A Deep Learning Library in C++ based on eigen with Python bindings originally written in the context of a praktikum at TUM. Henceforth called libdl for simplicity.
Clone the repository, create a build folder (eg: mkdir -p build) and run
make
in the build folder. By default the build in in \c RELEASE mode, you may change this by setting -DCMAKE_BUILD_TYPE=Debug but this is not reccomended since tests and examples become way slower.
This generates library, tests, and python bindings.
Please read the documentation.
In order to compile the documentation run
make doc_doxygen
in the build folder (known issue: issue with spaces in path, alternative read file Description.md). And check the results in the folder build/doc/html or build/doc/latex.
The documentation includes
- A Main Page, which gives details about the library design and important information (reccomended read). In case you don't want to use doxygen, you may read the file DESCRIPTION.md in the /doc folder.
- Information of the Classes being used as well as relevant functions (with class diagrams).
- Source Files with ommited doxygen blocks.
C++17 compiler.
For funtionality tests, check the folder /test.
For Higher-level tests on real data check the /python directory and run the corresponding notebook files.
The python notebook files mentioned above serve as examples. Additionally in the folder /examples provides some more example files.
Please refer to the documentation.
- Adria Font Calvarons
Refer to the folder examples.
You'll need to:
Clone the repository and run
make
or
make pybindings
in the build folder (just like in the CI). The important target is pybindings, this generates the python wrapper module.
Then open Jupyter notebook in the folder /python and run the script Classifier_MNIST_Deliverable (this one is simple, big strides and no max pool, but gets good enough results) or some other Classifier notebook. This files run an Example architecture with parameters that seem to work. The code for this architectures can be seen in the file /python/ClassifierExamples.h (lowest level example without network helper to run the code).
Note: To convince yourself that my code works and there is a learning actually taking place I suggest doing the following: Set the Learning rate to a higher value (eg: in the order of 0.1) and you will see the network doesn't learn anything and the accuracy is around 0.1, which is basically just the same as random. Reset the original Learning (0.005) and the accuracy should be above 0.9.
You'll need to do the same steps but open one of the Segmentation notebooks instead. The results are far from being state-of-the art but validate the algorithm. Similarly refer to /python/SegmentationExamples.h for code details.
Note: More information regarding the interaction with Python and the python bindings can be found in the Python readme.
Note: Latest developments regarding skip connections are in branch skip-connections-resnet.