecurrentLayer implementations (such as RNNLayer and LSTMLayer) for the definition of $ y $.
      2. +
    2. $ (T \times N \times D) $ the time-varying output $ y $, where $ D $ is recurrent_param.num_output(). Refer to documentation for particular RecurrentLayer implementations (such as RNNLayer and LSTMLayer) for the definition of $ y $.
    @@ -403,7 +403,7 @@

    -

    § LayerSetUp()

    +

    ◆ LayerSetUp()

    @@ -453,7 +453,7 @@

    -

    § MaxBottomBlobs()

    +

    ◆ MaxBottomBlobs()

    @@ -485,7 +485,7 @@

    -

    § MinBottomBlobs()

    +

    ◆ MinBottomBlobs()

    @@ -517,7 +517,7 @@

    -

    § Reshape()

    +

    ◆ Reshape()

    @@ -574,9 +574,9 @@

    -Generated on Wed Jan 18 2017 12:26:16 for Caffe by   +Generated on Fri Apr 14 2017 12:50:54 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1ReductionLayer-members.html b/doxygen/classcaffe_1_1ReductionLayer-members.html index 16f8d06c773..c4d9086abf8 100644 --- a/doxygen/classcaffe_1_1ReductionLayer-members.html +++ b/doxygen/classcaffe_1_1ReductionLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

    - + @@ -114,9 +114,9 @@
    -Generated on Wed Jan 18 2017 12:26:16 for Caffe by   +Generated on Fri Apr 14 2017 12:50:54 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1ReductionLayer.html b/doxygen/classcaffe_1_1ReductionLayer.html index e98a4c547e1..da0ce31ac9a 100644 --- a/doxygen/classcaffe_1_1ReductionLayer.html +++ b/doxygen/classcaffe_1_1ReductionLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::ReductionLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

    - + @@ -81,7 +81,7 @@
    - + caffe::Layer< Dtype >
    @@ -238,7 +238,7 @@

    TODO(dox): thorough documentation for Forward, Backward, and proto params.

    Member Function Documentation

    -

    § ExactNumBottomBlobs()

    +

    ◆ ExactNumBottomBlobs()

    @@ -270,7 +270,7 @@

    -

    § ExactNumTopBlobs()

    +

    ◆ ExactNumTopBlobs()

    @@ -302,7 +302,7 @@

    -

    § LayerSetUp()

    +

    ◆ LayerSetUp()

    @@ -352,7 +352,7 @@

    -

    § Reshape()

    +

    ◆ Reshape()

    @@ -408,9 +408,9 @@

    -Generated on Wed Jan 18 2017 12:26:16 for Caffe by   +Generated on Fri Apr 14 2017 12:50:54 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1ReshapeLayer-members.html b/doxygen/classcaffe_1_1ReshapeLayer-members.html index be7ec2b282d..c6080d02b5f 100644 --- a/doxygen/classcaffe_1_1ReshapeLayer-members.html +++ b/doxygen/classcaffe_1_1ReshapeLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

    - + @@ -111,9 +111,9 @@
    -Generated on Wed Jan 18 2017 12:26:17 for Caffe by   +Generated on Fri Apr 14 2017 12:50:55 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1ReshapeLayer.html b/doxygen/classcaffe_1_1ReshapeLayer.html index fd1ed619e7a..406dd83ccf2 100644 --- a/doxygen/classcaffe_1_1ReshapeLayer.html +++ b/doxygen/classcaffe_1_1ReshapeLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::ReshapeLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

    - + @@ -76,7 +76,7 @@
    - + caffe::Layer< Dtype >
    @@ -215,7 +215,7 @@

    Member Function Documentation

    -

    § ExactNumBottomBlobs()

    +

    ◆ ExactNumBottomBlobs()

    @@ -247,7 +247,7 @@

    -

    § ExactNumTopBlobs()

    +

    ◆ ExactNumTopBlobs()

    @@ -279,7 +279,7 @@

    -

    § LayerSetUp()

    +

    ◆ LayerSetUp()

    @@ -329,7 +329,7 @@

    -

    § Reshape()

    +

    ◆ Reshape()

    @@ -385,9 +385,9 @@

    -Generated on Wed Jan 18 2017 12:26:17 for Caffe by   +Generated on Fri Apr 14 2017 12:50:55 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SGDSolver-members.html b/doxygen/classcaffe_1_1SGDSolver-members.html index 9a5131e1b4a..0a20042dff6 100644 --- a/doxygen/classcaffe_1_1SGDSolver-members.html +++ b/doxygen/classcaffe_1_1SGDSolver-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

    - + @@ -132,9 +132,9 @@
    -Generated on Wed Jan 18 2017 12:26:17 for Caffe by   +Generated on Fri Apr 14 2017 12:50:55 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SGDSolver.html b/doxygen/classcaffe_1_1SGDSolver.html index 328c7f02b55..79bbf7f9f4f 100644 --- a/doxygen/classcaffe_1_1SGDSolver.html +++ b/doxygen/classcaffe_1_1SGDSolver.html @@ -3,7 +3,7 @@ - + Caffe: caffe::SGDSolver< Dtype > Class Template Reference @@ -29,7 +29,7 @@

    - + @@ -81,7 +81,7 @@
    - + caffe::Solver< Dtype > caffe::AdaDeltaSolver< Dtype > caffe::AdaGradSolver< Dtype > @@ -293,9 +293,9 @@
    -Generated on Wed Jan 18 2017 12:26:17 for Caffe by   +Generated on Fri Apr 14 2017 12:50:55 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SPPLayer-members.html b/doxygen/classcaffe_1_1SPPLayer-members.html index b99cbf7b093..041b22e1792 100644 --- a/doxygen/classcaffe_1_1SPPLayer-members.html +++ b/doxygen/classcaffe_1_1SPPLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@
    - + @@ -130,9 +130,9 @@
    -Generated on Wed Jan 18 2017 12:26:19 for Caffe by   +Generated on Fri Apr 14 2017 12:50:57 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SPPLayer.html b/doxygen/classcaffe_1_1SPPLayer.html index 75cae0921f8..9638e621cce 100644 --- a/doxygen/classcaffe_1_1SPPLayer.html +++ b/doxygen/classcaffe_1_1SPPLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::SPPLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

    - + @@ -81,7 +81,7 @@
    - + caffe::Layer< Dtype >
    @@ -290,7 +290,7 @@

    Does spatial pyramid pooling on the input image by taking the max, average, etc. within regions so that the result vector of different sized images are of the same size.

    Member Function Documentation

    -

    § ExactNumBottomBlobs()

    +

    ◆ ExactNumBottomBlobs()

    @@ -322,7 +322,7 @@

    -

    § ExactNumTopBlobs()

    +

    ◆ ExactNumTopBlobs()

    @@ -354,7 +354,7 @@

    -

    § LayerSetUp()

    +

    ◆ LayerSetUp()

    @@ -404,7 +404,7 @@

    -

    § Reshape()

    +

    ◆ Reshape()

    @@ -460,9 +460,9 @@

    -Generated on Wed Jan 18 2017 12:26:19 for Caffe by   +Generated on Fri Apr 14 2017 12:50:57 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1ScaleLayer-members.html b/doxygen/classcaffe_1_1ScaleLayer-members.html index fe8d018b1b5..dda9c9113fd 100644 --- a/doxygen/classcaffe_1_1ScaleLayer-members.html +++ b/doxygen/classcaffe_1_1ScaleLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

    - + @@ -119,9 +119,9 @@
    -Generated on Wed Jan 18 2017 12:26:17 for Caffe by   +Generated on Fri Apr 14 2017 12:50:55 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1ScaleLayer.html b/doxygen/classcaffe_1_1ScaleLayer.html index bae8011c691..1a763fd631f 100644 --- a/doxygen/classcaffe_1_1ScaleLayer.html +++ b/doxygen/classcaffe_1_1ScaleLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::ScaleLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

    - + @@ -81,7 +81,7 @@
    - + caffe::Layer< Dtype >
    @@ -245,7 +245,7 @@

    The latter, scale input may be omitted, in which case it's learned as parameter of the layer (as is the bias, if it is included).

    Member Function Documentation

    -

    § ExactNumTopBlobs()

    +

    ◆ ExactNumTopBlobs()

    @@ -277,7 +277,7 @@

    -

    § Forward_cpu()

    +

    ◆ Forward_cpu()

    @@ -311,16 +311,16 @@

    -

    In the below shape specifications, $ i $ denotes the value of the axis field given by this->layer_param_.scale_param().axis(), after canonicalization (i.e., conversion from negative to positive index, if applicable).

    +

    In the below shape specifications, $ i $ denotes the value of the axis field given by this->layer_param_.scale_param().axis(), after canonicalization (i.e., conversion from negative to positive index, if applicable).

    Parameters
    bottominput Blob vector (length 2)
      -
    1. $ (d_0 \times ... \times d_i \times ... \times d_j \times ... \times d_n) $ the first factor $ x $
      2. -
    2. $ (d_i \times ... \times d_j) $ the second factor $ y $
      3. +
    3. $ (d_0 \times ... \times d_i \times ... \times d_j \times ... \times d_n) $ the first factor $ x $
      4. +
    4. $ (d_i \times ... \times d_j) $ the second factor $ y $
    topoutput Blob vector (length 1)
      -
    1. $ (d_0 \times ... \times d_i \times ... \times d_j \times ... \times d_n) $ the product $ z = x y $ computed after "broadcasting" y. Equivalent to tiling $ y $ to have the same shape as $ x $, then computing the elementwise product.
      2. +
    2. $ (d_0 \times ... \times d_i \times ... \times d_j \times ... \times d_n) $ the product $ z = x y $ computed after "broadcasting" y. Equivalent to tiling $ y $ to have the same shape as $ x $, then computing the elementwise product.
    @@ -332,7 +332,7 @@

    -

    § LayerSetUp()

    +

    ◆ LayerSetUp()

    @@ -382,7 +382,7 @@

    -

    § MaxBottomBlobs()

    +

    ◆ MaxBottomBlobs()

    @@ -414,7 +414,7 @@

    -

    § MinBottomBlobs()

    +

    ◆ MinBottomBlobs()

    @@ -446,7 +446,7 @@

    -

    § Reshape()

    +

    ◆ Reshape()

    @@ -502,9 +502,9 @@

    -Generated on Wed Jan 18 2017 12:26:17 for Caffe by   +Generated on Fri Apr 14 2017 12:50:55 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SigmoidCrossEntropyLossLayer-members.html b/doxygen/classcaffe_1_1SigmoidCrossEntropyLossLayer-members.html index 35484bcf311..0aca893b194 100644 --- a/doxygen/classcaffe_1_1SigmoidCrossEntropyLossLayer-members.html +++ b/doxygen/classcaffe_1_1SigmoidCrossEntropyLossLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

    - + @@ -120,9 +120,9 @@
    -Generated on Wed Jan 18 2017 12:26:18 for Caffe by   +Generated on Fri Apr 14 2017 12:50:55 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SigmoidCrossEntropyLossLayer.html b/doxygen/classcaffe_1_1SigmoidCrossEntropyLossLayer.html index 2fcae43e615..81565291f5c 100644 --- a/doxygen/classcaffe_1_1SigmoidCrossEntropyLossLayer.html +++ b/doxygen/classcaffe_1_1SigmoidCrossEntropyLossLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::SigmoidCrossEntropyLossLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

    - + @@ -72,7 +72,7 @@
    -

    Computes the cross-entropy (logistic) loss $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $, often used for predicting targets interpreted as probabilities. +

    Computes the cross-entropy (logistic) loss $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $, often used for predicting targets interpreted as probabilities. More...

    #include <sigmoid_cross_entropy_loss_layer.hpp>

    @@ -81,7 +81,7 @@
    - + caffe::LossLayer< Dtype > caffe::Layer< Dtype > @@ -176,7 +176,7 @@

    Protected Member Functions

    virtual void Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) - Computes the cross-entropy (logistic) loss $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $, often used for predicting targets interpreted as probabilities. More...
    + Computes the cross-entropy (logistic) loss $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $, often used for predicting targets interpreted as probabilities. More...
      virtual void Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) @@ -252,17 +252,17 @@

    template<typename Dtype>
    class caffe::SigmoidCrossEntropyLossLayer< Dtype >

    -

    Computes the cross-entropy (logistic) loss $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $, often used for predicting targets interpreted as probabilities.

    +

    Computes the cross-entropy (logistic) loss $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $, often used for predicting targets interpreted as probabilities.

    This layer is implemented rather than separate SigmoidLayer + CrossEntropyLayer as its gradient computation is more numerically stable. At test time, this layer can be replaced simply by a SigmoidLayer.

    Parameters
    bottominput Blob vector (length 2)
      -
    1. $ (N \times C \times H \times W) $ the scores $ x \in [-\infty, +\infty]$, which this layer maps to probability predictions $ \hat{p}_n = \sigma(x_n) \in [0, 1] $ using the sigmoid function $ \sigma(.) $ (see SigmoidLayer).
      2. -
    2. $ (N \times C \times H \times W) $ the targets $ y \in [0, 1] $
      3. +
    3. $ (N \times C \times H \times W) $ the scores $ x \in [-\infty, +\infty]$, which this layer maps to probability predictions $ \hat{p}_n = \sigma(x_n) \in [0, 1] $ using the sigmoid function $ \sigma(.) $ (see SigmoidLayer).
      4. +
    4. $ (N \times C \times H \times W) $ the targets $ y \in [0, 1] $
    topoutput Blob vector (length 1)
      -
    1. $ (1 \times 1 \times 1 \times 1) $ the computed cross-entropy loss: $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $
      2. +
    2. $ (1 \times 1 \times 1 \times 1) $ the computed cross-entropy loss: $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $
    @@ -270,7 +270,7 @@

    Member Function Documentation

    -

    § Backward_cpu()

    +

    ◆ Backward_cpu()

    @@ -316,13 +316,13 @@

    Parameters
    topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
      -
    1. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
      2. +
    2. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
    propagate_downsee Layer::Backward. propagate_down[1] must be false as gradient computation with respect to the targets is not implemented.
    bottominput Blob vector (length 2)
      -
    1. $ (N \times C \times H \times W) $ the predictions $x$; Backward computes diff $ \frac{\partial E}{\partial x} = \frac{1}{n} \sum\limits_{n=1}^N (\hat{p}_n - p_n) $
      2. -
    2. $ (N \times 1 \times 1 \times 1) $ the labels – ignored as we can't compute their error gradients
      3. +
    3. $ (N \times C \times H \times W) $ the predictions $x$; Backward computes diff $ \frac{\partial E}{\partial x} = \frac{1}{n} \sum\limits_{n=1}^N (\hat{p}_n - p_n) $
      4. +
    4. $ (N \times 1 \times 1 \times 1) $ the labels – ignored as we can't compute their error gradients
    @@ -334,7 +334,7 @@

    -

    § Forward_cpu()

    +

    ◆ Forward_cpu()

    @@ -369,17 +369,17 @@

    -

    Computes the cross-entropy (logistic) loss $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $, often used for predicting targets interpreted as probabilities.

    +

    Computes the cross-entropy (logistic) loss $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $, often used for predicting targets interpreted as probabilities.

    This layer is implemented rather than separate SigmoidLayer + CrossEntropyLayer as its gradient computation is more numerically stable. At test time, this layer can be replaced simply by a SigmoidLayer.

    Parameters
    bottominput Blob vector (length 2)
      -
    1. $ (N \times C \times H \times W) $ the scores $ x \in [-\infty, +\infty]$, which this layer maps to probability predictions $ \hat{p}_n = \sigma(x_n) \in [0, 1] $ using the sigmoid function $ \sigma(.) $ (see SigmoidLayer).
      2. -
    2. $ (N \times C \times H \times W) $ the targets $ y \in [0, 1] $
      3. +
    3. $ (N \times C \times H \times W) $ the scores $ x \in [-\infty, +\infty]$, which this layer maps to probability predictions $ \hat{p}_n = \sigma(x_n) \in [0, 1] $ using the sigmoid function $ \sigma(.) $ (see SigmoidLayer).
      4. +
    4. $ (N \times C \times H \times W) $ the targets $ y \in [0, 1] $
    topoutput Blob vector (length 1)
      -
    1. $ (1 \times 1 \times 1 \times 1) $ the computed cross-entropy loss: $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $
      2. +
    2. $ (1 \times 1 \times 1 \times 1) $ the computed cross-entropy loss: $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $
    @@ -391,7 +391,7 @@

    -

    § get_normalizer()

    +

    ◆ get_normalizer()

    @@ -430,7 +430,7 @@

    -

    § LayerSetUp()

    +

    ◆ LayerSetUp()

    @@ -480,7 +480,7 @@

    -

    § Reshape()

    +

    ◆ Reshape()

    @@ -536,9 +536,9 @@

    -Generated on Wed Jan 18 2017 12:26:18 for Caffe by   +Generated on Fri Apr 14 2017 12:50:55 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SigmoidLayer-members.html b/doxygen/classcaffe_1_1SigmoidLayer-members.html index 1cab19ac40e..65033e36001 100644 --- a/doxygen/classcaffe_1_1SigmoidLayer-members.html +++ b/doxygen/classcaffe_1_1SigmoidLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

    - + @@ -109,9 +109,9 @@
    -Generated on Wed Jan 18 2017 12:26:18 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SigmoidLayer.html b/doxygen/classcaffe_1_1SigmoidLayer.html index 1a6f95a86f1..3c91d73385f 100644 --- a/doxygen/classcaffe_1_1SigmoidLayer.html +++ b/doxygen/classcaffe_1_1SigmoidLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::SigmoidLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

    - + @@ -71,7 +71,7 @@
    -

    Sigmoid function non-linearity $ y = (1 + \exp(-x))^{-1} $, a classic choice in neural networks. +

    Sigmoid function non-linearity $ y = (1 + \exp(-x))^{-1} $, a classic choice in neural networks. More...

    #include <sigmoid_layer.hpp>

    @@ -80,7 +80,7 @@
    - + caffe::NeuronLayer< Dtype > caffe::Layer< Dtype > @@ -211,11 +211,11 @@

    template<typename Dtype>
    class caffe::SigmoidLayer< Dtype >

    -

    Sigmoid function non-linearity $ y = (1 + \exp(-x))^{-1} $, a classic choice in neural networks.

    +

    Sigmoid function non-linearity $ y = (1 + \exp(-x))^{-1} $, a classic choice in neural networks.

    Note that the gradient vanishes as the values move away from 0. The ReLULayer is often a better choice for this reason.

    Member Function Documentation

    -

    § Backward_cpu()

    +

    ◆ Backward_cpu()

    @@ -260,12 +260,12 @@

    Parameters
    topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
      -
    1. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
      2. +
    2. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
    propagate_downsee Layer::Backward.
    bottominput Blob vector (length 1)
      -
    1. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} y (1 - y) $ if propagate_down[0]
      2. +
    2. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} y (1 - y) $ if propagate_down[0]
    @@ -277,7 +277,7 @@

    -

    § Forward_cpu()

    +

    ◆ Forward_cpu()

    @@ -314,11 +314,11 @@

    Parameters
    bottominput Blob vector (length 1)
      -
    1. $ (N \times C \times H \times W) $ the inputs $ x $
      2. +
    2. $ (N \times C \times H \times W) $ the inputs $ x $
    topoutput Blob vector (length 1)
      -
    1. $ (N \times C \times H \times W) $ the computed outputs $ y = (1 + \exp(-x))^{-1} $
      2. +
    2. $ (N \times C \times H \times W) $ the computed outputs $ y = (1 + \exp(-x))^{-1} $
    @@ -336,9 +336,9 @@

    -Generated on Wed Jan 18 2017 12:26:18 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SignalHandler-members.html b/doxygen/classcaffe_1_1SignalHandler-members.html index be6bcbbb1eb..c21b06c61a5 100644 --- a/doxygen/classcaffe_1_1SignalHandler-members.html +++ b/doxygen/classcaffe_1_1SignalHandler-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

    - + @@ -75,9 +75,9 @@
    -Generated on Wed Jan 18 2017 12:26:18 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SignalHandler.html b/doxygen/classcaffe_1_1SignalHandler.html index b69ef0c21c3..f25e6965685 100644 --- a/doxygen/classcaffe_1_1SignalHandler.html +++ b/doxygen/classcaffe_1_1SignalHandler.html @@ -3,7 +3,7 @@ - + Caffe: caffe::SignalHandler Class Reference @@ -29,7 +29,7 @@

    - + @@ -86,9 +86,9 @@
    -Generated on Wed Jan 18 2017 12:26:18 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SilenceLayer-members.html b/doxygen/classcaffe_1_1SilenceLayer-members.html index ea46ffb1c13..b6051fe3846 100644 --- a/doxygen/classcaffe_1_1SilenceLayer-members.html +++ b/doxygen/classcaffe_1_1SilenceLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@
    - + @@ -108,9 +108,9 @@
    -Generated on Wed Jan 18 2017 12:26:18 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SilenceLayer.html b/doxygen/classcaffe_1_1SilenceLayer.html index c7e449b623f..d40ad1b3172 100644 --- a/doxygen/classcaffe_1_1SilenceLayer.html +++ b/doxygen/classcaffe_1_1SilenceLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::SilenceLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@
    - + @@ -80,7 +80,7 @@
    - + caffe::Layer< Dtype >
    @@ -212,7 +212,7 @@

    Ignores bottom blobs while producing no top blobs. (This is useful to suppress outputs during testing.)

    Member Function Documentation

    -

    § ExactNumTopBlobs()

    +

    ◆ ExactNumTopBlobs()

    @@ -244,7 +244,7 @@

    -

    § MinBottomBlobs()

    +

    ◆ MinBottomBlobs()

    @@ -276,7 +276,7 @@

    -

    § Reshape()

    +

    ◆ Reshape()

    @@ -332,9 +332,9 @@

    -Generated on Wed Jan 18 2017 12:26:18 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SliceLayer-members.html b/doxygen/classcaffe_1_1SliceLayer-members.html index c11415f9f8c..2869a455a6e 100644 --- a/doxygen/classcaffe_1_1SliceLayer-members.html +++ b/doxygen/classcaffe_1_1SliceLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

    - + @@ -113,9 +113,9 @@
    -Generated on Wed Jan 18 2017 12:26:18 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SliceLayer.html b/doxygen/classcaffe_1_1SliceLayer.html index cb794310594..b3ea764e3b2 100644 --- a/doxygen/classcaffe_1_1SliceLayer.html +++ b/doxygen/classcaffe_1_1SliceLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::SliceLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

    - + @@ -81,7 +81,7 @@
    - + caffe::Layer< Dtype >
    @@ -229,7 +229,7 @@

    TODO(dox): thorough documentation for Forward, Backward, and proto params.

    Member Function Documentation

    -

    § ExactNumBottomBlobs()

    +

    ◆ ExactNumBottomBlobs()

    @@ -261,7 +261,7 @@

    -

    § LayerSetUp()

    +

    ◆ LayerSetUp()

    @@ -311,7 +311,7 @@

    -

    § MinTopBlobs()

    +

    ◆ MinTopBlobs()

    @@ -343,7 +343,7 @@

    -

    § Reshape()

    +

    ◆ Reshape()

    @@ -399,9 +399,9 @@

    -Generated on Wed Jan 18 2017 12:26:18 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SoftmaxLayer-members.html b/doxygen/classcaffe_1_1SoftmaxLayer-members.html index d9b551e9738..0699681bb7c 100644 --- a/doxygen/classcaffe_1_1SoftmaxLayer-members.html +++ b/doxygen/classcaffe_1_1SoftmaxLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

    - + @@ -113,9 +113,9 @@
    -Generated on Wed Jan 18 2017 12:26:18 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SoftmaxLayer.html b/doxygen/classcaffe_1_1SoftmaxLayer.html index 57112e7273d..d11ad4afeca 100644 --- a/doxygen/classcaffe_1_1SoftmaxLayer.html +++ b/doxygen/classcaffe_1_1SoftmaxLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::SoftmaxLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

    - + @@ -81,7 +81,7 @@
    - + caffe::Layer< Dtype >
    @@ -231,7 +231,7 @@

    TODO(dox): thorough documentation for Forward, Backward, and proto params.

    Member Function Documentation

    -

    § ExactNumBottomBlobs()

    +

    ◆ ExactNumBottomBlobs()

    @@ -263,7 +263,7 @@

    -

    § ExactNumTopBlobs()

    +

    ◆ ExactNumTopBlobs()

    @@ -295,7 +295,7 @@

    -

    § Reshape()

    +

    ◆ Reshape()

    @@ -351,9 +351,9 @@

    -Generated on Wed Jan 18 2017 12:26:18 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SoftmaxWithLossLayer-members.html b/doxygen/classcaffe_1_1SoftmaxWithLossLayer-members.html index d8fdb67fbee..88645473e89 100644 --- a/doxygen/classcaffe_1_1SoftmaxWithLossLayer-members.html +++ b/doxygen/classcaffe_1_1SoftmaxWithLossLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

    - + @@ -120,9 +120,9 @@
    -Generated on Wed Jan 18 2017 12:26:19 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SoftmaxWithLossLayer.html b/doxygen/classcaffe_1_1SoftmaxWithLossLayer.html index 651cb990630..3c469d8f65e 100644 --- a/doxygen/classcaffe_1_1SoftmaxWithLossLayer.html +++ b/doxygen/classcaffe_1_1SoftmaxWithLossLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::SoftmaxWithLossLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

    - + @@ -81,7 +81,7 @@
    - + caffe::LossLayer< Dtype > caffe::Layer< Dtype > @@ -257,12 +257,12 @@
    Parameters
    bottominput Blob vector (length 2)
      -
    1. $ (N \times C \times H \times W) $ the predictions $ x $, a Blob with values in $ [-\infty, +\infty] $ indicating the predicted score for each of the $ K = CHW $ classes. This layer maps these scores to a probability distribution over classes using the softmax function $ \hat{p}_{nk} = \exp(x_{nk}) / \left[\sum_{k'} \exp(x_{nk'})\right] $ (see SoftmaxLayer).
      2. -
    2. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
      3. +
    3. $ (N \times C \times H \times W) $ the predictions $ x $, a Blob with values in $ [-\infty, +\infty] $ indicating the predicted score for each of the $ K = CHW $ classes. This layer maps these scores to a probability distribution over classes using the softmax function $ \hat{p}_{nk} = \exp(x_{nk}) / \left[\sum_{k'} \exp(x_{nk'})\right] $ (see SoftmaxLayer).
      4. +
    4. $ (N \times 1 \times 1 \times 1) $ the labels $ l $, an integer-valued Blob with values $ l_n \in [0, 1, 2, ..., K - 1] $ indicating the correct class label among the $ K $ classes
    topoutput Blob vector (length 1)
      -
    1. $ (1 \times 1 \times 1 \times 1) $ the computed cross-entropy classification loss: $ E = \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n}) $, for softmax output class probabilites $ \hat{p} $
      2. +
    2. $ (1 \times 1 \times 1 \times 1) $ the computed cross-entropy classification loss: $ E = \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n}) $, for softmax output class probabilites $ \hat{p} $
    @@ -270,7 +270,7 @@

    Constructor & Destructor Documentation

    -

    § SoftmaxWithLossLayer()

    +

    ◆ SoftmaxWithLossLayer()

    @@ -309,7 +309,7 @@

    Member Function Documentation

    -

    § Backward_cpu()

    +

    ◆ Backward_cpu()

    @@ -355,13 +355,13 @@

    Parameters
    topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
      -
    1. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
      2. +
    2. $ (1 \times 1 \times 1 \times 1) $ This Blob's diff will simply contain the loss_weight* $ \lambda $, as $ \lambda $ is the coefficient of this layer's output $\ell_i$ in the overall Net loss $ E = \lambda_i \ell_i + \mbox{other loss terms}$; hence $ \frac{\partial E}{\partial \ell_i} = \lambda_i $. (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
    propagate_downsee Layer::Backward. propagate_down[1] must be false as we can't compute gradients with respect to the labels.
    bottominput Blob vector (length 2)
      -
    1. $ (N \times C \times H \times W) $ the predictions $ x $; Backward computes diff $ \frac{\partial E}{\partial x} $
      2. -
    2. $ (N \times 1 \times 1 \times 1) $ the labels – ignored as we can't compute their error gradients
      3. +
    3. $ (N \times C \times H \times W) $ the predictions $ x $; Backward computes diff $ \frac{\partial E}{\partial x} $
      4. +
    4. $ (N \times 1 \times 1 \times 1) $ the labels – ignored as we can't compute their error gradients
    @@ -373,7 +373,7 @@

    -

    § ExactNumTopBlobs()

    +

    ◆ ExactNumTopBlobs()

    @@ -405,7 +405,7 @@

    -

    § get_normalizer()

    +

    ◆ get_normalizer()

    @@ -444,7 +444,7 @@

    -

    § LayerSetUp()

    +

    ◆ LayerSetUp()

    @@ -494,7 +494,7 @@

    -

    § MaxTopBlobs()

    +

    ◆ MaxTopBlobs()

    @@ -526,7 +526,7 @@

    -

    § MinTopBlobs()

    +

    ◆ MinTopBlobs()

    @@ -558,7 +558,7 @@

    -

    § Reshape()

    +

    ◆ Reshape()

    @@ -614,9 +614,9 @@

    -Generated on Wed Jan 18 2017 12:26:19 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1Solver-members.html b/doxygen/classcaffe_1_1Solver-members.html index 3c6282a6145..7e1c8f70cab 100644 --- a/doxygen/classcaffe_1_1Solver-members.html +++ b/doxygen/classcaffe_1_1Solver-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

    - + @@ -117,9 +117,9 @@
    -Generated on Wed Jan 18 2017 12:26:19 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1Solver.html b/doxygen/classcaffe_1_1Solver.html index 0cd2e597afe..09fd033005f 100644 --- a/doxygen/classcaffe_1_1Solver.html +++ b/doxygen/classcaffe_1_1Solver.html @@ -3,7 +3,7 @@ - + Caffe: caffe::Solver< Dtype > Class Template Reference @@ -29,7 +29,7 @@

    - + @@ -82,7 +82,7 @@
    - + caffe::SGDSolver< Dtype > caffe::AdaDeltaSolver< Dtype > caffe::AdaGradSolver< Dtype > @@ -252,9 +252,9 @@
    -Generated on Wed Jan 18 2017 12:26:19 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
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    - + @@ -73,9 +73,9 @@
    -Generated on Wed Jan 18 2017 12:26:19 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
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    - + @@ -82,9 +82,9 @@
    -Generated on Wed Jan 18 2017 12:26:19 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SolverRegistry-members.html b/doxygen/classcaffe_1_1SolverRegistry-members.html index 2a5054bd257..6209f26e3f0 100644 --- a/doxygen/classcaffe_1_1SolverRegistry-members.html +++ b/doxygen/classcaffe_1_1SolverRegistry-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

    - + @@ -78,9 +78,9 @@
    -Generated on Wed Jan 18 2017 12:26:19 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SolverRegistry.html b/doxygen/classcaffe_1_1SolverRegistry.html index a39cbdabbc5..237f72d1c27 100644 --- a/doxygen/classcaffe_1_1SolverRegistry.html +++ b/doxygen/classcaffe_1_1SolverRegistry.html @@ -3,7 +3,7 @@ - + Caffe: caffe::SolverRegistry< Dtype > Class Template Reference @@ -29,7 +29,7 @@

    - + @@ -101,9 +101,9 @@
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    - + @@ -75,9 +75,9 @@
    -Generated on Wed Jan 18 2017 12:26:19 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1Solver_1_1Callback.html b/doxygen/classcaffe_1_1Solver_1_1Callback.html index f8cef28ab60..402eab50331 100644 --- a/doxygen/classcaffe_1_1Solver_1_1Callback.html +++ b/doxygen/classcaffe_1_1Solver_1_1Callback.html @@ -3,7 +3,7 @@ - + Caffe: caffe::Solver< Dtype >::Callback Class Reference @@ -29,7 +29,7 @@

    - + @@ -93,9 +93,9 @@
    -Generated on Wed Jan 18 2017 12:26:19 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1Solver_1_1Callback.png b/doxygen/classcaffe_1_1Solver_1_1Callback.png deleted file mode 100644 index 0ac96400656fe731a88b8e4ea897c8dfbb1d3865..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 792 zcmeAS@N?(olHy`uVBq!ia0vp^M}RnhgBeH`_G|F~DTx4|5ZC|z{{xvX-h3_XKQsZz z0^#8tTZ`oe9!TES?UxqhWU}U_|yiMU*S3QTu^~HiSRx+&L zQqmW@On;7!ctd#Q4#vXzh`k&i-!$C#BjOOfb+WR+`6as92KMzmxly0){#wN{L-^fZ z-NjFXE87a01yc4%swYnSod5UNhN2QZ$<>cPT-fwv?F)r9E2SepPbhwVjFkaPumw9M@)1-oqu+}O1SM{(4?%X zoRhvR<8!cSn(|~_I>T#k>pP<1W;XnX(tiF^U%s1f5o6$?-RDj2cMJ4?-~7*L+LCE; zPBXqtt516oJn!?)hF$t1kH79*y5)YB}T-d;Ch% zhj(QOZ<->MPCMRQ#A|+T>iLYef{!d5*)mm!xqEB{df$c=o3(WpvTocdl>69!&5<U$CY9`hdo-iXLvv~OYxyTHG`s)o7heWU#tfdWelF{r5}E)PwO^$G diff --git a/doxygen/classcaffe_1_1SplitLayer-members.html b/doxygen/classcaffe_1_1SplitLayer-members.html index d6c86e81148..9ea232f2b1b 100644 --- a/doxygen/classcaffe_1_1SplitLayer-members.html +++ b/doxygen/classcaffe_1_1SplitLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@
    - + @@ -109,9 +109,9 @@
    -Generated on Wed Jan 18 2017 12:26:19 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SplitLayer.html b/doxygen/classcaffe_1_1SplitLayer.html index 8de089e78df..764d0b62658 100644 --- a/doxygen/classcaffe_1_1SplitLayer.html +++ b/doxygen/classcaffe_1_1SplitLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::SplitLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@
    - + @@ -81,7 +81,7 @@
    - + caffe::Layer< Dtype >
    @@ -217,7 +217,7 @@

    TODO(dox): thorough documentation for Forward, Backward, and proto params.

    Member Function Documentation

    -

    § ExactNumBottomBlobs()

    +

    ◆ ExactNumBottomBlobs()

    @@ -249,7 +249,7 @@

    -

    § MinTopBlobs()

    +

    ◆ MinTopBlobs()

    @@ -281,7 +281,7 @@

    -

    § Reshape()

    +

    ◆ Reshape()

    @@ -337,9 +337,9 @@

    -Generated on Wed Jan 18 2017 12:26:19 for Caffe by   +Generated on Fri Apr 14 2017 12:50:56 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SyncedMemory-members.html b/doxygen/classcaffe_1_1SyncedMemory-members.html index 9f32c451613..f4e646067e5 100644 --- a/doxygen/classcaffe_1_1SyncedMemory-members.html +++ b/doxygen/classcaffe_1_1SyncedMemory-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

    - + @@ -89,9 +89,9 @@
    -Generated on Wed Jan 18 2017 12:26:19 for Caffe by   +Generated on Fri Apr 14 2017 12:50:57 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1SyncedMemory.html b/doxygen/classcaffe_1_1SyncedMemory.html index 0dec3cfa55f..d389cb56a57 100644 --- a/doxygen/classcaffe_1_1SyncedMemory.html +++ b/doxygen/classcaffe_1_1SyncedMemory.html @@ -3,7 +3,7 @@ - + Caffe: caffe::SyncedMemory Class Reference @@ -29,7 +29,7 @@

    - + @@ -128,9 +128,9 @@
    -Generated on Wed Jan 18 2017 12:26:19 for Caffe by   +Generated on Fri Apr 14 2017 12:50:57 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1TanHLayer-members.html b/doxygen/classcaffe_1_1TanHLayer-members.html index 6c0622eaaaa..1b155615e9b 100644 --- a/doxygen/classcaffe_1_1TanHLayer-members.html +++ b/doxygen/classcaffe_1_1TanHLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

    - + @@ -109,9 +109,9 @@
    -Generated on Wed Jan 18 2017 12:26:19 for Caffe by   +Generated on Fri Apr 14 2017 12:50:57 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1TanHLayer.html b/doxygen/classcaffe_1_1TanHLayer.html index 4da28e081b6..bbb9fc09740 100644 --- a/doxygen/classcaffe_1_1TanHLayer.html +++ b/doxygen/classcaffe_1_1TanHLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::TanHLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

    - + @@ -71,7 +71,7 @@
    -

    TanH hyperbolic tangent non-linearity $ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $, popular in auto-encoders. +

    TanH hyperbolic tangent non-linearity $ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $, popular in auto-encoders. More...

    #include <tanh_layer.hpp>

    @@ -80,7 +80,7 @@
    - + caffe::NeuronLayer< Dtype > caffe::Layer< Dtype > @@ -211,11 +211,11 @@

    template<typename Dtype>
    class caffe::TanHLayer< Dtype >

    -

    TanH hyperbolic tangent non-linearity $ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $, popular in auto-encoders.

    +

    TanH hyperbolic tangent non-linearity $ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $, popular in auto-encoders.

    Note that the gradient vanishes as the values move away from 0. The ReLULayer is often a better choice for this reason.

    Member Function Documentation

    -

    § Backward_cpu()

    +

    ◆ Backward_cpu()

    @@ -260,12 +260,12 @@

    Parameters
    topoutput Blob vector (length 1), providing the error gradient with respect to the outputs
      -
    1. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
      2. +
    2. $ (N \times C \times H \times W) $ containing error gradients $ \frac{\partial E}{\partial y} $ with respect to computed outputs $ y $
    propagate_downsee Layer::Backward.
    bottominput Blob vector (length 1)
      -
    1. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} \left(1 - \left[\frac{\exp(2x) - 1}{exp(2x) + 1} \right]^2 \right) = \frac{\partial E}{\partial y} (1 - y^2) $ if propagate_down[0]
      2. +
    2. $ (N \times C \times H \times W) $ the inputs $ x $; Backward fills their diff with gradients $ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} \left(1 - \left[\frac{\exp(2x) - 1}{exp(2x) + 1} \right]^2 \right) = \frac{\partial E}{\partial y} (1 - y^2) $ if propagate_down[0]
    @@ -277,7 +277,7 @@

    -

    § Forward_cpu()

    +

    ◆ Forward_cpu()

    @@ -314,11 +314,11 @@

    Parameters
    bottominput Blob vector (length 1)
      -
    1. $ (N \times C \times H \times W) $ the inputs $ x $
      2. +
    2. $ (N \times C \times H \times W) $ the inputs $ x $
    topoutput Blob vector (length 1)
      -
    1. $ (N \times C \times H \times W) $ the computed outputs $ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $
      2. +
    2. $ (N \times C \times H \times W) $ the computed outputs $ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $
    @@ -336,9 +336,9 @@

    -Generated on Wed Jan 18 2017 12:26:19 for Caffe by   +Generated on Fri Apr 14 2017 12:50:57 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1ThresholdLayer-members.html b/doxygen/classcaffe_1_1ThresholdLayer-members.html index 7a0a5eb77b0..4d3ea93a25f 100644 --- a/doxygen/classcaffe_1_1ThresholdLayer-members.html +++ b/doxygen/classcaffe_1_1ThresholdLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

    - + @@ -110,9 +110,9 @@
    -Generated on Wed Jan 18 2017 12:26:20 for Caffe by   +Generated on Fri Apr 14 2017 12:50:57 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1ThresholdLayer.html b/doxygen/classcaffe_1_1ThresholdLayer.html index 6f06461ef9e..76c6369028f 100644 --- a/doxygen/classcaffe_1_1ThresholdLayer.html +++ b/doxygen/classcaffe_1_1ThresholdLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::ThresholdLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

    - + @@ -81,7 +81,7 @@
    - + caffe::NeuronLayer< Dtype > caffe::Layer< Dtype > @@ -218,7 +218,7 @@

    Tests whether the input exceeds a threshold: outputs 1 for inputs above threshold; 0 otherwise.

    Constructor & Destructor Documentation

    -

    § ThresholdLayer()

    +

    ◆ ThresholdLayer()

    @@ -245,7 +245,7 @@

    Parameters
    paramprovides ThresholdParameter threshold_param, with ThresholdLayer options:
      -
    • threshold (optional, default 0). the threshold value $ t $ to which the input values are compared.
      • +
    • threshold (optional, default 0). the threshold value $ t $ to which the input values are compared.
    @@ -256,7 +256,7 @@

    Member Function Documentation

    -

    § Forward_cpu()

    +

    ◆ Forward_cpu()

    @@ -293,11 +293,11 @@

    Parameters
    bottominput Blob vector (length 1)
      -
    1. $ (N \times C \times H \times W) $ the inputs $ x $
      2. +
    2. $ (N \times C \times H \times W) $ the inputs $ x $
    topoutput Blob vector (length 1)
      -
    1. $ (N \times C \times H \times W) $ the computed outputs $ y = \left\{ \begin{array}{lr} 0 & \mathrm{if} \; x \le t \\ 1 & \mathrm{if} \; x > t \end{array} \right. $
      2. +
    2. $ (N \times C \times H \times W) $ the computed outputs $ y = \left\{ \begin{array}{lr} 0 & \mathrm{if} \; x \le t \\ 1 & \mathrm{if} \; x > t \end{array} \right. $
    @@ -309,7 +309,7 @@

    -

    § LayerSetUp()

    +

    ◆ LayerSetUp()

    @@ -365,9 +365,9 @@

    -Generated on Wed Jan 18 2017 12:26:20 for Caffe by   +Generated on Fri Apr 14 2017 12:50:57 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1TileLayer-members.html b/doxygen/classcaffe_1_1TileLayer-members.html index ad6b8cd2795..145dc36d4d0 100644 --- a/doxygen/classcaffe_1_1TileLayer-members.html +++ b/doxygen/classcaffe_1_1TileLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

    - + @@ -112,9 +112,9 @@
    -Generated on Wed Jan 18 2017 12:26:20 for Caffe by   +Generated on Fri Apr 14 2017 12:50:57 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1TileLayer.html b/doxygen/classcaffe_1_1TileLayer.html index 36db07bb497..9a4ee03abb4 100644 --- a/doxygen/classcaffe_1_1TileLayer.html +++ b/doxygen/classcaffe_1_1TileLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::TileLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@

    - + @@ -81,7 +81,7 @@
    - + caffe::Layer< Dtype >
    @@ -225,7 +225,7 @@

    Copy a Blob along specified dimensions.

    Member Function Documentation

    -

    § ExactNumBottomBlobs()

    +

    ◆ ExactNumBottomBlobs()

    @@ -257,7 +257,7 @@

    -

    § ExactNumTopBlobs()

    +

    ◆ ExactNumTopBlobs()

    @@ -289,7 +289,7 @@

    -

    § Reshape()

    +

    ◆ Reshape()

    @@ -345,9 +345,9 @@

    -Generated on Wed Jan 18 2017 12:26:20 for Caffe by   +Generated on Fri Apr 14 2017 12:50:57 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1Timer-members.html b/doxygen/classcaffe_1_1Timer-members.html index a1d7ecd94f3..e52d9e42d79 100644 --- a/doxygen/classcaffe_1_1Timer-members.html +++ b/doxygen/classcaffe_1_1Timer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

    - + @@ -92,9 +92,9 @@
    -Generated on Wed Jan 18 2017 12:26:20 for Caffe by   +Generated on Fri Apr 14 2017 12:50:57 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1Timer.html b/doxygen/classcaffe_1_1Timer.html index e3b635ab510..66bf3408cd2 100644 --- a/doxygen/classcaffe_1_1Timer.html +++ b/doxygen/classcaffe_1_1Timer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::Timer Class Reference @@ -29,7 +29,7 @@

    - + @@ -151,9 +151,9 @@
    -Generated on Wed Jan 18 2017 12:26:20 for Caffe by   +Generated on Fri Apr 14 2017 12:50:57 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1UniformFiller-members.html b/doxygen/classcaffe_1_1UniformFiller-members.html index 8756c4b81e8..065ac2f6bdf 100644 --- a/doxygen/classcaffe_1_1UniformFiller-members.html +++ b/doxygen/classcaffe_1_1UniformFiller-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@

    - + @@ -77,9 +77,9 @@
    -Generated on Wed Jan 18 2017 12:26:20 for Caffe by   +Generated on Fri Apr 14 2017 12:50:57 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1UniformFiller.html b/doxygen/classcaffe_1_1UniformFiller.html index db70f919bfa..e26d372039e 100644 --- a/doxygen/classcaffe_1_1UniformFiller.html +++ b/doxygen/classcaffe_1_1UniformFiller.html @@ -3,7 +3,7 @@ - + Caffe: caffe::UniformFiller< Dtype > Class Template Reference @@ -29,7 +29,7 @@

    - + @@ -70,7 +70,7 @@
    -

    Fills a Blob with uniformly distributed values $ x\sim U(a, b) $. +

    Fills a Blob with uniformly distributed values $ x\sim U(a, b) $. More...

    #include <filler.hpp>

    @@ -79,7 +79,7 @@
    - + caffe::Filler< Dtype >
    @@ -108,16 +108,16 @@

    template<typename Dtype>
    class caffe::UniformFiller< Dtype >

    -

    Fills a Blob with uniformly distributed values $ x\sim U(a, b) $.

    +

    Fills a Blob with uniformly distributed values $ x\sim U(a, b) $.

    The documentation for this class was generated from the following file:
    -Generated on Wed Jan 18 2017 12:26:20 for Caffe by   +Generated on Fri Apr 14 2017 12:50:57 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1WindowDataLayer-members.html b/doxygen/classcaffe_1_1WindowDataLayer-members.html index 22d3a7e3161..acb7229a2b8 100644 --- a/doxygen/classcaffe_1_1WindowDataLayer-members.html +++ b/doxygen/classcaffe_1_1WindowDataLayer-members.html @@ -3,7 +3,7 @@ - + Caffe: Member List @@ -29,7 +29,7 @@
    - + @@ -130,28 +130,27 @@ set_param_propagate_down(const int param_id, const bool value)caffe::Layer< Dtype >inline SetLossWeights(const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inlineprotected SetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)caffe::Layer< Dtype >inline - ShareInParallel() const (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >inlinevirtual - StartInternalThread()caffe::InternalThread - StopInternalThread()caffe::InternalThread - ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual - transform_param_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected - transformed_data_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected - type() constcaffe::WindowDataLayer< Dtype >inlinevirtual - WindowDataLayer(const LayerParameter &param) (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >inlineexplicit - WindowField enum name (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected - X1 enum value (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected - X2 enum value (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected - Y1 enum value (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected - Y2 enum value (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected - ~InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadvirtual - ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual - ~WindowDataLayer() (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >virtual + StartInternalThread()caffe::InternalThread + StopInternalThread()caffe::InternalThread + ToProto(LayerParameter *param, bool write_diff=false)caffe::Layer< Dtype >virtual + transform_param_ (defined in caffe::BaseDataLayer< Dtype >)caffe::BaseDataLayer< Dtype >protected + transformed_data_ (defined in caffe::BasePrefetchingDataLayer< Dtype >)caffe::BasePrefetchingDataLayer< Dtype >protected + type() constcaffe::WindowDataLayer< Dtype >inlinevirtual + WindowDataLayer(const LayerParameter &param) (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >inlineexplicit + WindowField enum name (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected + X1 enum value (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected + X2 enum value (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected + Y1 enum value (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected + Y2 enum value (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >protected + ~InternalThread() (defined in caffe::InternalThread)caffe::InternalThreadvirtual + ~Layer() (defined in caffe::Layer< Dtype >)caffe::Layer< Dtype >inlinevirtual + ~WindowDataLayer() (defined in caffe::WindowDataLayer< Dtype >)caffe::WindowDataLayer< Dtype >virtual
    -Generated on Wed Jan 18 2017 12:26:20 for Caffe by   +Generated on Fri Apr 14 2017 12:50:57 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1WindowDataLayer.html b/doxygen/classcaffe_1_1WindowDataLayer.html index a02530a7cf1..27ace9b7cb4 100644 --- a/doxygen/classcaffe_1_1WindowDataLayer.html +++ b/doxygen/classcaffe_1_1WindowDataLayer.html @@ -3,7 +3,7 @@ - + Caffe: caffe::WindowDataLayer< Dtype > Class Template Reference @@ -29,7 +29,7 @@
    - + @@ -82,7 +82,7 @@
    - + caffe::BasePrefetchingDataLayer< Dtype > caffe::BaseDataLayer< Dtype > caffe::InternalThread @@ -127,9 +127,6 @@  BaseDataLayer (const LayerParameter &param)   - -virtual bool ShareInParallel () const -  virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)  Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
      @@ -326,7 +323,7 @@

    TODO(dox): thorough documentation for Forward and proto params.

    Member Function Documentation

    -

    § ExactNumBottomBlobs()

    +

    ◆ ExactNumBottomBlobs()

    @@ -358,7 +355,7 @@

    -

    § ExactNumTopBlobs()

    +

    ◆ ExactNumTopBlobs()

    @@ -395,9 +392,9 @@

    -Generated on Wed Jan 18 2017 12:26:20 for Caffe by   +Generated on Fri Apr 14 2017 12:50:57 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/classcaffe_1_1WorkerSolver-members.html b/doxygen/classcaffe_1_1WorkerSolver-members.html deleted file mode 100644 index 41356eb3bf1..00000000000 --- a/doxygen/classcaffe_1_1WorkerSolver-members.html +++ /dev/null @@ -1,125 +0,0 @@ - - - - - - - -Caffe: Member List - - - - - - - - - -
    -
    - - - - - - -
    -
    Caffe -
    -
    -
    - - - - - - - - -
    -
    - - -
    - -
    - - -
    -
    -
    -
    caffe::WorkerSolver< Dtype > Member List
    -
    -
    - -

    This is the complete list of members for caffe::WorkerSolver< Dtype >, including all inherited members.

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    action_request_function_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
    add_callback(Callback *value) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
    ApplyUpdate() (defined in caffe::WorkerSolver< Dtype >)caffe::WorkerSolver< Dtype >inlineprotectedvirtual
    callbacks() const (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
    callbacks_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
    CheckSnapshotWritePermissions() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
    current_step_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
    DISABLE_COPY_AND_ASSIGN(Solver) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
    DisplayOutputBlobs(const int net_id) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
    GetRequestedAction() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
    Init(const SolverParameter &param) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
    InitTestNets() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
    InitTrainNet() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
    iter() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
    iter_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
    losses_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
    net() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
    net_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
    param() const (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
    param_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
    requested_early_exit_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
    Restore(const char *resume_file) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
    RestoreSolverStateFromBinaryProto(const string &state_file) (defined in caffe::WorkerSolver< Dtype >)caffe::WorkerSolver< Dtype >inlineprotectedvirtual
    RestoreSolverStateFromHDF5(const string &state_file) (defined in caffe::WorkerSolver< Dtype >)caffe::WorkerSolver< Dtype >inlineprotectedvirtual
    root_solver_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
    SetActionFunction(ActionCallback func) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
    smoothed_loss_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
    Snapshot() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
    SnapshotFilename(const string extension) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
    SnapshotSolverState(const string &model_filename) (defined in caffe::WorkerSolver< Dtype >)caffe::WorkerSolver< Dtype >inlineprotectedvirtual
    SnapshotToBinaryProto() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
    SnapshotToHDF5() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
    Solve(const char *resume_file=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >virtual
    Solve(const string resume_file) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
    Solver(const SolverParameter &param, const Solver *root_solver=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >explicit
    Solver(const string &param_file, const Solver *root_solver=NULL) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >explicit
    Step(int iters) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >
    Test(const int test_net_id=0) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
    test_nets() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inline
    test_nets_ (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
    TestAll() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
    type() constcaffe::Solver< Dtype >inlinevirtual
    UpdateSmoothedLoss(Dtype loss, int start_iter, int average_loss) (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >protected
    WorkerSolver(const SolverParameter &param, const Solver< Dtype > *root_solver=NULL) (defined in caffe::WorkerSolver< Dtype >)caffe::WorkerSolver< Dtype >inlineexplicit
    ~Solver() (defined in caffe::Solver< Dtype >)caffe::Solver< Dtype >inlinevirtual
    - -
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    - - diff --git a/doxygen/classcaffe_1_1WorkerSolver.html b/doxygen/classcaffe_1_1WorkerSolver.html deleted file mode 100644 index e3889b7faed..00000000000 --- a/doxygen/classcaffe_1_1WorkerSolver.html +++ /dev/null @@ -1,249 +0,0 @@ - - - - - - - -Caffe: caffe::WorkerSolver< Dtype > Class Template Reference - - - - - - - - - -
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    caffe::WorkerSolver< Dtype > Class Template Reference
    -
    -
    - -

    Solver that only computes gradients, used as worker for multi-GPU training. - More...

    - -

    #include <solver.hpp>

    -
    -Inheritance diagram for caffe::WorkerSolver< Dtype >:
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    -Public Member Functions

    - WorkerSolver (const SolverParameter &param, const Solver< Dtype > *root_solver=NULL)
     
    - Public Member Functions inherited from caffe::Solver< Dtype >
    - Solver (const SolverParameter &param, const Solver *root_solver=NULL)
     
    - Solver (const string &param_file, const Solver *root_solver=NULL)
     
    -void Init (const SolverParameter &param)
     
    -void InitTrainNet ()
     
    -void InitTestNets ()
     
    -void SetActionFunction (ActionCallback func)
     
    -SolverAction::Enum GetRequestedAction ()
     
    -virtual void Solve (const char *resume_file=NULL)
     
    -void Solve (const string resume_file)
     
    -void Step (int iters)
     
    -void Restore (const char *resume_file)
     
    -void Snapshot ()
     
    -const SolverParameter & param () const
     
    -shared_ptr< Net< Dtype > > net ()
     
    -const vector< shared_ptr< Net< Dtype > > > & test_nets ()
     
    -int iter ()
     
    -const vector< Callback * > & callbacks () const
     
    -void add_callback (Callback *value)
     
    -void CheckSnapshotWritePermissions ()
     
    -virtual const char * type () const
     Returns the solver type.
     
    - - - - - - - - - - - - - - - - - - - - - - - - - - -

    -Protected Member Functions

    -void ApplyUpdate ()
     
    -void SnapshotSolverState (const string &model_filename)
     
    -void RestoreSolverStateFromBinaryProto (const string &state_file)
     
    -void RestoreSolverStateFromHDF5 (const string &state_file)
     
    - Protected Member Functions inherited from caffe::Solver< Dtype >
    -string SnapshotFilename (const string extension)
     
    -string SnapshotToBinaryProto ()
     
    -string SnapshotToHDF5 ()
     
    -void TestAll ()
     
    -void Test (const int test_net_id=0)
     
    -void DisplayOutputBlobs (const int net_id)
     
    -void UpdateSmoothedLoss (Dtype loss, int start_iter, int average_loss)
     
    - DISABLE_COPY_AND_ASSIGN (Solver)
     
    - - - - - - - - - - - - - - - - - - - - - - - - -

    -Additional Inherited Members

    - Protected Attributes inherited from caffe::Solver< Dtype >
    -SolverParameter param_
     
    -int iter_
     
    -int current_step_
     
    -shared_ptr< Net< Dtype > > net_
     
    -vector< shared_ptr< Net< Dtype > > > test_nets_
     
    -vector< Callback * > callbacks_
     
    -vector< Dtype > losses_
     
    -Dtype smoothed_loss_
     
    -const Solver *const root_solver_
     
    -ActionCallback action_request_function_
     
    -bool requested_early_exit_
     
    -

    Detailed Description

    -

    template<typename Dtype>
    -class caffe::WorkerSolver< Dtype >

    - -

    Solver that only computes gradients, used as worker for multi-GPU training.

    -
    The documentation for this class was generated from the following file: -
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    diff --git a/doxygen/classcaffe_1_1XavierFiller.html b/doxygen/classcaffe_1_1XavierFiller.html index f9b9806b6e5..e50afbabf4f 100644 --- a/doxygen/classcaffe_1_1XavierFiller.html +++ b/doxygen/classcaffe_1_1XavierFiller.html @@ -3,7 +3,7 @@ - + Caffe: caffe::XavierFiller< Dtype > Class Template Reference @@ -29,7 +29,7 @@

    - + @@ -70,7 +70,7 @@
    -

    Fills a Blob with values $ x \sim U(-a, +a) $ where $ a $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average. +

    Fills a Blob with values $ x \sim U(-a, +a) $ where $ a $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average. More...

    #include <filler.hpp>

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    @@ -108,7 +108,7 @@

    template<typename Dtype>
    class caffe::XavierFiller< Dtype >

    -

    Fills a Blob with values $ x \sim U(-a, +a) $ where $ a $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average.

    +

    Fills a Blob with values $ x \sim U(-a, +a) $ where $ a $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average.

    A Filler based on the paper [Bengio and Glorot 2010]: Understanding the difficulty of training deep feedforward neuralnetworks.

    It fills the incoming matrix by randomly sampling uniform data from [-scale, scale] where scale = sqrt(3 / n) where n is the fan_in, fan_out, or their average, depending on the variance_norm option. You should make sure the input blob has shape (num, a, b, c) where a * b * c = fan_in and num * b * c = fan_out. Note that this is currently not the case for inner product layers.

    TODO(dox): make notation in above comment consistent with rest & use LaTeX.

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    Close() (defined in caffe::db::LMDB)caffe::db::LMDBinlinevirtual
    DB() (defined in caffe::db::DB)caffe::db::DBinline
    DISABLE_COPY_AND_ASSIGN(DB) (defined in caffe::db::DB)caffe::db::DB
    LMDB() (defined in caffe::db::LMDB)caffe::db::LMDBinline
    NewCursor() (defined in caffe::db::LMDB)caffe::db::LMDBvirtual
    NewTransaction() (defined in caffe::db::LMDB)caffe::db::LMDBvirtual
    Open(const string &source, Mode mode) (defined in caffe::db::LMDB)caffe::db::LMDBvirtual
    ~DB() (defined in caffe::db::DB)caffe::db::DBinlinevirtual
    ~LMDB() (defined in caffe::db::LMDB)caffe::db::LMDBinlinevirtual
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    -Public Member Functions

    -virtual void Open (const string &source, Mode mode)
     
    -virtual void Close ()
     
    -virtual LMDBCursor * NewCursor ()
     
    -virtual LMDBTransaction * NewTransaction ()
     
    - Public Member Functions inherited from caffe::db::DB
    - DISABLE_COPY_AND_ASSIGN (DB)
     
    -
    The documentation for this class was generated from the following files:
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    Cursor() (defined in caffe::db::Cursor)caffe::db::Cursorinline
    DISABLE_COPY_AND_ASSIGN(Cursor) (defined in caffe::db::Cursor)caffe::db::Cursor
    key() (defined in caffe::db::LMDBCursor)caffe::db::LMDBCursorinlinevirtual
    LMDBCursor(MDB_txn *mdb_txn, MDB_cursor *mdb_cursor) (defined in caffe::db::LMDBCursor)caffe::db::LMDBCursorinlineexplicit
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    valid() (defined in caffe::db::LMDBCursor)caffe::db::LMDBCursorinlinevirtual
    value() (defined in caffe::db::LMDBCursor)caffe::db::LMDBCursorinlinevirtual
    ~Cursor() (defined in caffe::db::Cursor)caffe::db::Cursorinlinevirtual
    ~LMDBCursor() (defined in caffe::db::LMDBCursor)caffe::db::LMDBCursorinlinevirtual
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    - LMDBCursor (MDB_txn *mdb_txn, MDB_cursor *mdb_cursor)
     
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    -virtual void Next ()
     
    -virtual string key ()
     
    -virtual string value ()
     
    -virtual bool valid ()
     
    - Public Member Functions inherited from caffe::db::Cursor
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    Commit() (defined in caffe::db::LMDBTransaction)caffe::db::LMDBTransactioninlinevirtual
    DISABLE_COPY_AND_ASSIGN(Transaction) (defined in caffe::db::Transaction)caffe::db::Transaction
    LMDBTransaction(MDB_dbi *mdb_dbi, MDB_txn *mdb_txn) (defined in caffe::db::LMDBTransaction)caffe::db::LMDBTransactioninlineexplicit
    Put(const string &key, const string &value) (defined in caffe::db::LMDBTransaction)caffe::db::LMDBTransactionvirtual
    Transaction() (defined in caffe::db::Transaction)caffe::db::Transactioninline
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    - LMDBTransaction (MDB_dbi *mdb_dbi, MDB_txn *mdb_txn)
     
    -virtual void Put (const string &key, const string &value)
     
    -virtual void Commit ()
     
    - Public Member Functions inherited from caffe::db::Transaction
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    Close() (defined in caffe::db::LevelDB)caffe::db::LevelDBinlinevirtual
    DB() (defined in caffe::db::DB)caffe::db::DBinline
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    ~LevelDB() (defined in caffe::db::LevelDB)caffe::db::LevelDBinlinevirtual
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    -virtual LevelDBCursor * NewCursor ()
     
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    Cursor() (defined in caffe::db::Cursor)caffe::db::Cursorinline
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    ~LevelDBCursor() (defined in caffe::db::LevelDBCursor)caffe::db::LevelDBCursorinline
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    - LevelDBCursor (leveldb::Iterator *iter)
     
    -virtual void SeekToFirst ()
     
    -virtual void Next ()
     
    -virtual string key ()
     
    -virtual string value ()
     
    -virtual bool valid ()
     
    - Public Member Functions inherited from caffe::db::Cursor
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    Commit() (defined in caffe::db::LevelDBTransaction)caffe::db::LevelDBTransactioninlinevirtual
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    - LevelDBTransaction (leveldb::DB *db)
     
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    -virtual void Commit ()
     
    - Public Member Functions inherited from caffe::db::Transaction
    - DISABLE_COPY_AND_ASSIGN (Transaction)
     
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    The documentation for this class was generated from the following file: -
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    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/common_8hpp_source.html b/doxygen/common_8hpp_source.html index 0e98bd40743..ae81a7450d9 100644 --- a/doxygen/common_8hpp_source.html +++ b/doxygen/common_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/common.hpp Source File @@ -29,7 +29,7 @@
    - + @@ -74,9 +74,9 @@
    -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/common__layers_8hpp_source.html b/doxygen/common__layers_8hpp_source.html deleted file mode 100644 index cb906af5d9d..00000000000 --- a/doxygen/common__layers_8hpp_source.html +++ /dev/null @@ -1,776 +0,0 @@ - - - - - - -Caffe: include/caffe/common_layers.hpp Source File - - - - - - - - - - -
    -
    - - - - - - -
    -
    Caffe -
    -
    -
    - - - - - - -
    -
    - - -
    - -
    - - -
    -
    -
    -
    common_layers.hpp
    -
    -
    -
    1 #ifndef CAFFE_COMMON_LAYERS_HPP_
    -
    2 #define CAFFE_COMMON_LAYERS_HPP_
    -
    3 
    -
    4 #include <string>
    -
    5 #include <utility>
    -
    6 #include <vector>
    -
    7 
    -
    8 #include "caffe/blob.hpp"
    -
    9 #include "caffe/common.hpp"
    -
    10 #include "caffe/data_layers.hpp"
    -
    11 #include "caffe/layer.hpp"
    -
    12 #include "caffe/loss_layers.hpp"
    -
    13 #include "caffe/neuron_layers.hpp"
    -
    14 #include "caffe/proto/caffe.pb.h"
    -
    15 
    -
    16 namespace caffe {
    -
    17 
    -
    29 template <typename Dtype>
    -
    30 class ArgMaxLayer : public Layer<Dtype> {
    -
    31  public:
    -
    44  explicit ArgMaxLayer(const LayerParameter& param)
    -
    45  : Layer<Dtype>(param) {}
    -
    46  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    47  const vector<Blob<Dtype>*>& top);
    -
    48  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    49  const vector<Blob<Dtype>*>& top);
    -
    50 
    -
    51  virtual inline const char* type() const { return "ArgMax"; }
    -
    52  virtual inline int ExactNumBottomBlobs() const { return 1; }
    -
    53  virtual inline int ExactNumTopBlobs() const { return 1; }
    -
    54 
    -
    55  protected:
    -
    68  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    69  const vector<Blob<Dtype>*>& top);
    -
    71  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    72  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
    -
    73  NOT_IMPLEMENTED;
    -
    74  }
    -
    75  bool out_max_val_;
    -
    76  size_t top_k_;
    -
    77  bool has_axis_;
    -
    78  int axis_;
    -
    79 };
    -
    80 
    -
    88 template <typename Dtype>
    -
    89 class BatchReindexLayer : public Layer<Dtype> {
    -
    90  public:
    -
    91  explicit BatchReindexLayer(const LayerParameter& param)
    -
    92  : Layer<Dtype>(param) {}
    -
    93  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    94  const vector<Blob<Dtype>*>& top);
    -
    95 
    -
    96  virtual inline const char* type() const { return "BatchReindex"; }
    -
    97  virtual inline int ExactNumBottomBlobs() const { return 2; }
    -
    98  virtual inline int ExactNumTopBlobs() const { return 1; }
    -
    99 
    -
    100  protected:
    -
    113  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    114  const vector<Blob<Dtype>*>& top);
    -
    115  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    -
    116  const vector<Blob<Dtype>*>& top);
    -
    117 
    -
    133  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    134  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    135  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    -
    136  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    137 
    -
    138  private:
    -
    139  struct pair_sort_first {
    -
    140  bool operator()(const std::pair<int, int> &left,
    -
    141  const std::pair<int, int> &right) {
    -
    142  return left.first < right.first;
    -
    143  }
    -
    144  };
    -
    145  void check_batch_reindex(int initial_num, int final_num,
    -
    146  const Dtype* ridx_data);
    -
    147 };
    -
    148 
    -
    149 
    -
    154 template <typename Dtype>
    -
    155 class ConcatLayer : public Layer<Dtype> {
    -
    156  public:
    -
    157  explicit ConcatLayer(const LayerParameter& param)
    -
    158  : Layer<Dtype>(param) {}
    -
    159  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    160  const vector<Blob<Dtype>*>& top);
    -
    161  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    162  const vector<Blob<Dtype>*>& top);
    -
    163 
    -
    164  virtual inline const char* type() const { return "Concat"; }
    -
    165  virtual inline int MinBottomBlobs() const { return 1; }
    -
    166  virtual inline int ExactNumTopBlobs() const { return 1; }
    -
    167 
    -
    168  protected:
    -
    185  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    186  const vector<Blob<Dtype>*>& top);
    -
    187  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    -
    188  const vector<Blob<Dtype>*>& top);
    -
    189 
    -
    212  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    213  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    214  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    -
    215  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    216 
    -
    217  int count_;
    -
    218  int num_concats_;
    -
    219  int concat_input_size_;
    -
    220  int concat_axis_;
    -
    221 };
    -
    222 
    -
    229 template <typename Dtype>
    -
    230 class EltwiseLayer : public Layer<Dtype> {
    -
    231  public:
    -
    232  explicit EltwiseLayer(const LayerParameter& param)
    -
    233  : Layer<Dtype>(param) {}
    -
    234  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    235  const vector<Blob<Dtype>*>& top);
    -
    236  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    237  const vector<Blob<Dtype>*>& top);
    -
    238 
    -
    239  virtual inline const char* type() const { return "Eltwise"; }
    -
    240  virtual inline int MinBottomBlobs() const { return 2; }
    -
    241  virtual inline int ExactNumTopBlobs() const { return 1; }
    -
    242 
    -
    243  protected:
    -
    244  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    245  const vector<Blob<Dtype>*>& top);
    -
    246  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    -
    247  const vector<Blob<Dtype>*>& top);
    -
    248  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    249  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    250  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    -
    251  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    252 
    -
    253  EltwiseParameter_EltwiseOp op_;
    -
    254  vector<Dtype> coeffs_;
    -
    255  Blob<int> max_idx_;
    -
    256 
    -
    257  bool stable_prod_grad_;
    -
    258 };
    -
    259 
    -
    267 template <typename Dtype>
    -
    268 class EmbedLayer : public Layer<Dtype> {
    -
    269  public:
    -
    270  explicit EmbedLayer(const LayerParameter& param)
    -
    271  : Layer<Dtype>(param) {}
    -
    272  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    273  const vector<Blob<Dtype>*>& top);
    -
    274  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    275  const vector<Blob<Dtype>*>& top);
    -
    276 
    -
    277  virtual inline const char* type() const { return "Embed"; }
    -
    278  virtual inline int ExactNumBottomBlobs() const { return 1; }
    -
    279  virtual inline int ExactNumTopBlobs() const { return 1; }
    -
    280 
    -
    281  protected:
    -
    282  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    283  const vector<Blob<Dtype>*>& top);
    -
    284  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    -
    285  const vector<Blob<Dtype>*>& top);
    -
    286  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    287  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    288  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    -
    289  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    290 
    -
    291  int M_;
    -
    292  int K_;
    -
    293  int N_;
    -
    294  bool bias_term_;
    -
    295  Blob<Dtype> bias_multiplier_;
    -
    296 };
    -
    297 
    -
    304 template <typename Dtype>
    -
    305 class FilterLayer : public Layer<Dtype> {
    -
    306  public:
    -
    307  explicit FilterLayer(const LayerParameter& param)
    -
    308  : Layer<Dtype>(param) {}
    -
    309  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    310  const vector<Blob<Dtype>*>& top);
    -
    311  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    312  const vector<Blob<Dtype>*>& top);
    -
    313 
    -
    314  virtual inline const char* type() const { return "Filter"; }
    -
    315  virtual inline int MinBottomBlobs() const { return 2; }
    -
    316  virtual inline int MinTopBlobs() const { return 1; }
    -
    317 
    -
    318  protected:
    -
    338  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    339  const vector<Blob<Dtype>*>& top);
    -
    340  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    -
    341  const vector<Blob<Dtype>*>& top);
    -
    342 
    -
    352  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    353  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    354  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    -
    355  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    356 
    -
    357  bool first_reshape_;
    -
    358  vector<int> indices_to_forward_;
    -
    359 };
    -
    360 
    -
    371 template <typename Dtype>
    -
    372 class FlattenLayer : public Layer<Dtype> {
    -
    373  public:
    -
    374  explicit FlattenLayer(const LayerParameter& param)
    -
    375  : Layer<Dtype>(param) {}
    -
    376  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    377  const vector<Blob<Dtype>*>& top);
    -
    378 
    -
    379  virtual inline const char* type() const { return "Flatten"; }
    -
    380  virtual inline int ExactNumBottomBlobs() const { return 1; }
    -
    381  virtual inline int ExactNumTopBlobs() const { return 1; }
    -
    382 
    -
    383  protected:
    -
    392  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    393  const vector<Blob<Dtype>*>& top);
    -
    394 
    -
    404  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    405  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    406 };
    -
    407 
    -
    414 template <typename Dtype>
    -
    415 class InnerProductLayer : public Layer<Dtype> {
    -
    416  public:
    -
    417  explicit InnerProductLayer(const LayerParameter& param)
    -
    418  : Layer<Dtype>(param) {}
    -
    419  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    420  const vector<Blob<Dtype>*>& top);
    -
    421  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    422  const vector<Blob<Dtype>*>& top);
    -
    423 
    -
    424  virtual inline const char* type() const { return "InnerProduct"; }
    -
    425  virtual inline int ExactNumBottomBlobs() const { return 1; }
    -
    426  virtual inline int ExactNumTopBlobs() const { return 1; }
    -
    427 
    -
    428  protected:
    -
    429  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    430  const vector<Blob<Dtype>*>& top);
    -
    431  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    -
    432  const vector<Blob<Dtype>*>& top);
    -
    433  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    434  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    435  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    -
    436  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    437 
    -
    438  int M_;
    -
    439  int K_;
    -
    440  int N_;
    -
    441  bool bias_term_;
    -
    442  Blob<Dtype> bias_multiplier_;
    -
    443 };
    -
    444 
    -
    450 template <typename Dtype>
    -
    451 class MVNLayer : public Layer<Dtype> {
    -
    452  public:
    -
    453  explicit MVNLayer(const LayerParameter& param)
    -
    454  : Layer<Dtype>(param) {}
    -
    455  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    456  const vector<Blob<Dtype>*>& top);
    -
    457 
    -
    458  virtual inline const char* type() const { return "MVN"; }
    -
    459  virtual inline int ExactNumBottomBlobs() const { return 1; }
    -
    460  virtual inline int ExactNumTopBlobs() const { return 1; }
    -
    461 
    -
    462  protected:
    -
    463  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    464  const vector<Blob<Dtype>*>& top);
    -
    465  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    -
    466  const vector<Blob<Dtype>*>& top);
    -
    467  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    468  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    469  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    -
    470  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    471 
    -
    472  Blob<Dtype> mean_, variance_, temp_;
    -
    473 
    -
    475  Blob<Dtype> sum_multiplier_;
    -
    476  Dtype eps_;
    -
    477 };
    -
    478 
    -
    479 /*
    -
    480  * @brief Reshapes the input Blob into an arbitrary-sized output Blob.
    -
    481  *
    -
    482  * Note: similarly to FlattenLayer, this layer does not change the input values
    -
    483  * (see FlattenLayer, Blob::ShareData and Blob::ShareDiff).
    -
    484  */
    -
    485 template <typename Dtype>
    -
    486 class ReshapeLayer : public Layer<Dtype> {
    -
    487  public:
    -
    488  explicit ReshapeLayer(const LayerParameter& param)
    -
    489  : Layer<Dtype>(param) {}
    -
    490  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    491  const vector<Blob<Dtype>*>& top);
    -
    492  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    493  const vector<Blob<Dtype>*>& top);
    -
    494 
    -
    495  virtual inline const char* type() const { return "Reshape"; }
    -
    496  virtual inline int ExactNumBottomBlobs() const { return 1; }
    -
    497  virtual inline int ExactNumTopBlobs() const { return 1; }
    -
    498 
    -
    499  protected:
    -
    500  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    501  const vector<Blob<Dtype>*>& top) {}
    -
    502  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    503  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
    -
    504  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    -
    505  const vector<Blob<Dtype>*>& top) {}
    -
    506  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    -
    507  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
    -
    508 
    -
    510  vector<int> copy_axes_;
    -
    512  int inferred_axis_;
    -
    514  int constant_count_;
    -
    515 };
    -
    516 
    -
    524 template <typename Dtype>
    -
    525 class ReductionLayer : public Layer<Dtype> {
    -
    526  public:
    -
    527  explicit ReductionLayer(const LayerParameter& param)
    -
    528  : Layer<Dtype>(param) {}
    -
    529  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    530  const vector<Blob<Dtype>*>& top);
    -
    531  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    532  const vector<Blob<Dtype>*>& top);
    -
    533 
    -
    534  virtual inline const char* type() const { return "Reduction"; }
    -
    535  virtual inline int ExactNumBottomBlobs() const { return 1; }
    -
    536  virtual inline int ExactNumTopBlobs() const { return 1; }
    -
    537 
    -
    538  protected:
    -
    539  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    540  const vector<Blob<Dtype>*>& top);
    -
    541  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    -
    542  const vector<Blob<Dtype>*>& top);
    -
    543  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    544  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    545  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    -
    546  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    547 
    -
    549  ReductionParameter_ReductionOp op_;
    -
    551  Dtype coeff_;
    -
    553  int axis_;
    -
    555  int num_;
    -
    557  int dim_;
    -
    559  Blob<Dtype> sum_multiplier_;
    -
    560 };
    -
    561 
    -
    566 template <typename Dtype>
    -
    567 class SilenceLayer : public Layer<Dtype> {
    -
    568  public:
    -
    569  explicit SilenceLayer(const LayerParameter& param)
    -
    570  : Layer<Dtype>(param) {}
    -
    571  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    572  const vector<Blob<Dtype>*>& top) {}
    -
    573 
    -
    574  virtual inline const char* type() const { return "Silence"; }
    -
    575  virtual inline int MinBottomBlobs() const { return 1; }
    -
    576  virtual inline int ExactNumTopBlobs() const { return 0; }
    -
    577 
    -
    578  protected:
    -
    579  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    580  const vector<Blob<Dtype>*>& top) {}
    -
    581  // We can't define Forward_gpu here, since STUB_GPU will provide
    -
    582  // its own definition for CPU_ONLY mode.
    -
    583  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    -
    584  const vector<Blob<Dtype>*>& top);
    -
    585  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    586  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    587  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    -
    588  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    589 };
    -
    590 
    -
    596 template <typename Dtype>
    -
    597 class SoftmaxLayer : public Layer<Dtype> {
    -
    598  public:
    -
    599  explicit SoftmaxLayer(const LayerParameter& param)
    -
    600  : Layer<Dtype>(param) {}
    -
    601  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    602  const vector<Blob<Dtype>*>& top);
    -
    603 
    -
    604  virtual inline const char* type() const { return "Softmax"; }
    -
    605  virtual inline int ExactNumBottomBlobs() const { return 1; }
    -
    606  virtual inline int ExactNumTopBlobs() const { return 1; }
    -
    607 
    -
    608  protected:
    -
    609  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    610  const vector<Blob<Dtype>*>& top);
    -
    611  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    -
    612  const vector<Blob<Dtype>*>& top);
    -
    613  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    614  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    615  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    -
    616  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    617 
    -
    618  int outer_num_;
    -
    619  int inner_num_;
    -
    620  int softmax_axis_;
    -
    622  Blob<Dtype> sum_multiplier_;
    -
    624  Blob<Dtype> scale_;
    -
    625 };
    -
    626 
    -
    627 #ifdef USE_CUDNN
    -
    628 
    -
    632 template <typename Dtype>
    -
    633 class CuDNNSoftmaxLayer : public SoftmaxLayer<Dtype> {
    -
    634  public:
    -
    635  explicit CuDNNSoftmaxLayer(const LayerParameter& param)
    -
    636  : SoftmaxLayer<Dtype>(param), handles_setup_(false) {}
    -
    637  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    638  const vector<Blob<Dtype>*>& top);
    -
    639  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    640  const vector<Blob<Dtype>*>& top);
    -
    641  virtual ~CuDNNSoftmaxLayer();
    -
    642 
    -
    643  protected:
    -
    644  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    -
    645  const vector<Blob<Dtype>*>& top);
    -
    646  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    -
    647  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    648 
    -
    649  bool handles_setup_;
    -
    650  cudnnHandle_t handle_;
    -
    651  cudnnTensorDescriptor_t bottom_desc_;
    -
    652  cudnnTensorDescriptor_t top_desc_;
    -
    653 };
    -
    654 #endif
    -
    655 
    -
    662 template <typename Dtype>
    -
    663 class SplitLayer : public Layer<Dtype> {
    -
    664  public:
    -
    665  explicit SplitLayer(const LayerParameter& param)
    -
    666  : Layer<Dtype>(param) {}
    -
    667  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    668  const vector<Blob<Dtype>*>& top);
    -
    669 
    -
    670  virtual inline const char* type() const { return "Split"; }
    -
    671  virtual inline int ExactNumBottomBlobs() const { return 1; }
    -
    672  virtual inline int MinTopBlobs() const { return 1; }
    -
    673 
    -
    674  protected:
    -
    675  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    676  const vector<Blob<Dtype>*>& top);
    -
    677  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    -
    678  const vector<Blob<Dtype>*>& top);
    -
    679  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    680  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    681  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    -
    682  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    683 
    -
    684  int count_;
    -
    685 };
    -
    686 
    -
    693 template <typename Dtype>
    -
    694 class SliceLayer : public Layer<Dtype> {
    -
    695  public:
    -
    696  explicit SliceLayer(const LayerParameter& param)
    -
    697  : Layer<Dtype>(param) {}
    -
    698  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    699  const vector<Blob<Dtype>*>& top);
    -
    700  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    701  const vector<Blob<Dtype>*>& top);
    -
    702 
    -
    703  virtual inline const char* type() const { return "Slice"; }
    -
    704  virtual inline int ExactNumBottomBlobs() const { return 1; }
    -
    705  virtual inline int MinTopBlobs() const { return 1; }
    -
    706 
    -
    707  protected:
    -
    708  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    709  const vector<Blob<Dtype>*>& top);
    -
    710  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    -
    711  const vector<Blob<Dtype>*>& top);
    -
    712  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    713  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    714  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    -
    715  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    716 
    -
    717  int count_;
    -
    718  int num_slices_;
    -
    719  int slice_size_;
    -
    720  int slice_axis_;
    -
    721  vector<int> slice_point_;
    -
    722 };
    -
    723 
    -
    727 template <typename Dtype>
    -
    728 class TileLayer : public Layer<Dtype> {
    -
    729  public:
    -
    730  explicit TileLayer(const LayerParameter& param)
    -
    731  : Layer<Dtype>(param) {}
    -
    732  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    733  const vector<Blob<Dtype>*>& top);
    -
    734 
    -
    735  virtual inline const char* type() const { return "Tile"; }
    -
    736  virtual inline int ExactNumBottomBlobs() const { return 1; }
    -
    737  virtual inline int ExactNumTopBlobs() const { return 1; }
    -
    738 
    -
    739  protected:
    -
    740  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    741  const vector<Blob<Dtype>*>& top);
    -
    742  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    -
    743  const vector<Blob<Dtype>*>& top);
    -
    744 
    -
    745  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    746  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    747  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    -
    748  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    749 
    -
    750  unsigned int axis_, tiles_, outer_dim_, inner_dim_;
    -
    751 };
    -
    752 
    -
    753 } // namespace caffe
    -
    754 
    -
    755 #endif // CAFFE_COMMON_LAYERS_HPP_
    -
    caffe::ReshapeLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: common_layers.hpp:506
    -
    caffe::SplitLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: split_layer.cpp:27
    -
    caffe::EltwiseLayer::MinBottomBlobs
    virtual int MinBottomBlobs() const
    Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
    Definition: common_layers.hpp:240
    -
    caffe::MVNLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:460
    -
    caffe::EmbedLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: embed_layer.cpp:68
    -
    caffe::ArgMaxLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: common_layers.hpp:51
    -
    caffe::InnerProductLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: inner_product_layer.cpp:13
    -
    caffe::ReductionLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    -
    caffe::TileLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: tile_layer.cpp:39
    -
    caffe::FlattenLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:381
    -
    caffe::EmbedLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: common_layers.hpp:277
    -
    caffe::TileLayer
    Copy a Blob along specified dimensions.
    Definition: common_layers.hpp:728
    -
    caffe::BatchReindexLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: batch_reindex_layer.cpp:10
    -
    caffe::ReshapeLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    Definition: common_layers.hpp:504
    -
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    -
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers could be called...
    Definition: blob.hpp:15
    -
    caffe::ConcatLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Definition: concat_layer.cpp:58
    -
    caffe::EmbedLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: embed_layer.cpp:52
    -
    caffe::ReductionLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: reduction_layer.cpp:80
    -
    caffe::ReductionLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: reduction_layer.cpp:45
    -
    caffe::InnerProductLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:426
    -
    caffe::SoftmaxLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:605
    -
    caffe::SoftmaxLayer::sum_multiplier_
    Blob< Dtype > sum_multiplier_
    sum_multiplier is used to carry out sum using BLAS
    Definition: common_layers.hpp:622
    -
    caffe::SliceLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:704
    -
    caffe::FlattenLayer
    Reshapes the input Blob into flat vectors.
    Definition: common_layers.hpp:372
    -
    caffe::ReshapeLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: common_layers.hpp:502
    -
    caffe::InnerProductLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: inner_product_layer.cpp:55
    -
    caffe::SoftmaxLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: softmax_layer.cpp:64
    -
    caffe::ReductionLayer::dim_
    int dim_
    the input size of each reduction
    Definition: common_layers.hpp:557
    -
    caffe::SliceLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: slice_layer.cpp:23
    -
    caffe::SilenceLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: common_layers.hpp:579
    -
    caffe::ReshapeLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: reshape_layer.cpp:9
    -
    caffe::FlattenLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: common_layers.hpp:379
    -
    caffe::MVNLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:459
    -
    caffe::SoftmaxLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:606
    -
    caffe::SilenceLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    -
    caffe::TileLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: tile_layer.cpp:25
    -
    caffe::InnerProductLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    -
    caffe::SliceLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    -
    caffe::ReshapeLayer::constant_count_
    int constant_count_
    the product of the "constant" output dimensions
    Definition: common_layers.hpp:514
    -
    caffe::FilterLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: filter_layer.cpp:18
    -
    caffe::ArgMaxLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: argmax_layer.cpp:34
    -
    caffe::ConcatLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Computes the error gradient w.r.t. the concatenate inputs.
    Definition: concat_layer.cpp:78
    -
    caffe::SliceLayer::MinTopBlobs
    virtual int MinTopBlobs() const
    Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
    Definition: common_layers.hpp:705
    -
    caffe::SoftmaxLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: common_layers.hpp:604
    -
    caffe::BatchReindexLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:97
    -
    caffe::ReshapeLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: reshape_layer.cpp:31
    -
    caffe::EmbedLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:278
    -
    caffe::SilenceLayer::MinBottomBlobs
    virtual int MinBottomBlobs() const
    Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
    Definition: common_layers.hpp:575
    -
    caffe::FilterLayer::MinBottomBlobs
    virtual int MinBottomBlobs() const
    Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
    Definition: common_layers.hpp:315
    -
    caffe::ConcatLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: concat_layer.cpp:10
    -
    caffe::EltwiseLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: eltwise_layer.cpp:11
    -
    caffe::ReshapeLayer::inferred_axis_
    int inferred_axis_
    the index of the axis whose dimension we infer, or -1 if none
    Definition: common_layers.hpp:512
    -
    caffe::EmbedLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: embed_layer.cpp:13
    -
    caffe::EmbedLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: embed_layer.cpp:89
    -
    caffe::ReductionLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: reduction_layer.cpp:18
    -
    caffe::MVNLayer
    Normalizes the input to have 0-mean and/or unit (1) variance.
    Definition: common_layers.hpp:451
    -
    caffe::EmbedLayer
    A layer for learning "embeddings" of one-hot vector input. Equivalent to an InnerProductLayer with on...
    Definition: common_layers.hpp:268
    -
    caffe::ReductionLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:535
    -
    caffe::FilterLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Definition: filter_layer.cpp:63
    -
    caffe::FilterLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Computes the error gradient w.r.t. the forwarded inputs.
    Definition: filter_layer.cpp:81
    -
    caffe::ArgMaxLayer
    Compute the index of the max values for each datum across all dimensions .
    Definition: common_layers.hpp:30
    -
    caffe::ArgMaxLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: argmax_layer.cpp:12
    -
    caffe::SoftmaxLayer
    Computes the softmax function.
    Definition: common_layers.hpp:597
    -
    caffe::SliceLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    -
    caffe::ReductionLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: common_layers.hpp:534
    -
    caffe::FilterLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    -
    caffe::TileLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    -
    caffe::SilenceLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: common_layers.hpp:574
    -
    caffe::InnerProductLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: inner_product_layer.cpp:81
    -
    caffe::MVNLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    -
    caffe::InnerProductLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    -
    caffe::EmbedLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    -
    caffe::EmbedLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    -
    caffe::SilenceLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: silence_layer.cpp:10
    -
    caffe::ConcatLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    -
    caffe::EltwiseLayer
    Compute elementwise operations, such as product and sum, along multiple input Blobs.
    Definition: common_layers.hpp:230
    -
    caffe::InnerProductLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: common_layers.hpp:424
    -
    caffe::ReshapeLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:497
    -
    caffe::FlattenLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:380
    -
    caffe::ArgMaxLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:53
    -
    caffe::BatchReindexLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    -
    caffe::TileLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:736
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    caffe::SilenceLayer
    Ignores bottom blobs while producing no top blobs. (This is useful to suppress outputs during testing...
    Definition: common_layers.hpp:567
    -
    caffe::ReductionLayer::sum_multiplier_
    Blob< Dtype > sum_multiplier_
    a helper Blob used for summation (op_ == SUM)
    Definition: common_layers.hpp:559
    -
    caffe::SilenceLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:576
    -
    caffe::ConcatLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    -
    caffe::EltwiseLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: eltwise_layer.cpp:32
    -
    caffe::SliceLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: slice_layer.cpp:77
    -
    caffe::TileLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    -
    caffe::MVNLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: common_layers.hpp:458
    -
    caffe::ArgMaxLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Not implemented (non-differentiable function)
    Definition: common_layers.hpp:71
    -
    caffe::ReshapeLayer
    Definition: common_layers.hpp:486
    -
    caffe::EltwiseLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: common_layers.hpp:239
    -
    caffe::SplitLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: common_layers.hpp:670
    -
    caffe::SliceLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: slice_layer.cpp:98
    -
    caffe::InnerProductLayer
    Also known as a "fully-connected" layer, computes an inner product with a set of learned weights...
    Definition: common_layers.hpp:415
    -
    caffe::ReshapeLayer::copy_axes_
    vector< int > copy_axes_
    vector of axes indices whose dimensions we'll copy from the bottom
    Definition: common_layers.hpp:510
    -
    caffe::FlattenLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: flatten_layer.cpp:10
    -
    caffe::FilterLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: common_layers.hpp:314
    -
    caffe::InnerProductLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: inner_product_layer.cpp:96
    -
    caffe::FilterLayer::MinTopBlobs
    virtual int MinTopBlobs() const
    Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
    Definition: common_layers.hpp:316
    -
    caffe::ReductionLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:536
    -
    caffe::SplitLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    -
    caffe::ReductionLayer::op_
    ReductionParameter_ReductionOp op_
    the reduction operation performed by the layer
    Definition: common_layers.hpp:549
    -
    caffe::ReductionLayer
    Compute "reductions" – operations that return a scalar output Blob for an input Blob of arbitrary si...
    Definition: common_layers.hpp:525
    -
    caffe::FilterLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    -
    caffe::BatchReindexLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Definition: batch_reindex_layer.cpp:34
    -
    caffe::TileLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:737
    -
    caffe::SplitLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: split_layer.cpp:35
    -
    caffe::EltwiseLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    -
    caffe::BatchReindexLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: common_layers.hpp:96
    -
    caffe::EmbedLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:279
    -
    caffe::ReductionLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: reduction_layer.cpp:12
    -
    caffe::MVNLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    -
    caffe::MVNLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: mvn_layer.cpp:11
    -
    caffe::BatchReindexLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Computes the error gradient w.r.t. the reordered input.
    Definition: batch_reindex_layer.cpp:53
    -
    caffe::TileLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: tile_layer.cpp:10
    -
    caffe::ArgMaxLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:52
    -
    caffe::EltwiseLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:241
    -
    caffe::EltwiseLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    -
    caffe::MVNLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: mvn_layer.cpp:76
    -
    caffe::ReductionLayer::coeff_
    Dtype coeff_
    a scalar coefficient applied to all outputs
    Definition: common_layers.hpp:551
    -
    caffe::ConcatLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: concat_layer.cpp:18
    -
    caffe::SoftmaxLayer::scale_
    Blob< Dtype > scale_
    scale is an intermediate Blob to hold temporary results.
    Definition: common_layers.hpp:624
    -
    caffe::ConcatLayer::MinBottomBlobs
    virtual int MinBottomBlobs() const
    Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
    Definition: common_layers.hpp:165
    -
    caffe::SoftmaxLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    -
    caffe::SoftmaxLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: softmax_layer.cpp:28
    -
    caffe::SilenceLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: common_layers.hpp:571
    -
    caffe::ConcatLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:166
    -
    caffe::SoftmaxLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    -
    caffe::SplitLayer
    Creates a "split" path in the network by copying the bottom Blob into multiple top Blobs to be used b...
    Definition: common_layers.hpp:663
    -
    caffe::SplitLayer::MinTopBlobs
    virtual int MinTopBlobs() const
    Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
    Definition: common_layers.hpp:672
    -
    caffe::ConcatLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: common_layers.hpp:164
    -
    caffe::BatchReindexLayer
    Index into the input blob along its first axis.
    Definition: common_layers.hpp:89
    -
    caffe::SliceLayer
    Takes a Blob and slices it along either the num or channel dimension, outputting multiple sliced Blob...
    Definition: common_layers.hpp:694
    -
    caffe::TileLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: common_layers.hpp:735
    -
    caffe::SilenceLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    -
    caffe::SplitLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:671
    -
    caffe::ReductionLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    -
    caffe::FlattenLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Definition: flatten_layer.cpp:30
    -
    caffe::EltwiseLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: eltwise_layer.cpp:101
    -
    caffe::ReshapeLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: common_layers.hpp:495
    -
    caffe::SliceLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: slice_layer.cpp:11
    -
    caffe::ArgMaxLayer::ArgMaxLayer
    ArgMaxLayer(const LayerParameter &param)
    Definition: common_layers.hpp:44
    -
    caffe::EltwiseLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: eltwise_layer.cpp:46
    -
    caffe::BatchReindexLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    -
    caffe::MVNLayer::sum_multiplier_
    Blob< Dtype > sum_multiplier_
    sum_multiplier is used to carry out sum using BLAS
    Definition: common_layers.hpp:475
    -
    caffe::SoftmaxLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: softmax_layer.cpp:11
    -
    caffe::MVNLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: mvn_layer.cpp:33
    -
    caffe::ReshapeLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: common_layers.hpp:500
    -
    caffe::SplitLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    -
    caffe::ReshapeLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:496
    -
    caffe::ReductionLayer::num_
    int num_
    the number of reductions performed
    Definition: common_layers.hpp:555
    -
    caffe::FlattenLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Computes the error gradient w.r.t. the concatenate inputs.
    Definition: flatten_layer.cpp:36
    -
    caffe::SplitLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: split_layer.cpp:10
    -
    caffe::SliceLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: common_layers.hpp:703
    -
    caffe::FilterLayer
    Takes two+ Blobs, interprets last Blob as a selector and filter remaining Blobs accordingly with sele...
    Definition: common_layers.hpp:305
    -
    caffe::BatchReindexLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:98
    -
    caffe::InnerProductLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: common_layers.hpp:425
    -
    caffe::ArgMaxLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Definition: argmax_layer.cpp:54
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    caffe::ReductionLayer::axis_
    int axis_
    the index of the first input axis to reduce
    Definition: common_layers.hpp:553
    -
    caffe::FilterLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: filter_layer.cpp:11
    -
    caffe::ConcatLayer
    Takes at least two Blobs and concatenates them along either the num or channel dimension, outputting the result.
    Definition: common_layers.hpp:155
    -
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:25
    -
    - -
    -Generated on Mon Oct 19 2015 15:25:08 for Caffe by   -doxygen - 1.8.9.1 -
    - - diff --git a/doxygen/concat__layer_8hpp_source.html b/doxygen/concat__layer_8hpp_source.html index beefed9e946..e8a38daefaa 100644 --- a/doxygen/concat__layer_8hpp_source.html +++ b/doxygen/concat__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/concat_layer.hpp Source File @@ -29,7 +29,7 @@
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    - + @@ -75,16 +75,16 @@
    caffe::CropLayer
    Takes a Blob and crop it, to the shape specified by the second input Blob, across all dimensions afte...
    Definition: crop_layer.hpp:21
    caffe::CropLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: crop_layer.cpp:36
    caffe::CropLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    -
    caffe::CropLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the CPU device, compute the layer output.
    Definition: crop_layer.cpp:115
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    caffe::CropLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: crop_layer.cpp:124
    +
    caffe::CropLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the CPU device, compute the layer output.
    Definition: crop_layer.cpp:113
    +
    caffe::CropLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: crop_layer.cpp:122
    caffe::CropLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: crop_layer.cpp:16
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24

    -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
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    cudnn.hpp

    -
    1 #ifndef CAFFE_UTIL_CUDNN_H_
    2 #define CAFFE_UTIL_CUDNN_H_
    3 #ifdef USE_CUDNN
    4 
    5 #include <cudnn.h>
    6 
    7 #include "caffe/common.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 #define CUDNN_VERSION_MIN(major, minor, patch) \
    11  (CUDNN_VERSION >= (major * 1000 + minor * 100 + patch))
    12 
    13 #define CUDNN_CHECK(condition) \
    14  do { \
    15  cudnnStatus_t status = condition; \
    16  CHECK_EQ(status, CUDNN_STATUS_SUCCESS) << " "\
    17  << cudnnGetErrorString(status); \
    18  } while (0)
    19 
    20 inline const char* cudnnGetErrorString(cudnnStatus_t status) {
    21  switch (status) {
    22  case CUDNN_STATUS_SUCCESS:
    23  return "CUDNN_STATUS_SUCCESS";
    24  case CUDNN_STATUS_NOT_INITIALIZED:
    25  return "CUDNN_STATUS_NOT_INITIALIZED";
    26  case CUDNN_STATUS_ALLOC_FAILED:
    27  return "CUDNN_STATUS_ALLOC_FAILED";
    28  case CUDNN_STATUS_BAD_PARAM:
    29  return "CUDNN_STATUS_BAD_PARAM";
    30  case CUDNN_STATUS_INTERNAL_ERROR:
    31  return "CUDNN_STATUS_INTERNAL_ERROR";
    32  case CUDNN_STATUS_INVALID_VALUE:
    33  return "CUDNN_STATUS_INVALID_VALUE";
    34  case CUDNN_STATUS_ARCH_MISMATCH:
    35  return "CUDNN_STATUS_ARCH_MISMATCH";
    36  case CUDNN_STATUS_MAPPING_ERROR:
    37  return "CUDNN_STATUS_MAPPING_ERROR";
    38  case CUDNN_STATUS_EXECUTION_FAILED:
    39  return "CUDNN_STATUS_EXECUTION_FAILED";
    40  case CUDNN_STATUS_NOT_SUPPORTED:
    41  return "CUDNN_STATUS_NOT_SUPPORTED";
    42  case CUDNN_STATUS_LICENSE_ERROR:
    43  return "CUDNN_STATUS_LICENSE_ERROR";
    44  }
    45  return "Unknown cudnn status";
    46 }
    47 
    48 namespace caffe {
    49 
    50 namespace cudnn {
    51 
    52 template <typename Dtype> class dataType;
    53 template<> class dataType<float> {
    54  public:
    55  static const cudnnDataType_t type = CUDNN_DATA_FLOAT;
    56  static float oneval, zeroval;
    57  static const void *one, *zero;
    58 };
    59 template<> class dataType<double> {
    60  public:
    61  static const cudnnDataType_t type = CUDNN_DATA_DOUBLE;
    62  static double oneval, zeroval;
    63  static const void *one, *zero;
    64 };
    65 
    66 template <typename Dtype>
    67 inline void createTensor4dDesc(cudnnTensorDescriptor_t* desc) {
    68  CUDNN_CHECK(cudnnCreateTensorDescriptor(desc));
    69 }
    70 
    71 template <typename Dtype>
    72 inline void setTensor4dDesc(cudnnTensorDescriptor_t* desc,
    73  int n, int c, int h, int w,
    74  int stride_n, int stride_c, int stride_h, int stride_w) {
    75  CUDNN_CHECK(cudnnSetTensor4dDescriptorEx(*desc, dataType<Dtype>::type,
    76  n, c, h, w, stride_n, stride_c, stride_h, stride_w));
    77 }
    78 
    79 template <typename Dtype>
    80 inline void setTensor4dDesc(cudnnTensorDescriptor_t* desc,
    81  int n, int c, int h, int w) {
    82  const int stride_w = 1;
    83  const int stride_h = w * stride_w;
    84  const int stride_c = h * stride_h;
    85  const int stride_n = c * stride_c;
    86  setTensor4dDesc<Dtype>(desc, n, c, h, w,
    87  stride_n, stride_c, stride_h, stride_w);
    88 }
    89 
    90 template <typename Dtype>
    91 inline void createFilterDesc(cudnnFilterDescriptor_t* desc,
    92  int n, int c, int h, int w) {
    93  CUDNN_CHECK(cudnnCreateFilterDescriptor(desc));
    94 #if CUDNN_VERSION_MIN(5, 0, 0)
    95  CUDNN_CHECK(cudnnSetFilter4dDescriptor(*desc, dataType<Dtype>::type,
    96  CUDNN_TENSOR_NCHW, n, c, h, w));
    97 #else
    98  CUDNN_CHECK(cudnnSetFilter4dDescriptor_v4(*desc, dataType<Dtype>::type,
    99  CUDNN_TENSOR_NCHW, n, c, h, w));
    100 #endif
    101 }
    102 
    103 template <typename Dtype>
    104 inline void createConvolutionDesc(cudnnConvolutionDescriptor_t* conv) {
    105  CUDNN_CHECK(cudnnCreateConvolutionDescriptor(conv));
    106 }
    107 
    108 template <typename Dtype>
    109 inline void setConvolutionDesc(cudnnConvolutionDescriptor_t* conv,
    110  cudnnTensorDescriptor_t bottom, cudnnFilterDescriptor_t filter,
    111  int pad_h, int pad_w, int stride_h, int stride_w) {
    112  CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv,
    113  pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION));
    114 }
    115 
    116 template <typename Dtype>
    117 inline void createPoolingDesc(cudnnPoolingDescriptor_t* pool_desc,
    118  PoolingParameter_PoolMethod poolmethod, cudnnPoolingMode_t* mode,
    119  int h, int w, int pad_h, int pad_w, int stride_h, int stride_w) {
    120  switch (poolmethod) {
    121  case PoolingParameter_PoolMethod_MAX:
    122  *mode = CUDNN_POOLING_MAX;
    123  break;
    124  case PoolingParameter_PoolMethod_AVE:
    125  *mode = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
    126  break;
    127  default:
    128  LOG(FATAL) << "Unknown pooling method.";
    129  }
    130  CUDNN_CHECK(cudnnCreatePoolingDescriptor(pool_desc));
    131 #if CUDNN_VERSION_MIN(5, 0, 0)
    132  CUDNN_CHECK(cudnnSetPooling2dDescriptor(*pool_desc, *mode,
    133  CUDNN_PROPAGATE_NAN, h, w, pad_h, pad_w, stride_h, stride_w));
    134 #else
    135  CUDNN_CHECK(cudnnSetPooling2dDescriptor_v4(*pool_desc, *mode,
    136  CUDNN_PROPAGATE_NAN, h, w, pad_h, pad_w, stride_h, stride_w));
    137 #endif
    138 }
    139 
    140 template <typename Dtype>
    141 inline void createActivationDescriptor(cudnnActivationDescriptor_t* activ_desc,
    142  cudnnActivationMode_t mode) {
    143  CUDNN_CHECK(cudnnCreateActivationDescriptor(activ_desc));
    144  CUDNN_CHECK(cudnnSetActivationDescriptor(*activ_desc, mode,
    145  CUDNN_PROPAGATE_NAN, Dtype(0)));
    146 }
    147 
    148 } // namespace cudnn
    149 
    150 } // namespace caffe
    151 
    152 #endif // USE_CUDNN
    153 #endif // CAFFE_UTIL_CUDNN_H_
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    1 #ifndef CAFFE_UTIL_CUDNN_H_
    2 #define CAFFE_UTIL_CUDNN_H_
    3 #ifdef USE_CUDNN
    4 
    5 #include <cudnn.h>
    6 
    7 #include "caffe/common.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 #define CUDNN_VERSION_MIN(major, minor, patch) \
    11  (CUDNN_VERSION >= (major * 1000 + minor * 100 + patch))
    12 
    13 #define CUDNN_CHECK(condition) \
    14  do { \
    15  cudnnStatus_t status = condition; \
    16  CHECK_EQ(status, CUDNN_STATUS_SUCCESS) << " "\
    17  << cudnnGetErrorString(status); \
    18  } while (0)
    19 
    20 inline const char* cudnnGetErrorString(cudnnStatus_t status) {
    21  switch (status) {
    22  case CUDNN_STATUS_SUCCESS:
    23  return "CUDNN_STATUS_SUCCESS";
    24  case CUDNN_STATUS_NOT_INITIALIZED:
    25  return "CUDNN_STATUS_NOT_INITIALIZED";
    26  case CUDNN_STATUS_ALLOC_FAILED:
    27  return "CUDNN_STATUS_ALLOC_FAILED";
    28  case CUDNN_STATUS_BAD_PARAM:
    29  return "CUDNN_STATUS_BAD_PARAM";
    30  case CUDNN_STATUS_INTERNAL_ERROR:
    31  return "CUDNN_STATUS_INTERNAL_ERROR";
    32  case CUDNN_STATUS_INVALID_VALUE:
    33  return "CUDNN_STATUS_INVALID_VALUE";
    34  case CUDNN_STATUS_ARCH_MISMATCH:
    35  return "CUDNN_STATUS_ARCH_MISMATCH";
    36  case CUDNN_STATUS_MAPPING_ERROR:
    37  return "CUDNN_STATUS_MAPPING_ERROR";
    38  case CUDNN_STATUS_EXECUTION_FAILED:
    39  return "CUDNN_STATUS_EXECUTION_FAILED";
    40  case CUDNN_STATUS_NOT_SUPPORTED:
    41  return "CUDNN_STATUS_NOT_SUPPORTED";
    42  case CUDNN_STATUS_LICENSE_ERROR:
    43  return "CUDNN_STATUS_LICENSE_ERROR";
    44 #if CUDNN_VERSION_MIN(6, 0, 0)
    45  case CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING:
    46  return "CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING";
    47 #endif
    48  }
    49  return "Unknown cudnn status";
    50 }
    51 
    52 namespace caffe {
    53 
    54 namespace cudnn {
    55 
    56 template <typename Dtype> class dataType;
    57 template<> class dataType<float> {
    58  public:
    59  static const cudnnDataType_t type = CUDNN_DATA_FLOAT;
    60  static float oneval, zeroval;
    61  static const void *one, *zero;
    62 };
    63 template<> class dataType<double> {
    64  public:
    65  static const cudnnDataType_t type = CUDNN_DATA_DOUBLE;
    66  static double oneval, zeroval;
    67  static const void *one, *zero;
    68 };
    69 
    70 template <typename Dtype>
    71 inline void createTensor4dDesc(cudnnTensorDescriptor_t* desc) {
    72  CUDNN_CHECK(cudnnCreateTensorDescriptor(desc));
    73 }
    74 
    75 template <typename Dtype>
    76 inline void setTensor4dDesc(cudnnTensorDescriptor_t* desc,
    77  int n, int c, int h, int w,
    78  int stride_n, int stride_c, int stride_h, int stride_w) {
    79  CUDNN_CHECK(cudnnSetTensor4dDescriptorEx(*desc, dataType<Dtype>::type,
    80  n, c, h, w, stride_n, stride_c, stride_h, stride_w));
    81 }
    82 
    83 template <typename Dtype>
    84 inline void setTensor4dDesc(cudnnTensorDescriptor_t* desc,
    85  int n, int c, int h, int w) {
    86  const int stride_w = 1;
    87  const int stride_h = w * stride_w;
    88  const int stride_c = h * stride_h;
    89  const int stride_n = c * stride_c;
    90  setTensor4dDesc<Dtype>(desc, n, c, h, w,
    91  stride_n, stride_c, stride_h, stride_w);
    92 }
    93 
    94 template <typename Dtype>
    95 inline void createFilterDesc(cudnnFilterDescriptor_t* desc,
    96  int n, int c, int h, int w) {
    97  CUDNN_CHECK(cudnnCreateFilterDescriptor(desc));
    98 #if CUDNN_VERSION_MIN(5, 0, 0)
    99  CUDNN_CHECK(cudnnSetFilter4dDescriptor(*desc, dataType<Dtype>::type,
    100  CUDNN_TENSOR_NCHW, n, c, h, w));
    101 #else
    102  CUDNN_CHECK(cudnnSetFilter4dDescriptor_v4(*desc, dataType<Dtype>::type,
    103  CUDNN_TENSOR_NCHW, n, c, h, w));
    104 #endif
    105 }
    106 
    107 template <typename Dtype>
    108 inline void createConvolutionDesc(cudnnConvolutionDescriptor_t* conv) {
    109  CUDNN_CHECK(cudnnCreateConvolutionDescriptor(conv));
    110 }
    111 
    112 template <typename Dtype>
    113 inline void setConvolutionDesc(cudnnConvolutionDescriptor_t* conv,
    114  cudnnTensorDescriptor_t bottom, cudnnFilterDescriptor_t filter,
    115  int pad_h, int pad_w, int stride_h, int stride_w) {
    116 #if CUDNN_VERSION_MIN(6, 0, 0)
    117  CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv,
    118  pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION,
    119  dataType<Dtype>::type));
    120 #else
    121  CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv,
    122  pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION));
    123 #endif
    124 }
    125 
    126 template <typename Dtype>
    127 inline void createPoolingDesc(cudnnPoolingDescriptor_t* pool_desc,
    128  PoolingParameter_PoolMethod poolmethod, cudnnPoolingMode_t* mode,
    129  int h, int w, int pad_h, int pad_w, int stride_h, int stride_w) {
    130  switch (poolmethod) {
    131  case PoolingParameter_PoolMethod_MAX:
    132  *mode = CUDNN_POOLING_MAX;
    133  break;
    134  case PoolingParameter_PoolMethod_AVE:
    135  *mode = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
    136  break;
    137  default:
    138  LOG(FATAL) << "Unknown pooling method.";
    139  }
    140  CUDNN_CHECK(cudnnCreatePoolingDescriptor(pool_desc));
    141 #if CUDNN_VERSION_MIN(5, 0, 0)
    142  CUDNN_CHECK(cudnnSetPooling2dDescriptor(*pool_desc, *mode,
    143  CUDNN_PROPAGATE_NAN, h, w, pad_h, pad_w, stride_h, stride_w));
    144 #else
    145  CUDNN_CHECK(cudnnSetPooling2dDescriptor_v4(*pool_desc, *mode,
    146  CUDNN_PROPAGATE_NAN, h, w, pad_h, pad_w, stride_h, stride_w));
    147 #endif
    148 }
    149 
    150 template <typename Dtype>
    151 inline void createActivationDescriptor(cudnnActivationDescriptor_t* activ_desc,
    152  cudnnActivationMode_t mode) {
    153  CUDNN_CHECK(cudnnCreateActivationDescriptor(activ_desc));
    154  CUDNN_CHECK(cudnnSetActivationDescriptor(*activ_desc, mode,
    155  CUDNN_PROPAGATE_NAN, Dtype(0)));
    156 }
    157 
    158 } // namespace cudnn
    159 
    160 } // namespace caffe
    161 
    162 #endif // USE_CUDNN
    163 #endif // CAFFE_UTIL_CUDNN_H_
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/cudnn__conv__layer_8hpp_source.html b/doxygen/cudnn__conv__layer_8hpp_source.html index f5d9c823791..a5e845a3386 100644 --- a/doxygen/cudnn__conv__layer_8hpp_source.html +++ b/doxygen/cudnn__conv__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/cudnn_conv_layer.hpp Source File @@ -29,7 +29,7 @@
    - + @@ -70,9 +70,9 @@

    -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
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    - + @@ -70,9 +70,9 @@

    -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
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    - + @@ -70,9 +70,9 @@

    -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
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    - + @@ -70,9 +70,9 @@
    -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/cudnn__relu__layer_8hpp_source.html b/doxygen/cudnn__relu__layer_8hpp_source.html index e1cd2f1a2aa..f5a16862af1 100644 --- a/doxygen/cudnn__relu__layer_8hpp_source.html +++ b/doxygen/cudnn__relu__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/cudnn_relu_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -70,9 +70,9 @@
    -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
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    -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
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    -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/data__layer_8hpp_source.html b/doxygen/data__layer_8hpp_source.html index 604f904e8a5..747c187a7ff 100644 --- a/doxygen/data__layer_8hpp_source.html +++ b/doxygen/data__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/data_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -66,21 +66,21 @@
    data_layer.hpp
    -
    1 #ifndef CAFFE_DATA_LAYER_HPP_
    2 #define CAFFE_DATA_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/data_transformer.hpp"
    8 #include "caffe/internal_thread.hpp"
    9 #include "caffe/layer.hpp"
    10 #include "caffe/layers/base_data_layer.hpp"
    11 #include "caffe/proto/caffe.pb.h"
    12 #include "caffe/util/db.hpp"
    13 
    14 namespace caffe {
    15 
    16 template <typename Dtype>
    17 class DataLayer : public BasePrefetchingDataLayer<Dtype> {
    18  public:
    19  explicit DataLayer(const LayerParameter& param);
    20  virtual ~DataLayer();
    21  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
    22  const vector<Blob<Dtype>*>& top);
    23  // DataLayer uses DataReader instead for sharing for parallelism
    24  virtual inline bool ShareInParallel() const { return false; }
    25  virtual inline const char* type() const { return "Data"; }
    26  virtual inline int ExactNumBottomBlobs() const { return 0; }
    27  virtual inline int MinTopBlobs() const { return 1; }
    28  virtual inline int MaxTopBlobs() const { return 2; }
    29 
    30  protected:
    31  void Next();
    32  bool Skip();
    33  virtual void load_batch(Batch<Dtype>* batch);
    34 
    35  shared_ptr<db::DB> db_;
    36  shared_ptr<db::Cursor> cursor_;
    37  uint64_t offset_;
    38 };
    39 
    40 } // namespace caffe
    41 
    42 #endif // CAFFE_DATA_LAYER_HPP_
    caffe::Batch
    Definition: base_data_layer.hpp:49
    -
    caffe::BasePrefetchingDataLayer
    Definition: base_data_layer.hpp:55
    +
    1 #ifndef CAFFE_DATA_LAYER_HPP_
    2 #define CAFFE_DATA_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/data_transformer.hpp"
    8 #include "caffe/internal_thread.hpp"
    9 #include "caffe/layer.hpp"
    10 #include "caffe/layers/base_data_layer.hpp"
    11 #include "caffe/proto/caffe.pb.h"
    12 #include "caffe/util/db.hpp"
    13 
    14 namespace caffe {
    15 
    16 template <typename Dtype>
    17 class DataLayer : public BasePrefetchingDataLayer<Dtype> {
    18  public:
    19  explicit DataLayer(const LayerParameter& param);
    20  virtual ~DataLayer();
    21  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
    22  const vector<Blob<Dtype>*>& top);
    23  virtual inline const char* type() const { return "Data"; }
    24  virtual inline int ExactNumBottomBlobs() const { return 0; }
    25  virtual inline int MinTopBlobs() const { return 1; }
    26  virtual inline int MaxTopBlobs() const { return 2; }
    27 
    28  protected:
    29  void Next();
    30  bool Skip();
    31  virtual void load_batch(Batch<Dtype>* batch);
    32 
    33  shared_ptr<db::DB> db_;
    34  shared_ptr<db::Cursor> cursor_;
    35  uint64_t offset_;
    36 };
    37 
    38 } // namespace caffe
    39 
    40 #endif // CAFFE_DATA_LAYER_HPP_
    caffe::Batch
    Definition: base_data_layer.hpp:47
    +
    caffe::BasePrefetchingDataLayer
    Definition: base_data_layer.hpp:53
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    caffe::DataLayer
    Definition: data_layer.hpp:17
    -
    caffe::DataLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: data_layer.hpp:26
    -
    caffe::DataLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: data_layer.hpp:25
    -
    caffe::DataLayer::MaxTopBlobs
    virtual int MaxTopBlobs() const
    Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required...
    Definition: data_layer.hpp:28
    -
    caffe::DataLayer::MinTopBlobs
    virtual int MinTopBlobs() const
    Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
    Definition: data_layer.hpp:27
    +
    caffe::DataLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: data_layer.hpp:24
    +
    caffe::DataLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: data_layer.hpp:23
    +
    caffe::DataLayer::MaxTopBlobs
    virtual int MaxTopBlobs() const
    Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required...
    Definition: data_layer.hpp:26
    +
    caffe::DataLayer::MinTopBlobs
    virtual int MinTopBlobs() const
    Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
    Definition: data_layer.hpp:25
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/data__layers_8hpp_source.html b/doxygen/data__layers_8hpp_source.html deleted file mode 100644 index bda072cae87..00000000000 --- a/doxygen/data__layers_8hpp_source.html +++ /dev/null @@ -1,475 +0,0 @@ - - - - - - -Caffe: include/caffe/data_layers.hpp Source File - - - - - - - - - - -
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    Caffe -
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    data_layers.hpp
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    1 #ifndef CAFFE_DATA_LAYERS_HPP_
    -
    2 #define CAFFE_DATA_LAYERS_HPP_
    -
    3 
    -
    4 #include <string>
    -
    5 #include <utility>
    -
    6 #include <vector>
    -
    7 #include "hdf5.h"
    -
    8 
    -
    9 #include "caffe/blob.hpp"
    -
    10 #include "caffe/common.hpp"
    -
    11 #include "caffe/data_reader.hpp"
    -
    12 #include "caffe/data_transformer.hpp"
    -
    13 #include "caffe/filler.hpp"
    -
    14 #include "caffe/internal_thread.hpp"
    -
    15 #include "caffe/layer.hpp"
    -
    16 #include "caffe/proto/caffe.pb.h"
    -
    17 #include "caffe/util/blocking_queue.hpp"
    -
    18 #include "caffe/util/db.hpp"
    -
    19 
    -
    20 namespace caffe {
    -
    21 
    -
    27 template <typename Dtype>
    -
    28 class BaseDataLayer : public Layer<Dtype> {
    -
    29  public:
    -
    30  explicit BaseDataLayer(const LayerParameter& param);
    -
    31  // LayerSetUp: implements common data layer setup functionality, and calls
    -
    32  // DataLayerSetUp to do special data layer setup for individual layer types.
    -
    33  // This method may not be overridden except by the BasePrefetchingDataLayer.
    -
    34  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    35  const vector<Blob<Dtype>*>& top);
    -
    36  // Data layers should be shared by multiple solvers in parallel
    -
    37  virtual inline bool ShareInParallel() const { return true; }
    -
    38  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    39  const vector<Blob<Dtype>*>& top) {}
    -
    40  // Data layers have no bottoms, so reshaping is trivial.
    -
    41  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    42  const vector<Blob<Dtype>*>& top) {}
    -
    43 
    -
    44  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    45  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
    -
    46  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    -
    47  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
    -
    48 
    -
    49  protected:
    -
    50  TransformationParameter transform_param_;
    -
    51  shared_ptr<DataTransformer<Dtype> > data_transformer_;
    -
    52  bool output_labels_;
    -
    53 };
    -
    54 
    -
    55 template <typename Dtype>
    -
    56 class Batch {
    -
    57  public:
    -
    58  Blob<Dtype> data_, label_;
    -
    59 };
    -
    60 
    -
    61 template <typename Dtype>
    -
    62 class BasePrefetchingDataLayer :
    -
    63  public BaseDataLayer<Dtype>, public InternalThread {
    -
    64  public:
    -
    65  explicit BasePrefetchingDataLayer(const LayerParameter& param);
    -
    66  // LayerSetUp: implements common data layer setup functionality, and calls
    -
    67  // DataLayerSetUp to do special data layer setup for individual layer types.
    -
    68  // This method may not be overridden.
    -
    69  void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    70  const vector<Blob<Dtype>*>& top);
    -
    71 
    -
    72  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    73  const vector<Blob<Dtype>*>& top);
    -
    74  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    -
    75  const vector<Blob<Dtype>*>& top);
    -
    76 
    -
    77  // Prefetches batches (asynchronously if to GPU memory)
    -
    78  static const int PREFETCH_COUNT = 3;
    -
    79 
    -
    80  protected:
    -
    81  virtual void InternalThreadEntry();
    -
    82  virtual void load_batch(Batch<Dtype>* batch) = 0;
    -
    83 
    -
    84  Batch<Dtype> prefetch_[PREFETCH_COUNT];
    -
    85  BlockingQueue<Batch<Dtype>*> prefetch_free_;
    -
    86  BlockingQueue<Batch<Dtype>*> prefetch_full_;
    -
    87 
    -
    88  Blob<Dtype> transformed_data_;
    -
    89 };
    -
    90 
    -
    91 template <typename Dtype>
    -
    92 class DataLayer : public BasePrefetchingDataLayer<Dtype> {
    -
    93  public:
    -
    94  explicit DataLayer(const LayerParameter& param);
    -
    95  virtual ~DataLayer();
    -
    96  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    97  const vector<Blob<Dtype>*>& top);
    -
    98  // DataLayer uses DataReader instead for sharing for parallelism
    -
    99  virtual inline bool ShareInParallel() const { return false; }
    -
    100  virtual inline const char* type() const { return "Data"; }
    -
    101  virtual inline int ExactNumBottomBlobs() const { return 0; }
    -
    102  virtual inline int MinTopBlobs() const { return 1; }
    -
    103  virtual inline int MaxTopBlobs() const { return 2; }
    -
    104 
    -
    105  protected:
    -
    106  virtual void load_batch(Batch<Dtype>* batch);
    -
    107 
    -
    108  DataReader reader_;
    -
    109 };
    -
    110 
    -
    116 template <typename Dtype>
    -
    117 class DummyDataLayer : public Layer<Dtype> {
    -
    118  public:
    -
    119  explicit DummyDataLayer(const LayerParameter& param)
    -
    120  : Layer<Dtype>(param) {}
    -
    121  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    122  const vector<Blob<Dtype>*>& top);
    -
    123  // Data layers should be shared by multiple solvers in parallel
    -
    124  virtual inline bool ShareInParallel() const { return true; }
    -
    125  // Data layers have no bottoms, so reshaping is trivial.
    -
    126  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    127  const vector<Blob<Dtype>*>& top) {}
    -
    128 
    -
    129  virtual inline const char* type() const { return "DummyData"; }
    -
    130  virtual inline int ExactNumBottomBlobs() const { return 0; }
    -
    131  virtual inline int MinTopBlobs() const { return 1; }
    -
    132 
    -
    133  protected:
    -
    134  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    135  const vector<Blob<Dtype>*>& top);
    -
    136  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    137  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
    -
    138  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    -
    139  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
    -
    140 
    -
    141  vector<shared_ptr<Filler<Dtype> > > fillers_;
    -
    142  vector<bool> refill_;
    -
    143 };
    -
    144 
    -
    150 template <typename Dtype>
    -
    151 class HDF5DataLayer : public Layer<Dtype> {
    -
    152  public:
    -
    153  explicit HDF5DataLayer(const LayerParameter& param)
    -
    154  : Layer<Dtype>(param) {}
    -
    155  virtual ~HDF5DataLayer();
    -
    156  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    157  const vector<Blob<Dtype>*>& top);
    -
    158  // Data layers should be shared by multiple solvers in parallel
    -
    159  virtual inline bool ShareInParallel() const { return true; }
    -
    160  // Data layers have no bottoms, so reshaping is trivial.
    -
    161  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    162  const vector<Blob<Dtype>*>& top) {}
    -
    163 
    -
    164  virtual inline const char* type() const { return "HDF5Data"; }
    -
    165  virtual inline int ExactNumBottomBlobs() const { return 0; }
    -
    166  virtual inline int MinTopBlobs() const { return 1; }
    -
    167 
    -
    168  protected:
    -
    169  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    170  const vector<Blob<Dtype>*>& top);
    -
    171  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    -
    172  const vector<Blob<Dtype>*>& top);
    -
    173  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    174  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
    -
    175  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    -
    176  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
    -
    177  virtual void LoadHDF5FileData(const char* filename);
    -
    178 
    -
    179  std::vector<std::string> hdf_filenames_;
    -
    180  unsigned int num_files_;
    -
    181  unsigned int current_file_;
    -
    182  hsize_t current_row_;
    -
    183  std::vector<shared_ptr<Blob<Dtype> > > hdf_blobs_;
    -
    184  std::vector<unsigned int> data_permutation_;
    -
    185  std::vector<unsigned int> file_permutation_;
    -
    186 };
    -
    187 
    -
    193 template <typename Dtype>
    -
    194 class HDF5OutputLayer : public Layer<Dtype> {
    -
    195  public:
    -
    196  explicit HDF5OutputLayer(const LayerParameter& param)
    -
    197  : Layer<Dtype>(param), file_opened_(false) {}
    -
    198  virtual ~HDF5OutputLayer();
    -
    199  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    200  const vector<Blob<Dtype>*>& top);
    -
    201  // Data layers should be shared by multiple solvers in parallel
    -
    202  virtual inline bool ShareInParallel() const { return true; }
    -
    203  // Data layers have no bottoms, so reshaping is trivial.
    -
    204  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    -
    205  const vector<Blob<Dtype>*>& top) {}
    -
    206 
    -
    207  virtual inline const char* type() const { return "HDF5Output"; }
    -
    208  // TODO: no limit on the number of blobs
    -
    209  virtual inline int ExactNumBottomBlobs() const { return 2; }
    -
    210  virtual inline int ExactNumTopBlobs() const { return 0; }
    -
    211 
    -
    212  inline std::string file_name() const { return file_name_; }
    -
    213 
    -
    214  protected:
    -
    215  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    216  const vector<Blob<Dtype>*>& top);
    -
    217  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    -
    218  const vector<Blob<Dtype>*>& top);
    -
    219  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    -
    220  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    221  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    -
    222  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    -
    223  virtual void SaveBlobs();
    -
    224 
    -
    225  bool file_opened_;
    -
    226  std::string file_name_;
    -
    227  hid_t file_id_;
    -
    228  Blob<Dtype> data_blob_;
    -
    229  Blob<Dtype> label_blob_;
    -
    230 };
    -
    231 
    -
    237 template <typename Dtype>
    -
    238 class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
    -
    239  public:
    -
    240  explicit ImageDataLayer(const LayerParameter& param)
    -
    241  : BasePrefetchingDataLayer<Dtype>(param) {}
    -
    242  virtual ~ImageDataLayer();
    -
    243  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    244  const vector<Blob<Dtype>*>& top);
    -
    245 
    -
    246  virtual inline const char* type() const { return "ImageData"; }
    -
    247  virtual inline int ExactNumBottomBlobs() const { return 0; }
    -
    248  virtual inline int ExactNumTopBlobs() const { return 2; }
    -
    249 
    -
    250  protected:
    -
    251  shared_ptr<Caffe::RNG> prefetch_rng_;
    -
    252  virtual void ShuffleImages();
    -
    253  virtual void load_batch(Batch<Dtype>* batch);
    -
    254 
    -
    255  vector<std::pair<std::string, int> > lines_;
    -
    256  int lines_id_;
    -
    257 };
    -
    258 
    -
    264 template <typename Dtype>
    -
    265 class MemoryDataLayer : public BaseDataLayer<Dtype> {
    -
    266  public:
    -
    267  explicit MemoryDataLayer(const LayerParameter& param)
    -
    268  : BaseDataLayer<Dtype>(param), has_new_data_(false) {}
    -
    269  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    270  const vector<Blob<Dtype>*>& top);
    -
    271 
    -
    272  virtual inline const char* type() const { return "MemoryData"; }
    -
    273  virtual inline int ExactNumBottomBlobs() const { return 0; }
    -
    274  virtual inline int ExactNumTopBlobs() const { return 2; }
    -
    275 
    -
    276  virtual void AddDatumVector(const vector<Datum>& datum_vector);
    -
    277 #ifdef USE_OPENCV
    -
    278  virtual void AddMatVector(const vector<cv::Mat>& mat_vector,
    -
    279  const vector<int>& labels);
    -
    280 #endif // USE_OPENCV
    -
    281 
    -
    282  // Reset should accept const pointers, but can't, because the memory
    -
    283  // will be given to Blob, which is mutable
    -
    284  void Reset(Dtype* data, Dtype* label, int n);
    -
    285  void set_batch_size(int new_size);
    -
    286 
    -
    287  int batch_size() { return batch_size_; }
    -
    288  int channels() { return channels_; }
    -
    289  int height() { return height_; }
    -
    290  int width() { return width_; }
    -
    291 
    -
    292  protected:
    -
    293  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    -
    294  const vector<Blob<Dtype>*>& top);
    -
    295 
    -
    296  int batch_size_, channels_, height_, width_, size_;
    -
    297  Dtype* data_;
    -
    298  Dtype* labels_;
    -
    299  int n_;
    -
    300  size_t pos_;
    -
    301  Blob<Dtype> added_data_;
    -
    302  Blob<Dtype> added_label_;
    -
    303  bool has_new_data_;
    -
    304 };
    -
    305 
    -
    312 template <typename Dtype>
    -
    313 class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
    -
    314  public:
    -
    315  explicit WindowDataLayer(const LayerParameter& param)
    -
    316  : BasePrefetchingDataLayer<Dtype>(param) {}
    -
    317  virtual ~WindowDataLayer();
    -
    318  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
    -
    319  const vector<Blob<Dtype>*>& top);
    -
    320 
    -
    321  virtual inline const char* type() const { return "WindowData"; }
    -
    322  virtual inline int ExactNumBottomBlobs() const { return 0; }
    -
    323  virtual inline int ExactNumTopBlobs() const { return 2; }
    -
    324 
    -
    325  protected:
    -
    326  virtual unsigned int PrefetchRand();
    -
    327  virtual void load_batch(Batch<Dtype>* batch);
    -
    328 
    -
    329  shared_ptr<Caffe::RNG> prefetch_rng_;
    -
    330  vector<std::pair<std::string, vector<int> > > image_database_;
    -
    331  enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM };
    -
    332  vector<vector<float> > fg_windows_;
    -
    333  vector<vector<float> > bg_windows_;
    -
    334  Blob<Dtype> data_mean_;
    -
    335  vector<Dtype> mean_values_;
    -
    336  bool has_mean_file_;
    -
    337  bool has_mean_values_;
    -
    338  bool cache_images_;
    -
    339  vector<std::pair<std::string, Datum > > image_database_cache_;
    -
    340 };
    -
    341 
    -
    342 } // namespace caffe
    -
    343 
    -
    344 #endif // CAFFE_DATA_LAYERS_HPP_
    -
    caffe::DataLayer::ShareInParallel
    virtual bool ShareInParallel() const
    Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
    Definition: data_layers.hpp:99
    -
    caffe::Batch
    Definition: data_layers.hpp:56
    -
    caffe::BasePrefetchingDataLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    -
    caffe::HDF5DataLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: data_layers.hpp:175
    -
    caffe::BasePrefetchingDataLayer
    Definition: data_layers.hpp:62
    -
    caffe::HDF5DataLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: data_layers.hpp:165
    -
    caffe::HDF5DataLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: hdf5_data_layer.cpp:73
    -
    caffe::HDF5OutputLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    -
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    -
    caffe::DummyDataLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: data_layers.hpp:136
    -
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers could be called...
    Definition: blob.hpp:15
    -
    caffe::WindowDataLayer
    Provides data to the Net from windows of images files, specified by a window data file...
    Definition: data_layers.hpp:313
    -
    caffe::HDF5OutputLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: data_layers.hpp:210
    -
    caffe::HDF5OutputLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: hdf5_output_layer.cpp:15
    -
    caffe::DummyDataLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: data_layers.hpp:126
    -
    caffe::HDF5OutputLayer
    Write blobs to disk as HDF5 files.
    Definition: data_layers.hpp:194
    -
    caffe::HDF5DataLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: hdf5_data_layer.cpp:129
    -
    caffe::BaseDataLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: base_data_layer.cpp:18
    -
    caffe::DummyDataLayer
    Provides data to the Net generated by a Filler.
    Definition: data_layers.hpp:117
    -
    caffe::DataLayer
    Definition: data_layers.hpp:92
    -
    caffe::DataLayer::MinTopBlobs
    virtual int MinTopBlobs() const
    Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
    Definition: data_layers.hpp:102
    -
    caffe::HDF5OutputLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: data_layers.hpp:207
    -
    caffe::MemoryDataLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: memory_data_layer.cpp:110
    -
    caffe::HDF5OutputLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    -
    caffe::DataLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: data_layers.hpp:101
    -
    caffe::BaseDataLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: data_layers.hpp:44
    -
    caffe::MemoryDataLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: data_layers.hpp:272
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    caffe::BaseDataLayer
    Provides base for data layers that feed blobs to the Net.
    Definition: data_layers.hpp:28
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    caffe::DummyDataLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: data_layers.hpp:130
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    caffe::HDF5OutputLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: hdf5_output_layer.cpp:44
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    caffe::BasePrefetchingDataLayer::LayerSetUp
    void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: base_data_layer.cpp:43
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    caffe::BaseDataLayer::ShareInParallel
    virtual bool ShareInParallel() const
    Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
    Definition: data_layers.hpp:37
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    caffe::ImageDataLayer
    Provides data to the Net from image files.
    Definition: data_layers.hpp:238
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    caffe::WindowDataLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: data_layers.hpp:322
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    caffe::ImageDataLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: data_layers.hpp:246
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    caffe::HDF5OutputLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: data_layers.hpp:209
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    caffe::WindowDataLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: data_layers.hpp:323
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    caffe::MemoryDataLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: data_layers.hpp:273
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    caffe::DummyDataLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: data_layers.hpp:129
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    caffe::HDF5DataLayer
    Provides data to the Net from HDF5 files.
    Definition: data_layers.hpp:151
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    caffe::DummyDataLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: data_layers.hpp:138
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    caffe::HDF5DataLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: data_layers.hpp:164
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    caffe::HDF5DataLayer::MinTopBlobs
    virtual int MinTopBlobs() const
    Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
    Definition: data_layers.hpp:166
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    caffe::HDF5DataLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    -
    caffe::DummyDataLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: dummy_data_layer.cpp:10
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    caffe::DataLayer::MaxTopBlobs
    virtual int MaxTopBlobs() const
    Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required...
    Definition: data_layers.hpp:103
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    caffe::HDF5OutputLayer::ShareInParallel
    virtual bool ShareInParallel() const
    Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
    Definition: data_layers.hpp:202
    -
    caffe::MemoryDataLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: data_layers.hpp:274
    -
    caffe::DummyDataLayer::MinTopBlobs
    virtual int MinTopBlobs() const
    Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
    Definition: data_layers.hpp:131
    -
    caffe::DataLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: data_layers.hpp:100
    -
    caffe::InternalThread
    Definition: internal_thread.hpp:19
    -
    caffe::HDF5DataLayer::ShareInParallel
    virtual bool ShareInParallel() const
    Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
    Definition: data_layers.hpp:159
    -
    caffe::HDF5OutputLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: hdf5_output_layer.cpp:65
    -
    caffe::BaseDataLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: data_layers.hpp:41
    -
    caffe::ImageDataLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: data_layers.hpp:248
    -
    caffe::BlockingQueue
    Definition: blocking_queue.hpp:12
    -
    caffe::WindowDataLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: data_layers.hpp:321
    -
    caffe::DummyDataLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: dummy_data_layer.cpp:102
    -
    caffe::HDF5DataLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: data_layers.hpp:161
    -
    caffe::DataReader
    Reads data from a source to queues available to data layers. A single reading thread is created per s...
    Definition: data_reader.hpp:23
    -
    caffe::BasePrefetchingDataLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Using the CPU device, compute the layer output.
    Definition: base_data_layer.cpp:104
    -
    caffe::DummyDataLayer::ShareInParallel
    virtual bool ShareInParallel() const
    Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
    Definition: data_layers.hpp:124
    -
    caffe::BaseDataLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: data_layers.hpp:46
    -
    caffe::HDF5DataLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: data_layers.hpp:173
    -
    caffe::ImageDataLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: data_layers.hpp:247
    -
    caffe::MemoryDataLayer
    Provides data to the Net from memory.
    Definition: data_layers.hpp:265
    -
    caffe::HDF5OutputLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: data_layers.hpp:204
    -
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:25
    -
    - -
    -Generated on Mon Oct 19 2015 15:25:08 for Caffe by   -doxygen - 1.8.9.1 -
    - - diff --git a/doxygen/data__reader_8hpp_source.html b/doxygen/data__reader_8hpp_source.html deleted file mode 100644 index 21be006d561..00000000000 --- a/doxygen/data__reader_8hpp_source.html +++ /dev/null @@ -1,84 +0,0 @@ - - - - - - - -Caffe: include/caffe/data_reader.hpp Source File - - - - - - - - - -
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    data_reader.hpp
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    -
    -
    1 #ifndef CAFFE_DATA_READER_HPP_
    2 #define CAFFE_DATA_READER_HPP_
    3 
    4 #include <map>
    5 #include <string>
    6 #include <vector>
    7 
    8 #include "caffe/common.hpp"
    9 #include "caffe/internal_thread.hpp"
    10 #include "caffe/util/blocking_queue.hpp"
    11 #include "caffe/util/db.hpp"
    12 
    13 namespace caffe {
    14 
    23 class DataReader {
    24  public:
    25  explicit DataReader(const LayerParameter& param);
    26  ~DataReader();
    27 
    28  inline BlockingQueue<Datum*>& free() const {
    29  return queue_pair_->free_;
    30  }
    31  inline BlockingQueue<Datum*>& full() const {
    32  return queue_pair_->full_;
    33  }
    34 
    35  protected:
    36  // Queue pairs are shared between a body and its readers
    37  class QueuePair {
    38  public:
    39  explicit QueuePair(int size);
    40  ~QueuePair();
    41 
    42  BlockingQueue<Datum*> free_;
    43  BlockingQueue<Datum*> full_;
    44 
    45  DISABLE_COPY_AND_ASSIGN(QueuePair);
    46  };
    47 
    48  // A single body is created per source
    49  class Body : public InternalThread {
    50  public:
    51  explicit Body(const LayerParameter& param);
    52  virtual ~Body();
    53 
    54  protected:
    55  void InternalThreadEntry();
    56  void read_one(db::Cursor* cursor, QueuePair* qp);
    57 
    58  const LayerParameter param_;
    59  BlockingQueue<shared_ptr<QueuePair> > new_queue_pairs_;
    60 
    61  friend class DataReader;
    62 
    63  DISABLE_COPY_AND_ASSIGN(Body);
    64  };
    65 
    66  // A source is uniquely identified by its layer name + path, in case
    67  // the same database is read from two different locations in the net.
    68  static inline string source_key(const LayerParameter& param) {
    69  return param.name() + ":" + param.data_param().source();
    70  }
    71 
    72  const shared_ptr<QueuePair> queue_pair_;
    73  shared_ptr<Body> body_;
    74 
    75  static map<const string, boost::weak_ptr<DataReader::Body> > bodies_;
    76 
    77 DISABLE_COPY_AND_ASSIGN(DataReader);
    78 };
    79 
    80 } // namespace caffe
    81 
    82 #endif // CAFFE_DATA_READER_HPP_
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
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    caffe::DataReader::Body
    Definition: data_reader.hpp:49
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    caffe::DataReader::QueuePair
    Definition: data_reader.hpp:37
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    caffe::InternalThread
    Definition: internal_thread.hpp:19
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    caffe::BlockingQueue
    Definition: blocking_queue.hpp:10
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    caffe::DataReader
    Reads data from a source to queues available to data layers. A single reading thread is created per s...
    Definition: data_reader.hpp:23
    -
    caffe::db::Cursor
    Definition: db.hpp:13
    -
    - -
    -Generated on Fri Jan 13 2017 13:56:34 for Caffe by   -doxygen - 1.8.12 -
    - - diff --git a/doxygen/data__transformer_8hpp_source.html b/doxygen/data__transformer_8hpp_source.html index 42ab00f68ff..7446a3e2cd6 100644 --- a/doxygen/data__transformer_8hpp_source.html +++ b/doxygen/data__transformer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/data_transformer.hpp Source File @@ -29,7 +29,7 @@
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    -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/db__leveldb_8hpp_source.html b/doxygen/db__leveldb_8hpp_source.html index 44701912631..ee2e9d47c4f 100644 --- a/doxygen/db__leveldb_8hpp_source.html +++ b/doxygen/db__leveldb_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/util/db_leveldb.hpp Source File @@ -29,7 +29,7 @@ - + @@ -66,13 +66,13 @@
    db_leveldb.hpp
    -
    1 #ifdef USE_LEVELDB
    2 #ifndef CAFFE_UTIL_DB_LEVELDB_HPP
    3 #define CAFFE_UTIL_DB_LEVELDB_HPP
    4 
    5 #include <string>
    6 
    7 #include "leveldb/db.h"
    8 #include "leveldb/write_batch.h"
    9 
    10 #include "caffe/util/db.hpp"
    11 
    12 namespace caffe { namespace db {
    13 
    14 class LevelDBCursor : public Cursor {
    15  public:
    16  explicit LevelDBCursor(leveldb::Iterator* iter)
    17  : iter_(iter) { SeekToFirst(); }
    18  ~LevelDBCursor() { delete iter_; }
    19  virtual void SeekToFirst() { iter_->SeekToFirst(); }
    20  virtual void Next() { iter_->Next(); }
    21  virtual string key() { return iter_->key().ToString(); }
    22  virtual string value() { return iter_->value().ToString(); }
    23  virtual bool valid() { return iter_->Valid(); }
    24 
    25  private:
    26  leveldb::Iterator* iter_;
    27 };
    28 
    29 class LevelDBTransaction : public Transaction {
    30  public:
    31  explicit LevelDBTransaction(leveldb::DB* db) : db_(db) { CHECK_NOTNULL(db_); }
    32  virtual void Put(const string& key, const string& value) {
    33  batch_.Put(key, value);
    34  }
    35  virtual void Commit() {
    36  leveldb::Status status = db_->Write(leveldb::WriteOptions(), &batch_);
    37  CHECK(status.ok()) << "Failed to write batch to leveldb "
    38  << std::endl << status.ToString();
    39  }
    40 
    41  private:
    42  leveldb::DB* db_;
    43  leveldb::WriteBatch batch_;
    44 
    45  DISABLE_COPY_AND_ASSIGN(LevelDBTransaction);
    46 };
    47 
    48 class LevelDB : public DB {
    49  public:
    50  LevelDB() : db_(NULL) { }
    51  virtual ~LevelDB() { Close(); }
    52  virtual void Open(const string& source, Mode mode);
    53  virtual void Close() {
    54  if (db_ != NULL) {
    55  delete db_;
    56  db_ = NULL;
    57  }
    58  }
    59  virtual LevelDBCursor* NewCursor() {
    60  return new LevelDBCursor(db_->NewIterator(leveldb::ReadOptions()));
    61  }
    62  virtual LevelDBTransaction* NewTransaction() {
    63  return new LevelDBTransaction(db_);
    64  }
    65 
    66  private:
    67  leveldb::DB* db_;
    68 };
    69 
    70 
    71 } // namespace db
    72 } // namespace caffe
    73 
    74 #endif // CAFFE_UTIL_DB_LEVELDB_HPP
    75 #endif // USE_LEVELDB
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    +
    1 #ifdef USE_LEVELDB
    2 #ifndef CAFFE_UTIL_DB_LEVELDB_HPP
    3 #define CAFFE_UTIL_DB_LEVELDB_HPP
    4 
    5 #include <string>
    6 
    7 #include "leveldb/db.h"
    8 #include "leveldb/write_batch.h"
    9 
    10 #include "caffe/util/db.hpp"
    11 
    12 namespace caffe { namespace db {
    13 
    14 class LevelDBCursor : public Cursor {
    15  public:
    16  explicit LevelDBCursor(leveldb::Iterator* iter)
    17  : iter_(iter) {
    18  SeekToFirst();
    19  CHECK(iter_->status().ok()) << iter_->status().ToString();
    20  }
    21  ~LevelDBCursor() { delete iter_; }
    22  virtual void SeekToFirst() { iter_->SeekToFirst(); }
    23  virtual void Next() { iter_->Next(); }
    24  virtual string key() { return iter_->key().ToString(); }
    25  virtual string value() { return iter_->value().ToString(); }
    26  virtual bool valid() { return iter_->Valid(); }
    27 
    28  private:
    29  leveldb::Iterator* iter_;
    30 };
    31 
    32 class LevelDBTransaction : public Transaction {
    33  public:
    34  explicit LevelDBTransaction(leveldb::DB* db) : db_(db) { CHECK_NOTNULL(db_); }
    35  virtual void Put(const string& key, const string& value) {
    36  batch_.Put(key, value);
    37  }
    38  virtual void Commit() {
    39  leveldb::Status status = db_->Write(leveldb::WriteOptions(), &batch_);
    40  CHECK(status.ok()) << "Failed to write batch to leveldb "
    41  << std::endl << status.ToString();
    42  }
    43 
    44  private:
    45  leveldb::DB* db_;
    46  leveldb::WriteBatch batch_;
    47 
    48  DISABLE_COPY_AND_ASSIGN(LevelDBTransaction);
    49 };
    50 
    51 class LevelDB : public DB {
    52  public:
    53  LevelDB() : db_(NULL) { }
    54  virtual ~LevelDB() { Close(); }
    55  virtual void Open(const string& source, Mode mode);
    56  virtual void Close() {
    57  if (db_ != NULL) {
    58  delete db_;
    59  db_ = NULL;
    60  }
    61  }
    62  virtual LevelDBCursor* NewCursor() {
    63  return new LevelDBCursor(db_->NewIterator(leveldb::ReadOptions()));
    64  }
    65  virtual LevelDBTransaction* NewTransaction() {
    66  return new LevelDBTransaction(db_);
    67  }
    68 
    69  private:
    70  leveldb::DB* db_;
    71 };
    72 
    73 
    74 } // namespace db
    75 } // namespace caffe
    76 
    77 #endif // CAFFE_UTIL_DB_LEVELDB_HPP
    78 #endif // USE_LEVELDB
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
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    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:50:58 for Caffe by   doxygen - 1.8.12 + 1.8.13
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    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:50:58 for Caffe by   doxygen - 1.8.12 + 1.8.13
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    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:50:58 for Caffe by   doxygen - 1.8.12 + 1.8.13
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    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:50:58 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/dir_b9606a76d4acbf8f90a06a4a51ec8b9e.html b/doxygen/dir_b9606a76d4acbf8f90a06a4a51ec8b9e.html index 86c09c2fbe0..bfc9cb0ba52 100644 --- a/doxygen/dir_b9606a76d4acbf8f90a06a4a51ec8b9e.html +++ b/doxygen/dir_b9606a76d4acbf8f90a06a4a51ec8b9e.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers Directory Reference @@ -29,7 +29,7 @@ - + @@ -69,9 +69,9 @@
    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:50:58 for Caffe by   doxygen - 1.8.12 + 1.8.13
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    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:50:58 for Caffe by   doxygen - 1.8.12 + 1.8.13
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    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:50:58 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/doxygen.css b/doxygen/doxygen.css index a2cf15fa876..4f1ab9195b4 100644 --- a/doxygen/doxygen.css +++ b/doxygen/doxygen.css @@ -1,9 +1,13 @@ -/* The standard CSS for doxygen 1.8.12 */ +/* The standard CSS for doxygen 1.8.13 */ body, table, div, p, dl { font: 400 14px/22px Roboto,sans-serif; } +p.reference, p.definition { + font: 400 14px/22px Roboto,sans-serif; +} + /* @group Heading Levels */ h1.groupheader { @@ -1211,6 +1215,11 @@ dl.section dd { text-align: center; } +.plantumlgraph +{ + text-align: center; +} + .diagraph { text-align: center; @@ -1506,3 +1515,82 @@ tr.heading h2 { } } +/* @group Markdown */ + +/* +table.markdownTable { + border-collapse:collapse; + margin-top: 4px; + margin-bottom: 4px; +} + +table.markdownTable td, table.markdownTable th { + border: 1px solid #2D4068; + padding: 3px 7px 2px; +} + +table.markdownTableHead tr { +} + +table.markdownTableBodyLeft td, table.markdownTable th { + border: 1px solid #2D4068; + padding: 3px 7px 2px; +} + +th.markdownTableHeadLeft th.markdownTableHeadRight th.markdownTableHeadCenter th.markdownTableHeadNone { + background-color: #374F7F; + color: #FFFFFF; + font-size: 110%; + padding-bottom: 4px; + padding-top: 5px; +} + +th.markdownTableHeadLeft { + text-align: left +} + +th.markdownTableHeadRight { + text-align: right +} + +th.markdownTableHeadCenter { + text-align: center +} +*/ + +table.markdownTable { + border-collapse:collapse; + margin-top: 4px; + margin-bottom: 4px; +} + +table.markdownTable td, table.markdownTable th { + border: 1px solid #2D4068; + padding: 3px 7px 2px; +} + +table.markdownTable tr { +} + +th.markdownTableHeadLeft, th.markdownTableHeadRight, th.markdownTableHeadCenter, th.markdownTableHeadNone { + background-color: #374F7F; + color: #FFFFFF; + font-size: 110%; + padding-bottom: 4px; + padding-top: 5px; +} + +th.markdownTableHeadLeft, td.markdownTableBodyLeft { + text-align: left +} + +th.markdownTableHeadRight, td.markdownTableBodyRight { + text-align: right +} + +th.markdownTableHeadCenter, td.markdownTableBodyCenter { + text-align: center +} + + +/* @end */ diff --git a/doxygen/dropout__layer_8hpp_source.html b/doxygen/dropout__layer_8hpp_source.html index cd66380f5b3..e9b04862e81 100644 --- a/doxygen/dropout__layer_8hpp_source.html +++ b/doxygen/dropout__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/dropout_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -84,9 +84,9 @@
    -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/dummy__data__layer_8hpp_source.html b/doxygen/dummy__data__layer_8hpp_source.html index feb248e0d14..d80bff75727 100644 --- a/doxygen/dummy__data__layer_8hpp_source.html +++ b/doxygen/dummy__data__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/dummy_data_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -66,24 +66,24 @@
    dummy_data_layer.hpp
    -
    1 #ifndef CAFFE_DUMMY_DATA_LAYER_HPP_
    2 #define CAFFE_DUMMY_DATA_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/filler.hpp"
    8 #include "caffe/layer.hpp"
    9 #include "caffe/proto/caffe.pb.h"
    10 
    11 namespace caffe {
    12 
    18 template <typename Dtype>
    19 class DummyDataLayer : public Layer<Dtype> {
    20  public:
    21  explicit DummyDataLayer(const LayerParameter& param)
    22  : Layer<Dtype>(param) {}
    23  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    24  const vector<Blob<Dtype>*>& top);
    25  // Data layers should be shared by multiple solvers in parallel
    26  virtual inline bool ShareInParallel() const { return true; }
    27  // Data layers have no bottoms, so reshaping is trivial.
    28  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    29  const vector<Blob<Dtype>*>& top) {}
    30 
    31  virtual inline const char* type() const { return "DummyData"; }
    32  virtual inline int ExactNumBottomBlobs() const { return 0; }
    33  virtual inline int MinTopBlobs() const { return 1; }
    34 
    35  protected:
    36  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    37  const vector<Blob<Dtype>*>& top);
    38  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    39  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
    40  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    41  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
    42 
    43  vector<shared_ptr<Filler<Dtype> > > fillers_;
    44  vector<bool> refill_;
    45 };
    46 
    47 } // namespace caffe
    48 
    49 #endif // CAFFE_DUMMY_DATA_LAYER_HPP_
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    +
    1 #ifndef CAFFE_DUMMY_DATA_LAYER_HPP_
    2 #define CAFFE_DUMMY_DATA_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/filler.hpp"
    8 #include "caffe/layer.hpp"
    9 #include "caffe/proto/caffe.pb.h"
    10 
    11 namespace caffe {
    12 
    18 template <typename Dtype>
    19 class DummyDataLayer : public Layer<Dtype> {
    20  public:
    21  explicit DummyDataLayer(const LayerParameter& param)
    22  : Layer<Dtype>(param) {}
    23  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    24  const vector<Blob<Dtype>*>& top);
    25  // Data layers have no bottoms, so reshaping is trivial.
    26  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    27  const vector<Blob<Dtype>*>& top) {}
    28 
    29  virtual inline const char* type() const { return "DummyData"; }
    30  virtual inline int ExactNumBottomBlobs() const { return 0; }
    31  virtual inline int MinTopBlobs() const { return 1; }
    32 
    33  protected:
    34  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    35  const vector<Blob<Dtype>*>& top);
    36  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    37  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
    38  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    39  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
    40 
    41  vector<shared_ptr<Filler<Dtype> > > fillers_;
    42  vector<bool> refill_;
    43 };
    44 
    45 } // namespace caffe
    46 
    47 #endif // CAFFE_DUMMY_DATA_LAYER_HPP_
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    -
    caffe::DummyDataLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: dummy_data_layer.hpp:40
    +
    caffe::DummyDataLayer::Backward_gpu
    virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: dummy_data_layer.hpp:38
    caffe::DummyDataLayer
    Provides data to the Net generated by a Filler.
    Definition: dummy_data_layer.hpp:19
    -
    caffe::DummyDataLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: dummy_data_layer.hpp:28
    +
    caffe::DummyDataLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: dummy_data_layer.hpp:26
    caffe::DummyDataLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: dummy_data_layer.cpp:9
    -
    caffe::DummyDataLayer::MinTopBlobs
    virtual int MinTopBlobs() const
    Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
    Definition: dummy_data_layer.hpp:33
    -
    caffe::DummyDataLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: dummy_data_layer.hpp:32
    -
    caffe::DummyDataLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: dummy_data_layer.hpp:31
    -
    caffe::DummyDataLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: dummy_data_layer.hpp:38
    +
    caffe::DummyDataLayer::MinTopBlobs
    virtual int MinTopBlobs() const
    Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
    Definition: dummy_data_layer.hpp:31
    +
    caffe::DummyDataLayer::ExactNumBottomBlobs
    virtual int ExactNumBottomBlobs() const
    Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
    Definition: dummy_data_layer.hpp:30
    +
    caffe::DummyDataLayer::type
    virtual const char * type() const
    Returns the layer type.
    Definition: dummy_data_layer.hpp:29
    +
    caffe::DummyDataLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: dummy_data_layer.hpp:36
    caffe::DummyDataLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the CPU device, compute the layer output.
    Definition: dummy_data_layer.cpp:101
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/eltwise__layer_8hpp_source.html b/doxygen/eltwise__layer_8hpp_source.html index 4d392e2569e..cef0bd422a9 100644 --- a/doxygen/eltwise__layer_8hpp_source.html +++ b/doxygen/eltwise__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/eltwise_layer.hpp Source File @@ -29,7 +29,7 @@
    - + @@ -66,7 +66,7 @@
    eltwise_layer.hpp
    -
    1 #ifndef CAFFE_ELTWISE_LAYER_HPP_
    2 #define CAFFE_ELTWISE_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 namespace caffe {
    11 
    18 template <typename Dtype>
    19 class EltwiseLayer : public Layer<Dtype> {
    20  public:
    21  explicit EltwiseLayer(const LayerParameter& param)
    22  : Layer<Dtype>(param) {}
    23  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    24  const vector<Blob<Dtype>*>& top);
    25  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    26  const vector<Blob<Dtype>*>& top);
    27 
    28  virtual inline const char* type() const { return "Eltwise"; }
    29  virtual inline int MinBottomBlobs() const { return 2; }
    30  virtual inline int ExactNumTopBlobs() const { return 1; }
    31 
    32  protected:
    33  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    34  const vector<Blob<Dtype>*>& top);
    35  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    36  const vector<Blob<Dtype>*>& top);
    37  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    38  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    39  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    40  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    41 
    42  EltwiseParameter_EltwiseOp op_;
    43  vector<Dtype> coeffs_;
    44  Blob<int> max_idx_;
    45 
    46  bool stable_prod_grad_;
    47 };
    48 
    49 } // namespace caffe
    50 
    51 #endif // CAFFE_ELTWISE_LAYER_HPP_
    caffe::EltwiseLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: eltwise_layer.cpp:100
    +
    1 #ifndef CAFFE_ELTWISE_LAYER_HPP_
    2 #define CAFFE_ELTWISE_LAYER_HPP_
    3 
    4 #include <vector>
    5 
    6 #include "caffe/blob.hpp"
    7 #include "caffe/layer.hpp"
    8 #include "caffe/proto/caffe.pb.h"
    9 
    10 namespace caffe {
    11 
    18 template <typename Dtype>
    19 class EltwiseLayer : public Layer<Dtype> {
    20  public:
    21  explicit EltwiseLayer(const LayerParameter& param)
    22  : Layer<Dtype>(param) {}
    23  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    24  const vector<Blob<Dtype>*>& top);
    25  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    26  const vector<Blob<Dtype>*>& top);
    27 
    28  virtual inline const char* type() const { return "Eltwise"; }
    29  virtual inline int MinBottomBlobs() const { return 2; }
    30  virtual inline int ExactNumTopBlobs() const { return 1; }
    31 
    32  protected:
    33  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    34  const vector<Blob<Dtype>*>& top);
    35  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    36  const vector<Blob<Dtype>*>& top);
    37  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    38  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    39  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    40  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    41 
    42  EltwiseParameter_EltwiseOp op_;
    43  vector<Dtype> coeffs_;
    44  Blob<int> max_idx_;
    45 
    46  bool stable_prod_grad_;
    47 };
    48 
    49 } // namespace caffe
    50 
    51 #endif // CAFFE_ELTWISE_LAYER_HPP_
    caffe::EltwiseLayer::Backward_cpu
    virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
    Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
    Definition: eltwise_layer.cpp:102
    caffe::EltwiseLayer::ExactNumTopBlobs
    virtual int ExactNumTopBlobs() const
    Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
    Definition: eltwise_layer.hpp:30
    caffe::Layer
    An interface for the units of computation which can be composed into a Net.
    Definition: layer.hpp:33
    caffe
    A layer factory that allows one to register layers. During runtime, registered layers can be called b...
    Definition: blob.hpp:14
    @@ -75,16 +75,16 @@
    caffe::EltwiseLayer
    Compute elementwise operations, such as product and sum, along multiple input Blobs.
    Definition: eltwise_layer.hpp:19
    caffe::EltwiseLayer::Reshape
    virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
    Definition: eltwise_layer.cpp:31
    caffe::EltwiseLayer::MinBottomBlobs
    virtual int MinBottomBlobs() const
    Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
    Definition: eltwise_layer.hpp:29
    -
    caffe::EltwiseLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the CPU device, compute the layer output.
    Definition: eltwise_layer.cpp:45
    +
    caffe::EltwiseLayer::Forward_cpu
    virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the CPU device, compute the layer output.
    Definition: eltwise_layer.cpp:47
    caffe::EltwiseLayer::Forward_gpu
    virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
    caffe::EltwiseLayer::LayerSetUp
    virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
    Does layer-specific setup: your layer should implement this function as well as Reshape.
    Definition: eltwise_layer.cpp:10
    caffe::Blob
    A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
    Definition: blob.hpp:24
    -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/elu__layer_8hpp_source.html b/doxygen/elu__layer_8hpp_source.html index b42b943421c..ed09da0218a 100644 --- a/doxygen/elu__layer_8hpp_source.html +++ b/doxygen/elu__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/elu_layer.hpp Source File @@ -29,7 +29,7 @@
    - + @@ -79,9 +79,9 @@
    -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
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    -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/formula.repository b/doxygen/formula.repository index 9b558c46cde..541d279d1fc 100644 --- a/doxygen/formula.repository +++ b/doxygen/formula.repository @@ -1,180 +1,175 @@ -\form#0:$ K $ -\form#1:$ (C \times H \times W) $ -\form#2:$ (N \times C \times H \times W) $ -\form#3:$ x $ -\form#4:$ (N \times 1 \times K \times 1) $ -\form#5:$ (N \times 2 \times K \times 1) $ -\form#6:$ y_n = \arg\max\limits_i x_{ni} $ -\form#7:$ K = 1 $ -\form#8:$ x_1 $ -\form#9:$ x_2 $ -\form#10:$ x_K $ -\form#11:$ (KN \times C \times H \times W) $ -\form#12:$ (N \times KC \times H \times W) $ -\form#13:$ y = [\begin{array}{cccc} x_1 & x_2 & ... & x_K \end{array}] $ -\form#14:$ \frac{\partial E}{\partial y} $ -\form#15:$ y $ -\form#16:$ \left[ \begin{array}{cccc} \frac{\partial E}{\partial x_1} & \frac{\partial E}{\partial x_2} & ... & \frac{\partial E}{\partial x_K} \end{array} \right] = \frac{\partial E}{\partial y} $ -\form#17:$ (N \times CHW \times 1 \times 1) $ -\form#18:$ x = 0 $ -\form#19:$ x\sim U(a, b) $ -\form#20:$ x = a $ -\form#21:$ x \in [0, 1] $ -\form#22:$ \forall i \sum_j x_{ij} = 1 $ -\form#23:$ x \sim U(-a, +a) $ -\form#24:$ a $ -\form#25:$ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d + \left(1-y\right) \max \left(margin-d, 0\right) $ -\form#26:$ d = \left| \left| a_n - b_n \right| \right|_2^2 $ -\form#27:$ (N \times C \times 1 \times 1) $ -\form#28:$ a \in [-\infty, +\infty]$ -\form#29:$ b \in [-\infty, +\infty]$ -\form#30:$ (N \times 1 \times 1 \times 1) $ -\form#31:$ s \in [0, 1]$ -\form#32:$ (1 \times 1 \times 1 \times 1) $ -\form#33:$ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ -\form#34:$ \hat{y} \in [-\infty, +\infty]$ -\form#35:$ y \in [-\infty, +\infty]$ -\form#36:$ E = \frac{1}{2n} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ -\form#37:$ t $ -\form#38:$ [-\infty, +\infty] $ -\form#39:$ K = CHW $ -\form#40:$ X^T W $ -\form#41:$ X \in \mathcal{R}^{D \times N} $ -\form#42:$ W \in \mathcal{R}^{D \times K} $ -\form#43:$ l $ -\form#44:$ l_n \in [0, 1, 2, ..., K - 1] $ -\form#45:$ E = \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p $ -\form#46:$ L^p $ -\form#47:$ p = 1 $ -\form#48:$ \delta\{\mathrm{condition}\} = \left\{ \begin{array}{lr} 1 & \mbox{if condition} \\ -1 & \mbox{otherwise} \end{array} \right. $ -\form#49:$ t \in \mathcal{R}^{N \times K} $ -\form#50:$k$ -\form#51:$ \hat{p} $ -\form#52:$ [0, 1] $ -\form#53:$ \hat{p}_n $ -\form#54:$ \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 $ -\form#55:$ (1 \times 1 \times K \times K) $ -\form#56:$ H $ -\form#57:$ H = I $ -\form#58:$ E = \frac{-1}{N} \sum\limits_{n=1}^N H_{l_n} \log(\hat{p}_n) = \frac{-1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^{K} H_{l_n,k} \log(\hat{p}_{n,k}) $ -\form#59:$ H_{l_n} $ -\form#60:$l_n$ -\form#61:$H$ -\form#62:$ E = \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n}) $ -\form#63:$ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $ -\form#64:$ x \in [-\infty, +\infty]$ -\form#65:$ \hat{p}_n = \sigma(x_n) \in [0, 1] $ -\form#66:$ \sigma(.) $ -\form#67:$ y \in [0, 1] $ -\form#68:$ \hat{p}_{nk} = \exp(x_{nk}) / \left[\sum_{k'} \exp(x_{nk'})\right] $ -\form#69:$ k $ -\form#70:$ k = 5 $ -\form#71:$ x_n $ -\form#72:$ \hat{l}_n $ -\form#73:$ \hat{l}_n = \arg\max\limits_k x_{nk} $ -\form#74:$ \frac{1}{N} \sum\limits_{n=1}^N \delta\{ \hat{l}_n = l_n \} $ -\form#75:$ \delta\{\mathrm{condition}\} = \left\{ \begin{array}{lr} 1 & \mbox{if condition} \\ 0 & \mbox{otherwise} \end{array} \right. $ -\form#76:$ \lambda $ -\form#77:$\ell_i$ -\form#78:$ E = \lambda_i \ell_i + \mbox{other loss terms}$ -\form#79:$ \frac{\partial E}{\partial \ell_i} = \lambda_i $ -\form#80:$a$ -\form#81:$b$ -\form#82:$\hat{y}$ -\form#83:$ \frac{\partial E}{\partial \hat{y}} = \frac{1}{n} \sum\limits_{n=1}^N (\hat{y}_n - y_n) $ -\form#84:$y$ -\form#85:$ \frac{\partial E}{\partial y} = \frac{1}{n} \sum\limits_{n=1}^N (y_n - \hat{y}_n) $ -\form#86:$t$ -\form#87:$ \frac{\partial E}{\partial t} $ -\form#88:$ \frac{\partial E}{\partial \hat{p}} $ -\form#89:$x$ -\form#90:$ \frac{\partial E}{\partial x} = \frac{1}{n} \sum\limits_{n=1}^N (\hat{p}_n - p_n) $ -\form#91:$ \frac{\partial E}{\partial x} $ -\form#92:$ y = |x| $ -\form#93:$ y = x + \log(1 + \exp(-x)) $ -\form#94:$ x > 0 $ -\form#95:$ y = \log(1 + \exp(x)) $ -\form#96:$ y = \left\{ \begin{array}{ll} x + \log(1 + \exp(-x)) & \mbox{if } x > 0 \\ \log(1 + \exp(x)) & \mbox{otherwise} \end{array} \right. $ -\form#97:$ y = \gamma ^ {\alpha x + \beta} $ -\form#98:$ \alpha $ -\form#99:$ \beta $ -\form#100:$ \gamma $ -\form#101:$ y = (\alpha x + \beta) ^ \gamma $ -\form#102:$ y = \max(0, x) $ -\form#103:$ y = (1 + \exp(-x))^{-1} $ -\form#104:$ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $ -\form#105:$ \frac{\partial E}{\partial x} = \mathrm{sign}(x) \frac{\partial E}{\partial y} $ -\form#106:$ p $ -\form#107:$ y_{\mbox{train}} = \left\{ \begin{array}{ll} \frac{x}{1 - p} & \mbox{if } u > p \\ 0 & \mbox{otherwise} \end{array} \right. $ -\form#108:$ u \sim U(0, 1)$ -\form#109:$ y_{\mbox{test}} = \mathbb{E}[y_{\mbox{train}}] = x $ -\form#110:$u\sim U(0,1)$ -\form#111:$ 1 / (1 - p) $ -\form#112:$ e \approx 2.718 $ -\form#113:$ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} y \alpha \log_e(gamma) $ -\form#114:$ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} \alpha \gamma (\alpha x + \beta) ^ {\gamma - 1} = \frac{\partial E}{\partial y} \frac{\alpha \gamma y}{\alpha x + \beta} $ -\form#115:$ \alpha \gamma $ -\form#116:$ \nu $ -\form#117:$ y = \max(0, x) + \nu \min(0, x) $ -\form#118:$ \frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} 0 & \mathrm{if} \; x \le 0 \\ \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0 \end{array} \right. $ -\form#119:$ \frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} \nu \frac{\partial E}{\partial y} & \mathrm{if} \; x \le 0 \\ \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0 \end{array} \right. $ -\form#120:$ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} y (1 - y) $ -\form#121:$ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} \left(1 - \left[\frac{\exp(2x) - 1}{exp(2x) + 1} \right]^2 \right) = \frac{\partial E}{\partial y} (1 - y^2) $ -\form#122:$ y = \left\{ \begin{array}{lr} 0 & \mathrm{if} \; x \le t \\ 1 & \mathrm{if} \; x > t \end{array} \right. $ -\form#123:$ \geq 1 $ -\form#124:$ x \sim N(0, \sigma^2) $ -\form#125:$ \sigma^2 $ -\form#126:$ y_i = \max(0, x_i) + a_i \min(0, x_i) $ -\form#127:$ (N \times C \times ...) $ -\form#128:$i$ -\form#129:$ \frac{\partial E}{\partial x_i} = \left\{ \begin{array}{lr} a_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\ \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i > 0 \end{array} \right. $ -\form#130:$ \frac{\partial E}{\partial a_i} = \left\{ \begin{array}{lr} \sum_{x_i} x_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\ 0 & \mathrm{if} \; x_i > 0 \end{array} \right. $ -\form#131:$ (S \times C \times H \times W) $ -\form#132:$ y = log_{\gamma}(\alpha x + \beta) $ -\form#133:$ (N \times K \times H \times W) $ -\form#134:$ (N \times ...) $ -\form#135:$ (M) $ -\form#136:$ (M \times ...) $ -\form#137:$ y = x_1[x_2] $ -\form#138:$ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ -\form#139:$ d = \left| \left| a_n - b_n \right| \right|_2 $ -\form#140:$ (N \times 1 \times K) $ -\form#141:$ (N \times 2 \times K) $ -\form#142:$ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ -\form#143:$ y = \left\{ \begin{array}{lr} x & \mathrm{if} \; x > 0 \\ \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 \end{array} \right. $ -\form#144:$ \frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} 1 & \mathrm{if} \; x > 0 \\ y + \alpha & \mathrm{if} \; x \le 0 \end{array} \right. $ -\form#145:$ i $ -\form#146:$ (d_0 \times ... \times d_i \times ... \times d_j \times ... \times d_n) $ -\form#147:$ (d_i \times ... \times d_j) $ -\form#148:$ z = x y $ -\form#149:$ (1 \times N \times D) $ -\form#150:$ c_{t-1} $ -\form#151:$ (1 \times N \times 4D) $ -\form#152:$ [i_t', f_t', o_t', g_t'] $ -\form#153:$ (1 \times N) $ -\form#154:$ \delta_t $ -\form#155:$ c_t $ -\form#156:$ h_t $ -\form#157:$ \frac{\partial E}{\partial c_t} $ -\form#158:$ \frac{\partial E}{\partial h_t} $ -\form#159:$ [ \frac{\partial E}{\partial i_t} \frac{\partial E}{\partial f_t} \frac{\partial E}{\partial o_t} \frac{\partial E}{\partial g_t} ] $ -\form#160:$ (1 \times 1 \times N) $ -\form#161:$ (T \times N \times ...) $ -\form#162:$ T $ -\form#163:$ N $ -\form#164:$ (N \times T \times ...) $ -\form#165:$ (T \times N) $ -\form#166:$ \delta $ -\form#167:$ \delta_{t,n} = 0 $ -\form#168:$ n $ -\form#169:$ h_{t-1} $ -\form#170:$ \delta_t = 0 $ -\form#171:$ \delta_{t,n} = 1 $ -\form#172:$ t-1 $ -\form#173:$ x_{static} $ -\form#174:$ x'_t = [x_t; x_{static}] $ -\form#175:$ (T \times N \times D) $ -\form#176:$ D $ -\form#177:$ x_t $ -\form#178:$ h_t := \tanh[ W_{hh} h_{t_1} + W_{xh} x_t + b_h ] $ -\form#179:$ o_t := \tanh[ W_{ho} h_t + b_o ] $ +\form#0:$ x = 0 $ +\form#1:$ x\sim U(a, b) $ +\form#2:$ x = a $ +\form#3:$ x \in [0, 1] $ +\form#4:$ \forall i \sum_j x_{ij} = 1 $ +\form#5:$ x \sim U(-a, +a) $ +\form#6:$ a $ +\form#7:$ x \sim N(0, \sigma^2) $ +\form#8:$ \sigma^2 $ +\form#9:$ y = |x| $ +\form#10:$ (N \times C \times H \times W) $ +\form#11:$ x $ +\form#12:$ \frac{\partial E}{\partial y} $ +\form#13:$ y $ +\form#14:$ \frac{\partial E}{\partial x} = \mathrm{sign}(x) \frac{\partial E}{\partial y} $ +\form#15:$ k $ +\form#16:$ k = 5 $ +\form#17:$ [-\infty, +\infty] $ +\form#18:$ K = CHW $ +\form#19:$ x_n $ +\form#20:$ \hat{l}_n $ +\form#21:$ \hat{l}_n = \arg\max\limits_k x_{nk} $ +\form#22:$ (N \times 1 \times 1 \times 1) $ +\form#23:$ l $ +\form#24:$ l_n \in [0, 1, 2, ..., K - 1] $ +\form#25:$ K $ +\form#26:$ (1 \times 1 \times 1 \times 1) $ +\form#27:$ \frac{1}{N} \sum\limits_{n=1}^N \delta\{ \hat{l}_n = l_n \} $ +\form#28:$ \delta\{\mathrm{condition}\} = \left\{ \begin{array}{lr} 1 & \mbox{if condition} \\ 0 & \mbox{otherwise} \end{array} \right. $ +\form#29:$ (C \times H \times W) $ +\form#30:$ (N \times 1 \times K) $ +\form#31:$ (N \times 2 \times K) $ +\form#32:$ (N \times K \times H \times W) $ +\form#33:$ y_n = \arg\max\limits_i x_{ni} $ +\form#34:$ K = 1 $ +\form#35:$ (N \times ...) $ +\form#36:$ x_1 $ +\form#37:$ (M) $ +\form#38:$ x_2 $ +\form#39:$ (M \times ...) $ +\form#40:$ y = x_1[x_2] $ +\form#41:$ y = x + \log(1 + \exp(-x)) $ +\form#42:$ x > 0 $ +\form#43:$ y = \log(1 + \exp(x)) $ +\form#44:$ y = \left\{ \begin{array}{ll} x + \log(1 + \exp(-x)) & \mbox{if } x > 0 \\ \log(1 + \exp(x)) & \mbox{otherwise} \end{array} \right. $ +\form#45:$ \frac{\partial E}{\partial x} $ +\form#46:$ x_K $ +\form#47:$ (KN \times C \times H \times W) $ +\form#48:$ (N \times KC \times H \times W) $ +\form#49:$ y = [\begin{array}{cccc} x_1 & x_2 & ... & x_K \end{array}] $ +\form#50:$ \left[ \begin{array}{cccc} \frac{\partial E}{\partial x_1} & \frac{\partial E}{\partial x_2} & ... & \frac{\partial E}{\partial x_K} \end{array} \right] = \frac{\partial E}{\partial y} $ +\form#51:$ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ +\form#52:$ d = \left| \left| a_n - b_n \right| \right|_2 $ +\form#53:$ (N \times C \times 1 \times 1) $ +\form#54:$ a \in [-\infty, +\infty]$ +\form#55:$ b \in [-\infty, +\infty]$ +\form#56:$ s \in [0, 1]$ +\form#57:$ \lambda $ +\form#58:$\ell_i$ +\form#59:$ E = \lambda_i \ell_i + \mbox{other loss terms}$ +\form#60:$ \frac{\partial E}{\partial \ell_i} = \lambda_i $ +\form#61:$a$ +\form#62:$b$ +\form#63:$ \geq 1 $ +\form#64:$x$ +\form#65:$ p $ +\form#66:$ y_{\mbox{train}} = \left\{ \begin{array}{ll} \frac{x}{1 - p} & \mbox{if } u > p \\ 0 & \mbox{otherwise} \end{array} \right. $ +\form#67:$ u \sim U(0, 1)$ +\form#68:$ y_{\mbox{test}} = \mathbb{E}[y_{\mbox{train}}] = x $ +\form#69:$u\sim U(0,1)$ +\form#70:$ 1 / (1 - p) $ +\form#71:$ y = \left\{ \begin{array}{lr} x & \mathrm{if} \; x > 0 \\ \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 \end{array} \right. $ +\form#72:$ \alpha $ +\form#73:$ \frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} 1 & \mathrm{if} \; x > 0 \\ y + \alpha & \mathrm{if} \; x \le 0 \end{array} \right. $ +\form#74:$ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ +\form#75:$ \hat{y} \in [-\infty, +\infty]$ +\form#76:$ y \in [-\infty, +\infty]$ +\form#77:$ E = \frac{1}{2n} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ +\form#78:$\hat{y}$ +\form#79:$ \frac{\partial E}{\partial \hat{y}} = \frac{1}{n} \sum\limits_{n=1}^N (\hat{y}_n - y_n) $ +\form#80:$y$ +\form#81:$ \frac{\partial E}{\partial y} = \frac{1}{n} \sum\limits_{n=1}^N (y_n - \hat{y}_n) $ +\form#82:$ y = \gamma ^ {\alpha x + \beta} $ +\form#83:$ \beta $ +\form#84:$ \gamma $ +\form#85:$ e \approx 2.718 $ +\form#86:$ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} y \alpha \log_e(gamma) $ +\form#87:$ (S \times C \times H \times W) $ +\form#88:$ (N \times CHW \times 1 \times 1) $ +\form#89:$ t $ +\form#90:$ X^T W $ +\form#91:$ X \in \mathcal{R}^{D \times N} $ +\form#92:$ W \in \mathcal{R}^{D \times K} $ +\form#93:$ E = \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p $ +\form#94:$ L^p $ +\form#95:$ p = 1 $ +\form#96:$ \delta\{\mathrm{condition}\} = \left\{ \begin{array}{lr} 1 & \mbox{if condition} \\ -1 & \mbox{otherwise} \end{array} \right. $ +\form#97:$ t \in \mathcal{R}^{N \times K} $ +\form#98:$k$ +\form#99:$t$ +\form#100:$ \frac{\partial E}{\partial t} $ +\form#101:$ \hat{p} $ +\form#102:$ [0, 1] $ +\form#103:$ \hat{p}_n $ +\form#104:$ \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 $ +\form#105:$ (1 \times 1 \times K \times K) $ +\form#106:$ H $ +\form#107:$ H = I $ +\form#108:$ E = \frac{-1}{N} \sum\limits_{n=1}^N H_{l_n} \log(\hat{p}_n) = \frac{-1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^{K} H_{l_n,k} \log(\hat{p}_{n,k}) $ +\form#109:$ H_{l_n} $ +\form#110:$l_n$ +\form#111:$H$ +\form#112:$ \frac{\partial E}{\partial \hat{p}} $ +\form#113:$ y = log_{\gamma}(\alpha x + \beta) $ +\form#114:$ (1 \times N \times D) $ +\form#115:$ c_{t-1} $ +\form#116:$ (1 \times N \times 4D) $ +\form#117:$ [i_t', f_t', o_t', g_t'] $ +\form#118:$ (1 \times N) $ +\form#119:$ \delta_t $ +\form#120:$ c_t $ +\form#121:$ h_t $ +\form#122:$ \frac{\partial E}{\partial c_t} $ +\form#123:$ \frac{\partial E}{\partial h_t} $ +\form#124:$ [ \frac{\partial E}{\partial i_t} \frac{\partial E}{\partial f_t} \frac{\partial E}{\partial o_t} \frac{\partial E}{\partial g_t} ] $ +\form#125:$ (1 \times 1 \times N) $ +\form#126:$ E = \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n}) $ +\form#127:$ y = (\alpha x + \beta) ^ \gamma $ +\form#128:$ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} \alpha \gamma (\alpha x + \beta) ^ {\gamma - 1} = \frac{\partial E}{\partial y} \frac{\alpha \gamma y}{\alpha x + \beta} $ +\form#129:$ \alpha \gamma $ +\form#130:$ y_i = \max(0, x_i) + a_i \min(0, x_i) $ +\form#131:$ (N \times C \times ...) $ +\form#132:$i$ +\form#133:$ \frac{\partial E}{\partial x_i} = \left\{ \begin{array}{lr} a_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\ \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i > 0 \end{array} \right. $ +\form#134:$ \frac{\partial E}{\partial a_i} = \left\{ \begin{array}{lr} \sum_{x_i} x_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\ 0 & \mathrm{if} \; x_i > 0 \end{array} \right. $ +\form#135:$ (T \times N \times ...) $ +\form#136:$ T $ +\form#137:$ N $ +\form#138:$ (N \times T \times ...) $ +\form#139:$ (T \times N) $ +\form#140:$ \delta $ +\form#141:$ \delta_{t,n} = 0 $ +\form#142:$ n $ +\form#143:$ h_{t-1} $ +\form#144:$ \delta_t = 0 $ +\form#145:$ \delta_{t,n} = 1 $ +\form#146:$ t-1 $ +\form#147:$ x_{static} $ +\form#148:$ x'_t = [x_t; x_{static}] $ +\form#149:$ (T \times N \times D) $ +\form#150:$ D $ +\form#151:$ y = \max(0, x) $ +\form#152:$ \nu $ +\form#153:$ y = \max(0, x) + \nu \min(0, x) $ +\form#154:$ \frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} 0 & \mathrm{if} \; x \le 0 \\ \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0 \end{array} \right. $ +\form#155:$ \frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} \nu \frac{\partial E}{\partial y} & \mathrm{if} \; x \le 0 \\ \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0 \end{array} \right. $ +\form#156:$ x_t $ +\form#157:$ h_t := \tanh[ W_{hh} h_{t_1} + W_{xh} x_t + b_h ] $ +\form#158:$ o_t := \tanh[ W_{ho} h_t + b_o ] $ +\form#159:$ i $ +\form#160:$ (d_0 \times ... \times d_i \times ... \times d_j \times ... \times d_n) $ +\form#161:$ (d_i \times ... \times d_j) $ +\form#162:$ z = x y $ +\form#163:$ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $ +\form#164:$ x \in [-\infty, +\infty]$ +\form#165:$ \hat{p}_n = \sigma(x_n) \in [0, 1] $ +\form#166:$ \sigma(.) $ +\form#167:$ y \in [0, 1] $ +\form#168:$ \frac{\partial E}{\partial x} = \frac{1}{n} \sum\limits_{n=1}^N (\hat{p}_n - p_n) $ +\form#169:$ y = (1 + \exp(-x))^{-1} $ +\form#170:$ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} y (1 - y) $ +\form#171:$ \hat{p}_{nk} = \exp(x_{nk}) / \left[\sum_{k'} \exp(x_{nk'})\right] $ +\form#172:$ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $ +\form#173:$ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} \left(1 - \left[\frac{\exp(2x) - 1}{exp(2x) + 1} \right]^2 \right) = \frac{\partial E}{\partial y} (1 - y^2) $ +\form#174:$ y = \left\{ \begin{array}{lr} 0 & \mathrm{if} \; x \le t \\ 1 & \mathrm{if} \; x > t \end{array} \right. $ diff --git a/doxygen/functions.html b/doxygen/functions.html index e51d0b99342..c0ec2c4d66c 100644 --- a/doxygen/functions.html +++ b/doxygen/functions.html @@ -3,7 +3,7 @@ - + Caffe: Class Members @@ -29,7 +29,7 @@ - + @@ -101,9 +101,9 @@
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    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
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    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
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    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
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    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/functions_func_l.html b/doxygen/functions_func_l.html index 0521d301c7b..c596fae65f4 100644 --- a/doxygen/functions_func_l.html +++ b/doxygen/functions_func_l.html @@ -3,7 +3,7 @@ - + Caffe: Class Members - Functions @@ -29,7 +29,7 @@ - + @@ -126,9 +126,9 @@
    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/functions_func_m.html b/doxygen/functions_func_m.html index 0d1d524ff83..4472b459073 100644 --- a/doxygen/functions_func_m.html +++ b/doxygen/functions_func_m.html @@ -3,7 +3,7 @@ - + Caffe: Class Members - Functions @@ -29,7 +29,7 @@ - + @@ -71,6 +71,7 @@
  • MaxTopBlobs() : caffe::AccuracyLayer< Dtype > , caffe::DataLayer< Dtype > +, caffe::InfogainLossLayer< Dtype > , caffe::Layer< Dtype > , caffe::PoolingLayer< Dtype > , caffe::SoftmaxWithLossLayer< Dtype > @@ -94,6 +95,7 @@ , caffe::DummyDataLayer< Dtype > , caffe::FilterLayer< Dtype > , caffe::HDF5DataLayer< Dtype > +, caffe::InfogainLossLayer< Dtype > , caffe::InputLayer< Dtype > , caffe::Layer< Dtype > , caffe::PoolingLayer< Dtype > @@ -105,9 +107,9 @@
    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/functions_func_n.html b/doxygen/functions_func_n.html index 5e565a4647f..889a24dc5ad 100644 --- a/doxygen/functions_func_n.html +++ b/doxygen/functions_func_n.html @@ -3,7 +3,7 @@ - + Caffe: Class Members - Functions @@ -29,7 +29,7 @@ - + @@ -74,9 +74,9 @@
    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/functions_func_o.html b/doxygen/functions_func_o.html index 025c92b857f..4264780fd8e 100644 --- a/doxygen/functions_func_o.html +++ b/doxygen/functions_func_o.html @@ -3,7 +3,7 @@ - + Caffe: Class Members - Functions @@ -29,7 +29,7 @@ - + @@ -70,9 +70,9 @@
    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/functions_func_p.html b/doxygen/functions_func_p.html index 635fbd5b8a9..cec4380cf32 100644 --- a/doxygen/functions_func_p.html +++ b/doxygen/functions_func_p.html @@ -3,7 +3,7 @@ - + Caffe: Class Members - Functions @@ -29,7 +29,7 @@ - + @@ -86,9 +86,9 @@
    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/functions_func_r.html b/doxygen/functions_func_r.html index 1d033cc4c41..a51c06c7e9d 100644 --- a/doxygen/functions_func_r.html +++ b/doxygen/functions_func_r.html @@ -3,7 +3,7 @@ - + Caffe: Class Members - Functions @@ -29,7 +29,7 @@ - + @@ -135,9 +135,9 @@
    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/functions_func_s.html b/doxygen/functions_func_s.html index d43c04f93b4..7190cf15e14 100644 --- a/doxygen/functions_func_s.html +++ b/doxygen/functions_func_s.html @@ -3,7 +3,7 @@ - + Caffe: Class Members - Functions @@ -29,7 +29,7 @@ - + @@ -106,6 +106,9 @@
  • StopInternalThread() : caffe::InternalThread
    • +
  • sum_rows_of_H() +: caffe::InfogainLossLayer< Dtype > +
  • sumsq_data() : caffe::Blob< Dtype >
    • @@ -116,9 +119,9 @@
    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/functions_func_t.html b/doxygen/functions_func_t.html index 060944d8a9a..29e5ee6861a 100644 --- a/doxygen/functions_func_t.html +++ b/doxygen/functions_func_t.html @@ -3,7 +3,7 @@ - + Caffe: Class Members - Functions @@ -29,7 +29,7 @@ - + @@ -154,9 +154,9 @@
    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/functions_func_u.html b/doxygen/functions_func_u.html index 779b394f0e8..9b1ede5a60c 100644 --- a/doxygen/functions_func_u.html +++ b/doxygen/functions_func_u.html @@ -3,7 +3,7 @@ - + Caffe: Class Members - Functions @@ -29,7 +29,7 @@ - + @@ -71,9 +71,9 @@
    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/functions_func_w.html b/doxygen/functions_func_w.html index fabc6922de6..8cb0a65e361 100644 --- a/doxygen/functions_func_w.html +++ b/doxygen/functions_func_w.html @@ -3,7 +3,7 @@ - + Caffe: Class Members - Functions @@ -29,7 +29,7 @@ - + @@ -68,9 +68,9 @@
    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/functions_g.html b/doxygen/functions_g.html index f3c154222c1..ecd606723dd 100644 --- a/doxygen/functions_g.html +++ b/doxygen/functions_g.html @@ -3,7 +3,7 @@ - + Caffe: Class Members @@ -29,7 +29,7 @@ - + @@ -62,16 +62,17 @@

    - g -

    -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
    diff --git a/doxygen/functions_h.html b/doxygen/functions_h.html index eeba84fa059..54458109807 100644 --- a/doxygen/functions_h.html +++ b/doxygen/functions_h.html @@ -3,7 +3,7 @@ - + Caffe: Class Members @@ -29,7 +29,7 @@ - + @@ -63,6 +63,7 @@

    - h -

    • has_ignore_label_ : caffe::AccuracyLayer< Dtype > +, caffe::InfogainLossLayer< Dtype > , caffe::SigmoidCrossEntropyLossLayer< Dtype > , caffe::SoftmaxWithLossLayer< Dtype >
      • @@ -76,9 +77,9 @@
      -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/functions_i.html b/doxygen/functions_i.html index 2d4e2594e86..2a84053e318 100644 --- a/doxygen/functions_i.html +++ b/doxygen/functions_i.html @@ -3,7 +3,7 @@ - + Caffe: Class Members @@ -29,7 +29,7 @@ - + @@ -63,6 +63,7 @@

      - i -

      -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/functions_r.html b/doxygen/functions_r.html index 6d0ffa5e2e2..37d5c83f56e 100644 --- a/doxygen/functions_r.html +++ b/doxygen/functions_r.html @@ -3,7 +3,7 @@ - + Caffe: Class Members @@ -29,7 +29,7 @@ - + @@ -138,9 +138,9 @@
      -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/functions_s.html b/doxygen/functions_s.html index 1a2f8023b03..d641f9cb10d 100644 --- a/doxygen/functions_s.html +++ b/doxygen/functions_s.html @@ -3,7 +3,7 @@ - + Caffe: Class Members @@ -29,7 +29,7 @@ - + @@ -115,13 +115,16 @@ : caffe::SigmoidCrossEntropyLossLayer< Dtype >
    • softmax_bottom_vec_ -: caffe::SoftmaxWithLossLayer< Dtype > +: caffe::InfogainLossLayer< Dtype > +, caffe::SoftmaxWithLossLayer< Dtype >
    • softmax_layer_ -: caffe::SoftmaxWithLossLayer< Dtype > +: caffe::InfogainLossLayer< Dtype > +, caffe::SoftmaxWithLossLayer< Dtype >
    • softmax_top_vec_ -: caffe::SoftmaxWithLossLayer< Dtype > +: caffe::InfogainLossLayer< Dtype > +, caffe::SoftmaxWithLossLayer< Dtype >
    • SoftmaxWithLossLayer() : caffe::SoftmaxWithLossLayer< Dtype > @@ -153,6 +156,9 @@ , caffe::ReductionLayer< Dtype > , caffe::SoftmaxLayer< Dtype >
      • +
    • sum_rows_of_H() +: caffe::InfogainLossLayer< Dtype > +
    • sumsq_data() : caffe::Blob< Dtype >
      • @@ -163,9 +169,9 @@
      -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/functions_t.html b/doxygen/functions_t.html index 4e480897a9b..0502d3e9b57 100644 --- a/doxygen/functions_t.html +++ b/doxygen/functions_t.html @@ -3,7 +3,7 @@ - + Caffe: Class Members @@ -29,7 +29,7 @@ - + @@ -166,9 +166,9 @@
      -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/functions_u.html b/doxygen/functions_u.html index ee03565e625..6cd5f79a3c8 100644 --- a/doxygen/functions_u.html +++ b/doxygen/functions_u.html @@ -3,7 +3,7 @@ - + Caffe: Class Members @@ -29,7 +29,7 @@ - + @@ -74,9 +74,9 @@
      -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
      diff --git a/doxygen/functions_vars.html b/doxygen/functions_vars.html index 72205b934a6..a148a71e8c3 100644 --- a/doxygen/functions_vars.html +++ b/doxygen/functions_vars.html @@ -3,7 +3,7 @@ - + Caffe: Class Members - Variables @@ -29,7 +29,7 @@ - + @@ -146,6 +146,7 @@

      - h -

      • has_ignore_label_ : caffe::AccuracyLayer< Dtype > +, caffe::InfogainLossLayer< Dtype > , caffe::SigmoidCrossEntropyLossLayer< Dtype > , caffe::SoftmaxWithLossLayer< Dtype >
        • @@ -158,6 +159,7 @@

        - i -

        @@ -305,13 +309,16 @@ : caffe::SigmoidCrossEntropyLossLayer< Dtype >
      • softmax_bottom_vec_ -: caffe::SoftmaxWithLossLayer< Dtype > +: caffe::InfogainLossLayer< Dtype > +, caffe::SoftmaxWithLossLayer< Dtype >
      • softmax_layer_ -: caffe::SoftmaxWithLossLayer< Dtype > +: caffe::InfogainLossLayer< Dtype > +, caffe::SoftmaxWithLossLayer< Dtype >
      • softmax_top_vec_ -: caffe::SoftmaxWithLossLayer< Dtype > +: caffe::InfogainLossLayer< Dtype > +, caffe::SoftmaxWithLossLayer< Dtype >
      • split_layer_ : caffe::SPPLayer< Dtype > @@ -358,9 +365,9 @@
        -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/functions_w.html b/doxygen/functions_w.html index 604e3f95430..8b85411dfff 100644 --- a/doxygen/functions_w.html +++ b/doxygen/functions_w.html @@ -3,7 +3,7 @@ - + Caffe: Class Members @@ -29,7 +29,7 @@ - + @@ -68,9 +68,9 @@
        -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/hdf5_8hpp_source.html b/doxygen/hdf5_8hpp_source.html index ea692f3b515..cbc8bd11890 100644 --- a/doxygen/hdf5_8hpp_source.html +++ b/doxygen/hdf5_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/util/hdf5.hpp Source File @@ -29,7 +29,7 @@ - + @@ -66,13 +66,13 @@
        hdf5.hpp
        -
        1 #ifndef CAFFE_UTIL_HDF5_H_
        2 #define CAFFE_UTIL_HDF5_H_
        3 
        4 #include <string>
        5 
        6 #include "hdf5.h"
        7 #include "hdf5_hl.h"
        8 
        9 #include "caffe/blob.hpp"
        10 
        11 namespace caffe {
        12 
        13 template <typename Dtype>
        14 void hdf5_load_nd_dataset_helper(
        15  hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
        16  Blob<Dtype>* blob);
        17 
        18 template <typename Dtype>
        19 void hdf5_load_nd_dataset(
        20  hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
        21  Blob<Dtype>* blob);
        22 
        23 template <typename Dtype>
        24 void hdf5_save_nd_dataset(
        25  const hid_t file_id, const string& dataset_name, const Blob<Dtype>& blob,
        26  bool write_diff = false);
        27 
        28 int hdf5_load_int(hid_t loc_id, const string& dataset_name);
        29 void hdf5_save_int(hid_t loc_id, const string& dataset_name, int i);
        30 string hdf5_load_string(hid_t loc_id, const string& dataset_name);
        31 void hdf5_save_string(hid_t loc_id, const string& dataset_name,
        32  const string& s);
        33 
        34 int hdf5_get_num_links(hid_t loc_id);
        35 string hdf5_get_name_by_idx(hid_t loc_id, int idx);
        36 
        37 } // namespace caffe
        38 
        39 #endif // CAFFE_UTIL_HDF5_H_
        caffe
        A layer factory that allows one to register layers. During runtime, registered layers can be called b...
        Definition: blob.hpp:14
        +
        1 #ifndef CAFFE_UTIL_HDF5_H_
        2 #define CAFFE_UTIL_HDF5_H_
        3 
        4 #include <string>
        5 
        6 #include "hdf5.h"
        7 #include "hdf5_hl.h"
        8 
        9 #include "caffe/blob.hpp"
        10 
        11 namespace caffe {
        12 
        13 template <typename Dtype>
        14 void hdf5_load_nd_dataset_helper(
        15  hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
        16  Blob<Dtype>* blob, bool reshape);
        17 
        18 template <typename Dtype>
        19 void hdf5_load_nd_dataset(
        20  hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
        21  Blob<Dtype>* blob, bool reshape = false);
        22 
        23 template <typename Dtype>
        24 void hdf5_save_nd_dataset(
        25  const hid_t file_id, const string& dataset_name, const Blob<Dtype>& blob,
        26  bool write_diff = false);
        27 
        28 int hdf5_load_int(hid_t loc_id, const string& dataset_name);
        29 void hdf5_save_int(hid_t loc_id, const string& dataset_name, int i);
        30 string hdf5_load_string(hid_t loc_id, const string& dataset_name);
        31 void hdf5_save_string(hid_t loc_id, const string& dataset_name,
        32  const string& s);
        33 
        34 int hdf5_get_num_links(hid_t loc_id);
        35 string hdf5_get_name_by_idx(hid_t loc_id, int idx);
        36 
        37 } // namespace caffe
        38 
        39 #endif // CAFFE_UTIL_HDF5_H_
        caffe
        A layer factory that allows one to register layers. During runtime, registered layers can be called b...
        Definition: blob.hpp:14
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/hdf5__data__layer_8hpp_source.html b/doxygen/hdf5__data__layer_8hpp_source.html index 8071550051b..6b9f305d207 100644 --- a/doxygen/hdf5__data__layer_8hpp_source.html +++ b/doxygen/hdf5__data__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/hdf5_data_layer.hpp Source File @@ -29,7 +29,7 @@
        - + @@ -66,25 +66,25 @@
        hdf5_data_layer.hpp
        -
        1 #ifndef CAFFE_HDF5_DATA_LAYER_HPP_
        2 #define CAFFE_HDF5_DATA_LAYER_HPP_
        3 
        4 #include "hdf5.h"
        5 
        6 #include <string>
        7 #include <vector>
        8 
        9 #include "caffe/blob.hpp"
        10 #include "caffe/layer.hpp"
        11 #include "caffe/proto/caffe.pb.h"
        12 
        13 #include "caffe/layers/base_data_layer.hpp"
        14 
        15 namespace caffe {
        16 
        22 template <typename Dtype>
        23 class HDF5DataLayer : public Layer<Dtype> {
        24  public:
        25  explicit HDF5DataLayer(const LayerParameter& param)
        26  : Layer<Dtype>(param), offset_() {}
        27  virtual ~HDF5DataLayer();
        28  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        29  const vector<Blob<Dtype>*>& top);
        30  // Data layers should be shared by multiple solvers in parallel
        31  virtual inline bool ShareInParallel() const { return true; }
        32  // Data layers have no bottoms, so reshaping is trivial.
        33  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        34  const vector<Blob<Dtype>*>& top) {}
        35 
        36  virtual inline const char* type() const { return "HDF5Data"; }
        37  virtual inline int ExactNumBottomBlobs() const { return 0; }
        38  virtual inline int MinTopBlobs() const { return 1; }
        39 
        40  protected:
        41  void Next();
        42  bool Skip();
        43 
        44  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        45  const vector<Blob<Dtype>*>& top);
        46  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        47  const vector<Blob<Dtype>*>& top);
        48  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        49  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
        50  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        51  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
        52  virtual void LoadHDF5FileData(const char* filename);
        53 
        54  std::vector<std::string> hdf_filenames_;
        55  unsigned int num_files_;
        56  unsigned int current_file_;
        57  hsize_t current_row_;
        58  std::vector<shared_ptr<Blob<Dtype> > > hdf_blobs_;
        59  std::vector<unsigned int> data_permutation_;
        60  std::vector<unsigned int> file_permutation_;
        61  uint64_t offset_;
        62 };
        63 
        64 } // namespace caffe
        65 
        66 #endif // CAFFE_HDF5_DATA_LAYER_HPP_
        caffe::HDF5DataLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::HDF5DataLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Using the CPU device, compute the layer output.
        Definition: hdf5_data_layer.cpp:161
        +
        1 #ifndef CAFFE_HDF5_DATA_LAYER_HPP_
        2 #define CAFFE_HDF5_DATA_LAYER_HPP_
        3 
        4 #include "hdf5.h"
        5 
        6 #include <string>
        7 #include <vector>
        8 
        9 #include "caffe/blob.hpp"
        10 #include "caffe/layer.hpp"
        11 #include "caffe/proto/caffe.pb.h"
        12 
        13 #include "caffe/layers/base_data_layer.hpp"
        14 
        15 namespace caffe {
        16 
        22 template <typename Dtype>
        23 class HDF5DataLayer : public Layer<Dtype> {
        24  public:
        25  explicit HDF5DataLayer(const LayerParameter& param)
        26  : Layer<Dtype>(param), offset_() {}
        27  virtual ~HDF5DataLayer();
        28  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        29  const vector<Blob<Dtype>*>& top);
        30  // Data layers have no bottoms, so reshaping is trivial.
        31  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        32  const vector<Blob<Dtype>*>& top) {}
        33 
        34  virtual inline const char* type() const { return "HDF5Data"; }
        35  virtual inline int ExactNumBottomBlobs() const { return 0; }
        36  virtual inline int MinTopBlobs() const { return 1; }
        37 
        38  protected:
        39  void Next();
        40  bool Skip();
        41 
        42  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        43  const vector<Blob<Dtype>*>& top);
        44  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        45  const vector<Blob<Dtype>*>& top);
        46  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        47  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
        48  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        49  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
        50  virtual void LoadHDF5FileData(const char* filename);
        51 
        52  std::vector<std::string> hdf_filenames_;
        53  unsigned int num_files_;
        54  unsigned int current_file_;
        55  hsize_t current_row_;
        56  std::vector<shared_ptr<Blob<Dtype> > > hdf_blobs_;
        57  std::vector<unsigned int> data_permutation_;
        58  std::vector<unsigned int> file_permutation_;
        59  uint64_t offset_;
        60 };
        61 
        62 } // namespace caffe
        63 
        64 #endif // CAFFE_HDF5_DATA_LAYER_HPP_
        caffe::HDF5DataLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        +
        caffe::HDF5DataLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Using the CPU device, compute the layer output.
        Definition: hdf5_data_layer.cpp:162
        caffe::Layer
        An interface for the units of computation which can be composed into a Net.
        Definition: layer.hpp:33
        caffe
        A layer factory that allows one to register layers. During runtime, registered layers can be called b...
        Definition: blob.hpp:14
        -
        caffe::HDF5DataLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        Definition: hdf5_data_layer.hpp:50
        -
        caffe::HDF5DataLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: hdf5_data_layer.hpp:33
        +
        caffe::HDF5DataLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        Definition: hdf5_data_layer.hpp:48
        +
        caffe::HDF5DataLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: hdf5_data_layer.hpp:31
        caffe::HDF5DataLayer
        Provides data to the Net from HDF5 files.
        Definition: hdf5_data_layer.hpp:23
        -
        caffe::HDF5DataLayer::MinTopBlobs
        virtual int MinTopBlobs() const
        Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
        Definition: hdf5_data_layer.hpp:38
        -
        caffe::HDF5DataLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: hdf5_data_layer.cpp:72
        -
        caffe::HDF5DataLayer::ExactNumBottomBlobs
        virtual int ExactNumBottomBlobs() const
        Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
        Definition: hdf5_data_layer.hpp:37
        -
        caffe::HDF5DataLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
        Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        Definition: hdf5_data_layer.hpp:48
        -
        caffe::HDF5DataLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: hdf5_data_layer.hpp:36
        +
        caffe::HDF5DataLayer::MinTopBlobs
        virtual int MinTopBlobs() const
        Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
        Definition: hdf5_data_layer.hpp:36
        +
        caffe::HDF5DataLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: hdf5_data_layer.cpp:73
        +
        caffe::HDF5DataLayer::ExactNumBottomBlobs
        virtual int ExactNumBottomBlobs() const
        Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
        Definition: hdf5_data_layer.hpp:35
        +
        caffe::HDF5DataLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
        Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        Definition: hdf5_data_layer.hpp:46
        +
        caffe::HDF5DataLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: hdf5_data_layer.hpp:34
        caffe::Blob
        A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
        Definition: blob.hpp:24
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/hdf5__output__layer_8hpp_source.html b/doxygen/hdf5__output__layer_8hpp_source.html index 7b9d78f509f..e270eb86a0e 100644 --- a/doxygen/hdf5__output__layer_8hpp_source.html +++ b/doxygen/hdf5__output__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/hdf5_output_layer.hpp Source File @@ -29,7 +29,7 @@
        - + @@ -66,25 +66,25 @@
        hdf5_output_layer.hpp
        -
        1 #ifndef CAFFE_HDF5_OUTPUT_LAYER_HPP_
        2 #define CAFFE_HDF5_OUTPUT_LAYER_HPP_
        3 
        4 #include "hdf5.h"
        5 
        6 #include <string>
        7 #include <vector>
        8 
        9 #include "caffe/blob.hpp"
        10 #include "caffe/layer.hpp"
        11 #include "caffe/proto/caffe.pb.h"
        12 
        13 namespace caffe {
        14 
        15 #define HDF5_DATA_DATASET_NAME "data"
        16 #define HDF5_DATA_LABEL_NAME "label"
        17 
        23 template <typename Dtype>
        24 class HDF5OutputLayer : public Layer<Dtype> {
        25  public:
        26  explicit HDF5OutputLayer(const LayerParameter& param)
        27  : Layer<Dtype>(param), file_opened_(false) {}
        28  virtual ~HDF5OutputLayer();
        29  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        30  const vector<Blob<Dtype>*>& top);
        31  // Data layers should be shared by multiple solvers in parallel
        32  virtual inline bool ShareInParallel() const { return true; }
        33  // Data layers have no bottoms, so reshaping is trivial.
        34  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        35  const vector<Blob<Dtype>*>& top) {}
        36 
        37  virtual inline const char* type() const { return "HDF5Output"; }
        38  // TODO: no limit on the number of blobs
        39  virtual inline int ExactNumBottomBlobs() const { return 2; }
        40  virtual inline int ExactNumTopBlobs() const { return 0; }
        41 
        42  inline std::string file_name() const { return file_name_; }
        43 
        44  protected:
        45  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        46  const vector<Blob<Dtype>*>& top);
        47  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        48  const vector<Blob<Dtype>*>& top);
        49  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        50  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        51  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        52  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        53  virtual void SaveBlobs();
        54 
        55  bool file_opened_;
        56  std::string file_name_;
        57  hid_t file_id_;
        58  Blob<Dtype> data_blob_;
        59  Blob<Dtype> label_blob_;
        60 };
        61 
        62 } // namespace caffe
        63 
        64 #endif // CAFFE_HDF5_OUTPUT_LAYER_HPP_
        caffe::Layer
        An interface for the units of computation which can be composed into a Net.
        Definition: layer.hpp:33
        +
        1 #ifndef CAFFE_HDF5_OUTPUT_LAYER_HPP_
        2 #define CAFFE_HDF5_OUTPUT_LAYER_HPP_
        3 
        4 #include "hdf5.h"
        5 
        6 #include <string>
        7 #include <vector>
        8 
        9 #include "caffe/blob.hpp"
        10 #include "caffe/layer.hpp"
        11 #include "caffe/proto/caffe.pb.h"
        12 
        13 namespace caffe {
        14 
        15 #define HDF5_DATA_DATASET_NAME "data"
        16 #define HDF5_DATA_LABEL_NAME "label"
        17 
        23 template <typename Dtype>
        24 class HDF5OutputLayer : public Layer<Dtype> {
        25  public:
        26  explicit HDF5OutputLayer(const LayerParameter& param)
        27  : Layer<Dtype>(param), file_opened_(false) {}
        28  virtual ~HDF5OutputLayer();
        29  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        30  const vector<Blob<Dtype>*>& top);
        31  // Data layers have no bottoms, so reshaping is trivial.
        32  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        33  const vector<Blob<Dtype>*>& top) {}
        34 
        35  virtual inline const char* type() const { return "HDF5Output"; }
        36  // TODO: no limit on the number of blobs
        37  virtual inline int ExactNumBottomBlobs() const { return 2; }
        38  virtual inline int ExactNumTopBlobs() const { return 0; }
        39 
        40  inline std::string file_name() const { return file_name_; }
        41 
        42  protected:
        43  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        44  const vector<Blob<Dtype>*>& top);
        45  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        46  const vector<Blob<Dtype>*>& top);
        47  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        48  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        49  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        50  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        51  virtual void SaveBlobs();
        52 
        53  bool file_opened_;
        54  std::string file_name_;
        55  hid_t file_id_;
        56  Blob<Dtype> data_blob_;
        57  Blob<Dtype> label_blob_;
        58 };
        59 
        60 } // namespace caffe
        61 
        62 #endif // CAFFE_HDF5_OUTPUT_LAYER_HPP_
        caffe::Layer
        An interface for the units of computation which can be composed into a Net.
        Definition: layer.hpp:33
        caffe
        A layer factory that allows one to register layers. During runtime, registered layers can be called b...
        Definition: blob.hpp:14
        caffe::HDF5OutputLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Using the CPU device, compute the layer output.
        Definition: hdf5_output_layer.cpp:41
        caffe::HDF5OutputLayer
        Write blobs to disk as HDF5 files.
        Definition: hdf5_output_layer.hpp:24
        -
        caffe::HDF5OutputLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: hdf5_output_layer.hpp:34
        -
        caffe::HDF5OutputLayer::ExactNumTopBlobs
        virtual int ExactNumTopBlobs() const
        Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
        Definition: hdf5_output_layer.hpp:40
        +
        caffe::HDF5OutputLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: hdf5_output_layer.hpp:32
        +
        caffe::HDF5OutputLayer::ExactNumTopBlobs
        virtual int ExactNumTopBlobs() const
        Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
        Definition: hdf5_output_layer.hpp:38
        caffe::HDF5OutputLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        caffe::HDF5OutputLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
        Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        Definition: hdf5_output_layer.cpp:62
        -
        caffe::HDF5OutputLayer::ExactNumBottomBlobs
        virtual int ExactNumBottomBlobs() const
        Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
        Definition: hdf5_output_layer.hpp:39
        -
        caffe::HDF5OutputLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: hdf5_output_layer.hpp:37
        +
        caffe::HDF5OutputLayer::ExactNumBottomBlobs
        virtual int ExactNumBottomBlobs() const
        Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
        Definition: hdf5_output_layer.hpp:37
        +
        caffe::HDF5OutputLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: hdf5_output_layer.hpp:35
        caffe::HDF5OutputLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: hdf5_output_layer.cpp:12
        caffe::HDF5OutputLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        caffe::Blob
        A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
        Definition: blob.hpp:24
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/hierarchy.html b/doxygen/hierarchy.html index 96fde267107..bc778cd9c30 100644 --- a/doxygen/hierarchy.html +++ b/doxygen/hierarchy.html @@ -3,7 +3,7 @@ - + Caffe: Class Hierarchy @@ -29,7 +29,7 @@
        - + @@ -78,12 +78,12 @@  Ccaffe::db::DB  ▼Ccaffe::Filler< Dtype >Fills a Blob with constant or randomly-generated data  Ccaffe::BilinearFiller< Dtype >Fills a Blob with coefficients for bilinear interpolation - Ccaffe::ConstantFiller< Dtype >Fills a Blob with constant values $ x = 0 $ - Ccaffe::GaussianFiller< Dtype >Fills a Blob with Gaussian-distributed values $ x = a $ - Ccaffe::MSRAFiller< Dtype >Fills a Blob with values $ x \sim N(0, \sigma^2) $ where $ \sigma^2 $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average - Ccaffe::PositiveUnitballFiller< Dtype >Fills a Blob with values $ x \in [0, 1] $ such that $ \forall i \sum_j x_{ij} = 1 $ - Ccaffe::UniformFiller< Dtype >Fills a Blob with uniformly distributed values $ x\sim U(a, b) $ - Ccaffe::XavierFiller< Dtype >Fills a Blob with values $ x \sim U(-a, +a) $ where $ a $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average + Ccaffe::ConstantFiller< Dtype >Fills a Blob with constant values $ x = 0 $ + Ccaffe::GaussianFiller< Dtype >Fills a Blob with Gaussian-distributed values $ x = a $ + Ccaffe::MSRAFiller< Dtype >Fills a Blob with values $ x \sim N(0, \sigma^2) $ where $ \sigma^2 $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average + Ccaffe::PositiveUnitballFiller< Dtype >Fills a Blob with values $ x \in [0, 1] $ such that $ \forall i \sum_j x_{ij} = 1 $ + Ccaffe::UniformFiller< Dtype >Fills a Blob with uniformly distributed values $ x\sim U(a, b) $ + Ccaffe::XavierFiller< Dtype >Fills a Blob with values $ x \sim U(-a, +a) $ where $ a $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average  Ccaffe::Caffe::RNG::Generator  ▼Ccaffe::InternalThread  ►Ccaffe::BasePrefetchingDataLayer< Dtype > @@ -92,7 +92,7 @@  Ccaffe::WindowDataLayer< Dtype >Provides data to the Net from windows of images files, specified by a window data file  ▼Ccaffe::Layer< Dtype >An interface for the units of computation which can be composed into a Net  Ccaffe::AccuracyLayer< Dtype >Computes the classification accuracy for a one-of-many classification task - Ccaffe::ArgMaxLayer< Dtype >Compute the index of the $ K $ max values for each datum across all dimensions $ (C \times H \times W) $ + Ccaffe::ArgMaxLayer< Dtype >Compute the index of the $ K $ max values for each datum across all dimensions $ (C \times H \times W) $  ►Ccaffe::BaseConvolutionLayer< Dtype >Abstract base class that factors out the BLAS code common to ConvolutionLayer and DeconvolutionLayer  Ccaffe::ConvolutionLayer< Dtype >Convolves the input image with a bank of learned filters, and (optionally) adds biases  Ccaffe::DeconvolutionLayer< Dtype >Convolve the input with a bank of learned filters, and (optionally) add biases, treating filters and convolution parameters in the opposite sense as ConvolutionLayer @@ -115,28 +115,28 @@  Ccaffe::InnerProductLayer< Dtype >Also known as a "fully-connected" layer, computes an inner product with a set of learned weights, and (optionally) adds biases  Ccaffe::InputLayer< Dtype >Provides data to the Net by assigning tops directly  ►Ccaffe::LossLayer< Dtype >An interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth labels – and output a singleton Blob representing the loss - Ccaffe::ContrastiveLossLayer< Dtype >Computes the contrastive loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks - Ccaffe::EuclideanLossLayer< Dtype >Computes the Euclidean (L2) loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ for real-valued regression tasks + Ccaffe::ContrastiveLossLayer< Dtype >Computes the contrastive loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks + Ccaffe::EuclideanLossLayer< Dtype >Computes the Euclidean (L2) loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ for real-valued regression tasks  Ccaffe::HingeLossLayer< Dtype >Computes the hinge loss for a one-of-many classification task  Ccaffe::InfogainLossLayer< Dtype >A generalization of MultinomialLogisticLossLayer that takes an "information gain" (infogain) matrix specifying the "value" of all label pairs  Ccaffe::MultinomialLogisticLossLayer< Dtype >Computes the multinomial logistic loss for a one-of-many classification task, directly taking a predicted probability distribution as input - Ccaffe::SigmoidCrossEntropyLossLayer< Dtype >Computes the cross-entropy (logistic) loss $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $, often used for predicting targets interpreted as probabilities + Ccaffe::SigmoidCrossEntropyLossLayer< Dtype >Computes the cross-entropy (logistic) loss $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $, often used for predicting targets interpreted as probabilities  Ccaffe::SoftmaxWithLossLayer< Dtype >Computes the multinomial logistic loss for a one-of-many classification task, passing real-valued predictions through a softmax to get a probability distribution over classes  Ccaffe::LRNLayer< Dtype >Normalize the input in a local region across or within feature maps  Ccaffe::LSTMUnitLayer< Dtype >A helper for LSTMLayer: computes a single timestep of the non-linearity of the LSTM, producing the updated cell and hidden states  Ccaffe::MVNLayer< Dtype >Normalizes the input to have 0-mean and/or unit (1) variance - ►Ccaffe::NeuronLayer< Dtype >An interface for layers that take one blob as input ( $ x $) and produce one equally-sized blob as output ( $ y $), where each element of the output depends only on the corresponding input element - Ccaffe::AbsValLayer< Dtype >Computes $ y = |x| $ - Ccaffe::BNLLLayer< Dtype >Computes $ y = x + \log(1 + \exp(-x)) $ if $ x > 0 $; $ y = \log(1 + \exp(x)) $ otherwise - Ccaffe::DropoutLayer< Dtype >During training only, sets a random portion of $x$ to 0, adjusting the rest of the vector magnitude accordingly - Ccaffe::ELULayer< Dtype >Exponential Linear Unit non-linearity $ y = \left\{ \begin{array}{lr} x & \mathrm{if} \; x > 0 \\ \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 \end{array} \right. $ - Ccaffe::ExpLayer< Dtype >Computes $ y = \gamma ^ {\alpha x + \beta} $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $ - Ccaffe::LogLayer< Dtype >Computes $ y = log_{\gamma}(\alpha x + \beta) $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $ - Ccaffe::PowerLayer< Dtype >Computes $ y = (\alpha x + \beta) ^ \gamma $, as specified by the scale $ \alpha $, shift $ \beta $, and power $ \gamma $ - Ccaffe::PReLULayer< Dtype >Parameterized Rectified Linear Unit non-linearity $ y_i = \max(0, x_i) + a_i \min(0, x_i) $. The differences from ReLULayer are 1) negative slopes are learnable though backprop and 2) negative slopes can vary across channels. The number of axes of input blob should be greater than or equal to 2. The 1st axis (0-based) is seen as channels - Ccaffe::ReLULayer< Dtype >Rectified Linear Unit non-linearity $ y = \max(0, x) $. The simple max is fast to compute, and the function does not saturate - Ccaffe::SigmoidLayer< Dtype >Sigmoid function non-linearity $ y = (1 + \exp(-x))^{-1} $, a classic choice in neural networks - Ccaffe::TanHLayer< Dtype >TanH hyperbolic tangent non-linearity $ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $, popular in auto-encoders + ►Ccaffe::NeuronLayer< Dtype >An interface for layers that take one blob as input ( $ x $) and produce one equally-sized blob as output ( $ y $), where each element of the output depends only on the corresponding input element + Ccaffe::AbsValLayer< Dtype >Computes $ y = |x| $ + Ccaffe::BNLLLayer< Dtype >Computes $ y = x + \log(1 + \exp(-x)) $ if $ x > 0 $; $ y = \log(1 + \exp(x)) $ otherwise + Ccaffe::DropoutLayer< Dtype >During training only, sets a random portion of $x$ to 0, adjusting the rest of the vector magnitude accordingly + Ccaffe::ELULayer< Dtype >Exponential Linear Unit non-linearity $ y = \left\{ \begin{array}{lr} x & \mathrm{if} \; x > 0 \\ \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 \end{array} \right. $ + Ccaffe::ExpLayer< Dtype >Computes $ y = \gamma ^ {\alpha x + \beta} $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $ + Ccaffe::LogLayer< Dtype >Computes $ y = log_{\gamma}(\alpha x + \beta) $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $ + Ccaffe::PowerLayer< Dtype >Computes $ y = (\alpha x + \beta) ^ \gamma $, as specified by the scale $ \alpha $, shift $ \beta $, and power $ \gamma $ + Ccaffe::PReLULayer< Dtype >Parameterized Rectified Linear Unit non-linearity $ y_i = \max(0, x_i) + a_i \min(0, x_i) $. The differences from ReLULayer are 1) negative slopes are learnable though backprop and 2) negative slopes can vary across channels. The number of axes of input blob should be greater than or equal to 2. The 1st axis (0-based) is seen as channels + Ccaffe::ReLULayer< Dtype >Rectified Linear Unit non-linearity $ y = \max(0, x) $. The simple max is fast to compute, and the function does not saturate + Ccaffe::SigmoidLayer< Dtype >Sigmoid function non-linearity $ y = (1 + \exp(-x))^{-1} $, a classic choice in neural networks + Ccaffe::TanHLayer< Dtype >TanH hyperbolic tangent non-linearity $ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $, popular in auto-encoders  Ccaffe::ThresholdLayer< Dtype >Tests whether the input exceeds a threshold: outputs 1 for inputs above threshold; 0 otherwise  Ccaffe::ParameterLayer< Dtype >  Ccaffe::PoolingLayer< Dtype >Pools the input image by taking the max, average, etc. within regions @@ -177,9 +177,9 @@
        -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/hinge__loss__layer_8hpp_source.html b/doxygen/hinge__loss__layer_8hpp_source.html index 68b182379d4..9c18986bbb7 100644 --- a/doxygen/hinge__loss__layer_8hpp_source.html +++ b/doxygen/hinge__loss__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/hinge_loss_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -76,9 +76,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/im2col_8hpp_source.html b/doxygen/im2col_8hpp_source.html index 3df8f57b976..58a4743da3b 100644 --- a/doxygen/im2col_8hpp_source.html +++ b/doxygen/im2col_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/util/im2col.hpp Source File @@ -29,7 +29,7 @@ - + @@ -70,9 +70,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/im2col__layer_8hpp_source.html b/doxygen/im2col__layer_8hpp_source.html index 63280f8b68c..6be27fa515e 100644 --- a/doxygen/im2col__layer_8hpp_source.html +++ b/doxygen/im2col__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/im2col_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -86,9 +86,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/image__data__layer_8hpp_source.html b/doxygen/image__data__layer_8hpp_source.html index 0316d67c914..561ff05bbde 100644 --- a/doxygen/image__data__layer_8hpp_source.html +++ b/doxygen/image__data__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/image_data_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -66,8 +66,8 @@
        image_data_layer.hpp
        -
        1 #ifndef CAFFE_IMAGE_DATA_LAYER_HPP_
        2 #define CAFFE_IMAGE_DATA_LAYER_HPP_
        3 
        4 #include <string>
        5 #include <utility>
        6 #include <vector>
        7 
        8 #include "caffe/blob.hpp"
        9 #include "caffe/data_transformer.hpp"
        10 #include "caffe/internal_thread.hpp"
        11 #include "caffe/layer.hpp"
        12 #include "caffe/layers/base_data_layer.hpp"
        13 #include "caffe/proto/caffe.pb.h"
        14 
        15 namespace caffe {
        16 
        22 template <typename Dtype>
        23 class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
        24  public:
        25  explicit ImageDataLayer(const LayerParameter& param)
        26  : BasePrefetchingDataLayer<Dtype>(param) {}
        27  virtual ~ImageDataLayer();
        28  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
        29  const vector<Blob<Dtype>*>& top);
        30 
        31  virtual inline const char* type() const { return "ImageData"; }
        32  virtual inline int ExactNumBottomBlobs() const { return 0; }
        33  virtual inline int ExactNumTopBlobs() const { return 2; }
        34 
        35  protected:
        36  shared_ptr<Caffe::RNG> prefetch_rng_;
        37  virtual void ShuffleImages();
        38  virtual void load_batch(Batch<Dtype>* batch);
        39 
        40  vector<std::pair<std::string, int> > lines_;
        41  int lines_id_;
        42 };
        43 
        44 
        45 } // namespace caffe
        46 
        47 #endif // CAFFE_IMAGE_DATA_LAYER_HPP_
        caffe::Batch
        Definition: base_data_layer.hpp:49
        -
        caffe::BasePrefetchingDataLayer
        Definition: base_data_layer.hpp:55
        +
        1 #ifndef CAFFE_IMAGE_DATA_LAYER_HPP_
        2 #define CAFFE_IMAGE_DATA_LAYER_HPP_
        3 
        4 #include <string>
        5 #include <utility>
        6 #include <vector>
        7 
        8 #include "caffe/blob.hpp"
        9 #include "caffe/data_transformer.hpp"
        10 #include "caffe/internal_thread.hpp"
        11 #include "caffe/layer.hpp"
        12 #include "caffe/layers/base_data_layer.hpp"
        13 #include "caffe/proto/caffe.pb.h"
        14 
        15 namespace caffe {
        16 
        22 template <typename Dtype>
        23 class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
        24  public:
        25  explicit ImageDataLayer(const LayerParameter& param)
        26  : BasePrefetchingDataLayer<Dtype>(param) {}
        27  virtual ~ImageDataLayer();
        28  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
        29  const vector<Blob<Dtype>*>& top);
        30 
        31  virtual inline const char* type() const { return "ImageData"; }
        32  virtual inline int ExactNumBottomBlobs() const { return 0; }
        33  virtual inline int ExactNumTopBlobs() const { return 2; }
        34 
        35  protected:
        36  shared_ptr<Caffe::RNG> prefetch_rng_;
        37  virtual void ShuffleImages();
        38  virtual void load_batch(Batch<Dtype>* batch);
        39 
        40  vector<std::pair<std::string, int> > lines_;
        41  int lines_id_;
        42 };
        43 
        44 
        45 } // namespace caffe
        46 
        47 #endif // CAFFE_IMAGE_DATA_LAYER_HPP_
        caffe::Batch
        Definition: base_data_layer.hpp:47
        +
        caffe::BasePrefetchingDataLayer
        Definition: base_data_layer.hpp:53
        caffe
        A layer factory that allows one to register layers. During runtime, registered layers can be called b...
        Definition: blob.hpp:14
        caffe::ImageDataLayer
        Provides data to the Net from image files.
        Definition: image_data_layer.hpp:23
        caffe::ImageDataLayer::ExactNumTopBlobs
        virtual int ExactNumTopBlobs() const
        Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
        Definition: image_data_layer.hpp:33
        @@ -77,9 +77,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/index.html b/doxygen/index.html index 7e734005ee9..d3e97249eb7 100644 --- a/doxygen/index.html +++ b/doxygen/index.html @@ -3,7 +3,7 @@ - + Caffe: Main Page @@ -29,7 +29,7 @@
        - + @@ -65,9 +65,9 @@
        -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/infogain__loss__layer_8hpp_source.html b/doxygen/infogain__loss__layer_8hpp_source.html index d4b817d1bfc..8e3628188ce 100644 --- a/doxygen/infogain__loss__layer_8hpp_source.html +++ b/doxygen/infogain__loss__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/infogain_loss_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -66,24 +66,36 @@
        infogain_loss_layer.hpp
        -
        1 #ifndef CAFFE_INFOGAIN_LOSS_LAYER_HPP_
        2 #define CAFFE_INFOGAIN_LOSS_LAYER_HPP_
        3 
        4 #include <vector>
        5 
        6 #include "caffe/blob.hpp"
        7 #include "caffe/layer.hpp"
        8 #include "caffe/proto/caffe.pb.h"
        9 
        10 #include "caffe/layers/loss_layer.hpp"
        11 
        12 namespace caffe {
        13 
        46 template <typename Dtype>
        47 class InfogainLossLayer : public LossLayer<Dtype> {
        48  public:
        49  explicit InfogainLossLayer(const LayerParameter& param)
        50  : LossLayer<Dtype>(param), infogain_() {}
        51  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        52  const vector<Blob<Dtype>*>& top);
        53  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        54  const vector<Blob<Dtype>*>& top);
        55 
        56  // InfogainLossLayer takes 2-3 bottom Blobs; if there are 3 the third should
        57  // be the infogain matrix. (Otherwise the infogain matrix is loaded from a
        58  // file specified by LayerParameter.)
        59  virtual inline int ExactNumBottomBlobs() const { return -1; }
        60  virtual inline int MinBottomBlobs() const { return 2; }
        61  virtual inline int MaxBottomBlobs() const { return 3; }
        62 
        63  virtual inline const char* type() const { return "InfogainLoss"; }
        64 
        65  protected:
        67  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        68  const vector<Blob<Dtype>*>& top);
        69 
        102  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        103  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        104 
        105  Blob<Dtype> infogain_;
        106 };
        107 
        108 } // namespace caffe
        109 
        110 #endif // CAFFE_INFOGAIN_LOSS_LAYER_HPP_
        caffe::InfogainLossLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: infogain_loss_layer.cpp:11
        +
        1 #ifndef CAFFE_INFOGAIN_LOSS_LAYER_HPP_
        2 #define CAFFE_INFOGAIN_LOSS_LAYER_HPP_
        3 
        4 #include <vector>
        5 
        6 #include "caffe/blob.hpp"
        7 #include "caffe/layer.hpp"
        8 #include "caffe/proto/caffe.pb.h"
        9 
        10 #include "caffe/layers/loss_layer.hpp"
        11 #include "caffe/layers/softmax_layer.hpp"
        12 
        13 namespace caffe {
        14 
        47 template <typename Dtype>
        48 class InfogainLossLayer : public LossLayer<Dtype> {
        49  public:
        50  explicit InfogainLossLayer(const LayerParameter& param)
        51  : LossLayer<Dtype>(param), infogain_() {}
        52  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        53  const vector<Blob<Dtype>*>& top);
        54  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        55  const vector<Blob<Dtype>*>& top);
        56 
        57  // InfogainLossLayer takes 2-3 bottom Blobs; if there are 3 the third should
        58  // be the infogain matrix. (Otherwise the infogain matrix is loaded from a
        59  // file specified by LayerParameter.)
        60  virtual inline int ExactNumBottomBlobs() const { return -1; }
        61  virtual inline int MinBottomBlobs() const { return 2; }
        62  virtual inline int MaxBottomBlobs() const { return 3; }
        63 
        64  // InfogainLossLayer computes softmax prob internally.
        65  // optional second "top" outputs the softmax prob
        66  virtual inline int ExactNumTopBlobs() const { return -1; }
        67  virtual inline int MinTopBlobs() const { return 1; }
        68  virtual inline int MaxTopBlobs() const { return 2; }
        69 
        70  virtual inline const char* type() const { return "InfogainLoss"; }
        71 
        72  protected:
        74  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        75  const vector<Blob<Dtype>*>& top);
        76 
        109  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        110  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        111 
        116  virtual Dtype get_normalizer(
        117  LossParameter_NormalizationMode normalization_mode, int valid_count);
        119  virtual void sum_rows_of_H(const Blob<Dtype>* H);
        120 
        122  shared_ptr<Layer<Dtype> > softmax_layer_;
        124  Blob<Dtype> prob_;
        126  vector<Blob<Dtype>*> softmax_bottom_vec_;
        128  vector<Blob<Dtype>*> softmax_top_vec_;
        129 
        130  Blob<Dtype> infogain_;
        131  Blob<Dtype> sum_rows_H_; // cache the row sums of H.
        132 
        134  bool has_ignore_label_;
        136  int ignore_label_;
        138  LossParameter_NormalizationMode normalization_;
        139 
        140  int infogain_axis_, outer_num_, inner_num_, num_labels_;
        141 };
        142 
        143 } // namespace caffe
        144 
        145 #endif // CAFFE_INFOGAIN_LOSS_LAYER_HPP_
        caffe::InfogainLossLayer::MinTopBlobs
        virtual int MinTopBlobs() const
        Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
        Definition: infogain_loss_layer.hpp:67
        +
        caffe::InfogainLossLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: infogain_loss_layer.cpp:12
        +
        caffe::InfogainLossLayer::softmax_bottom_vec_
        vector< Blob< Dtype > * > softmax_bottom_vec_
        bottom vector holder used in call to the underlying SoftmaxLayer::Forward
        Definition: infogain_loss_layer.hpp:126
        +
        caffe::InfogainLossLayer::ignore_label_
        int ignore_label_
        The label indicating that an instance should be ignored.
        Definition: infogain_loss_layer.hpp:136
        +
        caffe::InfogainLossLayer::prob_
        Blob< Dtype > prob_
        prob stores the output probability predictions from the SoftmaxLayer.
        Definition: infogain_loss_layer.hpp:124
        caffe
        A layer factory that allows one to register layers. During runtime, registered layers can be called b...
        Definition: blob.hpp:14
        -
        caffe::InfogainLossLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: infogain_loss_layer.hpp:63
        -
        caffe::InfogainLossLayer
        A generalization of MultinomialLogisticLossLayer that takes an "information gain" (infogain) matrix s...
        Definition: infogain_loss_layer.hpp:47
        -
        caffe::InfogainLossLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
        Computes the infogain loss error gradient w.r.t. the predictions.
        Definition: infogain_loss_layer.cpp:71
        -
        caffe::InfogainLossLayer::MaxBottomBlobs
        virtual int MaxBottomBlobs() const
        Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is requi...
        Definition: infogain_loss_layer.hpp:61
        -
        caffe::InfogainLossLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        A generalization of MultinomialLogisticLossLayer that takes an "information gain" (infogain) matrix s...
        Definition: infogain_loss_layer.cpp:47
        -
        caffe::InfogainLossLayer::ExactNumBottomBlobs
        virtual int ExactNumBottomBlobs() const
        Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
        Definition: infogain_loss_layer.hpp:59
        +
        caffe::InfogainLossLayer::ExactNumTopBlobs
        virtual int ExactNumTopBlobs() const
        Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
        Definition: infogain_loss_layer.hpp:66
        +
        caffe::InfogainLossLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: infogain_loss_layer.hpp:70
        +
        caffe::InfogainLossLayer::normalization_
        LossParameter_NormalizationMode normalization_
        How to normalize the output loss.
        Definition: infogain_loss_layer.hpp:138
        +
        caffe::InfogainLossLayer
        A generalization of MultinomialLogisticLossLayer that takes an "information gain" (infogain) matrix s...
        Definition: infogain_loss_layer.hpp:48
        +
        caffe::InfogainLossLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
        Computes the infogain loss error gradient w.r.t. the predictions.
        Definition: infogain_loss_layer.cpp:168
        +
        caffe::InfogainLossLayer::softmax_layer_
        shared_ptr< Layer< Dtype > > softmax_layer_
        The internal SoftmaxLayer used to map predictions to a distribution.
        Definition: infogain_loss_layer.hpp:122
        +
        caffe::InfogainLossLayer::MaxBottomBlobs
        virtual int MaxBottomBlobs() const
        Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is requi...
        Definition: infogain_loss_layer.hpp:62
        +
        caffe::InfogainLossLayer::has_ignore_label_
        bool has_ignore_label_
        Whether to ignore instances with a certain label.
        Definition: infogain_loss_layer.hpp:134
        +
        caffe::InfogainLossLayer::softmax_top_vec_
        vector< Blob< Dtype > * > softmax_top_vec_
        top vector holder used in call to the underlying SoftmaxLayer::Forward
        Definition: infogain_loss_layer.hpp:128
        +
        caffe::InfogainLossLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        A generalization of MultinomialLogisticLossLayer that takes an "information gain" (infogain) matrix s...
        Definition: infogain_loss_layer.cpp:129
        +
        caffe::InfogainLossLayer::ExactNumBottomBlobs
        virtual int ExactNumBottomBlobs() const
        Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
        Definition: infogain_loss_layer.hpp:60
        +
        caffe::InfogainLossLayer::MaxTopBlobs
        virtual int MaxTopBlobs() const
        Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required...
        Definition: infogain_loss_layer.hpp:68
        +
        caffe::InfogainLossLayer::sum_rows_of_H
        virtual void sum_rows_of_H(const Blob< Dtype > *H)
        fill sum_rows_H_ according to matrix H
        Definition: infogain_loss_layer.cpp:115
        caffe::LossLayer
        An interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth ...
        Definition: loss_layer.hpp:23
        -
        caffe::InfogainLossLayer::MinBottomBlobs
        virtual int MinBottomBlobs() const
        Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
        Definition: infogain_loss_layer.hpp:60
        -
        caffe::InfogainLossLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: infogain_loss_layer.cpp:25
        +
        caffe::InfogainLossLayer::get_normalizer
        virtual Dtype get_normalizer(LossParameter_NormalizationMode normalization_mode, int valid_count)
        Definition: infogain_loss_layer.cpp:85
        +
        caffe::InfogainLossLayer::MinBottomBlobs
        virtual int MinBottomBlobs() const
        Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
        Definition: infogain_loss_layer.hpp:61
        +
        caffe::InfogainLossLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: infogain_loss_layer.cpp:51
        caffe::Blob
        A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
        Definition: blob.hpp:24
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/inner__product__layer_8hpp_source.html b/doxygen/inner__product__layer_8hpp_source.html index 2301df5c856..ff795e52e0f 100644 --- a/doxygen/inner__product__layer_8hpp_source.html +++ b/doxygen/inner__product__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/inner_product_layer.hpp Source File @@ -29,7 +29,7 @@
        - + @@ -83,9 +83,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/input__layer_8hpp_source.html b/doxygen/input__layer_8hpp_source.html index d7ef52b6bf0..58be6ee7c5f 100644 --- a/doxygen/input__layer_8hpp_source.html +++ b/doxygen/input__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/input_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -66,23 +66,23 @@
        input_layer.hpp
        -
        1 #ifndef CAFFE_INPUT_LAYER_HPP_
        2 #define CAFFE_INPUT_LAYER_HPP_
        3 
        4 #include <vector>
        5 
        6 #include "caffe/blob.hpp"
        7 #include "caffe/layer.hpp"
        8 #include "caffe/proto/caffe.pb.h"
        9 
        10 namespace caffe {
        11 
        18 template <typename Dtype>
        19 class InputLayer : public Layer<Dtype> {
        20  public:
        21  explicit InputLayer(const LayerParameter& param)
        22  : Layer<Dtype>(param) {}
        23  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        24  const vector<Blob<Dtype>*>& top);
        25  // Data layers should be shared by multiple solvers in parallel
        26  virtual inline bool ShareInParallel() const { return true; }
        27  // Data layers have no bottoms, so reshaping is trivial.
        28  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        29  const vector<Blob<Dtype>*>& top) {}
        30 
        31  virtual inline const char* type() const { return "Input"; }
        32  virtual inline int ExactNumBottomBlobs() const { return 0; }
        33  virtual inline int MinTopBlobs() const { return 1; }
        34 
        35  protected:
        36  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        37  const vector<Blob<Dtype>*>& top) {}
        38  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        39  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
        40 };
        41 
        42 } // namespace caffe
        43 
        44 #endif // CAFFE_INPUT_LAYER_HPP_
        caffe::Layer
        An interface for the units of computation which can be composed into a Net.
        Definition: layer.hpp:33
        +
        1 #ifndef CAFFE_INPUT_LAYER_HPP_
        2 #define CAFFE_INPUT_LAYER_HPP_
        3 
        4 #include <vector>
        5 
        6 #include "caffe/blob.hpp"
        7 #include "caffe/layer.hpp"
        8 #include "caffe/proto/caffe.pb.h"
        9 
        10 namespace caffe {
        11 
        18 template <typename Dtype>
        19 class InputLayer : public Layer<Dtype> {
        20  public:
        21  explicit InputLayer(const LayerParameter& param)
        22  : Layer<Dtype>(param) {}
        23  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        24  const vector<Blob<Dtype>*>& top);
        25  // Data layers have no bottoms, so reshaping is trivial.
        26  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        27  const vector<Blob<Dtype>*>& top) {}
        28 
        29  virtual inline const char* type() const { return "Input"; }
        30  virtual inline int ExactNumBottomBlobs() const { return 0; }
        31  virtual inline int MinTopBlobs() const { return 1; }
        32 
        33  protected:
        34  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        35  const vector<Blob<Dtype>*>& top) {}
        36  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        37  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
        38 };
        39 
        40 } // namespace caffe
        41 
        42 #endif // CAFFE_INPUT_LAYER_HPP_
        caffe::Layer
        An interface for the units of computation which can be composed into a Net.
        Definition: layer.hpp:33
        caffe
        A layer factory that allows one to register layers. During runtime, registered layers can be called b...
        Definition: blob.hpp:14
        -
        caffe::InputLayer::ExactNumBottomBlobs
        virtual int ExactNumBottomBlobs() const
        Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
        Definition: input_layer.hpp:32
        +
        caffe::InputLayer::ExactNumBottomBlobs
        virtual int ExactNumBottomBlobs() const
        Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
        Definition: input_layer.hpp:30
        caffe::InputLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: input_layer.cpp:8
        -
        caffe::InputLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Using the CPU device, compute the layer output.
        Definition: input_layer.hpp:36
        -
        caffe::InputLayer::MinTopBlobs
        virtual int MinTopBlobs() const
        Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
        Definition: input_layer.hpp:33
        +
        caffe::InputLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Using the CPU device, compute the layer output.
        Definition: input_layer.hpp:34
        +
        caffe::InputLayer::MinTopBlobs
        virtual int MinTopBlobs() const
        Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
        Definition: input_layer.hpp:31
        caffe::InputLayer
        Provides data to the Net by assigning tops directly.
        Definition: input_layer.hpp:19
        -
        caffe::InputLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: input_layer.hpp:31
        -
        caffe::InputLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
        Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        Definition: input_layer.hpp:38
        -
        caffe::InputLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: input_layer.hpp:28
        +
        caffe::InputLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: input_layer.hpp:29
        +
        caffe::InputLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
        Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        Definition: input_layer.hpp:36
        +
        caffe::InputLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: input_layer.hpp:26
        caffe::Blob
        A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
        Definition: blob.hpp:24
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/insert__splits_8hpp_source.html b/doxygen/insert__splits_8hpp_source.html index c09d83dcdef..636b90d5a56 100644 --- a/doxygen/insert__splits_8hpp_source.html +++ b/doxygen/insert__splits_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/util/insert_splits.hpp Source File @@ -29,7 +29,7 @@
        - + @@ -70,9 +70,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
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        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/io_8hpp_source.html b/doxygen/io_8hpp_source.html index 47b26226754..fa1a5ce2878 100644 --- a/doxygen/io_8hpp_source.html +++ b/doxygen/io_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/util/io.hpp Source File @@ -29,7 +29,7 @@ - + @@ -70,9 +70,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/layer_8hpp_source.html b/doxygen/layer_8hpp_source.html index 07f8fc7aa32..d580bc3f2c9 100644 --- a/doxygen/layer_8hpp_source.html +++ b/doxygen/layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -102,9 +102,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/layer__factory_8hpp_source.html b/doxygen/layer__factory_8hpp_source.html index 81db5b70d81..41db22b9675 100644 --- a/doxygen/layer__factory_8hpp_source.html +++ b/doxygen/layer__factory_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layer_factory.hpp Source File @@ -29,7 +29,7 @@ - + @@ -73,9 +73,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/log__layer_8hpp_source.html b/doxygen/log__layer_8hpp_source.html index 4a2588dd626..a33d868067a 100644 --- a/doxygen/log__layer_8hpp_source.html +++ b/doxygen/log__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/log_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -80,9 +80,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/loss__layer_8hpp_source.html b/doxygen/loss__layer_8hpp_source.html index 0503f6f57fc..00ba470d37f 100644 --- a/doxygen/loss__layer_8hpp_source.html +++ b/doxygen/loss__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/loss_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -79,9 +79,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/loss__layers_8hpp_source.html b/doxygen/loss__layers_8hpp_source.html deleted file mode 100644 index 16f743e7f36..00000000000 --- a/doxygen/loss__layers_8hpp_source.html +++ /dev/null @@ -1,456 +0,0 @@ - - - - - - -Caffe: include/caffe/loss_layers.hpp Source File - - - - - - - - - - -
        -
        - - - - - - -
        -
        Caffe -
        -
        -
        - - - - - - -
        -
        - - -
        - -
        - - -
        -
        -
        -
        loss_layers.hpp
        -
        -
        -
        1 #ifndef CAFFE_LOSS_LAYERS_HPP_
        -
        2 #define CAFFE_LOSS_LAYERS_HPP_
        -
        3 
        -
        4 #include <string>
        -
        5 #include <utility>
        -
        6 #include <vector>
        -
        7 
        -
        8 #include "caffe/blob.hpp"
        -
        9 #include "caffe/common.hpp"
        -
        10 #include "caffe/layer.hpp"
        -
        11 #include "caffe/neuron_layers.hpp"
        -
        12 #include "caffe/proto/caffe.pb.h"
        -
        13 
        -
        14 namespace caffe {
        -
        15 
        -
        16 const float kLOG_THRESHOLD = 1e-20;
        -
        17 
        -
        22 template <typename Dtype>
        -
        23 class AccuracyLayer : public Layer<Dtype> {
        -
        24  public:
        -
        33  explicit AccuracyLayer(const LayerParameter& param)
        -
        34  : Layer<Dtype>(param) {}
        -
        35  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        36  const vector<Blob<Dtype>*>& top);
        -
        37  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        38  const vector<Blob<Dtype>*>& top);
        -
        39 
        -
        40  virtual inline const char* type() const { return "Accuracy"; }
        -
        41  virtual inline int ExactNumBottomBlobs() const { return 2; }
        -
        42 
        -
        43  // If there are two top blobs, then the second blob will contain
        -
        44  // accuracies per class.
        -
        45  virtual inline int MinTopBlobs() const { return 1; }
        -
        46  virtual inline int MaxTopBlos() const { return 2; }
        -
        47 
        -
        48  protected:
        -
        73  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        74  const vector<Blob<Dtype>*>& top);
        -
        75 
        -
        76 
        -
        78  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        79  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
        -
        80  for (int i = 0; i < propagate_down.size(); ++i) {
        -
        81  if (propagate_down[i]) { NOT_IMPLEMENTED; }
        -
        82  }
        -
        83  }
        -
        84 
        -
        85  int label_axis_, outer_num_, inner_num_;
        -
        86 
        -
        87  int top_k_;
        -
        88 
        -
        90  bool has_ignore_label_;
        -
        92  int ignore_label_;
        -
        94  Blob<Dtype> nums_buffer_;
        -
        95 };
        -
        96 
        -
        105 template <typename Dtype>
        -
        106 class LossLayer : public Layer<Dtype> {
        -
        107  public:
        -
        108  explicit LossLayer(const LayerParameter& param)
        -
        109  : Layer<Dtype>(param) {}
        -
        110  virtual void LayerSetUp(
        -
        111  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
        -
        112  virtual void Reshape(
        -
        113  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
        -
        114 
        -
        115  virtual inline int ExactNumBottomBlobs() const { return 2; }
        -
        116 
        -
        123  virtual inline bool AutoTopBlobs() const { return true; }
        -
        124  virtual inline int ExactNumTopBlobs() const { return 1; }
        -
        129  virtual inline bool AllowForceBackward(const int bottom_index) const {
        -
        130  return bottom_index != 1;
        -
        131  }
        -
        132 };
        -
        133 
        -
        158 template <typename Dtype>
        -
        159 class ContrastiveLossLayer : public LossLayer<Dtype> {
        -
        160  public:
        -
        161  explicit ContrastiveLossLayer(const LayerParameter& param)
        -
        162  : LossLayer<Dtype>(param), diff_() {}
        -
        163  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        164  const vector<Blob<Dtype>*>& top);
        -
        165 
        -
        166  virtual inline int ExactNumBottomBlobs() const { return 3; }
        -
        167  virtual inline const char* type() const { return "ContrastiveLoss"; }
        -
        172  virtual inline bool AllowForceBackward(const int bottom_index) const {
        -
        173  return bottom_index != 2;
        -
        174  }
        -
        175 
        -
        176  protected:
        -
        178  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        179  const vector<Blob<Dtype>*>& top);
        -
        180  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        181  const vector<Blob<Dtype>*>& top);
        -
        182 
        -
        208  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        209  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        210  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        211  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        212 
        -
        213  Blob<Dtype> diff_; // cached for backward pass
        -
        214  Blob<Dtype> dist_sq_; // cached for backward pass
        -
        215  Blob<Dtype> diff_sq_; // tmp storage for gpu forward pass
        -
        216  Blob<Dtype> summer_vec_; // tmp storage for gpu forward pass
        -
        217 };
        -
        218 
        -
        245 template <typename Dtype>
        -
        246 class EuclideanLossLayer : public LossLayer<Dtype> {
        -
        247  public:
        -
        248  explicit EuclideanLossLayer(const LayerParameter& param)
        -
        249  : LossLayer<Dtype>(param), diff_() {}
        -
        250  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        251  const vector<Blob<Dtype>*>& top);
        -
        252 
        -
        253  virtual inline const char* type() const { return "EuclideanLoss"; }
        -
        258  virtual inline bool AllowForceBackward(const int bottom_index) const {
        -
        259  return true;
        -
        260  }
        -
        261 
        -
        262  protected:
        -
        264  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        265  const vector<Blob<Dtype>*>& top);
        -
        266  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        267  const vector<Blob<Dtype>*>& top);
        -
        268 
        -
        302  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        303  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        304  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        305  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        306 
        -
        307  Blob<Dtype> diff_;
        -
        308 };
        -
        309 
        -
        353 template <typename Dtype>
        -
        354 class HingeLossLayer : public LossLayer<Dtype> {
        -
        355  public:
        -
        356  explicit HingeLossLayer(const LayerParameter& param)
        -
        357  : LossLayer<Dtype>(param) {}
        -
        358 
        -
        359  virtual inline const char* type() const { return "HingeLoss"; }
        -
        360 
        -
        361  protected:
        -
        363  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        364  const vector<Blob<Dtype>*>& top);
        -
        365 
        -
        393  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        394  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        395 };
        -
        396 
        -
        429 template <typename Dtype>
        -
        430 class InfogainLossLayer : public LossLayer<Dtype> {
        -
        431  public:
        -
        432  explicit InfogainLossLayer(const LayerParameter& param)
        -
        433  : LossLayer<Dtype>(param), infogain_() {}
        -
        434  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        435  const vector<Blob<Dtype>*>& top);
        -
        436  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        437  const vector<Blob<Dtype>*>& top);
        -
        438 
        -
        439  // InfogainLossLayer takes 2-3 bottom Blobs; if there are 3 the third should
        -
        440  // be the infogain matrix. (Otherwise the infogain matrix is loaded from a
        -
        441  // file specified by LayerParameter.)
        -
        442  virtual inline int ExactNumBottomBlobs() const { return -1; }
        -
        443  virtual inline int MinBottomBlobs() const { return 2; }
        -
        444  virtual inline int MaxBottomBlobs() const { return 3; }
        -
        445 
        -
        446  virtual inline const char* type() const { return "InfogainLoss"; }
        -
        447 
        -
        448  protected:
        -
        450  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        451  const vector<Blob<Dtype>*>& top);
        -
        452 
        -
        485  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        486  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        487 
        -
        488  Blob<Dtype> infogain_;
        -
        489 };
        -
        490 
        -
        520 template <typename Dtype>
        -
        521 class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
        -
        522  public:
        -
        523  explicit MultinomialLogisticLossLayer(const LayerParameter& param)
        -
        524  : LossLayer<Dtype>(param) {}
        -
        525  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        526  const vector<Blob<Dtype>*>& top);
        -
        527 
        -
        528  virtual inline const char* type() const { return "MultinomialLogisticLoss"; }
        -
        529 
        -
        530  protected:
        -
        532  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        533  const vector<Blob<Dtype>*>& top);
        -
        534 
        -
        563  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        564  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        565 };
        -
        566 
        -
        596 template <typename Dtype>
        -
        597 class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
        -
        598  public:
        -
        599  explicit SigmoidCrossEntropyLossLayer(const LayerParameter& param)
        -
        600  : LossLayer<Dtype>(param),
        -
        601  sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
        -
        602  sigmoid_output_(new Blob<Dtype>()) {}
        -
        603  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        604  const vector<Blob<Dtype>*>& top);
        -
        605  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        606  const vector<Blob<Dtype>*>& top);
        -
        607 
        -
        608  virtual inline const char* type() const { return "SigmoidCrossEntropyLoss"; }
        -
        609 
        -
        610  protected:
        -
        612  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        613  const vector<Blob<Dtype>*>& top);
        -
        614 
        -
        645  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        646  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        647  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        648  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        649 
        -
        651  shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
        -
        653  shared_ptr<Blob<Dtype> > sigmoid_output_;
        -
        655  vector<Blob<Dtype>*> sigmoid_bottom_vec_;
        -
        657  vector<Blob<Dtype>*> sigmoid_top_vec_;
        -
        658 };
        -
        659 
        -
        660 // Forward declare SoftmaxLayer for use in SoftmaxWithLossLayer.
        -
        661 template <typename Dtype> class SoftmaxLayer;
        -
        662 
        -
        691 template <typename Dtype>
        -
        692 class SoftmaxWithLossLayer : public LossLayer<Dtype> {
        -
        693  public:
        -
        702  explicit SoftmaxWithLossLayer(const LayerParameter& param)
        -
        703  : LossLayer<Dtype>(param) {}
        -
        704  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        705  const vector<Blob<Dtype>*>& top);
        -
        706  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        707  const vector<Blob<Dtype>*>& top);
        -
        708 
        -
        709  virtual inline const char* type() const { return "SoftmaxWithLoss"; }
        -
        710  virtual inline int ExactNumTopBlobs() const { return -1; }
        -
        711  virtual inline int MinTopBlobs() const { return 1; }
        -
        712  virtual inline int MaxTopBlobs() const { return 2; }
        -
        713 
        -
        714  protected:
        -
        715  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        716  const vector<Blob<Dtype>*>& top);
        -
        717  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        718  const vector<Blob<Dtype>*>& top);
        -
        746  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        747  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        748  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        749  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        750 
        -
        751 
        -
        753  shared_ptr<Layer<Dtype> > softmax_layer_;
        -
        755  Blob<Dtype> prob_;
        -
        757  vector<Blob<Dtype>*> softmax_bottom_vec_;
        -
        759  vector<Blob<Dtype>*> softmax_top_vec_;
        -
        761  bool has_ignore_label_;
        -
        763  int ignore_label_;
        -
        766  bool normalize_;
        -
        767 
        -
        768  int softmax_axis_, outer_num_, inner_num_;
        -
        769 };
        -
        770 
        -
        771 } // namespace caffe
        -
        772 
        -
        773 #endif // CAFFE_LOSS_LAYERS_HPP_
        -
        caffe::LossLayer::AutoTopBlobs
        virtual bool AutoTopBlobs() const
        For convenience and backwards compatibility, instruct the Net to automatically allocate a single top ...
        Definition: loss_layers.hpp:123
        -
        caffe::SigmoidCrossEntropyLossLayer::sigmoid_bottom_vec_
        vector< Blob< Dtype > * > sigmoid_bottom_vec_
        bottom vector holder to call the underlying SigmoidLayer::Forward
        Definition: loss_layers.hpp:655
        -
        caffe::SoftmaxWithLossLayer::softmax_layer_
        shared_ptr< Layer< Dtype > > softmax_layer_
        The internal SoftmaxLayer used to map predictions to a distribution.
        Definition: loss_layers.hpp:753
        -
        caffe::ContrastiveLossLayer::AllowForceBackward
        virtual bool AllowForceBackward(const int bottom_index) const
        Definition: loss_layers.hpp:172
        -
        caffe::MultinomialLogisticLossLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: loss_layers.hpp:528
        -
        caffe::Layer
        An interface for the units of computation which can be composed into a Net.
        Definition: layer.hpp:33
        -
        caffe
        A layer factory that allows one to register layers. During runtime, registered layers could be called...
        Definition: blob.hpp:15
        -
        caffe::AccuracyLayer::MinTopBlobs
        virtual int MinTopBlobs() const
        Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
        Definition: loss_layers.hpp:45
        -
        caffe::AccuracyLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: accuracy_layer.cpp:26
        -
        caffe::SigmoidLayer
        Sigmoid function non-linearity , a classic choice in neural networks.
        Definition: neuron_layers.hpp:515
        -
        caffe::SigmoidCrossEntropyLossLayer::sigmoid_output_
        shared_ptr< Blob< Dtype > > sigmoid_output_
        sigmoid_output stores the output of the SigmoidLayer.
        Definition: loss_layers.hpp:653
        -
        caffe::SigmoidCrossEntropyLossLayer::sigmoid_top_vec_
        vector< Blob< Dtype > * > sigmoid_top_vec_
        top vector holder to call the underlying SigmoidLayer::Forward
        Definition: loss_layers.hpp:657
        -
        caffe::InfogainLossLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: infogain_loss_layer.cpp:14
        -
        caffe::ContrastiveLossLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Computes the Contrastive error gradient w.r.t. the inputs.
        Definition: contrastive_loss_layer.cpp:66
        -
        caffe::LossLayer::ExactNumBottomBlobs
        virtual int ExactNumBottomBlobs() const
        Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
        Definition: loss_layers.hpp:115
        -
        caffe::InfogainLossLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: loss_layers.hpp:446
        -
        caffe::HingeLossLayer
        Computes the hinge loss for a one-of-many classification task.
        Definition: loss_layers.hpp:354
        -
        caffe::InfogainLossLayer
        A generalization of MultinomialLogisticLossLayer that takes an "information gain" (infogain) matrix s...
        Definition: loss_layers.hpp:430
        -
        caffe::SigmoidCrossEntropyLossLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: sigmoid_cross_entropy_loss_layer.cpp:23
        -
        caffe::AccuracyLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Not implemented – AccuracyLayer cannot be used as a loss.
        Definition: loss_layers.hpp:78
        -
        caffe::LossLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: loss_layer.cpp:23
        -
        caffe::EuclideanLossLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Computes the Euclidean (L2) loss for real-valued regression tasks.
        Definition: euclidean_loss_layer.cpp:20
        -
        caffe::HingeLossLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Computes the hinge loss for a one-of-many classification task.
        Definition: hinge_loss_layer.cpp:14
        -
        caffe::SoftmaxWithLossLayer::SoftmaxWithLossLayer
        SoftmaxWithLossLayer(const LayerParameter &param)
        Definition: loss_layers.hpp:702
        -
        caffe::SoftmaxWithLossLayer::ExactNumTopBlobs
        virtual int ExactNumTopBlobs() const
        Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
        Definition: loss_layers.hpp:710
        -
        caffe::SoftmaxWithLossLayer::MinTopBlobs
        virtual int MinTopBlobs() const
        Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
        Definition: loss_layers.hpp:711
        -
        caffe::SigmoidCrossEntropyLossLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Computes the sigmoid cross-entropy loss error gradient w.r.t. the predictions.
        Definition: sigmoid_cross_entropy_loss_layer.cpp:52
        -
        caffe::LossLayer::AllowForceBackward
        virtual bool AllowForceBackward(const int bottom_index) const
        Definition: loss_layers.hpp:129
        -
        caffe::SoftmaxWithLossLayer::softmax_top_vec_
        vector< Blob< Dtype > * > softmax_top_vec_
        top vector holder used in call to the underlying SoftmaxLayer::Forward
        Definition: loss_layers.hpp:759
        -
        caffe::AccuracyLayer::ignore_label_
        int ignore_label_
        The label indicating that an instance should be ignored.
        Definition: loss_layers.hpp:92
        -
        caffe::AccuracyLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: accuracy_layer.cpp:14
        -
        caffe::SigmoidCrossEntropyLossLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        -
        caffe::SigmoidCrossEntropyLossLayer::sigmoid_layer_
        shared_ptr< SigmoidLayer< Dtype > > sigmoid_layer_
        The internal SigmoidLayer used to map predictions to probabilities.
        Definition: loss_layers.hpp:651
        -
        caffe::SoftmaxWithLossLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: softmax_loss_layer.cpp:13
        -
        caffe::AccuracyLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Definition: accuracy_layer.cpp:51
        -
        caffe::AccuracyLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: loss_layers.hpp:40
        -
        caffe::SoftmaxLayer
        Computes the softmax function.
        Definition: common_layers.hpp:597
        -
        caffe::InfogainLossLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: infogain_loss_layer.cpp:28
        -
        caffe::SoftmaxWithLossLayer::MaxTopBlobs
        virtual int MaxTopBlobs() const
        Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required...
        Definition: loss_layers.hpp:712
        -
        caffe::AccuracyLayer
        Computes the classification accuracy for a one-of-many classification task.
        Definition: loss_layers.hpp:23
        -
        caffe::HingeLossLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Computes the hinge loss error gradient w.r.t. the predictions.
        Definition: hinge_loss_layer.cpp:47
        -
        caffe::EuclideanLossLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: euclidean_loss_layer.cpp:11
        -
        caffe::EuclideanLossLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: loss_layers.hpp:253
        -
        caffe::AccuracyLayer::has_ignore_label_
        bool has_ignore_label_
        Whether to ignore instances with a certain label.
        Definition: loss_layers.hpp:90
        -
        caffe::InfogainLossLayer::MinBottomBlobs
        virtual int MinBottomBlobs() const
        Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
        Definition: loss_layers.hpp:443
        -
        caffe::SigmoidCrossEntropyLossLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: sigmoid_cross_entropy_loss_layer.cpp:12
        -
        caffe::LossLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: loss_layer.cpp:14
        -
        caffe::EuclideanLossLayer::AllowForceBackward
        virtual bool AllowForceBackward(const int bottom_index) const
        Definition: loss_layers.hpp:258
        -
        caffe::EuclideanLossLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        -
        caffe::MultinomialLogisticLossLayer
        Computes the multinomial logistic loss for a one-of-many classification task, directly taking a predi...
        Definition: loss_layers.hpp:521
        -
        caffe::ContrastiveLossLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Computes the contrastive loss where . This can be used to train siamese networks.
        Definition: contrastive_loss_layer.cpp:33
        -
        caffe::AccuracyLayer::AccuracyLayer
        AccuracyLayer(const LayerParameter &param)
        Definition: loss_layers.hpp:33
        -
        caffe::SoftmaxWithLossLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        -
        caffe::SigmoidCrossEntropyLossLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Computes the cross-entropy (logistic) loss , often used for predicting targets interpreted as probabi...
        Definition: sigmoid_cross_entropy_loss_layer.cpp:32
        -
        caffe::SigmoidCrossEntropyLossLayer
        Computes the cross-entropy (logistic) loss , often used for predicting targets interpreted as probabi...
        Definition: loss_layers.hpp:597
        -
        caffe::SigmoidCrossEntropyLossLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: loss_layers.hpp:608
        -
        caffe::SoftmaxWithLossLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Computes the softmax loss error gradient w.r.t. the predictions.
        Definition: softmax_loss_layer.cpp:87
        -
        caffe::SoftmaxWithLossLayer
        Computes the multinomial logistic loss for a one-of-many classification task, passing real-valued pre...
        Definition: loss_layers.hpp:692
        -
        caffe::SoftmaxWithLossLayer::softmax_bottom_vec_
        vector< Blob< Dtype > * > softmax_bottom_vec_
        bottom vector holder used in call to the underlying SoftmaxLayer::Forward
        Definition: loss_layers.hpp:757
        -
        caffe::SoftmaxWithLossLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: softmax_loss_layer.cpp:34
        -
        caffe::SoftmaxWithLossLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::ContrastiveLossLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        -
        caffe::ContrastiveLossLayer::ExactNumBottomBlobs
        virtual int ExactNumBottomBlobs() const
        Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
        Definition: loss_layers.hpp:166
        -
        caffe::ContrastiveLossLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::SoftmaxWithLossLayer::normalize_
        bool normalize_
        Definition: loss_layers.hpp:766
        -
        caffe::EuclideanLossLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Computes the Euclidean error gradient w.r.t. the inputs.
        Definition: euclidean_loss_layer.cpp:34
        -
        caffe::SoftmaxWithLossLayer::ignore_label_
        int ignore_label_
        The label indicating that an instance should be ignored.
        Definition: loss_layers.hpp:763
        -
        caffe::InfogainLossLayer::MaxBottomBlobs
        virtual int MaxBottomBlobs() const
        Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is requi...
        Definition: loss_layers.hpp:444
        -
        caffe::LossLayer
        An interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth ...
        Definition: loss_layers.hpp:106
        -
        caffe::SoftmaxWithLossLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the CPU device, compute the layer output.
        Definition: softmax_loss_layer.cpp:54
        -
        caffe::InfogainLossLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        A generalization of MultinomialLogisticLossLayer that takes an "information gain" (infogain) matrix s...
        Definition: infogain_loss_layer.cpp:50
        -
        caffe::ContrastiveLossLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: contrastive_loss_layer.cpp:12
        -
        caffe::SoftmaxWithLossLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: loss_layers.hpp:709
        -
        caffe::EuclideanLossLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::LossLayer::ExactNumTopBlobs
        virtual int ExactNumTopBlobs() const
        Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
        Definition: loss_layers.hpp:124
        -
        caffe::ContrastiveLossLayer
        Computes the contrastive loss where . This can be used to train siamese networks.
        Definition: loss_layers.hpp:159
        -
        caffe::InfogainLossLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Computes the infogain loss error gradient w.r.t. the predictions.
        Definition: infogain_loss_layer.cpp:74
        -
        caffe::AccuracyLayer::ExactNumBottomBlobs
        virtual int ExactNumBottomBlobs() const
        Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
        Definition: loss_layers.hpp:41
        -
        caffe::AccuracyLayer::nums_buffer_
        Blob< Dtype > nums_buffer_
        Keeps counts of the number of samples per class.
        Definition: loss_layers.hpp:94
        -
        caffe::HingeLossLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: loss_layers.hpp:359
        -
        caffe::InfogainLossLayer::ExactNumBottomBlobs
        virtual int ExactNumBottomBlobs() const
        Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
        Definition: loss_layers.hpp:442
        -
        caffe::MultinomialLogisticLossLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Computes the multinomial logistic loss for a one-of-many classification task, directly taking a predi...
        Definition: multinomial_logistic_loss_layer.cpp:23
        -
        caffe::EuclideanLossLayer
        Computes the Euclidean (L2) loss for real-valued regression tasks.
        Definition: loss_layers.hpp:246
        -
        caffe::SoftmaxWithLossLayer::prob_
        Blob< Dtype > prob_
        prob stores the output probability predictions from the SoftmaxLayer.
        Definition: loss_layers.hpp:755
        -
        caffe::MultinomialLogisticLossLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: multinomial_logistic_loss_layer.cpp:14
        -
        caffe::ContrastiveLossLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: loss_layers.hpp:167
        -
        caffe::MultinomialLogisticLossLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Computes the multinomial logistic loss error gradient w.r.t. the predictions.
        Definition: multinomial_logistic_loss_layer.cpp:40
        -
        caffe::Blob
        A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
        Definition: blob.hpp:25
        -
        caffe::SoftmaxWithLossLayer::has_ignore_label_
        bool has_ignore_label_
        Whether to ignore instances with a certain label.
        Definition: loss_layers.hpp:761
        -
        - -
        -Generated on Mon Oct 19 2015 15:25:08 for Caffe by   -doxygen - 1.8.9.1 -
        - - diff --git a/doxygen/lrn__layer_8hpp_source.html b/doxygen/lrn__layer_8hpp_source.html index ee165e55dd4..05b73b715bd 100644 --- a/doxygen/lrn__layer_8hpp_source.html +++ b/doxygen/lrn__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/lrn_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -82,9 +82,9 @@
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        diff --git a/doxygen/math__functions_8hpp_source.html b/doxygen/math__functions_8hpp_source.html index c66e4396b50..00eee8c7176 100644 --- a/doxygen/math__functions_8hpp_source.html +++ b/doxygen/math__functions_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/util/math_functions.hpp Source File @@ -29,7 +29,7 @@ - + @@ -66,13 +66,13 @@
        math_functions.hpp
        -
        1 #ifndef CAFFE_UTIL_MATH_FUNCTIONS_H_
        2 #define CAFFE_UTIL_MATH_FUNCTIONS_H_
        3 
        4 #include <stdint.h>
        5 #include <cmath> // for std::fabs and std::signbit
        6 
        7 #include "glog/logging.h"
        8 
        9 #include "caffe/common.hpp"
        10 #include "caffe/util/device_alternate.hpp"
        11 #include "caffe/util/mkl_alternate.hpp"
        12 
        13 namespace caffe {
        14 
        15 // Caffe gemm provides a simpler interface to the gemm functions, with the
        16 // limitation that the data has to be contiguous in memory.
        17 template <typename Dtype>
        18 void caffe_cpu_gemm(const CBLAS_TRANSPOSE TransA,
        19  const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
        20  const Dtype alpha, const Dtype* A, const Dtype* B, const Dtype beta,
        21  Dtype* C);
        22 
        23 template <typename Dtype>
        24 void caffe_cpu_gemv(const CBLAS_TRANSPOSE TransA, const int M, const int N,
        25  const Dtype alpha, const Dtype* A, const Dtype* x, const Dtype beta,
        26  Dtype* y);
        27 
        28 template <typename Dtype>
        29 void caffe_axpy(const int N, const Dtype alpha, const Dtype* X,
        30  Dtype* Y);
        31 
        32 template <typename Dtype>
        33 void caffe_cpu_axpby(const int N, const Dtype alpha, const Dtype* X,
        34  const Dtype beta, Dtype* Y);
        35 
        36 template <typename Dtype>
        37 void caffe_copy(const int N, const Dtype *X, Dtype *Y);
        38 
        39 template <typename Dtype>
        40 void caffe_set(const int N, const Dtype alpha, Dtype *X);
        41 
        42 inline void caffe_memset(const size_t N, const int alpha, void* X) {
        43  memset(X, alpha, N); // NOLINT(caffe/alt_fn)
        44 }
        45 
        46 template <typename Dtype>
        47 void caffe_add_scalar(const int N, const Dtype alpha, Dtype *X);
        48 
        49 template <typename Dtype>
        50 void caffe_scal(const int N, const Dtype alpha, Dtype *X);
        51 
        52 template <typename Dtype>
        53 void caffe_sqr(const int N, const Dtype* a, Dtype* y);
        54 
        55 template <typename Dtype>
        56 void caffe_add(const int N, const Dtype* a, const Dtype* b, Dtype* y);
        57 
        58 template <typename Dtype>
        59 void caffe_sub(const int N, const Dtype* a, const Dtype* b, Dtype* y);
        60 
        61 template <typename Dtype>
        62 void caffe_mul(const int N, const Dtype* a, const Dtype* b, Dtype* y);
        63 
        64 template <typename Dtype>
        65 void caffe_div(const int N, const Dtype* a, const Dtype* b, Dtype* y);
        66 
        67 template <typename Dtype>
        68 void caffe_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
        69 
        70 unsigned int caffe_rng_rand();
        71 
        72 template <typename Dtype>
        73 Dtype caffe_nextafter(const Dtype b);
        74 
        75 template <typename Dtype>
        76 void caffe_rng_uniform(const int n, const Dtype a, const Dtype b, Dtype* r);
        77 
        78 template <typename Dtype>
        79 void caffe_rng_gaussian(const int n, const Dtype mu, const Dtype sigma,
        80  Dtype* r);
        81 
        82 template <typename Dtype>
        83 void caffe_rng_bernoulli(const int n, const Dtype p, int* r);
        84 
        85 template <typename Dtype>
        86 void caffe_rng_bernoulli(const int n, const Dtype p, unsigned int* r);
        87 
        88 template <typename Dtype>
        89 void caffe_exp(const int n, const Dtype* a, Dtype* y);
        90 
        91 template <typename Dtype>
        92 void caffe_log(const int n, const Dtype* a, Dtype* y);
        93 
        94 template <typename Dtype>
        95 void caffe_abs(const int n, const Dtype* a, Dtype* y);
        96 
        97 template <typename Dtype>
        98 Dtype caffe_cpu_dot(const int n, const Dtype* x, const Dtype* y);
        99 
        100 template <typename Dtype>
        101 Dtype caffe_cpu_strided_dot(const int n, const Dtype* x, const int incx,
        102  const Dtype* y, const int incy);
        103 
        104 // Returns the sum of the absolute values of the elements of vector x
        105 template <typename Dtype>
        106 Dtype caffe_cpu_asum(const int n, const Dtype* x);
        107 
        108 // the branchless, type-safe version from
        109 // http://stackoverflow.com/questions/1903954/is-there-a-standard-sign-function-signum-sgn-in-c-c
        110 template<typename Dtype>
        111 inline int8_t caffe_sign(Dtype val) {
        112  return (Dtype(0) < val) - (val < Dtype(0));
        113 }
        114 
        115 // The following two macros are modifications of DEFINE_VSL_UNARY_FUNC
        116 // in include/caffe/util/mkl_alternate.hpp authored by @Rowland Depp.
        117 // Please refer to commit 7e8ef25c7 of the boost-eigen branch.
        118 // Git cherry picking that commit caused a conflict hard to resolve and
        119 // copying that file in convenient for code reviewing.
        120 // So they have to be pasted here temporarily.
        121 #define DEFINE_CAFFE_CPU_UNARY_FUNC(name, operation) \
        122  template<typename Dtype> \
        123  void caffe_cpu_##name(const int n, const Dtype* x, Dtype* y) { \
        124  CHECK_GT(n, 0); CHECK(x); CHECK(y); \
        125  for (int i = 0; i < n; ++i) { \
        126  operation; \
        127  } \
        128  }
        129 
        130 // output is 1 for the positives, 0 for zero, and -1 for the negatives
        131 DEFINE_CAFFE_CPU_UNARY_FUNC(sign, y[i] = caffe_sign<Dtype>(x[i]));
        132 
        133 // This returns a nonzero value if the input has its sign bit set.
        134 // The name sngbit is meant to avoid conflicts with std::signbit in the macro.
        135 // The extra parens are needed because CUDA < 6.5 defines signbit as a macro,
        136 // and we don't want that to expand here when CUDA headers are also included.
        137 DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, \
        138  y[i] = static_cast<bool>((std::signbit)(x[i])));
        139 
        140 DEFINE_CAFFE_CPU_UNARY_FUNC(fabs, y[i] = std::fabs(x[i]));
        141 
        142 template <typename Dtype>
        143 void caffe_cpu_scale(const int n, const Dtype alpha, const Dtype *x, Dtype* y);
        144 
        145 #ifndef CPU_ONLY // GPU
        146 
        147 // Decaf gpu gemm provides an interface that is almost the same as the cpu
        148 // gemm function - following the c convention and calling the fortran-order
        149 // gpu code under the hood.
        150 template <typename Dtype>
        151 void caffe_gpu_gemm(const CBLAS_TRANSPOSE TransA,
        152  const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
        153  const Dtype alpha, const Dtype* A, const Dtype* B, const Dtype beta,
        154  Dtype* C);
        155 
        156 template <typename Dtype>
        157 void caffe_gpu_gemv(const CBLAS_TRANSPOSE TransA, const int M, const int N,
        158  const Dtype alpha, const Dtype* A, const Dtype* x, const Dtype beta,
        159  Dtype* y);
        160 
        161 template <typename Dtype>
        162 void caffe_gpu_axpy(const int N, const Dtype alpha, const Dtype* X,
        163  Dtype* Y);
        164 
        165 template <typename Dtype>
        166 void caffe_gpu_axpby(const int N, const Dtype alpha, const Dtype* X,
        167  const Dtype beta, Dtype* Y);
        168 
        169 void caffe_gpu_memcpy(const size_t N, const void *X, void *Y);
        170 
        171 template <typename Dtype>
        172 void caffe_gpu_set(const int N, const Dtype alpha, Dtype *X);
        173 
        174 inline void caffe_gpu_memset(const size_t N, const int alpha, void* X) {
        175 #ifndef CPU_ONLY
        176  CUDA_CHECK(cudaMemset(X, alpha, N)); // NOLINT(caffe/alt_fn)
        177 #else
        178  NO_GPU;
        179 #endif
        180 }
        181 
        182 template <typename Dtype>
        183 void caffe_gpu_add_scalar(const int N, const Dtype alpha, Dtype *X);
        184 
        185 template <typename Dtype>
        186 void caffe_gpu_scal(const int N, const Dtype alpha, Dtype *X);
        187 
        188 #ifndef CPU_ONLY
        189 template <typename Dtype>
        190 void caffe_gpu_scal(const int N, const Dtype alpha, Dtype* X, cudaStream_t str);
        191 #endif
        192 
        193 template <typename Dtype>
        194 void caffe_gpu_add(const int N, const Dtype* a, const Dtype* b, Dtype* y);
        195 
        196 template <typename Dtype>
        197 void caffe_gpu_sub(const int N, const Dtype* a, const Dtype* b, Dtype* y);
        198 
        199 template <typename Dtype>
        200 void caffe_gpu_mul(const int N, const Dtype* a, const Dtype* b, Dtype* y);
        201 
        202 template <typename Dtype>
        203 void caffe_gpu_div(const int N, const Dtype* a, const Dtype* b, Dtype* y);
        204 
        205 template <typename Dtype>
        206 void caffe_gpu_abs(const int n, const Dtype* a, Dtype* y);
        207 
        208 template <typename Dtype>
        209 void caffe_gpu_exp(const int n, const Dtype* a, Dtype* y);
        210 
        211 template <typename Dtype>
        212 void caffe_gpu_log(const int n, const Dtype* a, Dtype* y);
        213 
        214 template <typename Dtype>
        215 void caffe_gpu_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
        216 
        217 // caffe_gpu_rng_uniform with two arguments generates integers in the range
        218 // [0, UINT_MAX].
        219 void caffe_gpu_rng_uniform(const int n, unsigned int* r);
        220 
        221 // caffe_gpu_rng_uniform with four arguments generates floats in the range
        222 // (a, b] (strictly greater than a, less than or equal to b) due to the
        223 // specification of curandGenerateUniform. With a = 0, b = 1, just calls
        224 // curandGenerateUniform; with other limits will shift and scale the outputs
        225 // appropriately after calling curandGenerateUniform.
        226 template <typename Dtype>
        227 void caffe_gpu_rng_uniform(const int n, const Dtype a, const Dtype b, Dtype* r);
        228 
        229 template <typename Dtype>
        230 void caffe_gpu_rng_gaussian(const int n, const Dtype mu, const Dtype sigma,
        231  Dtype* r);
        232 
        233 template <typename Dtype>
        234 void caffe_gpu_rng_bernoulli(const int n, const Dtype p, int* r);
        235 
        236 template <typename Dtype>
        237 void caffe_gpu_dot(const int n, const Dtype* x, const Dtype* y, Dtype* out);
        238 
        239 template <typename Dtype>
        240 void caffe_gpu_asum(const int n, const Dtype* x, Dtype* y);
        241 
        242 template<typename Dtype>
        243 void caffe_gpu_sign(const int n, const Dtype* x, Dtype* y);
        244 
        245 template<typename Dtype>
        246 void caffe_gpu_sgnbit(const int n, const Dtype* x, Dtype* y);
        247 
        248 template <typename Dtype>
        249 void caffe_gpu_fabs(const int n, const Dtype* x, Dtype* y);
        250 
        251 template <typename Dtype>
        252 void caffe_gpu_scale(const int n, const Dtype alpha, const Dtype *x, Dtype* y);
        253 
        254 #define DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(name, operation) \
        255 template<typename Dtype> \
        256 __global__ void name##_kernel(const int n, const Dtype* x, Dtype* y) { \
        257  CUDA_KERNEL_LOOP(index, n) { \
        258  operation; \
        259  } \
        260 } \
        261 template <> \
        262 void caffe_gpu_##name<float>(const int n, const float* x, float* y) { \
        263  /* NOLINT_NEXT_LINE(whitespace/operators) */ \
        264  name##_kernel<float><<<CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS>>>( \
        265  n, x, y); \
        266 } \
        267 template <> \
        268 void caffe_gpu_##name<double>(const int n, const double* x, double* y) { \
        269  /* NOLINT_NEXT_LINE(whitespace/operators) */ \
        270  name##_kernel<double><<<CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS>>>( \
        271  n, x, y); \
        272 }
        273 
        274 #endif // !CPU_ONLY
        275 
        276 } // namespace caffe
        277 
        278 #endif // CAFFE_UTIL_MATH_FUNCTIONS_H_
        caffe
        A layer factory that allows one to register layers. During runtime, registered layers can be called b...
        Definition: blob.hpp:14
        +
        1 #ifndef CAFFE_UTIL_MATH_FUNCTIONS_H_
        2 #define CAFFE_UTIL_MATH_FUNCTIONS_H_
        3 
        4 #include <stdint.h>
        5 #include <cmath> // for std::fabs and std::signbit
        6 
        7 #include "glog/logging.h"
        8 
        9 #include "caffe/common.hpp"
        10 #include "caffe/util/device_alternate.hpp"
        11 #include "caffe/util/mkl_alternate.hpp"
        12 
        13 namespace caffe {
        14 
        15 // Caffe gemm provides a simpler interface to the gemm functions, with the
        16 // limitation that the data has to be contiguous in memory.
        17 template <typename Dtype>
        18 void caffe_cpu_gemm(const CBLAS_TRANSPOSE TransA,
        19  const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
        20  const Dtype alpha, const Dtype* A, const Dtype* B, const Dtype beta,
        21  Dtype* C);
        22 
        23 template <typename Dtype>
        24 void caffe_cpu_gemv(const CBLAS_TRANSPOSE TransA, const int M, const int N,
        25  const Dtype alpha, const Dtype* A, const Dtype* x, const Dtype beta,
        26  Dtype* y);
        27 
        28 template <typename Dtype>
        29 void caffe_axpy(const int N, const Dtype alpha, const Dtype* X,
        30  Dtype* Y);
        31 
        32 template <typename Dtype>
        33 void caffe_cpu_axpby(const int N, const Dtype alpha, const Dtype* X,
        34  const Dtype beta, Dtype* Y);
        35 
        36 template <typename Dtype>
        37 void caffe_copy(const int N, const Dtype *X, Dtype *Y);
        38 
        39 template <typename Dtype>
        40 void caffe_set(const int N, const Dtype alpha, Dtype *X);
        41 
        42 inline void caffe_memset(const size_t N, const int alpha, void* X) {
        43  memset(X, alpha, N); // NOLINT(caffe/alt_fn)
        44 }
        45 
        46 template <typename Dtype>
        47 void caffe_add_scalar(const int N, const Dtype alpha, Dtype *X);
        48 
        49 template <typename Dtype>
        50 void caffe_scal(const int N, const Dtype alpha, Dtype *X);
        51 
        52 template <typename Dtype>
        53 void caffe_sqr(const int N, const Dtype* a, Dtype* y);
        54 
        55 template <typename Dtype>
        56 void caffe_sqrt(const int N, const Dtype* a, Dtype* y);
        57 
        58 template <typename Dtype>
        59 void caffe_add(const int N, const Dtype* a, const Dtype* b, Dtype* y);
        60 
        61 template <typename Dtype>
        62 void caffe_sub(const int N, const Dtype* a, const Dtype* b, Dtype* y);
        63 
        64 template <typename Dtype>
        65 void caffe_mul(const int N, const Dtype* a, const Dtype* b, Dtype* y);
        66 
        67 template <typename Dtype>
        68 void caffe_div(const int N, const Dtype* a, const Dtype* b, Dtype* y);
        69 
        70 template <typename Dtype>
        71 void caffe_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
        72 
        73 unsigned int caffe_rng_rand();
        74 
        75 template <typename Dtype>
        76 Dtype caffe_nextafter(const Dtype b);
        77 
        78 template <typename Dtype>
        79 void caffe_rng_uniform(const int n, const Dtype a, const Dtype b, Dtype* r);
        80 
        81 template <typename Dtype>
        82 void caffe_rng_gaussian(const int n, const Dtype mu, const Dtype sigma,
        83  Dtype* r);
        84 
        85 template <typename Dtype>
        86 void caffe_rng_bernoulli(const int n, const Dtype p, int* r);
        87 
        88 template <typename Dtype>
        89 void caffe_rng_bernoulli(const int n, const Dtype p, unsigned int* r);
        90 
        91 template <typename Dtype>
        92 void caffe_exp(const int n, const Dtype* a, Dtype* y);
        93 
        94 template <typename Dtype>
        95 void caffe_log(const int n, const Dtype* a, Dtype* y);
        96 
        97 template <typename Dtype>
        98 void caffe_abs(const int n, const Dtype* a, Dtype* y);
        99 
        100 template <typename Dtype>
        101 Dtype caffe_cpu_dot(const int n, const Dtype* x, const Dtype* y);
        102 
        103 template <typename Dtype>
        104 Dtype caffe_cpu_strided_dot(const int n, const Dtype* x, const int incx,
        105  const Dtype* y, const int incy);
        106 
        107 // Returns the sum of the absolute values of the elements of vector x
        108 template <typename Dtype>
        109 Dtype caffe_cpu_asum(const int n, const Dtype* x);
        110 
        111 // the branchless, type-safe version from
        112 // http://stackoverflow.com/questions/1903954/is-there-a-standard-sign-function-signum-sgn-in-c-c
        113 template<typename Dtype>
        114 inline int8_t caffe_sign(Dtype val) {
        115  return (Dtype(0) < val) - (val < Dtype(0));
        116 }
        117 
        118 // The following two macros are modifications of DEFINE_VSL_UNARY_FUNC
        119 // in include/caffe/util/mkl_alternate.hpp authored by @Rowland Depp.
        120 // Please refer to commit 7e8ef25c7 of the boost-eigen branch.
        121 // Git cherry picking that commit caused a conflict hard to resolve and
        122 // copying that file in convenient for code reviewing.
        123 // So they have to be pasted here temporarily.
        124 #define DEFINE_CAFFE_CPU_UNARY_FUNC(name, operation) \
        125  template<typename Dtype> \
        126  void caffe_cpu_##name(const int n, const Dtype* x, Dtype* y) { \
        127  CHECK_GT(n, 0); CHECK(x); CHECK(y); \
        128  for (int i = 0; i < n; ++i) { \
        129  operation; \
        130  } \
        131  }
        132 
        133 // output is 1 for the positives, 0 for zero, and -1 for the negatives
        134 DEFINE_CAFFE_CPU_UNARY_FUNC(sign, y[i] = caffe_sign<Dtype>(x[i]))
        135 
        136 // This returns a nonzero value if the input has its sign bit set.
        137 // The name sngbit is meant to avoid conflicts with std::signbit in the macro.
        138 // The extra parens are needed because CUDA < 6.5 defines signbit as a macro,
        139 // and we don't want that to expand here when CUDA headers are also included.
        140 DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, \
        141  y[i] = static_cast<bool>((std::signbit)(x[i])))
        142 
        143 DEFINE_CAFFE_CPU_UNARY_FUNC(fabs, y[i] = std::fabs(x[i]))
        144 
        145 template <typename Dtype>
        146 void caffe_cpu_scale(const int n, const Dtype alpha, const Dtype *x, Dtype* y);
        147 
        148 #ifndef CPU_ONLY // GPU
        149 
        150 // Decaf gpu gemm provides an interface that is almost the same as the cpu
        151 // gemm function - following the c convention and calling the fortran-order
        152 // gpu code under the hood.
        153 template <typename Dtype>
        154 void caffe_gpu_gemm(const CBLAS_TRANSPOSE TransA,
        155  const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
        156  const Dtype alpha, const Dtype* A, const Dtype* B, const Dtype beta,
        157  Dtype* C);
        158 
        159 template <typename Dtype>
        160 void caffe_gpu_gemv(const CBLAS_TRANSPOSE TransA, const int M, const int N,
        161  const Dtype alpha, const Dtype* A, const Dtype* x, const Dtype beta,
        162  Dtype* y);
        163 
        164 template <typename Dtype>
        165 void caffe_gpu_axpy(const int N, const Dtype alpha, const Dtype* X,
        166  Dtype* Y);
        167 
        168 template <typename Dtype>
        169 void caffe_gpu_axpby(const int N, const Dtype alpha, const Dtype* X,
        170  const Dtype beta, Dtype* Y);
        171 
        172 void caffe_gpu_memcpy(const size_t N, const void *X, void *Y);
        173 
        174 template <typename Dtype>
        175 void caffe_gpu_set(const int N, const Dtype alpha, Dtype *X);
        176 
        177 inline void caffe_gpu_memset(const size_t N, const int alpha, void* X) {
        178 #ifndef CPU_ONLY
        179  CUDA_CHECK(cudaMemset(X, alpha, N)); // NOLINT(caffe/alt_fn)
        180 #else
        181  NO_GPU;
        182 #endif
        183 }
        184 
        185 template <typename Dtype>
        186 void caffe_gpu_add_scalar(const int N, const Dtype alpha, Dtype *X);
        187 
        188 template <typename Dtype>
        189 void caffe_gpu_scal(const int N, const Dtype alpha, Dtype *X);
        190 
        191 #ifndef CPU_ONLY
        192 template <typename Dtype>
        193 void caffe_gpu_scal(const int N, const Dtype alpha, Dtype* X, cudaStream_t str);
        194 #endif
        195 
        196 template <typename Dtype>
        197 void caffe_gpu_add(const int N, const Dtype* a, const Dtype* b, Dtype* y);
        198 
        199 template <typename Dtype>
        200 void caffe_gpu_sub(const int N, const Dtype* a, const Dtype* b, Dtype* y);
        201 
        202 template <typename Dtype>
        203 void caffe_gpu_mul(const int N, const Dtype* a, const Dtype* b, Dtype* y);
        204 
        205 template <typename Dtype>
        206 void caffe_gpu_div(const int N, const Dtype* a, const Dtype* b, Dtype* y);
        207 
        208 template <typename Dtype>
        209 void caffe_gpu_abs(const int n, const Dtype* a, Dtype* y);
        210 
        211 template <typename Dtype>
        212 void caffe_gpu_exp(const int n, const Dtype* a, Dtype* y);
        213 
        214 template <typename Dtype>
        215 void caffe_gpu_log(const int n, const Dtype* a, Dtype* y);
        216 
        217 template <typename Dtype>
        218 void caffe_gpu_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
        219 
        220 template <typename Dtype>
        221 void caffe_gpu_sqrt(const int n, const Dtype* a, Dtype* y);
        222 
        223 // caffe_gpu_rng_uniform with two arguments generates integers in the range
        224 // [0, UINT_MAX].
        225 void caffe_gpu_rng_uniform(const int n, unsigned int* r);
        226 
        227 // caffe_gpu_rng_uniform with four arguments generates floats in the range
        228 // (a, b] (strictly greater than a, less than or equal to b) due to the
        229 // specification of curandGenerateUniform. With a = 0, b = 1, just calls
        230 // curandGenerateUniform; with other limits will shift and scale the outputs
        231 // appropriately after calling curandGenerateUniform.
        232 template <typename Dtype>
        233 void caffe_gpu_rng_uniform(const int n, const Dtype a, const Dtype b, Dtype* r);
        234 
        235 template <typename Dtype>
        236 void caffe_gpu_rng_gaussian(const int n, const Dtype mu, const Dtype sigma,
        237  Dtype* r);
        238 
        239 template <typename Dtype>
        240 void caffe_gpu_rng_bernoulli(const int n, const Dtype p, int* r);
        241 
        242 template <typename Dtype>
        243 void caffe_gpu_dot(const int n, const Dtype* x, const Dtype* y, Dtype* out);
        244 
        245 template <typename Dtype>
        246 void caffe_gpu_asum(const int n, const Dtype* x, Dtype* y);
        247 
        248 template<typename Dtype>
        249 void caffe_gpu_sign(const int n, const Dtype* x, Dtype* y);
        250 
        251 template<typename Dtype>
        252 void caffe_gpu_sgnbit(const int n, const Dtype* x, Dtype* y);
        253 
        254 template <typename Dtype>
        255 void caffe_gpu_fabs(const int n, const Dtype* x, Dtype* y);
        256 
        257 template <typename Dtype>
        258 void caffe_gpu_scale(const int n, const Dtype alpha, const Dtype *x, Dtype* y);
        259 
        260 #define DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(name, operation) \
        261 template<typename Dtype> \
        262 __global__ void name##_kernel(const int n, const Dtype* x, Dtype* y) { \
        263  CUDA_KERNEL_LOOP(index, n) { \
        264  operation; \
        265  } \
        266 } \
        267 template <> \
        268 void caffe_gpu_##name<float>(const int n, const float* x, float* y) { \
        269  /* NOLINT_NEXT_LINE(whitespace/operators) */ \
        270  name##_kernel<float><<<CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS>>>( \
        271  n, x, y); \
        272 } \
        273 template <> \
        274 void caffe_gpu_##name<double>(const int n, const double* x, double* y) { \
        275  /* NOLINT_NEXT_LINE(whitespace/operators) */ \
        276  name##_kernel<double><<<CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS>>>( \
        277  n, x, y); \
        278 }
        279 
        280 #endif // !CPU_ONLY
        281 
        282 } // namespace caffe
        283 
        284 #endif // CAFFE_UTIL_MATH_FUNCTIONS_H_
        caffe
        A layer factory that allows one to register layers. During runtime, registered layers can be called b...
        Definition: blob.hpp:14
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
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+{text:"Functions",url:"namespacemembers_func.html"}, +{text:"Typedefs",url:"namespacemembers_type.html"}]}]}, +{text:"Classes",url:"annotated.html",children:[ +{text:"Class List",url:"annotated.html"}, +{text:"Class Index",url:"classes.html"}, +{text:"Class Hierarchy",url:"hierarchy.html"}, +{text:"Class Members",url:"functions.html",children:[ +{text:"All",url:"functions.html",children:[ +{text:"a",url:"functions.html#index_a"}, +{text:"b",url:"functions_b.html#index_b"}, +{text:"c",url:"functions_c.html#index_c"}, +{text:"d",url:"functions_d.html#index_d"}, +{text:"e",url:"functions_e.html#index_e"}, +{text:"f",url:"functions_f.html#index_f"}, +{text:"g",url:"functions_g.html#index_g"}, +{text:"h",url:"functions_h.html#index_h"}, +{text:"i",url:"functions_i.html#index_i"}, +{text:"k",url:"functions_k.html#index_k"}, +{text:"l",url:"functions_l.html#index_l"}, +{text:"m",url:"functions_m.html#index_m"}, +{text:"n",url:"functions_n.html#index_n"}, +{text:"o",url:"functions_o.html#index_o"}, +{text:"p",url:"functions_p.html#index_p"}, +{text:"r",url:"functions_r.html#index_r"}, +{text:"s",url:"functions_s.html#index_s"}, +{text:"t",url:"functions_t.html#index_t"}, +{text:"u",url:"functions_u.html#index_u"}, +{text:"w",url:"functions_w.html#index_w"}]}, +{text:"Functions",url:"functions_func.html",children:[ +{text:"a",url:"functions_func.html#index_a"}, +{text:"b",url:"functions_func_b.html#index_b"}, +{text:"c",url:"functions_func_c.html#index_c"}, +{text:"d",url:"functions_func_d.html#index_d"}, +{text:"e",url:"functions_func_e.html#index_e"}, +{text:"f",url:"functions_func_f.html#index_f"}, +{text:"g",url:"functions_func_g.html#index_g"}, +{text:"h",url:"functions_func_h.html#index_h"}, +{text:"i",url:"functions_func_i.html#index_i"}, +{text:"l",url:"functions_func_l.html#index_l"}, +{text:"m",url:"functions_func_m.html#index_m"}, +{text:"n",url:"functions_func_n.html#index_n"}, +{text:"o",url:"functions_func_o.html#index_o"}, +{text:"p",url:"functions_func_p.html#index_p"}, +{text:"r",url:"functions_func_r.html#index_r"}, +{text:"s",url:"functions_func_s.html#index_s"}, +{text:"t",url:"functions_func_t.html#index_t"}, +{text:"u",url:"functions_func_u.html#index_u"}, +{text:"w",url:"functions_func_w.html#index_w"}]}, +{text:"Variables",url:"functions_vars.html",children:[ +{text:"a",url:"functions_vars.html#index_a"}, +{text:"b",url:"functions_vars.html#index_b"}, +{text:"c",url:"functions_vars.html#index_c"}, +{text:"d",url:"functions_vars.html#index_d"}, +{text:"e",url:"functions_vars.html#index_e"}, +{text:"f",url:"functions_vars.html#index_f"}, +{text:"h",url:"functions_vars.html#index_h"}, +{text:"i",url:"functions_vars.html#index_i"}, +{text:"k",url:"functions_vars.html#index_k"}, +{text:"l",url:"functions_vars.html#index_l"}, +{text:"m",url:"functions_vars.html#index_m"}, +{text:"n",url:"functions_vars.html#index_n"}, +{text:"o",url:"functions_vars.html#index_o"}, +{text:"p",url:"functions_vars.html#index_p"}, +{text:"r",url:"functions_vars.html#index_r"}, +{text:"s",url:"functions_vars.html#index_s"}, +{text:"t",url:"functions_vars.html#index_t"}, +{text:"u",url:"functions_vars.html#index_u"}]}]}]}, +{text:"Files",url:"files.html",children:[ +{text:"File List",url:"files.html"}]}]} diff --git a/doxygen/mkl__alternate_8hpp_source.html b/doxygen/mkl__alternate_8hpp_source.html index 2e2f10171c0..77b9b2c7b67 100644 --- a/doxygen/mkl__alternate_8hpp_source.html +++ b/doxygen/mkl__alternate_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/util/mkl_alternate.hpp Source File @@ -29,7 +29,7 @@ - + @@ -66,12 +66,12 @@
        mkl_alternate.hpp
        -
        1 #ifndef CAFFE_UTIL_MKL_ALTERNATE_H_
        2 #define CAFFE_UTIL_MKL_ALTERNATE_H_
        3 
        4 #ifdef USE_MKL
        5 
        6 #include <mkl.h>
        7 
        8 #else // If use MKL, simply include the MKL header
        9 
        10 #ifdef USE_ACCELERATE
        11 #include <Accelerate/Accelerate.h>
        12 #else
        13 extern "C" {
        14 #include <cblas.h>
        15 }
        16 #endif // USE_ACCELERATE
        17 
        18 #include <math.h>
        19 
        20 // Functions that caffe uses but are not present if MKL is not linked.
        21 
        22 // A simple way to define the vsl unary functions. The operation should
        23 // be in the form e.g. y[i] = sqrt(a[i])
        24 #define DEFINE_VSL_UNARY_FUNC(name, operation) \
        25  template<typename Dtype> \
        26  void v##name(const int n, const Dtype* a, Dtype* y) { \
        27  CHECK_GT(n, 0); CHECK(a); CHECK(y); \
        28  for (int i = 0; i < n; ++i) { operation; } \
        29  } \
        30  inline void vs##name( \
        31  const int n, const float* a, float* y) { \
        32  v##name<float>(n, a, y); \
        33  } \
        34  inline void vd##name( \
        35  const int n, const double* a, double* y) { \
        36  v##name<double>(n, a, y); \
        37  }
        38 
        39 DEFINE_VSL_UNARY_FUNC(Sqr, y[i] = a[i] * a[i]);
        40 DEFINE_VSL_UNARY_FUNC(Exp, y[i] = exp(a[i]));
        41 DEFINE_VSL_UNARY_FUNC(Ln, y[i] = log(a[i]));
        42 DEFINE_VSL_UNARY_FUNC(Abs, y[i] = fabs(a[i]));
        43 
        44 // A simple way to define the vsl unary functions with singular parameter b.
        45 // The operation should be in the form e.g. y[i] = pow(a[i], b)
        46 #define DEFINE_VSL_UNARY_FUNC_WITH_PARAM(name, operation) \
        47  template<typename Dtype> \
        48  void v##name(const int n, const Dtype* a, const Dtype b, Dtype* y) { \
        49  CHECK_GT(n, 0); CHECK(a); CHECK(y); \
        50  for (int i = 0; i < n; ++i) { operation; } \
        51  } \
        52  inline void vs##name( \
        53  const int n, const float* a, const float b, float* y) { \
        54  v##name<float>(n, a, b, y); \
        55  } \
        56  inline void vd##name( \
        57  const int n, const double* a, const float b, double* y) { \
        58  v##name<double>(n, a, b, y); \
        59  }
        60 
        61 DEFINE_VSL_UNARY_FUNC_WITH_PARAM(Powx, y[i] = pow(a[i], b));
        62 
        63 // A simple way to define the vsl binary functions. The operation should
        64 // be in the form e.g. y[i] = a[i] + b[i]
        65 #define DEFINE_VSL_BINARY_FUNC(name, operation) \
        66  template<typename Dtype> \
        67  void v##name(const int n, const Dtype* a, const Dtype* b, Dtype* y) { \
        68  CHECK_GT(n, 0); CHECK(a); CHECK(b); CHECK(y); \
        69  for (int i = 0; i < n; ++i) { operation; } \
        70  } \
        71  inline void vs##name( \
        72  const int n, const float* a, const float* b, float* y) { \
        73  v##name<float>(n, a, b, y); \
        74  } \
        75  inline void vd##name( \
        76  const int n, const double* a, const double* b, double* y) { \
        77  v##name<double>(n, a, b, y); \
        78  }
        79 
        80 DEFINE_VSL_BINARY_FUNC(Add, y[i] = a[i] + b[i]);
        81 DEFINE_VSL_BINARY_FUNC(Sub, y[i] = a[i] - b[i]);
        82 DEFINE_VSL_BINARY_FUNC(Mul, y[i] = a[i] * b[i]);
        83 DEFINE_VSL_BINARY_FUNC(Div, y[i] = a[i] / b[i]);
        84 
        85 // In addition, MKL comes with an additional function axpby that is not present
        86 // in standard blas. We will simply use a two-step (inefficient, of course) way
        87 // to mimic that.
        88 inline void cblas_saxpby(const int N, const float alpha, const float* X,
        89  const int incX, const float beta, float* Y,
        90  const int incY) {
        91  cblas_sscal(N, beta, Y, incY);
        92  cblas_saxpy(N, alpha, X, incX, Y, incY);
        93 }
        94 inline void cblas_daxpby(const int N, const double alpha, const double* X,
        95  const int incX, const double beta, double* Y,
        96  const int incY) {
        97  cblas_dscal(N, beta, Y, incY);
        98  cblas_daxpy(N, alpha, X, incX, Y, incY);
        99 }
        100 
        101 #endif // USE_MKL
        102 #endif // CAFFE_UTIL_MKL_ALTERNATE_H_
        +
        1 #ifndef CAFFE_UTIL_MKL_ALTERNATE_H_
        2 #define CAFFE_UTIL_MKL_ALTERNATE_H_
        3 
        4 #ifdef USE_MKL
        5 
        6 #include <mkl.h>
        7 
        8 #else // If use MKL, simply include the MKL header
        9 
        10 #ifdef USE_ACCELERATE
        11 #include <Accelerate/Accelerate.h>
        12 #else
        13 extern "C" {
        14 #include <cblas.h>
        15 }
        16 #endif // USE_ACCELERATE
        17 
        18 #include <math.h>
        19 
        20 // Functions that caffe uses but are not present if MKL is not linked.
        21 
        22 // A simple way to define the vsl unary functions. The operation should
        23 // be in the form e.g. y[i] = sqrt(a[i])
        24 #define DEFINE_VSL_UNARY_FUNC(name, operation) \
        25  template<typename Dtype> \
        26  void v##name(const int n, const Dtype* a, Dtype* y) { \
        27  CHECK_GT(n, 0); CHECK(a); CHECK(y); \
        28  for (int i = 0; i < n; ++i) { operation; } \
        29  } \
        30  inline void vs##name( \
        31  const int n, const float* a, float* y) { \
        32  v##name<float>(n, a, y); \
        33  } \
        34  inline void vd##name( \
        35  const int n, const double* a, double* y) { \
        36  v##name<double>(n, a, y); \
        37  }
        38 
        39 DEFINE_VSL_UNARY_FUNC(Sqr, y[i] = a[i] * a[i])
        40 DEFINE_VSL_UNARY_FUNC(Sqrt, y[i] = sqrt(a[i]))
        41 DEFINE_VSL_UNARY_FUNC(Exp, y[i] = exp(a[i]))
        42 DEFINE_VSL_UNARY_FUNC(Ln, y[i] = log(a[i]))
        43 DEFINE_VSL_UNARY_FUNC(Abs, y[i] = fabs(a[i]))
        44 
        45 // A simple way to define the vsl unary functions with singular parameter b.
        46 // The operation should be in the form e.g. y[i] = pow(a[i], b)
        47 #define DEFINE_VSL_UNARY_FUNC_WITH_PARAM(name, operation) \
        48  template<typename Dtype> \
        49  void v##name(const int n, const Dtype* a, const Dtype b, Dtype* y) { \
        50  CHECK_GT(n, 0); CHECK(a); CHECK(y); \
        51  for (int i = 0; i < n; ++i) { operation; } \
        52  } \
        53  inline void vs##name( \
        54  const int n, const float* a, const float b, float* y) { \
        55  v##name<float>(n, a, b, y); \
        56  } \
        57  inline void vd##name( \
        58  const int n, const double* a, const float b, double* y) { \
        59  v##name<double>(n, a, b, y); \
        60  }
        61 
        62 DEFINE_VSL_UNARY_FUNC_WITH_PARAM(Powx, y[i] = pow(a[i], b))
        63 
        64 // A simple way to define the vsl binary functions. The operation should
        65 // be in the form e.g. y[i] = a[i] + b[i]
        66 #define DEFINE_VSL_BINARY_FUNC(name, operation) \
        67  template<typename Dtype> \
        68  void v##name(const int n, const Dtype* a, const Dtype* b, Dtype* y) { \
        69  CHECK_GT(n, 0); CHECK(a); CHECK(b); CHECK(y); \
        70  for (int i = 0; i < n; ++i) { operation; } \
        71  } \
        72  inline void vs##name( \
        73  const int n, const float* a, const float* b, float* y) { \
        74  v##name<float>(n, a, b, y); \
        75  } \
        76  inline void vd##name( \
        77  const int n, const double* a, const double* b, double* y) { \
        78  v##name<double>(n, a, b, y); \
        79  }
        80 
        81 DEFINE_VSL_BINARY_FUNC(Add, y[i] = a[i] + b[i])
        82 DEFINE_VSL_BINARY_FUNC(Sub, y[i] = a[i] - b[i])
        83 DEFINE_VSL_BINARY_FUNC(Mul, y[i] = a[i] * b[i])
        84 DEFINE_VSL_BINARY_FUNC(Div, y[i] = a[i] / b[i])
        85 
        86 // In addition, MKL comes with an additional function axpby that is not present
        87 // in standard blas. We will simply use a two-step (inefficient, of course) way
        88 // to mimic that.
        89 inline void cblas_saxpby(const int N, const float alpha, const float* X,
        90  const int incX, const float beta, float* Y,
        91  const int incY) {
        92  cblas_sscal(N, beta, Y, incY);
        93  cblas_saxpy(N, alpha, X, incX, Y, incY);
        94 }
        95 inline void cblas_daxpby(const int N, const double alpha, const double* X,
        96  const int incX, const double beta, double* Y,
        97  const int incY) {
        98  cblas_dscal(N, beta, Y, incY);
        99  cblas_daxpy(N, alpha, X, incX, Y, incY);
        100 }
        101 
        102 #endif // USE_MKL
        103 #endif // CAFFE_UTIL_MKL_ALTERNATE_H_
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/multinomial__logistic__loss__layer_8hpp_source.html b/doxygen/multinomial__logistic__loss__layer_8hpp_source.html index 5277fff523a..1c96344da0b 100644 --- a/doxygen/multinomial__logistic__loss__layer_8hpp_source.html +++ b/doxygen/multinomial__logistic__loss__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/multinomial_logistic_loss_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -77,9 +77,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/mvn__layer_8hpp_source.html b/doxygen/mvn__layer_8hpp_source.html index e07b4a117f6..64436a07555 100644 --- a/doxygen/mvn__layer_8hpp_source.html +++ b/doxygen/mvn__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/mvn_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -82,9 +82,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/namespaceboost.html b/doxygen/namespaceboost.html index cf2bc570f1b..eb5b51fe1cc 100644 --- a/doxygen/namespaceboost.html +++ b/doxygen/namespaceboost.html @@ -3,7 +3,7 @@ - + Caffe: boost Namespace Reference @@ -29,7 +29,7 @@ - + @@ -67,9 +67,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/namespacecaffe.html b/doxygen/namespacecaffe.html index f5d6e0f61c0..8243c370b10 100644 --- a/doxygen/namespacecaffe.html +++ b/doxygen/namespacecaffe.html @@ -3,7 +3,7 @@ - + Caffe: caffe Namespace Reference @@ -29,7 +29,7 @@ - + @@ -81,7 +81,7 @@

        Classes

        class  AbsValLayer - Computes $ y = |x| $. More...
        + Computes $ y = |x| $. More...
          class  AccuracyLayer  Computes the classification accuracy for a one-of-many classification task. More...
        @@ -94,7 +94,7 @@  AdamSolver, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. Described in [1]. More...
          class  ArgMaxLayer - Compute the index of the $ K $ max values for each datum across all dimensions $ (C \times H \times W) $. More...
        + Compute the index of the $ K $ max values for each datum across all dimensions $ (C \times H \times W) $. More...
          class  BaseConvolutionLayer  Abstract base class that factors out the BLAS code common to ConvolutionLayer and DeconvolutionLayer. More...
        @@ -124,7 +124,7 @@ class  BlockingQueue   class  BNLLLayer - Computes $ y = x + \log(1 + \exp(-x)) $ if $ x > 0 $; $ y = \log(1 + \exp(x)) $ otherwise. More...
        + Computes $ y = x + \log(1 + \exp(-x)) $ if $ x > 0 $; $ y = \log(1 + \exp(x)) $ otherwise. More...
          class  Caffe   @@ -132,10 +132,10 @@  Takes at least two Blobs and concatenates them along either the num or channel dimension, outputting the result. More...
          class  ConstantFiller - Fills a Blob with constant values $ x = 0 $. More...
        + Fills a Blob with constant values $ x = 0 $. More...
          class  ContrastiveLossLayer - Computes the contrastive loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks. More...
        + Computes the contrastive loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $ where $ d = \left| \left| a_n - b_n \right| \right|_2 $. This can be used to train siamese networks. More...
          class  ConvolutionLayer  Convolves the input image with a bank of learned filters, and (optionally) adds biases. More...
        @@ -154,7 +154,7 @@  Convolve the input with a bank of learned filters, and (optionally) add biases, treating filters and convolution parameters in the opposite sense as ConvolutionLayer. More...
          class  DropoutLayer - During training only, sets a random portion of $x$ to 0, adjusting the rest of the vector magnitude accordingly. More...
        + During training only, sets a random portion of $x$ to 0, adjusting the rest of the vector magnitude accordingly. More...
          class  DummyDataLayer  Provides data to the Net generated by a Filler. More...
        @@ -163,16 +163,16 @@  Compute elementwise operations, such as product and sum, along multiple input Blobs. More...
          class  ELULayer - Exponential Linear Unit non-linearity $ y = \left\{ \begin{array}{lr} x & \mathrm{if} \; x > 0 \\ \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 \end{array} \right. $. More...
        + Exponential Linear Unit non-linearity $ y = \left\{ \begin{array}{lr} x & \mathrm{if} \; x > 0 \\ \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 \end{array} \right. $. More...
          class  EmbedLayer  A layer for learning "embeddings" of one-hot vector input. Equivalent to an InnerProductLayer with one-hot vectors as input, but for efficiency the input is the "hot" index of each column itself. More...
          class  EuclideanLossLayer - Computes the Euclidean (L2) loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ for real-valued regression tasks. More...
        + Computes the Euclidean (L2) loss $ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $ for real-valued regression tasks. More...
          class  ExpLayer - Computes $ y = \gamma ^ {\alpha x + \beta} $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $. More...
        + Computes $ y = \gamma ^ {\alpha x + \beta} $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $. More...
          class  Filler  Fills a Blob with constant or randomly-generated data. More...
        @@ -184,7 +184,7 @@  Reshapes the input Blob into flat vectors. More...
          class  GaussianFiller - Fills a Blob with Gaussian-distributed values $ x = a $. More...
        + Fills a Blob with Gaussian-distributed values $ x = a $. More...
          class  HDF5DataLayer  Provides data to the Net from HDF5 files. More...
        @@ -220,7 +220,7 @@ class  LayerRegistry   class  LogLayer - Computes $ y = log_{\gamma}(\alpha x + \beta) $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $. More...
        + Computes $ y = log_{\gamma}(\alpha x + \beta) $, as specified by the scale $ \alpha $, shift $ \beta $, and base $ \gamma $. More...
          class  LossLayer  An interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth labels – and output a singleton Blob representing the loss. More...
        @@ -238,7 +238,7 @@  Provides data to the Net from memory. More...
          class  MSRAFiller - Fills a Blob with values $ x \sim N(0, \sigma^2) $ where $ \sigma^2 $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average. More...
        + Fills a Blob with values $ x \sim N(0, \sigma^2) $ where $ \sigma^2 $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average. More...
          class  MultinomialLogisticLossLayer  Computes the multinomial logistic loss for a one-of-many classification task, directly taking a predicted probability distribution as input. More...
        @@ -252,7 +252,7 @@  Connects Layers together into a directed acyclic graph (DAG) specified by a NetParameter. More...
          class  NeuronLayer - An interface for layers that take one blob as input ( $ x $) and produce one equally-sized blob as output ( $ y $), where each element of the output depends only on the corresponding input element. More...
        + An interface for layers that take one blob as input ( $ x $) and produce one equally-sized blob as output ( $ y $), where each element of the output depends only on the corresponding input element. More...
          class  ParameterLayer   @@ -260,13 +260,13 @@  Pools the input image by taking the max, average, etc. within regions. More...
          class  PositiveUnitballFiller - Fills a Blob with values $ x \in [0, 1] $ such that $ \forall i \sum_j x_{ij} = 1 $. More...
        + Fills a Blob with values $ x \in [0, 1] $ such that $ \forall i \sum_j x_{ij} = 1 $. More...
          class  PowerLayer - Computes $ y = (\alpha x + \beta) ^ \gamma $, as specified by the scale $ \alpha $, shift $ \beta $, and power $ \gamma $. More...
        + Computes $ y = (\alpha x + \beta) ^ \gamma $, as specified by the scale $ \alpha $, shift $ \beta $, and power $ \gamma $. More...
          class  PReLULayer - Parameterized Rectified Linear Unit non-linearity $ y_i = \max(0, x_i) + a_i \min(0, x_i) $. The differences from ReLULayer are 1) negative slopes are learnable though backprop and 2) negative slopes can vary across channels. The number of axes of input blob should be greater than or equal to 2. The 1st axis (0-based) is seen as channels. More...
        + Parameterized Rectified Linear Unit non-linearity $ y_i = \max(0, x_i) + a_i \min(0, x_i) $. The differences from ReLULayer are 1) negative slopes are learnable though backprop and 2) negative slopes can vary across channels. The number of axes of input blob should be greater than or equal to 2. The 1st axis (0-based) is seen as channels. More...
          class  PythonLayer   @@ -277,7 +277,7 @@  Compute "reductions" – operations that return a scalar output Blob for an input Blob of arbitrary size, such as the sum, absolute sum, and sum of squares. More...
          class  ReLULayer - Rectified Linear Unit non-linearity $ y = \max(0, x) $. The simple max is fast to compute, and the function does not saturate. More...
        + Rectified Linear Unit non-linearity $ y = \max(0, x) $. The simple max is fast to compute, and the function does not saturate. More...
          class  ReshapeLayer   @@ -293,10 +293,10 @@  Optimizes the parameters of a Net using stochastic gradient descent (SGD) with momentum. More...
          class  SigmoidCrossEntropyLossLayer - Computes the cross-entropy (logistic) loss $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $, often used for predicting targets interpreted as probabilities. More...
        + Computes the cross-entropy (logistic) loss $ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $, often used for predicting targets interpreted as probabilities. More...
          class  SigmoidLayer - Sigmoid function non-linearity $ y = (1 + \exp(-x))^{-1} $, a classic choice in neural networks. More...
        + Sigmoid function non-linearity $ y = (1 + \exp(-x))^{-1} $, a classic choice in neural networks. More...
          class  SignalHandler   @@ -329,7 +329,7 @@  Manages memory allocation and synchronization between the host (CPU) and device (GPU). More...
          class  TanHLayer - TanH hyperbolic tangent non-linearity $ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $, popular in auto-encoders. More...
        + TanH hyperbolic tangent non-linearity $ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $, popular in auto-encoders. More...
          class  ThresholdLayer  Tests whether the input exceeds a threshold: outputs 1 for inputs above threshold; 0 otherwise. More...
        @@ -340,13 +340,13 @@ class  Timer   class  UniformFiller - Fills a Blob with uniformly distributed values $ x\sim U(a, b) $. More...
        + Fills a Blob with uniformly distributed values $ x\sim U(a, b) $. More...
          class  WindowDataLayer  Provides data to the Net from windows of images files, specified by a window data file. More...
          class  XavierFiller - Fills a Blob with values $ x \sim U(-a, +a) $ where $ a $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average. More...
        + Fills a Blob with values $ x \sim U(-a, +a) $ where $ a $ is set inversely proportional to number of incoming nodes, outgoing nodes, or their average. More...
          - - - - + + - - + + @@ -571,6 +571,10 @@ template<typename Dtype > + + + @@ -642,19 +646,9 @@ template<typename Dtype > - - - - - - - - - + + @@ -725,6 +719,10 @@ template<typename Dtype > + + + @@ -1312,14 +1310,14 @@ - - - - + + - - + + @@ -1476,6 +1474,14 @@ template<> + + + + + + @@ -1569,6 +1575,15 @@ + + + + + + @@ -1600,7 +1615,7 @@

        Note that each solver type should only be registered once.

        Function Documentation

        -

        § GetFiller()

        +

        ◆ GetFiller()

        @@ -1625,9 +1640,9 @@

        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/namespacecaffe_1_1SolverAction.html b/doxygen/namespacecaffe_1_1SolverAction.html index 98ee8da46bb..497435df292 100644 --- a/doxygen/namespacecaffe_1_1SolverAction.html +++ b/doxygen/namespacecaffe_1_1SolverAction.html @@ -3,7 +3,7 @@ - + Caffe: caffe::SolverAction Namespace Reference @@ -29,7 +29,7 @@

        @@ -386,14 +386,14 @@

        std::string format_int (int n, int numberOfLeadingZeros=0)
         
        +
        template<typename Dtype >
        void hdf5_load_nd_dataset_helper (hid_t file_id, const char *dataset_name_, int min_dim, int max_dim, Blob< Dtype > *blob)
         
        +
        void hdf5_load_nd_dataset_helper (hid_t file_id, const char *dataset_name_, int min_dim, int max_dim, Blob< Dtype > *blob, bool reshape)
         
        template<typename Dtype >
        void hdf5_load_nd_dataset (hid_t file_id, const char *dataset_name_, int min_dim, int max_dim, Blob< Dtype > *blob)
         
        void hdf5_load_nd_dataset (hid_t file_id, const char *dataset_name_, int min_dim, int max_dim, Blob< Dtype > *blob, bool reshape=false)
         
        template<typename Dtype >
        void hdf5_save_nd_dataset (const hid_t file_id, const string &dataset_name, const Blob< Dtype > &blob, bool write_diff=false)
        void caffe_sqr (const int N, const Dtype *a, Dtype *y)
         
        +template<typename Dtype >
        void caffe_sqrt (const int N, const Dtype *a, Dtype *y)
         
        template<typename Dtype >
        void caffe_add (const int N, const Dtype *a, const Dtype *b, Dtype *y)
        int8_t caffe_sign (Dtype val)
         
        - DEFINE_CAFFE_CPU_UNARY_FUNC (sign, y[i]=caffe_sign< Dtype >(x[i]))
         
        - DEFINE_CAFFE_CPU_UNARY_FUNC (sgnbit, y[i]=static_cast< bool >((std::signbit)(x[i])))
         
        - DEFINE_CAFFE_CPU_UNARY_FUNC (fabs, y[i]=std::fabs(x[i]))
         
        -template<typename Dtype >
        void caffe_cpu_scale (const int n, const Dtype alpha, const Dtype *x, Dtype *y)
         
        + DEFINE_CAFFE_CPU_UNARY_FUNC (sgnbit, y[i]=static_cast< bool >((std::signbit)(x[i]))) template< typename Dtype > void caffe_cpu_scale(const int n
         
        template<typename Dtype >
        void caffe_gpu_gemm (const CBLAS_TRANSPOSE TransA, const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K, const Dtype alpha, const Dtype *A, const Dtype *B, const Dtype beta, Dtype *C)
        void caffe_gpu_powx (const int n, const Dtype *a, const Dtype b, Dtype *y)
         
        +template<typename Dtype >
        void caffe_gpu_sqrt (const int n, const Dtype *a, Dtype *y)
         
        void caffe_gpu_rng_uniform (const int n, unsigned int *r)
         
         REGISTER_SOLVER_CLASS (SGD)
         
        +
        template<>
        void hdf5_load_nd_dataset< float > (hid_t file_id, const char *dataset_name_, int min_dim, int max_dim, Blob< float > *blob)
         
        +
        void hdf5_load_nd_dataset< float > (hid_t file_id, const char *dataset_name_, int min_dim, int max_dim, Blob< float > *blob, bool reshape)
         
        template<>
        void hdf5_load_nd_dataset< double > (hid_t file_id, const char *dataset_name_, int min_dim, int max_dim, Blob< double > *blob)
         
        void hdf5_load_nd_dataset< double > (hid_t file_id, const char *dataset_name_, int min_dim, int max_dim, Blob< double > *blob, bool reshape)
         
        template<>
        void hdf5_save_nd_dataset< float > (const hid_t file_id, const string &dataset_name, const Blob< float > &blob, bool write_diff)
        void caffe_sqr< double > (const int n, const double *a, double *y)
         
        +template<>
        void caffe_sqrt< float > (const int n, const float *a, float *y)
         
        +template<>
        void caffe_sqrt< double > (const int n, const double *a, double *y)
         
        template<>
        void caffe_exp< float > (const int n, const float *a, float *y)
        const int CAFFE_CUDA_NUM_THREADS = 512
         
        +const Dtype alpha
         
        +const Dtype const Dtype * x
         
        +const Dtype const Dtype Dtype * y
         
        const float kBNLL_THRESHOLD = 50.
         
        - + @@ -85,9 +85,9 @@
        -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:50:58 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/namespacemembers.html b/doxygen/namespacemembers.html index 96422292e1a..990f6580066 100644 --- a/doxygen/namespacemembers.html +++ b/doxygen/namespacemembers.html @@ -3,7 +3,7 @@ - + Caffe: Namespace Members @@ -29,7 +29,7 @@ - + @@ -69,9 +69,9 @@
        -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/namespacemembers_func.html b/doxygen/namespacemembers_func.html index 7deeafbd052..8ad52df6426 100644 --- a/doxygen/namespacemembers_func.html +++ b/doxygen/namespacemembers_func.html @@ -3,7 +3,7 @@ - + Caffe: Namespace Members @@ -29,7 +29,7 @@ - + @@ -66,9 +66,9 @@
        -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/namespacemembers_type.html b/doxygen/namespacemembers_type.html index c715bd02b59..970977f2552 100644 --- a/doxygen/namespacemembers_type.html +++ b/doxygen/namespacemembers_type.html @@ -3,7 +3,7 @@ - + Caffe: Namespace Members @@ -29,7 +29,7 @@ - + @@ -66,9 +66,9 @@
        -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/namespaces.html b/doxygen/namespaces.html index b8fa5dbf019..e88cf6e6488 100644 --- a/doxygen/namespaces.html +++ b/doxygen/namespaces.html @@ -3,7 +3,7 @@ - + Caffe: Namespace List @@ -29,7 +29,7 @@ - + @@ -72,9 +72,9 @@
        -Generated on Wed Jan 18 2017 12:26:21 for Caffe by   +Generated on Fri Apr 14 2017 12:51:38 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/nccl_8hpp_source.html b/doxygen/nccl_8hpp_source.html index 49535d41e38..75e8b5baf1d 100644 --- a/doxygen/nccl_8hpp_source.html +++ b/doxygen/nccl_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/util/nccl.hpp Source File @@ -29,7 +29,7 @@ - + @@ -70,9 +70,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/net_8hpp_source.html b/doxygen/net_8hpp_source.html index 640e802f0cf..19bb96d4dea 100644 --- a/doxygen/net_8hpp_source.html +++ b/doxygen/net_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/net.hpp Source File @@ -29,7 +29,7 @@ - + @@ -66,48 +66,48 @@
        net.hpp
        -
        1 #ifndef CAFFE_NET_HPP_
        2 #define CAFFE_NET_HPP_
        3 
        4 #include <map>
        5 #include <set>
        6 #include <string>
        7 #include <utility>
        8 #include <vector>
        9 
        10 #include "caffe/blob.hpp"
        11 #include "caffe/common.hpp"
        12 #include "caffe/layer.hpp"
        13 #include "caffe/proto/caffe.pb.h"
        14 
        15 namespace caffe {
        16 
        23 template <typename Dtype>
        24 class Net {
        25  public:
        26  explicit Net(const NetParameter& param);
        27  explicit Net(const string& param_file, Phase phase,
        28  const int level = 0, const vector<string>* stages = NULL);
        29  virtual ~Net() {}
        30 
        32  void Init(const NetParameter& param);
        33 
        38  const vector<Blob<Dtype>*>& Forward(Dtype* loss = NULL);
        40  const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL) {
        41  LOG_EVERY_N(WARNING, 1000) << "DEPRECATED: ForwardPrefilled() "
        42  << "will be removed in a future version. Use Forward().";
        43  return Forward(loss);
        44  }
        45 
        54  Dtype ForwardFromTo(int start, int end);
        55  Dtype ForwardFrom(int start);
        56  Dtype ForwardTo(int end);
        58  const vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom,
        59  Dtype* loss = NULL);
        60 
        65  void ClearParamDiffs();
        66 
        72  void Backward();
        73  void BackwardFromTo(int start, int end);
        74  void BackwardFrom(int start);
        75  void BackwardTo(int end);
        76 
        83  void Reshape();
        84 
        85  Dtype ForwardBackward() {
        86  Dtype loss;
        87  Forward(&loss);
        88  Backward();
        89  return loss;
        90  }
        91 
        93  void Update();
        100  void ShareWeights();
        101 
        106  void ShareTrainedLayersWith(const Net* other);
        107  // For an already initialized net, CopyTrainedLayersFrom() copies the already
        108  // trained layers from another net parameter instance.
        113  void CopyTrainedLayersFrom(const NetParameter& param);
        114  void CopyTrainedLayersFrom(const string trained_filename);
        115  void CopyTrainedLayersFromBinaryProto(const string trained_filename);
        116  void CopyTrainedLayersFromHDF5(const string trained_filename);
        118  void ToProto(NetParameter* param, bool write_diff = false) const;
        120  void ToHDF5(const string& filename, bool write_diff = false) const;
        121 
        123  inline const string& name() const { return name_; }
        125  inline const vector<string>& layer_names() const { return layer_names_; }
        127  inline const vector<string>& blob_names() const { return blob_names_; }
        129  inline const vector<shared_ptr<Blob<Dtype> > >& blobs() const {
        130  return blobs_;
        131  }
        133  inline const vector<shared_ptr<Layer<Dtype> > >& layers() const {
        134  return layers_;
        135  }
        137  inline Phase phase() const { return phase_; }
        142  inline const vector<vector<Blob<Dtype>*> >& bottom_vecs() const {
        143  return bottom_vecs_;
        144  }
        149  inline const vector<vector<Blob<Dtype>*> >& top_vecs() const {
        150  return top_vecs_;
        151  }
        153  inline const vector<int> & top_ids(int i) const {
        154  CHECK_GE(i, 0) << "Invalid layer id";
        155  CHECK_LT(i, top_id_vecs_.size()) << "Invalid layer id";
        156  return top_id_vecs_[i];
        157  }
        159  inline const vector<int> & bottom_ids(int i) const {
        160  CHECK_GE(i, 0) << "Invalid layer id";
        161  CHECK_LT(i, bottom_id_vecs_.size()) << "Invalid layer id";
        162  return bottom_id_vecs_[i];
        163  }
        164  inline const vector<vector<bool> >& bottom_need_backward() const {
        165  return bottom_need_backward_;
        166  }
        167  inline const vector<Dtype>& blob_loss_weights() const {
        168  return blob_loss_weights_;
        169  }
        170  inline const vector<bool>& layer_need_backward() const {
        171  return layer_need_backward_;
        172  }
        174  inline const vector<shared_ptr<Blob<Dtype> > >& params() const {
        175  return params_;
        176  }
        177  inline const vector<Blob<Dtype>*>& learnable_params() const {
        178  return learnable_params_;
        179  }
        181  inline const vector<float>& params_lr() const { return params_lr_; }
        182  inline const vector<bool>& has_params_lr() const { return has_params_lr_; }
        184  inline const vector<float>& params_weight_decay() const {
        185  return params_weight_decay_;
        186  }
        187  inline const vector<bool>& has_params_decay() const {
        188  return has_params_decay_;
        189  }
        190  const map<string, int>& param_names_index() const {
        191  return param_names_index_;
        192  }
        193  inline const vector<int>& param_owners() const { return param_owners_; }
        194  inline const vector<string>& param_display_names() const {
        195  return param_display_names_;
        196  }
        198  inline int num_inputs() const { return net_input_blobs_.size(); }
        199  inline int num_outputs() const { return net_output_blobs_.size(); }
        200  inline const vector<Blob<Dtype>*>& input_blobs() const {
        201  return net_input_blobs_;
        202  }
        203  inline const vector<Blob<Dtype>*>& output_blobs() const {
        204  return net_output_blobs_;
        205  }
        206  inline const vector<int>& input_blob_indices() const {
        207  return net_input_blob_indices_;
        208  }
        209  inline const vector<int>& output_blob_indices() const {
        210  return net_output_blob_indices_;
        211  }
        212  bool has_blob(const string& blob_name) const;
        213  const shared_ptr<Blob<Dtype> > blob_by_name(const string& blob_name) const;
        214  bool has_layer(const string& layer_name) const;
        215  const shared_ptr<Layer<Dtype> > layer_by_name(const string& layer_name) const;
        216 
        217  void set_debug_info(const bool value) { debug_info_ = value; }
        218 
        219  // Helpers for Init.
        224  static void FilterNet(const NetParameter& param,
        225  NetParameter* param_filtered);
        227  static bool StateMeetsRule(const NetState& state, const NetStateRule& rule,
        228  const string& layer_name);
        229 
        230  // Invoked at specific points during an iteration
        231  class Callback {
        232  protected:
        233  virtual void run(int layer) = 0;
        234 
        235  template <typename T>
        236  friend class Net;
        237  };
        238  const vector<Callback*>& before_forward() const { return before_forward_; }
        239  void add_before_forward(Callback* value) {
        240  before_forward_.push_back(value);
        241  }
        242  const vector<Callback*>& after_forward() const { return after_forward_; }
        243  void add_after_forward(Callback* value) {
        244  after_forward_.push_back(value);
        245  }
        246  const vector<Callback*>& before_backward() const { return before_backward_; }
        247  void add_before_backward(Callback* value) {
        248  before_backward_.push_back(value);
        249  }
        250  const vector<Callback*>& after_backward() const { return after_backward_; }
        251  void add_after_backward(Callback* value) {
        252  after_backward_.push_back(value);
        253  }
        254 
        255  protected:
        256  // Helpers for Init.
        258  void AppendTop(const NetParameter& param, const int layer_id,
        259  const int top_id, set<string>* available_blobs,
        260  map<string, int>* blob_name_to_idx);
        262  int AppendBottom(const NetParameter& param, const int layer_id,
        263  const int bottom_id, set<string>* available_blobs,
        264  map<string, int>* blob_name_to_idx);
        266  void AppendParam(const NetParameter& param, const int layer_id,
        267  const int param_id);
        268 
        270  void ForwardDebugInfo(const int layer_id);
        272  void BackwardDebugInfo(const int layer_id);
        274  void UpdateDebugInfo(const int param_id);
        275 
        277  string name_;
        279  Phase phase_;
        281  vector<shared_ptr<Layer<Dtype> > > layers_;
        282  vector<string> layer_names_;
        283  map<string, int> layer_names_index_;
        284  vector<bool> layer_need_backward_;
        286  vector<shared_ptr<Blob<Dtype> > > blobs_;
        287  vector<string> blob_names_;
        288  map<string, int> blob_names_index_;
        289  vector<bool> blob_need_backward_;
        293  vector<vector<Blob<Dtype>*> > bottom_vecs_;
        294  vector<vector<int> > bottom_id_vecs_;
        295  vector<vector<bool> > bottom_need_backward_;
        297  vector<vector<Blob<Dtype>*> > top_vecs_;
        298  vector<vector<int> > top_id_vecs_;
        301  vector<Dtype> blob_loss_weights_;
        302  vector<vector<int> > param_id_vecs_;
        303  vector<int> param_owners_;
        304  vector<string> param_display_names_;
        305  vector<pair<int, int> > param_layer_indices_;
        306  map<string, int> param_names_index_;
        308  vector<int> net_input_blob_indices_;
        309  vector<int> net_output_blob_indices_;
        310  vector<Blob<Dtype>*> net_input_blobs_;
        311  vector<Blob<Dtype>*> net_output_blobs_;
        313  vector<shared_ptr<Blob<Dtype> > > params_;
        314  vector<Blob<Dtype>*> learnable_params_;
        322  vector<int> learnable_param_ids_;
        324  vector<float> params_lr_;
        325  vector<bool> has_params_lr_;
        327  vector<float> params_weight_decay_;
        328  vector<bool> has_params_decay_;
        330  size_t memory_used_;
        332  bool debug_info_;
        333  // Callbacks
        334  vector<Callback*> before_forward_;
        335  vector<Callback*> after_forward_;
        336  vector<Callback*> before_backward_;
        337  vector<Callback*> after_backward_;
        338 
        339 DISABLE_COPY_AND_ASSIGN(Net);
        340 };
        341 
        342 
        343 } // namespace caffe
        344 
        345 #endif // CAFFE_NET_HPP_
        caffe::Net::AppendTop
        void AppendTop(const NetParameter &param, const int layer_id, const int top_id, set< string > *available_blobs, map< string, int > *blob_name_to_idx)
        Append a new top blob to the net.
        Definition: net.cpp:358
        +
        1 #ifndef CAFFE_NET_HPP_
        2 #define CAFFE_NET_HPP_
        3 
        4 #include <map>
        5 #include <set>
        6 #include <string>
        7 #include <utility>
        8 #include <vector>
        9 
        10 #include "caffe/blob.hpp"
        11 #include "caffe/common.hpp"
        12 #include "caffe/layer.hpp"
        13 #include "caffe/proto/caffe.pb.h"
        14 
        15 namespace caffe {
        16 
        23 template <typename Dtype>
        24 class Net {
        25  public:
        26  explicit Net(const NetParameter& param);
        27  explicit Net(const string& param_file, Phase phase,
        28  const int level = 0, const vector<string>* stages = NULL);
        29  virtual ~Net() {}
        30 
        32  void Init(const NetParameter& param);
        33 
        38  const vector<Blob<Dtype>*>& Forward(Dtype* loss = NULL);
        40  const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL) {
        41  LOG_EVERY_N(WARNING, 1000) << "DEPRECATED: ForwardPrefilled() "
        42  << "will be removed in a future version. Use Forward().";
        43  return Forward(loss);
        44  }
        45 
        54  Dtype ForwardFromTo(int start, int end);
        55  Dtype ForwardFrom(int start);
        56  Dtype ForwardTo(int end);
        58  const vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom,
        59  Dtype* loss = NULL);
        60 
        65  void ClearParamDiffs();
        66 
        72  void Backward();
        73  void BackwardFromTo(int start, int end);
        74  void BackwardFrom(int start);
        75  void BackwardTo(int end);
        76 
        83  void Reshape();
        84 
        85  Dtype ForwardBackward() {
        86  Dtype loss;
        87  Forward(&loss);
        88  Backward();
        89  return loss;
        90  }
        91 
        93  void Update();
        100  void ShareWeights();
        101 
        106  void ShareTrainedLayersWith(const Net* other);
        107  // For an already initialized net, CopyTrainedLayersFrom() copies the already
        108  // trained layers from another net parameter instance.
        113  void CopyTrainedLayersFrom(const NetParameter& param);
        114  void CopyTrainedLayersFrom(const string trained_filename);
        115  void CopyTrainedLayersFromBinaryProto(const string trained_filename);
        116  void CopyTrainedLayersFromHDF5(const string trained_filename);
        118  void ToProto(NetParameter* param, bool write_diff = false) const;
        120  void ToHDF5(const string& filename, bool write_diff = false) const;
        121 
        123  inline const string& name() const { return name_; }
        125  inline const vector<string>& layer_names() const { return layer_names_; }
        127  inline const vector<string>& blob_names() const { return blob_names_; }
        129  inline const vector<shared_ptr<Blob<Dtype> > >& blobs() const {
        130  return blobs_;
        131  }
        133  inline const vector<shared_ptr<Layer<Dtype> > >& layers() const {
        134  return layers_;
        135  }
        137  inline Phase phase() const { return phase_; }
        142  inline const vector<vector<Blob<Dtype>*> >& bottom_vecs() const {
        143  return bottom_vecs_;
        144  }
        149  inline const vector<vector<Blob<Dtype>*> >& top_vecs() const {
        150  return top_vecs_;
        151  }
        153  inline const vector<int> & top_ids(int i) const {
        154  CHECK_GE(i, 0) << "Invalid layer id";
        155  CHECK_LT(i, top_id_vecs_.size()) << "Invalid layer id";
        156  return top_id_vecs_[i];
        157  }
        159  inline const vector<int> & bottom_ids(int i) const {
        160  CHECK_GE(i, 0) << "Invalid layer id";
        161  CHECK_LT(i, bottom_id_vecs_.size()) << "Invalid layer id";
        162  return bottom_id_vecs_[i];
        163  }
        164  inline const vector<vector<bool> >& bottom_need_backward() const {
        165  return bottom_need_backward_;
        166  }
        167  inline const vector<Dtype>& blob_loss_weights() const {
        168  return blob_loss_weights_;
        169  }
        170  inline const vector<bool>& layer_need_backward() const {
        171  return layer_need_backward_;
        172  }
        174  inline const vector<shared_ptr<Blob<Dtype> > >& params() const {
        175  return params_;
        176  }
        177  inline const vector<Blob<Dtype>*>& learnable_params() const {
        178  return learnable_params_;
        179  }
        181  inline const vector<float>& params_lr() const { return params_lr_; }
        182  inline const vector<bool>& has_params_lr() const { return has_params_lr_; }
        184  inline const vector<float>& params_weight_decay() const {
        185  return params_weight_decay_;
        186  }
        187  inline const vector<bool>& has_params_decay() const {
        188  return has_params_decay_;
        189  }
        190  const map<string, int>& param_names_index() const {
        191  return param_names_index_;
        192  }
        193  inline const vector<int>& param_owners() const { return param_owners_; }
        194  inline const vector<string>& param_display_names() const {
        195  return param_display_names_;
        196  }
        198  inline int num_inputs() const { return net_input_blobs_.size(); }
        199  inline int num_outputs() const { return net_output_blobs_.size(); }
        200  inline const vector<Blob<Dtype>*>& input_blobs() const {
        201  return net_input_blobs_;
        202  }
        203  inline const vector<Blob<Dtype>*>& output_blobs() const {
        204  return net_output_blobs_;
        205  }
        206  inline const vector<int>& input_blob_indices() const {
        207  return net_input_blob_indices_;
        208  }
        209  inline const vector<int>& output_blob_indices() const {
        210  return net_output_blob_indices_;
        211  }
        212  bool has_blob(const string& blob_name) const;
        213  const shared_ptr<Blob<Dtype> > blob_by_name(const string& blob_name) const;
        214  bool has_layer(const string& layer_name) const;
        215  const shared_ptr<Layer<Dtype> > layer_by_name(const string& layer_name) const;
        216 
        217  void set_debug_info(const bool value) { debug_info_ = value; }
        218 
        219  // Helpers for Init.
        224  static void FilterNet(const NetParameter& param,
        225  NetParameter* param_filtered);
        227  static bool StateMeetsRule(const NetState& state, const NetStateRule& rule,
        228  const string& layer_name);
        229 
        230  // Invoked at specific points during an iteration
        231  class Callback {
        232  protected:
        233  virtual void run(int layer) = 0;
        234 
        235  template <typename T>
        236  friend class Net;
        237  };
        238  const vector<Callback*>& before_forward() const { return before_forward_; }
        239  void add_before_forward(Callback* value) {
        240  before_forward_.push_back(value);
        241  }
        242  const vector<Callback*>& after_forward() const { return after_forward_; }
        243  void add_after_forward(Callback* value) {
        244  after_forward_.push_back(value);
        245  }
        246  const vector<Callback*>& before_backward() const { return before_backward_; }
        247  void add_before_backward(Callback* value) {
        248  before_backward_.push_back(value);
        249  }
        250  const vector<Callback*>& after_backward() const { return after_backward_; }
        251  void add_after_backward(Callback* value) {
        252  after_backward_.push_back(value);
        253  }
        254 
        255  protected:
        256  // Helpers for Init.
        258  void AppendTop(const NetParameter& param, const int layer_id,
        259  const int top_id, set<string>* available_blobs,
        260  map<string, int>* blob_name_to_idx);
        262  int AppendBottom(const NetParameter& param, const int layer_id,
        263  const int bottom_id, set<string>* available_blobs,
        264  map<string, int>* blob_name_to_idx);
        266  void AppendParam(const NetParameter& param, const int layer_id,
        267  const int param_id);
        268 
        270  void ForwardDebugInfo(const int layer_id);
        272  void BackwardDebugInfo(const int layer_id);
        274  void UpdateDebugInfo(const int param_id);
        275 
        277  string name_;
        279  Phase phase_;
        281  vector<shared_ptr<Layer<Dtype> > > layers_;
        282  vector<string> layer_names_;
        283  map<string, int> layer_names_index_;
        284  vector<bool> layer_need_backward_;
        286  vector<shared_ptr<Blob<Dtype> > > blobs_;
        287  vector<string> blob_names_;
        288  map<string, int> blob_names_index_;
        289  vector<bool> blob_need_backward_;
        293  vector<vector<Blob<Dtype>*> > bottom_vecs_;
        294  vector<vector<int> > bottom_id_vecs_;
        295  vector<vector<bool> > bottom_need_backward_;
        297  vector<vector<Blob<Dtype>*> > top_vecs_;
        298  vector<vector<int> > top_id_vecs_;
        301  vector<Dtype> blob_loss_weights_;
        302  vector<vector<int> > param_id_vecs_;
        303  vector<int> param_owners_;
        304  vector<string> param_display_names_;
        305  vector<pair<int, int> > param_layer_indices_;
        306  map<string, int> param_names_index_;
        308  vector<int> net_input_blob_indices_;
        309  vector<int> net_output_blob_indices_;
        310  vector<Blob<Dtype>*> net_input_blobs_;
        311  vector<Blob<Dtype>*> net_output_blobs_;
        313  vector<shared_ptr<Blob<Dtype> > > params_;
        314  vector<Blob<Dtype>*> learnable_params_;
        322  vector<int> learnable_param_ids_;
        324  vector<float> params_lr_;
        325  vector<bool> has_params_lr_;
        327  vector<float> params_weight_decay_;
        328  vector<bool> has_params_decay_;
        330  size_t memory_used_;
        332  bool debug_info_;
        333  // Callbacks
        334  vector<Callback*> before_forward_;
        335  vector<Callback*> after_forward_;
        336  vector<Callback*> before_backward_;
        337  vector<Callback*> after_backward_;
        338 
        339 DISABLE_COPY_AND_ASSIGN(Net);
        340 };
        341 
        342 
        343 } // namespace caffe
        344 
        345 #endif // CAFFE_NET_HPP_
        caffe::Net::AppendTop
        void AppendTop(const NetParameter &param, const int layer_id, const int top_id, set< string > *available_blobs, map< string, int > *blob_name_to_idx)
        Append a new top blob to the net.
        Definition: net.cpp:356
        caffe::Net::ForwardPrefilled
        const vector< Blob< Dtype > * > & ForwardPrefilled(Dtype *loss=NULL)
        DEPRECATED; use Forward() instead.
        Definition: net.hpp:40
        caffe::Net::layers_
        vector< shared_ptr< Layer< Dtype > > > layers_
        Individual layers in the net.
        Definition: net.hpp:281
        caffe::Net::num_inputs
        int num_inputs() const
        Input and output blob numbers.
        Definition: net.hpp:198
        caffe
        A layer factory that allows one to register layers. During runtime, registered layers can be called b...
        Definition: blob.hpp:14
        caffe::Net::blob_names
        const vector< string > & blob_names() const
        returns the blob names
        Definition: net.hpp:127
        caffe::Net::blob_loss_weights_
        vector< Dtype > blob_loss_weights_
        Definition: net.hpp:301
        -
        caffe::Net::AppendParam
        void AppendParam(const NetParameter &param, const int layer_id, const int param_id)
        Append a new parameter blob to the net.
        Definition: net.cpp:423
        +
        caffe::Net::AppendParam
        void AppendParam(const NetParameter &param, const int layer_id, const int param_id)
        Append a new parameter blob to the net.
        Definition: net.cpp:421
        caffe::Net::Callback
        Definition: net.hpp:231
        -
        caffe::Net::UpdateDebugInfo
        void UpdateDebugInfo(const int param_id)
        Helper for displaying debug info in Update.
        Definition: net.cpp:641
        -
        caffe::Net::Reshape
        void Reshape()
        Reshape all layers from bottom to top.
        Definition: net.cpp:728
        -
        caffe::Net::ForwardFromTo
        Dtype ForwardFromTo(int start, int end)
        Definition: net.cpp:518
        +
        caffe::Net::UpdateDebugInfo
        void UpdateDebugInfo(const int param_id)
        Helper for displaying debug info in Update.
        Definition: net.cpp:639
        +
        caffe::Net::Reshape
        void Reshape()
        Reshape all layers from bottom to top.
        Definition: net.cpp:726
        +
        caffe::Net::ForwardFromTo
        Dtype ForwardFromTo(int start, int end)
        Definition: net.cpp:516
        caffe::Net::layers
        const vector< shared_ptr< Layer< Dtype > > > & layers() const
        returns the layers
        Definition: net.hpp:133
        caffe::Net::memory_used_
        size_t memory_used_
        The bytes of memory used by this net.
        Definition: net.hpp:330
        -
        caffe::Net::Update
        void Update()
        Updates the network weights based on the diff values computed.
        Definition: net.cpp:910
        +
        caffe::Net::Update
        void Update()
        Updates the network weights based on the diff values computed.
        Definition: net.cpp:907
        caffe::Net::debug_info_
        bool debug_info_
        Whether to compute and display debug info for the net.
        Definition: net.hpp:332
        caffe::Net::blobs
        const vector< shared_ptr< Blob< Dtype > > > & blobs() const
        returns the blobs
        Definition: net.hpp:129
        caffe::Net::top_vecs_
        vector< vector< Blob< Dtype > * > > top_vecs_
        top_vecs stores the vectors containing the output for each layer
        Definition: net.hpp:297
        caffe::Net::name
        const string & name() const
        returns the network name.
        Definition: net.hpp:123
        -
        caffe::Net::ClearParamDiffs
        void ClearParamDiffs()
        Zeroes out the diffs of all net parameters. Should be run before Backward.
        Definition: net.cpp:917
        -
        caffe::Net::BackwardDebugInfo
        void BackwardDebugInfo(const int layer_id)
        Helper for displaying debug info in Backward.
        Definition: net.cpp:614
        -
        caffe::Net::ShareWeights
        void ShareWeights()
        Shares weight data of owner blobs with shared blobs.
        Definition: net.cpp:938
        -
        caffe::Net::Init
        void Init(const NetParameter &param)
        Initialize a network with a NetParameter.
        Definition: net.cpp:46
        -
        caffe::Net::Backward
        void Backward()
        Definition: net.cpp:709
        +
        caffe::Net::ClearParamDiffs
        void ClearParamDiffs()
        Zeroes out the diffs of all net parameters. Should be run before Backward.
        Definition: net.cpp:914
        +
        caffe::Net::BackwardDebugInfo
        void BackwardDebugInfo(const int layer_id)
        Helper for displaying debug info in Backward.
        Definition: net.cpp:612
        +
        caffe::Net::ShareWeights
        void ShareWeights()
        Shares weight data of owner blobs with shared blobs.
        Definition: net.cpp:935
        +
        caffe::Net::Init
        void Init(const NetParameter &param)
        Initialize a network with a NetParameter.
        Definition: net.cpp:44
        +
        caffe::Net::Backward
        void Backward()
        Definition: net.cpp:707
        caffe::Net::top_vecs
        const vector< vector< Blob< Dtype > * > > & top_vecs() const
        returns the top vecs for each layer – usually you won&#39;t need this unless you do per-layer checks suc...
        Definition: net.hpp:149
        -
        caffe::Net::ShareTrainedLayersWith
        void ShareTrainedLayersWith(const Net *other)
        For an already initialized net, implicitly copies (i.e., using no additional memory) the pre-trained ...
        Definition: net.cpp:667
        -
        caffe::Net::AppendBottom
        int AppendBottom(const NetParameter &param, const int layer_id, const int bottom_id, set< string > *available_blobs, map< string, int > *blob_name_to_idx)
        Append a new bottom blob to the net.
        Definition: net.cpp:398
        +
        caffe::Net::ShareTrainedLayersWith
        void ShareTrainedLayersWith(const Net *other)
        For an already initialized net, implicitly copies (i.e., using no additional memory) the pre-trained ...
        Definition: net.cpp:665
        +
        caffe::Net::AppendBottom
        int AppendBottom(const NetParameter &param, const int layer_id, const int bottom_id, set< string > *available_blobs, map< string, int > *blob_name_to_idx)
        Append a new bottom blob to the net.
        Definition: net.cpp:396
        caffe::Net::params_weight_decay
        const vector< float > & params_weight_decay() const
        returns the learnable parameter decay multipliers
        Definition: net.hpp:184
        caffe::Net::params_weight_decay_
        vector< float > params_weight_decay_
        the weight decay multipliers for learnable_params_
        Definition: net.hpp:327
        -
        caffe::Net::FilterNet
        static void FilterNet(const NetParameter &param, NetParameter *param_filtered)
        Remove layers that the user specified should be excluded given the current phase, level...
        Definition: net.cpp:261
        +
        caffe::Net::FilterNet
        static void FilterNet(const NetParameter &param, NetParameter *param_filtered)
        Remove layers that the user specified should be excluded given the current phase, level...
        Definition: net.cpp:259
        caffe::Net::top_ids
        const vector< int > & top_ids(int i) const
        returns the ids of the top blobs of layer i
        Definition: net.hpp:153
        caffe::Net::bottom_ids
        const vector< int > & bottom_ids(int i) const
        returns the ids of the bottom blobs of layer i
        Definition: net.hpp:159
        -
        caffe::Net::ToProto
        void ToProto(NetParameter *param, bool write_diff=false) const
        Writes the net to a proto.
        Definition: net.cpp:841
        -
        caffe::Net::ToHDF5
        void ToHDF5(const string &filename, bool write_diff=false) const
        Writes the net to an HDF5 file.
        Definition: net.cpp:853
        +
        caffe::Net::ToProto
        void ToProto(NetParameter *param, bool write_diff=false) const
        Writes the net to a proto.
        Definition: net.cpp:838
        +
        caffe::Net::ToHDF5
        void ToHDF5(const string &filename, bool write_diff=false) const
        Writes the net to an HDF5 file.
        Definition: net.cpp:850
        caffe::Net::learnable_param_ids_
        vector< int > learnable_param_ids_
        Definition: net.hpp:322
        caffe::Net::params_
        vector< shared_ptr< Blob< Dtype > > > params_
        The parameters in the network.
        Definition: net.hpp:313
        caffe::Net::phase_
        Phase phase_
        The phase: TRAIN or TEST.
        Definition: net.hpp:279
        -
        caffe::Net::CopyTrainedLayersFrom
        void CopyTrainedLayersFrom(const NetParameter &param)
        For an already initialized net, copies the pre-trained layers from another Net.
        Definition: net.cpp:735
        -
        caffe::Net::StateMeetsRule
        static bool StateMeetsRule(const NetState &state, const NetStateRule &rule, const string &layer_name)
        return whether NetState state meets NetStateRule rule
        Definition: net.cpp:291
        -
        caffe::Net::Forward
        const vector< Blob< Dtype > * > & Forward(Dtype *loss=NULL)
        Run Forward and return the result.
        Definition: net.cpp:547
        +
        caffe::Net::CopyTrainedLayersFrom
        void CopyTrainedLayersFrom(const NetParameter &param)
        For an already initialized net, copies the pre-trained layers from another Net.
        Definition: net.cpp:733
        +
        caffe::Net::StateMeetsRule
        static bool StateMeetsRule(const NetState &state, const NetStateRule &rule, const string &layer_name)
        return whether NetState state meets NetStateRule rule
        Definition: net.cpp:289
        +
        caffe::Net::Forward
        const vector< Blob< Dtype > * > & Forward(Dtype *loss=NULL)
        Run Forward and return the result.
        Definition: net.cpp:545
        caffe::Net::params
        const vector< shared_ptr< Blob< Dtype > > > & params() const
        returns the parameters
        Definition: net.hpp:174
        -
        caffe::Net::ForwardDebugInfo
        void ForwardDebugInfo(const int layer_id)
        Helper for displaying debug info in Forward.
        Definition: net.cpp:588
        +
        caffe::Net::ForwardDebugInfo
        void ForwardDebugInfo(const int layer_id)
        Helper for displaying debug info in Forward.
        Definition: net.cpp:586
        caffe::Net::params_lr_
        vector< float > params_lr_
        the learning rate multipliers for learnable_params_
        Definition: net.hpp:324
        caffe::Net::net_input_blob_indices_
        vector< int > net_input_blob_indices_
        blob indices for the input and the output of the net
        Definition: net.hpp:308
        caffe::Net::layer_names
        const vector< string > & layer_names() const
        returns the layer names
        Definition: net.hpp:125
        @@ -122,9 +122,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/neuron__layer_8hpp_source.html b/doxygen/neuron__layer_8hpp_source.html index 049b16c201c..779b9cabd5e 100644 --- a/doxygen/neuron__layer_8hpp_source.html +++ b/doxygen/neuron__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/neuron_layer.hpp Source File @@ -29,7 +29,7 @@
        - + @@ -76,9 +76,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/neuron__layers_8hpp_source.html b/doxygen/neuron__layers_8hpp_source.html deleted file mode 100644 index 0b9c643b660..00000000000 --- a/doxygen/neuron__layers_8hpp_source.html +++ /dev/null @@ -1,569 +0,0 @@ - - - - - - -Caffe: include/caffe/neuron_layers.hpp Source File - - - - - - - - - - -
        -
        - - - - - - -
        -
        Caffe -
        -
        -
        - - - - - - -
        -
        - - -
        - -
        - - -
        -
        -
        -
        neuron_layers.hpp
        -
        -
        -
        1 #ifndef CAFFE_NEURON_LAYERS_HPP_
        -
        2 #define CAFFE_NEURON_LAYERS_HPP_
        -
        3 
        -
        4 #include <string>
        -
        5 #include <utility>
        -
        6 #include <vector>
        -
        7 
        -
        8 #include "caffe/blob.hpp"
        -
        9 #include "caffe/common.hpp"
        -
        10 #include "caffe/layer.hpp"
        -
        11 #include "caffe/proto/caffe.pb.h"
        -
        12 
        -
        13 #define HDF5_DATA_DATASET_NAME "data"
        -
        14 #define HDF5_DATA_LABEL_NAME "label"
        -
        15 
        -
        16 namespace caffe {
        -
        17 
        -
        24 template <typename Dtype>
        -
        25 class NeuronLayer : public Layer<Dtype> {
        -
        26  public:
        -
        27  explicit NeuronLayer(const LayerParameter& param)
        -
        28  : Layer<Dtype>(param) {}
        -
        29  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        30  const vector<Blob<Dtype>*>& top);
        -
        31 
        -
        32  virtual inline int ExactNumBottomBlobs() const { return 1; }
        -
        33  virtual inline int ExactNumTopBlobs() const { return 1; }
        -
        34 };
        -
        35 
        -
        46 template <typename Dtype>
        -
        47 class AbsValLayer : public NeuronLayer<Dtype> {
        -
        48  public:
        -
        49  explicit AbsValLayer(const LayerParameter& param)
        -
        50  : NeuronLayer<Dtype>(param) {}
        -
        51  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        52  const vector<Blob<Dtype>*>& top);
        -
        53 
        -
        54  virtual inline const char* type() const { return "AbsVal"; }
        -
        55  virtual inline int ExactNumBottomBlobs() const { return 1; }
        -
        56  virtual inline int ExactNumTopBlobs() const { return 1; }
        -
        57 
        -
        58  protected:
        -
        60  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        61  const vector<Blob<Dtype>*>& top);
        -
        62  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        63  const vector<Blob<Dtype>*>& top);
        -
        64 
        -
        82  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        83  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        84  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        85  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        86 };
        -
        87 
        -
        105 template <typename Dtype>
        -
        106 class BNLLLayer : public NeuronLayer<Dtype> {
        -
        107  public:
        -
        108  explicit BNLLLayer(const LayerParameter& param)
        -
        109  : NeuronLayer<Dtype>(param) {}
        -
        110 
        -
        111  virtual inline const char* type() const { return "BNLL"; }
        -
        112 
        -
        113  protected:
        -
        115  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        116  const vector<Blob<Dtype>*>& top);
        -
        117  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        118  const vector<Blob<Dtype>*>& top);
        -
        119 
        -
        136  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        137  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        138  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        139  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        140 };
        -
        141 
        -
        153 template <typename Dtype>
        -
        154 class DropoutLayer : public NeuronLayer<Dtype> {
        -
        155  public:
        -
        162  explicit DropoutLayer(const LayerParameter& param)
        -
        163  : NeuronLayer<Dtype>(param) {}
        -
        164  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        165  const vector<Blob<Dtype>*>& top);
        -
        166  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        167  const vector<Blob<Dtype>*>& top);
        -
        168 
        -
        169  virtual inline const char* type() const { return "Dropout"; }
        -
        170 
        -
        171  protected:
        -
        188  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        189  const vector<Blob<Dtype>*>& top);
        -
        190  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        191  const vector<Blob<Dtype>*>& top);
        -
        192  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        193  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        194  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        195  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        196 
        -
        198  Blob<unsigned int> rand_vec_;
        -
        200  Dtype threshold_;
        -
        202  Dtype scale_;
        -
        203  unsigned int uint_thres_;
        -
        204 };
        -
        205 
        -
        211 template <typename Dtype>
        -
        212 class ExpLayer : public NeuronLayer<Dtype> {
        -
        213  public:
        -
        222  explicit ExpLayer(const LayerParameter& param)
        -
        223  : NeuronLayer<Dtype>(param) {}
        -
        224  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        225  const vector<Blob<Dtype>*>& top);
        -
        226 
        -
        227  virtual inline const char* type() const { return "Exp"; }
        -
        228 
        -
        229  protected:
        -
        240  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        241  const vector<Blob<Dtype>*>& top);
        -
        242  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        243  const vector<Blob<Dtype>*>& top);
        -
        244 
        -
        262  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        263  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        264  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        265  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        266 
        -
        267  Dtype inner_scale_, outer_scale_;
        -
        268 };
        -
        269 
        -
        275 template <typename Dtype>
        -
        276 class LogLayer : public NeuronLayer<Dtype> {
        -
        277  public:
        -
        286  explicit LogLayer(const LayerParameter& param)
        -
        287  : NeuronLayer<Dtype>(param) {}
        -
        288  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        289  const vector<Blob<Dtype>*>& top);
        -
        290 
        -
        291  virtual inline const char* type() const { return "Log"; }
        -
        292 
        -
        293  protected:
        -
        304  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        305  const vector<Blob<Dtype>*>& top);
        -
        306  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        307  const vector<Blob<Dtype>*>& top);
        -
        308 
        -
        326  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        327  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        328  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        329  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        330 
        -
        331  Dtype base_scale_;
        -
        332  Dtype input_scale_, input_shift_;
        -
        333  Dtype backward_num_scale_;
        -
        334 };
        -
        335 
        -
        341 template <typename Dtype>
        -
        342 class PowerLayer : public NeuronLayer<Dtype> {
        -
        343  public:
        -
        351  explicit PowerLayer(const LayerParameter& param)
        -
        352  : NeuronLayer<Dtype>(param) {}
        -
        353  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        354  const vector<Blob<Dtype>*>& top);
        -
        355 
        -
        356  virtual inline const char* type() const { return "Power"; }
        -
        357 
        -
        358  protected:
        -
        369  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        370  const vector<Blob<Dtype>*>& top);
        -
        371  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        372  const vector<Blob<Dtype>*>& top);
        -
        373 
        -
        394  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        395  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        396  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        397  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        398 
        -
        400  Dtype power_;
        -
        402  Dtype scale_;
        -
        404  Dtype shift_;
        -
        406  Dtype diff_scale_;
        -
        407 };
        -
        408 
        -
        413 template <typename Dtype>
        -
        414 class ReLULayer : public NeuronLayer<Dtype> {
        -
        415  public:
        -
        422  explicit ReLULayer(const LayerParameter& param)
        -
        423  : NeuronLayer<Dtype>(param) {}
        -
        424 
        -
        425  virtual inline const char* type() const { return "ReLU"; }
        -
        426 
        -
        427  protected:
        -
        439  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        440  const vector<Blob<Dtype>*>& top);
        -
        441  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        442  const vector<Blob<Dtype>*>& top);
        -
        443 
        -
        472  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        473  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        474  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        475  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        476 };
        -
        477 
        -
        478 #ifdef USE_CUDNN
        -
        479 
        -
        482 template <typename Dtype>
        -
        483 class CuDNNReLULayer : public ReLULayer<Dtype> {
        -
        484  public:
        -
        485  explicit CuDNNReLULayer(const LayerParameter& param)
        -
        486  : ReLULayer<Dtype>(param), handles_setup_(false) {}
        -
        487  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        488  const vector<Blob<Dtype>*>& top);
        -
        489  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        490  const vector<Blob<Dtype>*>& top);
        -
        491  virtual ~CuDNNReLULayer();
        -
        492 
        -
        493  protected:
        -
        494  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        495  const vector<Blob<Dtype>*>& top);
        -
        496  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        497  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        498 
        -
        499  bool handles_setup_;
        -
        500  cudnnHandle_t handle_;
        -
        501  cudnnTensorDescriptor_t bottom_desc_;
        -
        502  cudnnTensorDescriptor_t top_desc_;
        -
        503 };
        -
        504 #endif
        -
        505 
        -
        514 template <typename Dtype>
        -
        515 class SigmoidLayer : public NeuronLayer<Dtype> {
        -
        516  public:
        -
        517  explicit SigmoidLayer(const LayerParameter& param)
        -
        518  : NeuronLayer<Dtype>(param) {}
        -
        519 
        -
        520  virtual inline const char* type() const { return "Sigmoid"; }
        -
        521 
        -
        522  protected:
        -
        533  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        534  const vector<Blob<Dtype>*>& top);
        -
        535  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        536  const vector<Blob<Dtype>*>& top);
        -
        537 
        -
        555  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        556  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        557  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        558  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        559 };
        -
        560 
        -
        561 #ifdef USE_CUDNN
        -
        562 
        -
        565 template <typename Dtype>
        -
        566 class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
        -
        567  public:
        -
        568  explicit CuDNNSigmoidLayer(const LayerParameter& param)
        -
        569  : SigmoidLayer<Dtype>(param), handles_setup_(false) {}
        -
        570  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        571  const vector<Blob<Dtype>*>& top);
        -
        572  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        573  const vector<Blob<Dtype>*>& top);
        -
        574  virtual ~CuDNNSigmoidLayer();
        -
        575 
        -
        576  protected:
        -
        577  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        578  const vector<Blob<Dtype>*>& top);
        -
        579  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        580  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        581 
        -
        582  bool handles_setup_;
        -
        583  cudnnHandle_t handle_;
        -
        584  cudnnTensorDescriptor_t bottom_desc_;
        -
        585  cudnnTensorDescriptor_t top_desc_;
        -
        586 };
        -
        587 #endif
        -
        588 
        -
        597 template <typename Dtype>
        -
        598 class TanHLayer : public NeuronLayer<Dtype> {
        -
        599  public:
        -
        600  explicit TanHLayer(const LayerParameter& param)
        -
        601  : NeuronLayer<Dtype>(param) {}
        -
        602 
        -
        603  virtual inline const char* type() const { return "TanH"; }
        -
        604 
        -
        605  protected:
        -
        616  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        617  const vector<Blob<Dtype>*>& top);
        -
        618  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        619  const vector<Blob<Dtype>*>& top);
        -
        620 
        -
        640  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        641  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        642  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        643  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        644 };
        -
        645 
        -
        646 #ifdef USE_CUDNN
        -
        647 
        -
        650 template <typename Dtype>
        -
        651 class CuDNNTanHLayer : public TanHLayer<Dtype> {
        -
        652  public:
        -
        653  explicit CuDNNTanHLayer(const LayerParameter& param)
        -
        654  : TanHLayer<Dtype>(param), handles_setup_(false) {}
        -
        655  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        656  const vector<Blob<Dtype>*>& top);
        -
        657  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        658  const vector<Blob<Dtype>*>& top);
        -
        659  virtual ~CuDNNTanHLayer();
        -
        660 
        -
        661  protected:
        -
        662  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        663  const vector<Blob<Dtype>*>& top);
        -
        664  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        665  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        666 
        -
        667  bool handles_setup_;
        -
        668  cudnnHandle_t handle_;
        -
        669  cudnnTensorDescriptor_t bottom_desc_;
        -
        670  cudnnTensorDescriptor_t top_desc_;
        -
        671 };
        -
        672 #endif
        -
        673 
        -
        678 template <typename Dtype>
        -
        679 class ThresholdLayer : public NeuronLayer<Dtype> {
        -
        680  public:
        -
        687  explicit ThresholdLayer(const LayerParameter& param)
        -
        688  : NeuronLayer<Dtype>(param) {}
        -
        689  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        690  const vector<Blob<Dtype>*>& top);
        -
        691 
        -
        692  virtual inline const char* type() const { return "Threshold"; }
        -
        693 
        -
        694  protected:
        -
        709  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        710  const vector<Blob<Dtype>*>& top);
        -
        711  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        712  const vector<Blob<Dtype>*>& top);
        -
        714  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        715  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
        -
        716  NOT_IMPLEMENTED;
        -
        717  }
        -
        718 
        -
        719  Dtype threshold_;
        -
        720 };
        -
        721 
        -
        730 template <typename Dtype>
        -
        731 class PReLULayer : public NeuronLayer<Dtype> {
        -
        732  public:
        -
        741  explicit PReLULayer(const LayerParameter& param)
        -
        742  : NeuronLayer<Dtype>(param) {}
        -
        743 
        -
        744  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        745  const vector<Blob<Dtype>*>& top);
        -
        746 
        -
        747  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        748  const vector<Blob<Dtype>*>& top);
        -
        749 
        -
        750  virtual inline const char* type() const { return "PReLU"; }
        -
        751 
        -
        752  protected:
        -
        763  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        764  const vector<Blob<Dtype>*>& top);
        -
        765  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        766  const vector<Blob<Dtype>*>& top);
        -
        767 
        -
        796  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        797  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        798  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        799  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        800 
        -
        801  bool channel_shared_;
        -
        802  Blob<Dtype> multiplier_; // dot multiplier for backward computation of params
        -
        803  Blob<Dtype> backward_buff_; // temporary buffer for backward computation
        -
        804  Blob<Dtype> bottom_memory_; // memory for in-place computation
        -
        805 };
        -
        806 
        -
        807 } // namespace caffe
        -
        808 
        -
        809 #endif // CAFFE_NEURON_LAYERS_HPP_
        -
        caffe::ThresholdLayer
        Tests whether the input exceeds a threshold: outputs 1 for inputs above threshold; 0 otherwise...
        Definition: neuron_layers.hpp:679
        -
        caffe::AbsValLayer::ExactNumTopBlobs
        virtual int ExactNumTopBlobs() const
        Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
        Definition: neuron_layers.hpp:56
        -
        caffe::DropoutLayer::rand_vec_
        Blob< unsigned int > rand_vec_
        when divided by UINT_MAX, the randomly generated values
        Definition: neuron_layers.hpp:198
        -
        caffe::SigmoidLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Definition: sigmoid_layer.cpp:16
        -
        caffe::ThresholdLayer::ThresholdLayer
        ThresholdLayer(const LayerParameter &param)
        Definition: neuron_layers.hpp:687
        -
        caffe::NeuronLayer::ExactNumBottomBlobs
        virtual int ExactNumBottomBlobs() const
        Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
        Definition: neuron_layers.hpp:32
        -
        caffe::TanHLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        -
        caffe::Layer
        An interface for the units of computation which can be composed into a Net.
        Definition: layer.hpp:33
        -
        caffe::DropoutLayer::scale_
        Dtype scale_
        the scale for undropped inputs at train time
        Definition: neuron_layers.hpp:202
        -
        caffe
        A layer factory that allows one to register layers. During runtime, registered layers could be called...
        Definition: blob.hpp:15
        -
        caffe::ExpLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Definition: exp_layer.cpp:32
        -
        caffe::SigmoidLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: neuron_layers.hpp:520
        -
        caffe::SigmoidLayer
        Sigmoid function non-linearity , a classic choice in neural networks.
        Definition: neuron_layers.hpp:515
        -
        caffe::DropoutLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::ThresholdLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Definition: threshold_layer.cpp:17
        -
        caffe::BNLLLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Computes the error gradient w.r.t. the BNLL inputs.
        Definition: bnll_layer.cpp:25
        -
        caffe::PowerLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: power_layer.cpp:11
        -
        caffe::LogLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::PowerLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::NeuronLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: neuron_layer.cpp:9
        -
        caffe::TanHLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Definition: tanh_layer.cpp:13
        -
        caffe::AbsValLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Computes the error gradient w.r.t. the absolute value inputs.
        Definition: absval_layer.cpp:26
        -
        caffe::ExpLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        -
        caffe::PowerLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        -
        caffe::AbsValLayer
        Computes .
        Definition: neuron_layers.hpp:47
        -
        caffe::ExpLayer
        Computes , as specified by the scale , shift , and base .
        Definition: neuron_layers.hpp:212
        -
        caffe::TanHLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: neuron_layers.hpp:603
        -
        caffe::PowerLayer::power_
        Dtype power_
        from layer_param_.power_param()
        Definition: neuron_layers.hpp:400
        -
        caffe::PowerLayer::diff_scale_
        Dtype diff_scale_
        Result of .
        Definition: neuron_layers.hpp:406
        -
        caffe::LogLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: neuron_layers.hpp:291
        -
        caffe::PowerLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: neuron_layers.hpp:356
        -
        caffe::DropoutLayer::DropoutLayer
        DropoutLayer(const LayerParameter &param)
        Definition: neuron_layers.hpp:162
        -
        caffe::ThresholdLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::LogLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: log_layer.cpp:11
        -
        caffe::PReLULayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: prelu_layer.cpp:54
        -
        caffe::ReLULayer
        Rectified Linear Unit non-linearity . The simple max is fast to compute, and the function does not sa...
        Definition: neuron_layers.hpp:414
        -
        caffe::ThresholdLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Not implemented (non-differentiable function)
        Definition: neuron_layers.hpp:714
        -
        caffe::SigmoidLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        -
        caffe::PowerLayer
        Computes , as specified by the scale , shift , and power .
        Definition: neuron_layers.hpp:342
        -
        caffe::ExpLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::ReLULayer::ReLULayer
        ReLULayer(const LayerParameter &param)
        Definition: neuron_layers.hpp:422
        -
        caffe::DropoutLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: dropout_layer.cpp:14
        -
        caffe::SigmoidLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Computes the error gradient w.r.t. the sigmoid inputs.
        Definition: sigmoid_layer.cpp:27
        -
        caffe::DropoutLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        -
        caffe::PReLULayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: prelu_layer.cpp:11
        -
        caffe::NeuronLayer::ExactNumTopBlobs
        virtual int ExactNumTopBlobs() const
        Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
        Definition: neuron_layers.hpp:33
        -
        caffe::PReLULayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: neuron_layers.hpp:750
        -
        caffe::ReLULayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Computes the error gradient w.r.t. the ReLU inputs.
        Definition: relu_layer.cpp:23
        -
        caffe::PowerLayer::shift_
        Dtype shift_
        from layer_param_.power_param()
        Definition: neuron_layers.hpp:404
        -
        caffe::LogLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        -
        caffe::PowerLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Computes the error gradient w.r.t. the power inputs.
        Definition: power_layer.cpp:46
        -
        caffe::BNLLLayer
        Computes if ; otherwise.
        Definition: neuron_layers.hpp:106
        -
        caffe::LogLayer
        Computes , as specified by the scale , shift , and base .
        Definition: neuron_layers.hpp:276
        -
        caffe::DropoutLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Definition: dropout_layer.cpp:34
        -
        caffe::SigmoidLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::ReLULayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: neuron_layers.hpp:425
        -
        caffe::DropoutLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: neuron_layers.hpp:169
        -
        caffe::PReLULayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Computes the error gradient w.r.t. the PReLU inputs.
        Definition: prelu_layer.cpp:91
        -
        caffe::ExpLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Computes the error gradient w.r.t. the exp inputs.
        Definition: exp_layer.cpp:49
        -
        caffe::NeuronLayer
        An interface for layers that take one blob as input ( ) and produce one equally-sized blob as output ...
        Definition: neuron_layers.hpp:25
        -
        caffe::PowerLayer::scale_
        Dtype scale_
        from layer_param_.power_param()
        Definition: neuron_layers.hpp:402
        -
        caffe::DropoutLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        Definition: dropout_layer.cpp:52
        -
        caffe::AbsValLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        -
        caffe::PReLULayer
        Parameterized Rectified Linear Unit non-linearity . The differences from ReLULayer are 1) negative sl...
        Definition: neuron_layers.hpp:731
        -
        caffe::PReLULayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        -
        caffe::PowerLayer::PowerLayer
        PowerLayer(const LayerParameter &param)
        Definition: neuron_layers.hpp:351
        -
        caffe::ReLULayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::LogLayer::LogLayer
        LogLayer(const LayerParameter &param)
        Definition: neuron_layers.hpp:286
        -
        caffe::ReLULayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        -
        caffe::BNLLLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::AbsValLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: absval_layer.cpp:10
        -
        caffe::ThresholdLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: neuron_layers.hpp:692
        -
        caffe::ExpLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: neuron_layers.hpp:227
        -
        caffe::PReLULayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Definition: prelu_layer.cpp:66
        -
        caffe::AbsValLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Computes .
        Definition: absval_layer.cpp:18
        -
        caffe::BNLLLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        -
        caffe::PowerLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Definition: power_layer.cpp:22
        -
        caffe::PReLULayer::PReLULayer
        PReLULayer(const LayerParameter &param)
        Definition: neuron_layers.hpp:741
        -
        caffe::ReLULayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Definition: relu_layer.cpp:10
        -
        caffe::TanHLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::LogLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Definition: log_layer.cpp:36
        -
        caffe::DropoutLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: dropout_layer.cpp:25
        -
        caffe::BNLLLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Computes if ; otherwise.
        Definition: bnll_layer.cpp:12
        -
        caffe::PReLULayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::AbsValLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: neuron_layers.hpp:54
        -
        caffe::AbsValLayer::ExactNumBottomBlobs
        virtual int ExactNumBottomBlobs() const
        Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
        Definition: neuron_layers.hpp:55
        -
        caffe::AbsValLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::ExpLayer::ExpLayer
        ExpLayer(const LayerParameter &param)
        Definition: neuron_layers.hpp:222
        -
        caffe::DropoutLayer
        During training only, sets a random portion of to 0, adjusting the rest of the vector magnitude acco...
        Definition: neuron_layers.hpp:154
        -
        caffe::ThresholdLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: threshold_layer.cpp:10
        -
        caffe::DropoutLayer::threshold_
        Dtype threshold_
        the probability of dropping any input
        Definition: neuron_layers.hpp:200
        -
        caffe::TanHLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Computes the error gradient w.r.t. the sigmoid inputs.
        Definition: tanh_layer.cpp:24
        -
        caffe::ExpLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: exp_layer.cpp:11
        -
        caffe::LogLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Computes the error gradient w.r.t. the exp inputs.
        Definition: log_layer.cpp:59
        -
        caffe::BNLLLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: neuron_layers.hpp:111
        -
        caffe::TanHLayer
        TanH hyperbolic tangent non-linearity , popular in auto-encoders.
        Definition: neuron_layers.hpp:598
        -
        caffe::Blob
        A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
        Definition: blob.hpp:25
        -
        - -
        -Generated on Mon Oct 19 2015 15:25:08 for Caffe by   -doxygen - 1.8.9.1 -
        - - diff --git a/doxygen/parallel_8hpp_source.html b/doxygen/parallel_8hpp_source.html index 0e041d6d90e..68aee43618c 100644 --- a/doxygen/parallel_8hpp_source.html +++ b/doxygen/parallel_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/parallel.hpp Source File @@ -29,7 +29,7 @@ - + @@ -70,9 +70,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/parameter__layer_8hpp_source.html b/doxygen/parameter__layer_8hpp_source.html index 83b730bc231..95f74a5e0a5 100644 --- a/doxygen/parameter__layer_8hpp_source.html +++ b/doxygen/parameter__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/parameter_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -82,9 +82,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/pooling__layer_8hpp_source.html b/doxygen/pooling__layer_8hpp_source.html index d1b0c052fa9..d67e3b92447 100644 --- a/doxygen/pooling__layer_8hpp_source.html +++ b/doxygen/pooling__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/pooling_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -84,9 +84,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/power__layer_8hpp_source.html b/doxygen/power__layer_8hpp_source.html index 115362888d6..ddbd8619324 100644 --- a/doxygen/power__layer_8hpp_source.html +++ b/doxygen/power__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/power_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -84,9 +84,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/prelu__layer_8hpp_source.html b/doxygen/prelu__layer_8hpp_source.html index 59635da6b46..68726aca5e3 100644 --- a/doxygen/prelu__layer_8hpp_source.html +++ b/doxygen/prelu__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/prelu_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -81,9 +81,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/python__layer_8hpp_source.html b/doxygen/python__layer_8hpp_source.html index 7d3e211bf24..8305aae70b7 100644 --- a/doxygen/python__layer_8hpp_source.html +++ b/doxygen/python__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/python_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -66,23 +66,23 @@
        python_layer.hpp
        -
        1 #ifndef CAFFE_PYTHON_LAYER_HPP_
        2 #define CAFFE_PYTHON_LAYER_HPP_
        3 
        4 #include <boost/python.hpp>
        5 #include <vector>
        6 
        7 #include "caffe/layer.hpp"
        8 
        9 namespace bp = boost::python;
        10 
        11 namespace caffe {
        12 
        13 template <typename Dtype>
        14 class PythonLayer : public Layer<Dtype> {
        15  public:
        16  PythonLayer(PyObject* self, const LayerParameter& param)
        17  : Layer<Dtype>(param), self_(bp::handle<>(bp::borrowed(self))) { }
        18 
        19  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        20  const vector<Blob<Dtype>*>& top) {
        21  // Disallow PythonLayer in MultiGPU training stage, due to GIL issues
        22  // Details: https://github.com/BVLC/caffe/issues/2936
        23  if (this->phase_ == TRAIN && Caffe::solver_count() > 1
        24  && !Caffe::multiprocess()) {
        25  LOG(FATAL) << "PythonLayer does not support CLI Multi-GPU, use train.py";
        26  }
        27  self_.attr("param_str") = bp::str(
        28  this->layer_param_.python_param().param_str());
        29  self_.attr("phase") = static_cast<int>(this->phase_);
        30  self_.attr("setup")(bottom, top);
        31  }
        32  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        33  const vector<Blob<Dtype>*>& top) {
        34  self_.attr("reshape")(bottom, top);
        35  }
        36 
        37  virtual inline bool ShareInParallel() const {
        38  return this->layer_param_.python_param().share_in_parallel();
        39  }
        40 
        41  virtual inline const char* type() const { return "Python"; }
        42 
        43  protected:
        44  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        45  const vector<Blob<Dtype>*>& top) {
        46  self_.attr("forward")(bottom, top);
        47  }
        48  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        49  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
        50  self_.attr("backward")(top, propagate_down, bottom);
        51  }
        52 
        53  private:
        54  bp::object self_;
        55 };
        56 
        57 } // namespace caffe
        58 
        59 #endif
        caffe::Layer
        An interface for the units of computation which can be composed into a Net.
        Definition: layer.hpp:33
        +
        1 #ifndef CAFFE_PYTHON_LAYER_HPP_
        2 #define CAFFE_PYTHON_LAYER_HPP_
        3 
        4 #include <boost/python.hpp>
        5 #include <vector>
        6 
        7 #include "caffe/layer.hpp"
        8 
        9 namespace bp = boost::python;
        10 
        11 namespace caffe {
        12 
        13 template <typename Dtype>
        14 class PythonLayer : public Layer<Dtype> {
        15  public:
        16  PythonLayer(PyObject* self, const LayerParameter& param)
        17  : Layer<Dtype>(param), self_(bp::handle<>(bp::borrowed(self))) { }
        18 
        19  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        20  const vector<Blob<Dtype>*>& top) {
        21  // Disallow PythonLayer in MultiGPU training stage, due to GIL issues
        22  // Details: https://github.com/BVLC/caffe/issues/2936
        23  if (this->phase_ == TRAIN && Caffe::solver_count() > 1
        24  && !Caffe::multiprocess()) {
        25  LOG(FATAL) << "PythonLayer does not support CLI Multi-GPU, use train.py";
        26  }
        27  self_.attr("param_str") = bp::str(
        28  this->layer_param_.python_param().param_str());
        29  self_.attr("phase") = static_cast<int>(this->phase_);
        30  self_.attr("setup")(bottom, top);
        31  }
        32  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        33  const vector<Blob<Dtype>*>& top) {
        34  self_.attr("reshape")(bottom, top);
        35  }
        36 
        37  virtual inline const char* type() const { return "Python"; }
        38 
        39  protected:
        40  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        41  const vector<Blob<Dtype>*>& top) {
        42  self_.attr("forward")(bottom, top);
        43  }
        44  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        45  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
        46  self_.attr("backward")(top, propagate_down, bottom);
        47  }
        48 
        49  private:
        50  bp::object self_;
        51 };
        52 
        53 } // namespace caffe
        54 
        55 #endif
        caffe::Layer
        An interface for the units of computation which can be composed into a Net.
        Definition: layer.hpp:33
        caffe
        A layer factory that allows one to register layers. During runtime, registered layers can be called b...
        Definition: blob.hpp:14
        -
        caffe::PythonLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
        Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        Definition: python_layer.hpp:48
        +
        caffe::PythonLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
        Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        Definition: python_layer.hpp:44
        caffe::PythonLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: python_layer.hpp:19
        caffe::PythonLayer
        Definition: python_layer.hpp:14
        -
        caffe::PythonLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Using the CPU device, compute the layer output.
        Definition: python_layer.hpp:44
        +
        caffe::PythonLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Using the CPU device, compute the layer output.
        Definition: python_layer.hpp:40
        caffe::Layer::phase_
        Phase phase_
        Definition: layer.hpp:299
        caffe::Layer::layer_param_
        LayerParameter layer_param_
        Definition: layer.hpp:297
        -
        caffe::PythonLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: python_layer.hpp:41
        +
        caffe::PythonLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: python_layer.hpp:37
        caffe::PythonLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: python_layer.hpp:32
        caffe::Blob
        A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
        Definition: blob.hpp:24
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/recurrent__layer_8hpp_source.html b/doxygen/recurrent__layer_8hpp_source.html index b60f52c1b87..54ad5fb2b79 100644 --- a/doxygen/recurrent__layer_8hpp_source.html +++ b/doxygen/recurrent__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/recurrent_layer.hpp Source File @@ -29,7 +29,7 @@
        - + @@ -94,9 +94,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/reduction__layer_8hpp_source.html b/doxygen/reduction__layer_8hpp_source.html index ab0ce85c2fb..3b63332a010 100644 --- a/doxygen/reduction__layer_8hpp_source.html +++ b/doxygen/reduction__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/reduction_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -88,9 +88,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/relu__layer_8hpp_source.html b/doxygen/relu__layer_8hpp_source.html index 4e606ab4270..60e9378dd81 100644 --- a/doxygen/relu__layer_8hpp_source.html +++ b/doxygen/relu__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/relu_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -79,9 +79,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/doxygen/reshape__layer_8hpp_source.html b/doxygen/reshape__layer_8hpp_source.html index 1e1eb615cae..d88033b990f 100644 --- a/doxygen/reshape__layer_8hpp_source.html +++ b/doxygen/reshape__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/reshape_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -85,9 +85,9 @@
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        vision_layers.hpp
        -
        -
        -
        1 #ifndef CAFFE_VISION_LAYERS_HPP_
        -
        2 #define CAFFE_VISION_LAYERS_HPP_
        -
        3 
        -
        4 #include <string>
        -
        5 #include <utility>
        -
        6 #include <vector>
        -
        7 
        -
        8 #include "caffe/blob.hpp"
        -
        9 #include "caffe/common.hpp"
        -
        10 #include "caffe/common_layers.hpp"
        -
        11 #include "caffe/data_layers.hpp"
        -
        12 #include "caffe/layer.hpp"
        -
        13 #include "caffe/loss_layers.hpp"
        -
        14 #include "caffe/neuron_layers.hpp"
        -
        15 #include "caffe/proto/caffe.pb.h"
        -
        16 
        -
        17 namespace caffe {
        -
        18 
        -
        23 template <typename Dtype>
        -
        24 class BaseConvolutionLayer : public Layer<Dtype> {
        -
        25  public:
        -
        26  explicit BaseConvolutionLayer(const LayerParameter& param)
        -
        27  : Layer<Dtype>(param) {}
        -
        28  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        29  const vector<Blob<Dtype>*>& top);
        -
        30  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        31  const vector<Blob<Dtype>*>& top);
        -
        32 
        -
        33  virtual inline int MinBottomBlobs() const { return 1; }
        -
        34  virtual inline int MinTopBlobs() const { return 1; }
        -
        35  virtual inline bool EqualNumBottomTopBlobs() const { return true; }
        -
        36 
        -
        37  protected:
        -
        38  // Helper functions that abstract away the column buffer and gemm arguments.
        -
        39  // The last argument in forward_cpu_gemm is so that we can skip the im2col if
        -
        40  // we just called weight_cpu_gemm with the same input.
        -
        41  void forward_cpu_gemm(const Dtype* input, const Dtype* weights,
        -
        42  Dtype* output, bool skip_im2col = false);
        -
        43  void forward_cpu_bias(Dtype* output, const Dtype* bias);
        -
        44  void backward_cpu_gemm(const Dtype* input, const Dtype* weights,
        -
        45  Dtype* output);
        -
        46  void weight_cpu_gemm(const Dtype* input, const Dtype* output, Dtype*
        -
        47  weights);
        -
        48  void backward_cpu_bias(Dtype* bias, const Dtype* input);
        -
        49 
        -
        50 #ifndef CPU_ONLY
        -
        51  void forward_gpu_gemm(const Dtype* col_input, const Dtype* weights,
        -
        52  Dtype* output, bool skip_im2col = false);
        -
        53  void forward_gpu_bias(Dtype* output, const Dtype* bias);
        -
        54  void backward_gpu_gemm(const Dtype* input, const Dtype* weights,
        -
        55  Dtype* col_output);
        -
        56  void weight_gpu_gemm(const Dtype* col_input, const Dtype* output, Dtype*
        -
        57  weights);
        -
        58  void backward_gpu_bias(Dtype* bias, const Dtype* input);
        -
        59 #endif
        -
        60 
        -
        62  inline int input_shape(int i) {
        -
        63  return (*bottom_shape_)[channel_axis_ + i];
        -
        64  }
        -
        65  // reverse_dimensions should return true iff we are implementing deconv, so
        -
        66  // that conv helpers know which dimensions are which.
        -
        67  virtual bool reverse_dimensions() = 0;
        -
        68  // Compute height_out_ and width_out_ from other parameters.
        -
        69  virtual void compute_output_shape() = 0;
        -
        70 
        -
        72  Blob<int> kernel_shape_;
        -
        74  Blob<int> stride_;
        -
        76  Blob<int> pad_;
        -
        78  Blob<int> conv_input_shape_;
        -
        80  vector<int> col_buffer_shape_;
        -
        82  vector<int> output_shape_;
        -
        83  const vector<int>* bottom_shape_;
        -
        84 
        -
        85  int num_spatial_axes_;
        -
        86  int bottom_dim_;
        -
        87  int top_dim_;
        -
        88 
        -
        89  int channel_axis_;
        -
        90  int num_;
        -
        91  int channels_;
        -
        92  int group_;
        -
        93  int out_spatial_dim_;
        -
        94  int weight_offset_;
        -
        95  int num_output_;
        -
        96  bool bias_term_;
        -
        97  bool is_1x1_;
        -
        98  bool force_nd_im2col_;
        -
        99 
        -
        100  private:
        -
        101  // wrap im2col/col2im so we don't have to remember the (long) argument lists
        -
        102  inline void conv_im2col_cpu(const Dtype* data, Dtype* col_buff) {
        -
        103  if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
        -
        104  im2col_cpu(data, conv_in_channels_,
        -
        105  conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
        -
        106  kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
        -
        107  pad_.cpu_data()[0], pad_.cpu_data()[1],
        -
        108  stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff);
        -
        109  } else {
        -
        110  im2col_nd_cpu(data, num_spatial_axes_, conv_input_shape_.cpu_data(),
        -
        111  col_buffer_shape_.data(), kernel_shape_.cpu_data(),
        -
        112  pad_.cpu_data(), stride_.cpu_data(), col_buff);
        -
        113  }
        -
        114  }
        -
        115  inline void conv_col2im_cpu(const Dtype* col_buff, Dtype* data) {
        -
        116  if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
        -
        117  col2im_cpu(col_buff, conv_in_channels_,
        -
        118  conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
        -
        119  kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
        -
        120  pad_.cpu_data()[0], pad_.cpu_data()[1],
        -
        121  stride_.cpu_data()[0], stride_.cpu_data()[1], data);
        -
        122  } else {
        -
        123  col2im_nd_cpu(col_buff, num_spatial_axes_, conv_input_shape_.cpu_data(),
        -
        124  col_buffer_shape_.data(), kernel_shape_.cpu_data(),
        -
        125  pad_.cpu_data(), stride_.cpu_data(), data);
        -
        126  }
        -
        127  }
        -
        128 #ifndef CPU_ONLY
        -
        129  inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) {
        -
        130  if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
        -
        131  im2col_gpu(data, conv_in_channels_,
        -
        132  conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
        -
        133  kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
        -
        134  pad_.cpu_data()[0], pad_.cpu_data()[1],
        -
        135  stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff);
        -
        136  } else {
        -
        137  im2col_nd_gpu(data, num_spatial_axes_, num_kernels_im2col_,
        -
        138  conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
        -
        139  kernel_shape_.gpu_data(), pad_.gpu_data(),
        -
        140  stride_.gpu_data(), col_buff);
        -
        141  }
        -
        142  }
        -
        143  inline void conv_col2im_gpu(const Dtype* col_buff, Dtype* data) {
        -
        144  if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
        -
        145  col2im_gpu(col_buff, conv_in_channels_,
        -
        146  conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
        -
        147  kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
        -
        148  pad_.cpu_data()[0], pad_.cpu_data()[1],
        -
        149  stride_.cpu_data()[0], stride_.cpu_data()[1], data);
        -
        150  } else {
        -
        151  col2im_nd_gpu(col_buff, num_spatial_axes_, num_kernels_col2im_,
        -
        152  conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
        -
        153  kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(),
        -
        154  data);
        -
        155  }
        -
        156  }
        -
        157 #endif
        -
        158 
        -
        159  int num_kernels_im2col_;
        -
        160  int num_kernels_col2im_;
        -
        161  int conv_out_channels_;
        -
        162  int conv_in_channels_;
        -
        163  int conv_out_spatial_dim_;
        -
        164  int kernel_dim_;
        -
        165  int col_offset_;
        -
        166  int output_offset_;
        -
        167 
        -
        168  Blob<Dtype> col_buffer_;
        -
        169  Blob<Dtype> bias_multiplier_;
        -
        170 };
        -
        171 
        -
        188 template <typename Dtype>
        -
        189 class ConvolutionLayer : public BaseConvolutionLayer<Dtype> {
        -
        190  public:
        -
        219  explicit ConvolutionLayer(const LayerParameter& param)
        -
        220  : BaseConvolutionLayer<Dtype>(param) {}
        -
        221 
        -
        222  virtual inline const char* type() const { return "Convolution"; }
        -
        223 
        -
        224  protected:
        -
        225  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        226  const vector<Blob<Dtype>*>& top);
        -
        227  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        228  const vector<Blob<Dtype>*>& top);
        -
        229  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        230  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        231  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        232  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        233  virtual inline bool reverse_dimensions() { return false; }
        -
        234  virtual void compute_output_shape();
        -
        235 };
        -
        236 
        -
        251 template <typename Dtype>
        -
        252 class DeconvolutionLayer : public BaseConvolutionLayer<Dtype> {
        -
        253  public:
        -
        254  explicit DeconvolutionLayer(const LayerParameter& param)
        -
        255  : BaseConvolutionLayer<Dtype>(param) {}
        -
        256 
        -
        257  virtual inline const char* type() const { return "Deconvolution"; }
        -
        258 
        -
        259  protected:
        -
        260  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        261  const vector<Blob<Dtype>*>& top);
        -
        262  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        263  const vector<Blob<Dtype>*>& top);
        -
        264  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        265  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        266  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        267  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        268  virtual inline bool reverse_dimensions() { return true; }
        -
        269  virtual void compute_output_shape();
        -
        270 };
        -
        271 
        -
        272 #ifdef USE_CUDNN
        -
        273 /*
        -
        274  * @brief cuDNN implementation of ConvolutionLayer.
        -
        275  * Fallback to ConvolutionLayer for CPU mode.
        -
        276  *
        -
        277  * cuDNN accelerates convolution through forward kernels for filtering and bias
        -
        278  * plus backward kernels for the gradient w.r.t. the filters, biases, and
        -
        279  * inputs. Caffe + cuDNN further speeds up the computation through forward
        -
        280  * parallelism across groups and backward parallelism across gradients.
        -
        281  *
        -
        282  * The CUDNN engine does not have memory overhead for matrix buffers. For many
        -
        283  * input and filter regimes the CUDNN engine is faster than the CAFFE engine,
        -
        284  * but for fully-convolutional models and large inputs the CAFFE engine can be
        -
        285  * faster as long as it fits in memory.
        -
        286 */
        -
        287 template <typename Dtype>
        -
        288 class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
        -
        289  public:
        -
        290  explicit CuDNNConvolutionLayer(const LayerParameter& param)
        -
        291  : ConvolutionLayer<Dtype>(param), handles_setup_(false) {}
        -
        292  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        293  const vector<Blob<Dtype>*>& top);
        -
        294  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        295  const vector<Blob<Dtype>*>& top);
        -
        296  virtual ~CuDNNConvolutionLayer();
        -
        297 
        -
        298  protected:
        -
        299  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        300  const vector<Blob<Dtype>*>& top);
        -
        301  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        302  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        303 
        -
        304  bool handles_setup_;
        -
        305  cudnnHandle_t* handle_;
        -
        306  cudaStream_t* stream_;
        -
        307 
        -
        308  // algorithms for forward and backwards convolutions
        -
        309  cudnnConvolutionFwdAlgo_t *fwd_algo_;
        -
        310  cudnnConvolutionBwdFilterAlgo_t *bwd_filter_algo_;
        -
        311  cudnnConvolutionBwdDataAlgo_t *bwd_data_algo_;
        -
        312 
        -
        313  vector<cudnnTensorDescriptor_t> bottom_descs_, top_descs_;
        -
        314  cudnnTensorDescriptor_t bias_desc_;
        -
        315  cudnnFilterDescriptor_t filter_desc_;
        -
        316  vector<cudnnConvolutionDescriptor_t> conv_descs_;
        -
        317  int bottom_offset_, top_offset_, bias_offset_;
        -
        318 
        -
        319  size_t *workspace_fwd_sizes_;
        -
        320  size_t *workspace_bwd_data_sizes_;
        -
        321  size_t *workspace_bwd_filter_sizes_;
        -
        322  size_t workspaceSizeInBytes; // size of underlying storage
        -
        323  void *workspaceData; // underlying storage
        -
        324  void **workspace; // aliases into workspaceData
        -
        325 };
        -
        326 #endif
        -
        327 
        -
        335 template <typename Dtype>
        -
        336 class Im2colLayer : public Layer<Dtype> {
        -
        337  public:
        -
        338  explicit Im2colLayer(const LayerParameter& param)
        -
        339  : Layer<Dtype>(param) {}
        -
        340  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        341  const vector<Blob<Dtype>*>& top);
        -
        342  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        343  const vector<Blob<Dtype>*>& top);
        -
        344 
        -
        345  virtual inline const char* type() const { return "Im2col"; }
        -
        346  virtual inline int ExactNumBottomBlobs() const { return 1; }
        -
        347  virtual inline int ExactNumTopBlobs() const { return 1; }
        -
        348 
        -
        349  protected:
        -
        350  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        351  const vector<Blob<Dtype>*>& top);
        -
        352  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        353  const vector<Blob<Dtype>*>& top);
        -
        354  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        355  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        356  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        357  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        358 
        -
        360  Blob<int> kernel_shape_;
        -
        362  Blob<int> stride_;
        -
        364  Blob<int> pad_;
        -
        365 
        -
        366  int num_spatial_axes_;
        -
        367  int bottom_dim_;
        -
        368  int top_dim_;
        -
        369 
        -
        370  int channel_axis_;
        -
        371  int num_;
        -
        372  int channels_;
        -
        373 
        -
        374  bool force_nd_im2col_;
        -
        375 };
        -
        376 
        -
        377 // Forward declare PoolingLayer and SplitLayer for use in LRNLayer.
        -
        378 template <typename Dtype> class PoolingLayer;
        -
        379 template <typename Dtype> class SplitLayer;
        -
        380 
        -
        386 template <typename Dtype>
        -
        387 class LRNLayer : public Layer<Dtype> {
        -
        388  public:
        -
        389  explicit LRNLayer(const LayerParameter& param)
        -
        390  : Layer<Dtype>(param) {}
        -
        391  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        392  const vector<Blob<Dtype>*>& top);
        -
        393  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        394  const vector<Blob<Dtype>*>& top);
        -
        395 
        -
        396  virtual inline const char* type() const { return "LRN"; }
        -
        397  virtual inline int ExactNumBottomBlobs() const { return 1; }
        -
        398  virtual inline int ExactNumTopBlobs() const { return 1; }
        -
        399 
        -
        400  protected:
        -
        401  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        402  const vector<Blob<Dtype>*>& top);
        -
        403  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        404  const vector<Blob<Dtype>*>& top);
        -
        405  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        406  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        407  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        408  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        409 
        -
        410  virtual void CrossChannelForward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        411  const vector<Blob<Dtype>*>& top);
        -
        412  virtual void CrossChannelForward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        413  const vector<Blob<Dtype>*>& top);
        -
        414  virtual void WithinChannelForward(const vector<Blob<Dtype>*>& bottom,
        -
        415  const vector<Blob<Dtype>*>& top);
        -
        416  virtual void CrossChannelBackward_cpu(const vector<Blob<Dtype>*>& top,
        -
        417  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        418  virtual void CrossChannelBackward_gpu(const vector<Blob<Dtype>*>& top,
        -
        419  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        420  virtual void WithinChannelBackward(const vector<Blob<Dtype>*>& top,
        -
        421  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        422 
        -
        423  int size_;
        -
        424  int pre_pad_;
        -
        425  Dtype alpha_;
        -
        426  Dtype beta_;
        -
        427  Dtype k_;
        -
        428  int num_;
        -
        429  int channels_;
        -
        430  int height_;
        -
        431  int width_;
        -
        432 
        -
        433  // Fields used for normalization ACROSS_CHANNELS
        -
        434  // scale_ stores the intermediate summing results
        -
        435  Blob<Dtype> scale_;
        -
        436 
        -
        437  // Fields used for normalization WITHIN_CHANNEL
        -
        438  shared_ptr<SplitLayer<Dtype> > split_layer_;
        -
        439  vector<Blob<Dtype>*> split_top_vec_;
        -
        440  shared_ptr<PowerLayer<Dtype> > square_layer_;
        -
        441  Blob<Dtype> square_input_;
        -
        442  Blob<Dtype> square_output_;
        -
        443  vector<Blob<Dtype>*> square_bottom_vec_;
        -
        444  vector<Blob<Dtype>*> square_top_vec_;
        -
        445  shared_ptr<PoolingLayer<Dtype> > pool_layer_;
        -
        446  Blob<Dtype> pool_output_;
        -
        447  vector<Blob<Dtype>*> pool_top_vec_;
        -
        448  shared_ptr<PowerLayer<Dtype> > power_layer_;
        -
        449  Blob<Dtype> power_output_;
        -
        450  vector<Blob<Dtype>*> power_top_vec_;
        -
        451  shared_ptr<EltwiseLayer<Dtype> > product_layer_;
        -
        452  Blob<Dtype> product_input_;
        -
        453  vector<Blob<Dtype>*> product_bottom_vec_;
        -
        454 };
        -
        455 
        -
        456 #ifdef USE_CUDNN
        -
        457 
        -
        458 template <typename Dtype>
        -
        459 class CuDNNLRNLayer : public LRNLayer<Dtype> {
        -
        460  public:
        -
        461  explicit CuDNNLRNLayer(const LayerParameter& param)
        -
        462  : LRNLayer<Dtype>(param), handles_setup_(false) {}
        -
        463  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        464  const vector<Blob<Dtype>*>& top);
        -
        465  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        466  const vector<Blob<Dtype>*>& top);
        -
        467  virtual ~CuDNNLRNLayer();
        -
        468 
        -
        469  protected:
        -
        470  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        471  const vector<Blob<Dtype>*>& top);
        -
        472  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        473  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        474 
        -
        475  bool handles_setup_;
        -
        476  cudnnHandle_t handle_;
        -
        477  cudnnLRNDescriptor_t norm_desc_;
        -
        478  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
        -
        479 
        -
        480  int size_;
        -
        481  Dtype alpha_, beta_, k_;
        -
        482 };
        -
        483 
        -
        484 template <typename Dtype>
        -
        485 class CuDNNLCNLayer : public LRNLayer<Dtype> {
        -
        486  public:
        -
        487  explicit CuDNNLCNLayer(const LayerParameter& param)
        -
        488  : LRNLayer<Dtype>(param), handles_setup_(false), tempDataSize(0),
        -
        489  tempData1(NULL), tempData2(NULL) {}
        -
        490  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        491  const vector<Blob<Dtype>*>& top);
        -
        492  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        493  const vector<Blob<Dtype>*>& top);
        -
        494  virtual ~CuDNNLCNLayer();
        -
        495 
        -
        496  protected:
        -
        497  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        498  const vector<Blob<Dtype>*>& top);
        -
        499  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        500  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        501 
        -
        502  bool handles_setup_;
        -
        503  cudnnHandle_t handle_;
        -
        504  cudnnLRNDescriptor_t norm_desc_;
        -
        505  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
        -
        506 
        -
        507  int size_, pre_pad_;
        -
        508  Dtype alpha_, beta_, k_;
        -
        509 
        -
        510  size_t tempDataSize;
        -
        511  void *tempData1, *tempData2;
        -
        512 };
        -
        513 
        -
        514 #endif
        -
        515 
        -
        521 template <typename Dtype>
        -
        522 class PoolingLayer : public Layer<Dtype> {
        -
        523  public:
        -
        524  explicit PoolingLayer(const LayerParameter& param)
        -
        525  : Layer<Dtype>(param) {}
        -
        526  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        527  const vector<Blob<Dtype>*>& top);
        -
        528  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        529  const vector<Blob<Dtype>*>& top);
        -
        530 
        -
        531  virtual inline const char* type() const { return "Pooling"; }
        -
        532  virtual inline int ExactNumBottomBlobs() const { return 1; }
        -
        533  virtual inline int MinTopBlobs() const { return 1; }
        -
        534  // MAX POOL layers can output an extra top blob for the mask;
        -
        535  // others can only output the pooled inputs.
        -
        536  virtual inline int MaxTopBlobs() const {
        -
        537  return (this->layer_param_.pooling_param().pool() ==
        -
        538  PoolingParameter_PoolMethod_MAX) ? 2 : 1;
        -
        539  }
        -
        540 
        -
        541  protected:
        -
        542  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        543  const vector<Blob<Dtype>*>& top);
        -
        544  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        545  const vector<Blob<Dtype>*>& top);
        -
        546  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        547  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        548  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        549  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        550 
        -
        551  int kernel_h_, kernel_w_;
        -
        552  int stride_h_, stride_w_;
        -
        553  int pad_h_, pad_w_;
        -
        554  int channels_;
        -
        555  int height_, width_;
        -
        556  int pooled_height_, pooled_width_;
        -
        557  bool global_pooling_;
        -
        558  Blob<Dtype> rand_idx_;
        -
        559  Blob<int> max_idx_;
        -
        560 };
        -
        561 
        -
        562 #ifdef USE_CUDNN
        -
        563 /*
        -
        564  * @brief cuDNN implementation of PoolingLayer.
        -
        565  * Fallback to PoolingLayer for CPU mode.
        -
        566 */
        -
        567 template <typename Dtype>
        -
        568 class CuDNNPoolingLayer : public PoolingLayer<Dtype> {
        -
        569  public:
        -
        570  explicit CuDNNPoolingLayer(const LayerParameter& param)
        -
        571  : PoolingLayer<Dtype>(param), handles_setup_(false) {}
        -
        572  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        573  const vector<Blob<Dtype>*>& top);
        -
        574  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        575  const vector<Blob<Dtype>*>& top);
        -
        576  virtual ~CuDNNPoolingLayer();
        -
        577  // Currently, cuDNN does not support the extra top blob.
        -
        578  virtual inline int MinTopBlobs() const { return -1; }
        -
        579  virtual inline int ExactNumTopBlobs() const { return 1; }
        -
        580 
        -
        581  protected:
        -
        582  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        -
        583  const vector<Blob<Dtype>*>& top);
        -
        584  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        -
        585  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        586 
        -
        587  bool handles_setup_;
        -
        588  cudnnHandle_t handle_;
        -
        589  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
        -
        590  cudnnPoolingDescriptor_t pooling_desc_;
        -
        591  cudnnPoolingMode_t mode_;
        -
        592 };
        -
        593 #endif
        -
        594 
        -
        601 template <typename Dtype>
        -
        602 class SPPLayer : public Layer<Dtype> {
        -
        603  public:
        -
        604  explicit SPPLayer(const LayerParameter& param)
        -
        605  : Layer<Dtype>(param) {}
        -
        606  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        -
        607  const vector<Blob<Dtype>*>& top);
        -
        608  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        -
        609  const vector<Blob<Dtype>*>& top);
        -
        610 
        -
        611  virtual inline const char* type() const { return "SPP"; }
        -
        612  virtual inline int ExactNumBottomBlobs() const { return 1; }
        -
        613  virtual inline int ExactNumTopBlobs() const { return 1; }
        -
        614 
        -
        615  protected:
        -
        616  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        -
        617  const vector<Blob<Dtype>*>& top);
        -
        618  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        -
        619  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
        -
        620  // calculates the kernel and stride dimensions for the pooling layer,
        -
        621  // returns a correctly configured LayerParameter for a PoolingLayer
        -
        622  virtual LayerParameter GetPoolingParam(const int pyramid_level,
        -
        623  const int bottom_h, const int bottom_w, const SPPParameter spp_param);
        -
        624 
        -
        625  int pyramid_height_;
        -
        626  int bottom_h_, bottom_w_;
        -
        627  int num_;
        -
        628  int channels_;
        -
        629  int kernel_h_, kernel_w_;
        -
        630  int pad_h_, pad_w_;
        -
        631  bool reshaped_first_time_;
        -
        632 
        -
        634  shared_ptr<SplitLayer<Dtype> > split_layer_;
        -
        636  vector<Blob<Dtype>*> split_top_vec_;
        -
        638  vector<vector<Blob<Dtype>*>*> pooling_bottom_vecs_;
        -
        640  vector<shared_ptr<PoolingLayer<Dtype> > > pooling_layers_;
        -
        642  vector<vector<Blob<Dtype>*>*> pooling_top_vecs_;
        -
        644  vector<Blob<Dtype>*> pooling_outputs_;
        -
        646  vector<FlattenLayer<Dtype>*> flatten_layers_;
        -
        648  vector<vector<Blob<Dtype>*>*> flatten_top_vecs_;
        -
        650  vector<Blob<Dtype>*> flatten_outputs_;
        -
        652  vector<Blob<Dtype>*> concat_bottom_vec_;
        -
        654  shared_ptr<ConcatLayer<Dtype> > concat_layer_;
        -
        655 };
        -
        656 
        -
        657 } // namespace caffe
        -
        658 
        -
        659 #endif // CAFFE_VISION_LAYERS_HPP_
        -
        caffe::BaseConvolutionLayer::kernel_shape_
        Blob< int > kernel_shape_
        The spatial dimensions of a filter kernel.
        Definition: vision_layers.hpp:72
        -
        caffe::LRNLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: lrn_layer.cpp:10
        -
        caffe::Im2colLayer
        A helper for image operations that rearranges image regions into column vectors. Used by ConvolutionL...
        Definition: vision_layers.hpp:336
        -
        caffe::PoolingLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        -
        caffe::LRNLayer::ExactNumTopBlobs
        virtual int ExactNumTopBlobs() const
        Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
        Definition: vision_layers.hpp:398
        -
        caffe::Layer
        An interface for the units of computation which can be composed into a Net.
        Definition: layer.hpp:33
        -
        caffe
        A layer factory that allows one to register layers. During runtime, registered layers could be called...
        Definition: blob.hpp:15
        -
        caffe::ConvolutionLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: vision_layers.hpp:222
        -
        caffe::PoolingLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::LRNLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::BaseConvolutionLayer::EqualNumBottomTopBlobs
        virtual bool EqualNumBottomTopBlobs() const
        Returns true if the layer requires an equal number of bottom and top blobs.
        Definition: vision_layers.hpp:35
        -
        caffe::Im2colLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: im2col_layer.cpp:95
        -
        caffe::PoolingLayer::ExactNumBottomBlobs
        virtual int ExactNumBottomBlobs() const
        Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
        Definition: vision_layers.hpp:532
        -
        caffe::SPPLayer::pooling_layers_
        vector< shared_ptr< PoolingLayer< Dtype > > > pooling_layers_
        the internal Pooling layers of different kernel sizes
        Definition: vision_layers.hpp:640
        -
        caffe::PoolingLayer::MaxTopBlobs
        virtual int MaxTopBlobs() const
        Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required...
        Definition: vision_layers.hpp:536
        -
        caffe::ConvolutionLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        Definition: conv_layer.cpp:45
        -
        caffe::SPPLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: spp_layer.cpp:65
        -
        caffe::SPPLayer::flatten_top_vecs_
        vector< vector< Blob< Dtype > * > * > flatten_top_vecs_
        top vector holders used in call to the underlying FlattenLayer::Forward
        Definition: vision_layers.hpp:648
        -
        caffe::SPPLayer::pooling_top_vecs_
        vector< vector< Blob< Dtype > * > * > pooling_top_vecs_
        top vector holders used in call to the underlying PoolingLayer::Forward
        Definition: vision_layers.hpp:642
        -
        caffe::LRNLayer::ExactNumBottomBlobs
        virtual int ExactNumBottomBlobs() const
        Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
        Definition: vision_layers.hpp:397
        -
        caffe::SPPLayer::split_top_vec_
        vector< Blob< Dtype > * > split_top_vec_
        top vector holder used in call to the underlying SplitLayer::Forward
        Definition: vision_layers.hpp:636
        -
        caffe::DeconvolutionLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the CPU device, compute the layer output.
        Definition: deconv_layer.cpp:27
        -
        caffe::SPPLayer::ExactNumBottomBlobs
        virtual int ExactNumBottomBlobs() const
        Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
        Definition: vision_layers.hpp:612
        -
        caffe::BaseConvolutionLayer::input_shape
        int input_shape(int i)
        The spatial dimensions of the input.
        Definition: vision_layers.hpp:62
        -
        caffe::BaseConvolutionLayer::MinBottomBlobs
        virtual int MinBottomBlobs() const
        Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...
        Definition: vision_layers.hpp:33
        -
        caffe::SPPLayer
        Does spatial pyramid pooling on the input image by taking the max, average, etc. within regions so th...
        Definition: vision_layers.hpp:602
        -
        caffe::Im2colLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::PoolingLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: vision_layers.hpp:531
        -
        caffe::Im2colLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        -
        caffe::Im2colLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: vision_layers.hpp:345
        -
        caffe::SPPLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the CPU device, compute the layer output.
        Definition: spp_layer.cpp:188
        -
        caffe::Im2colLayer::pad_
        Blob< int > pad_
        The spatial dimensions of the padding.
        Definition: vision_layers.hpp:364
        -
        caffe::DeconvolutionLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: vision_layers.hpp:257
        -
        caffe::SPPLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: vision_layers.hpp:611
        -
        caffe::BaseConvolutionLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: base_conv_layer.cpp:173
        -
        caffe::PoolingLayer::MinTopBlobs
        virtual int MinTopBlobs() const
        Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
        Definition: vision_layers.hpp:533
        -
        caffe::ConvolutionLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        -
        caffe::BaseConvolutionLayer
        Abstract base class that factors out the BLAS code common to ConvolutionLayer and DeconvolutionLayer...
        Definition: vision_layers.hpp:24
        -
        caffe::LRNLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        Definition: lrn_layer.cpp:166
        -
        caffe::Im2colLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: im2col_layer.cpp:11
        -
        caffe::SPPLayer::pooling_bottom_vecs_
        vector< vector< Blob< Dtype > * > * > pooling_bottom_vecs_
        bottom vector holder used in call to the underlying PoolingLayer::Forward
        Definition: vision_layers.hpp:638
        -
        caffe::SPPLayer::concat_bottom_vec_
        vector< Blob< Dtype > * > concat_bottom_vec_
        bottom vector holder used in call to the underlying ConcatLayer::Forward
        Definition: vision_layers.hpp:652
        -
        caffe::PoolingLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: pooling_layer.cpp:82
        -
        caffe::BaseConvolutionLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: base_conv_layer.cpp:13
        -
        caffe::ConvolutionLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the CPU device, compute the layer output.
        Definition: conv_layer.cpp:27
        -
        caffe::BaseConvolutionLayer::pad_
        Blob< int > pad_
        The spatial dimensions of the padding.
        Definition: vision_layers.hpp:76
        -
        caffe::ConvolutionLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::SPPLayer::flatten_layers_
        vector< FlattenLayer< Dtype > * > flatten_layers_
        the internal Flatten layers that the Pooling layers feed into
        Definition: vision_layers.hpp:646
        -
        caffe::PoolingLayer::LayerSetUp
        virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Does layer-specific setup: your layer should implement this function as well as Reshape.
        Definition: pooling_layer.cpp:17
        -
        caffe::SPPLayer::ExactNumTopBlobs
        virtual int ExactNumTopBlobs() const
        Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
        Definition: vision_layers.hpp:613
        -
        caffe::LRNLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: lrn_layer.cpp:70
        -
        caffe::Im2colLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        Definition: im2col_layer.cpp:146
        -
        caffe::Layer::Layer
        Layer(const LayerParameter &param)
        Definition: layer.hpp:40
        -
        caffe::SPPLayer::flatten_outputs_
        vector< Blob< Dtype > * > flatten_outputs_
        flatten_outputs stores the outputs of the FlattenLayers
        Definition: vision_layers.hpp:650
        -
        caffe::LRNLayer::type
        virtual const char * type() const
        Returns the layer type.
        Definition: vision_layers.hpp:396
        -
        caffe::BaseConvolutionLayer::col_buffer_shape_
        vector< int > col_buffer_shape_
        The spatial dimensions of the col_buffer.
        Definition: vision_layers.hpp:80
        -
        caffe::SPPLayer::Reshape
        virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
        Definition: spp_layer.cpp:146
        -
        caffe::Im2colLayer::ExactNumBottomBlobs
        virtual int ExactNumBottomBlobs() const
        Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
        Definition: vision_layers.hpp:346
        -
        caffe::LRNLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        -
        caffe::ConvolutionLayer::ConvolutionLayer
        ConvolutionLayer(const LayerParameter &param)
        Definition: vision_layers.hpp:219
        -
        caffe::DeconvolutionLayer
        Convolve the input with a bank of learned filters, and (optionally) add biases, treating filters and ...
        Definition: vision_layers.hpp:252
        -
        caffe::Im2colLayer::kernel_shape_
        Blob< int > kernel_shape_
        The spatial dimensions of a filter kernel.
        Definition: vision_layers.hpp:360
        -
        caffe::Layer::layer_param_
        LayerParameter layer_param_
        Definition: layer.hpp:322
        -
        caffe::PoolingLayer
        Pools the input image by taking the max, average, etc. within regions.
        Definition: vision_layers.hpp:378
        -
        caffe::SplitLayer
        Creates a "split" path in the network by copying the bottom Blob into multiple top Blobs to be used b...
        Definition: common_layers.hpp:663
        -
        caffe::DeconvolutionLayer::Forward_gpu
        virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
        -
        caffe::DeconvolutionLayer::Backward_gpu
        virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        -
        caffe::SPPLayer::concat_layer_
        shared_ptr< ConcatLayer< Dtype > > concat_layer_
        the internal Concat layers that the Flatten layers feed into
        Definition: vision_layers.hpp:654
        -
        caffe::BaseConvolutionLayer::output_shape_
        vector< int > output_shape_
        The spatial dimensions of the output.
        Definition: vision_layers.hpp:82
        -
        caffe::SPPLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        Definition: spp_layer.cpp:205
        -
        caffe::Im2colLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the CPU device, compute the layer output.
        Definition: im2col_layer.cpp:117
        -
        caffe::SPPLayer::split_layer_
        shared_ptr< SplitLayer< Dtype > > split_layer_
        the internal Split layer that feeds the pooling layers
        Definition: vision_layers.hpp:634
        -
        caffe::ConvolutionLayer
        Convolves the input image with a bank of learned filters, and (optionally) adds biases.
        Definition: vision_layers.hpp:189
        -
        caffe::BaseConvolutionLayer::conv_input_shape_
        Blob< int > conv_input_shape_
        The spatial dimensions of the convolution input.
        Definition: vision_layers.hpp:78
        -
        caffe::DeconvolutionLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        Definition: deconv_layer.cpp:45
        -
        caffe::PoolingLayer::Backward_cpu
        virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
        Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
        Definition: pooling_layer.cpp:233
        -
        caffe::PoolingLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the CPU device, compute the layer output.
        Definition: pooling_layer.cpp:131
        -
        caffe::Im2colLayer::ExactNumTopBlobs
        virtual int ExactNumTopBlobs() const
        Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...
        Definition: vision_layers.hpp:347
        -
        caffe::LRNLayer
        Normalize the input in a local region across or within feature maps.
        Definition: vision_layers.hpp:387
        -
        caffe::SPPLayer::pooling_outputs_
        vector< Blob< Dtype > * > pooling_outputs_
        pooling_outputs stores the outputs of the PoolingLayers
        Definition: vision_layers.hpp:644
        -
        caffe::Im2colLayer::stride_
        Blob< int > stride_
        The spatial dimensions of the stride.
        Definition: vision_layers.hpp:362
        -
        caffe::BaseConvolutionLayer::stride_
        Blob< int > stride_
        The spatial dimensions of the stride.
        Definition: vision_layers.hpp:74
        -
        caffe::Blob
        A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
        Definition: blob.hpp:25
        -
        caffe::BaseConvolutionLayer::MinTopBlobs
        virtual int MinTopBlobs() const
        Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
        Definition: vision_layers.hpp:34
        -
        caffe::LRNLayer::Forward_cpu
        virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
        Using the CPU device, compute the layer output.
        Definition: lrn_layer.cpp:94
        -
        - -
        -Generated on Mon Oct 19 2015 15:25:08 for Caffe by   -doxygen - 1.8.9.1 -
        - - diff --git a/doxygen/window__data__layer_8hpp_source.html b/doxygen/window__data__layer_8hpp_source.html index fff6b7fcc83..403c2b77bcb 100644 --- a/doxygen/window__data__layer_8hpp_source.html +++ b/doxygen/window__data__layer_8hpp_source.html @@ -3,7 +3,7 @@ - + Caffe: include/caffe/layers/window_data_layer.hpp Source File @@ -29,7 +29,7 @@ - + @@ -66,8 +66,8 @@
        window_data_layer.hpp
        -
        1 #ifndef CAFFE_WINDOW_DATA_LAYER_HPP_
        2 #define CAFFE_WINDOW_DATA_LAYER_HPP_
        3 
        4 #include <string>
        5 #include <utility>
        6 #include <vector>
        7 
        8 #include "caffe/blob.hpp"
        9 #include "caffe/data_transformer.hpp"
        10 #include "caffe/internal_thread.hpp"
        11 #include "caffe/layer.hpp"
        12 #include "caffe/layers/base_data_layer.hpp"
        13 #include "caffe/proto/caffe.pb.h"
        14 
        15 namespace caffe {
        16 
        23 template <typename Dtype>
        24 class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
        25  public:
        26  explicit WindowDataLayer(const LayerParameter& param)
        27  : BasePrefetchingDataLayer<Dtype>(param) {}
        28  virtual ~WindowDataLayer();
        29  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
        30  const vector<Blob<Dtype>*>& top);
        31 
        32  virtual inline const char* type() const { return "WindowData"; }
        33  virtual inline int ExactNumBottomBlobs() const { return 0; }
        34  virtual inline int ExactNumTopBlobs() const { return 2; }
        35 
        36  protected:
        37  virtual unsigned int PrefetchRand();
        38  virtual void load_batch(Batch<Dtype>* batch);
        39 
        40  shared_ptr<Caffe::RNG> prefetch_rng_;
        41  vector<std::pair<std::string, vector<int> > > image_database_;
        42  enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM };
        43  vector<vector<float> > fg_windows_;
        44  vector<vector<float> > bg_windows_;
        45  Blob<Dtype> data_mean_;
        46  vector<Dtype> mean_values_;
        47  bool has_mean_file_;
        48  bool has_mean_values_;
        49  bool cache_images_;
        50  vector<std::pair<std::string, Datum > > image_database_cache_;
        51 };
        52 
        53 } // namespace caffe
        54 
        55 #endif // CAFFE_WINDOW_DATA_LAYER_HPP_
        caffe::Batch
        Definition: base_data_layer.hpp:49
        -
        caffe::BasePrefetchingDataLayer
        Definition: base_data_layer.hpp:55
        +
        1 #ifndef CAFFE_WINDOW_DATA_LAYER_HPP_
        2 #define CAFFE_WINDOW_DATA_LAYER_HPP_
        3 
        4 #include <string>
        5 #include <utility>
        6 #include <vector>
        7 
        8 #include "caffe/blob.hpp"
        9 #include "caffe/data_transformer.hpp"
        10 #include "caffe/internal_thread.hpp"
        11 #include "caffe/layer.hpp"
        12 #include "caffe/layers/base_data_layer.hpp"
        13 #include "caffe/proto/caffe.pb.h"
        14 
        15 namespace caffe {
        16 
        23 template <typename Dtype>
        24 class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
        25  public:
        26  explicit WindowDataLayer(const LayerParameter& param)
        27  : BasePrefetchingDataLayer<Dtype>(param) {}
        28  virtual ~WindowDataLayer();
        29  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
        30  const vector<Blob<Dtype>*>& top);
        31 
        32  virtual inline const char* type() const { return "WindowData"; }
        33  virtual inline int ExactNumBottomBlobs() const { return 0; }
        34  virtual inline int ExactNumTopBlobs() const { return 2; }
        35 
        36  protected:
        37  virtual unsigned int PrefetchRand();
        38  virtual void load_batch(Batch<Dtype>* batch);
        39 
        40  shared_ptr<Caffe::RNG> prefetch_rng_;
        41  vector<std::pair<std::string, vector<int> > > image_database_;
        42  enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM };
        43  vector<vector<float> > fg_windows_;
        44  vector<vector<float> > bg_windows_;
        45  Blob<Dtype> data_mean_;
        46  vector<Dtype> mean_values_;
        47  bool has_mean_file_;
        48  bool has_mean_values_;
        49  bool cache_images_;
        50  vector<std::pair<std::string, Datum > > image_database_cache_;
        51 };
        52 
        53 } // namespace caffe
        54 
        55 #endif // CAFFE_WINDOW_DATA_LAYER_HPP_
        caffe::Batch
        Definition: base_data_layer.hpp:47
        +
        caffe::BasePrefetchingDataLayer
        Definition: base_data_layer.hpp:53
        caffe
        A layer factory that allows one to register layers. During runtime, registered layers can be called b...
        Definition: blob.hpp:14
        caffe::WindowDataLayer
        Provides data to the Net from windows of images files, specified by a window data file...
        Definition: window_data_layer.hpp:24
        caffe::WindowDataLayer::ExactNumBottomBlobs
        virtual int ExactNumBottomBlobs() const
        Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
        Definition: window_data_layer.hpp:33
        @@ -77,9 +77,9 @@
        -Generated on Wed Jan 18 2017 12:26:04 for Caffe by   +Generated on Fri Apr 14 2017 12:50:45 for Caffe by   doxygen - 1.8.12 + 1.8.13
        diff --git a/gathered/examples/00-classification.ipynb b/gathered/examples/00-classification.ipynb index 6cce2dc7d5d..a4cdbf98829 100644 --- a/gathered/examples/00-classification.ipynb +++ b/gathered/examples/00-classification.ipynb @@ -36,7 +36,7 @@

        Caffe

        - Deep learning framework by the BVLC + Deep learning framework by BAIR

        Created by diff --git a/gathered/examples/01-learning-lenet.ipynb b/gathered/examples/01-learning-lenet.ipynb index 15fcd2448d4..e764383e389 100644 --- a/gathered/examples/01-learning-lenet.ipynb +++ b/gathered/examples/01-learning-lenet.ipynb @@ -36,7 +36,7 @@

        Caffe

        - Deep learning framework by the BVLC + Deep learning framework by BAIR

        Created by diff --git a/gathered/examples/02-fine-tuning.ipynb b/gathered/examples/02-fine-tuning.ipynb index beed35a5a9e..8c3ce152cae 100644 --- a/gathered/examples/02-fine-tuning.ipynb +++ b/gathered/examples/02-fine-tuning.ipynb @@ -36,7 +36,7 @@

        Caffe

        - Deep learning framework by the BVLC + Deep learning framework by BAIR

        Created by @@ -202,7 +202,7 @@ "outputs": [], "source": [ "import os\n", - "weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n", + "weights = os.path.join(caffe_root, 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n", "assert os.path.exists(weights)" ] }, diff --git a/gathered/examples/brewing-logreg.ipynb b/gathered/examples/brewing-logreg.ipynb index a75bd257be7..1981e4420f0 100644 --- a/gathered/examples/brewing-logreg.ipynb +++ b/gathered/examples/brewing-logreg.ipynb @@ -36,7 +36,7 @@

        Caffe

        - Deep learning framework by the BVLC + Deep learning framework by BAIR

        Created by diff --git a/gathered/examples/cifar10.html b/gathered/examples/cifar10.html index 6956d286115..d099b5504ec 100644 --- a/gathered/examples/cifar10.html +++ b/gathered/examples/cifar10.html @@ -36,7 +36,7 @@

        Caffe

        - Deep learning framework by the BVLC + Deep learning framework by BAIR

        Created by diff --git a/gathered/examples/cpp_classification.html b/gathered/examples/cpp_classification.html index b8ee3e543b7..72ff1efb979 100644 --- a/gathered/examples/cpp_classification.html +++ b/gathered/examples/cpp_classification.html @@ -36,7 +36,7 @@

        Caffe

        - Deep learning framework by the BVLC + Deep learning framework by BAIR

        Created by @@ -83,34 +83,34 @@

        Usage

        To use the pre-trained CaffeNet model with the classification example, you need to download it from the “Model Zoo” using the following -script: - -./scripts/download_model_binary.py models/bvlc_reference_caffenet - -The ImageNet labels file (also called the synset file) is also -required in order to map a prediction to the name of the class: - -./data/ilsvrc12/get_ilsvrc_aux.sh - -Using the files that were downloaded, we can classify the provided cat -image (examples/images/cat.jpg) using this command: - -./build/examples/cpp_classification/classification.bin \ +script:

        +
        ./scripts/download_model_binary.py models/bvlc_reference_caffenet
+
        +
        +

        The ImageNet labels file (also called the synset file) is also +required in order to map a prediction to the name of the class:

        +
        ./data/ilsvrc12/get_ilsvrc_aux.sh
+
        +
        +

        Using the files that were downloaded, we can classify the provided cat +image (examples/images/cat.jpg) using this command:

        +
        ./build/examples/cpp_classification/classification.bin \
   models/bvlc_reference_caffenet/deploy.prototxt \
   models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \
   data/ilsvrc12/imagenet_mean.binaryproto \
   data/ilsvrc12/synset_words.txt \
   examples/images/cat.jpg
-
-The output should look like this:
-
----------- Prediction for examples/images/cat.jpg ----------
+
        +
        +

        The output should look like this:

        +
        ---------- Prediction for examples/images/cat.jpg ----------
 0.3134 - "n02123045 tabby, tabby cat"
 0.2380 - "n02123159 tiger cat"
 0.1235 - "n02124075 Egyptian cat"
 0.1003 - "n02119022 red fox, Vulpes vulpes"
 0.0715 - "n02127052 lynx, catamount"
-
        
+
        +

        Improving Performance

        diff --git a/gathered/examples/detection.ipynb b/gathered/examples/detection.ipynb index 86cb3d0eb56..6b21d86818e 100644 --- a/gathered/examples/detection.ipynb +++ b/gathered/examples/detection.ipynb @@ -36,7 +36,7 @@

        Caffe

        - Deep learning framework by the BVLC + Deep learning framework by BAIR

        Created by diff --git a/gathered/examples/feature_extraction.html b/gathered/examples/feature_extraction.html index fe73c5cab3c..2e8a69647ec 100644 --- a/gathered/examples/feature_extraction.html +++ b/gathered/examples/feature_extraction.html @@ -36,7 +36,7 @@

        Caffe

        - Deep learning framework by the BVLC + Deep learning framework by BAIR

        Created by diff --git a/gathered/examples/finetune_flickr_style.html b/gathered/examples/finetune_flickr_style.html index ada1fe19025..f178b7698bd 100644 --- a/gathered/examples/finetune_flickr_style.html +++ b/gathered/examples/finetune_flickr_style.html @@ -36,7 +36,7 @@

        Caffe

        - Deep learning framework by the BVLC + Deep learning framework by BAIR

        Created by @@ -57,7 +57,7 @@

        Caffe

        Fine-tuning CaffeNet for Style Recognition on “Flickr Style” Data

        Fine-tuning takes an already learned model, adapts the architecture, and resumes training from the already learned model weights. -Let’s fine-tune the BVLC-distributed CaffeNet model on a different dataset, Flickr Style, to predict image style instead of object category.

        +Let’s fine-tune the BAIR-distributed CaffeNet model on a different dataset, Flickr Style, to predict image style instead of object category.

        Explanation

        diff --git a/gathered/examples/imagenet.html b/gathered/examples/imagenet.html index 8e9e4575695..68a6619f947 100644 --- a/gathered/examples/imagenet.html +++ b/gathered/examples/imagenet.html @@ -36,7 +36,7 @@

        Caffe

        - Deep learning framework by the BVLC + Deep learning framework by BAIR

        Created by diff --git a/gathered/examples/mnist.html b/gathered/examples/mnist.html index 4bbf2e2909e..92539e6ed77 100644 --- a/gathered/examples/mnist.html +++ b/gathered/examples/mnist.html @@ -36,7 +36,7 @@

        Caffe

        - Deep learning framework by the BVLC + Deep learning framework by BAIR

        Created by diff --git a/gathered/examples/net_surgery.ipynb b/gathered/examples/net_surgery.ipynb index 3c1ddaf6511..a88da6008eb 100644 --- a/gathered/examples/net_surgery.ipynb +++ b/gathered/examples/net_surgery.ipynb @@ -36,7 +36,7 @@

        Caffe

        - Deep learning framework by the BVLC + Deep learning framework by BAIR

        Created by diff --git a/gathered/examples/pascal-multilabel-with-datalayer.ipynb b/gathered/examples/pascal-multilabel-with-datalayer.ipynb index 34aa94e334d..f3865298b6f 100644 --- a/gathered/examples/pascal-multilabel-with-datalayer.ipynb +++ b/gathered/examples/pascal-multilabel-with-datalayer.ipynb @@ -36,7 +36,7 @@

        Caffe

        - Deep learning framework by the BVLC + Deep learning framework by BAIR

        Created by diff --git a/gathered/examples/siamese.html b/gathered/examples/siamese.html index d28447d9e32..e4503e56559 100644 --- a/gathered/examples/siamese.html +++ b/gathered/examples/siamese.html @@ -36,7 +36,7 @@

        Caffe

        - Deep learning framework by the BVLC + Deep learning framework by BAIR

        Created by diff --git a/gathered/examples/siamese/mnist_siamese.ipynb b/gathered/examples/siamese/mnist_siamese.ipynb index ffeda21ade3..d4ae7a9b72b 100644 --- a/gathered/examples/siamese/mnist_siamese.ipynb +++ b/gathered/examples/siamese/mnist_siamese.ipynb @@ -36,7 +36,7 @@

        Caffe

        - Deep learning framework by the BVLC + Deep learning framework by BAIR

        Created by diff --git a/gathered/examples/web_demo.html b/gathered/examples/web_demo.html index fbec5daa457..05fa5d717fc 100644 --- a/gathered/examples/web_demo.html +++ b/gathered/examples/web_demo.html @@ -36,7 +36,7 @@

        Caffe

        - Deep learning framework by the BVLC + Deep learning framework by BAIR

        Created by diff --git a/index.html b/index.html index 9318597dd5c..991f978ad97 100644 --- a/index.html +++ b/index.html @@ -36,7 +36,7 @@

        Caffe

        - Deep learning framework by the BVLC + Deep learning framework by BAIR

        Created by @@ -57,7 +57,7 @@

        Caffe

        Caffe

        Caffe is a deep learning framework made with expression, speed, and modularity in mind. -It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. +It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license.

        @@ -75,22 +75,21 @@

        Why Caffe?

        Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU*. -That’s 1 ms/image for inference and 4 ms/image for learning. -We believe that Caffe is the fastest convnet implementation available.

        +That’s 1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still. +We believe that Caffe is among the fastest convnet implementations available.

        Community: Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Join our community of brewers on the caffe-users group and Github.

        -* With the ILSVRC2012-winning SuperVision model and caching IO. -Consult performance details. +* With the ILSVRC2012-winning SuperVision model and prefetching IO.

        Documentation

        -

        Examples

        +

        Notebook Examples

        -

        Notebook Examples

        +

        Command Line Examples

        @@ -176,8 +177,7 @@

        Citing Caffe

        -

        If you do publish a paper where Caffe helped your research, we encourage you to update the publications wiki. -Citations are also tracked automatically by Google Scholar.

        +

        If you do publish a paper where Caffe helped your research, we encourage you to cite the framework for tracking by Google Scholar.

        Contacting Us

        @@ -185,17 +185,12 @@

        Contacting Us

        Framework development discussions and thorough bug reports are collected on Issues.

        -

        Contact caffe-dev if you have a confidential proposal for the framework and the ability to act on it. -Requests for features, explanations, or personal help will be ignored; post to caffe-users instead.

        - -

        The core Caffe developers offer consulting services for appropriate projects.

        -

        Acknowledgements

        -

        The BVLC Caffe developers would like to thank NVIDIA for GPU donation, A9 and Amazon Web Services for a research grant in support of Caffe development and reproducible research in deep learning, and BVLC PI Trevor Darrell for guidance.

        +

        The BAIR Caffe developers would like to thank NVIDIA for GPU donation, A9 and Amazon Web Services for a research grant in support of Caffe development and reproducible research in deep learning, and BAIR PI Trevor Darrell for guidance.

        -

        The BVLC members who have contributed to Caffe are (alphabetical by first name): -Eric Tzeng, Evan Shelhamer, Jeff Donahue, Jon Long, Ross Girshick, Sergey Karayev, Sergio Guadarrama, and Yangqing Jia.

        +

        The BAIR members who have contributed to Caffe are (alphabetical by first name): +Carl Doersch, Eric Tzeng, Evan Shelhamer, Jeff Donahue, Jon Long, Philipp Krähenbühl, Ronghang Hu, Ross Girshick, Sergey Karayev, Sergio Guadarrama, Takuya Narihira, and Yangqing Jia.

        The open-source community plays an important and growing role in Caffe’s development. Check out the Github project pulse for recent activity and the contributors for the full list.

        @@ -203,7 +198,7 @@

        Acknowledgements

        We sincerely appreciate your interest and contributions! If you’d like to contribute, please read the developing & contributing guide.

        -

        Yangqing would like to give a personal thanks to the NVIDIA Academic program for providing GPUs, Oriol Vinyals for discussions along the journey, and BVLC PI Trevor Darrell for advice.

        +

        Yangqing would like to give a personal thanks to the NVIDIA Academic program for providing GPUs, Oriol Vinyals for discussions along the journey, and BAIR PI Trevor Darrell for advice.

        diff --git a/install_apt.html b/install_apt.html index ed4601d1d74..d97677603b1 100644 --- a/install_apt.html +++ b/install_apt.html @@ -36,7 +36,7 @@

        Caffe

        - Deep learning framework by the BVLC + Deep learning framework by BAIR

        Created by @@ -68,7 +68,7 @@

        Ubuntu Installation

        If installing from packages, install the library and latest driver separately; the driver bundled with the library is usually out-of-date. This can be skipped for CPU-only installation.

        -

        BLAS: install ATLAS by sudo apt-get install libatlas-base-dev or install OpenBLAS or MKL for better CPU performance.

        +

        BLAS: install ATLAS by sudo apt-get install libatlas-base-dev or install OpenBLAS by sudo apt-get install libopenblas-dev or MKL for better CPU performance.

        Python (optional): if you use the default Python you will need to sudo apt-get install the python-dev package to have the Python headers for building the pycaffe interface.

        @@ -89,8 +89,8 @@

        Ubuntu Installation

        These dependencies need manual installation in 12.04.

        # glog
-wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
-tar zxvf glog-0.3.3.tar.gz
+wget https://github.com/google/glog/archive/v0.3.3.tar.gz
+tar zxvf v0.3.3.tar.gz
 cd glog-0.3.3
 ./configure
 make && make install
diff --git a/install_apt_debian.html b/install_apt_debian.html
index b0f7319d283..fc9bc8ac6d0 100644
--- a/install_apt_debian.html
+++ b/install_apt_debian.html
@@ -36,7 +36,7 @@
       
        
         
        Caffe
        
         
        
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
        
         
        
           Created by
@@ -57,12 +57,12 @@ 
        Caffe
        
       
        Debian Installation
        
 
 
        Caffe packages are available for several Debian versions, as shown in the
-following chart
        
+following chart:
        
 
 
        Your Distro     |  CPU_ONLY  |  CUDA  |     Alias
 ----------------+------------+--------+-------------------
 Debian/stable   |     ✘      |   ✘    | Debian Jessie
-Debian/testing  |     ✔      |   ☐    | Debian Stretch/Sid
+Debian/testing  |     ✔      |   ✔    | Debian Stretch/Sid
 Debian/unstable |     ✔      |   ✔    | Debian Sid
 
        
 
        
@@ -74,33 +74,34 @@ 
        Debian Installation
        
   
      • 
     
        ✔  You can install caffe with a single command line following this guide.
        
   
        • 
-  
      • 
-    
        ☐  The same with ✔ . However it will not work any more when Debian/Stretch becomes the stable branch.
        
-  
        •
      -

      Last update: 2017-01-05

      +

      Last update: 2017-02-01

      Binary installation with APT

      Apart from the installation methods based on source, Debian/unstable -and Debian/testing users can install pre-compiled Caffe packages via the official archive.

      +and Debian/testing users can install pre-compiled Caffe packages from +the official archive.

      -

      Make sure that there is something like the follows in your /etc/apt/sources.list: - -deb http://MIRROR/debian CODENAME main contrib non-free - -where MIRROR is your favorate Debian mirror, and CODENAME ∈ {testing,stretch,sid}.

      +

      Make sure that your /etc/apt/sources.list contains contrib and non-free +sections if you want to install the CUDA version, for instance:

      + +
      deb http://ftp2.cn.debian.org/debian sid main contrib non-free
+
      +

      Then we update APT cache and directly install Caffe. Note, the cpu version and -the cuda version cannot be installed at the same time. - -# apt update -# apt install [ caffe-cpu | caffe-cuda ] -# caffe # command line interface working -# python3 -c 'import caffe; print(caffe.__path__)' # python3 interface working - -It should work out of box.

      +the cuda version cannot coexist.

      + +
      $ sudo apt update
+$ sudo apt install [ caffe-cpu | caffe-cuda ]
+$ caffe                                              # command line interface working
+$ python3 -c 'import caffe; print(caffe.__path__)'   # python3 interface working
+
      +
      + +

      These Caffe packages should work for you out of box.

      Customizing caffe packages

      @@ -108,48 +109,50 @@

      Customizing caffe packages

      the package is beyond this guide. Here is only a brief guide of producing the customized .deb packages.

      -

      Make sure that there is something like this in your /etc/apt/sources.list: - -deb http://ftp2.cn.debian.org/debian sid main contrib non-free +

      Make sure that there is a dec-src source in your /etc/apt/sources.list, +for instance:

      + +
      deb http://ftp2.cn.debian.org/debian sid main contrib non-free
 deb-src http://ftp2.cn.debian.org/debian sid main contrib non-free
-
      
-
-
      Then we build caffe deb files with the following commands:
-
-$ sudo apt update
-$ sudo apt install build-essential debhelper devscripts    # standard package building tools
-$ sudo apt build-dep [ caffe-cpu | caffe-cuda ]            # the most elegant way to pull caffe build dependencies
-$ apt source [ caffe-cpu | caffe-cuda ]               # download the source tarball and extract
+
      +
      + +

      Then we build caffe deb files with the following commands:

      + +
      $ sudo apt update
+$ sudo apt install build-essential debhelper devscripts  # standard package building tools
+$ sudo apt build-dep [ caffe-cpu | caffe-cuda ]          # the most elegant way to pull caffe build dependencies
+$ apt source [ caffe-cpu | caffe-cuda ]                  # download the source tarball and extract
 $ cd caffe-XXXX
-[ ... optional, customize caffe code/build ... ]
-$ dch -llocal "Modified XXX in order to XXX"          # write your one-line changelog
-$ debuild -B -j4                                      # build caffe with 4 parallel jobs (similar to make -j4)
+[ ... optional, customizing caffe code/build ... ]
+$ dch --local "Modified XXX"                             # bump package version and write changelog
+$ debuild -B -j4                                         # build caffe with 4 parallel jobs (similar to make -j4)
 [ ... building ...]
-$ debc                                                # optional, if you want to check the package contents
-$ sudo debi                                           # optional, install the generated packages
-
-The resulting deb packages can be found under the parent directory of the source tree.
      
-
-
      Note, the dch ... command line above is for bumping the package version number
-and adding an entry to the package changelog. If you would like to write
-more than one changelog entry, use subsequent dch command (see man 1 dch)
-instead of manually modifing debian/changelog unless you know how to keep its format correct.
+$ debc                                                   # optional, if you want to check the package contents
+$ sudo debi                                              # optional, install the generated packages
+$ ls ../                                                 # optional, you will see the resulting packages
+
      +
      + +

      It is a BUG if the package failed to build without any change. The changelog will be installed at e.g. /usr/share/doc/caffe-cpu/changelog.Debian.gz.

      Source installation

      -

      Source installation under Debian/unstable is similar to that of Ubuntu, but -here is a more elegant way to pull caffe build dependencies: - -$ sudo apt build-dep [ caffe-cpu | caffe-cuda ] - -Note, this requires a deb-src entry in your /etc/apt/sources.list.

      +

      Source installation under Debian/unstable and Debian/testing is similar to that of Ubuntu, but +here is a more elegant way to pull caffe build dependencies:

      + +
      $ sudo apt build-dep [ caffe-cpu | caffe-cuda ]
+
      +
      + +

      Note, this requires a deb-src entry in your /etc/apt/sources.list.

      Compiler Combinations

      -

      Some users may find their favorate compiler doesn’t work well with CUDA. - -CXX compiler | CUDA 7.5 | CUDA 8.0 | +

      Some users may find their favorate compiler doesn’t work with CUDA.

      + +
      CXX compiler |  CUDA 7.5  |  CUDA 8.0  |
 -------------+------------+------------+-
 GCC-7        |     ?      |     ?      |
 GCC-6        |     ✘      |     ✘      |
@@ -157,7 +160,8 @@ 
      Compiler Combinations
      
 CLANG-4.0    |     ?      |     ?      |
 CLANG-3.9    |     ✘      |     ✘      |
 CLANG-3.8    |     ?      |     ✔      |
-
      
+
      +

      [1] CUDA 7.5 ‘s host_config.h must be patched before working with GCC-5.

      @@ -215,8 +219,8 @@

      FAQ

    • Where are the examples, the models and other documentation stuff?
    -
    sudo apt install caffe-doc
-dpkg -L caffe-doc
+
    $ sudo apt install caffe-doc
+$ dpkg -L caffe-doc
 
    
 
    
 
@@ -224,9 +228,10 @@ 
    FAQ
    
   
  • Where can I find the Debian package status?
    • 
 
 
-
    https://tracker.debian.org/pkg/caffe  (for the CPU_ONLY version)
    
-
-
    https://tracker.debian.org/pkg/caffe-contrib  (for the CUDA version)
    
+
    https://tracker.debian.org/pkg/caffe          (for the CPU_ONLY version)
+https://tracker.debian.org/pkg/caffe-contrib  (for the CUDA version)
+
    
+
    
 
 
       
diff --git a/install_osx.html b/install_osx.html
index 063b1670dd0..745b63bc60b 100644
--- a/install_osx.html
+++ b/install_osx.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
diff --git a/install_yum.html b/install_yum.html
index 4abb76c8e8c..9482842451c 100644
--- a/install_yum.html
+++ b/install_yum.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
diff --git a/installation.html b/installation.html
index dd5d3f24573..521ccddad25 100644
--- a/installation.html
+++ b/installation.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -104,7 +104,7 @@ 
    Prerequisites
    
 
      
   
    • OpenCV >= 2.4 including 3.0
      • 
   
    • IO libraries: lmdb, leveldb (note: leveldb requires snappy)
      • 
-  
    • cuDNN for GPU acceleration (v5)
      • 
+  
    • cuDNN for GPU acceleration (v6)
      • 
 
    
 
 
    Pycaffe and Matcaffe interfaces have their own natural needs.
    
@@ -114,7 +114,7 @@ 
    Prerequisites
    
   
  • For MATLAB Caffe: MATLAB with the mex compiler.
    • 
 
 
-
    cuDNN Caffe: for fastest operation Caffe is accelerated by drop-in integration of NVIDIA cuDNN. To speed up your Caffe models, install cuDNN then uncomment the USE_CUDNN := 1 flag in Makefile.config when installing Caffe. Acceleration is automatic. The current version is cuDNN v5; older versions are supported in older Caffe.
    
+
    cuDNN Caffe: for fastest operation Caffe is accelerated by drop-in integration of NVIDIA cuDNN. To speed up your Caffe models, install cuDNN then uncomment the USE_CUDNN := 1 flag in Makefile.config when installing Caffe. Acceleration is automatic. The current version is cuDNN v6; older versions are supported in older Caffe.
    
 
 
    CPU-only Caffe: for cold-brewed CPU-only Caffe uncomment the CPU_ONLY := 1 flag in Makefile.config to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.
    
 
@@ -130,7 +130,7 @@ 
    CUDA and BLAS
    
 
 
      
   
    • ATLAS: free, open source, and so the default for Caffe.
      • 
-  
    • Intel MKL: commercial and optimized for Intel CPUs, with a free trial and student licenses.
+  
    • Intel MKL: commercial and optimized for Intel CPUs, with free licenses.
     
        
       
      1. Install MKL.
        2. 
       
      2. Set up MKL environment (Details: Linux, OS X). Example: source /opt/intel/mkl/bin/mklvars.sh intel64
        3. 
diff --git a/model_zoo.html b/model_zoo.html
index 9bc145287df..db3e36928e4 100644
--- a/model_zoo.html
+++ b/model_zoo.html
@@ -36,7 +36,7 @@
       
        
         
        Caffe
        
         
        
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
        
         
        
           Created by
@@ -56,7 +56,7 @@ 
        Caffe
        
 
       
        Caffe Model Zoo
        
 
-
        Lots of researchers and engineers have made Caffe models for different tasks with all kinds of architectures and data.
+
        Lots of researchers and engineers have made Caffe models for different tasks with all kinds of architectures and data: check out the model zoo!
 These models are learned and applied for problems ranging from simple regression, to large-scale visual classification, to Siamese networks for image similarity, to speech and robotics applications.
        
 
 
        To help share these models, we introduce the model zoo framework:
        
@@ -69,19 +69,19 @@ 
        Caffe Model Zoo
        
 
 
        Where to get trained models
        
 
-
        First of all, we bundle BVLC-trained models for unrestricted, out of the box use.
+
        First of all, we bundle BAIR-trained models for unrestricted, out of the box use.
 
        
-See the BVLC model license for details.
+See the BAIR model license for details.
 Each one of these can be downloaded by running scripts/download_model_binary.py <dirname> where <dirname> is specified below:
        
 
 
          
-  
        • BVLC Reference CaffeNet in models/bvlc_reference_caffenet: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in ImageNet classification with deep convolutional neural networks by Krizhevsky et al. in NIPS 2012. (Trained by Jeff Donahue @jeffdonahue)
          • 
-  
        • BVLC AlexNet in models/bvlc_alexnet: AlexNet trained on ILSVRC 2012, almost exactly as described in ImageNet classification with deep convolutional neural networks by Krizhevsky et al. in NIPS 2012. (Trained by Evan Shelhamer @shelhamer)
          • 
-  
        • BVLC Reference R-CNN ILSVRC-2013 in models/bvlc_reference_rcnn_ilsvrc13: pure Caffe implementation of R-CNN as described by Girshick et al. in CVPR 2014. (Trained by Ross Girshick @rbgirshick)
          • 
-  
        • BVLC GoogLeNet in models/bvlc_googlenet: GoogLeNet trained on ILSVRC 2012, almost exactly as described in Going Deeper with Convolutions by Szegedy et al. in ILSVRC 2014. (Trained by Sergio Guadarrama @sguada)
          • 
+  
        • BAIR Reference CaffeNet in models/bvlc_reference_caffenet: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in ImageNet classification with deep convolutional neural networks by Krizhevsky et al. in NIPS 2012. (Trained by Jeff Donahue @jeffdonahue)
          • 
+  
        • BAIR AlexNet in models/bvlc_alexnet: AlexNet trained on ILSVRC 2012, almost exactly as described in ImageNet classification with deep convolutional neural networks by Krizhevsky et al. in NIPS 2012. (Trained by Evan Shelhamer @shelhamer)
          • 
+  
        • BAIR Reference R-CNN ILSVRC-2013 in models/bvlc_reference_rcnn_ilsvrc13: pure Caffe implementation of R-CNN as described by Girshick et al. in CVPR 2014. (Trained by Ross Girshick @rbgirshick)
          • 
+  
        • BAIR GoogLeNet in models/bvlc_googlenet: GoogLeNet trained on ILSVRC 2012, almost exactly as described in Going Deeper with Convolutions by Szegedy et al. in ILSVRC 2014. (Trained by Sergio Guadarrama @sguada)
          • 
 
        
 
-
        Community models made by Caffe users are posted to a publicly editable wiki page.
+
        Community models made by Caffe users are posted to a publicly editable model zoo wiki page.
 These models are subject to conditions of their respective authors such as citation and license.
 Thank you for sharing your models!
        
 
@@ -107,6 +107,8 @@ 
        Model info format
        
   
      3. [optional] Other helpful scripts.
        4. 
 
    
 
+
    This simple format can be handled through bundled scripts or manually if need be.
    
+
 
    Hosting model info
    
 
 
    Github Gist is a good format for model info distribution because it can contain multiple files, is versionable, and has in-browser syntax highlighting and markdown rendering.
    
@@ -120,14 +122,14 @@ 
    Hosting model info
    
 
    Hosting trained models
    
 
 
    It is up to the user where to host the .caffemodel file.
-We host our BVLC-provided models on our own server.
+We host our BAIR-provided models on our own server.
 Dropbox also works fine (tip: make sure that ?dl=1 is appended to the end of the URL).
    
 
 
    scripts/download_model_binary.py <dirname> downloads the .caffemodel from the URL specified in the <dirname>/readme.md frontmatter and confirms SHA1.
    
 
-
    BVLC model license
    
+
    BAIR model license
    
 
-
    The Caffe models bundled by the BVLC are released for unrestricted use.
    
+
    The Caffe models bundled by the BAIR are released for unrestricted use.
    
 
 
    These models are trained on data from the ImageNet project and training data includes internet photos that may be subject to copyright.
    
 
diff --git a/multigpu.html b/multigpu.html
index 8dea5a3e466..6f8302f60bd 100644
--- a/multigpu.html
+++ b/multigpu.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
diff --git a/performance_hardware.html b/performance_hardware.html
deleted file mode 100644
index a9119d2d42e..00000000000
--- a/performance_hardware.html
+++ /dev/null
@@ -1,142 +0,0 @@
-
-
-  
-    
-    
-    
-    
-    
-      Caffe | Performance and Hardware Configuration
-    
-
-    
-
-    
-    
-    
-
-    
-    
-  
-  
-  
-    
    
-      
    
-        
    Caffe
    
-        
    
-          Deep learning framework by the BVLC
-        
    
-        
    
-          Created by
-          
    
-          Yangqing Jia
-          
    
-          Lead Developer
-          
    
-          Evan Shelhamer
-        
    
-      
    
-      
    
-
-      
    Performance and Hardware Configuration
    
-
-
    To measure performance on different NVIDIA GPUs we use CaffeNet, the Caffe reference ImageNet model.
    
-
-
    For training, each time point is 20 iterations/minibatches of 256 images for 5,120 images total. For testing, a 50,000 image validation set is classified.
    
-
-
    Acknowledgements: BVLC members are very grateful to NVIDIA for providing several GPUs to conduct this research.
    
-
-
    NVIDIA K40
    
-
-
    Performance is best with ECC off and boost clock enabled. While ECC makes a negligible difference in speed, disabling it frees ~1 GB of GPU memory.
    
-
-
    Best settings with ECC off and maximum clock speed in standard Caffe:
    
-
-
      
-  
    • Training is 26.5 secs / 20 iterations (5,120 images)
      • 
-  
    • Testing is 100 secs / validation set (50,000 images)
      • 
-
    
-
-
    Best settings with Caffe + cuDNN acceleration:
    
-
-
      
-  
    • Training is 19.2 secs / 20 iterations (5,120 images)
      • 
-  
    • Testing is 60.7 secs / validation set (50,000 images)
      • 
-
    
-
-
    Other settings:
    
-
-
      
-  
    • ECC on, max speed: training 26.7 secs / 20 iterations, test 101 secs / validation set
      • 
-  
    • ECC on, default speed: training 31 secs / 20 iterations, test 117 secs / validation set
      • 
-  
    • ECC off, default speed: training 31 secs / 20 iterations, test 118 secs / validation set
      • 
-
    
-
-
    K40 configuration tips
    
-
-
    For maximum K40 performance, turn off ECC and boost the clock speed (at your own risk).
    
-
-
    To turn off ECC, do
    
-
-
    sudo nvidia-smi -i 0 --ecc-config=0    # repeat with -i x for each GPU ID
-
    
-
    
-
-
    then reboot.
    
-
-
    Set the “persistence” mode of the GPU settings by
    
-
-
    sudo nvidia-smi -pm 1
-
    
-
    
-
-
    and then set the clock speed with
    
-
-
    sudo nvidia-smi -i 0 -ac 3004,875    # repeat with -i x for each GPU ID
-
    
-
    
-
-
    but note that this configuration resets across driver reloading / rebooting. Include these commands in a boot script to initialize these settings. For a simple fix, add these commands to /etc/rc.local (on Ubuntu).
    
-
-
    NVIDIA Titan
    
-
-
    Training: 26.26 secs / 20 iterations (5,120 images).
-Testing: 100 secs / validation set (50,000 images).
    
-
-
    cuDNN Training: 20.25 secs / 20 iterations (5,120 images).
-cuDNN Testing: 66.3 secs / validation set (50,000 images).
    
-
-
    NVIDIA K20
    
-
-
    Training: 36.0 secs / 20 iterations (5,120 images).
-Testing: 133 secs / validation set (50,000 images).
    
-
-
    NVIDIA GTX 770
    
-
-
    Training: 33.0 secs / 20 iterations (5,120 images).
-Testing: 129 secs / validation set (50,000 images).
    
-
-
    cuDNN Training: 24.3 secs / 20 iterations (5,120 images).
-cuDNN Testing: 104 secs / validation set (50,000 images).
    
-
-
-      
    
-  
    
-  
-
diff --git a/tutorial/convolution.html b/tutorial/convolution.html
index 75abe25e561..924569651a2 100644
--- a/tutorial/convolution.html
+++ b/tutorial/convolution.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
diff --git a/tutorial/data.html b/tutorial/data.html
index 5fd01ba86d1..e967c652a1e 100644
--- a/tutorial/data.html
+++ b/tutorial/data.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
diff --git a/tutorial/forward_backward.html b/tutorial/forward_backward.html
index 38ae26dceca..b98b6786152 100644
--- a/tutorial/forward_backward.html
+++ b/tutorial/forward_backward.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
diff --git a/tutorial/index.html b/tutorial/index.html
index 99b0cf4f319..8027a112bae 100644
--- a/tutorial/index.html
+++ b/tutorial/index.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
diff --git a/tutorial/interfaces.html b/tutorial/interfaces.html
index 49542d7bcfe..5150d569519 100644
--- a/tutorial/interfaces.html
+++ b/tutorial/interfaces.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -162,7 +162,7 @@ 
    MATLAB
    
   
  • Intermingle arbitrary Matlab code with gradient steps
    • 
 
 
-
    An ILSVRC image classification demo is in caffe/matlab/demo/classification_demo.m (you need to download BVLC CaffeNet from Model Zoo to run it).
    
+
    An ILSVRC image classification demo is in caffe/matlab/demo/classification_demo.m (you need to download BAIR CaffeNet from Model Zoo to run it).
    
 
 
    Build MatCaffe
    
 
@@ -189,7 +189,7 @@ 
    Use MatCaffe
    
 
 
    MatCaffe is very similar to PyCaffe in usage.
    
 
-
    Examples below shows detailed usages and assumes you have downloaded BVLC CaffeNet from Model Zoo and started matlab from caffe root folder.
    
+
    Examples below shows detailed usages and assumes you have downloaded BAIR CaffeNet from Model Zoo and started matlab from caffe root folder.
    
 
 
    model = './models/bvlc_reference_caffenet/deploy.prototxt';
 weights = './models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel';
diff --git a/tutorial/layers.html b/tutorial/layers.html
index c71cbe00ea8..00da376169d 100644
--- a/tutorial/layers.html
+++ b/tutorial/layers.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -207,7 +207,7 @@ 
    Loss Layers
    
   
  • Infogain Loss - a generalization of MultinomialLogisticLossLayer.
    • 
   
  • Softmax with Loss - computes the multinomial logistic loss of the softmax of its inputs. It’s conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.
    • 
   
  • Sum-of-Squares / Euclidean - computes the sum of squares of differences of its two inputs, .
    • 
-  
  • Hinge / Margin - The hinge loss layer computes a one-vs-all hinge (L1) or squared hinge loss (L2).
    • 
+  
  • Hinge / Margin - The hinge loss layer computes a one-vs-all hinge (L1) or squared hinge loss (L2).
    • 
   
  • Sigmoid Cross-Entropy Loss - computes the cross-entropy (logistic) loss, often used for predicting targets interpreted as probabilities.
    • 
   
  • Accuracy / Top-k layer - scores the output as an accuracy with respect to target – it is not actually a loss and has no backward step.
    • 
   
  • Contrastive Loss
    • 
diff --git a/tutorial/layers/absval.html b/tutorial/layers/absval.html
index 563074c3cfa..59ba51bc185 100644
--- a/tutorial/layers/absval.html
+++ b/tutorial/layers/absval.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
diff --git a/tutorial/layers/accuracy.html b/tutorial/layers/accuracy.html
index bf45619e352..d4a2252e0a4 100644
--- a/tutorial/layers/accuracy.html
+++ b/tutorial/layers/accuracy.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -63,7 +63,6 @@ 
    Accuracy and Top-k
    
   
  • Doxygen Documentation
    • 
   
  • Header: ./include/caffe/layers/accuracy_layer.hpp
    • 
   
  • CPU implementation: ./src/caffe/layers/accuracy_layer.cpp
    • 
-  
  • CUDA GPU implementation: ./src/caffe/layers/accuracy_layer.cu
    • 
 
 
 
    Parameters
    
@@ -72,22 +71,23 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto):
    • 
 
 
-
    message AccuracyParameter {
-  // When computing accuracy, count as correct by comparing the true label to
-  // the top k scoring classes.  By default, only compare to the top scoring
-  // class (i.e. argmax).
-  optional uint32 top_k = 1 [default = 1];
+
    message AccuracyParameter {
+  // When computing accuracy, count as correct by comparing the true label to
+  // the top k scoring classes.  By default, only compare to the top scoring
+  // class (i.e. argmax).
+  optional uint32 top_k = 1 [default = 1];
 
-  // The "label" axis of the prediction blob, whose argmax corresponds to the
-  // predicted label -- may be negative to index from the end (e.g., -1 for the
-  // last axis).  For example, if axis == 1 and the predictions are
-  // (N x C x H x W), the label blob is expected to contain N*H*W ground truth
-  // labels with integer values in {0, 1, ..., C-1}.
-  optional int32 axis = 2 [default = 1];
+  // The "label" axis of the prediction blob, whose argmax corresponds to the
+  // predicted label -- may be negative to index from the end (e.g., -1 for the
+  // last axis).  For example, if axis == 1 and the predictions are
+  // (N x C x H x W), the label blob is expected to contain N*H*W ground truth
+  // labels with integer values in {0, 1, ..., C-1}.
+  optional int32 axis = 2 [default = 1];
+
+  // If specified, ignore instances with the given label.
+  optional int32 ignore_label = 3;
+}
    
 
-  // If specified, ignore instances with the given label.
-  optional int32 ignore_label = 3;
-}
    
 
 
       
diff --git a/tutorial/layers/argmax.html b/tutorial/layers/argmax.html
index 57cc8b0ea6b..9ecde5ab5b4 100644
--- a/tutorial/layers/argmax.html
+++ b/tutorial/layers/argmax.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -61,7 +61,6 @@ 
    ArgMax Layer
    
   
  • Doxygen Documentation
    • 
   
  • Header: ./include/caffe/layers/argmax_layer.hpp
    • 
   
  • CPU implementation: ./src/caffe/layers/argmax_layer.cpp
    • 
-  
  • CUDA GPU implementation: ./src/caffe/layers/argmax_layer.cu
    • 
 
 
 
    Parameters
    
@@ -70,16 +69,17 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto):
    • 
 
 
-
    message ArgMaxParameter {
-  // If true produce pairs (argmax, maxval)
-  optional bool out_max_val = 1 [default = false];
-  optional uint32 top_k = 2 [default = 1];
-  // The axis along which to maximise -- may be negative to index from the
-  // end (e.g., -1 for the last axis).
-  // By default ArgMaxLayer maximizes over the flattened trailing dimensions
-  // for each index of the first / num dimension.
-  optional int32 axis = 3;
-}
    
+
    message ArgMaxParameter {
+  // If true produce pairs (argmax, maxval)
+  optional bool out_max_val = 1 [default = false];
+  optional uint32 top_k = 2 [default = 1];
+  // The axis along which to maximise -- may be negative to index from the
+  // end (e.g., -1 for the last axis).
+  // By default ArgMaxLayer maximizes over the flattened trailing dimensions
+  // for each index of the first / num dimension.
+  optional int32 axis = 3;
+}
    
+
 
 
       
diff --git a/tutorial/layers/batchnorm.html b/tutorial/layers/batchnorm.html
index e09a24f9bc7..9620e87b002 100644
--- a/tutorial/layers/batchnorm.html
+++ b/tutorial/layers/batchnorm.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -71,17 +71,27 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    message BatchNormParameter {
-  // If false, accumulate global mean/variance values via a moving average. If
-  // true, use those accumulated values instead of computing mean/variance
-  // across the batch.
-  optional bool use_global_stats = 1;
-  // How much does the moving average decay each iteration?
-  optional float moving_average_fraction = 2 [default = .999];
-  // Small value to add to the variance estimate so that we don't divide by
-  // zero.
-  optional float eps = 3 [default = 1e-5];
-}
    
+
    message BatchNormParameter {
+  // If false, normalization is performed over the current mini-batch
+  // and global statistics are accumulated (but not yet used) by a moving
+  // average.
+  // If true, those accumulated mean and variance values are used for the
+  // normalization.
+  // By default, it is set to false when the network is in the training
+  // phase and true when the network is in the testing phase.
+  optional bool use_global_stats = 1;
+  // What fraction of the moving average remains each iteration?
+  // Smaller values make the moving average decay faster, giving more
+  // weight to the recent values.
+  // Each iteration updates the moving average @f$S_{t-1}@f$ with the
+  // current mean @f$ Y_t @f$ by
+  // @f$ S_t = (1-\beta)Y_t + \beta \cdot S_{t-1} @f$, where @f$ \beta @f$
+  // is the moving_average_fraction parameter.
+  optional float moving_average_fraction = 2 [default = .999];
+  // Small value to add to the variance estimate so that we don't divide by
+  // zero.
+  optional float eps = 3 [default = 1e-5];
+}
    
 
 
 
diff --git a/tutorial/layers/batchreindex.html b/tutorial/layers/batchreindex.html
index 4d549719e6a..25333e25d46 100644
--- a/tutorial/layers/batchreindex.html
+++ b/tutorial/layers/batchreindex.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
diff --git a/tutorial/layers/bias.html b/tutorial/layers/bias.html
index cdb5fc1a9b3..6c418693763 100644
--- a/tutorial/layers/bias.html
+++ b/tutorial/layers/bias.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -70,37 +70,37 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto):
    • 
 
 
-
    message BiasParameter {
-  // The first axis of bottom[0] (the first input Blob) along which to apply
-  // bottom[1] (the second input Blob).  May be negative to index from the end
-  // (e.g., -1 for the last axis).
-  //
-  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
-  // top[0] will have the same shape, and bottom[1] may have any of the
-  // following shapes (for the given value of axis):
-  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
-  //    (axis == 1 == -3)          3;     3x40;     3x40x60
-  //    (axis == 2 == -2)                   40;       40x60
-  //    (axis == 3 == -1)                                60
-  // Furthermore, bottom[1] may have the empty shape (regardless of the value of
-  // "axis") -- a scalar bias.
-  optional int32 axis = 1 [default = 1];
+
    message BiasParameter {
+  // The first axis of bottom[0] (the first input Blob) along which to apply
+  // bottom[1] (the second input Blob).  May be negative to index from the end
+  // (e.g., -1 for the last axis).
+  //
+  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
+  // top[0] will have the same shape, and bottom[1] may have any of the
+  // following shapes (for the given value of axis):
+  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
+  //    (axis == 1 == -3)          3;     3x40;     3x40x60
+  //    (axis == 2 == -2)                   40;       40x60
+  //    (axis == 3 == -1)                                60
+  // Furthermore, bottom[1] may have the empty shape (regardless of the value of
+  // "axis") -- a scalar bias.
+  optional int32 axis = 1 [default = 1];
 
-  // (num_axes is ignored unless just one bottom is given and the bias is
-  // a learned parameter of the layer.  Otherwise, num_axes is determined by the
-  // number of axes by the second bottom.)
-  // The number of axes of the input (bottom[0]) covered by the bias
-  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
-  // Set num_axes := 0, to add a zero-axis Blob: a scalar.
-  optional int32 num_axes = 2 [default = 1];
+  // (num_axes is ignored unless just one bottom is given and the bias is
+  // a learned parameter of the layer.  Otherwise, num_axes is determined by the
+  // number of axes by the second bottom.)
+  // The number of axes of the input (bottom[0]) covered by the bias
+  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
+  // Set num_axes := 0, to add a zero-axis Blob: a scalar.
+  optional int32 num_axes = 2 [default = 1];
 
-  // (filler is ignored unless just one bottom is given and the bias is
-  // a learned parameter of the layer.)
-  // The initialization for the learned bias parameter.
-  // Default is the zero (0) initialization, resulting in the BiasLayer
-  // initially performing the identity operation.
-  optional FillerParameter filler = 3;
-}
    
+  // (filler is ignored unless just one bottom is given and the bias is
+  // a learned parameter of the layer.)
+  // The initialization for the learned bias parameter.
+  // Default is the zero (0) initialization, resulting in the BiasLayer
+  // initially performing the identity operation.
+  optional FillerParameter filler = 3;
+}
    
 
 
 
diff --git a/tutorial/layers/bnll.html b/tutorial/layers/bnll.html
index 505ce3837a5..0c12d47f664 100644
--- a/tutorial/layers/bnll.html
+++ b/tutorial/layers/bnll.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
diff --git a/tutorial/layers/concat.html b/tutorial/layers/concat.html
index 7c52c655312..eb9db91c025 100644
--- a/tutorial/layers/concat.html
+++ b/tutorial/layers/concat.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -107,16 +107,16 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto):
    • 
 
 
-
    message ConcatParameter {
-  // The axis along which to concatenate -- may be negative to index from the
-  // end (e.g., -1 for the last axis).  Other axes must have the
-  // same dimension for all the bottom blobs.
-  // By default, ConcatLayer concatenates blobs along the "channels" axis (1).
-  optional int32 axis = 2 [default = 1];
+
    message ConcatParameter {
+  // The axis along which to concatenate -- may be negative to index from the
+  // end (e.g., -1 for the last axis).  Other axes must have the
+  // same dimension for all the bottom blobs.
+  // By default, ConcatLayer concatenates blobs along the "channels" axis (1).
+  optional int32 axis = 2 [default = 1];
 
-  // DEPRECATED: alias for "axis" -- does not support negative indexing.
-  optional uint32 concat_dim = 1 [default = 1];
-}
    
+  // DEPRECATED: alias for "axis" -- does not support negative indexing.
+  optional uint32 concat_dim = 1 [default = 1];
+}
    
 
 
 
diff --git a/tutorial/layers/contrastiveloss.html b/tutorial/layers/contrastiveloss.html
index 59876042856..d383afa114b 100644
--- a/tutorial/layers/contrastiveloss.html
+++ b/tutorial/layers/contrastiveloss.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -71,17 +71,17 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto):
    • 
 
 
-
    message ContrastiveLossParameter {
-  // margin for dissimilar pair
-  optional float margin = 1 [default = 1.0];
-  // The first implementation of this cost did not exactly match the cost of
-  // Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2.
-  // legacy_version = false (the default) uses (margin - d)^2 as proposed in the
-  // Hadsell paper. New models should probably use this version.
-  // legacy_version = true uses (margin - d^2). This is kept to support /
-  // reproduce existing models and results
-  optional bool legacy_version = 2 [default = false];
-}
    
+
    message ContrastiveLossParameter {
+  // margin for dissimilar pair
+  optional float margin = 1 [default = 1.0];
+  // The first implementation of this cost did not exactly match the cost of
+  // Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2.
+  // legacy_version = false (the default) uses (margin - d)^2 as proposed in the
+  // Hadsell paper. New models should probably use this version.
+  // legacy_version = true uses (margin - d^2). This is kept to support /
+  // reproduce existing models and results
+  optional bool legacy_version = 2 [default = false];
+}
    
 
 
 
diff --git a/tutorial/layers/convolution.html b/tutorial/layers/convolution.html
index a1dafe34ada..34837d2807a 100644
--- a/tutorial/layers/convolution.html
+++ b/tutorial/layers/convolution.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -134,58 +134,58 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto):
    • 
 
 
-
    message ConvolutionParameter {
-  optional uint32 num_output = 1; // The number of outputs for the layer
-  optional bool bias_term = 2 [default = true]; // whether to have bias terms
-
-  // Pad, kernel size, and stride are all given as a single value for equal
-  // dimensions in all spatial dimensions, or once per spatial dimension.
-  repeated uint32 pad = 3; // The padding size; defaults to 0
-  repeated uint32 kernel_size = 4; // The kernel size
-  repeated uint32 stride = 6; // The stride; defaults to 1
-  // Factor used to dilate the kernel, (implicitly) zero-filling the resulting
-  // holes. (Kernel dilation is sometimes referred to by its use in the
-  // algorithme à trous from Holschneider et al. 1987.)
-  repeated uint32 dilation = 18; // The dilation; defaults to 1
-
-  // For 2D convolution only, the *_h and *_w versions may also be used to
-  // specify both spatial dimensions.
-  optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
-  optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
-  optional uint32 kernel_h = 11; // The kernel height (2D only)
-  optional uint32 kernel_w = 12; // The kernel width (2D only)
-  optional uint32 stride_h = 13; // The stride height (2D only)
-  optional uint32 stride_w = 14; // The stride width (2D only)
-
-  optional uint32 group = 5 [default = 1]; // The group size for group conv
-
-  optional FillerParameter weight_filler = 7; // The filler for the weight
-  optional FillerParameter bias_filler = 8; // The filler for the bias
-  enum Engine {
-    DEFAULT = 0;
-    CAFFE = 1;
-    CUDNN = 2;
-  }
-  optional Engine engine = 15 [default = DEFAULT];
-
-  // The axis to interpret as "channels" when performing convolution.
-  // Preceding dimensions are treated as independent inputs;
-  // succeeding dimensions are treated as "spatial".
-  // With (N, C, H, W) inputs, and axis == 1 (the default), we perform
-  // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
-  // groups g>1) filters across the spatial axes (H, W) of the input.
-  // With (N, C, D, H, W) inputs, and axis == 1, we perform
-  // N independent 3D convolutions, sliding (C/g)-channels
-  // filters across the spatial axes (D, H, W) of the input.
-  optional int32 axis = 16 [default = 1];
-
-  // Whether to force use of the general ND convolution, even if a specific
-  // implementation for blobs of the appropriate number of spatial dimensions
-  // is available. (Currently, there is only a 2D-specific convolution
-  // implementation; for input blobs with num_axes != 2, this option is
-  // ignored and the ND implementation will be used.)
-  optional bool force_nd_im2col = 17 [default = false];
-}
    
+
    message ConvolutionParameter {
+  optional uint32 num_output = 1; // The number of outputs for the layer
+  optional bool bias_term = 2 [default = true]; // whether to have bias terms
+
+  // Pad, kernel size, and stride are all given as a single value for equal
+  // dimensions in all spatial dimensions, or once per spatial dimension.
+  repeated uint32 pad = 3; // The padding size; defaults to 0
+  repeated uint32 kernel_size = 4; // The kernel size
+  repeated uint32 stride = 6; // The stride; defaults to 1
+  // Factor used to dilate the kernel, (implicitly) zero-filling the resulting
+  // holes. (Kernel dilation is sometimes referred to by its use in the
+  // algorithme à trous from Holschneider et al. 1987.)
+  repeated uint32 dilation = 18; // The dilation; defaults to 1
+
+  // For 2D convolution only, the *_h and *_w versions may also be used to
+  // specify both spatial dimensions.
+  optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
+  optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
+  optional uint32 kernel_h = 11; // The kernel height (2D only)
+  optional uint32 kernel_w = 12; // The kernel width (2D only)
+  optional uint32 stride_h = 13; // The stride height (2D only)
+  optional uint32 stride_w = 14; // The stride width (2D only)
+
+  optional uint32 group = 5 [default = 1]; // The group size for group conv
+
+  optional FillerParameter weight_filler = 7; // The filler for the weight
+  optional FillerParameter bias_filler = 8; // The filler for the bias
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 15 [default = DEFAULT];
+
+  // The axis to interpret as "channels" when performing convolution.
+  // Preceding dimensions are treated as independent inputs;
+  // succeeding dimensions are treated as "spatial".
+  // With (N, C, H, W) inputs, and axis == 1 (the default), we perform
+  // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
+  // groups g>1) filters across the spatial axes (H, W) of the input.
+  // With (N, C, D, H, W) inputs, and axis == 1, we perform
+  // N independent 3D convolutions, sliding (C/g)-channels
+  // filters across the spatial axes (D, H, W) of the input.
+  optional int32 axis = 16 [default = 1];
+
+  // Whether to force use of the general ND convolution, even if a specific
+  // implementation for blobs of the appropriate number of spatial dimensions
+  // is available. (Currently, there is only a 2D-specific convolution
+  // implementation; for input blobs with num_axes != 2, this option is
+  // ignored and the ND implementation will be used.)
+  optional bool force_nd_im2col = 17 [default = false];
+}
    
 
 
 
diff --git a/tutorial/layers/crop.html b/tutorial/layers/crop.html
index 360d83bc3b3..30e82637684 100644
--- a/tutorial/layers/crop.html
+++ b/tutorial/layers/crop.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -71,23 +71,23 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto):
    • 
 
 
-
    message CropParameter {
-  // To crop, elements of the first bottom are selected to fit the dimensions
-  // of the second, reference bottom. The crop is configured by
-  // - the crop `axis` to pick the dimensions for cropping
-  // - the crop `offset` to set the shift for all/each dimension
-  // to align the cropped bottom with the reference bottom.
-  // All dimensions up to but excluding `axis` are preserved, while
-  // the dimensions including and trailing `axis` are cropped.
-  // If only one `offset` is set, then all dimensions are offset by this amount.
-  // Otherwise, the number of offsets must equal the number of cropped axes to
-  // shift the crop in each dimension accordingly.
-  // Note: standard dimensions are N,C,H,W so the default is a spatial crop,
-  // and `axis` may be negative to index from the end (e.g., -1 for the last
-  // axis).
-  optional int32 axis = 1 [default = 2];
-  repeated uint32 offset = 2;
-}
    
+
    message CropParameter {
+  // To crop, elements of the first bottom are selected to fit the dimensions
+  // of the second, reference bottom. The crop is configured by
+  // - the crop `axis` to pick the dimensions for cropping
+  // - the crop `offset` to set the shift for all/each dimension
+  // to align the cropped bottom with the reference bottom.
+  // All dimensions up to but excluding `axis` are preserved, while
+  // the dimensions including and trailing `axis` are cropped.
+  // If only one `offset` is set, then all dimensions are offset by this amount.
+  // Otherwise, the number of offsets must equal the number of cropped axes to
+  // shift the crop in each dimension accordingly.
+  // Note: standard dimensions are N,C,H,W so the default is a spatial crop,
+  // and `axis` may be negative to index from the end (e.g., -1 for the last
+  // axis).
+  optional int32 axis = 1 [default = 2];
+  repeated uint32 offset = 2;
+}
    
 
 
 
diff --git a/tutorial/layers/data.html b/tutorial/layers/data.html
index ab851d8799c..faf488565d8 100644
--- a/tutorial/layers/data.html
+++ b/tutorial/layers/data.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -70,39 +70,39 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto):
    • 
 
 
-
    message DataParameter {
-  enum DB {
-    LEVELDB = 0;
-    LMDB = 1;
-  }
-  // Specify the data source.
-  optional string source = 1;
-  // Specify the batch size.
-  optional uint32 batch_size = 4;
-  // The rand_skip variable is for the data layer to skip a few data points
-  // to avoid all asynchronous sgd clients to start at the same point. The skip
-  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
-  // be larger than the number of keys in the database.
-  // DEPRECATED. Each solver accesses a different subset of the database.
-  optional uint32 rand_skip = 7 [default = 0];
-  optional DB backend = 8 [default = LEVELDB];
-  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
-  // simple scaling and subtracting the data mean, if provided. Note that the
-  // mean subtraction is always carried out before scaling.
-  optional float scale = 2 [default = 1];
-  optional string mean_file = 3;
-  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
-  // crop an image.
-  optional uint32 crop_size = 5 [default = 0];
-  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
-  // data.
-  optional bool mirror = 6 [default = false];
-  // Force the encoded image to have 3 color channels
-  optional bool force_encoded_color = 9 [default = false];
-  // Prefetch queue (Increase if data feeding bandwidth varies, within the
-  // limit of device memory for GPU training)
-  optional uint32 prefetch = 10 [default = 4];
-}
    
+
    message DataParameter {
+  enum DB {
+    LEVELDB = 0;
+    LMDB = 1;
+  }
+  // Specify the data source.
+  optional string source = 1;
+  // Specify the batch size.
+  optional uint32 batch_size = 4;
+  // The rand_skip variable is for the data layer to skip a few data points
+  // to avoid all asynchronous sgd clients to start at the same point. The skip
+  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
+  // be larger than the number of keys in the database.
+  // DEPRECATED. Each solver accesses a different subset of the database.
+  optional uint32 rand_skip = 7 [default = 0];
+  optional DB backend = 8 [default = LEVELDB];
+  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
+  // simple scaling and subtracting the data mean, if provided. Note that the
+  // mean subtraction is always carried out before scaling.
+  optional float scale = 2 [default = 1];
+  optional string mean_file = 3;
+  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
+  // crop an image.
+  optional uint32 crop_size = 5 [default = 0];
+  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
+  // data.
+  optional bool mirror = 6 [default = false];
+  // Force the encoded image to have 3 color channels
+  optional bool force_encoded_color = 9 [default = false];
+  // Prefetch queue (Increase if data feeding bandwidth varies, within the
+  // limit of device memory for GPU training)
+  optional uint32 prefetch = 10 [default = 4];
+}
    
 
 
      
   
    • Parameters
diff --git a/tutorial/layers/deconvolution.html b/tutorial/layers/deconvolution.html
index 94cc26ebd4f..050d28945d2 100644
--- a/tutorial/layers/deconvolution.html
+++ b/tutorial/layers/deconvolution.html
@@ -36,7 +36,7 @@
       
      
         
      Caffe
      
         
      
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
      
         
      
           Created by
@@ -73,58 +73,58 @@ 
      Parameters
      
   
    • From ./src/caffe/proto/caffe.proto):
      • 
 
    
 
-
    message ConvolutionParameter {
-  optional uint32 num_output = 1; // The number of outputs for the layer
-  optional bool bias_term = 2 [default = true]; // whether to have bias terms
-
-  // Pad, kernel size, and stride are all given as a single value for equal
-  // dimensions in all spatial dimensions, or once per spatial dimension.
-  repeated uint32 pad = 3; // The padding size; defaults to 0
-  repeated uint32 kernel_size = 4; // The kernel size
-  repeated uint32 stride = 6; // The stride; defaults to 1
-  // Factor used to dilate the kernel, (implicitly) zero-filling the resulting
-  // holes. (Kernel dilation is sometimes referred to by its use in the
-  // algorithme à trous from Holschneider et al. 1987.)
-  repeated uint32 dilation = 18; // The dilation; defaults to 1
-
-  // For 2D convolution only, the *_h and *_w versions may also be used to
-  // specify both spatial dimensions.
-  optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
-  optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
-  optional uint32 kernel_h = 11; // The kernel height (2D only)
-  optional uint32 kernel_w = 12; // The kernel width (2D only)
-  optional uint32 stride_h = 13; // The stride height (2D only)
-  optional uint32 stride_w = 14; // The stride width (2D only)
-
-  optional uint32 group = 5 [default = 1]; // The group size for group conv
-
-  optional FillerParameter weight_filler = 7; // The filler for the weight
-  optional FillerParameter bias_filler = 8; // The filler for the bias
-  enum Engine {
-    DEFAULT = 0;
-    CAFFE = 1;
-    CUDNN = 2;
-  }
-  optional Engine engine = 15 [default = DEFAULT];
-
-  // The axis to interpret as "channels" when performing convolution.
-  // Preceding dimensions are treated as independent inputs;
-  // succeeding dimensions are treated as "spatial".
-  // With (N, C, H, W) inputs, and axis == 1 (the default), we perform
-  // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
-  // groups g>1) filters across the spatial axes (H, W) of the input.
-  // With (N, C, D, H, W) inputs, and axis == 1, we perform
-  // N independent 3D convolutions, sliding (C/g)-channels
-  // filters across the spatial axes (D, H, W) of the input.
-  optional int32 axis = 16 [default = 1];
-
-  // Whether to force use of the general ND convolution, even if a specific
-  // implementation for blobs of the appropriate number of spatial dimensions
-  // is available. (Currently, there is only a 2D-specific convolution
-  // implementation; for input blobs with num_axes != 2, this option is
-  // ignored and the ND implementation will be used.)
-  optional bool force_nd_im2col = 17 [default = false];
-}
    
+
    message ConvolutionParameter {
+  optional uint32 num_output = 1; // The number of outputs for the layer
+  optional bool bias_term = 2 [default = true]; // whether to have bias terms
+
+  // Pad, kernel size, and stride are all given as a single value for equal
+  // dimensions in all spatial dimensions, or once per spatial dimension.
+  repeated uint32 pad = 3; // The padding size; defaults to 0
+  repeated uint32 kernel_size = 4; // The kernel size
+  repeated uint32 stride = 6; // The stride; defaults to 1
+  // Factor used to dilate the kernel, (implicitly) zero-filling the resulting
+  // holes. (Kernel dilation is sometimes referred to by its use in the
+  // algorithme à trous from Holschneider et al. 1987.)
+  repeated uint32 dilation = 18; // The dilation; defaults to 1
+
+  // For 2D convolution only, the *_h and *_w versions may also be used to
+  // specify both spatial dimensions.
+  optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
+  optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
+  optional uint32 kernel_h = 11; // The kernel height (2D only)
+  optional uint32 kernel_w = 12; // The kernel width (2D only)
+  optional uint32 stride_h = 13; // The stride height (2D only)
+  optional uint32 stride_w = 14; // The stride width (2D only)
+
+  optional uint32 group = 5 [default = 1]; // The group size for group conv
+
+  optional FillerParameter weight_filler = 7; // The filler for the weight
+  optional FillerParameter bias_filler = 8; // The filler for the bias
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 15 [default = DEFAULT];
+
+  // The axis to interpret as "channels" when performing convolution.
+  // Preceding dimensions are treated as independent inputs;
+  // succeeding dimensions are treated as "spatial".
+  // With (N, C, H, W) inputs, and axis == 1 (the default), we perform
+  // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
+  // groups g>1) filters across the spatial axes (H, W) of the input.
+  // With (N, C, D, H, W) inputs, and axis == 1, we perform
+  // N independent 3D convolutions, sliding (C/g)-channels
+  // filters across the spatial axes (D, H, W) of the input.
+  optional int32 axis = 16 [default = 1];
+
+  // Whether to force use of the general ND convolution, even if a specific
+  // implementation for blobs of the appropriate number of spatial dimensions
+  // is available. (Currently, there is only a 2D-specific convolution
+  // implementation; for input blobs with num_axes != 2, this option is
+  // ignored and the ND implementation will be used.)
+  optional bool force_nd_im2col = 17 [default = false];
+}
    
 
 
 
diff --git a/tutorial/layers/dropout.html b/tutorial/layers/dropout.html
index 8118ed725fb..17061eb7c27 100644
--- a/tutorial/layers/dropout.html
+++ b/tutorial/layers/dropout.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -71,9 +71,9 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto):
    • 
 
 
-
    message DropoutParameter {
-  optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio
-}
    
+
    message DropoutParameter {
+  optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio
+}
    
 
 
 
diff --git a/tutorial/layers/dummydata.html b/tutorial/layers/dummydata.html
index 28ff17d0ef3..dcfe4124b07 100644
--- a/tutorial/layers/dummydata.html
+++ b/tutorial/layers/dummydata.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -70,24 +70,24 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto):
    • 
 
 
-
    // DummyDataLayer fills any number of arbitrarily shaped blobs with random
+
    // DummyDataLayer fills any number of arbitrarily shaped blobs with random
 // (or constant) data generated by "Fillers" (see "message FillerParameter").
-message DummyDataParameter {
-  // This layer produces N >= 1 top blobs.  DummyDataParameter must specify 1 or N
-  // shape fields, and 0, 1 or N data_fillers.
-  //
-  // If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.
-  // If 1 data_filler is specified, it is applied to all top blobs.  If N are
-  // specified, the ith is applied to the ith top blob.
-  repeated FillerParameter data_filler = 1;
-  repeated BlobShape shape = 6;
+message DummyDataParameter {
+  // This layer produces N >= 1 top blobs.  DummyDataParameter must specify 1 or N
+  // shape fields, and 0, 1 or N data_fillers.
+  //
+  // If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.
+  // If 1 data_filler is specified, it is applied to all top blobs.  If N are
+  // specified, the ith is applied to the ith top blob.
+  repeated FillerParameter data_filler = 1;
+  repeated BlobShape shape = 6;
 
-  // 4D dimensions -- deprecated.  Use "shape" instead.
-  repeated uint32 num = 2;
-  repeated uint32 channels = 3;
-  repeated uint32 height = 4;
-  repeated uint32 width = 5;
-}
    
+  // 4D dimensions -- deprecated.  Use "shape" instead.
+  repeated uint32 num = 2;
+  repeated uint32 channels = 3;
+  repeated uint32 height = 4;
+  repeated uint32 width = 5;
+}
    
 
 
 
diff --git a/tutorial/layers/eltwise.html b/tutorial/layers/eltwise.html
index 9a3572eb36e..b333ad9e0ec 100644
--- a/tutorial/layers/eltwise.html
+++ b/tutorial/layers/eltwise.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -71,19 +71,19 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto):
    • 
 
 
-
    message EltwiseParameter {
-  enum EltwiseOp {
-    PROD = 0;
-    SUM = 1;
-    MAX = 2;
-  }
-  optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
-  repeated float coeff = 2; // blob-wise coefficient for SUM operation
-
-  // Whether to use an asymptotically slower (for >2 inputs) but stabler method
-  // of computing the gradient for the PROD operation. (No effect for SUM op.)
-  optional bool stable_prod_grad = 3 [default = true];
-}
    
+
    message EltwiseParameter {
+  enum EltwiseOp {
+    PROD = 0;
+    SUM = 1;
+    MAX = 2;
+  }
+  optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
+  repeated float coeff = 2; // blob-wise coefficient for SUM operation
+
+  // Whether to use an asymptotically slower (for >2 inputs) but stabler method
+  // of computing the gradient for the PROD operation. (No effect for SUM op.)
+  optional bool stable_prod_grad = 3 [default = true];
+}
    
 
 
 
diff --git a/tutorial/layers/elu.html b/tutorial/layers/elu.html
index 7553469bc34..6140499e3c2 100644
--- a/tutorial/layers/elu.html
+++ b/tutorial/layers/elu.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -78,13 +78,13 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    // Message that stores parameters used by ELULayer
-message ELUParameter {
-  // Described in:
-  // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate
-  // Deep Network Learning by Exponential Linear Units (ELUs). arXiv
-  optional float alpha = 1 [default = 1];
-}
    
+
    // Message that stores parameters used by ELULayer
+message ELUParameter {
+  // Described in:
+  // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate
+  // Deep Network Learning by Exponential Linear Units (ELUs). arXiv
+  optional float alpha = 1 [default = 1];
+}
    
 
 
 
diff --git a/tutorial/layers/embed.html b/tutorial/layers/embed.html
index 9d99ac2441a..dcc900f4768 100644
--- a/tutorial/layers/embed.html
+++ b/tutorial/layers/embed.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -71,19 +71,19 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    // Message that stores parameters used by EmbedLayer
-message EmbedParameter {
-  optional uint32 num_output = 1; // The number of outputs for the layer
-  // The input is given as integers to be interpreted as one-hot
-  // vector indices with dimension num_input.  Hence num_input should be
-  // 1 greater than the maximum possible input value.
-  optional uint32 input_dim = 2;
+
    // Message that stores parameters used by EmbedLayer
+message EmbedParameter {
+  optional uint32 num_output = 1; // The number of outputs for the layer
+  // The input is given as integers to be interpreted as one-hot
+  // vector indices with dimension num_input.  Hence num_input should be
+  // 1 greater than the maximum possible input value.
+  optional uint32 input_dim = 2;
 
-  optional bool bias_term = 3 [default = true]; // Whether to use a bias term
-  optional FillerParameter weight_filler = 4; // The filler for the weight
-  optional FillerParameter bias_filler = 5; // The filler for the bias
-
-}
    
+  optional bool bias_term = 3 [default = true]; // Whether to use a bias term
+  optional FillerParameter weight_filler = 4; // The filler for the weight
+  optional FillerParameter bias_filler = 5; // The filler for the bias
+
+}
    
 
 
 
diff --git a/tutorial/layers/euclideanloss.html b/tutorial/layers/euclideanloss.html
index 16a8981522a..4b4d1adf7e4 100644
--- a/tutorial/layers/euclideanloss.html
+++ b/tutorial/layers/euclideanloss.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
diff --git a/tutorial/layers/exp.html b/tutorial/layers/exp.html
index b60d0f1e0e9..70ad0228808 100644
--- a/tutorial/layers/exp.html
+++ b/tutorial/layers/exp.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -71,15 +71,15 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    // Message that stores parameters used by ExpLayer
-message ExpParameter {
-  // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.
-  // Or if base is set to the default (-1), base is set to e,
-  // so y = exp(shift + scale * x).
-  optional float base = 1 [default = -1.0];
-  optional float scale = 2 [default = 1.0];
-  optional float shift = 3 [default = 0.0];
-}
    
+
    // Message that stores parameters used by ExpLayer
+message ExpParameter {
+  // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.
+  // Or if base is set to the default (-1), base is set to e,
+  // so y = exp(shift + scale * x).
+  optional float base = 1 [default = -1.0];
+  optional float scale = 2 [default = 1.0];
+  optional float shift = 3 [default = 0.0];
+}
    
 
 
    See also
    
 
diff --git a/tutorial/layers/filter.html b/tutorial/layers/filter.html
index 580e874d9e1..495089575a6 100644
--- a/tutorial/layers/filter.html
+++ b/tutorial/layers/filter.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
diff --git a/tutorial/layers/flatten.html b/tutorial/layers/flatten.html
index 4433e131134..525f829e62e 100644
--- a/tutorial/layers/flatten.html
+++ b/tutorial/layers/flatten.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -72,17 +72,17 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    /// Message that stores parameters used by FlattenLayer
-message FlattenParameter {
-  // The first axis to flatten: all preceding axes are retained in the output.
-  // May be negative to index from the end (e.g., -1 for the last axis).
-  optional int32 axis = 1 [default = 1];
+
    /// Message that stores parameters used by FlattenLayer
+message FlattenParameter {
+  // The first axis to flatten: all preceding axes are retained in the output.
+  // May be negative to index from the end (e.g., -1 for the last axis).
+  optional int32 axis = 1 [default = 1];
 
-  // The last axis to flatten: all following axes are retained in the output.
-  // May be negative to index from the end (e.g., the default -1 for the last
-  // axis).
-  optional int32 end_axis = 2 [default = -1];
-}
    
+  // The last axis to flatten: all following axes are retained in the output.
+  // May be negative to index from the end (e.g., the default -1 for the last
+  // axis).
+  optional int32 end_axis = 2 [default = -1];
+}
    
 
 
 
diff --git a/tutorial/layers/hdf5data.html b/tutorial/layers/hdf5data.html
index 1f94f1cb372..cf94af6e389 100644
--- a/tutorial/layers/hdf5data.html
+++ b/tutorial/layers/hdf5data.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -71,20 +71,20 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    // Message that stores parameters used by HDF5DataLayer
-message HDF5DataParameter {
-  // Specify the data source.
-  optional string source = 1;
-  // Specify the batch size.
-  optional uint32 batch_size = 2;
+
    // Message that stores parameters used by HDF5DataLayer
+message HDF5DataParameter {
+  // Specify the data source.
+  optional string source = 1;
+  // Specify the batch size.
+  optional uint32 batch_size = 2;
 
-  // Specify whether to shuffle the data.
-  // If shuffle == true, the ordering of the HDF5 files is shuffled,
-  // and the ordering of data within any given HDF5 file is shuffled,
-  // but data between different files are not interleaved; all of a file's
-  // data are output (in a random order) before moving onto another file.
-  optional bool shuffle = 3 [default = false];
-}
    
+  // Specify whether to shuffle the data.
+  // If shuffle == true, the ordering of the HDF5 files is shuffled,
+  // and the ordering of data within any given HDF5 file is shuffled,
+  // but data between different files are not interleaved; all of a file's
+  // data are output (in a random order) before moving onto another file.
+  optional bool shuffle = 3 [default = false];
+}
    
 
 
 
diff --git a/tutorial/layers/hdf5output.html b/tutorial/layers/hdf5output.html
index 26ab5e2e9ad..eb9f06f1a97 100644
--- a/tutorial/layers/hdf5output.html
+++ b/tutorial/layers/hdf5output.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -81,9 +81,9 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    message HDF5OutputParameter {
-  optional string file_name = 1;
-}
    
+
    message HDF5OutputParameter {
+  optional string file_name = 1;
+}
    
 
 
 
diff --git a/tutorial/layers/hingeloss.html b/tutorial/layers/hingeloss.html
index 614af426659..26612e696c5 100644
--- a/tutorial/layers/hingeloss.html
+++ b/tutorial/layers/hingeloss.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -70,14 +70,14 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    message HingeLossParameter {
-  enum Norm {
-    L1 = 1;
-    L2 = 2;
-  }
-  // Specify the Norm to use L1 or L2
-  optional Norm norm = 1 [default = L1];
-}
    
+
    message HingeLossParameter {
+  enum Norm {
+    L1 = 1;
+    L2 = 2;
+  }
+  // Specify the Norm to use L1 or L2
+  optional Norm norm = 1 [default = L1];
+}
    
 
 
 
diff --git a/tutorial/layers/im2col.html b/tutorial/layers/im2col.html
index 64bc2aa4ee0..d2e4c52fc46 100644
--- a/tutorial/layers/im2col.html
+++ b/tutorial/layers/im2col.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
diff --git a/tutorial/layers/imagedata.html b/tutorial/layers/imagedata.html
index 2cfe013d255..906e8635fb4 100644
--- a/tutorial/layers/imagedata.html
+++ b/tutorial/layers/imagedata.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -86,36 +86,36 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    message ImageDataParameter {
-  // Specify the data source.
-  optional string source = 1;
-  // Specify the batch size.
-  optional uint32 batch_size = 4 [default = 1];
-  // The rand_skip variable is for the data layer to skip a few data points
-  // to avoid all asynchronous sgd clients to start at the same point. The skip
-  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
-  // be larger than the number of keys in the database.
-  optional uint32 rand_skip = 7 [default = 0];
-  // Whether or not ImageLayer should shuffle the list of files at every epoch.
-  optional bool shuffle = 8 [default = false];
-  // It will also resize images if new_height or new_width are not zero.
-  optional uint32 new_height = 9 [default = 0];
-  optional uint32 new_width = 10 [default = 0];
-  // Specify if the images are color or gray
-  optional bool is_color = 11 [default = true];
-  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
-  // simple scaling and subtracting the data mean, if provided. Note that the
-  // mean subtraction is always carried out before scaling.
-  optional float scale = 2 [default = 1];
-  optional string mean_file = 3;
-  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
-  // crop an image.
-  optional uint32 crop_size = 5 [default = 0];
-  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
-  // data.
-  optional bool mirror = 6 [default = false];
-  optional string root_folder = 12 [default = ""];
-}
    
+
    message ImageDataParameter {
+  // Specify the data source.
+  optional string source = 1;
+  // Specify the batch size.
+  optional uint32 batch_size = 4 [default = 1];
+  // The rand_skip variable is for the data layer to skip a few data points
+  // to avoid all asynchronous sgd clients to start at the same point. The skip
+  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
+  // be larger than the number of keys in the database.
+  optional uint32 rand_skip = 7 [default = 0];
+  // Whether or not ImageLayer should shuffle the list of files at every epoch.
+  optional bool shuffle = 8 [default = false];
+  // It will also resize images if new_height or new_width are not zero.
+  optional uint32 new_height = 9 [default = 0];
+  optional uint32 new_width = 10 [default = 0];
+  // Specify if the images are color or gray
+  optional bool is_color = 11 [default = true];
+  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
+  // simple scaling and subtracting the data mean, if provided. Note that the
+  // mean subtraction is always carried out before scaling.
+  optional float scale = 2 [default = 1];
+  optional string mean_file = 3;
+  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
+  // crop an image.
+  optional uint32 crop_size = 5 [default = 0];
+  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
+  // data.
+  optional bool mirror = 6 [default = false];
+  optional string root_folder = 12 [default = ""];
+}
    
 
 
 
diff --git a/tutorial/layers/infogainloss.html b/tutorial/layers/infogainloss.html
index 3d243dd4968..d32041bc1d0 100644
--- a/tutorial/layers/infogainloss.html
+++ b/tutorial/layers/infogainloss.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -61,12 +61,11 @@ 
    Infogain Loss Layer
    
   
  • Doxygen Documentation
    • 
   
  • Header: ./include/caffe/layers/infogain_loss_layer.hpp
    • 
   
  • CPU implementation: ./src/caffe/layers/infogain_loss_layer.cpp
    • 
-  
  • CUDA GPU implementation: ./src/caffe/layers/infogain_loss_layer.cu
    • 
 
 
-
    A generalization of MultinomialLogisticLossLayer that takes an “information gain” (infogain) matrix specifying the “value” of all label pairs.
    
+
    A generalization of MultinomialLogisticLossLayer that takes an “information gain” (infogain) matrix specifying the “value” of all label pairs.
    
 
-
    Equivalent to the MultinomialLogisticLossLayer if the infogain matrix is the identity.
    
+
    Equivalent to the MultinomialLogisticLossLayer if the infogain matrix is the identity.
    
 
 
    Parameters
    
 
@@ -75,10 +74,11 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    message InfogainLossParameter {
-  // Specify the infogain matrix source.
-  optional string source = 1;
-}
    
+
    message InfogainLossParameter {
+  // Specify the infogain matrix source.
+  optional string source = 1;
+  optional int32 axis = 2 [default = 1]; // axis of prob
+}
    
 
 
 
diff --git a/tutorial/layers/innerproduct.html b/tutorial/layers/innerproduct.html
index 764326e545d..eb6c4f07295 100644
--- a/tutorial/layers/innerproduct.html
+++ b/tutorial/layers/innerproduct.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -131,22 +131,22 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    message InnerProductParameter {
-  optional uint32 num_output = 1; // The number of outputs for the layer
-  optional bool bias_term = 2 [default = true]; // whether to have bias terms
-  optional FillerParameter weight_filler = 3; // The filler for the weight
-  optional FillerParameter bias_filler = 4; // The filler for the bias
-
-  // The first axis to be lumped into a single inner product computation;
-  // all preceding axes are retained in the output.
-  // May be negative to index from the end (e.g., -1 for the last axis).
-  optional int32 axis = 5 [default = 1];
-  // Specify whether to transpose the weight matrix or not.
-  // If transpose == true, any operations will be performed on the transpose
-  // of the weight matrix. The weight matrix itself is not going to be transposed
-  // but rather the transfer flag of operations will be toggled accordingly.
-  optional bool transpose = 6 [default = false];
-}
    
+
    message InnerProductParameter {
+  optional uint32 num_output = 1; // The number of outputs for the layer
+  optional bool bias_term = 2 [default = true]; // whether to have bias terms
+  optional FillerParameter weight_filler = 3; // The filler for the weight
+  optional FillerParameter bias_filler = 4; // The filler for the bias
+
+  // The first axis to be lumped into a single inner product computation;
+  // all preceding axes are retained in the output.
+  // May be negative to index from the end (e.g., -1 for the last axis).
+  optional int32 axis = 5 [default = 1];
+  // Specify whether to transpose the weight matrix or not.
+  // If transpose == true, any operations will be performed on the transpose
+  // of the weight matrix. The weight matrix itself is not going to be transposed
+  // but rather the transfer flag of operations will be toggled accordingly.
+  optional bool transpose = 6 [default = false];
+}
    
 
 
 
diff --git a/tutorial/layers/input.html b/tutorial/layers/input.html
index 73f3501046b..e735c047065 100644
--- a/tutorial/layers/input.html
+++ b/tutorial/layers/input.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -70,13 +70,13 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto):
    • 
 
 
-
    message InputParameter {
-  // This layer produces N >= 1 top blob(s) to be assigned manually.
-  // Define N shapes to set a shape for each top.
-  // Define 1 shape to set the same shape for every top.
-  // Define no shape to defer to reshaping manually.
-  repeated BlobShape shape = 1;
-}
    
+
    message InputParameter {
+  // This layer produces N >= 1 top blob(s) to be assigned manually.
+  // Define N shapes to set a shape for each top.
+  // Define 1 shape to set the same shape for every top.
+  // Define no shape to defer to reshaping manually.
+  repeated BlobShape shape = 1;
+}
    
 
 
 
diff --git a/tutorial/layers/log.html b/tutorial/layers/log.html
index 07d3e1ac041..fa3eacc4ba5 100644
--- a/tutorial/layers/log.html
+++ b/tutorial/layers/log.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -71,15 +71,15 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    // Message that stores parameters used by LogLayer
-message LogParameter {
-  // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.
-  // Or if base is set to the default (-1), base is set to e,
-  // so y = ln(shift + scale * x) = log_e(shift + scale * x)
-  optional float base = 1 [default = -1.0];
-  optional float scale = 2 [default = 1.0];
-  optional float shift = 3 [default = 0.0];
-}
    
+
    // Message that stores parameters used by LogLayer
+message LogParameter {
+  // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.
+  // Or if base is set to the default (-1), base is set to e,
+  // so y = ln(shift + scale * x) = log_e(shift + scale * x)
+  optional float base = 1 [default = -1.0];
+  optional float scale = 2 [default = 1.0];
+  optional float shift = 3 [default = 0.0];
+}
    
 
 
 
diff --git a/tutorial/layers/lrn.html b/tutorial/layers/lrn.html
index 5a70fc56b30..98ce688868c 100644
--- a/tutorial/layers/lrn.html
+++ b/tutorial/layers/lrn.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -81,21 +81,28 @@ 
    Local Response Normalization (LRN)
    Parameters
 
 
 
-
    message BatchNormParameter {
-  // If false, accumulate global mean/variance values via a moving average. If
-  // true, use those accumulated values instead of computing mean/variance
-  // across the batch.
-  optional bool use_global_stats = 1;
-  // How much does the moving average decay each iteration?
-  optional float moving_average_fraction = 2 [default = .999];
-  // Small value to add to the variance estimate so that we don't divide by
-  // zero.
-  optional float eps = 3 [default = 1e-5];
-}
    
+
    // Message that stores parameters used by LRNLayer
+message LRNParameter {
+  optional uint32 local_size = 1 [default = 5];
+  optional float alpha = 2 [default = 1.];
+  optional float beta = 3 [default = 0.75];
+  enum NormRegion {
+    ACROSS_CHANNELS = 0;
+    WITHIN_CHANNEL = 1;
+  }
+  optional NormRegion norm_region = 4 [default = ACROSS_CHANNELS];
+  optional float k = 5 [default = 1.];
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 6 [default = DEFAULT];
+}
    
 
 
 
diff --git a/tutorial/layers/lstm.html b/tutorial/layers/lstm.html
index 18cb0bbca95..b59759af844 100644
--- a/tutorial/layers/lstm.html
+++ b/tutorial/layers/lstm.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -72,24 +72,24 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    // Message that stores parameters used by RecurrentLayer
-message RecurrentParameter {
-  // The dimension of the output (and usually hidden state) representation --
-  // must be explicitly set to non-zero.
-  optional uint32 num_output = 1 [default = 0];
+
    // Message that stores parameters used by RecurrentLayer
+message RecurrentParameter {
+  // The dimension of the output (and usually hidden state) representation --
+  // must be explicitly set to non-zero.
+  optional uint32 num_output = 1 [default = 0];
 
-  optional FillerParameter weight_filler = 2; // The filler for the weight
-  optional FillerParameter bias_filler = 3; // The filler for the bias
+  optional FillerParameter weight_filler = 2; // The filler for the weight
+  optional FillerParameter bias_filler = 3; // The filler for the bias
+
+  // Whether to enable displaying debug_info in the unrolled recurrent net.
+  optional bool debug_info = 4 [default = false];
 
-  // Whether to enable displaying debug_info in the unrolled recurrent net.
-  optional bool debug_info = 4 [default = false];
-
-  // Whether to add as additional inputs (bottoms) the initial hidden state
-  // blobs, and add as additional outputs (tops) the final timestep hidden state
-  // blobs.  The number of additional bottom/top blobs required depends on the
-  // recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.
-  optional bool expose_hidden = 5 [default = false];
-}
    
+  // Whether to add as additional inputs (bottoms) the initial hidden state
+  // blobs, and add as additional outputs (tops) the final timestep hidden state
+  // blobs.  The number of additional bottom/top blobs required depends on the
+  // recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.
+  optional bool expose_hidden = 5 [default = false];
+}
    
 
 
 
diff --git a/tutorial/layers/memorydata.html b/tutorial/layers/memorydata.html
index 75360f8a158..406d87c2b19 100644
--- a/tutorial/layers/memorydata.html
+++ b/tutorial/layers/memorydata.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -60,7 +60,7 @@ 
    Memory Data Layer
    
   
  • Layer type: MemoryData
    • 
   
  • Doxygen Documentation
    • 
   
  • Header: ./include/caffe/layers/memory_data_layer.hpp
    • 
-  
  • CPU implementation: ./src/caffe/layers/memory_data_layer.cpu
    • 
+  
  • CPU implementation: ./src/caffe/layers/memory_data_layer.cpp
    • 
 
 
 
    The memory data layer reads data directly from memory, without copying it. In order to use it, one must call MemoryDataLayer::Reset (from C++) or Net.set_input_arrays (from Python) in order to specify a source of contiguous data (as 4D row major array), which is read one batch-sized chunk at a time.
    
@@ -72,12 +72,12 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    message MemoryDataParameter {
-  optional uint32 batch_size = 1;
-  optional uint32 channels = 2;
-  optional uint32 height = 3;
-  optional uint32 width = 4;
-}
    
+
    message MemoryDataParameter {
+  optional uint32 batch_size = 1;
+  optional uint32 channels = 2;
+  optional uint32 height = 3;
+  optional uint32 width = 4;
+}
    
 
 
 
 
    Parameters
    
@@ -70,34 +70,34 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    // Message that stores parameters shared by loss layers
-message LossParameter {
-  // If specified, ignore instances with the given label.
-  optional int32 ignore_label = 1;
-  // How to normalize the loss for loss layers that aggregate across batches,
-  // spatial dimensions, or other dimensions.  Currently only implemented in
-  // SoftmaxWithLoss and SigmoidCrossEntropyLoss layers.
-  enum NormalizationMode {
-    // Divide by the number of examples in the batch times spatial dimensions.
-    // Outputs that receive the ignore label will NOT be ignored in computing
-    // the normalization factor.
-    FULL = 0;
-    // Divide by the total number of output locations that do not take the
-    // ignore_label.  If ignore_label is not set, this behaves like FULL.
-    VALID = 1;
-    // Divide by the batch size.
-    BATCH_SIZE = 2;
-    // Do not normalize the loss.
-    NONE = 3;
-  }
-  // For historical reasons, the default normalization for
-  // SigmoidCrossEntropyLoss is BATCH_SIZE and *not* VALID.
-  optional NormalizationMode normalization = 3 [default = VALID];
-  // Deprecated.  Ignored if normalization is specified.  If normalization
-  // is not specified, then setting this to false will be equivalent to
-  // normalization = BATCH_SIZE to be consistent with previous behavior.
-  optional bool normalize = 2;
-}
    
+
    // Message that stores parameters shared by loss layers
+message LossParameter {
+  // If specified, ignore instances with the given label.
+  optional int32 ignore_label = 1;
+  // How to normalize the loss for loss layers that aggregate across batches,
+  // spatial dimensions, or other dimensions.  Currently only implemented in
+  // SoftmaxWithLoss and SigmoidCrossEntropyLoss layers.
+  enum NormalizationMode {
+    // Divide by the number of examples in the batch times spatial dimensions.
+    // Outputs that receive the ignore label will NOT be ignored in computing
+    // the normalization factor.
+    FULL = 0;
+    // Divide by the total number of output locations that do not take the
+    // ignore_label.  If ignore_label is not set, this behaves like FULL.
+    VALID = 1;
+    // Divide by the batch size.
+    BATCH_SIZE = 2;
+    // Do not normalize the loss.
+    NONE = 3;
+  }
+  // For historical reasons, the default normalization for
+  // SigmoidCrossEntropyLoss is BATCH_SIZE and *not* VALID.
+  optional NormalizationMode normalization = 3 [default = VALID];
+  // Deprecated.  Ignored if normalization is specified.  If normalization
+  // is not specified, then setting this to false will be equivalent to
+  // normalization = BATCH_SIZE to be consistent with previous behavior.
+  optional bool normalize = 2;
+}
    
 
 
 
diff --git a/tutorial/layers/mvn.html b/tutorial/layers/mvn.html
index 5aa125a33a1..f9a71dcbb51 100644
--- a/tutorial/layers/mvn.html
+++ b/tutorial/layers/mvn.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -71,16 +71,16 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    message MVNParameter {
-  // This parameter can be set to false to normalize mean only
-  optional bool normalize_variance = 1 [default = true];
+
    message MVNParameter {
+  // This parameter can be set to false to normalize mean only
+  optional bool normalize_variance = 1 [default = true];
 
-  // This parameter can be set to true to perform DNN-like MVN
-  optional bool across_channels = 2 [default = false];
+  // This parameter can be set to true to perform DNN-like MVN
+  optional bool across_channels = 2 [default = false];
 
-  // Epsilon for not dividing by zero while normalizing variance
-  optional float eps = 3 [default = 1e-9];
-}
    
+  // Epsilon for not dividing by zero while normalizing variance
+  optional float eps = 3 [default = 1e-9];
+}
    
 
 
 
diff --git a/tutorial/layers/parameter.html b/tutorial/layers/parameter.html
index 8da63b86525..d45fa4a92c6 100644
--- a/tutorial/layers/parameter.html
+++ b/tutorial/layers/parameter.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -72,9 +72,9 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    message ParameterParameter {
-  optional BlobShape shape = 1;
-}
    
+
    message ParameterParameter {
+  optional BlobShape shape = 1;
+}
    
 
 
 
diff --git a/tutorial/layers/pooling.html b/tutorial/layers/pooling.html
index 07e6ff0d9b3..7784aae4e2e 100644
--- a/tutorial/layers/pooling.html
+++ b/tutorial/layers/pooling.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -98,34 +98,34 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    message PoolingParameter {
-  enum PoolMethod {
-    MAX = 0;
-    AVE = 1;
-    STOCHASTIC = 2;
-  }
-  optional PoolMethod pool = 1 [default = MAX]; // The pooling method
-  // Pad, kernel size, and stride are all given as a single value for equal
-  // dimensions in height and width or as Y, X pairs.
-  optional uint32 pad = 4 [default = 0]; // The padding size (equal in Y, X)
-  optional uint32 pad_h = 9 [default = 0]; // The padding height
-  optional uint32 pad_w = 10 [default = 0]; // The padding width
-  optional uint32 kernel_size = 2; // The kernel size (square)
-  optional uint32 kernel_h = 5; // The kernel height
-  optional uint32 kernel_w = 6; // The kernel width
-  optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X)
-  optional uint32 stride_h = 7; // The stride height
-  optional uint32 stride_w = 8; // The stride width
-  enum Engine {
-    DEFAULT = 0;
-    CAFFE = 1;
-    CUDNN = 2;
-  }
-  optional Engine engine = 11 [default = DEFAULT];
-  // If global_pooling then it will pool over the size of the bottom by doing
-  // kernel_h = bottom->height and kernel_w = bottom->width
-  optional bool global_pooling = 12 [default = false];
-}
    
+
    message PoolingParameter {
+  enum PoolMethod {
+    MAX = 0;
+    AVE = 1;
+    STOCHASTIC = 2;
+  }
+  optional PoolMethod pool = 1 [default = MAX]; // The pooling method
+  // Pad, kernel size, and stride are all given as a single value for equal
+  // dimensions in height and width or as Y, X pairs.
+  optional uint32 pad = 4 [default = 0]; // The padding size (equal in Y, X)
+  optional uint32 pad_h = 9 [default = 0]; // The padding height
+  optional uint32 pad_w = 10 [default = 0]; // The padding width
+  optional uint32 kernel_size = 2; // The kernel size (square)
+  optional uint32 kernel_h = 5; // The kernel height
+  optional uint32 kernel_w = 6; // The kernel width
+  optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X)
+  optional uint32 stride_h = 7; // The stride height
+  optional uint32 stride_w = 8; // The stride width
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 11 [default = DEFAULT];
+  // If global_pooling then it will pool over the size of the bottom by doing
+  // kernel_h = bottom->height and kernel_w = bottom->width
+  optional bool global_pooling = 12 [default = false];
+}
    
 
 
    Sample
    
 
      
diff --git a/tutorial/layers/power.html b/tutorial/layers/power.html
index 11f59141c19..6b32d905e62 100644
--- a/tutorial/layers/power.html
+++ b/tutorial/layers/power.html
@@ -36,7 +36,7 @@
       
      
         
      Caffe
      
         
      
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
      
         
      
           Created by
@@ -82,12 +82,12 @@ 
      Parameters
      
   
    • From ./src/caffe/proto/caffe.proto:
      • 
 
    
 
-
    message PowerParameter {
-  // PowerLayer computes outputs y = (shift + scale * x) ^ power.
-  optional float power = 1 [default = 1.0];
-  optional float scale = 2 [default = 1.0];
-  optional float shift = 3 [default = 0.0];
-}
    
+
    message PowerParameter {
+  // PowerLayer computes outputs y = (shift + scale * x) ^ power.
+  optional float power = 1 [default = 1.0];
+  optional float scale = 2 [default = 1.0];
+  optional float shift = 3 [default = 0.0];
+}
    
 
 
    Sample
    
 
diff --git a/tutorial/layers/prelu.html b/tutorial/layers/prelu.html
index 0afe36d323e..30bca82d778 100644
--- a/tutorial/layers/prelu.html
+++ b/tutorial/layers/prelu.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -71,15 +71,15 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    message PReLUParameter {
-  // Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:
-  // Surpassing Human-Level Performance on ImageNet Classification, 2015.
-
-  // Initial value of a_i. Default is a_i=0.25 for all i.
-  optional FillerParameter filler = 1;
-  // Whether or not slope parameters are shared across channels.
-  optional bool channel_shared = 2 [default = false];
-}
    
+
    message PReLUParameter {
+  // Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:
+  // Surpassing Human-Level Performance on ImageNet Classification, 2015.
+
+  // Initial value of a_i. Default is a_i=0.25 for all i.
+  optional FillerParameter filler = 1;
+  // Whether or not slope parameters are shared across channels.
+  optional bool channel_shared = 2 [default = false];
+}
    
 
 
 
diff --git a/tutorial/layers/python.html b/tutorial/layers/python.html
index 162c1cafe8c..928b823b5ef 100644
--- a/tutorial/layers/python.html
+++ b/tutorial/layers/python.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -71,19 +71,17 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    message PythonParameter {
-  optional string module = 1;
-  optional string layer = 2;
-  // This value is set to the attribute `param_str` of the `PythonLayer` object
-  // in Python before calling the `setup()` method. This could be a number,
-  // string, dictionary in Python dict format, JSON, etc. You may parse this
-  // string in `setup` method and use it in `forward` and `backward`.
-  optional string param_str = 3 [default = ''];
-  // Whether this PythonLayer is shared among worker solvers during data parallelism.
-  // If true, each worker solver sequentially run forward from this layer.
-  // This value should be set true if you are using it as a data layer.
-  optional bool share_in_parallel = 4 [default = false];
-}
    
+
    message PythonParameter {
+  optional string module = 1;
+  optional string layer = 2;
+  // This value is set to the attribute `param_str` of the `PythonLayer` object
+  // in Python before calling the `setup()` method. This could be a number,
+  // string, dictionary in Python dict format, JSON, etc. You may parse this
+  // string in `setup` method and use it in `forward` and `backward`.
+  optional string param_str = 3 [default = ''];
+  // DEPRECATED
+  optional bool share_in_parallel = 4 [default = false];
+}
    
 
 
    Examples and tutorials
    
 
diff --git a/tutorial/layers/recurrent.html b/tutorial/layers/recurrent.html
index c81774e95bc..13641a53100 100644
--- a/tutorial/layers/recurrent.html
+++ b/tutorial/layers/recurrent.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -71,24 +71,24 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    // Message that stores parameters used by RecurrentLayer
-message RecurrentParameter {
-  // The dimension of the output (and usually hidden state) representation --
-  // must be explicitly set to non-zero.
-  optional uint32 num_output = 1 [default = 0];
+
    // Message that stores parameters used by RecurrentLayer
+message RecurrentParameter {
+  // The dimension of the output (and usually hidden state) representation --
+  // must be explicitly set to non-zero.
+  optional uint32 num_output = 1 [default = 0];
 
-  optional FillerParameter weight_filler = 2; // The filler for the weight
-  optional FillerParameter bias_filler = 3; // The filler for the bias
+  optional FillerParameter weight_filler = 2; // The filler for the weight
+  optional FillerParameter bias_filler = 3; // The filler for the bias
+
+  // Whether to enable displaying debug_info in the unrolled recurrent net.
+  optional bool debug_info = 4 [default = false];
 
-  // Whether to enable displaying debug_info in the unrolled recurrent net.
-  optional bool debug_info = 4 [default = false];
-
-  // Whether to add as additional inputs (bottoms) the initial hidden state
-  // blobs, and add as additional outputs (tops) the final timestep hidden state
-  // blobs.  The number of additional bottom/top blobs required depends on the
-  // recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.
-  optional bool expose_hidden = 5 [default = false];
-}
    
+  // Whether to add as additional inputs (bottoms) the initial hidden state
+  // blobs, and add as additional outputs (tops) the final timestep hidden state
+  // blobs.  The number of additional bottom/top blobs required depends on the
+  // recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.
+  optional bool expose_hidden = 5 [default = false];
+}
    
 
 
 
diff --git a/tutorial/layers/reduction.html b/tutorial/layers/reduction.html
index ead0f1fff7a..38ab174d065 100644
--- a/tutorial/layers/reduction.html
+++ b/tutorial/layers/reduction.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -71,34 +71,34 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    // Message that stores parameters used by ReductionLayer
-message ReductionParameter {
-  enum ReductionOp {
-    SUM = 1;
-    ASUM = 2;
-    SUMSQ = 3;
-    MEAN = 4;
-  }
+
    // Message that stores parameters used by ReductionLayer
+message ReductionParameter {
+  enum ReductionOp {
+    SUM = 1;
+    ASUM = 2;
+    SUMSQ = 3;
+    MEAN = 4;
+  }
 
-  optional ReductionOp operation = 1 [default = SUM]; // reduction operation
+  optional ReductionOp operation = 1 [default = SUM]; // reduction operation
+
+  // The first axis to reduce to a scalar -- may be negative to index from the
+  // end (e.g., -1 for the last axis).
+  // (Currently, only reduction along ALL "tail" axes is supported; reduction
+  // of axis M through N, where N < num_axes - 1, is unsupported.)
+  // Suppose we have an n-axis bottom Blob with shape:
+  //     (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)).
+  // If axis == m, the output Blob will have shape
+  //     (d0, d1, d2, ..., d(m-1)),
+  // and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1))
+  // times, each including (dm * d(m+1) * ... * d(n-1)) individual data.
+  // If axis == 0 (the default), the output Blob always has the empty shape
+  // (count 1), performing reduction across the entire input --
+  // often useful for creating new loss functions.
+  optional int32 axis = 2 [default = 0];
 
-  // The first axis to reduce to a scalar -- may be negative to index from the
-  // end (e.g., -1 for the last axis).
-  // (Currently, only reduction along ALL "tail" axes is supported; reduction
-  // of axis M through N, where N < num_axes - 1, is unsupported.)
-  // Suppose we have an n-axis bottom Blob with shape:
-  //     (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)).
-  // If axis == m, the output Blob will have shape
-  //     (d0, d1, d2, ..., d(m-1)),
-  // and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1))
-  // times, each including (dm * d(m+1) * ... * d(n-1)) individual data.
-  // If axis == 0 (the default), the output Blob always has the empty shape
-  // (count 1), performing reduction across the entire input --
-  // often useful for creating new loss functions.
-  optional int32 axis = 2 [default = 0];
-
-  optional float coeff = 3 [default = 1.0]; // coefficient for output
-}
    
+  optional float coeff = 3 [default = 1.0]; // coefficient for output
+}
    
 
 
 
diff --git a/tutorial/layers/relu.html b/tutorial/layers/relu.html
index 1fdbfbbed78..1714ef77d35 100644
--- a/tutorial/layers/relu.html
+++ b/tutorial/layers/relu.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -93,21 +93,21 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    // Message that stores parameters used by ReLULayer
-message ReLUParameter {
-  // Allow non-zero slope for negative inputs to speed up optimization
-  // Described in:
-  // Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities
-  // improve neural network acoustic models. In ICML Workshop on Deep Learning
-  // for Audio, Speech, and Language Processing.
-  optional float negative_slope = 1 [default = 0];
-  enum Engine {
-    DEFAULT = 0;
-    CAFFE = 1;
-    CUDNN = 2;
-  }
-  optional Engine engine = 2 [default = DEFAULT];
-}
    
+
    // Message that stores parameters used by ReLULayer
+message ReLUParameter {
+  // Allow non-zero slope for negative inputs to speed up optimization
+  // Described in:
+  // Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities
+  // improve neural network acoustic models. In ICML Workshop on Deep Learning
+  // for Audio, Speech, and Language Processing.
+  optional float negative_slope = 1 [default = 0];
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 2 [default = DEFAULT];
+}
    
 
 
 
diff --git a/tutorial/layers/reshape.html b/tutorial/layers/reshape.html
index 90bee0588fc..0cbe1d39c25 100644
--- a/tutorial/layers/reshape.html
+++ b/tutorial/layers/reshape.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -120,69 +120,69 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    message ReshapeParameter {
-  // Specify the output dimensions. If some of the dimensions are set to 0,
-  // the corresponding dimension from the bottom layer is used (unchanged).
-  // Exactly one dimension may be set to -1, in which case its value is
-  // inferred from the count of the bottom blob and the remaining dimensions.
-  // For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8:
-  //
-  //   layer {
-  //     type: "Reshape" bottom: "input" top: "output"
-  //     reshape_param { ... }
-  //   }
-  //
-  // If "input" is 2D with shape 2 x 8, then the following reshape_param
-  // specifications are all equivalent, producing a 3D blob "output" with shape
-  // 2 x 2 x 4:
-  //
-  //   reshape_param { shape { dim:  2  dim: 2  dim:  4 } }
-  //   reshape_param { shape { dim:  0  dim: 2  dim:  4 } }
-  //   reshape_param { shape { dim:  0  dim: 2  dim: -1 } }
-  //   reshape_param { shape { dim:  0  dim:-1  dim:  4 } }
-  //
-  optional BlobShape shape = 1;
-
-  // axis and num_axes control the portion of the bottom blob's shape that are
-  // replaced by (included in) the reshape. By default (axis == 0 and
-  // num_axes == -1), the entire bottom blob shape is included in the reshape,
-  // and hence the shape field must specify the entire output shape.
-  //
-  // axis may be non-zero to retain some portion of the beginning of the input
-  // shape (and may be negative to index from the end; e.g., -1 to begin the
-  // reshape after the last axis, including nothing in the reshape,
-  // -2 to include only the last axis, etc.).
-  //
-  // For example, suppose "input" is a 2D blob with shape 2 x 8.
-  // Then the following ReshapeLayer specifications are all equivalent,
-  // producing a blob "output" with shape 2 x 2 x 4:
-  //
-  //   reshape_param { shape { dim: 2  dim: 2  dim: 4 } }
-  //   reshape_param { shape { dim: 2  dim: 4 } axis:  1 }
-  //   reshape_param { shape { dim: 2  dim: 4 } axis: -3 }
-  //
-  // num_axes specifies the extent of the reshape.
-  // If num_axes >= 0 (and axis >= 0), the reshape will be performed only on
-  // input axes in the range [axis, axis+num_axes].
-  // num_axes may also be -1, the default, to include all remaining axes
-  // (starting from axis).
-  //
-  // For example, suppose "input" is a 2D blob with shape 2 x 8.
-  // Then the following ReshapeLayer specifications are equivalent,
-  // producing a blob "output" with shape 1 x 2 x 8.
-  //
-  //   reshape_param { shape { dim:  1  dim: 2  dim:  8 } }
-  //   reshape_param { shape { dim:  1  dim: 2  }  num_axes: 1 }
-  //   reshape_param { shape { dim:  1  }  num_axes: 0 }
-  //
-  // On the other hand, these would produce output blob shape 2 x 1 x 8:
-  //
-  //   reshape_param { shape { dim: 2  dim: 1  dim: 8  }  }
-  //   reshape_param { shape { dim: 1 }  axis: 1  num_axes: 0 }
-  //
-  optional int32 axis = 2 [default = 0];
-  optional int32 num_axes = 3 [default = -1];
-}
    
+
    message ReshapeParameter {
+  // Specify the output dimensions. If some of the dimensions are set to 0,
+  // the corresponding dimension from the bottom layer is used (unchanged).
+  // Exactly one dimension may be set to -1, in which case its value is
+  // inferred from the count of the bottom blob and the remaining dimensions.
+  // For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8:
+  //
+  //   layer {
+  //     type: "Reshape" bottom: "input" top: "output"
+  //     reshape_param { ... }
+  //   }
+  //
+  // If "input" is 2D with shape 2 x 8, then the following reshape_param
+  // specifications are all equivalent, producing a 3D blob "output" with shape
+  // 2 x 2 x 4:
+  //
+  //   reshape_param { shape { dim:  2  dim: 2  dim:  4 } }
+  //   reshape_param { shape { dim:  0  dim: 2  dim:  4 } }
+  //   reshape_param { shape { dim:  0  dim: 2  dim: -1 } }
+  //   reshape_param { shape { dim:  0  dim:-1  dim:  4 } }
+  //
+  optional BlobShape shape = 1;
+
+  // axis and num_axes control the portion of the bottom blob's shape that are
+  // replaced by (included in) the reshape. By default (axis == 0 and
+  // num_axes == -1), the entire bottom blob shape is included in the reshape,
+  // and hence the shape field must specify the entire output shape.
+  //
+  // axis may be non-zero to retain some portion of the beginning of the input
+  // shape (and may be negative to index from the end; e.g., -1 to begin the
+  // reshape after the last axis, including nothing in the reshape,
+  // -2 to include only the last axis, etc.).
+  //
+  // For example, suppose "input" is a 2D blob with shape 2 x 8.
+  // Then the following ReshapeLayer specifications are all equivalent,
+  // producing a blob "output" with shape 2 x 2 x 4:
+  //
+  //   reshape_param { shape { dim: 2  dim: 2  dim: 4 } }
+  //   reshape_param { shape { dim: 2  dim: 4 } axis:  1 }
+  //   reshape_param { shape { dim: 2  dim: 4 } axis: -3 }
+  //
+  // num_axes specifies the extent of the reshape.
+  // If num_axes >= 0 (and axis >= 0), the reshape will be performed only on
+  // input axes in the range [axis, axis+num_axes].
+  // num_axes may also be -1, the default, to include all remaining axes
+  // (starting from axis).
+  //
+  // For example, suppose "input" is a 2D blob with shape 2 x 8.
+  // Then the following ReshapeLayer specifications are equivalent,
+  // producing a blob "output" with shape 1 x 2 x 8.
+  //
+  //   reshape_param { shape { dim:  1  dim: 2  dim:  8 } }
+  //   reshape_param { shape { dim:  1  dim: 2  }  num_axes: 1 }
+  //   reshape_param { shape { dim:  1  }  num_axes: 0 }
+  //
+  // On the other hand, these would produce output blob shape 2 x 1 x 8:
+  //
+  //   reshape_param { shape { dim: 2  dim: 1  dim: 8  }  }
+  //   reshape_param { shape { dim: 1 }  axis: 1  num_axes: 0 }
+  //
+  optional int32 axis = 2 [default = 0];
+  optional int32 num_axes = 3 [default = -1];
+}
    
 
 
 
diff --git a/tutorial/layers/rnn.html b/tutorial/layers/rnn.html
index 1e82e0870ae..d26f3ca6c20 100644
--- a/tutorial/layers/rnn.html
+++ b/tutorial/layers/rnn.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -70,24 +70,24 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    // Message that stores parameters used by RecurrentLayer
-message RecurrentParameter {
-  // The dimension of the output (and usually hidden state) representation --
-  // must be explicitly set to non-zero.
-  optional uint32 num_output = 1 [default = 0];
+
    // Message that stores parameters used by RecurrentLayer
+message RecurrentParameter {
+  // The dimension of the output (and usually hidden state) representation --
+  // must be explicitly set to non-zero.
+  optional uint32 num_output = 1 [default = 0];
 
-  optional FillerParameter weight_filler = 2; // The filler for the weight
-  optional FillerParameter bias_filler = 3; // The filler for the bias
+  optional FillerParameter weight_filler = 2; // The filler for the weight
+  optional FillerParameter bias_filler = 3; // The filler for the bias
+
+  // Whether to enable displaying debug_info in the unrolled recurrent net.
+  optional bool debug_info = 4 [default = false];
 
-  // Whether to enable displaying debug_info in the unrolled recurrent net.
-  optional bool debug_info = 4 [default = false];
-
-  // Whether to add as additional inputs (bottoms) the initial hidden state
-  // blobs, and add as additional outputs (tops) the final timestep hidden state
-  // blobs.  The number of additional bottom/top blobs required depends on the
-  // recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.
-  optional bool expose_hidden = 5 [default = false];
-}
    
+  // Whether to add as additional inputs (bottoms) the initial hidden state
+  // blobs, and add as additional outputs (tops) the final timestep hidden state
+  // blobs.  The number of additional bottom/top blobs required depends on the
+  // recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.
+  optional bool expose_hidden = 5 [default = false];
+}
    
 
 
 
diff --git a/tutorial/layers/scale.html b/tutorial/layers/scale.html
index 9ab2970015f..4d6e9c504a4 100644
--- a/tutorial/layers/scale.html
+++ b/tutorial/layers/scale.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -71,42 +71,42 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    message ScaleParameter {
-  // The first axis of bottom[0] (the first input Blob) along which to apply
-  // bottom[1] (the second input Blob).  May be negative to index from the end
-  // (e.g., -1 for the last axis).
-  //
-  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
-  // top[0] will have the same shape, and bottom[1] may have any of the
-  // following shapes (for the given value of axis):
-  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
-  //    (axis == 1 == -3)          3;     3x40;     3x40x60
-  //    (axis == 2 == -2)                   40;       40x60
-  //    (axis == 3 == -1)                                60
-  // Furthermore, bottom[1] may have the empty shape (regardless of the value of
-  // "axis") -- a scalar multiplier.
-  optional int32 axis = 1 [default = 1];
+
    message ScaleParameter {
+  // The first axis of bottom[0] (the first input Blob) along which to apply
+  // bottom[1] (the second input Blob).  May be negative to index from the end
+  // (e.g., -1 for the last axis).
+  //
+  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
+  // top[0] will have the same shape, and bottom[1] may have any of the
+  // following shapes (for the given value of axis):
+  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
+  //    (axis == 1 == -3)          3;     3x40;     3x40x60
+  //    (axis == 2 == -2)                   40;       40x60
+  //    (axis == 3 == -1)                                60
+  // Furthermore, bottom[1] may have the empty shape (regardless of the value of
+  // "axis") -- a scalar multiplier.
+  optional int32 axis = 1 [default = 1];
 
-  // (num_axes is ignored unless just one bottom is given and the scale is
-  // a learned parameter of the layer.  Otherwise, num_axes is determined by the
-  // number of axes by the second bottom.)
-  // The number of axes of the input (bottom[0]) covered by the scale
-  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
-  // Set num_axes := 0, to multiply with a zero-axis Blob: a scalar.
-  optional int32 num_axes = 2 [default = 1];
+  // (num_axes is ignored unless just one bottom is given and the scale is
+  // a learned parameter of the layer.  Otherwise, num_axes is determined by the
+  // number of axes by the second bottom.)
+  // The number of axes of the input (bottom[0]) covered by the scale
+  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
+  // Set num_axes := 0, to multiply with a zero-axis Blob: a scalar.
+  optional int32 num_axes = 2 [default = 1];
 
-  // (filler is ignored unless just one bottom is given and the scale is
-  // a learned parameter of the layer.)
-  // The initialization for the learned scale parameter.
-  // Default is the unit (1) initialization, resulting in the ScaleLayer
-  // initially performing the identity operation.
-  optional FillerParameter filler = 3;
+  // (filler is ignored unless just one bottom is given and the scale is
+  // a learned parameter of the layer.)
+  // The initialization for the learned scale parameter.
+  // Default is the unit (1) initialization, resulting in the ScaleLayer
+  // initially performing the identity operation.
+  optional FillerParameter filler = 3;
 
-  // Whether to also learn a bias (equivalent to a ScaleLayer+BiasLayer, but
-  // may be more efficient).  Initialized with bias_filler (defaults to 0).
-  optional bool bias_term = 4 [default = false];
-  optional FillerParameter bias_filler = 5;
-}
    
+  // Whether to also learn a bias (equivalent to a ScaleLayer+BiasLayer, but
+  // may be more efficient).  Initialized with bias_filler (defaults to 0).
+  optional bool bias_term = 4 [default = false];
+  optional FillerParameter bias_filler = 5;
+}
    
 
 
 
diff --git a/tutorial/layers/sigmoid.html b/tutorial/layers/sigmoid.html
index 995f8662221..498acdffdc4 100644
--- a/tutorial/layers/sigmoid.html
+++ b/tutorial/layers/sigmoid.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -62,8 +62,22 @@ 
    Sigmoid Layer
    
   
  • Header: ./include/caffe/layers/sigmoid_layer.hpp
    • 
   
  • CPU implementation: ./src/caffe/layers/sigmoid_layer.cpp
    • 
   
  • CUDA GPU implementation: ./src/caffe/layers/sigmoid_layer.cu
    • 
+  
  • 
+    
    Example (from ./examples/mnist/mnist_autoencoder.prototxt):
    
+
+    
    layer {
+  name: "encode1neuron"
+  bottom: "encode1"
+  top: "encode1neuron"
+  type: "Sigmoid"
+}
+
    
+    
    
+  
    • 
 
 
+
    The Sigmoid layer computes sigmoid(x) for each element x in the bottom blob.
    
+
 
    Parameters
    
 
 
 
-
    message SigmoidParameter {
-  enum Engine {
-    DEFAULT = 0;
-    CAFFE = 1;
-    CUDNN = 2;
-  }
-  optional Engine engine = 1 [default = DEFAULT];
-}
    
+
    message SigmoidParameter {
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 1 [default = DEFAULT];
+}
    
 
 
 
diff --git a/tutorial/layers/sigmoidcrossentropyloss.html b/tutorial/layers/sigmoidcrossentropyloss.html
index feed41fb1f1..1d63a724754 100644
--- a/tutorial/layers/sigmoidcrossentropyloss.html
+++ b/tutorial/layers/sigmoidcrossentropyloss.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
diff --git a/tutorial/layers/silence.html b/tutorial/layers/silence.html
index b109c64b424..416b9a9fb0f 100644
--- a/tutorial/layers/silence.html
+++ b/tutorial/layers/silence.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -68,23 +68,7 @@ 
    Silence Layer
    
 
 
    Parameters
    
 
-
-
-
    message BatchNormParameter {
-  // If false, accumulate global mean/variance values via a moving average. If
-  // true, use those accumulated values instead of computing mean/variance
-  // across the batch.
-  optional bool use_global_stats = 1;
-  // How much does the moving average decay each iteration?
-  optional float moving_average_fraction = 2 [default = .999];
-  // Small value to add to the variance estimate so that we don't divide by
-  // zero.
-  optional float eps = 3 [default = 1e-5];
-}
    
-
+
    No parameters.
    
 
 
       
diff --git a/tutorial/layers/slice.html b/tutorial/layers/slice.html
index d9f2ceab083..d15bb56da24 100644
--- a/tutorial/layers/slice.html
+++ b/tutorial/layers/slice.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -98,16 +98,16 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    message SliceParameter {
-  // The axis along which to slice -- may be negative to index from the end
-  // (e.g., -1 for the last axis).
-  // By default, SliceLayer concatenates blobs along the "channels" axis (1).
-  optional int32 axis = 3 [default = 1];
-  repeated uint32 slice_point = 2;
+
    message SliceParameter {
+  // The axis along which to slice -- may be negative to index from the end
+  // (e.g., -1 for the last axis).
+  // By default, SliceLayer concatenates blobs along the "channels" axis (1).
+  optional int32 axis = 3 [default = 1];
+  repeated uint32 slice_point = 2;
 
-  // DEPRECATED: alias for "axis" -- does not support negative indexing.
-  optional uint32 slice_dim = 1 [default = 1];
-}
    
+  // DEPRECATED: alias for "axis" -- does not support negative indexing.
+  optional uint32 slice_dim = 1 [default = 1];
+}
    
 
 
 
diff --git a/tutorial/layers/softmax.html b/tutorial/layers/softmax.html
index 8f78f711eb5..fc6978b7faa 100644
--- a/tutorial/layers/softmax.html
+++ b/tutorial/layers/softmax.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -71,20 +71,20 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    // Message that stores parameters used by SoftmaxLayer, SoftmaxWithLossLayer
-message SoftmaxParameter {
-  enum Engine {
-    DEFAULT = 0;
-    CAFFE = 1;
-    CUDNN = 2;
-  }
-  optional Engine engine = 1 [default = DEFAULT];
+
    // Message that stores parameters used by SoftmaxLayer, SoftmaxWithLossLayer
+message SoftmaxParameter {
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 1 [default = DEFAULT];
 
-  // The axis along which to perform the softmax -- may be negative to index
-  // from the end (e.g., -1 for the last axis).
-  // Any other axes will be evaluated as independent softmaxes.
-  optional int32 axis = 2 [default = 1];
-}
    
+  // The axis along which to perform the softmax -- may be negative to index
+  // from the end (e.g., -1 for the last axis).
+  // Any other axes will be evaluated as independent softmaxes.
+  optional int32 axis = 2 [default = 1];
+}
    
 
 
    See also
    
 
diff --git a/tutorial/layers/softmaxwithloss.html b/tutorial/layers/softmaxwithloss.html
index 5bee89450de..5f0a557a943 100644
--- a/tutorial/layers/softmaxwithloss.html
+++ b/tutorial/layers/softmaxwithloss.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -73,54 +73,54 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    // Message that stores parameters used by SoftmaxLayer, SoftmaxWithLossLayer
-message SoftmaxParameter {
-  enum Engine {
-    DEFAULT = 0;
-    CAFFE = 1;
-    CUDNN = 2;
-  }
-  optional Engine engine = 1 [default = DEFAULT];
-
-  // The axis along which to perform the softmax -- may be negative to index
-  // from the end (e.g., -1 for the last axis).
-  // Any other axes will be evaluated as independent softmaxes.
-  optional int32 axis = 2 [default = 1];
-}
    
+
    // Message that stores parameters used by SoftmaxLayer, SoftmaxWithLossLayer
+message SoftmaxParameter {
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 1 [default = DEFAULT];
+
+  // The axis along which to perform the softmax -- may be negative to index
+  // from the end (e.g., -1 for the last axis).
+  // Any other axes will be evaluated as independent softmaxes.
+  optional int32 axis = 2 [default = 1];
+}
    
 
 
 
-
    // Message that stores parameters shared by loss layers
-message LossParameter {
-  // If specified, ignore instances with the given label.
-  optional int32 ignore_label = 1;
-  // How to normalize the loss for loss layers that aggregate across batches,
-  // spatial dimensions, or other dimensions.  Currently only implemented in
-  // SoftmaxWithLoss and SigmoidCrossEntropyLoss layers.
-  enum NormalizationMode {
-    // Divide by the number of examples in the batch times spatial dimensions.
-    // Outputs that receive the ignore label will NOT be ignored in computing
-    // the normalization factor.
-    FULL = 0;
-    // Divide by the total number of output locations that do not take the
-    // ignore_label.  If ignore_label is not set, this behaves like FULL.
-    VALID = 1;
-    // Divide by the batch size.
-    BATCH_SIZE = 2;
-    // Do not normalize the loss.
-    NONE = 3;
-  }
-  // For historical reasons, the default normalization for
-  // SigmoidCrossEntropyLoss is BATCH_SIZE and *not* VALID.
-  optional NormalizationMode normalization = 3 [default = VALID];
-  // Deprecated.  Ignored if normalization is specified.  If normalization
-  // is not specified, then setting this to false will be equivalent to
-  // normalization = BATCH_SIZE to be consistent with previous behavior.
-  optional bool normalize = 2;
-}
    
+
    // Message that stores parameters shared by loss layers
+message LossParameter {
+  // If specified, ignore instances with the given label.
+  optional int32 ignore_label = 1;
+  // How to normalize the loss for loss layers that aggregate across batches,
+  // spatial dimensions, or other dimensions.  Currently only implemented in
+  // SoftmaxWithLoss and SigmoidCrossEntropyLoss layers.
+  enum NormalizationMode {
+    // Divide by the number of examples in the batch times spatial dimensions.
+    // Outputs that receive the ignore label will NOT be ignored in computing
+    // the normalization factor.
+    FULL = 0;
+    // Divide by the total number of output locations that do not take the
+    // ignore_label.  If ignore_label is not set, this behaves like FULL.
+    VALID = 1;
+    // Divide by the batch size.
+    BATCH_SIZE = 2;
+    // Do not normalize the loss.
+    NONE = 3;
+  }
+  // For historical reasons, the default normalization for
+  // SigmoidCrossEntropyLoss is BATCH_SIZE and *not* VALID.
+  optional NormalizationMode normalization = 3 [default = VALID];
+  // Deprecated.  Ignored if normalization is specified.  If normalization
+  // is not specified, then setting this to false will be equivalent to
+  // normalization = BATCH_SIZE to be consistent with previous behavior.
+  optional bool normalize = 2;
+}
    
 
 
    See also
    
 
diff --git a/tutorial/layers/split.html b/tutorial/layers/split.html
index 0d2ba9c0adb..35ada75abbc 100644
--- a/tutorial/layers/split.html
+++ b/tutorial/layers/split.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
diff --git a/tutorial/layers/spp.html b/tutorial/layers/spp.html
index f46cbd87c6f..5a71ade0704 100644
--- a/tutorial/layers/spp.html
+++ b/tutorial/layers/spp.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -70,21 +70,21 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    message SPPParameter {
-  enum PoolMethod {
-    MAX = 0;
-    AVE = 1;
-    STOCHASTIC = 2;
-  }
-  optional uint32 pyramid_height = 1;
-  optional PoolMethod pool = 2 [default = MAX]; // The pooling method
-  enum Engine {
-    DEFAULT = 0;
-    CAFFE = 1;
-    CUDNN = 2;
-  }
-  optional Engine engine = 6 [default = DEFAULT];
-}
    
+
    message SPPParameter {
+  enum PoolMethod {
+    MAX = 0;
+    AVE = 1;
+    STOCHASTIC = 2;
+  }
+  optional uint32 pyramid_height = 1;
+  optional PoolMethod pool = 2 [default = MAX]; // The pooling method
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 6 [default = DEFAULT];
+}
    
 
 
 
diff --git a/tutorial/layers/tanh.html b/tutorial/layers/tanh.html
index 442331700d3..fa591027faf 100644
--- a/tutorial/layers/tanh.html
+++ b/tutorial/layers/tanh.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -69,14 +69,14 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    message TanHParameter {
-  enum Engine {
-    DEFAULT = 0;
-    CAFFE = 1;
-    CUDNN = 2;
-  }
-  optional Engine engine = 1 [default = DEFAULT];
-}
    
+
    message TanHParameter {
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 1 [default = DEFAULT];
+}
    
 
 
 
diff --git a/tutorial/layers/threshold.html b/tutorial/layers/threshold.html
index 00c91c9b7a2..ec5f5a955fd 100644
--- a/tutorial/layers/threshold.html
+++ b/tutorial/layers/threshold.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -69,10 +69,10 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    // Message that stores parameters used by ThresholdLayer
-message ThresholdParameter {
-  optional float threshold = 1 [default = 0]; // Strictly positive values
-}
    
+
    // Message that stores parameters used by ThresholdLayer
+message ThresholdParameter {
+  optional float threshold = 1 [default = 0]; // Strictly positive values
+}
    
 
 
 
diff --git a/tutorial/layers/tile.html b/tutorial/layers/tile.html
index da508757d34..d769ed04ef3 100644
--- a/tutorial/layers/tile.html
+++ b/tutorial/layers/tile.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -71,14 +71,14 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    // Message that stores parameters used by TileLayer
-message TileParameter {
-  // The index of the axis to tile.
-  optional int32 axis = 1 [default = 1];
+
    // Message that stores parameters used by TileLayer
+message TileParameter {
+  // The index of the axis to tile.
+  optional int32 axis = 1 [default = 1];
 
-  // The number of copies (tiles) of the blob to output.
-  optional int32 tiles = 2;
-}
    
+  // The number of copies (tiles) of the blob to output.
+  optional int32 tiles = 2;
+}
    
 
 
 
diff --git a/tutorial/layers/windowdata.html b/tutorial/layers/windowdata.html
index 6967e82f899..c671f41af43 100644
--- a/tutorial/layers/windowdata.html
+++ b/tutorial/layers/windowdata.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -70,38 +70,38 @@ 
    Parameters
    
   
  • From ./src/caffe/proto/caffe.proto:
    • 
 
 
-
    message WindowDataParameter {
-  // Specify the data source.
-  optional string source = 1;
-  // For data pre-processing, we can do simple scaling and subtracting the
-  // data mean, if provided. Note that the mean subtraction is always carried
-  // out before scaling.
-  optional float scale = 2 [default = 1];
-  optional string mean_file = 3;
-  // Specify the batch size.
-  optional uint32 batch_size = 4;
-  // Specify if we would like to randomly crop an image.
-  optional uint32 crop_size = 5 [default = 0];
-  // Specify if we want to randomly mirror data.
-  optional bool mirror = 6 [default = false];
-  // Foreground (object) overlap threshold
-  optional float fg_threshold = 7 [default = 0.5];
-  // Background (non-object) overlap threshold
-  optional float bg_threshold = 8 [default = 0.5];
-  // Fraction of batch that should be foreground objects
-  optional float fg_fraction = 9 [default = 0.25];
-  // Amount of contextual padding to add around a window
-  // (used only by the window_data_layer)
-  optional uint32 context_pad = 10 [default = 0];
-  // Mode for cropping out a detection window
-  // warp: cropped window is warped to a fixed size and aspect ratio
-  // square: the tightest square around the window is cropped
-  optional string crop_mode = 11 [default = "warp"];
-  // cache_images: will load all images in memory for faster access
-  optional bool cache_images = 12 [default = false];
-  // append root_folder to locate images
-  optional string root_folder = 13 [default = ""];
-}
    
+
    message WindowDataParameter {
+  // Specify the data source.
+  optional string source = 1;
+  // For data pre-processing, we can do simple scaling and subtracting the
+  // data mean, if provided. Note that the mean subtraction is always carried
+  // out before scaling.
+  optional float scale = 2 [default = 1];
+  optional string mean_file = 3;
+  // Specify the batch size.
+  optional uint32 batch_size = 4;
+  // Specify if we would like to randomly crop an image.
+  optional uint32 crop_size = 5 [default = 0];
+  // Specify if we want to randomly mirror data.
+  optional bool mirror = 6 [default = false];
+  // Foreground (object) overlap threshold
+  optional float fg_threshold = 7 [default = 0.5];
+  // Background (non-object) overlap threshold
+  optional float bg_threshold = 8 [default = 0.5];
+  // Fraction of batch that should be foreground objects
+  optional float fg_fraction = 9 [default = 0.25];
+  // Amount of contextual padding to add around a window
+  // (used only by the window_data_layer)
+  optional uint32 context_pad = 10 [default = 0];
+  // Mode for cropping out a detection window
+  // warp: cropped window is warped to a fixed size and aspect ratio
+  // square: the tightest square around the window is cropped
+  optional string crop_mode = 11 [default = "warp"];
+  // cache_images: will load all images in memory for faster access
+  optional bool cache_images = 12 [default = false];
+  // append root_folder to locate images
+  optional string root_folder = 13 [default = ""];
+}
    
 
 
 
diff --git a/tutorial/loss.html b/tutorial/loss.html
index 674171c6d9c..5ccbf59abcb 100644
--- a/tutorial/loss.html
+++ b/tutorial/loss.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
diff --git a/tutorial/net_layer_blob.html b/tutorial/net_layer_blob.html
index 0171d45d00e..bd8bcca5fb8 100644
--- a/tutorial/net_layer_blob.html
+++ b/tutorial/net_layer_blob.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
diff --git a/tutorial/solver.html b/tutorial/solver.html
index 3aef87463d8..982ba6124dc 100644
--- a/tutorial/solver.html
+++ b/tutorial/solver.html
@@ -36,7 +36,7 @@
       
    
         
    Caffe
    
         
    
-          Deep learning framework by the BVLC
+          Deep learning framework by BAIR
         
    
         
    
           Created by
@@ -128,7 +128,7 @@ 
    SGD
    
     Stochastic Gradient Descent Tricks.
     Neural Networks: Tricks of the Trade: Springer, 2012.
    
 
-
    Rules of thumb for setting the learning rate  and momentum 
    
+
    Rules of thumb for setting the learning rate  and momentum 
    
 
 
    A good strategy for deep learning with SGD is to initialize the learning rate  to a value around , and dropping it by a constant factor (e.g., 10) throughout training when the loss begins to reach an apparent “plateau”, repeating this several times.
 Generally, you probably want to use a momentum  or similar value.