diff --git a/README.md b/README.md
index 54a93a7ef..120b2c743 100644
--- a/README.md
+++ b/README.md
@@ -1,76 +1,76 @@
-# Caffe
-[![Build Status](https://travis-ci.org/BVLC/caffe.svg?branch=master)](https://travis-ci.org/BVLC/caffe)
-[![License](https://img.shields.io/badge/license-BSD-blue.svg)](LICENSE)
-
-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](http://bvlc.eecs.berkeley.edu)) and community contributors.
-
-Check out the [project site](http://caffe.berkeleyvision.org) for all the details like
-- [DIY Deep Learning for Vision with Caffe](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.p)
-- [Tutorial Documentation](http://caffe.berkeleyvision.org/tutorial/)
-- [BVLC reference models](http://caffe.berkeleyvision.org/model_zoo.html) and the [community model zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo)
-- [Installation instructions](http://caffe.berkeleyvision.org/installation.html)
-
-and step-by-step examples.
-
-[![Join the chat at https://gitter.im/BVLC/caffe](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/BVLC/caffe?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
-
-Please join the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) or [gitter chat](https://gitter.im/BVLC/caffe) to ask questions and talk about methods and models.
-Framework development discussions and thorough bug reports are collected on [Issues](https://github.com/BVLC/caffe/issues).
-
-Happy brewing!
-
-
-# SSD: Single Shot MultiBox Detector
-This repository contains merged code issued as pull request to BVLC caffe written by:
-[Wei Liu](http://www.cs.unc.edu/~wliu/), [Dragomir Anguelov](https://www.linkedin.com/in/dragomiranguelov), [Dumitru Erhan](http://research.google.com/pubs/DumitruErhan.html), [Christian Szegedy](http://research.google.com/pubs/ChristianSzegedy.html), [Scott Reed](http://www-personal.umich.edu/~reedscot/), [Cheng-Yang Fu](http://www.cs.unc.edu/~cyfu/), [Alexander C. Berg](http://acberg.com).
-
-Original branch can be found at https://github.com/weiliu89/caffe/tree/ssd.
-
-Read our [wiki page](https://github.com/intel/caffe/wiki/SSD:-Single-Shot-MultiBox-Detector) for more details.
-
-# Intel® Distribution of Caffe*
-This fork is dedicated to improving Caffe performance when running on CPU, in particular Intel® Xeon processors (HSW, BDW, Xeon Phi)
-
-## Building
-Build procedure is the same as on bvlc-caffe-master branch. Both Make and CMake can be used.
-When OpenMP is available will be used automatically.
-
-## Running
-Run procedure is the same as on bvlc-caffe-master branch.
-
-Current implementation uses OpenMP threads. By default the number of OpenMP threads is set
-to the number of CPU cores. Each one thread is bound to a single core to achieve best
-performance results. It is however possible to use own configuration by providing right
-one through OpenMP environmental variables like OMP_NUM_THREADS or GOMP_CPU_AFFINITY.
-
-If some system tool like numactl is used to control CPU affinity, by default caffe will prevent
-to use more than one thread per core. When less than required cores are specified, caffe will
-limit execution of OpenMP threads to specified cores only.
-
-## Best performance solution
-Please read [our Wiki](https://github.com/intel/caffe/wiki/Recommendations-to-achieve-best-performance) for our recommendations and configuration to achieve best performance on Intel CPUs. 
-
-## Multinode Training
-Intel® Distribution of Caffe* multi-node allows you to execute deep neural network training on multiple machines.
-
-To understand how it works and read some tutorials, go to our Wiki. Start from [Multinode guide](https://github.com/intel/caffe/wiki/Multinode-guide).
-
-## License and Citation
-Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE).
-The BVLC reference models are released for unrestricted use.
-
-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}
-    }
-
-***
- *Other names and brands may be claimed as the property of others
-
-
-
+# Caffe
+[![Build Status](https://travis-ci.org/BVLC/caffe.svg?branch=master)](https://travis-ci.org/BVLC/caffe)
+[![License](https://img.shields.io/badge/license-BSD-blue.svg)](LICENSE)
+
+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](http://bvlc.eecs.berkeley.edu)) and community contributors.
+
+Check out the [project site](http://caffe.berkeleyvision.org) for all the details like
+- [DIY Deep Learning for Vision with Caffe](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.p)
+- [Tutorial Documentation](http://caffe.berkeleyvision.org/tutorial/)
+- [BVLC reference models](http://caffe.berkeleyvision.org/model_zoo.html) and the [community model zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo)
+- [Installation instructions](https://github.com/intel/caffe/wiki/Installation)
+
+and step-by-step examples.
+
+[![Join the chat at https://gitter.im/BVLC/caffe](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/BVLC/caffe?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+
+Please join the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) or [gitter chat](https://gitter.im/BVLC/caffe) to ask questions and talk about methods and models.
+Framework development discussions and thorough bug reports are collected on [Issues](https://github.com/BVLC/caffe/issues).
+
+Happy brewing!
+
+
+# SSD: Single Shot MultiBox Detector
+This repository contains merged code issued as pull request to BVLC caffe written by:
+[Wei Liu](http://www.cs.unc.edu/~wliu/), [Dragomir Anguelov](https://www.linkedin.com/in/dragomiranguelov), [Dumitru Erhan](http://research.google.com/pubs/DumitruErhan.html), [Christian Szegedy](http://research.google.com/pubs/ChristianSzegedy.html), [Scott Reed](http://www-personal.umich.edu/~reedscot/), [Cheng-Yang Fu](http://www.cs.unc.edu/~cyfu/), [Alexander C. Berg](http://acberg.com).
+
+Original branch can be found at https://github.com/weiliu89/caffe/tree/ssd.
+
+Read our [wiki page](https://github.com/intel/caffe/wiki/SSD:-Single-Shot-MultiBox-Detector) for more details.
+
+# Intel® Distribution of Caffe*
+This fork is dedicated to improving Caffe performance when running on CPU, in particular Intel® Xeon processors (HSW, BDW, Xeon Phi)
+
+## Building
+Build procedure is the same as on bvlc-caffe-master branch. Both Make and CMake can be used.
+When OpenMP is available will be used automatically.
+
+## Running
+Run procedure is the same as on bvlc-caffe-master branch.
+
+Current implementation uses OpenMP threads. By default the number of OpenMP threads is set
+to the number of CPU cores. Each one thread is bound to a single core to achieve best
+performance results. It is however possible to use own configuration by providing right
+one through OpenMP environmental variables like OMP_NUM_THREADS or GOMP_CPU_AFFINITY.
+
+If some system tool like numactl is used to control CPU affinity, by default caffe will prevent
+to use more than one thread per core. When less than required cores are specified, caffe will
+limit execution of OpenMP threads to specified cores only.
+
+## Best performance solution
+Please read [our Wiki](https://github.com/intel/caffe/wiki/Recommendations-to-achieve-best-performance) for our recommendations and configuration to achieve best performance on Intel CPUs. 
+
+## Multinode Training
+Intel® Distribution of Caffe* multi-node allows you to execute deep neural network training on multiple machines.
+
+To understand how it works and read some tutorials, go to our Wiki. Start from [Multinode guide](https://github.com/intel/caffe/wiki/Multinode-guide).
+
+## License and Citation
+Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE).
+The BVLC reference models are released for unrestricted use.
+
+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}
+    }
+
+***
+ *Other names and brands may be claimed as the property of others
+
+
+
diff --git a/models/intel_optimized_models/alexnet/bdw/train_val_dummydata.prototxt b/models/intel_optimized_models/alexnet/bdw/train_val_dummydata.prototxt
new file mode 100644
index 000000000..fc47551c3
--- /dev/null
+++ b/models/intel_optimized_models/alexnet/bdw/train_val_dummydata.prototxt
@@ -0,0 +1,394 @@
+name: "AlexNet"
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  dummy_data_param {
+    data_filler {
+        type: "constant"
+        value: 0.01
+    }
+    shape: { dim: 256 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 256 dim: 1 dim: 1   dim: 1   }
+  }
+}
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.01
+    }
+    shape: { dim: 256 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 256 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.005
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.005
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  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
+    }
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+}
+layer {
+  name: "loss3/top-1"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss3/top-1"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss3/top-5"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss3/top-5"
+  include {
+    phase: TEST
+  }
+  accuracy_param {
+    top_k: 5
+  }
+}
diff --git a/models/intel_optimized_models/alexnet/knl/train_val_dummydata.prototxt b/models/intel_optimized_models/alexnet/knl/train_val_dummydata.prototxt
new file mode 100644
index 000000000..fc47551c3
--- /dev/null
+++ b/models/intel_optimized_models/alexnet/knl/train_val_dummydata.prototxt
@@ -0,0 +1,394 @@
+name: "AlexNet"
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  dummy_data_param {
+    data_filler {
+        type: "constant"
+        value: 0.01
+    }
+    shape: { dim: 256 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 256 dim: 1 dim: 1   dim: 1   }
+  }
+}
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.01
+    }
+    shape: { dim: 256 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 256 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.005
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.005
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  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
+    }
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+}
+layer {
+  name: "loss3/top-1"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss3/top-1"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss3/top-5"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss3/top-5"
+  include {
+    phase: TEST
+  }
+  accuracy_param {
+    top_k: 5
+  }
+}
diff --git a/models/intel_optimized_models/alexnet/skx/train_val_dummydata.prototxt b/models/intel_optimized_models/alexnet/skx/train_val_dummydata.prototxt
new file mode 100644
index 000000000..bc064140e
--- /dev/null
+++ b/models/intel_optimized_models/alexnet/skx/train_val_dummydata.prototxt
@@ -0,0 +1,394 @@
+name: "AlexNet"
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  dummy_data_param {
+    data_filler {
+        type: "constant"
+        value: 0.01
+    }
+    shape: { dim: 1024 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 1024 dim: 1 dim: 1   dim: 1   }
+  }
+}
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.01
+    }
+    shape: { dim: 512 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 512 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.005
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.005
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  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
+    }
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+}
+layer {
+  name: "loss3/top-1"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss3/top-1"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss3/top-5"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss3/top-5"
+  include {
+    phase: TEST
+  }
+  accuracy_param {
+    top_k: 5
+  }
+}
diff --git a/models/intel_optimized_models/googlenet/bdw/train_val_dummydata.prototxt b/models/intel_optimized_models/googlenet/bdw/train_val_dummydata.prototxt
new file mode 100644
index 000000000..bf411dfe7
--- /dev/null
+++ b/models/intel_optimized_models/googlenet/bdw/train_val_dummydata.prototxt
@@ -0,0 +1,2427 @@
+name: "GoogleNet"
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.01
+    }
+    shape: { dim: 64 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 64 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.01
+    }
+    shape: { dim: 32 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 32 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+layer {
+  name: "conv1/7x7_s2"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1/7x7_s2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 3
+    kernel_size: 7
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "conv1/relu_7x7"
+  type: "ReLU"
+  bottom: "conv1/7x7_s2"
+  top: "conv1/7x7_s2"
+}
+layer {
+  name: "pool1/3x3_s2"
+  type: "Pooling"
+  bottom: "conv1/7x7_s2"
+  top: "pool1/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "pool1/norm1"
+  type: "LRN"
+  bottom: "pool1/3x3_s2"
+  top: "pool1/norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layer {
+  name: "conv2/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool1/norm1"
+  top: "conv2/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "conv2/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "conv2/3x3_reduce"
+  top: "conv2/3x3_reduce"
+}
+layer {
+  name: "conv2/3x3"
+  type: "Convolution"
+  bottom: "conv2/3x3_reduce"
+  top: "conv2/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "conv2/relu_3x3"
+  type: "ReLU"
+  bottom: "conv2/3x3"
+  top: "conv2/3x3"
+}
+layer {
+  name: "conv2/norm2"
+  type: "LRN"
+  bottom: "conv2/3x3"
+  top: "conv2/norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2/3x3_s2"
+  type: "Pooling"
+  bottom: "conv2/norm2"
+  top: "pool2/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "inception_3a/1x1"
+  type: "Convolution"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_3a/1x1"
+  top: "inception_3a/1x1"
+}
+layer {
+  name: "inception_3a/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_3a/3x3_reduce"
+  top: "inception_3a/3x3_reduce"
+}
+layer {
+  name: "inception_3a/3x3"
+  type: "Convolution"
+  bottom: "inception_3a/3x3_reduce"
+  top: "inception_3a/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_3a/3x3"
+  top: "inception_3a/3x3"
+}
+layer {
+  name: "inception_3a/5x5_reduce"
+  type: "Convolution"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_3a/5x5_reduce"
+  top: "inception_3a/5x5_reduce"
+}
+layer {
+  name: "inception_3a/5x5"
+  type: "Convolution"
+  bottom: "inception_3a/5x5_reduce"
+  top: "inception_3a/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_3a/5x5"
+  top: "inception_3a/5x5"
+}
+layer {
+  name: "inception_3a/pool"
+  type: "Pooling"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_3a/pool_proj"
+  type: "Convolution"
+  bottom: "inception_3a/pool"
+  top: "inception_3a/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_3a/pool_proj"
+  top: "inception_3a/pool_proj"
+}
+layer {
+  name: "inception_3a/output"
+  type: "Concat"
+  bottom: "inception_3a/1x1"
+  bottom: "inception_3a/3x3"
+  bottom: "inception_3a/5x5"
+  bottom: "inception_3a/pool_proj"
+  top: "inception_3a/output"
+}
+layer {
+  name: "inception_3b/1x1"
+  type: "Convolution"
+  bottom: "inception_3a/output"
+  top: "inception_3b/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_3b/1x1"
+  top: "inception_3b/1x1"
+}
+layer {
+  name: "inception_3b/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_3a/output"
+  top: "inception_3b/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_3b/3x3_reduce"
+  top: "inception_3b/3x3_reduce"
+}
+layer {
+  name: "inception_3b/3x3"
+  type: "Convolution"
+  bottom: "inception_3b/3x3_reduce"
+  top: "inception_3b/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_3b/3x3"
+  top: "inception_3b/3x3"
+}
+layer {
+  name: "inception_3b/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_3a/output"
+  top: "inception_3b/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_3b/5x5_reduce"
+  top: "inception_3b/5x5_reduce"
+}
+layer {
+  name: "inception_3b/5x5"
+  type: "Convolution"
+  bottom: "inception_3b/5x5_reduce"
+  top: "inception_3b/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 96
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_3b/5x5"
+  top: "inception_3b/5x5"
+}
+layer {
+  name: "inception_3b/pool"
+  type: "Pooling"
+  bottom: "inception_3a/output"
+  top: "inception_3b/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_3b/pool_proj"
+  type: "Convolution"
+  bottom: "inception_3b/pool"
+  top: "inception_3b/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_3b/pool_proj"
+  top: "inception_3b/pool_proj"
+}
+layer {
+  name: "inception_3b/output"
+  type: "Concat"
+  bottom: "inception_3b/1x1"
+  bottom: "inception_3b/3x3"
+  bottom: "inception_3b/5x5"
+  bottom: "inception_3b/pool_proj"
+  top: "inception_3b/output"
+}
+layer {
+  name: "pool3/3x3_s2"
+  type: "Pooling"
+  bottom: "inception_3b/output"
+  top: "pool3/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "inception_4a/1x1"
+  type: "Convolution"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4a/1x1"
+  top: "inception_4a/1x1"
+}
+layer {
+  name: "inception_4a/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4a/3x3_reduce"
+  top: "inception_4a/3x3_reduce"
+}
+layer {
+  name: "inception_4a/3x3"
+  type: "Convolution"
+  bottom: "inception_4a/3x3_reduce"
+  top: "inception_4a/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 208
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4a/3x3"
+  top: "inception_4a/3x3"
+}
+layer {
+  name: "inception_4a/5x5_reduce"
+  type: "Convolution"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4a/5x5_reduce"
+  top: "inception_4a/5x5_reduce"
+}
+layer {
+  name: "inception_4a/5x5"
+  type: "Convolution"
+  bottom: "inception_4a/5x5_reduce"
+  top: "inception_4a/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 48
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4a/5x5"
+  top: "inception_4a/5x5"
+}
+layer {
+  name: "inception_4a/pool"
+  type: "Pooling"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4a/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4a/pool"
+  top: "inception_4a/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4a/pool_proj"
+  top: "inception_4a/pool_proj"
+}
+layer {
+  name: "inception_4a/output"
+  type: "Concat"
+  bottom: "inception_4a/1x1"
+  bottom: "inception_4a/3x3"
+  bottom: "inception_4a/5x5"
+  bottom: "inception_4a/pool_proj"
+  top: "inception_4a/output"
+}
+layer {
+  name: "loss1/ave_pool"
+  type: "Pooling"
+  bottom: "inception_4a/output"
+  top: "loss1/ave_pool"
+  pooling_param {
+    pool: AVE
+    kernel_size: 5
+    stride: 3
+  }
+}
+layer {
+  name: "loss1/conv"
+  type: "Convolution"
+  bottom: "loss1/ave_pool"
+  top: "loss1/conv"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "loss1/relu_conv"
+  type: "ReLU"
+  bottom: "loss1/conv"
+  top: "loss1/conv"
+}
+layer {
+  name: "loss1/fc"
+  type: "InnerProduct"
+  bottom: "loss1/conv"
+  top: "loss1/fc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1024
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "loss1/relu_fc"
+  type: "ReLU"
+  bottom: "loss1/fc"
+  top: "loss1/fc"
+}
+layer {
+  name: "loss1/drop_fc"
+  type: "Dropout"
+  bottom: "loss1/fc"
+  top: "loss1/fc"
+  dropout_param {
+    dropout_ratio: 0.7
+  }
+}
+layer {
+  name: "loss1/classifier"
+  type: "InnerProduct"
+  bottom: "loss1/fc"
+  top: "loss1/classifier"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "loss1/loss"
+  type: "SoftmaxWithLoss"
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/loss1"
+  loss_weight: 0.3
+}
+layer {
+  name: "loss1/top-1"
+  type: "Accuracy"
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/top-1"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss1/top-5"
+  type: "Accuracy"
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/top-5"
+  include {
+    phase: TEST
+  }
+  accuracy_param {
+    top_k: 5
+  }
+}
+layer {
+  name: "inception_4b/1x1"
+  type: "Convolution"
+  bottom: "inception_4a/output"
+  top: "inception_4b/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4b/1x1"
+  top: "inception_4b/1x1"
+}
+layer {
+  name: "inception_4b/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4a/output"
+  top: "inception_4b/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 112
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4b/3x3_reduce"
+  top: "inception_4b/3x3_reduce"
+}
+layer {
+  name: "inception_4b/3x3"
+  type: "Convolution"
+  bottom: "inception_4b/3x3_reduce"
+  top: "inception_4b/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4b/3x3"
+  top: "inception_4b/3x3"
+}
+layer {
+  name: "inception_4b/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4a/output"
+  top: "inception_4b/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 24
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4b/5x5_reduce"
+  top: "inception_4b/5x5_reduce"
+}
+layer {
+  name: "inception_4b/5x5"
+  type: "Convolution"
+  bottom: "inception_4b/5x5_reduce"
+  top: "inception_4b/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4b/5x5"
+  top: "inception_4b/5x5"
+}
+layer {
+  name: "inception_4b/pool"
+  type: "Pooling"
+  bottom: "inception_4a/output"
+  top: "inception_4b/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4b/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4b/pool"
+  top: "inception_4b/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4b/pool_proj"
+  top: "inception_4b/pool_proj"
+}
+layer {
+  name: "inception_4b/output"
+  type: "Concat"
+  bottom: "inception_4b/1x1"
+  bottom: "inception_4b/3x3"
+  bottom: "inception_4b/5x5"
+  bottom: "inception_4b/pool_proj"
+  top: "inception_4b/output"
+}
+layer {
+  name: "inception_4c/1x1"
+  type: "Convolution"
+  bottom: "inception_4b/output"
+  top: "inception_4c/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4c/1x1"
+  top: "inception_4c/1x1"
+}
+layer {
+  name: "inception_4c/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4b/output"
+  top: "inception_4c/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4c/3x3_reduce"
+  top: "inception_4c/3x3_reduce"
+}
+layer {
+  name: "inception_4c/3x3"
+  type: "Convolution"
+  bottom: "inception_4c/3x3_reduce"
+  top: "inception_4c/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4c/3x3"
+  top: "inception_4c/3x3"
+}
+layer {
+  name: "inception_4c/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4b/output"
+  top: "inception_4c/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 24
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4c/5x5_reduce"
+  top: "inception_4c/5x5_reduce"
+}
+layer {
+  name: "inception_4c/5x5"
+  type: "Convolution"
+  bottom: "inception_4c/5x5_reduce"
+  top: "inception_4c/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4c/5x5"
+  top: "inception_4c/5x5"
+}
+layer {
+  name: "inception_4c/pool"
+  type: "Pooling"
+  bottom: "inception_4b/output"
+  top: "inception_4c/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4c/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4c/pool"
+  top: "inception_4c/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4c/pool_proj"
+  top: "inception_4c/pool_proj"
+}
+layer {
+  name: "inception_4c/output"
+  type: "Concat"
+  bottom: "inception_4c/1x1"
+  bottom: "inception_4c/3x3"
+  bottom: "inception_4c/5x5"
+  bottom: "inception_4c/pool_proj"
+  top: "inception_4c/output"
+}
+layer {
+  name: "inception_4d/1x1"
+  type: "Convolution"
+  bottom: "inception_4c/output"
+  top: "inception_4d/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 112
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4d/1x1"
+  top: "inception_4d/1x1"
+}
+layer {
+  name: "inception_4d/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4c/output"
+  top: "inception_4d/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 144
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4d/3x3_reduce"
+  top: "inception_4d/3x3_reduce"
+}
+layer {
+  name: "inception_4d/3x3"
+  type: "Convolution"
+  bottom: "inception_4d/3x3_reduce"
+  top: "inception_4d/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 288
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4d/3x3"
+  top: "inception_4d/3x3"
+}
+layer {
+  name: "inception_4d/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4c/output"
+  top: "inception_4d/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4d/5x5_reduce"
+  top: "inception_4d/5x5_reduce"
+}
+layer {
+  name: "inception_4d/5x5"
+  type: "Convolution"
+  bottom: "inception_4d/5x5_reduce"
+  top: "inception_4d/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4d/5x5"
+  top: "inception_4d/5x5"
+}
+layer {
+  name: "inception_4d/pool"
+  type: "Pooling"
+  bottom: "inception_4c/output"
+  top: "inception_4d/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4d/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4d/pool"
+  top: "inception_4d/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4d/pool_proj"
+  top: "inception_4d/pool_proj"
+}
+layer {
+  name: "inception_4d/output"
+  type: "Concat"
+  bottom: "inception_4d/1x1"
+  bottom: "inception_4d/3x3"
+  bottom: "inception_4d/5x5"
+  bottom: "inception_4d/pool_proj"
+  top: "inception_4d/output"
+}
+layer {
+  name: "loss2/ave_pool"
+  type: "Pooling"
+  bottom: "inception_4d/output"
+  top: "loss2/ave_pool"
+  pooling_param {
+    pool: AVE
+    kernel_size: 5
+    stride: 3
+  }
+}
+layer {
+  name: "loss2/conv"
+  type: "Convolution"
+  bottom: "loss2/ave_pool"
+  top: "loss2/conv"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "loss2/relu_conv"
+  type: "ReLU"
+  bottom: "loss2/conv"
+  top: "loss2/conv"
+}
+layer {
+  name: "loss2/fc"
+  type: "InnerProduct"
+  bottom: "loss2/conv"
+  top: "loss2/fc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1024
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "loss2/relu_fc"
+  type: "ReLU"
+  bottom: "loss2/fc"
+  top: "loss2/fc"
+}
+layer {
+  name: "loss2/drop_fc"
+  type: "Dropout"
+  bottom: "loss2/fc"
+  top: "loss2/fc"
+  dropout_param {
+    dropout_ratio: 0.7
+  }
+}
+layer {
+  name: "loss2/classifier"
+  type: "InnerProduct"
+  bottom: "loss2/fc"
+  top: "loss2/classifier"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "loss2/loss"
+  type: "SoftmaxWithLoss"
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/loss1"
+  loss_weight: 0.3
+}
+layer {
+  name: "loss2/top-1"
+  type: "Accuracy"
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/top-1"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss2/top-5"
+  type: "Accuracy"
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/top-5"
+  include {
+    phase: TEST
+  }
+  accuracy_param {
+    top_k: 5
+  }
+}
+layer {
+  name: "inception_4e/1x1"
+  type: "Convolution"
+  bottom: "inception_4d/output"
+  top: "inception_4e/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4e/1x1"
+  top: "inception_4e/1x1"
+}
+layer {
+  name: "inception_4e/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4d/output"
+  top: "inception_4e/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4e/3x3_reduce"
+  top: "inception_4e/3x3_reduce"
+}
+layer {
+  name: "inception_4e/3x3"
+  type: "Convolution"
+  bottom: "inception_4e/3x3_reduce"
+  top: "inception_4e/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4e/3x3"
+  top: "inception_4e/3x3"
+}
+layer {
+  name: "inception_4e/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4d/output"
+  top: "inception_4e/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4e/5x5_reduce"
+  top: "inception_4e/5x5_reduce"
+}
+layer {
+  name: "inception_4e/5x5"
+  type: "Convolution"
+  bottom: "inception_4e/5x5_reduce"
+  top: "inception_4e/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4e/5x5"
+  top: "inception_4e/5x5"
+}
+layer {
+  name: "inception_4e/pool"
+  type: "Pooling"
+  bottom: "inception_4d/output"
+  top: "inception_4e/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4e/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4e/pool"
+  top: "inception_4e/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4e/pool_proj"
+  top: "inception_4e/pool_proj"
+}
+layer {
+  name: "inception_4e/output"
+  type: "Concat"
+  bottom: "inception_4e/1x1"
+  bottom: "inception_4e/3x3"
+  bottom: "inception_4e/5x5"
+  bottom: "inception_4e/pool_proj"
+  top: "inception_4e/output"
+}
+layer {
+  name: "pool4/3x3_s2"
+  type: "Pooling"
+  bottom: "inception_4e/output"
+  top: "pool4/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "inception_5a/1x1"
+  type: "Convolution"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_5a/1x1"
+  top: "inception_5a/1x1"
+}
+layer {
+  name: "inception_5a/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_5a/3x3_reduce"
+  top: "inception_5a/3x3_reduce"
+}
+layer {
+  name: "inception_5a/3x3"
+  type: "Convolution"
+  bottom: "inception_5a/3x3_reduce"
+  top: "inception_5a/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_5a/3x3"
+  top: "inception_5a/3x3"
+}
+layer {
+  name: "inception_5a/5x5_reduce"
+  type: "Convolution"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_5a/5x5_reduce"
+  top: "inception_5a/5x5_reduce"
+}
+layer {
+  name: "inception_5a/5x5"
+  type: "Convolution"
+  bottom: "inception_5a/5x5_reduce"
+  top: "inception_5a/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_5a/5x5"
+  top: "inception_5a/5x5"
+}
+layer {
+  name: "inception_5a/pool"
+  type: "Pooling"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_5a/pool_proj"
+  type: "Convolution"
+  bottom: "inception_5a/pool"
+  top: "inception_5a/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_5a/pool_proj"
+  top: "inception_5a/pool_proj"
+}
+layer {
+  name: "inception_5a/output"
+  type: "Concat"
+  bottom: "inception_5a/1x1"
+  bottom: "inception_5a/3x3"
+  bottom: "inception_5a/5x5"
+  bottom: "inception_5a/pool_proj"
+  top: "inception_5a/output"
+}
+layer {
+  name: "inception_5b/1x1"
+  type: "Convolution"
+  bottom: "inception_5a/output"
+  top: "inception_5b/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 384
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_5b/1x1"
+  top: "inception_5b/1x1"
+}
+layer {
+  name: "inception_5b/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_5a/output"
+  top: "inception_5b/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_5b/3x3_reduce"
+  top: "inception_5b/3x3_reduce"
+}
+layer {
+  name: "inception_5b/3x3"
+  type: "Convolution"
+  bottom: "inception_5b/3x3_reduce"
+  top: "inception_5b/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_5b/3x3"
+  top: "inception_5b/3x3"
+}
+layer {
+  name: "inception_5b/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_5a/output"
+  top: "inception_5b/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 48
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_5b/5x5_reduce"
+  top: "inception_5b/5x5_reduce"
+}
+layer {
+  name: "inception_5b/5x5"
+  type: "Convolution"
+  bottom: "inception_5b/5x5_reduce"
+  top: "inception_5b/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_5b/5x5"
+  top: "inception_5b/5x5"
+}
+layer {
+  name: "inception_5b/pool"
+  type: "Pooling"
+  bottom: "inception_5a/output"
+  top: "inception_5b/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_5b/pool_proj"
+  type: "Convolution"
+  bottom: "inception_5b/pool"
+  top: "inception_5b/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_5b/pool_proj"
+  top: "inception_5b/pool_proj"
+}
+layer {
+  name: "inception_5b/output"
+  type: "Concat"
+  bottom: "inception_5b/1x1"
+  bottom: "inception_5b/3x3"
+  bottom: "inception_5b/5x5"
+  bottom: "inception_5b/pool_proj"
+  top: "inception_5b/output"
+}
+layer {
+  name: "pool5/7x7_s1"
+  type: "Pooling"
+  bottom: "inception_5b/output"
+  top: "pool5/7x7_s1"
+  pooling_param {
+    pool: AVE
+    kernel_size: 7
+    stride: 1
+  }
+}
+layer {
+  name: "pool5/drop_7x7_s1"
+  type: "Dropout"
+  bottom: "pool5/7x7_s1"
+  top: "pool5/7x7_s1"
+  dropout_param {
+    dropout_ratio: 0.4
+  }
+}
+layer {
+  name: "loss3/classifier"
+  type: "InnerProduct"
+  bottom: "pool5/7x7_s1"
+  top: "loss3/classifier"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "loss3/loss3"
+  type: "SoftmaxWithLoss"
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/loss3"
+  loss_weight: 1
+}
+layer {
+  name: "loss3/top-1"
+  type: "Accuracy"
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/top-1"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss3/top-5"
+  type: "Accuracy"
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/top-5"
+  include {
+    phase: TEST
+  }
+  accuracy_param {
+    top_k: 5
+  }
+}
diff --git a/models/intel_optimized_models/googlenet/knl/train_val_dummydata.prototxt b/models/intel_optimized_models/googlenet/knl/train_val_dummydata.prototxt
new file mode 100644
index 000000000..602416008
--- /dev/null
+++ b/models/intel_optimized_models/googlenet/knl/train_val_dummydata.prototxt
@@ -0,0 +1,2427 @@
+name: "GoogleNet"
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.01
+    }
+    shape: { dim: 128 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 128 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.01
+    }
+    shape: { dim: 128 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 128 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+layer {
+  name: "conv1/7x7_s2"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1/7x7_s2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 3
+    kernel_size: 7
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "conv1/relu_7x7"
+  type: "ReLU"
+  bottom: "conv1/7x7_s2"
+  top: "conv1/7x7_s2"
+}
+layer {
+  name: "pool1/3x3_s2"
+  type: "Pooling"
+  bottom: "conv1/7x7_s2"
+  top: "pool1/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "pool1/norm1"
+  type: "LRN"
+  bottom: "pool1/3x3_s2"
+  top: "pool1/norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layer {
+  name: "conv2/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool1/norm1"
+  top: "conv2/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "conv2/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "conv2/3x3_reduce"
+  top: "conv2/3x3_reduce"
+}
+layer {
+  name: "conv2/3x3"
+  type: "Convolution"
+  bottom: "conv2/3x3_reduce"
+  top: "conv2/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "conv2/relu_3x3"
+  type: "ReLU"
+  bottom: "conv2/3x3"
+  top: "conv2/3x3"
+}
+layer {
+  name: "conv2/norm2"
+  type: "LRN"
+  bottom: "conv2/3x3"
+  top: "conv2/norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2/3x3_s2"
+  type: "Pooling"
+  bottom: "conv2/norm2"
+  top: "pool2/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "inception_3a/1x1"
+  type: "Convolution"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_3a/1x1"
+  top: "inception_3a/1x1"
+}
+layer {
+  name: "inception_3a/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_3a/3x3_reduce"
+  top: "inception_3a/3x3_reduce"
+}
+layer {
+  name: "inception_3a/3x3"
+  type: "Convolution"
+  bottom: "inception_3a/3x3_reduce"
+  top: "inception_3a/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_3a/3x3"
+  top: "inception_3a/3x3"
+}
+layer {
+  name: "inception_3a/5x5_reduce"
+  type: "Convolution"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_3a/5x5_reduce"
+  top: "inception_3a/5x5_reduce"
+}
+layer {
+  name: "inception_3a/5x5"
+  type: "Convolution"
+  bottom: "inception_3a/5x5_reduce"
+  top: "inception_3a/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_3a/5x5"
+  top: "inception_3a/5x5"
+}
+layer {
+  name: "inception_3a/pool"
+  type: "Pooling"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_3a/pool_proj"
+  type: "Convolution"
+  bottom: "inception_3a/pool"
+  top: "inception_3a/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_3a/pool_proj"
+  top: "inception_3a/pool_proj"
+}
+layer {
+  name: "inception_3a/output"
+  type: "Concat"
+  bottom: "inception_3a/1x1"
+  bottom: "inception_3a/3x3"
+  bottom: "inception_3a/5x5"
+  bottom: "inception_3a/pool_proj"
+  top: "inception_3a/output"
+}
+layer {
+  name: "inception_3b/1x1"
+  type: "Convolution"
+  bottom: "inception_3a/output"
+  top: "inception_3b/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_3b/1x1"
+  top: "inception_3b/1x1"
+}
+layer {
+  name: "inception_3b/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_3a/output"
+  top: "inception_3b/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_3b/3x3_reduce"
+  top: "inception_3b/3x3_reduce"
+}
+layer {
+  name: "inception_3b/3x3"
+  type: "Convolution"
+  bottom: "inception_3b/3x3_reduce"
+  top: "inception_3b/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_3b/3x3"
+  top: "inception_3b/3x3"
+}
+layer {
+  name: "inception_3b/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_3a/output"
+  top: "inception_3b/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_3b/5x5_reduce"
+  top: "inception_3b/5x5_reduce"
+}
+layer {
+  name: "inception_3b/5x5"
+  type: "Convolution"
+  bottom: "inception_3b/5x5_reduce"
+  top: "inception_3b/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 96
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_3b/5x5"
+  top: "inception_3b/5x5"
+}
+layer {
+  name: "inception_3b/pool"
+  type: "Pooling"
+  bottom: "inception_3a/output"
+  top: "inception_3b/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_3b/pool_proj"
+  type: "Convolution"
+  bottom: "inception_3b/pool"
+  top: "inception_3b/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_3b/pool_proj"
+  top: "inception_3b/pool_proj"
+}
+layer {
+  name: "inception_3b/output"
+  type: "Concat"
+  bottom: "inception_3b/1x1"
+  bottom: "inception_3b/3x3"
+  bottom: "inception_3b/5x5"
+  bottom: "inception_3b/pool_proj"
+  top: "inception_3b/output"
+}
+layer {
+  name: "pool3/3x3_s2"
+  type: "Pooling"
+  bottom: "inception_3b/output"
+  top: "pool3/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "inception_4a/1x1"
+  type: "Convolution"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4a/1x1"
+  top: "inception_4a/1x1"
+}
+layer {
+  name: "inception_4a/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4a/3x3_reduce"
+  top: "inception_4a/3x3_reduce"
+}
+layer {
+  name: "inception_4a/3x3"
+  type: "Convolution"
+  bottom: "inception_4a/3x3_reduce"
+  top: "inception_4a/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 208
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4a/3x3"
+  top: "inception_4a/3x3"
+}
+layer {
+  name: "inception_4a/5x5_reduce"
+  type: "Convolution"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4a/5x5_reduce"
+  top: "inception_4a/5x5_reduce"
+}
+layer {
+  name: "inception_4a/5x5"
+  type: "Convolution"
+  bottom: "inception_4a/5x5_reduce"
+  top: "inception_4a/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 48
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4a/5x5"
+  top: "inception_4a/5x5"
+}
+layer {
+  name: "inception_4a/pool"
+  type: "Pooling"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4a/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4a/pool"
+  top: "inception_4a/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4a/pool_proj"
+  top: "inception_4a/pool_proj"
+}
+layer {
+  name: "inception_4a/output"
+  type: "Concat"
+  bottom: "inception_4a/1x1"
+  bottom: "inception_4a/3x3"
+  bottom: "inception_4a/5x5"
+  bottom: "inception_4a/pool_proj"
+  top: "inception_4a/output"
+}
+layer {
+  name: "loss1/ave_pool"
+  type: "Pooling"
+  bottom: "inception_4a/output"
+  top: "loss1/ave_pool"
+  pooling_param {
+    pool: AVE
+    kernel_size: 5
+    stride: 3
+  }
+}
+layer {
+  name: "loss1/conv"
+  type: "Convolution"
+  bottom: "loss1/ave_pool"
+  top: "loss1/conv"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "loss1/relu_conv"
+  type: "ReLU"
+  bottom: "loss1/conv"
+  top: "loss1/conv"
+}
+layer {
+  name: "loss1/fc"
+  type: "InnerProduct"
+  bottom: "loss1/conv"
+  top: "loss1/fc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1024
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "loss1/relu_fc"
+  type: "ReLU"
+  bottom: "loss1/fc"
+  top: "loss1/fc"
+}
+layer {
+  name: "loss1/drop_fc"
+  type: "Dropout"
+  bottom: "loss1/fc"
+  top: "loss1/fc"
+  dropout_param {
+    dropout_ratio: 0.7
+  }
+}
+layer {
+  name: "loss1/classifier"
+  type: "InnerProduct"
+  bottom: "loss1/fc"
+  top: "loss1/classifier"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "loss1/loss"
+  type: "SoftmaxWithLoss"
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/loss1"
+  loss_weight: 0.3
+}
+layer {
+  name: "loss1/top-1"
+  type: "Accuracy"
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/top-1"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss1/top-5"
+  type: "Accuracy"
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/top-5"
+  include {
+    phase: TEST
+  }
+  accuracy_param {
+    top_k: 5
+  }
+}
+layer {
+  name: "inception_4b/1x1"
+  type: "Convolution"
+  bottom: "inception_4a/output"
+  top: "inception_4b/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4b/1x1"
+  top: "inception_4b/1x1"
+}
+layer {
+  name: "inception_4b/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4a/output"
+  top: "inception_4b/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 112
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4b/3x3_reduce"
+  top: "inception_4b/3x3_reduce"
+}
+layer {
+  name: "inception_4b/3x3"
+  type: "Convolution"
+  bottom: "inception_4b/3x3_reduce"
+  top: "inception_4b/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4b/3x3"
+  top: "inception_4b/3x3"
+}
+layer {
+  name: "inception_4b/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4a/output"
+  top: "inception_4b/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 24
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4b/5x5_reduce"
+  top: "inception_4b/5x5_reduce"
+}
+layer {
+  name: "inception_4b/5x5"
+  type: "Convolution"
+  bottom: "inception_4b/5x5_reduce"
+  top: "inception_4b/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4b/5x5"
+  top: "inception_4b/5x5"
+}
+layer {
+  name: "inception_4b/pool"
+  type: "Pooling"
+  bottom: "inception_4a/output"
+  top: "inception_4b/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4b/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4b/pool"
+  top: "inception_4b/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4b/pool_proj"
+  top: "inception_4b/pool_proj"
+}
+layer {
+  name: "inception_4b/output"
+  type: "Concat"
+  bottom: "inception_4b/1x1"
+  bottom: "inception_4b/3x3"
+  bottom: "inception_4b/5x5"
+  bottom: "inception_4b/pool_proj"
+  top: "inception_4b/output"
+}
+layer {
+  name: "inception_4c/1x1"
+  type: "Convolution"
+  bottom: "inception_4b/output"
+  top: "inception_4c/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4c/1x1"
+  top: "inception_4c/1x1"
+}
+layer {
+  name: "inception_4c/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4b/output"
+  top: "inception_4c/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4c/3x3_reduce"
+  top: "inception_4c/3x3_reduce"
+}
+layer {
+  name: "inception_4c/3x3"
+  type: "Convolution"
+  bottom: "inception_4c/3x3_reduce"
+  top: "inception_4c/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4c/3x3"
+  top: "inception_4c/3x3"
+}
+layer {
+  name: "inception_4c/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4b/output"
+  top: "inception_4c/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 24
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4c/5x5_reduce"
+  top: "inception_4c/5x5_reduce"
+}
+layer {
+  name: "inception_4c/5x5"
+  type: "Convolution"
+  bottom: "inception_4c/5x5_reduce"
+  top: "inception_4c/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4c/5x5"
+  top: "inception_4c/5x5"
+}
+layer {
+  name: "inception_4c/pool"
+  type: "Pooling"
+  bottom: "inception_4b/output"
+  top: "inception_4c/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4c/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4c/pool"
+  top: "inception_4c/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4c/pool_proj"
+  top: "inception_4c/pool_proj"
+}
+layer {
+  name: "inception_4c/output"
+  type: "Concat"
+  bottom: "inception_4c/1x1"
+  bottom: "inception_4c/3x3"
+  bottom: "inception_4c/5x5"
+  bottom: "inception_4c/pool_proj"
+  top: "inception_4c/output"
+}
+layer {
+  name: "inception_4d/1x1"
+  type: "Convolution"
+  bottom: "inception_4c/output"
+  top: "inception_4d/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 112
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4d/1x1"
+  top: "inception_4d/1x1"
+}
+layer {
+  name: "inception_4d/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4c/output"
+  top: "inception_4d/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 144
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4d/3x3_reduce"
+  top: "inception_4d/3x3_reduce"
+}
+layer {
+  name: "inception_4d/3x3"
+  type: "Convolution"
+  bottom: "inception_4d/3x3_reduce"
+  top: "inception_4d/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 288
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4d/3x3"
+  top: "inception_4d/3x3"
+}
+layer {
+  name: "inception_4d/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4c/output"
+  top: "inception_4d/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4d/5x5_reduce"
+  top: "inception_4d/5x5_reduce"
+}
+layer {
+  name: "inception_4d/5x5"
+  type: "Convolution"
+  bottom: "inception_4d/5x5_reduce"
+  top: "inception_4d/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4d/5x5"
+  top: "inception_4d/5x5"
+}
+layer {
+  name: "inception_4d/pool"
+  type: "Pooling"
+  bottom: "inception_4c/output"
+  top: "inception_4d/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4d/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4d/pool"
+  top: "inception_4d/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4d/pool_proj"
+  top: "inception_4d/pool_proj"
+}
+layer {
+  name: "inception_4d/output"
+  type: "Concat"
+  bottom: "inception_4d/1x1"
+  bottom: "inception_4d/3x3"
+  bottom: "inception_4d/5x5"
+  bottom: "inception_4d/pool_proj"
+  top: "inception_4d/output"
+}
+layer {
+  name: "loss2/ave_pool"
+  type: "Pooling"
+  bottom: "inception_4d/output"
+  top: "loss2/ave_pool"
+  pooling_param {
+    pool: AVE
+    kernel_size: 5
+    stride: 3
+  }
+}
+layer {
+  name: "loss2/conv"
+  type: "Convolution"
+  bottom: "loss2/ave_pool"
+  top: "loss2/conv"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "loss2/relu_conv"
+  type: "ReLU"
+  bottom: "loss2/conv"
+  top: "loss2/conv"
+}
+layer {
+  name: "loss2/fc"
+  type: "InnerProduct"
+  bottom: "loss2/conv"
+  top: "loss2/fc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1024
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "loss2/relu_fc"
+  type: "ReLU"
+  bottom: "loss2/fc"
+  top: "loss2/fc"
+}
+layer {
+  name: "loss2/drop_fc"
+  type: "Dropout"
+  bottom: "loss2/fc"
+  top: "loss2/fc"
+  dropout_param {
+    dropout_ratio: 0.7
+  }
+}
+layer {
+  name: "loss2/classifier"
+  type: "InnerProduct"
+  bottom: "loss2/fc"
+  top: "loss2/classifier"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "loss2/loss"
+  type: "SoftmaxWithLoss"
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/loss1"
+  loss_weight: 0.3
+}
+layer {
+  name: "loss2/top-1"
+  type: "Accuracy"
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/top-1"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss2/top-5"
+  type: "Accuracy"
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/top-5"
+  include {
+    phase: TEST
+  }
+  accuracy_param {
+    top_k: 5
+  }
+}
+layer {
+  name: "inception_4e/1x1"
+  type: "Convolution"
+  bottom: "inception_4d/output"
+  top: "inception_4e/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4e/1x1"
+  top: "inception_4e/1x1"
+}
+layer {
+  name: "inception_4e/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4d/output"
+  top: "inception_4e/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4e/3x3_reduce"
+  top: "inception_4e/3x3_reduce"
+}
+layer {
+  name: "inception_4e/3x3"
+  type: "Convolution"
+  bottom: "inception_4e/3x3_reduce"
+  top: "inception_4e/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4e/3x3"
+  top: "inception_4e/3x3"
+}
+layer {
+  name: "inception_4e/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4d/output"
+  top: "inception_4e/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4e/5x5_reduce"
+  top: "inception_4e/5x5_reduce"
+}
+layer {
+  name: "inception_4e/5x5"
+  type: "Convolution"
+  bottom: "inception_4e/5x5_reduce"
+  top: "inception_4e/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4e/5x5"
+  top: "inception_4e/5x5"
+}
+layer {
+  name: "inception_4e/pool"
+  type: "Pooling"
+  bottom: "inception_4d/output"
+  top: "inception_4e/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4e/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4e/pool"
+  top: "inception_4e/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4e/pool_proj"
+  top: "inception_4e/pool_proj"
+}
+layer {
+  name: "inception_4e/output"
+  type: "Concat"
+  bottom: "inception_4e/1x1"
+  bottom: "inception_4e/3x3"
+  bottom: "inception_4e/5x5"
+  bottom: "inception_4e/pool_proj"
+  top: "inception_4e/output"
+}
+layer {
+  name: "pool4/3x3_s2"
+  type: "Pooling"
+  bottom: "inception_4e/output"
+  top: "pool4/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "inception_5a/1x1"
+  type: "Convolution"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_5a/1x1"
+  top: "inception_5a/1x1"
+}
+layer {
+  name: "inception_5a/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_5a/3x3_reduce"
+  top: "inception_5a/3x3_reduce"
+}
+layer {
+  name: "inception_5a/3x3"
+  type: "Convolution"
+  bottom: "inception_5a/3x3_reduce"
+  top: "inception_5a/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_5a/3x3"
+  top: "inception_5a/3x3"
+}
+layer {
+  name: "inception_5a/5x5_reduce"
+  type: "Convolution"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_5a/5x5_reduce"
+  top: "inception_5a/5x5_reduce"
+}
+layer {
+  name: "inception_5a/5x5"
+  type: "Convolution"
+  bottom: "inception_5a/5x5_reduce"
+  top: "inception_5a/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_5a/5x5"
+  top: "inception_5a/5x5"
+}
+layer {
+  name: "inception_5a/pool"
+  type: "Pooling"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_5a/pool_proj"
+  type: "Convolution"
+  bottom: "inception_5a/pool"
+  top: "inception_5a/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_5a/pool_proj"
+  top: "inception_5a/pool_proj"
+}
+layer {
+  name: "inception_5a/output"
+  type: "Concat"
+  bottom: "inception_5a/1x1"
+  bottom: "inception_5a/3x3"
+  bottom: "inception_5a/5x5"
+  bottom: "inception_5a/pool_proj"
+  top: "inception_5a/output"
+}
+layer {
+  name: "inception_5b/1x1"
+  type: "Convolution"
+  bottom: "inception_5a/output"
+  top: "inception_5b/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 384
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_5b/1x1"
+  top: "inception_5b/1x1"
+}
+layer {
+  name: "inception_5b/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_5a/output"
+  top: "inception_5b/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_5b/3x3_reduce"
+  top: "inception_5b/3x3_reduce"
+}
+layer {
+  name: "inception_5b/3x3"
+  type: "Convolution"
+  bottom: "inception_5b/3x3_reduce"
+  top: "inception_5b/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_5b/3x3"
+  top: "inception_5b/3x3"
+}
+layer {
+  name: "inception_5b/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_5a/output"
+  top: "inception_5b/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 48
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_5b/5x5_reduce"
+  top: "inception_5b/5x5_reduce"
+}
+layer {
+  name: "inception_5b/5x5"
+  type: "Convolution"
+  bottom: "inception_5b/5x5_reduce"
+  top: "inception_5b/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_5b/5x5"
+  top: "inception_5b/5x5"
+}
+layer {
+  name: "inception_5b/pool"
+  type: "Pooling"
+  bottom: "inception_5a/output"
+  top: "inception_5b/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_5b/pool_proj"
+  type: "Convolution"
+  bottom: "inception_5b/pool"
+  top: "inception_5b/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_5b/pool_proj"
+  top: "inception_5b/pool_proj"
+}
+layer {
+  name: "inception_5b/output"
+  type: "Concat"
+  bottom: "inception_5b/1x1"
+  bottom: "inception_5b/3x3"
+  bottom: "inception_5b/5x5"
+  bottom: "inception_5b/pool_proj"
+  top: "inception_5b/output"
+}
+layer {
+  name: "pool5/7x7_s1"
+  type: "Pooling"
+  bottom: "inception_5b/output"
+  top: "pool5/7x7_s1"
+  pooling_param {
+    pool: AVE
+    kernel_size: 7
+    stride: 1
+  }
+}
+layer {
+  name: "pool5/drop_7x7_s1"
+  type: "Dropout"
+  bottom: "pool5/7x7_s1"
+  top: "pool5/7x7_s1"
+  dropout_param {
+    dropout_ratio: 0.4
+  }
+}
+layer {
+  name: "loss3/classifier"
+  type: "InnerProduct"
+  bottom: "pool5/7x7_s1"
+  top: "loss3/classifier"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "loss3/loss3"
+  type: "SoftmaxWithLoss"
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/loss3"
+  loss_weight: 1
+}
+layer {
+  name: "loss3/top-1"
+  type: "Accuracy"
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/top-1"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss3/top-5"
+  type: "Accuracy"
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/top-5"
+  include {
+    phase: TEST
+  }
+  accuracy_param {
+    top_k: 5
+  }
+}
diff --git a/models/intel_optimized_models/googlenet/skx/train_val_dummydata.prototxt b/models/intel_optimized_models/googlenet/skx/train_val_dummydata.prototxt
new file mode 100644
index 000000000..602416008
--- /dev/null
+++ b/models/intel_optimized_models/googlenet/skx/train_val_dummydata.prototxt
@@ -0,0 +1,2427 @@
+name: "GoogleNet"
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.01
+    }
+    shape: { dim: 128 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 128 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.01
+    }
+    shape: { dim: 128 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 128 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+layer {
+  name: "conv1/7x7_s2"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1/7x7_s2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 3
+    kernel_size: 7
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "conv1/relu_7x7"
+  type: "ReLU"
+  bottom: "conv1/7x7_s2"
+  top: "conv1/7x7_s2"
+}
+layer {
+  name: "pool1/3x3_s2"
+  type: "Pooling"
+  bottom: "conv1/7x7_s2"
+  top: "pool1/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "pool1/norm1"
+  type: "LRN"
+  bottom: "pool1/3x3_s2"
+  top: "pool1/norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layer {
+  name: "conv2/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool1/norm1"
+  top: "conv2/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "conv2/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "conv2/3x3_reduce"
+  top: "conv2/3x3_reduce"
+}
+layer {
+  name: "conv2/3x3"
+  type: "Convolution"
+  bottom: "conv2/3x3_reduce"
+  top: "conv2/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "conv2/relu_3x3"
+  type: "ReLU"
+  bottom: "conv2/3x3"
+  top: "conv2/3x3"
+}
+layer {
+  name: "conv2/norm2"
+  type: "LRN"
+  bottom: "conv2/3x3"
+  top: "conv2/norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2/3x3_s2"
+  type: "Pooling"
+  bottom: "conv2/norm2"
+  top: "pool2/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "inception_3a/1x1"
+  type: "Convolution"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_3a/1x1"
+  top: "inception_3a/1x1"
+}
+layer {
+  name: "inception_3a/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_3a/3x3_reduce"
+  top: "inception_3a/3x3_reduce"
+}
+layer {
+  name: "inception_3a/3x3"
+  type: "Convolution"
+  bottom: "inception_3a/3x3_reduce"
+  top: "inception_3a/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_3a/3x3"
+  top: "inception_3a/3x3"
+}
+layer {
+  name: "inception_3a/5x5_reduce"
+  type: "Convolution"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_3a/5x5_reduce"
+  top: "inception_3a/5x5_reduce"
+}
+layer {
+  name: "inception_3a/5x5"
+  type: "Convolution"
+  bottom: "inception_3a/5x5_reduce"
+  top: "inception_3a/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_3a/5x5"
+  top: "inception_3a/5x5"
+}
+layer {
+  name: "inception_3a/pool"
+  type: "Pooling"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_3a/pool_proj"
+  type: "Convolution"
+  bottom: "inception_3a/pool"
+  top: "inception_3a/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_3a/pool_proj"
+  top: "inception_3a/pool_proj"
+}
+layer {
+  name: "inception_3a/output"
+  type: "Concat"
+  bottom: "inception_3a/1x1"
+  bottom: "inception_3a/3x3"
+  bottom: "inception_3a/5x5"
+  bottom: "inception_3a/pool_proj"
+  top: "inception_3a/output"
+}
+layer {
+  name: "inception_3b/1x1"
+  type: "Convolution"
+  bottom: "inception_3a/output"
+  top: "inception_3b/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_3b/1x1"
+  top: "inception_3b/1x1"
+}
+layer {
+  name: "inception_3b/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_3a/output"
+  top: "inception_3b/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_3b/3x3_reduce"
+  top: "inception_3b/3x3_reduce"
+}
+layer {
+  name: "inception_3b/3x3"
+  type: "Convolution"
+  bottom: "inception_3b/3x3_reduce"
+  top: "inception_3b/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_3b/3x3"
+  top: "inception_3b/3x3"
+}
+layer {
+  name: "inception_3b/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_3a/output"
+  top: "inception_3b/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_3b/5x5_reduce"
+  top: "inception_3b/5x5_reduce"
+}
+layer {
+  name: "inception_3b/5x5"
+  type: "Convolution"
+  bottom: "inception_3b/5x5_reduce"
+  top: "inception_3b/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 96
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_3b/5x5"
+  top: "inception_3b/5x5"
+}
+layer {
+  name: "inception_3b/pool"
+  type: "Pooling"
+  bottom: "inception_3a/output"
+  top: "inception_3b/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_3b/pool_proj"
+  type: "Convolution"
+  bottom: "inception_3b/pool"
+  top: "inception_3b/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_3b/pool_proj"
+  top: "inception_3b/pool_proj"
+}
+layer {
+  name: "inception_3b/output"
+  type: "Concat"
+  bottom: "inception_3b/1x1"
+  bottom: "inception_3b/3x3"
+  bottom: "inception_3b/5x5"
+  bottom: "inception_3b/pool_proj"
+  top: "inception_3b/output"
+}
+layer {
+  name: "pool3/3x3_s2"
+  type: "Pooling"
+  bottom: "inception_3b/output"
+  top: "pool3/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "inception_4a/1x1"
+  type: "Convolution"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4a/1x1"
+  top: "inception_4a/1x1"
+}
+layer {
+  name: "inception_4a/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4a/3x3_reduce"
+  top: "inception_4a/3x3_reduce"
+}
+layer {
+  name: "inception_4a/3x3"
+  type: "Convolution"
+  bottom: "inception_4a/3x3_reduce"
+  top: "inception_4a/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 208
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4a/3x3"
+  top: "inception_4a/3x3"
+}
+layer {
+  name: "inception_4a/5x5_reduce"
+  type: "Convolution"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4a/5x5_reduce"
+  top: "inception_4a/5x5_reduce"
+}
+layer {
+  name: "inception_4a/5x5"
+  type: "Convolution"
+  bottom: "inception_4a/5x5_reduce"
+  top: "inception_4a/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 48
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4a/5x5"
+  top: "inception_4a/5x5"
+}
+layer {
+  name: "inception_4a/pool"
+  type: "Pooling"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4a/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4a/pool"
+  top: "inception_4a/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4a/pool_proj"
+  top: "inception_4a/pool_proj"
+}
+layer {
+  name: "inception_4a/output"
+  type: "Concat"
+  bottom: "inception_4a/1x1"
+  bottom: "inception_4a/3x3"
+  bottom: "inception_4a/5x5"
+  bottom: "inception_4a/pool_proj"
+  top: "inception_4a/output"
+}
+layer {
+  name: "loss1/ave_pool"
+  type: "Pooling"
+  bottom: "inception_4a/output"
+  top: "loss1/ave_pool"
+  pooling_param {
+    pool: AVE
+    kernel_size: 5
+    stride: 3
+  }
+}
+layer {
+  name: "loss1/conv"
+  type: "Convolution"
+  bottom: "loss1/ave_pool"
+  top: "loss1/conv"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "loss1/relu_conv"
+  type: "ReLU"
+  bottom: "loss1/conv"
+  top: "loss1/conv"
+}
+layer {
+  name: "loss1/fc"
+  type: "InnerProduct"
+  bottom: "loss1/conv"
+  top: "loss1/fc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1024
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "loss1/relu_fc"
+  type: "ReLU"
+  bottom: "loss1/fc"
+  top: "loss1/fc"
+}
+layer {
+  name: "loss1/drop_fc"
+  type: "Dropout"
+  bottom: "loss1/fc"
+  top: "loss1/fc"
+  dropout_param {
+    dropout_ratio: 0.7
+  }
+}
+layer {
+  name: "loss1/classifier"
+  type: "InnerProduct"
+  bottom: "loss1/fc"
+  top: "loss1/classifier"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "loss1/loss"
+  type: "SoftmaxWithLoss"
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/loss1"
+  loss_weight: 0.3
+}
+layer {
+  name: "loss1/top-1"
+  type: "Accuracy"
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/top-1"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss1/top-5"
+  type: "Accuracy"
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/top-5"
+  include {
+    phase: TEST
+  }
+  accuracy_param {
+    top_k: 5
+  }
+}
+layer {
+  name: "inception_4b/1x1"
+  type: "Convolution"
+  bottom: "inception_4a/output"
+  top: "inception_4b/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4b/1x1"
+  top: "inception_4b/1x1"
+}
+layer {
+  name: "inception_4b/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4a/output"
+  top: "inception_4b/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 112
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4b/3x3_reduce"
+  top: "inception_4b/3x3_reduce"
+}
+layer {
+  name: "inception_4b/3x3"
+  type: "Convolution"
+  bottom: "inception_4b/3x3_reduce"
+  top: "inception_4b/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4b/3x3"
+  top: "inception_4b/3x3"
+}
+layer {
+  name: "inception_4b/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4a/output"
+  top: "inception_4b/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 24
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4b/5x5_reduce"
+  top: "inception_4b/5x5_reduce"
+}
+layer {
+  name: "inception_4b/5x5"
+  type: "Convolution"
+  bottom: "inception_4b/5x5_reduce"
+  top: "inception_4b/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4b/5x5"
+  top: "inception_4b/5x5"
+}
+layer {
+  name: "inception_4b/pool"
+  type: "Pooling"
+  bottom: "inception_4a/output"
+  top: "inception_4b/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4b/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4b/pool"
+  top: "inception_4b/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4b/pool_proj"
+  top: "inception_4b/pool_proj"
+}
+layer {
+  name: "inception_4b/output"
+  type: "Concat"
+  bottom: "inception_4b/1x1"
+  bottom: "inception_4b/3x3"
+  bottom: "inception_4b/5x5"
+  bottom: "inception_4b/pool_proj"
+  top: "inception_4b/output"
+}
+layer {
+  name: "inception_4c/1x1"
+  type: "Convolution"
+  bottom: "inception_4b/output"
+  top: "inception_4c/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4c/1x1"
+  top: "inception_4c/1x1"
+}
+layer {
+  name: "inception_4c/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4b/output"
+  top: "inception_4c/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4c/3x3_reduce"
+  top: "inception_4c/3x3_reduce"
+}
+layer {
+  name: "inception_4c/3x3"
+  type: "Convolution"
+  bottom: "inception_4c/3x3_reduce"
+  top: "inception_4c/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4c/3x3"
+  top: "inception_4c/3x3"
+}
+layer {
+  name: "inception_4c/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4b/output"
+  top: "inception_4c/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 24
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4c/5x5_reduce"
+  top: "inception_4c/5x5_reduce"
+}
+layer {
+  name: "inception_4c/5x5"
+  type: "Convolution"
+  bottom: "inception_4c/5x5_reduce"
+  top: "inception_4c/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4c/5x5"
+  top: "inception_4c/5x5"
+}
+layer {
+  name: "inception_4c/pool"
+  type: "Pooling"
+  bottom: "inception_4b/output"
+  top: "inception_4c/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4c/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4c/pool"
+  top: "inception_4c/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4c/pool_proj"
+  top: "inception_4c/pool_proj"
+}
+layer {
+  name: "inception_4c/output"
+  type: "Concat"
+  bottom: "inception_4c/1x1"
+  bottom: "inception_4c/3x3"
+  bottom: "inception_4c/5x5"
+  bottom: "inception_4c/pool_proj"
+  top: "inception_4c/output"
+}
+layer {
+  name: "inception_4d/1x1"
+  type: "Convolution"
+  bottom: "inception_4c/output"
+  top: "inception_4d/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 112
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4d/1x1"
+  top: "inception_4d/1x1"
+}
+layer {
+  name: "inception_4d/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4c/output"
+  top: "inception_4d/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 144
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4d/3x3_reduce"
+  top: "inception_4d/3x3_reduce"
+}
+layer {
+  name: "inception_4d/3x3"
+  type: "Convolution"
+  bottom: "inception_4d/3x3_reduce"
+  top: "inception_4d/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 288
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4d/3x3"
+  top: "inception_4d/3x3"
+}
+layer {
+  name: "inception_4d/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4c/output"
+  top: "inception_4d/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4d/5x5_reduce"
+  top: "inception_4d/5x5_reduce"
+}
+layer {
+  name: "inception_4d/5x5"
+  type: "Convolution"
+  bottom: "inception_4d/5x5_reduce"
+  top: "inception_4d/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4d/5x5"
+  top: "inception_4d/5x5"
+}
+layer {
+  name: "inception_4d/pool"
+  type: "Pooling"
+  bottom: "inception_4c/output"
+  top: "inception_4d/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4d/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4d/pool"
+  top: "inception_4d/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4d/pool_proj"
+  top: "inception_4d/pool_proj"
+}
+layer {
+  name: "inception_4d/output"
+  type: "Concat"
+  bottom: "inception_4d/1x1"
+  bottom: "inception_4d/3x3"
+  bottom: "inception_4d/5x5"
+  bottom: "inception_4d/pool_proj"
+  top: "inception_4d/output"
+}
+layer {
+  name: "loss2/ave_pool"
+  type: "Pooling"
+  bottom: "inception_4d/output"
+  top: "loss2/ave_pool"
+  pooling_param {
+    pool: AVE
+    kernel_size: 5
+    stride: 3
+  }
+}
+layer {
+  name: "loss2/conv"
+  type: "Convolution"
+  bottom: "loss2/ave_pool"
+  top: "loss2/conv"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "loss2/relu_conv"
+  type: "ReLU"
+  bottom: "loss2/conv"
+  top: "loss2/conv"
+}
+layer {
+  name: "loss2/fc"
+  type: "InnerProduct"
+  bottom: "loss2/conv"
+  top: "loss2/fc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1024
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "loss2/relu_fc"
+  type: "ReLU"
+  bottom: "loss2/fc"
+  top: "loss2/fc"
+}
+layer {
+  name: "loss2/drop_fc"
+  type: "Dropout"
+  bottom: "loss2/fc"
+  top: "loss2/fc"
+  dropout_param {
+    dropout_ratio: 0.7
+  }
+}
+layer {
+  name: "loss2/classifier"
+  type: "InnerProduct"
+  bottom: "loss2/fc"
+  top: "loss2/classifier"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "loss2/loss"
+  type: "SoftmaxWithLoss"
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/loss1"
+  loss_weight: 0.3
+}
+layer {
+  name: "loss2/top-1"
+  type: "Accuracy"
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/top-1"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss2/top-5"
+  type: "Accuracy"
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/top-5"
+  include {
+    phase: TEST
+  }
+  accuracy_param {
+    top_k: 5
+  }
+}
+layer {
+  name: "inception_4e/1x1"
+  type: "Convolution"
+  bottom: "inception_4d/output"
+  top: "inception_4e/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4e/1x1"
+  top: "inception_4e/1x1"
+}
+layer {
+  name: "inception_4e/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4d/output"
+  top: "inception_4e/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4e/3x3_reduce"
+  top: "inception_4e/3x3_reduce"
+}
+layer {
+  name: "inception_4e/3x3"
+  type: "Convolution"
+  bottom: "inception_4e/3x3_reduce"
+  top: "inception_4e/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4e/3x3"
+  top: "inception_4e/3x3"
+}
+layer {
+  name: "inception_4e/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4d/output"
+  top: "inception_4e/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4e/5x5_reduce"
+  top: "inception_4e/5x5_reduce"
+}
+layer {
+  name: "inception_4e/5x5"
+  type: "Convolution"
+  bottom: "inception_4e/5x5_reduce"
+  top: "inception_4e/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4e/5x5"
+  top: "inception_4e/5x5"
+}
+layer {
+  name: "inception_4e/pool"
+  type: "Pooling"
+  bottom: "inception_4d/output"
+  top: "inception_4e/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4e/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4e/pool"
+  top: "inception_4e/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4e/pool_proj"
+  top: "inception_4e/pool_proj"
+}
+layer {
+  name: "inception_4e/output"
+  type: "Concat"
+  bottom: "inception_4e/1x1"
+  bottom: "inception_4e/3x3"
+  bottom: "inception_4e/5x5"
+  bottom: "inception_4e/pool_proj"
+  top: "inception_4e/output"
+}
+layer {
+  name: "pool4/3x3_s2"
+  type: "Pooling"
+  bottom: "inception_4e/output"
+  top: "pool4/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "inception_5a/1x1"
+  type: "Convolution"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_5a/1x1"
+  top: "inception_5a/1x1"
+}
+layer {
+  name: "inception_5a/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_5a/3x3_reduce"
+  top: "inception_5a/3x3_reduce"
+}
+layer {
+  name: "inception_5a/3x3"
+  type: "Convolution"
+  bottom: "inception_5a/3x3_reduce"
+  top: "inception_5a/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_5a/3x3"
+  top: "inception_5a/3x3"
+}
+layer {
+  name: "inception_5a/5x5_reduce"
+  type: "Convolution"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_5a/5x5_reduce"
+  top: "inception_5a/5x5_reduce"
+}
+layer {
+  name: "inception_5a/5x5"
+  type: "Convolution"
+  bottom: "inception_5a/5x5_reduce"
+  top: "inception_5a/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_5a/5x5"
+  top: "inception_5a/5x5"
+}
+layer {
+  name: "inception_5a/pool"
+  type: "Pooling"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_5a/pool_proj"
+  type: "Convolution"
+  bottom: "inception_5a/pool"
+  top: "inception_5a/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_5a/pool_proj"
+  top: "inception_5a/pool_proj"
+}
+layer {
+  name: "inception_5a/output"
+  type: "Concat"
+  bottom: "inception_5a/1x1"
+  bottom: "inception_5a/3x3"
+  bottom: "inception_5a/5x5"
+  bottom: "inception_5a/pool_proj"
+  top: "inception_5a/output"
+}
+layer {
+  name: "inception_5b/1x1"
+  type: "Convolution"
+  bottom: "inception_5a/output"
+  top: "inception_5b/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 384
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_5b/1x1"
+  top: "inception_5b/1x1"
+}
+layer {
+  name: "inception_5b/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_5a/output"
+  top: "inception_5b/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_5b/3x3_reduce"
+  top: "inception_5b/3x3_reduce"
+}
+layer {
+  name: "inception_5b/3x3"
+  type: "Convolution"
+  bottom: "inception_5b/3x3_reduce"
+  top: "inception_5b/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_5b/3x3"
+  top: "inception_5b/3x3"
+}
+layer {
+  name: "inception_5b/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_5a/output"
+  top: "inception_5b/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 48
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_5b/5x5_reduce"
+  top: "inception_5b/5x5_reduce"
+}
+layer {
+  name: "inception_5b/5x5"
+  type: "Convolution"
+  bottom: "inception_5b/5x5_reduce"
+  top: "inception_5b/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_5b/5x5"
+  top: "inception_5b/5x5"
+}
+layer {
+  name: "inception_5b/pool"
+  type: "Pooling"
+  bottom: "inception_5a/output"
+  top: "inception_5b/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_5b/pool_proj"
+  type: "Convolution"
+  bottom: "inception_5b/pool"
+  top: "inception_5b/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_5b/pool_proj"
+  top: "inception_5b/pool_proj"
+}
+layer {
+  name: "inception_5b/output"
+  type: "Concat"
+  bottom: "inception_5b/1x1"
+  bottom: "inception_5b/3x3"
+  bottom: "inception_5b/5x5"
+  bottom: "inception_5b/pool_proj"
+  top: "inception_5b/output"
+}
+layer {
+  name: "pool5/7x7_s1"
+  type: "Pooling"
+  bottom: "inception_5b/output"
+  top: "pool5/7x7_s1"
+  pooling_param {
+    pool: AVE
+    kernel_size: 7
+    stride: 1
+  }
+}
+layer {
+  name: "pool5/drop_7x7_s1"
+  type: "Dropout"
+  bottom: "pool5/7x7_s1"
+  top: "pool5/7x7_s1"
+  dropout_param {
+    dropout_ratio: 0.4
+  }
+}
+layer {
+  name: "loss3/classifier"
+  type: "InnerProduct"
+  bottom: "pool5/7x7_s1"
+  top: "loss3/classifier"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "loss3/loss3"
+  type: "SoftmaxWithLoss"
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/loss3"
+  loss_weight: 1
+}
+layer {
+  name: "loss3/top-1"
+  type: "Accuracy"
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/top-1"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss3/top-5"
+  type: "Accuracy"
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/top-5"
+  include {
+    phase: TEST
+  }
+  accuracy_param {
+    top_k: 5
+  }
+}
diff --git a/models/intel_optimized_models/googlenet_v2/bdw/train_val_dummydata.prototxt b/models/intel_optimized_models/googlenet_v2/bdw/train_val_dummydata.prototxt
new file mode 100644
index 000000000..2908c3841
--- /dev/null
+++ b/models/intel_optimized_models/googlenet_v2/bdw/train_val_dummydata.prototxt
@@ -0,0 +1,4034 @@
+# Inception Network (GoogLeNet Batch Normalization Network)
+name: "InceptionNetwork"
+### Training Set
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  dummy_data_param {
+    data_filler {
+        type: "constant"
+        value: 0.01
+    }
+    shape: { dim: 96 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 96 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+### Validation Set
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.01
+    }
+    shape: { dim: 32 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 32 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+layer {
+  bottom: "data"
+  top: "conv1/7x7_s2"
+  name: "conv1/7x7_s2"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 3
+    kernel_size: 7
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "conv1/7x7_s2"
+  name: "conv1/7x7_s2/bn"
+  top: "conv1/7x7_s2/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "conv1/7x7_s2/bn"
+  top: "conv1/7x7_s2/bn/sc"
+  name: "conv1/7x7_s2/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "conv1/7x7_s2/bn/sc"
+  top: "conv1/7x7_s2/bn/sc"
+  name: "conv1/7x7_s2/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "conv1/7x7_s2/bn/sc"
+  top: "pool1/3x3_s2"
+  name: "pool1/3x3_s2"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+ bottom: "pool1/3x3_s2"
+  top: "conv2/3x3_reduce"
+  name: "conv2/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "conv2/3x3_reduce"
+  name: "conv2/3x3_reduce/bn"
+  top: "conv2/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "conv2/3x3_reduce/bn"
+  top: "conv2/3x3_reduce/bn/sc"
+  name: "conv2/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "conv2/3x3_reduce/bn/sc"
+  top: "conv2/3x3_reduce/bn/sc"
+  name: "conv2/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "conv2/3x3_reduce/bn/sc"
+  top: "conv2/3x3"
+  name: "conv2/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "conv2/3x3"
+  name: "conv2/3x3/bn"
+  top: "conv2/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "conv2/3x3/bn"
+  top: "conv2/3x3/bn/sc"
+  name: "conv2/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "conv2/3x3/bn/sc"
+  top: "conv2/3x3/bn/sc"
+  name: "conv2/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "conv2/3x3/bn/sc"
+  top: "pool2/3x3_s2"
+  name: "pool2/3x3_s2"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+ bottom: "pool2/3x3_s2"
+  top: "inception_3a/1x1"
+  name: "inception_3a/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/1x1"
+  name: "inception_3a/1x1/bn"
+  top: "inception_3a/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/1x1/bn"
+  top: "inception_3a/1x1/bn/sc"
+  name: "inception_3a/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/1x1/bn/sc"
+  top: "inception_3a/1x1/bn/sc"
+  name: "inception_3a/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "pool2/3x3_s2"
+  top: "inception_3a/3x3_reduce"
+  name: "inception_3a/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/3x3_reduce"
+  name: "inception_3a/3x3_reduce/bn"
+  top: "inception_3a/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/3x3_reduce/bn"
+  top: "inception_3a/3x3_reduce/bn/sc"
+  name: "inception_3a/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/3x3_reduce/bn/sc"
+  top: "inception_3a/3x3_reduce/bn/sc"
+  name: "inception_3a/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3a/3x3_reduce/bn/sc"
+  top: "inception_3a/3x3"
+  name: "inception_3a/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/3x3"
+  name: "inception_3a/3x3/bn"
+  top: "inception_3a/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/3x3/bn"
+  top: "inception_3a/3x3/bn/sc"
+  name: "inception_3a/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/3x3/bn/sc"
+  top: "inception_3a/3x3/bn/sc"
+  name: "inception_3a/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "pool2/3x3_s2"
+  top: "inception_3a/double3x3_reduce"
+  name: "inception_3a/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3_reduce"
+  name: "inception_3a/double3x3_reduce/bn"
+  top: "inception_3a/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3_reduce/bn"
+  top: "inception_3a/double3x3_reduce/bn/sc"
+  name: "inception_3a/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3_reduce/bn/sc"
+  top: "inception_3a/double3x3_reduce/bn/sc"
+  name: "inception_3a/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3a/double3x3_reduce/bn/sc"
+  top: "inception_3a/double3x3a"
+  name: "inception_3a/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3a"
+  name: "inception_3a/double3x3a/bn"
+  top: "inception_3a/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3a/bn"
+  top: "inception_3a/double3x3a/bn/sc"
+  name: "inception_3a/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3a/bn/sc"
+  top: "inception_3a/double3x3a/bn/sc"
+  name: "inception_3a/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3a/double3x3a/bn/sc"
+  top: "inception_3a/double3x3b"
+  name: "inception_3a/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3b"
+  name: "inception_3a/double3x3b/bn"
+  top: "inception_3a/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3b/bn"
+  top: "inception_3a/double3x3b/bn/sc"
+  name: "inception_3a/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3b/bn/sc"
+  top: "inception_3a/double3x3b/bn/sc"
+  name: "inception_3a/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/pool"
+  name: "inception_3a/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_3a/pool"
+  top: "inception_3a/pool_proj"
+  name: "inception_3a/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 32
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/pool_proj"
+  name: "inception_3a/pool_proj/bn"
+  top: "inception_3a/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/pool_proj/bn"
+  top: "inception_3a/pool_proj/bn/sc"
+  name: "inception_3a/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/pool_proj/bn/sc"
+  top: "inception_3a/pool_proj/bn/sc"
+  name: "inception_3a/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/1x1/bn/sc"
+  bottom: "inception_3a/3x3/bn/sc"
+  bottom: "inception_3a/double3x3b/bn/sc"
+  bottom: "inception_3a/pool_proj/bn/sc"
+  top: "inception_3a/output"
+  name: "inception_3a/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3a/output"
+  top: "inception_3b/1x1"
+  name: "inception_3b/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/1x1"
+  name: "inception_3b/1x1/bn"
+  top: "inception_3b/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/1x1/bn"
+  top: "inception_3b/1x1/bn/sc"
+  name: "inception_3b/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/1x1/bn/sc"
+  top: "inception_3b/1x1/bn/sc"
+  name: "inception_3b/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3a/output"
+  top: "inception_3b/3x3_reduce"
+  name: "inception_3b/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/3x3_reduce"
+  name: "inception_3b/3x3_reduce/bn"
+  top: "inception_3b/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/3x3_reduce/bn"
+  top: "inception_3b/3x3_reduce/bn/sc"
+  name: "inception_3b/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/3x3_reduce/bn/sc"
+  top: "inception_3b/3x3_reduce/bn/sc"
+  name: "inception_3b/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3b/3x3_reduce/bn/sc"
+  top: "inception_3b/3x3"
+  name: "inception_3b/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/3x3"
+  name: "inception_3b/3x3/bn"
+  top: "inception_3b/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/3x3/bn"
+  top: "inception_3b/3x3/bn/sc"
+  name: "inception_3b/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/3x3/bn/sc"
+  top: "inception_3b/3x3/bn/sc"
+  name: "inception_3b/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3a/output"
+  top: "inception_3b/double3x3_reduce"
+  name: "inception_3b/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3_reduce"
+  name: "inception_3b/double3x3_reduce/bn"
+  top: "inception_3b/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3_reduce/bn"
+  top: "inception_3b/double3x3_reduce/bn/sc"
+  name: "inception_3b/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3_reduce/bn/sc"
+  top: "inception_3b/double3x3_reduce/bn/sc"
+  name: "inception_3b/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3b/double3x3_reduce/bn/sc"
+  top: "inception_3b/double3x3a"
+  name: "inception_3b/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3a"
+  name: "inception_3b/double3x3a/bn"
+  top: "inception_3b/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3a/bn"
+  top: "inception_3b/double3x3a/bn/sc"
+  name: "inception_3b/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3a/bn/sc"
+  top: "inception_3b/double3x3a/bn/sc"
+  name: "inception_3b/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3b/double3x3a/bn/sc"
+  top: "inception_3b/double3x3b"
+  name: "inception_3b/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3b"
+  name: "inception_3b/double3x3b/bn"
+  top: "inception_3b/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3b/bn"
+  top: "inception_3b/double3x3b/bn/sc"
+  name: "inception_3b/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3b/bn/sc"
+  top: "inception_3b/double3x3b/bn/sc"
+  name: "inception_3b/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/output"
+  top: "inception_3b/pool"
+  name: "inception_3b/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_3b/pool"
+  top: "inception_3b/pool_proj"
+  name: "inception_3b/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/pool_proj"
+  name: "inception_3b/pool_proj/bn"
+  top: "inception_3b/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/pool_proj/bn"
+  top: "inception_3b/pool_proj/bn/sc"
+  name: "inception_3b/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/pool_proj/bn/sc"
+  top: "inception_3b/pool_proj/bn/sc"
+  name: "inception_3b/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/1x1/bn/sc"
+  bottom: "inception_3b/3x3/bn/sc"
+  bottom: "inception_3b/double3x3b/bn/sc"
+  bottom: "inception_3b/pool_proj/bn/sc"
+  top: "inception_3b/output"
+  name: "inception_3b/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3b/output"
+  top: "inception_3c/3x3_reduce"
+  name: "inception_3c/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3c/3x3_reduce"
+  name: "inception_3c/3x3_reduce/bn"
+  top: "inception_3c/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/3x3_reduce/bn"
+  top: "inception_3c/3x3_reduce/bn/sc"
+  name: "inception_3c/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3c/3x3_reduce/bn/sc"
+  top: "inception_3c/3x3_reduce/bn/sc"
+  name: "inception_3c/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3c/3x3_reduce/bn/sc"
+  top: "inception_3c/3x3"
+  name: "inception_3c/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3c/3x3"
+  name: "inception_3c/3x3/bn"
+  top: "inception_3c/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/3x3/bn"
+  top: "inception_3c/3x3/bn/sc"
+  name: "inception_3c/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3c/3x3/bn/sc"
+  top: "inception_3c/3x3/bn/sc"
+  name: "inception_3c/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3b/output"
+  top: "inception_3c/double3x3_reduce"
+  name: "inception_3c/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3_reduce"
+  name: "inception_3c/double3x3_reduce/bn"
+  top: "inception_3c/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3_reduce/bn"
+  top: "inception_3c/double3x3_reduce/bn/sc"
+  name: "inception_3c/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3_reduce/bn/sc"
+  top: "inception_3c/double3x3_reduce/bn/sc"
+  name: "inception_3c/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3c/double3x3_reduce/bn/sc"
+  top: "inception_3c/double3x3a"
+  name: "inception_3c/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3a"
+  name: "inception_3c/double3x3a/bn"
+  top: "inception_3c/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3a/bn"
+  top: "inception_3c/double3x3a/bn/sc"
+  name: "inception_3c/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3a/bn/sc"
+  top: "inception_3c/double3x3a/bn/sc"
+  name: "inception_3c/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3c/double3x3a/bn/sc"
+  top: "inception_3c/double3x3b"
+  name: "inception_3c/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3b"
+  name: "inception_3c/double3x3b/bn"
+  top: "inception_3c/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3b/bn"
+  top: "inception_3c/double3x3b/bn/sc"
+  name: "inception_3c/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3b/bn/sc"
+  top: "inception_3c/double3x3b/bn/sc"
+  name: "inception_3c/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/output"
+  top: "inception_3c/pool"
+  name: "inception_3c/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  bottom: "inception_3c/3x3/bn/sc"
+  bottom: "inception_3c/double3x3b/bn/sc"
+  bottom: "inception_3c/pool"
+  top: "inception_3c/output"
+  name: "inception_3c/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/output"
+  top: "pool3/5x5_s3"
+  name: "pool3/5x5_s3"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 5
+    stride: 3
+  }
+}
+layer {
+ bottom: "pool3/5x5_s3"
+  top: "loss1/conv"
+  name: "loss1/conv"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "loss1/conv"
+  name: "loss1/conv/bn"
+  top: "loss1/conv/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "loss1/conv/bn"
+  top: "loss1/conv/bn/sc"
+  name: "loss1/conv/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "loss1/conv/bn/sc"
+  top: "loss1/conv/bn/sc"
+  name: "loss1/conv/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "loss1/conv/bn/sc"
+  top: "loss1/fc"
+  name: "loss1/fc"
+  type: "InnerProduct"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  inner_product_param {
+    num_output: 1024
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "loss1/fc"
+  name: "loss1/fc/bn"
+  top: "loss1/fc/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "loss1/fc/bn"
+  top: "loss1/fc/bn/sc"
+  name: "loss1/fc/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "loss1/fc/bn/sc"
+  top: "loss1/fc/bn/sc"
+  name: "loss1/fc/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "loss1/fc/bn/sc"
+  top: "loss1/classifier"
+  name: "loss1/classifier"
+  type: "InnerProduct"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/loss"
+  name: "loss1/loss"
+  type: "SoftmaxWithLoss"
+  loss_weight: 0.3
+}
+layer {
+  bottom: "loss1/classifier"
+  top: "loss1/prob"
+  name: "loss1/prob"
+  type: "Softmax"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  bottom: "loss1/prob"
+  bottom: "label"
+  top: "loss1/top-1"
+  name: "loss1/top-1"
+  type: "Accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  bottom: "loss1/prob"
+  bottom: "label"
+  top: "loss1/top-5"
+  name: "loss1/top-5"
+  type: "Accuracy"
+  accuracy_param {
+    top_k: 5
+  }
+  include {
+    phase: TEST
+  }
+}
+layer {
+ bottom: "inception_3c/output"
+  top: "inception_4a/1x1"
+  name: "inception_4a/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 224
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/1x1"
+  name: "inception_4a/1x1/bn"
+  top: "inception_4a/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/1x1/bn"
+  top: "inception_4a/1x1/bn/sc"
+  name: "inception_4a/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/1x1/bn/sc"
+  top: "inception_4a/1x1/bn/sc"
+  name: "inception_4a/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3c/output"
+  top: "inception_4a/3x3_reduce"
+  name: "inception_4a/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/3x3_reduce"
+  name: "inception_4a/3x3_reduce/bn"
+  top: "inception_4a/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/3x3_reduce/bn"
+  top: "inception_4a/3x3_reduce/bn/sc"
+  name: "inception_4a/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/3x3_reduce/bn/sc"
+  top: "inception_4a/3x3_reduce/bn/sc"
+  name: "inception_4a/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4a/3x3_reduce/bn/sc"
+  top: "inception_4a/3x3"
+  name: "inception_4a/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/3x3"
+  name: "inception_4a/3x3/bn"
+  top: "inception_4a/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/3x3/bn"
+  top: "inception_4a/3x3/bn/sc"
+  name: "inception_4a/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/3x3/bn/sc"
+  top: "inception_4a/3x3/bn/sc"
+  name: "inception_4a/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3c/output"
+  top: "inception_4a/double3x3_reduce"
+  name: "inception_4a/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3_reduce"
+  name: "inception_4a/double3x3_reduce/bn"
+  top: "inception_4a/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3_reduce/bn"
+  top: "inception_4a/double3x3_reduce/bn/sc"
+  name: "inception_4a/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3_reduce/bn/sc"
+  top: "inception_4a/double3x3_reduce/bn/sc"
+  name: "inception_4a/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4a/double3x3_reduce/bn/sc"
+  top: "inception_4a/double3x3a"
+  name: "inception_4a/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3a"
+  name: "inception_4a/double3x3a/bn"
+  top: "inception_4a/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3a/bn"
+  top: "inception_4a/double3x3a/bn/sc"
+  name: "inception_4a/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3a/bn/sc"
+  top: "inception_4a/double3x3a/bn/sc"
+  name: "inception_4a/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4a/double3x3a/bn/sc"
+  top: "inception_4a/double3x3b"
+  name: "inception_4a/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3b"
+  name: "inception_4a/double3x3b/bn"
+  top: "inception_4a/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3b/bn"
+  top: "inception_4a/double3x3b/bn/sc"
+  name: "inception_4a/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3b/bn/sc"
+  top: "inception_4a/double3x3b/bn/sc"
+  name: "inception_4a/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/output"
+  top: "inception_4a/pool"
+  name: "inception_4a/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_4a/pool"
+  top: "inception_4a/pool_proj"
+  name: "inception_4a/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/pool_proj"
+  name: "inception_4a/pool_proj/bn"
+  top: "inception_4a/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/pool_proj/bn"
+  top: "inception_4a/pool_proj/bn/sc"
+  name: "inception_4a/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/pool_proj/bn/sc"
+  top: "inception_4a/pool_proj/bn/sc"
+  name: "inception_4a/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/1x1/bn/sc"
+  bottom: "inception_4a/3x3/bn/sc"
+  bottom: "inception_4a/double3x3b/bn/sc"
+  bottom: "inception_4a/pool_proj/bn/sc"
+  top: "inception_4a/output"
+  name: "inception_4a/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4a/output"
+  top: "inception_4b/1x1"
+  name: "inception_4b/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/1x1"
+  name: "inception_4b/1x1/bn"
+  top: "inception_4b/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/1x1/bn"
+  top: "inception_4b/1x1/bn/sc"
+  name: "inception_4b/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/1x1/bn/sc"
+  top: "inception_4b/1x1/bn/sc"
+  name: "inception_4b/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4a/output"
+  top: "inception_4b/3x3_reduce"
+  name: "inception_4b/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/3x3_reduce"
+  name: "inception_4b/3x3_reduce/bn"
+  top: "inception_4b/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/3x3_reduce/bn"
+  top: "inception_4b/3x3_reduce/bn/sc"
+  name: "inception_4b/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/3x3_reduce/bn/sc"
+  top: "inception_4b/3x3_reduce/bn/sc"
+  name: "inception_4b/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4b/3x3_reduce/bn/sc"
+  top: "inception_4b/3x3"
+  name: "inception_4b/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/3x3"
+  name: "inception_4b/3x3/bn"
+  top: "inception_4b/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/3x3/bn"
+  top: "inception_4b/3x3/bn/sc"
+  name: "inception_4b/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/3x3/bn/sc"
+  top: "inception_4b/3x3/bn/sc"
+  name: "inception_4b/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4a/output"
+  top: "inception_4b/double3x3_reduce"
+  name: "inception_4b/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3_reduce"
+  name: "inception_4b/double3x3_reduce/bn"
+  top: "inception_4b/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3_reduce/bn"
+  top: "inception_4b/double3x3_reduce/bn/sc"
+  name: "inception_4b/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3_reduce/bn/sc"
+  top: "inception_4b/double3x3_reduce/bn/sc"
+  name: "inception_4b/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4b/double3x3_reduce/bn/sc"
+  top: "inception_4b/double3x3a"
+  name: "inception_4b/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3a"
+  name: "inception_4b/double3x3a/bn"
+  top: "inception_4b/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3a/bn"
+  top: "inception_4b/double3x3a/bn/sc"
+  name: "inception_4b/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3a/bn/sc"
+  top: "inception_4b/double3x3a/bn/sc"
+  name: "inception_4b/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4b/double3x3a/bn/sc"
+  top: "inception_4b/double3x3b"
+  name: "inception_4b/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3b"
+  name: "inception_4b/double3x3b/bn"
+  top: "inception_4b/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3b/bn"
+  top: "inception_4b/double3x3b/bn/sc"
+  name: "inception_4b/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3b/bn/sc"
+  top: "inception_4b/double3x3b/bn/sc"
+  name: "inception_4b/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/output"
+  top: "inception_4b/pool"
+  name: "inception_4b/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_4b/pool"
+  top: "inception_4b/pool_proj"
+  name: "inception_4b/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/pool_proj"
+  name: "inception_4b/pool_proj/bn"
+  top: "inception_4b/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/pool_proj/bn"
+  top: "inception_4b/pool_proj/bn/sc"
+  name: "inception_4b/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/pool_proj/bn/sc"
+  top: "inception_4b/pool_proj/bn/sc"
+  name: "inception_4b/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/1x1/bn/sc"
+  bottom: "inception_4b/3x3/bn/sc"
+  bottom: "inception_4b/double3x3b/bn/sc"
+  bottom: "inception_4b/pool_proj/bn/sc"
+  top: "inception_4b/output"
+  name: "inception_4b/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4b/output"
+  top: "inception_4c/1x1"
+  name: "inception_4c/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/1x1"
+  name: "inception_4c/1x1/bn"
+  top: "inception_4c/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/1x1/bn"
+  top: "inception_4c/1x1/bn/sc"
+  name: "inception_4c/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/1x1/bn/sc"
+  top: "inception_4c/1x1/bn/sc"
+  name: "inception_4c/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4b/output"
+  top: "inception_4c/3x3_reduce"
+  name: "inception_4c/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/3x3_reduce"
+  name: "inception_4c/3x3_reduce/bn"
+  top: "inception_4c/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/3x3_reduce/bn"
+  top: "inception_4c/3x3_reduce/bn/sc"
+  name: "inception_4c/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/3x3_reduce/bn/sc"
+  top: "inception_4c/3x3_reduce/bn/sc"
+  name: "inception_4c/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4c/3x3_reduce/bn/sc"
+  top: "inception_4c/3x3"
+  name: "inception_4c/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/3x3"
+  name: "inception_4c/3x3/bn"
+  top: "inception_4c/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/3x3/bn"
+  top: "inception_4c/3x3/bn/sc"
+  name: "inception_4c/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/3x3/bn/sc"
+  top: "inception_4c/3x3/bn/sc"
+  name: "inception_4c/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4b/output"
+  top: "inception_4c/double3x3_reduce"
+  name: "inception_4c/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3_reduce"
+  name: "inception_4c/double3x3_reduce/bn"
+  top: "inception_4c/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3_reduce/bn"
+  top: "inception_4c/double3x3_reduce/bn/sc"
+  name: "inception_4c/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3_reduce/bn/sc"
+  top: "inception_4c/double3x3_reduce/bn/sc"
+  name: "inception_4c/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4c/double3x3_reduce/bn/sc"
+  top: "inception_4c/double3x3a"
+  name: "inception_4c/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3a"
+  name: "inception_4c/double3x3a/bn"
+  top: "inception_4c/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3a/bn"
+  top: "inception_4c/double3x3a/bn/sc"
+  name: "inception_4c/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3a/bn/sc"
+  top: "inception_4c/double3x3a/bn/sc"
+  name: "inception_4c/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4c/double3x3a/bn/sc"
+  top: "inception_4c/double3x3b"
+  name: "inception_4c/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3b"
+  name: "inception_4c/double3x3b/bn"
+  top: "inception_4c/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3b/bn"
+  top: "inception_4c/double3x3b/bn/sc"
+  name: "inception_4c/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3b/bn/sc"
+  top: "inception_4c/double3x3b/bn/sc"
+  name: "inception_4c/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/output"
+  top: "inception_4c/pool"
+  name: "inception_4c/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_4c/pool"
+  top: "inception_4c/pool_proj"
+  name: "inception_4c/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/pool_proj"
+  name: "inception_4c/pool_proj/bn"
+  top: "inception_4c/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/pool_proj/bn"
+  top: "inception_4c/pool_proj/bn/sc"
+  name: "inception_4c/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/pool_proj/bn/sc"
+  top: "inception_4c/pool_proj/bn/sc"
+  name: "inception_4c/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/1x1/bn/sc"
+  bottom: "inception_4c/3x3/bn/sc"
+  bottom: "inception_4c/double3x3b/bn/sc"
+  bottom: "inception_4c/pool_proj/bn/sc"
+  top: "inception_4c/output"
+  name: "inception_4c/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4c/output"
+  top: "inception_4d/1x1"
+  name: "inception_4d/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/1x1"
+  name: "inception_4d/1x1/bn"
+  top: "inception_4d/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/1x1/bn"
+  top: "inception_4d/1x1/bn/sc"
+  name: "inception_4d/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/1x1/bn/sc"
+  top: "inception_4d/1x1/bn/sc"
+  name: "inception_4d/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4c/output"
+  top: "inception_4d/3x3_reduce"
+  name: "inception_4d/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/3x3_reduce"
+  name: "inception_4d/3x3_reduce/bn"
+  top: "inception_4d/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/3x3_reduce/bn"
+  top: "inception_4d/3x3_reduce/bn/sc"
+  name: "inception_4d/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/3x3_reduce/bn/sc"
+  top: "inception_4d/3x3_reduce/bn/sc"
+  name: "inception_4d/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4d/3x3_reduce/bn/sc"
+  top: "inception_4d/3x3"
+  name: "inception_4d/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/3x3"
+  name: "inception_4d/3x3/bn"
+  top: "inception_4d/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/3x3/bn"
+  top: "inception_4d/3x3/bn/sc"
+  name: "inception_4d/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/3x3/bn/sc"
+  top: "inception_4d/3x3/bn/sc"
+  name: "inception_4d/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4c/output"
+  top: "inception_4d/double3x3_reduce"
+  name: "inception_4d/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3_reduce"
+  name: "inception_4d/double3x3_reduce/bn"
+  top: "inception_4d/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3_reduce/bn"
+  top: "inception_4d/double3x3_reduce/bn/sc"
+  name: "inception_4d/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3_reduce/bn/sc"
+  top: "inception_4d/double3x3_reduce/bn/sc"
+  name: "inception_4d/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4d/double3x3_reduce/bn/sc"
+  top: "inception_4d/double3x3a"
+  name: "inception_4d/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3a"
+  name: "inception_4d/double3x3a/bn"
+  top: "inception_4d/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3a/bn"
+  top: "inception_4d/double3x3a/bn/sc"
+  name: "inception_4d/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3a/bn/sc"
+  top: "inception_4d/double3x3a/bn/sc"
+  name: "inception_4d/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4d/double3x3a/bn/sc"
+  top: "inception_4d/double3x3b"
+  name: "inception_4d/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3b"
+  name: "inception_4d/double3x3b/bn"
+  top: "inception_4d/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3b/bn"
+  top: "inception_4d/double3x3b/bn/sc"
+  name: "inception_4d/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3b/bn/sc"
+  top: "inception_4d/double3x3b/bn/sc"
+  name: "inception_4d/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/output"
+  top: "inception_4d/pool"
+  name: "inception_4d/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_4d/pool"
+  top: "inception_4d/pool_proj"
+  name: "inception_4d/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/pool_proj"
+  name: "inception_4d/pool_proj/bn"
+  top: "inception_4d/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/pool_proj/bn"
+  top: "inception_4d/pool_proj/bn/sc"
+  name: "inception_4d/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/pool_proj/bn/sc"
+  top: "inception_4d/pool_proj/bn/sc"
+  name: "inception_4d/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/1x1/bn/sc"
+  bottom: "inception_4d/3x3/bn/sc"
+  bottom: "inception_4d/double3x3b/bn/sc"
+  bottom: "inception_4d/pool_proj/bn/sc"
+  top: "inception_4d/output"
+  name: "inception_4d/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4d/output"
+  top: "inception_4e/3x3_reduce"
+  name: "inception_4e/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4e/3x3_reduce"
+  name: "inception_4e/3x3_reduce/bn"
+  top: "inception_4e/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/3x3_reduce/bn"
+  top: "inception_4e/3x3_reduce/bn/sc"
+  name: "inception_4e/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4e/3x3_reduce/bn/sc"
+  top: "inception_4e/3x3_reduce/bn/sc"
+  name: "inception_4e/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4e/3x3_reduce/bn/sc"
+  top: "inception_4e/3x3"
+  name: "inception_4e/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4e/3x3"
+  name: "inception_4e/3x3/bn"
+  top: "inception_4e/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/3x3/bn"
+  top: "inception_4e/3x3/bn/sc"
+  name: "inception_4e/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4e/3x3/bn/sc"
+  top: "inception_4e/3x3/bn/sc"
+  name: "inception_4e/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4d/output"
+  top: "inception_4e/double3x3_reduce"
+  name: "inception_4e/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3_reduce"
+  name: "inception_4e/double3x3_reduce/bn"
+  top: "inception_4e/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3_reduce/bn"
+  top: "inception_4e/double3x3_reduce/bn/sc"
+  name: "inception_4e/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3_reduce/bn/sc"
+  top: "inception_4e/double3x3_reduce/bn/sc"
+  name: "inception_4e/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4e/double3x3_reduce/bn/sc"
+  top: "inception_4e/double3x3a"
+  name: "inception_4e/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3a"
+  name: "inception_4e/double3x3a/bn"
+  top: "inception_4e/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3a/bn"
+  top: "inception_4e/double3x3a/bn/sc"
+  name: "inception_4e/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3a/bn/sc"
+  top: "inception_4e/double3x3a/bn/sc"
+  name: "inception_4e/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4e/double3x3a/bn/sc"
+  top: "inception_4e/double3x3b"
+  name: "inception_4e/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3b"
+  name: "inception_4e/double3x3b/bn"
+  top: "inception_4e/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3b/bn"
+  top: "inception_4e/double3x3b/bn/sc"
+  name: "inception_4e/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3b/bn/sc"
+  top: "inception_4e/double3x3b/bn/sc"
+  name: "inception_4e/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/output"
+  top: "inception_4e/pool"
+  name: "inception_4e/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  bottom: "inception_4e/3x3/bn/sc"
+  bottom: "inception_4e/double3x3b/bn/sc"
+  bottom: "inception_4e/pool"
+  top: "inception_4e/output"
+  name: "inception_4e/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/output"
+  top: "pool4/5x5_s3"
+  name: "pool4/5x5_s3"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 5
+    stride: 3
+  }
+}
+layer {
+ bottom: "pool4/5x5_s3"
+  top: "loss2/conv"
+  name: "loss2/conv"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "loss2/conv"
+  name: "loss2/conv/bn"
+  top: "loss2/conv/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "loss2/conv/bn"
+  top: "loss2/conv/bn/sc"
+  name: "loss2/conv/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "loss2/conv/bn/sc"
+  top: "loss2/conv/bn/sc"
+  name: "loss2/conv/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "loss2/conv/bn/sc"
+  top: "loss2/fc"
+  name: "loss2/fc"
+  type: "InnerProduct"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  inner_product_param {
+    num_output: 1024
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "loss2/fc"
+  name: "loss2/fc/bn"
+  top: "loss2/fc/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "loss2/fc/bn"
+  top: "loss2/fc/bn/sc"
+  name: "loss2/fc/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "loss2/fc/bn/sc"
+  top: "loss2/fc/bn/sc"
+  name: "loss2/fc/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "loss2/fc/bn/sc"
+  top: "loss2/classifier"
+  name: "loss2/classifier"
+  type: "InnerProduct"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/loss"
+  name: "loss2/loss"
+  type: "SoftmaxWithLoss"
+  loss_weight: 0.3
+}
+layer {
+  bottom: "loss2/classifier"
+  top: "loss2/prob"
+  name: "loss2/prob"
+  type: "Softmax"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  bottom: "loss2/prob"
+  bottom: "label"
+  top: "loss2/top-1"
+  name: "loss2/top-1"
+  type: "Accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  bottom: "loss2/prob"
+  bottom: "label"
+  top: "loss2/top-5"
+  name: "loss2/top-5"
+  type: "Accuracy"
+  accuracy_param {
+    top_k: 5
+  }
+  include {
+    phase: TEST
+  }
+}
+layer {
+ bottom: "inception_4e/output"
+  top: "inception_5a/1x1"
+  name: "inception_5a/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 352
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/1x1"
+  name: "inception_5a/1x1/bn"
+  top: "inception_5a/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/1x1/bn"
+  top: "inception_5a/1x1/bn/sc"
+  name: "inception_5a/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/1x1/bn/sc"
+  top: "inception_5a/1x1/bn/sc"
+  name: "inception_5a/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4e/output"
+  top: "inception_5a/3x3_reduce"
+  name: "inception_5a/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/3x3_reduce"
+  name: "inception_5a/3x3_reduce/bn"
+  top: "inception_5a/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/3x3_reduce/bn"
+  top: "inception_5a/3x3_reduce/bn/sc"
+  name: "inception_5a/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/3x3_reduce/bn/sc"
+  top: "inception_5a/3x3_reduce/bn/sc"
+  name: "inception_5a/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5a/3x3_reduce/bn/sc"
+  top: "inception_5a/3x3"
+  name: "inception_5a/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/3x3"
+  name: "inception_5a/3x3/bn"
+  top: "inception_5a/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/3x3/bn"
+  top: "inception_5a/3x3/bn/sc"
+  name: "inception_5a/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/3x3/bn/sc"
+  top: "inception_5a/3x3/bn/sc"
+  name: "inception_5a/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4e/output"
+  top: "inception_5a/double3x3_reduce"
+  name: "inception_5a/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3_reduce"
+  name: "inception_5a/double3x3_reduce/bn"
+  top: "inception_5a/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3_reduce/bn"
+  top: "inception_5a/double3x3_reduce/bn/sc"
+  name: "inception_5a/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3_reduce/bn/sc"
+  top: "inception_5a/double3x3_reduce/bn/sc"
+  name: "inception_5a/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5a/double3x3_reduce/bn/sc"
+  top: "inception_5a/double3x3a"
+  name: "inception_5a/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3a"
+  name: "inception_5a/double3x3a/bn"
+  top: "inception_5a/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3a/bn"
+  top: "inception_5a/double3x3a/bn/sc"
+  name: "inception_5a/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3a/bn/sc"
+  top: "inception_5a/double3x3a/bn/sc"
+  name: "inception_5a/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5a/double3x3a/bn/sc"
+  top: "inception_5a/double3x3b"
+  name: "inception_5a/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3b"
+  name: "inception_5a/double3x3b/bn"
+  top: "inception_5a/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3b/bn"
+  top: "inception_5a/double3x3b/bn/sc"
+  name: "inception_5a/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3b/bn/sc"
+  top: "inception_5a/double3x3b/bn/sc"
+  name: "inception_5a/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/output"
+  top: "inception_5a/pool"
+  name: "inception_5a/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_5a/pool"
+  top: "inception_5a/pool_proj"
+  name: "inception_5a/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/pool_proj"
+  name: "inception_5a/pool_proj/bn"
+  top: "inception_5a/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/pool_proj/bn"
+  top: "inception_5a/pool_proj/bn/sc"
+  name: "inception_5a/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/pool_proj/bn/sc"
+  top: "inception_5a/pool_proj/bn/sc"
+  name: "inception_5a/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/1x1/bn/sc"
+  bottom: "inception_5a/3x3/bn/sc"
+  bottom: "inception_5a/double3x3b/bn/sc"
+  bottom: "inception_5a/pool_proj/bn/sc"
+  top: "inception_5a/output"
+  name: "inception_5a/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5a/output"
+  top: "inception_5b/1x1"
+  name: "inception_5b/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 352
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/1x1"
+  name: "inception_5b/1x1/bn"
+  top: "inception_5b/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/1x1/bn"
+  top: "inception_5b/1x1/bn/sc"
+  name: "inception_5b/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/1x1/bn/sc"
+  top: "inception_5b/1x1/bn/sc"
+  name: "inception_5b/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5a/output"
+  top: "inception_5b/3x3_reduce"
+  name: "inception_5b/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/3x3_reduce"
+  name: "inception_5b/3x3_reduce/bn"
+  top: "inception_5b/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/3x3_reduce/bn"
+  top: "inception_5b/3x3_reduce/bn/sc"
+  name: "inception_5b/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/3x3_reduce/bn/sc"
+  top: "inception_5b/3x3_reduce/bn/sc"
+  name: "inception_5b/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5b/3x3_reduce/bn/sc"
+  top: "inception_5b/3x3"
+  name: "inception_5b/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/3x3"
+  name: "inception_5b/3x3/bn"
+  top: "inception_5b/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/3x3/bn"
+  top: "inception_5b/3x3/bn/sc"
+  name: "inception_5b/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/3x3/bn/sc"
+  top: "inception_5b/3x3/bn/sc"
+  name: "inception_5b/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5a/output"
+  top: "inception_5b/double3x3_reduce"
+  name: "inception_5b/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3_reduce"
+  name: "inception_5b/double3x3_reduce/bn"
+  top: "inception_5b/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3_reduce/bn"
+  top: "inception_5b/double3x3_reduce/bn/sc"
+  name: "inception_5b/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3_reduce/bn/sc"
+  top: "inception_5b/double3x3_reduce/bn/sc"
+  name: "inception_5b/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5b/double3x3_reduce/bn/sc"
+  top: "inception_5b/double3x3a"
+  name: "inception_5b/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3a"
+  name: "inception_5b/double3x3a/bn"
+  top: "inception_5b/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3a/bn"
+  top: "inception_5b/double3x3a/bn/sc"
+  name: "inception_5b/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3a/bn/sc"
+  top: "inception_5b/double3x3a/bn/sc"
+  name: "inception_5b/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5b/double3x3a/bn/sc"
+  top: "inception_5b/double3x3b"
+  name: "inception_5b/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3b"
+  name: "inception_5b/double3x3b/bn"
+  top: "inception_5b/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3b/bn"
+  top: "inception_5b/double3x3b/bn/sc"
+  name: "inception_5b/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3b/bn/sc"
+  top: "inception_5b/double3x3b/bn/sc"
+  name: "inception_5b/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/output"
+  top: "inception_5b/pool"
+  name: "inception_5b/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_5b/pool"
+  top: "inception_5b/pool_proj"
+  name: "inception_5b/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/pool_proj"
+  name: "inception_5b/pool_proj/bn"
+  top: "inception_5b/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/pool_proj/bn"
+  top: "inception_5b/pool_proj/bn/sc"
+  name: "inception_5b/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/pool_proj/bn/sc"
+  top: "inception_5b/pool_proj/bn/sc"
+  name: "inception_5b/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/1x1/bn/sc"
+  bottom: "inception_5b/3x3/bn/sc"
+  bottom: "inception_5b/double3x3b/bn/sc"
+  bottom: "inception_5b/pool_proj/bn/sc"
+  top: "inception_5b/output"
+  name: "inception_5b/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/output"
+  top: "pool5/7x7_s1"
+  name: "pool5/7x7_s1"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 7
+    stride: 1
+  }
+}
+layer {
+  bottom: "pool5/7x7_s1"
+  top: "loss3/classifier"
+  name: "loss3/classifier"
+  type: "InnerProduct"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/loss"
+  name: "loss3/loss"
+  type: "SoftmaxWithLoss"
+  loss_weight: 1
+}
+layer {
+  bottom: "loss3/classifier"
+  top: "loss3/prob"
+  name: "loss3/prob"
+  type: "Softmax"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  bottom: "loss3/prob"
+  bottom: "label"
+  top: "loss3/top-1"
+  name: "loss3/top-1"
+  type: "Accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  bottom: "loss3/prob"
+  bottom: "label"
+  top: "loss3/top-5"
+  name: "loss3/top-5"
+  type: "Accuracy"
+  accuracy_param {
+    top_k: 5
+  }
+  include {
+    phase: TEST
+  }
+}
diff --git a/models/intel_optimized_models/googlenet_v2/knl/train_val_dummydata.prototxt b/models/intel_optimized_models/googlenet_v2/knl/train_val_dummydata.prototxt
new file mode 100644
index 000000000..28a89b023
--- /dev/null
+++ b/models/intel_optimized_models/googlenet_v2/knl/train_val_dummydata.prototxt
@@ -0,0 +1,4034 @@
+# Inception Network (GoogLeNet Batch Normalization Network)
+name: "InceptionNetwork"
+### Training Set
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  dummy_data_param {
+    data_filler {
+        type: "constant"
+        value: 0.01
+    }
+    shape: { dim: 96 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 96 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+### Validation Set
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.01
+    }
+    shape: { dim: 192 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 192 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+layer {
+  bottom: "data"
+  top: "conv1/7x7_s2"
+  name: "conv1/7x7_s2"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 3
+    kernel_size: 7
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "conv1/7x7_s2"
+  name: "conv1/7x7_s2/bn"
+  top: "conv1/7x7_s2/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "conv1/7x7_s2/bn"
+  top: "conv1/7x7_s2/bn/sc"
+  name: "conv1/7x7_s2/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "conv1/7x7_s2/bn/sc"
+  top: "conv1/7x7_s2/bn/sc"
+  name: "conv1/7x7_s2/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "conv1/7x7_s2/bn/sc"
+  top: "pool1/3x3_s2"
+  name: "pool1/3x3_s2"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+ bottom: "pool1/3x3_s2"
+  top: "conv2/3x3_reduce"
+  name: "conv2/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "conv2/3x3_reduce"
+  name: "conv2/3x3_reduce/bn"
+  top: "conv2/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "conv2/3x3_reduce/bn"
+  top: "conv2/3x3_reduce/bn/sc"
+  name: "conv2/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "conv2/3x3_reduce/bn/sc"
+  top: "conv2/3x3_reduce/bn/sc"
+  name: "conv2/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "conv2/3x3_reduce/bn/sc"
+  top: "conv2/3x3"
+  name: "conv2/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "conv2/3x3"
+  name: "conv2/3x3/bn"
+  top: "conv2/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "conv2/3x3/bn"
+  top: "conv2/3x3/bn/sc"
+  name: "conv2/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "conv2/3x3/bn/sc"
+  top: "conv2/3x3/bn/sc"
+  name: "conv2/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "conv2/3x3/bn/sc"
+  top: "pool2/3x3_s2"
+  name: "pool2/3x3_s2"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+ bottom: "pool2/3x3_s2"
+  top: "inception_3a/1x1"
+  name: "inception_3a/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/1x1"
+  name: "inception_3a/1x1/bn"
+  top: "inception_3a/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/1x1/bn"
+  top: "inception_3a/1x1/bn/sc"
+  name: "inception_3a/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/1x1/bn/sc"
+  top: "inception_3a/1x1/bn/sc"
+  name: "inception_3a/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "pool2/3x3_s2"
+  top: "inception_3a/3x3_reduce"
+  name: "inception_3a/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/3x3_reduce"
+  name: "inception_3a/3x3_reduce/bn"
+  top: "inception_3a/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/3x3_reduce/bn"
+  top: "inception_3a/3x3_reduce/bn/sc"
+  name: "inception_3a/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/3x3_reduce/bn/sc"
+  top: "inception_3a/3x3_reduce/bn/sc"
+  name: "inception_3a/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3a/3x3_reduce/bn/sc"
+  top: "inception_3a/3x3"
+  name: "inception_3a/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/3x3"
+  name: "inception_3a/3x3/bn"
+  top: "inception_3a/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/3x3/bn"
+  top: "inception_3a/3x3/bn/sc"
+  name: "inception_3a/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/3x3/bn/sc"
+  top: "inception_3a/3x3/bn/sc"
+  name: "inception_3a/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "pool2/3x3_s2"
+  top: "inception_3a/double3x3_reduce"
+  name: "inception_3a/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3_reduce"
+  name: "inception_3a/double3x3_reduce/bn"
+  top: "inception_3a/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3_reduce/bn"
+  top: "inception_3a/double3x3_reduce/bn/sc"
+  name: "inception_3a/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3_reduce/bn/sc"
+  top: "inception_3a/double3x3_reduce/bn/sc"
+  name: "inception_3a/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3a/double3x3_reduce/bn/sc"
+  top: "inception_3a/double3x3a"
+  name: "inception_3a/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3a"
+  name: "inception_3a/double3x3a/bn"
+  top: "inception_3a/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3a/bn"
+  top: "inception_3a/double3x3a/bn/sc"
+  name: "inception_3a/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3a/bn/sc"
+  top: "inception_3a/double3x3a/bn/sc"
+  name: "inception_3a/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3a/double3x3a/bn/sc"
+  top: "inception_3a/double3x3b"
+  name: "inception_3a/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3b"
+  name: "inception_3a/double3x3b/bn"
+  top: "inception_3a/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3b/bn"
+  top: "inception_3a/double3x3b/bn/sc"
+  name: "inception_3a/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3b/bn/sc"
+  top: "inception_3a/double3x3b/bn/sc"
+  name: "inception_3a/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/pool"
+  name: "inception_3a/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_3a/pool"
+  top: "inception_3a/pool_proj"
+  name: "inception_3a/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 32
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/pool_proj"
+  name: "inception_3a/pool_proj/bn"
+  top: "inception_3a/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/pool_proj/bn"
+  top: "inception_3a/pool_proj/bn/sc"
+  name: "inception_3a/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/pool_proj/bn/sc"
+  top: "inception_3a/pool_proj/bn/sc"
+  name: "inception_3a/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/1x1/bn/sc"
+  bottom: "inception_3a/3x3/bn/sc"
+  bottom: "inception_3a/double3x3b/bn/sc"
+  bottom: "inception_3a/pool_proj/bn/sc"
+  top: "inception_3a/output"
+  name: "inception_3a/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3a/output"
+  top: "inception_3b/1x1"
+  name: "inception_3b/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/1x1"
+  name: "inception_3b/1x1/bn"
+  top: "inception_3b/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/1x1/bn"
+  top: "inception_3b/1x1/bn/sc"
+  name: "inception_3b/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/1x1/bn/sc"
+  top: "inception_3b/1x1/bn/sc"
+  name: "inception_3b/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3a/output"
+  top: "inception_3b/3x3_reduce"
+  name: "inception_3b/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/3x3_reduce"
+  name: "inception_3b/3x3_reduce/bn"
+  top: "inception_3b/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/3x3_reduce/bn"
+  top: "inception_3b/3x3_reduce/bn/sc"
+  name: "inception_3b/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/3x3_reduce/bn/sc"
+  top: "inception_3b/3x3_reduce/bn/sc"
+  name: "inception_3b/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3b/3x3_reduce/bn/sc"
+  top: "inception_3b/3x3"
+  name: "inception_3b/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/3x3"
+  name: "inception_3b/3x3/bn"
+  top: "inception_3b/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/3x3/bn"
+  top: "inception_3b/3x3/bn/sc"
+  name: "inception_3b/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/3x3/bn/sc"
+  top: "inception_3b/3x3/bn/sc"
+  name: "inception_3b/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3a/output"
+  top: "inception_3b/double3x3_reduce"
+  name: "inception_3b/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3_reduce"
+  name: "inception_3b/double3x3_reduce/bn"
+  top: "inception_3b/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3_reduce/bn"
+  top: "inception_3b/double3x3_reduce/bn/sc"
+  name: "inception_3b/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3_reduce/bn/sc"
+  top: "inception_3b/double3x3_reduce/bn/sc"
+  name: "inception_3b/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3b/double3x3_reduce/bn/sc"
+  top: "inception_3b/double3x3a"
+  name: "inception_3b/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3a"
+  name: "inception_3b/double3x3a/bn"
+  top: "inception_3b/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3a/bn"
+  top: "inception_3b/double3x3a/bn/sc"
+  name: "inception_3b/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3a/bn/sc"
+  top: "inception_3b/double3x3a/bn/sc"
+  name: "inception_3b/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3b/double3x3a/bn/sc"
+  top: "inception_3b/double3x3b"
+  name: "inception_3b/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3b"
+  name: "inception_3b/double3x3b/bn"
+  top: "inception_3b/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3b/bn"
+  top: "inception_3b/double3x3b/bn/sc"
+  name: "inception_3b/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3b/bn/sc"
+  top: "inception_3b/double3x3b/bn/sc"
+  name: "inception_3b/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/output"
+  top: "inception_3b/pool"
+  name: "inception_3b/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_3b/pool"
+  top: "inception_3b/pool_proj"
+  name: "inception_3b/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/pool_proj"
+  name: "inception_3b/pool_proj/bn"
+  top: "inception_3b/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/pool_proj/bn"
+  top: "inception_3b/pool_proj/bn/sc"
+  name: "inception_3b/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/pool_proj/bn/sc"
+  top: "inception_3b/pool_proj/bn/sc"
+  name: "inception_3b/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/1x1/bn/sc"
+  bottom: "inception_3b/3x3/bn/sc"
+  bottom: "inception_3b/double3x3b/bn/sc"
+  bottom: "inception_3b/pool_proj/bn/sc"
+  top: "inception_3b/output"
+  name: "inception_3b/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3b/output"
+  top: "inception_3c/3x3_reduce"
+  name: "inception_3c/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3c/3x3_reduce"
+  name: "inception_3c/3x3_reduce/bn"
+  top: "inception_3c/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/3x3_reduce/bn"
+  top: "inception_3c/3x3_reduce/bn/sc"
+  name: "inception_3c/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3c/3x3_reduce/bn/sc"
+  top: "inception_3c/3x3_reduce/bn/sc"
+  name: "inception_3c/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3c/3x3_reduce/bn/sc"
+  top: "inception_3c/3x3"
+  name: "inception_3c/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3c/3x3"
+  name: "inception_3c/3x3/bn"
+  top: "inception_3c/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/3x3/bn"
+  top: "inception_3c/3x3/bn/sc"
+  name: "inception_3c/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3c/3x3/bn/sc"
+  top: "inception_3c/3x3/bn/sc"
+  name: "inception_3c/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3b/output"
+  top: "inception_3c/double3x3_reduce"
+  name: "inception_3c/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3_reduce"
+  name: "inception_3c/double3x3_reduce/bn"
+  top: "inception_3c/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3_reduce/bn"
+  top: "inception_3c/double3x3_reduce/bn/sc"
+  name: "inception_3c/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3_reduce/bn/sc"
+  top: "inception_3c/double3x3_reduce/bn/sc"
+  name: "inception_3c/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3c/double3x3_reduce/bn/sc"
+  top: "inception_3c/double3x3a"
+  name: "inception_3c/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3a"
+  name: "inception_3c/double3x3a/bn"
+  top: "inception_3c/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3a/bn"
+  top: "inception_3c/double3x3a/bn/sc"
+  name: "inception_3c/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3a/bn/sc"
+  top: "inception_3c/double3x3a/bn/sc"
+  name: "inception_3c/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3c/double3x3a/bn/sc"
+  top: "inception_3c/double3x3b"
+  name: "inception_3c/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3b"
+  name: "inception_3c/double3x3b/bn"
+  top: "inception_3c/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3b/bn"
+  top: "inception_3c/double3x3b/bn/sc"
+  name: "inception_3c/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3b/bn/sc"
+  top: "inception_3c/double3x3b/bn/sc"
+  name: "inception_3c/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/output"
+  top: "inception_3c/pool"
+  name: "inception_3c/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  bottom: "inception_3c/3x3/bn/sc"
+  bottom: "inception_3c/double3x3b/bn/sc"
+  bottom: "inception_3c/pool"
+  top: "inception_3c/output"
+  name: "inception_3c/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/output"
+  top: "pool3/5x5_s3"
+  name: "pool3/5x5_s3"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 5
+    stride: 3
+  }
+}
+layer {
+ bottom: "pool3/5x5_s3"
+  top: "loss1/conv"
+  name: "loss1/conv"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "loss1/conv"
+  name: "loss1/conv/bn"
+  top: "loss1/conv/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "loss1/conv/bn"
+  top: "loss1/conv/bn/sc"
+  name: "loss1/conv/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "loss1/conv/bn/sc"
+  top: "loss1/conv/bn/sc"
+  name: "loss1/conv/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "loss1/conv/bn/sc"
+  top: "loss1/fc"
+  name: "loss1/fc"
+  type: "InnerProduct"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  inner_product_param {
+    num_output: 1024
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "loss1/fc"
+  name: "loss1/fc/bn"
+  top: "loss1/fc/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "loss1/fc/bn"
+  top: "loss1/fc/bn/sc"
+  name: "loss1/fc/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "loss1/fc/bn/sc"
+  top: "loss1/fc/bn/sc"
+  name: "loss1/fc/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "loss1/fc/bn/sc"
+  top: "loss1/classifier"
+  name: "loss1/classifier"
+  type: "InnerProduct"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/loss"
+  name: "loss1/loss"
+  type: "SoftmaxWithLoss"
+  loss_weight: 0.3
+}
+layer {
+  bottom: "loss1/classifier"
+  top: "loss1/prob"
+  name: "loss1/prob"
+  type: "Softmax"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  bottom: "loss1/prob"
+  bottom: "label"
+  top: "loss1/top-1"
+  name: "loss1/top-1"
+  type: "Accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  bottom: "loss1/prob"
+  bottom: "label"
+  top: "loss1/top-5"
+  name: "loss1/top-5"
+  type: "Accuracy"
+  accuracy_param {
+    top_k: 5
+  }
+  include {
+    phase: TEST
+  }
+}
+layer {
+ bottom: "inception_3c/output"
+  top: "inception_4a/1x1"
+  name: "inception_4a/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 224
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/1x1"
+  name: "inception_4a/1x1/bn"
+  top: "inception_4a/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/1x1/bn"
+  top: "inception_4a/1x1/bn/sc"
+  name: "inception_4a/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/1x1/bn/sc"
+  top: "inception_4a/1x1/bn/sc"
+  name: "inception_4a/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3c/output"
+  top: "inception_4a/3x3_reduce"
+  name: "inception_4a/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/3x3_reduce"
+  name: "inception_4a/3x3_reduce/bn"
+  top: "inception_4a/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/3x3_reduce/bn"
+  top: "inception_4a/3x3_reduce/bn/sc"
+  name: "inception_4a/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/3x3_reduce/bn/sc"
+  top: "inception_4a/3x3_reduce/bn/sc"
+  name: "inception_4a/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4a/3x3_reduce/bn/sc"
+  top: "inception_4a/3x3"
+  name: "inception_4a/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/3x3"
+  name: "inception_4a/3x3/bn"
+  top: "inception_4a/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/3x3/bn"
+  top: "inception_4a/3x3/bn/sc"
+  name: "inception_4a/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/3x3/bn/sc"
+  top: "inception_4a/3x3/bn/sc"
+  name: "inception_4a/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3c/output"
+  top: "inception_4a/double3x3_reduce"
+  name: "inception_4a/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3_reduce"
+  name: "inception_4a/double3x3_reduce/bn"
+  top: "inception_4a/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3_reduce/bn"
+  top: "inception_4a/double3x3_reduce/bn/sc"
+  name: "inception_4a/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3_reduce/bn/sc"
+  top: "inception_4a/double3x3_reduce/bn/sc"
+  name: "inception_4a/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4a/double3x3_reduce/bn/sc"
+  top: "inception_4a/double3x3a"
+  name: "inception_4a/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3a"
+  name: "inception_4a/double3x3a/bn"
+  top: "inception_4a/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3a/bn"
+  top: "inception_4a/double3x3a/bn/sc"
+  name: "inception_4a/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3a/bn/sc"
+  top: "inception_4a/double3x3a/bn/sc"
+  name: "inception_4a/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4a/double3x3a/bn/sc"
+  top: "inception_4a/double3x3b"
+  name: "inception_4a/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3b"
+  name: "inception_4a/double3x3b/bn"
+  top: "inception_4a/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3b/bn"
+  top: "inception_4a/double3x3b/bn/sc"
+  name: "inception_4a/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3b/bn/sc"
+  top: "inception_4a/double3x3b/bn/sc"
+  name: "inception_4a/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/output"
+  top: "inception_4a/pool"
+  name: "inception_4a/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_4a/pool"
+  top: "inception_4a/pool_proj"
+  name: "inception_4a/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/pool_proj"
+  name: "inception_4a/pool_proj/bn"
+  top: "inception_4a/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/pool_proj/bn"
+  top: "inception_4a/pool_proj/bn/sc"
+  name: "inception_4a/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/pool_proj/bn/sc"
+  top: "inception_4a/pool_proj/bn/sc"
+  name: "inception_4a/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/1x1/bn/sc"
+  bottom: "inception_4a/3x3/bn/sc"
+  bottom: "inception_4a/double3x3b/bn/sc"
+  bottom: "inception_4a/pool_proj/bn/sc"
+  top: "inception_4a/output"
+  name: "inception_4a/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4a/output"
+  top: "inception_4b/1x1"
+  name: "inception_4b/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/1x1"
+  name: "inception_4b/1x1/bn"
+  top: "inception_4b/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/1x1/bn"
+  top: "inception_4b/1x1/bn/sc"
+  name: "inception_4b/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/1x1/bn/sc"
+  top: "inception_4b/1x1/bn/sc"
+  name: "inception_4b/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4a/output"
+  top: "inception_4b/3x3_reduce"
+  name: "inception_4b/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/3x3_reduce"
+  name: "inception_4b/3x3_reduce/bn"
+  top: "inception_4b/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/3x3_reduce/bn"
+  top: "inception_4b/3x3_reduce/bn/sc"
+  name: "inception_4b/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/3x3_reduce/bn/sc"
+  top: "inception_4b/3x3_reduce/bn/sc"
+  name: "inception_4b/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4b/3x3_reduce/bn/sc"
+  top: "inception_4b/3x3"
+  name: "inception_4b/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/3x3"
+  name: "inception_4b/3x3/bn"
+  top: "inception_4b/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/3x3/bn"
+  top: "inception_4b/3x3/bn/sc"
+  name: "inception_4b/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/3x3/bn/sc"
+  top: "inception_4b/3x3/bn/sc"
+  name: "inception_4b/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4a/output"
+  top: "inception_4b/double3x3_reduce"
+  name: "inception_4b/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3_reduce"
+  name: "inception_4b/double3x3_reduce/bn"
+  top: "inception_4b/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3_reduce/bn"
+  top: "inception_4b/double3x3_reduce/bn/sc"
+  name: "inception_4b/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3_reduce/bn/sc"
+  top: "inception_4b/double3x3_reduce/bn/sc"
+  name: "inception_4b/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4b/double3x3_reduce/bn/sc"
+  top: "inception_4b/double3x3a"
+  name: "inception_4b/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3a"
+  name: "inception_4b/double3x3a/bn"
+  top: "inception_4b/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3a/bn"
+  top: "inception_4b/double3x3a/bn/sc"
+  name: "inception_4b/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3a/bn/sc"
+  top: "inception_4b/double3x3a/bn/sc"
+  name: "inception_4b/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4b/double3x3a/bn/sc"
+  top: "inception_4b/double3x3b"
+  name: "inception_4b/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3b"
+  name: "inception_4b/double3x3b/bn"
+  top: "inception_4b/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3b/bn"
+  top: "inception_4b/double3x3b/bn/sc"
+  name: "inception_4b/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3b/bn/sc"
+  top: "inception_4b/double3x3b/bn/sc"
+  name: "inception_4b/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/output"
+  top: "inception_4b/pool"
+  name: "inception_4b/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_4b/pool"
+  top: "inception_4b/pool_proj"
+  name: "inception_4b/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/pool_proj"
+  name: "inception_4b/pool_proj/bn"
+  top: "inception_4b/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/pool_proj/bn"
+  top: "inception_4b/pool_proj/bn/sc"
+  name: "inception_4b/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/pool_proj/bn/sc"
+  top: "inception_4b/pool_proj/bn/sc"
+  name: "inception_4b/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/1x1/bn/sc"
+  bottom: "inception_4b/3x3/bn/sc"
+  bottom: "inception_4b/double3x3b/bn/sc"
+  bottom: "inception_4b/pool_proj/bn/sc"
+  top: "inception_4b/output"
+  name: "inception_4b/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4b/output"
+  top: "inception_4c/1x1"
+  name: "inception_4c/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/1x1"
+  name: "inception_4c/1x1/bn"
+  top: "inception_4c/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/1x1/bn"
+  top: "inception_4c/1x1/bn/sc"
+  name: "inception_4c/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/1x1/bn/sc"
+  top: "inception_4c/1x1/bn/sc"
+  name: "inception_4c/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4b/output"
+  top: "inception_4c/3x3_reduce"
+  name: "inception_4c/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/3x3_reduce"
+  name: "inception_4c/3x3_reduce/bn"
+  top: "inception_4c/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/3x3_reduce/bn"
+  top: "inception_4c/3x3_reduce/bn/sc"
+  name: "inception_4c/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/3x3_reduce/bn/sc"
+  top: "inception_4c/3x3_reduce/bn/sc"
+  name: "inception_4c/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4c/3x3_reduce/bn/sc"
+  top: "inception_4c/3x3"
+  name: "inception_4c/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/3x3"
+  name: "inception_4c/3x3/bn"
+  top: "inception_4c/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/3x3/bn"
+  top: "inception_4c/3x3/bn/sc"
+  name: "inception_4c/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/3x3/bn/sc"
+  top: "inception_4c/3x3/bn/sc"
+  name: "inception_4c/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4b/output"
+  top: "inception_4c/double3x3_reduce"
+  name: "inception_4c/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3_reduce"
+  name: "inception_4c/double3x3_reduce/bn"
+  top: "inception_4c/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3_reduce/bn"
+  top: "inception_4c/double3x3_reduce/bn/sc"
+  name: "inception_4c/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3_reduce/bn/sc"
+  top: "inception_4c/double3x3_reduce/bn/sc"
+  name: "inception_4c/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4c/double3x3_reduce/bn/sc"
+  top: "inception_4c/double3x3a"
+  name: "inception_4c/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3a"
+  name: "inception_4c/double3x3a/bn"
+  top: "inception_4c/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3a/bn"
+  top: "inception_4c/double3x3a/bn/sc"
+  name: "inception_4c/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3a/bn/sc"
+  top: "inception_4c/double3x3a/bn/sc"
+  name: "inception_4c/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4c/double3x3a/bn/sc"
+  top: "inception_4c/double3x3b"
+  name: "inception_4c/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3b"
+  name: "inception_4c/double3x3b/bn"
+  top: "inception_4c/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3b/bn"
+  top: "inception_4c/double3x3b/bn/sc"
+  name: "inception_4c/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3b/bn/sc"
+  top: "inception_4c/double3x3b/bn/sc"
+  name: "inception_4c/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/output"
+  top: "inception_4c/pool"
+  name: "inception_4c/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_4c/pool"
+  top: "inception_4c/pool_proj"
+  name: "inception_4c/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/pool_proj"
+  name: "inception_4c/pool_proj/bn"
+  top: "inception_4c/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/pool_proj/bn"
+  top: "inception_4c/pool_proj/bn/sc"
+  name: "inception_4c/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/pool_proj/bn/sc"
+  top: "inception_4c/pool_proj/bn/sc"
+  name: "inception_4c/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/1x1/bn/sc"
+  bottom: "inception_4c/3x3/bn/sc"
+  bottom: "inception_4c/double3x3b/bn/sc"
+  bottom: "inception_4c/pool_proj/bn/sc"
+  top: "inception_4c/output"
+  name: "inception_4c/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4c/output"
+  top: "inception_4d/1x1"
+  name: "inception_4d/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/1x1"
+  name: "inception_4d/1x1/bn"
+  top: "inception_4d/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/1x1/bn"
+  top: "inception_4d/1x1/bn/sc"
+  name: "inception_4d/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/1x1/bn/sc"
+  top: "inception_4d/1x1/bn/sc"
+  name: "inception_4d/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4c/output"
+  top: "inception_4d/3x3_reduce"
+  name: "inception_4d/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/3x3_reduce"
+  name: "inception_4d/3x3_reduce/bn"
+  top: "inception_4d/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/3x3_reduce/bn"
+  top: "inception_4d/3x3_reduce/bn/sc"
+  name: "inception_4d/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/3x3_reduce/bn/sc"
+  top: "inception_4d/3x3_reduce/bn/sc"
+  name: "inception_4d/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4d/3x3_reduce/bn/sc"
+  top: "inception_4d/3x3"
+  name: "inception_4d/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/3x3"
+  name: "inception_4d/3x3/bn"
+  top: "inception_4d/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/3x3/bn"
+  top: "inception_4d/3x3/bn/sc"
+  name: "inception_4d/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/3x3/bn/sc"
+  top: "inception_4d/3x3/bn/sc"
+  name: "inception_4d/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4c/output"
+  top: "inception_4d/double3x3_reduce"
+  name: "inception_4d/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3_reduce"
+  name: "inception_4d/double3x3_reduce/bn"
+  top: "inception_4d/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3_reduce/bn"
+  top: "inception_4d/double3x3_reduce/bn/sc"
+  name: "inception_4d/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3_reduce/bn/sc"
+  top: "inception_4d/double3x3_reduce/bn/sc"
+  name: "inception_4d/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4d/double3x3_reduce/bn/sc"
+  top: "inception_4d/double3x3a"
+  name: "inception_4d/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3a"
+  name: "inception_4d/double3x3a/bn"
+  top: "inception_4d/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3a/bn"
+  top: "inception_4d/double3x3a/bn/sc"
+  name: "inception_4d/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3a/bn/sc"
+  top: "inception_4d/double3x3a/bn/sc"
+  name: "inception_4d/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4d/double3x3a/bn/sc"
+  top: "inception_4d/double3x3b"
+  name: "inception_4d/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3b"
+  name: "inception_4d/double3x3b/bn"
+  top: "inception_4d/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3b/bn"
+  top: "inception_4d/double3x3b/bn/sc"
+  name: "inception_4d/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3b/bn/sc"
+  top: "inception_4d/double3x3b/bn/sc"
+  name: "inception_4d/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/output"
+  top: "inception_4d/pool"
+  name: "inception_4d/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_4d/pool"
+  top: "inception_4d/pool_proj"
+  name: "inception_4d/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/pool_proj"
+  name: "inception_4d/pool_proj/bn"
+  top: "inception_4d/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/pool_proj/bn"
+  top: "inception_4d/pool_proj/bn/sc"
+  name: "inception_4d/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/pool_proj/bn/sc"
+  top: "inception_4d/pool_proj/bn/sc"
+  name: "inception_4d/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/1x1/bn/sc"
+  bottom: "inception_4d/3x3/bn/sc"
+  bottom: "inception_4d/double3x3b/bn/sc"
+  bottom: "inception_4d/pool_proj/bn/sc"
+  top: "inception_4d/output"
+  name: "inception_4d/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4d/output"
+  top: "inception_4e/3x3_reduce"
+  name: "inception_4e/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4e/3x3_reduce"
+  name: "inception_4e/3x3_reduce/bn"
+  top: "inception_4e/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/3x3_reduce/bn"
+  top: "inception_4e/3x3_reduce/bn/sc"
+  name: "inception_4e/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4e/3x3_reduce/bn/sc"
+  top: "inception_4e/3x3_reduce/bn/sc"
+  name: "inception_4e/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4e/3x3_reduce/bn/sc"
+  top: "inception_4e/3x3"
+  name: "inception_4e/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4e/3x3"
+  name: "inception_4e/3x3/bn"
+  top: "inception_4e/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/3x3/bn"
+  top: "inception_4e/3x3/bn/sc"
+  name: "inception_4e/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4e/3x3/bn/sc"
+  top: "inception_4e/3x3/bn/sc"
+  name: "inception_4e/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4d/output"
+  top: "inception_4e/double3x3_reduce"
+  name: "inception_4e/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3_reduce"
+  name: "inception_4e/double3x3_reduce/bn"
+  top: "inception_4e/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3_reduce/bn"
+  top: "inception_4e/double3x3_reduce/bn/sc"
+  name: "inception_4e/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3_reduce/bn/sc"
+  top: "inception_4e/double3x3_reduce/bn/sc"
+  name: "inception_4e/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4e/double3x3_reduce/bn/sc"
+  top: "inception_4e/double3x3a"
+  name: "inception_4e/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3a"
+  name: "inception_4e/double3x3a/bn"
+  top: "inception_4e/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3a/bn"
+  top: "inception_4e/double3x3a/bn/sc"
+  name: "inception_4e/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3a/bn/sc"
+  top: "inception_4e/double3x3a/bn/sc"
+  name: "inception_4e/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4e/double3x3a/bn/sc"
+  top: "inception_4e/double3x3b"
+  name: "inception_4e/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3b"
+  name: "inception_4e/double3x3b/bn"
+  top: "inception_4e/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3b/bn"
+  top: "inception_4e/double3x3b/bn/sc"
+  name: "inception_4e/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3b/bn/sc"
+  top: "inception_4e/double3x3b/bn/sc"
+  name: "inception_4e/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/output"
+  top: "inception_4e/pool"
+  name: "inception_4e/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  bottom: "inception_4e/3x3/bn/sc"
+  bottom: "inception_4e/double3x3b/bn/sc"
+  bottom: "inception_4e/pool"
+  top: "inception_4e/output"
+  name: "inception_4e/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/output"
+  top: "pool4/5x5_s3"
+  name: "pool4/5x5_s3"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 5
+    stride: 3
+  }
+}
+layer {
+ bottom: "pool4/5x5_s3"
+  top: "loss2/conv"
+  name: "loss2/conv"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "loss2/conv"
+  name: "loss2/conv/bn"
+  top: "loss2/conv/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "loss2/conv/bn"
+  top: "loss2/conv/bn/sc"
+  name: "loss2/conv/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "loss2/conv/bn/sc"
+  top: "loss2/conv/bn/sc"
+  name: "loss2/conv/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "loss2/conv/bn/sc"
+  top: "loss2/fc"
+  name: "loss2/fc"
+  type: "InnerProduct"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  inner_product_param {
+    num_output: 1024
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "loss2/fc"
+  name: "loss2/fc/bn"
+  top: "loss2/fc/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "loss2/fc/bn"
+  top: "loss2/fc/bn/sc"
+  name: "loss2/fc/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "loss2/fc/bn/sc"
+  top: "loss2/fc/bn/sc"
+  name: "loss2/fc/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "loss2/fc/bn/sc"
+  top: "loss2/classifier"
+  name: "loss2/classifier"
+  type: "InnerProduct"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/loss"
+  name: "loss2/loss"
+  type: "SoftmaxWithLoss"
+  loss_weight: 0.3
+}
+layer {
+  bottom: "loss2/classifier"
+  top: "loss2/prob"
+  name: "loss2/prob"
+  type: "Softmax"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  bottom: "loss2/prob"
+  bottom: "label"
+  top: "loss2/top-1"
+  name: "loss2/top-1"
+  type: "Accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  bottom: "loss2/prob"
+  bottom: "label"
+  top: "loss2/top-5"
+  name: "loss2/top-5"
+  type: "Accuracy"
+  accuracy_param {
+    top_k: 5
+  }
+  include {
+    phase: TEST
+  }
+}
+layer {
+ bottom: "inception_4e/output"
+  top: "inception_5a/1x1"
+  name: "inception_5a/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 352
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/1x1"
+  name: "inception_5a/1x1/bn"
+  top: "inception_5a/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/1x1/bn"
+  top: "inception_5a/1x1/bn/sc"
+  name: "inception_5a/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/1x1/bn/sc"
+  top: "inception_5a/1x1/bn/sc"
+  name: "inception_5a/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4e/output"
+  top: "inception_5a/3x3_reduce"
+  name: "inception_5a/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/3x3_reduce"
+  name: "inception_5a/3x3_reduce/bn"
+  top: "inception_5a/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/3x3_reduce/bn"
+  top: "inception_5a/3x3_reduce/bn/sc"
+  name: "inception_5a/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/3x3_reduce/bn/sc"
+  top: "inception_5a/3x3_reduce/bn/sc"
+  name: "inception_5a/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5a/3x3_reduce/bn/sc"
+  top: "inception_5a/3x3"
+  name: "inception_5a/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/3x3"
+  name: "inception_5a/3x3/bn"
+  top: "inception_5a/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/3x3/bn"
+  top: "inception_5a/3x3/bn/sc"
+  name: "inception_5a/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/3x3/bn/sc"
+  top: "inception_5a/3x3/bn/sc"
+  name: "inception_5a/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4e/output"
+  top: "inception_5a/double3x3_reduce"
+  name: "inception_5a/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3_reduce"
+  name: "inception_5a/double3x3_reduce/bn"
+  top: "inception_5a/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3_reduce/bn"
+  top: "inception_5a/double3x3_reduce/bn/sc"
+  name: "inception_5a/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3_reduce/bn/sc"
+  top: "inception_5a/double3x3_reduce/bn/sc"
+  name: "inception_5a/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5a/double3x3_reduce/bn/sc"
+  top: "inception_5a/double3x3a"
+  name: "inception_5a/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3a"
+  name: "inception_5a/double3x3a/bn"
+  top: "inception_5a/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3a/bn"
+  top: "inception_5a/double3x3a/bn/sc"
+  name: "inception_5a/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3a/bn/sc"
+  top: "inception_5a/double3x3a/bn/sc"
+  name: "inception_5a/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5a/double3x3a/bn/sc"
+  top: "inception_5a/double3x3b"
+  name: "inception_5a/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3b"
+  name: "inception_5a/double3x3b/bn"
+  top: "inception_5a/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3b/bn"
+  top: "inception_5a/double3x3b/bn/sc"
+  name: "inception_5a/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3b/bn/sc"
+  top: "inception_5a/double3x3b/bn/sc"
+  name: "inception_5a/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/output"
+  top: "inception_5a/pool"
+  name: "inception_5a/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_5a/pool"
+  top: "inception_5a/pool_proj"
+  name: "inception_5a/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/pool_proj"
+  name: "inception_5a/pool_proj/bn"
+  top: "inception_5a/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/pool_proj/bn"
+  top: "inception_5a/pool_proj/bn/sc"
+  name: "inception_5a/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/pool_proj/bn/sc"
+  top: "inception_5a/pool_proj/bn/sc"
+  name: "inception_5a/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/1x1/bn/sc"
+  bottom: "inception_5a/3x3/bn/sc"
+  bottom: "inception_5a/double3x3b/bn/sc"
+  bottom: "inception_5a/pool_proj/bn/sc"
+  top: "inception_5a/output"
+  name: "inception_5a/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5a/output"
+  top: "inception_5b/1x1"
+  name: "inception_5b/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 352
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/1x1"
+  name: "inception_5b/1x1/bn"
+  top: "inception_5b/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/1x1/bn"
+  top: "inception_5b/1x1/bn/sc"
+  name: "inception_5b/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/1x1/bn/sc"
+  top: "inception_5b/1x1/bn/sc"
+  name: "inception_5b/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5a/output"
+  top: "inception_5b/3x3_reduce"
+  name: "inception_5b/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/3x3_reduce"
+  name: "inception_5b/3x3_reduce/bn"
+  top: "inception_5b/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/3x3_reduce/bn"
+  top: "inception_5b/3x3_reduce/bn/sc"
+  name: "inception_5b/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/3x3_reduce/bn/sc"
+  top: "inception_5b/3x3_reduce/bn/sc"
+  name: "inception_5b/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5b/3x3_reduce/bn/sc"
+  top: "inception_5b/3x3"
+  name: "inception_5b/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/3x3"
+  name: "inception_5b/3x3/bn"
+  top: "inception_5b/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/3x3/bn"
+  top: "inception_5b/3x3/bn/sc"
+  name: "inception_5b/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/3x3/bn/sc"
+  top: "inception_5b/3x3/bn/sc"
+  name: "inception_5b/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5a/output"
+  top: "inception_5b/double3x3_reduce"
+  name: "inception_5b/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3_reduce"
+  name: "inception_5b/double3x3_reduce/bn"
+  top: "inception_5b/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3_reduce/bn"
+  top: "inception_5b/double3x3_reduce/bn/sc"
+  name: "inception_5b/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3_reduce/bn/sc"
+  top: "inception_5b/double3x3_reduce/bn/sc"
+  name: "inception_5b/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5b/double3x3_reduce/bn/sc"
+  top: "inception_5b/double3x3a"
+  name: "inception_5b/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3a"
+  name: "inception_5b/double3x3a/bn"
+  top: "inception_5b/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3a/bn"
+  top: "inception_5b/double3x3a/bn/sc"
+  name: "inception_5b/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3a/bn/sc"
+  top: "inception_5b/double3x3a/bn/sc"
+  name: "inception_5b/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5b/double3x3a/bn/sc"
+  top: "inception_5b/double3x3b"
+  name: "inception_5b/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3b"
+  name: "inception_5b/double3x3b/bn"
+  top: "inception_5b/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3b/bn"
+  top: "inception_5b/double3x3b/bn/sc"
+  name: "inception_5b/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3b/bn/sc"
+  top: "inception_5b/double3x3b/bn/sc"
+  name: "inception_5b/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/output"
+  top: "inception_5b/pool"
+  name: "inception_5b/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_5b/pool"
+  top: "inception_5b/pool_proj"
+  name: "inception_5b/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/pool_proj"
+  name: "inception_5b/pool_proj/bn"
+  top: "inception_5b/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/pool_proj/bn"
+  top: "inception_5b/pool_proj/bn/sc"
+  name: "inception_5b/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/pool_proj/bn/sc"
+  top: "inception_5b/pool_proj/bn/sc"
+  name: "inception_5b/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/1x1/bn/sc"
+  bottom: "inception_5b/3x3/bn/sc"
+  bottom: "inception_5b/double3x3b/bn/sc"
+  bottom: "inception_5b/pool_proj/bn/sc"
+  top: "inception_5b/output"
+  name: "inception_5b/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/output"
+  top: "pool5/7x7_s1"
+  name: "pool5/7x7_s1"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 7
+    stride: 1
+  }
+}
+layer {
+  bottom: "pool5/7x7_s1"
+  top: "loss3/classifier"
+  name: "loss3/classifier"
+  type: "InnerProduct"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/loss"
+  name: "loss3/loss"
+  type: "SoftmaxWithLoss"
+  loss_weight: 1
+}
+layer {
+  bottom: "loss3/classifier"
+  top: "loss3/prob"
+  name: "loss3/prob"
+  type: "Softmax"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  bottom: "loss3/prob"
+  bottom: "label"
+  top: "loss3/top-1"
+  name: "loss3/top-1"
+  type: "Accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  bottom: "loss3/prob"
+  bottom: "label"
+  top: "loss3/top-5"
+  name: "loss3/top-5"
+  type: "Accuracy"
+  accuracy_param {
+    top_k: 5
+  }
+  include {
+    phase: TEST
+  }
+}
diff --git a/models/intel_optimized_models/googlenet_v2/skx/train_val_dummydata.prototxt b/models/intel_optimized_models/googlenet_v2/skx/train_val_dummydata.prototxt
new file mode 100644
index 000000000..63ca66d97
--- /dev/null
+++ b/models/intel_optimized_models/googlenet_v2/skx/train_val_dummydata.prototxt
@@ -0,0 +1,4034 @@
+# Inception Network (GoogLeNet Batch Normalization Network)
+name: "InceptionNetwork"
+### Training Set
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  dummy_data_param {
+    data_filler {
+        type: "constant"
+        value: 0.01
+    }
+    shape: { dim: 128 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 128 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+### Validation Set
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.01
+    }
+    shape: { dim: 192 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 192 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+layer {
+  bottom: "data"
+  top: "conv1/7x7_s2"
+  name: "conv1/7x7_s2"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 3
+    kernel_size: 7
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "conv1/7x7_s2"
+  name: "conv1/7x7_s2/bn"
+  top: "conv1/7x7_s2/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "conv1/7x7_s2/bn"
+  top: "conv1/7x7_s2/bn/sc"
+  name: "conv1/7x7_s2/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "conv1/7x7_s2/bn/sc"
+  top: "conv1/7x7_s2/bn/sc"
+  name: "conv1/7x7_s2/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "conv1/7x7_s2/bn/sc"
+  top: "pool1/3x3_s2"
+  name: "pool1/3x3_s2"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+ bottom: "pool1/3x3_s2"
+  top: "conv2/3x3_reduce"
+  name: "conv2/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "conv2/3x3_reduce"
+  name: "conv2/3x3_reduce/bn"
+  top: "conv2/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "conv2/3x3_reduce/bn"
+  top: "conv2/3x3_reduce/bn/sc"
+  name: "conv2/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "conv2/3x3_reduce/bn/sc"
+  top: "conv2/3x3_reduce/bn/sc"
+  name: "conv2/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "conv2/3x3_reduce/bn/sc"
+  top: "conv2/3x3"
+  name: "conv2/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "conv2/3x3"
+  name: "conv2/3x3/bn"
+  top: "conv2/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "conv2/3x3/bn"
+  top: "conv2/3x3/bn/sc"
+  name: "conv2/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "conv2/3x3/bn/sc"
+  top: "conv2/3x3/bn/sc"
+  name: "conv2/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "conv2/3x3/bn/sc"
+  top: "pool2/3x3_s2"
+  name: "pool2/3x3_s2"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+ bottom: "pool2/3x3_s2"
+  top: "inception_3a/1x1"
+  name: "inception_3a/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/1x1"
+  name: "inception_3a/1x1/bn"
+  top: "inception_3a/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/1x1/bn"
+  top: "inception_3a/1x1/bn/sc"
+  name: "inception_3a/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/1x1/bn/sc"
+  top: "inception_3a/1x1/bn/sc"
+  name: "inception_3a/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "pool2/3x3_s2"
+  top: "inception_3a/3x3_reduce"
+  name: "inception_3a/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/3x3_reduce"
+  name: "inception_3a/3x3_reduce/bn"
+  top: "inception_3a/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/3x3_reduce/bn"
+  top: "inception_3a/3x3_reduce/bn/sc"
+  name: "inception_3a/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/3x3_reduce/bn/sc"
+  top: "inception_3a/3x3_reduce/bn/sc"
+  name: "inception_3a/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3a/3x3_reduce/bn/sc"
+  top: "inception_3a/3x3"
+  name: "inception_3a/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/3x3"
+  name: "inception_3a/3x3/bn"
+  top: "inception_3a/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/3x3/bn"
+  top: "inception_3a/3x3/bn/sc"
+  name: "inception_3a/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/3x3/bn/sc"
+  top: "inception_3a/3x3/bn/sc"
+  name: "inception_3a/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "pool2/3x3_s2"
+  top: "inception_3a/double3x3_reduce"
+  name: "inception_3a/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3_reduce"
+  name: "inception_3a/double3x3_reduce/bn"
+  top: "inception_3a/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3_reduce/bn"
+  top: "inception_3a/double3x3_reduce/bn/sc"
+  name: "inception_3a/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3_reduce/bn/sc"
+  top: "inception_3a/double3x3_reduce/bn/sc"
+  name: "inception_3a/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3a/double3x3_reduce/bn/sc"
+  top: "inception_3a/double3x3a"
+  name: "inception_3a/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3a"
+  name: "inception_3a/double3x3a/bn"
+  top: "inception_3a/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3a/bn"
+  top: "inception_3a/double3x3a/bn/sc"
+  name: "inception_3a/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3a/bn/sc"
+  top: "inception_3a/double3x3a/bn/sc"
+  name: "inception_3a/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3a/double3x3a/bn/sc"
+  top: "inception_3a/double3x3b"
+  name: "inception_3a/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3b"
+  name: "inception_3a/double3x3b/bn"
+  top: "inception_3a/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3b/bn"
+  top: "inception_3a/double3x3b/bn/sc"
+  name: "inception_3a/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/double3x3b/bn/sc"
+  top: "inception_3a/double3x3b/bn/sc"
+  name: "inception_3a/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/pool"
+  name: "inception_3a/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_3a/pool"
+  top: "inception_3a/pool_proj"
+  name: "inception_3a/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 32
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3a/pool_proj"
+  name: "inception_3a/pool_proj/bn"
+  top: "inception_3a/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/pool_proj/bn"
+  top: "inception_3a/pool_proj/bn/sc"
+  name: "inception_3a/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3a/pool_proj/bn/sc"
+  top: "inception_3a/pool_proj/bn/sc"
+  name: "inception_3a/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/1x1/bn/sc"
+  bottom: "inception_3a/3x3/bn/sc"
+  bottom: "inception_3a/double3x3b/bn/sc"
+  bottom: "inception_3a/pool_proj/bn/sc"
+  top: "inception_3a/output"
+  name: "inception_3a/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3a/output"
+  top: "inception_3b/1x1"
+  name: "inception_3b/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/1x1"
+  name: "inception_3b/1x1/bn"
+  top: "inception_3b/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/1x1/bn"
+  top: "inception_3b/1x1/bn/sc"
+  name: "inception_3b/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/1x1/bn/sc"
+  top: "inception_3b/1x1/bn/sc"
+  name: "inception_3b/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3a/output"
+  top: "inception_3b/3x3_reduce"
+  name: "inception_3b/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/3x3_reduce"
+  name: "inception_3b/3x3_reduce/bn"
+  top: "inception_3b/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/3x3_reduce/bn"
+  top: "inception_3b/3x3_reduce/bn/sc"
+  name: "inception_3b/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/3x3_reduce/bn/sc"
+  top: "inception_3b/3x3_reduce/bn/sc"
+  name: "inception_3b/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3b/3x3_reduce/bn/sc"
+  top: "inception_3b/3x3"
+  name: "inception_3b/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/3x3"
+  name: "inception_3b/3x3/bn"
+  top: "inception_3b/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/3x3/bn"
+  top: "inception_3b/3x3/bn/sc"
+  name: "inception_3b/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/3x3/bn/sc"
+  top: "inception_3b/3x3/bn/sc"
+  name: "inception_3b/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3a/output"
+  top: "inception_3b/double3x3_reduce"
+  name: "inception_3b/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3_reduce"
+  name: "inception_3b/double3x3_reduce/bn"
+  top: "inception_3b/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3_reduce/bn"
+  top: "inception_3b/double3x3_reduce/bn/sc"
+  name: "inception_3b/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3_reduce/bn/sc"
+  top: "inception_3b/double3x3_reduce/bn/sc"
+  name: "inception_3b/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3b/double3x3_reduce/bn/sc"
+  top: "inception_3b/double3x3a"
+  name: "inception_3b/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3a"
+  name: "inception_3b/double3x3a/bn"
+  top: "inception_3b/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3a/bn"
+  top: "inception_3b/double3x3a/bn/sc"
+  name: "inception_3b/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3a/bn/sc"
+  top: "inception_3b/double3x3a/bn/sc"
+  name: "inception_3b/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3b/double3x3a/bn/sc"
+  top: "inception_3b/double3x3b"
+  name: "inception_3b/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3b"
+  name: "inception_3b/double3x3b/bn"
+  top: "inception_3b/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3b/bn"
+  top: "inception_3b/double3x3b/bn/sc"
+  name: "inception_3b/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/double3x3b/bn/sc"
+  top: "inception_3b/double3x3b/bn/sc"
+  name: "inception_3b/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3a/output"
+  top: "inception_3b/pool"
+  name: "inception_3b/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_3b/pool"
+  top: "inception_3b/pool_proj"
+  name: "inception_3b/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3b/pool_proj"
+  name: "inception_3b/pool_proj/bn"
+  top: "inception_3b/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/pool_proj/bn"
+  top: "inception_3b/pool_proj/bn/sc"
+  name: "inception_3b/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3b/pool_proj/bn/sc"
+  top: "inception_3b/pool_proj/bn/sc"
+  name: "inception_3b/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/1x1/bn/sc"
+  bottom: "inception_3b/3x3/bn/sc"
+  bottom: "inception_3b/double3x3b/bn/sc"
+  bottom: "inception_3b/pool_proj/bn/sc"
+  top: "inception_3b/output"
+  name: "inception_3b/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3b/output"
+  top: "inception_3c/3x3_reduce"
+  name: "inception_3c/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3c/3x3_reduce"
+  name: "inception_3c/3x3_reduce/bn"
+  top: "inception_3c/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/3x3_reduce/bn"
+  top: "inception_3c/3x3_reduce/bn/sc"
+  name: "inception_3c/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3c/3x3_reduce/bn/sc"
+  top: "inception_3c/3x3_reduce/bn/sc"
+  name: "inception_3c/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3c/3x3_reduce/bn/sc"
+  top: "inception_3c/3x3"
+  name: "inception_3c/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3c/3x3"
+  name: "inception_3c/3x3/bn"
+  top: "inception_3c/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/3x3/bn"
+  top: "inception_3c/3x3/bn/sc"
+  name: "inception_3c/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3c/3x3/bn/sc"
+  top: "inception_3c/3x3/bn/sc"
+  name: "inception_3c/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3b/output"
+  top: "inception_3c/double3x3_reduce"
+  name: "inception_3c/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3_reduce"
+  name: "inception_3c/double3x3_reduce/bn"
+  top: "inception_3c/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3_reduce/bn"
+  top: "inception_3c/double3x3_reduce/bn/sc"
+  name: "inception_3c/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3_reduce/bn/sc"
+  top: "inception_3c/double3x3_reduce/bn/sc"
+  name: "inception_3c/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3c/double3x3_reduce/bn/sc"
+  top: "inception_3c/double3x3a"
+  name: "inception_3c/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3a"
+  name: "inception_3c/double3x3a/bn"
+  top: "inception_3c/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3a/bn"
+  top: "inception_3c/double3x3a/bn/sc"
+  name: "inception_3c/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3a/bn/sc"
+  top: "inception_3c/double3x3a/bn/sc"
+  name: "inception_3c/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3c/double3x3a/bn/sc"
+  top: "inception_3c/double3x3b"
+  name: "inception_3c/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3b"
+  name: "inception_3c/double3x3b/bn"
+  top: "inception_3c/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3b/bn"
+  top: "inception_3c/double3x3b/bn/sc"
+  name: "inception_3c/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_3c/double3x3b/bn/sc"
+  top: "inception_3c/double3x3b/bn/sc"
+  name: "inception_3c/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3b/output"
+  top: "inception_3c/pool"
+  name: "inception_3c/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  bottom: "inception_3c/3x3/bn/sc"
+  bottom: "inception_3c/double3x3b/bn/sc"
+  bottom: "inception_3c/pool"
+  top: "inception_3c/output"
+  name: "inception_3c/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/output"
+  top: "pool3/5x5_s3"
+  name: "pool3/5x5_s3"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 5
+    stride: 3
+  }
+}
+layer {
+ bottom: "pool3/5x5_s3"
+  top: "loss1/conv"
+  name: "loss1/conv"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "loss1/conv"
+  name: "loss1/conv/bn"
+  top: "loss1/conv/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "loss1/conv/bn"
+  top: "loss1/conv/bn/sc"
+  name: "loss1/conv/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "loss1/conv/bn/sc"
+  top: "loss1/conv/bn/sc"
+  name: "loss1/conv/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "loss1/conv/bn/sc"
+  top: "loss1/fc"
+  name: "loss1/fc"
+  type: "InnerProduct"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  inner_product_param {
+    num_output: 1024
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "loss1/fc"
+  name: "loss1/fc/bn"
+  top: "loss1/fc/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "loss1/fc/bn"
+  top: "loss1/fc/bn/sc"
+  name: "loss1/fc/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "loss1/fc/bn/sc"
+  top: "loss1/fc/bn/sc"
+  name: "loss1/fc/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "loss1/fc/bn/sc"
+  top: "loss1/classifier"
+  name: "loss1/classifier"
+  type: "InnerProduct"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/loss"
+  name: "loss1/loss"
+  type: "SoftmaxWithLoss"
+  loss_weight: 0.3
+}
+layer {
+  bottom: "loss1/classifier"
+  top: "loss1/prob"
+  name: "loss1/prob"
+  type: "Softmax"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  bottom: "loss1/prob"
+  bottom: "label"
+  top: "loss1/top-1"
+  name: "loss1/top-1"
+  type: "Accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  bottom: "loss1/prob"
+  bottom: "label"
+  top: "loss1/top-5"
+  name: "loss1/top-5"
+  type: "Accuracy"
+  accuracy_param {
+    top_k: 5
+  }
+  include {
+    phase: TEST
+  }
+}
+layer {
+ bottom: "inception_3c/output"
+  top: "inception_4a/1x1"
+  name: "inception_4a/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 224
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/1x1"
+  name: "inception_4a/1x1/bn"
+  top: "inception_4a/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/1x1/bn"
+  top: "inception_4a/1x1/bn/sc"
+  name: "inception_4a/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/1x1/bn/sc"
+  top: "inception_4a/1x1/bn/sc"
+  name: "inception_4a/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3c/output"
+  top: "inception_4a/3x3_reduce"
+  name: "inception_4a/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/3x3_reduce"
+  name: "inception_4a/3x3_reduce/bn"
+  top: "inception_4a/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/3x3_reduce/bn"
+  top: "inception_4a/3x3_reduce/bn/sc"
+  name: "inception_4a/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/3x3_reduce/bn/sc"
+  top: "inception_4a/3x3_reduce/bn/sc"
+  name: "inception_4a/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4a/3x3_reduce/bn/sc"
+  top: "inception_4a/3x3"
+  name: "inception_4a/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/3x3"
+  name: "inception_4a/3x3/bn"
+  top: "inception_4a/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/3x3/bn"
+  top: "inception_4a/3x3/bn/sc"
+  name: "inception_4a/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/3x3/bn/sc"
+  top: "inception_4a/3x3/bn/sc"
+  name: "inception_4a/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_3c/output"
+  top: "inception_4a/double3x3_reduce"
+  name: "inception_4a/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3_reduce"
+  name: "inception_4a/double3x3_reduce/bn"
+  top: "inception_4a/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3_reduce/bn"
+  top: "inception_4a/double3x3_reduce/bn/sc"
+  name: "inception_4a/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3_reduce/bn/sc"
+  top: "inception_4a/double3x3_reduce/bn/sc"
+  name: "inception_4a/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4a/double3x3_reduce/bn/sc"
+  top: "inception_4a/double3x3a"
+  name: "inception_4a/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3a"
+  name: "inception_4a/double3x3a/bn"
+  top: "inception_4a/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3a/bn"
+  top: "inception_4a/double3x3a/bn/sc"
+  name: "inception_4a/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3a/bn/sc"
+  top: "inception_4a/double3x3a/bn/sc"
+  name: "inception_4a/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4a/double3x3a/bn/sc"
+  top: "inception_4a/double3x3b"
+  name: "inception_4a/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3b"
+  name: "inception_4a/double3x3b/bn"
+  top: "inception_4a/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3b/bn"
+  top: "inception_4a/double3x3b/bn/sc"
+  name: "inception_4a/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/double3x3b/bn/sc"
+  top: "inception_4a/double3x3b/bn/sc"
+  name: "inception_4a/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_3c/output"
+  top: "inception_4a/pool"
+  name: "inception_4a/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_4a/pool"
+  top: "inception_4a/pool_proj"
+  name: "inception_4a/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4a/pool_proj"
+  name: "inception_4a/pool_proj/bn"
+  top: "inception_4a/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/pool_proj/bn"
+  top: "inception_4a/pool_proj/bn/sc"
+  name: "inception_4a/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4a/pool_proj/bn/sc"
+  top: "inception_4a/pool_proj/bn/sc"
+  name: "inception_4a/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/1x1/bn/sc"
+  bottom: "inception_4a/3x3/bn/sc"
+  bottom: "inception_4a/double3x3b/bn/sc"
+  bottom: "inception_4a/pool_proj/bn/sc"
+  top: "inception_4a/output"
+  name: "inception_4a/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4a/output"
+  top: "inception_4b/1x1"
+  name: "inception_4b/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/1x1"
+  name: "inception_4b/1x1/bn"
+  top: "inception_4b/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/1x1/bn"
+  top: "inception_4b/1x1/bn/sc"
+  name: "inception_4b/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/1x1/bn/sc"
+  top: "inception_4b/1x1/bn/sc"
+  name: "inception_4b/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4a/output"
+  top: "inception_4b/3x3_reduce"
+  name: "inception_4b/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/3x3_reduce"
+  name: "inception_4b/3x3_reduce/bn"
+  top: "inception_4b/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/3x3_reduce/bn"
+  top: "inception_4b/3x3_reduce/bn/sc"
+  name: "inception_4b/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/3x3_reduce/bn/sc"
+  top: "inception_4b/3x3_reduce/bn/sc"
+  name: "inception_4b/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4b/3x3_reduce/bn/sc"
+  top: "inception_4b/3x3"
+  name: "inception_4b/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/3x3"
+  name: "inception_4b/3x3/bn"
+  top: "inception_4b/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/3x3/bn"
+  top: "inception_4b/3x3/bn/sc"
+  name: "inception_4b/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/3x3/bn/sc"
+  top: "inception_4b/3x3/bn/sc"
+  name: "inception_4b/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4a/output"
+  top: "inception_4b/double3x3_reduce"
+  name: "inception_4b/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3_reduce"
+  name: "inception_4b/double3x3_reduce/bn"
+  top: "inception_4b/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3_reduce/bn"
+  top: "inception_4b/double3x3_reduce/bn/sc"
+  name: "inception_4b/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3_reduce/bn/sc"
+  top: "inception_4b/double3x3_reduce/bn/sc"
+  name: "inception_4b/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4b/double3x3_reduce/bn/sc"
+  top: "inception_4b/double3x3a"
+  name: "inception_4b/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3a"
+  name: "inception_4b/double3x3a/bn"
+  top: "inception_4b/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3a/bn"
+  top: "inception_4b/double3x3a/bn/sc"
+  name: "inception_4b/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3a/bn/sc"
+  top: "inception_4b/double3x3a/bn/sc"
+  name: "inception_4b/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4b/double3x3a/bn/sc"
+  top: "inception_4b/double3x3b"
+  name: "inception_4b/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3b"
+  name: "inception_4b/double3x3b/bn"
+  top: "inception_4b/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3b/bn"
+  top: "inception_4b/double3x3b/bn/sc"
+  name: "inception_4b/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/double3x3b/bn/sc"
+  top: "inception_4b/double3x3b/bn/sc"
+  name: "inception_4b/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4a/output"
+  top: "inception_4b/pool"
+  name: "inception_4b/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_4b/pool"
+  top: "inception_4b/pool_proj"
+  name: "inception_4b/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4b/pool_proj"
+  name: "inception_4b/pool_proj/bn"
+  top: "inception_4b/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/pool_proj/bn"
+  top: "inception_4b/pool_proj/bn/sc"
+  name: "inception_4b/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4b/pool_proj/bn/sc"
+  top: "inception_4b/pool_proj/bn/sc"
+  name: "inception_4b/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/1x1/bn/sc"
+  bottom: "inception_4b/3x3/bn/sc"
+  bottom: "inception_4b/double3x3b/bn/sc"
+  bottom: "inception_4b/pool_proj/bn/sc"
+  top: "inception_4b/output"
+  name: "inception_4b/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4b/output"
+  top: "inception_4c/1x1"
+  name: "inception_4c/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/1x1"
+  name: "inception_4c/1x1/bn"
+  top: "inception_4c/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/1x1/bn"
+  top: "inception_4c/1x1/bn/sc"
+  name: "inception_4c/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/1x1/bn/sc"
+  top: "inception_4c/1x1/bn/sc"
+  name: "inception_4c/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4b/output"
+  top: "inception_4c/3x3_reduce"
+  name: "inception_4c/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/3x3_reduce"
+  name: "inception_4c/3x3_reduce/bn"
+  top: "inception_4c/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/3x3_reduce/bn"
+  top: "inception_4c/3x3_reduce/bn/sc"
+  name: "inception_4c/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/3x3_reduce/bn/sc"
+  top: "inception_4c/3x3_reduce/bn/sc"
+  name: "inception_4c/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4c/3x3_reduce/bn/sc"
+  top: "inception_4c/3x3"
+  name: "inception_4c/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/3x3"
+  name: "inception_4c/3x3/bn"
+  top: "inception_4c/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/3x3/bn"
+  top: "inception_4c/3x3/bn/sc"
+  name: "inception_4c/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/3x3/bn/sc"
+  top: "inception_4c/3x3/bn/sc"
+  name: "inception_4c/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4b/output"
+  top: "inception_4c/double3x3_reduce"
+  name: "inception_4c/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3_reduce"
+  name: "inception_4c/double3x3_reduce/bn"
+  top: "inception_4c/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3_reduce/bn"
+  top: "inception_4c/double3x3_reduce/bn/sc"
+  name: "inception_4c/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3_reduce/bn/sc"
+  top: "inception_4c/double3x3_reduce/bn/sc"
+  name: "inception_4c/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4c/double3x3_reduce/bn/sc"
+  top: "inception_4c/double3x3a"
+  name: "inception_4c/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3a"
+  name: "inception_4c/double3x3a/bn"
+  top: "inception_4c/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3a/bn"
+  top: "inception_4c/double3x3a/bn/sc"
+  name: "inception_4c/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3a/bn/sc"
+  top: "inception_4c/double3x3a/bn/sc"
+  name: "inception_4c/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4c/double3x3a/bn/sc"
+  top: "inception_4c/double3x3b"
+  name: "inception_4c/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3b"
+  name: "inception_4c/double3x3b/bn"
+  top: "inception_4c/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3b/bn"
+  top: "inception_4c/double3x3b/bn/sc"
+  name: "inception_4c/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/double3x3b/bn/sc"
+  top: "inception_4c/double3x3b/bn/sc"
+  name: "inception_4c/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4b/output"
+  top: "inception_4c/pool"
+  name: "inception_4c/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_4c/pool"
+  top: "inception_4c/pool_proj"
+  name: "inception_4c/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4c/pool_proj"
+  name: "inception_4c/pool_proj/bn"
+  top: "inception_4c/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/pool_proj/bn"
+  top: "inception_4c/pool_proj/bn/sc"
+  name: "inception_4c/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4c/pool_proj/bn/sc"
+  top: "inception_4c/pool_proj/bn/sc"
+  name: "inception_4c/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/1x1/bn/sc"
+  bottom: "inception_4c/3x3/bn/sc"
+  bottom: "inception_4c/double3x3b/bn/sc"
+  bottom: "inception_4c/pool_proj/bn/sc"
+  top: "inception_4c/output"
+  name: "inception_4c/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4c/output"
+  top: "inception_4d/1x1"
+  name: "inception_4d/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/1x1"
+  name: "inception_4d/1x1/bn"
+  top: "inception_4d/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/1x1/bn"
+  top: "inception_4d/1x1/bn/sc"
+  name: "inception_4d/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/1x1/bn/sc"
+  top: "inception_4d/1x1/bn/sc"
+  name: "inception_4d/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4c/output"
+  top: "inception_4d/3x3_reduce"
+  name: "inception_4d/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/3x3_reduce"
+  name: "inception_4d/3x3_reduce/bn"
+  top: "inception_4d/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/3x3_reduce/bn"
+  top: "inception_4d/3x3_reduce/bn/sc"
+  name: "inception_4d/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/3x3_reduce/bn/sc"
+  top: "inception_4d/3x3_reduce/bn/sc"
+  name: "inception_4d/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4d/3x3_reduce/bn/sc"
+  top: "inception_4d/3x3"
+  name: "inception_4d/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/3x3"
+  name: "inception_4d/3x3/bn"
+  top: "inception_4d/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/3x3/bn"
+  top: "inception_4d/3x3/bn/sc"
+  name: "inception_4d/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/3x3/bn/sc"
+  top: "inception_4d/3x3/bn/sc"
+  name: "inception_4d/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4c/output"
+  top: "inception_4d/double3x3_reduce"
+  name: "inception_4d/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3_reduce"
+  name: "inception_4d/double3x3_reduce/bn"
+  top: "inception_4d/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3_reduce/bn"
+  top: "inception_4d/double3x3_reduce/bn/sc"
+  name: "inception_4d/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3_reduce/bn/sc"
+  top: "inception_4d/double3x3_reduce/bn/sc"
+  name: "inception_4d/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4d/double3x3_reduce/bn/sc"
+  top: "inception_4d/double3x3a"
+  name: "inception_4d/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3a"
+  name: "inception_4d/double3x3a/bn"
+  top: "inception_4d/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3a/bn"
+  top: "inception_4d/double3x3a/bn/sc"
+  name: "inception_4d/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3a/bn/sc"
+  top: "inception_4d/double3x3a/bn/sc"
+  name: "inception_4d/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4d/double3x3a/bn/sc"
+  top: "inception_4d/double3x3b"
+  name: "inception_4d/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3b"
+  name: "inception_4d/double3x3b/bn"
+  top: "inception_4d/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3b/bn"
+  top: "inception_4d/double3x3b/bn/sc"
+  name: "inception_4d/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/double3x3b/bn/sc"
+  top: "inception_4d/double3x3b/bn/sc"
+  name: "inception_4d/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4c/output"
+  top: "inception_4d/pool"
+  name: "inception_4d/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_4d/pool"
+  top: "inception_4d/pool_proj"
+  name: "inception_4d/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 96
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4d/pool_proj"
+  name: "inception_4d/pool_proj/bn"
+  top: "inception_4d/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/pool_proj/bn"
+  top: "inception_4d/pool_proj/bn/sc"
+  name: "inception_4d/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4d/pool_proj/bn/sc"
+  top: "inception_4d/pool_proj/bn/sc"
+  name: "inception_4d/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/1x1/bn/sc"
+  bottom: "inception_4d/3x3/bn/sc"
+  bottom: "inception_4d/double3x3b/bn/sc"
+  bottom: "inception_4d/pool_proj/bn/sc"
+  top: "inception_4d/output"
+  name: "inception_4d/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4d/output"
+  top: "inception_4e/3x3_reduce"
+  name: "inception_4e/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4e/3x3_reduce"
+  name: "inception_4e/3x3_reduce/bn"
+  top: "inception_4e/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/3x3_reduce/bn"
+  top: "inception_4e/3x3_reduce/bn/sc"
+  name: "inception_4e/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4e/3x3_reduce/bn/sc"
+  top: "inception_4e/3x3_reduce/bn/sc"
+  name: "inception_4e/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4e/3x3_reduce/bn/sc"
+  top: "inception_4e/3x3"
+  name: "inception_4e/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4e/3x3"
+  name: "inception_4e/3x3/bn"
+  top: "inception_4e/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/3x3/bn"
+  top: "inception_4e/3x3/bn/sc"
+  name: "inception_4e/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4e/3x3/bn/sc"
+  top: "inception_4e/3x3/bn/sc"
+  name: "inception_4e/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4d/output"
+  top: "inception_4e/double3x3_reduce"
+  name: "inception_4e/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3_reduce"
+  name: "inception_4e/double3x3_reduce/bn"
+  top: "inception_4e/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3_reduce/bn"
+  top: "inception_4e/double3x3_reduce/bn/sc"
+  name: "inception_4e/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3_reduce/bn/sc"
+  top: "inception_4e/double3x3_reduce/bn/sc"
+  name: "inception_4e/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4e/double3x3_reduce/bn/sc"
+  top: "inception_4e/double3x3a"
+  name: "inception_4e/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3a"
+  name: "inception_4e/double3x3a/bn"
+  top: "inception_4e/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3a/bn"
+  top: "inception_4e/double3x3a/bn/sc"
+  name: "inception_4e/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3a/bn/sc"
+  top: "inception_4e/double3x3a/bn/sc"
+  name: "inception_4e/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4e/double3x3a/bn/sc"
+  top: "inception_4e/double3x3b"
+  name: "inception_4e/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3b"
+  name: "inception_4e/double3x3b/bn"
+  top: "inception_4e/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3b/bn"
+  top: "inception_4e/double3x3b/bn/sc"
+  name: "inception_4e/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_4e/double3x3b/bn/sc"
+  top: "inception_4e/double3x3b/bn/sc"
+  name: "inception_4e/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4d/output"
+  top: "inception_4e/pool"
+  name: "inception_4e/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  bottom: "inception_4e/3x3/bn/sc"
+  bottom: "inception_4e/double3x3b/bn/sc"
+  bottom: "inception_4e/pool"
+  top: "inception_4e/output"
+  name: "inception_4e/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/output"
+  top: "pool4/5x5_s3"
+  name: "pool4/5x5_s3"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 5
+    stride: 3
+  }
+}
+layer {
+ bottom: "pool4/5x5_s3"
+  top: "loss2/conv"
+  name: "loss2/conv"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "loss2/conv"
+  name: "loss2/conv/bn"
+  top: "loss2/conv/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "loss2/conv/bn"
+  top: "loss2/conv/bn/sc"
+  name: "loss2/conv/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "loss2/conv/bn/sc"
+  top: "loss2/conv/bn/sc"
+  name: "loss2/conv/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "loss2/conv/bn/sc"
+  top: "loss2/fc"
+  name: "loss2/fc"
+  type: "InnerProduct"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  inner_product_param {
+    num_output: 1024
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "loss2/fc"
+  name: "loss2/fc/bn"
+  top: "loss2/fc/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "loss2/fc/bn"
+  top: "loss2/fc/bn/sc"
+  name: "loss2/fc/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "loss2/fc/bn/sc"
+  top: "loss2/fc/bn/sc"
+  name: "loss2/fc/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "loss2/fc/bn/sc"
+  top: "loss2/classifier"
+  name: "loss2/classifier"
+  type: "InnerProduct"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/loss"
+  name: "loss2/loss"
+  type: "SoftmaxWithLoss"
+  loss_weight: 0.3
+}
+layer {
+  bottom: "loss2/classifier"
+  top: "loss2/prob"
+  name: "loss2/prob"
+  type: "Softmax"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  bottom: "loss2/prob"
+  bottom: "label"
+  top: "loss2/top-1"
+  name: "loss2/top-1"
+  type: "Accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  bottom: "loss2/prob"
+  bottom: "label"
+  top: "loss2/top-5"
+  name: "loss2/top-5"
+  type: "Accuracy"
+  accuracy_param {
+    top_k: 5
+  }
+  include {
+    phase: TEST
+  }
+}
+layer {
+ bottom: "inception_4e/output"
+  top: "inception_5a/1x1"
+  name: "inception_5a/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 352
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/1x1"
+  name: "inception_5a/1x1/bn"
+  top: "inception_5a/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/1x1/bn"
+  top: "inception_5a/1x1/bn/sc"
+  name: "inception_5a/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/1x1/bn/sc"
+  top: "inception_5a/1x1/bn/sc"
+  name: "inception_5a/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4e/output"
+  top: "inception_5a/3x3_reduce"
+  name: "inception_5a/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/3x3_reduce"
+  name: "inception_5a/3x3_reduce/bn"
+  top: "inception_5a/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/3x3_reduce/bn"
+  top: "inception_5a/3x3_reduce/bn/sc"
+  name: "inception_5a/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/3x3_reduce/bn/sc"
+  top: "inception_5a/3x3_reduce/bn/sc"
+  name: "inception_5a/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5a/3x3_reduce/bn/sc"
+  top: "inception_5a/3x3"
+  name: "inception_5a/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/3x3"
+  name: "inception_5a/3x3/bn"
+  top: "inception_5a/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/3x3/bn"
+  top: "inception_5a/3x3/bn/sc"
+  name: "inception_5a/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/3x3/bn/sc"
+  top: "inception_5a/3x3/bn/sc"
+  name: "inception_5a/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_4e/output"
+  top: "inception_5a/double3x3_reduce"
+  name: "inception_5a/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 160
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3_reduce"
+  name: "inception_5a/double3x3_reduce/bn"
+  top: "inception_5a/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3_reduce/bn"
+  top: "inception_5a/double3x3_reduce/bn/sc"
+  name: "inception_5a/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3_reduce/bn/sc"
+  top: "inception_5a/double3x3_reduce/bn/sc"
+  name: "inception_5a/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5a/double3x3_reduce/bn/sc"
+  top: "inception_5a/double3x3a"
+  name: "inception_5a/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3a"
+  name: "inception_5a/double3x3a/bn"
+  top: "inception_5a/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3a/bn"
+  top: "inception_5a/double3x3a/bn/sc"
+  name: "inception_5a/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3a/bn/sc"
+  top: "inception_5a/double3x3a/bn/sc"
+  name: "inception_5a/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5a/double3x3a/bn/sc"
+  top: "inception_5a/double3x3b"
+  name: "inception_5a/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3b"
+  name: "inception_5a/double3x3b/bn"
+  top: "inception_5a/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3b/bn"
+  top: "inception_5a/double3x3b/bn/sc"
+  name: "inception_5a/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/double3x3b/bn/sc"
+  top: "inception_5a/double3x3b/bn/sc"
+  name: "inception_5a/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_4e/output"
+  top: "inception_5a/pool"
+  name: "inception_5a/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_5a/pool"
+  top: "inception_5a/pool_proj"
+  name: "inception_5a/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5a/pool_proj"
+  name: "inception_5a/pool_proj/bn"
+  top: "inception_5a/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/pool_proj/bn"
+  top: "inception_5a/pool_proj/bn/sc"
+  name: "inception_5a/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5a/pool_proj/bn/sc"
+  top: "inception_5a/pool_proj/bn/sc"
+  name: "inception_5a/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/1x1/bn/sc"
+  bottom: "inception_5a/3x3/bn/sc"
+  bottom: "inception_5a/double3x3b/bn/sc"
+  bottom: "inception_5a/pool_proj/bn/sc"
+  top: "inception_5a/output"
+  name: "inception_5a/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5a/output"
+  top: "inception_5b/1x1"
+  name: "inception_5b/1x1"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 352
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/1x1"
+  name: "inception_5b/1x1/bn"
+  top: "inception_5b/1x1/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/1x1/bn"
+  top: "inception_5b/1x1/bn/sc"
+  name: "inception_5b/1x1/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/1x1/bn/sc"
+  top: "inception_5b/1x1/bn/sc"
+  name: "inception_5b/1x1/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5a/output"
+  top: "inception_5b/3x3_reduce"
+  name: "inception_5b/3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/3x3_reduce"
+  name: "inception_5b/3x3_reduce/bn"
+  top: "inception_5b/3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/3x3_reduce/bn"
+  top: "inception_5b/3x3_reduce/bn/sc"
+  name: "inception_5b/3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/3x3_reduce/bn/sc"
+  top: "inception_5b/3x3_reduce/bn/sc"
+  name: "inception_5b/3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5b/3x3_reduce/bn/sc"
+  top: "inception_5b/3x3"
+  name: "inception_5b/3x3"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/3x3"
+  name: "inception_5b/3x3/bn"
+  top: "inception_5b/3x3/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/3x3/bn"
+  top: "inception_5b/3x3/bn/sc"
+  name: "inception_5b/3x3/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/3x3/bn/sc"
+  top: "inception_5b/3x3/bn/sc"
+  name: "inception_5b/3x3/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5a/output"
+  top: "inception_5b/double3x3_reduce"
+  name: "inception_5b/double3x3_reduce"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 192
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3_reduce"
+  name: "inception_5b/double3x3_reduce/bn"
+  top: "inception_5b/double3x3_reduce/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3_reduce/bn"
+  top: "inception_5b/double3x3_reduce/bn/sc"
+  name: "inception_5b/double3x3_reduce/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3_reduce/bn/sc"
+  top: "inception_5b/double3x3_reduce/bn/sc"
+  name: "inception_5b/double3x3_reduce/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5b/double3x3_reduce/bn/sc"
+  top: "inception_5b/double3x3a"
+  name: "inception_5b/double3x3a"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3a"
+  name: "inception_5b/double3x3a/bn"
+  top: "inception_5b/double3x3a/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3a/bn"
+  top: "inception_5b/double3x3a/bn/sc"
+  name: "inception_5b/double3x3a/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3a/bn/sc"
+  top: "inception_5b/double3x3a/bn/sc"
+  name: "inception_5b/double3x3a/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+ bottom: "inception_5b/double3x3a/bn/sc"
+  top: "inception_5b/double3x3b"
+  name: "inception_5b/double3x3b"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3b"
+  name: "inception_5b/double3x3b/bn"
+  top: "inception_5b/double3x3b/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3b/bn"
+  top: "inception_5b/double3x3b/bn/sc"
+  name: "inception_5b/double3x3b/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/double3x3b/bn/sc"
+  top: "inception_5b/double3x3b/bn/sc"
+  name: "inception_5b/double3x3b/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5a/output"
+  top: "inception_5b/pool"
+  name: "inception_5b/pool"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+ bottom: "inception_5b/pool"
+  top: "inception_5b/pool_proj"
+  name: "inception_5b/pool_proj"
+  type: "Convolution"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  convolution_param {
+    
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_term: false
+  }
+}
+layer {
+  bottom: "inception_5b/pool_proj"
+  name: "inception_5b/pool_proj/bn"
+  top: "inception_5b/pool_proj/bn"
+  type: "BatchNorm"
+  batch_norm_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/pool_proj/bn"
+  top: "inception_5b/pool_proj/bn/sc"
+  name: "inception_5b/pool_proj/bn/sc"
+  type: "Scale"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  bottom: "inception_5b/pool_proj/bn/sc"
+  top: "inception_5b/pool_proj/bn/sc"
+  name: "inception_5b/pool_proj/bn/sc/relu"
+  type: "ReLU"
+  relu_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/1x1/bn/sc"
+  bottom: "inception_5b/3x3/bn/sc"
+  bottom: "inception_5b/double3x3b/bn/sc"
+  bottom: "inception_5b/pool_proj/bn/sc"
+  top: "inception_5b/output"
+  name: "inception_5b/output"
+  type: "Concat"
+  concat_param {
+    
+  }
+}
+layer {
+  bottom: "inception_5b/output"
+  top: "pool5/7x7_s1"
+  name: "pool5/7x7_s1"
+  type: "Pooling"
+  pooling_param {
+    
+    pool: AVE
+    kernel_size: 7
+    stride: 1
+  }
+}
+layer {
+  bottom: "pool5/7x7_s1"
+  top: "loss3/classifier"
+  name: "loss3/classifier"
+  type: "InnerProduct"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/loss"
+  name: "loss3/loss"
+  type: "SoftmaxWithLoss"
+  loss_weight: 1
+}
+layer {
+  bottom: "loss3/classifier"
+  top: "loss3/prob"
+  name: "loss3/prob"
+  type: "Softmax"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  bottom: "loss3/prob"
+  bottom: "label"
+  top: "loss3/top-1"
+  name: "loss3/top-1"
+  type: "Accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  bottom: "loss3/prob"
+  bottom: "label"
+  top: "loss3/top-5"
+  name: "loss3/top-5"
+  type: "Accuracy"
+  accuracy_param {
+    top_k: 5
+  }
+  include {
+    phase: TEST
+  }
+}
diff --git a/models/intel_optimized_models/resnet_50/bdw/train_val_dummydata.prototxt b/models/intel_optimized_models/resnet_50/bdw/train_val_dummydata.prototxt
new file mode 100644
index 000000000..aa37d1c88
--- /dev/null
+++ b/models/intel_optimized_models/resnet_50/bdw/train_val_dummydata.prototxt
@@ -0,0 +1,2294 @@
+#This is Intel(R) optimized (in terms of time to train) version of topology described in the [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) publication.
+#
+#Top-5 and Top-1 results achieved with this topology:
+#Top-5: 92%
+#Top-1: 73.9%
+#Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor.
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  dummy_data_param {
+    data_filler {
+        type: "constant"
+        value: 0.01
+    }
+    shape: { dim: 32 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 32 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.01
+    }
+    shape: { dim: 32 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 32 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+
+layer {
+name: "conv1"
+type: "Convolution"
+bottom: "data"
+top: "conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+param {
+  lr_mult: 2.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  pad: 3
+  kernel_size: 7
+  stride: 2
+  weight_filler {
+    type: "msra"
+    variance_norm: FAN_OUT
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "conv1_bn"
+type: "BatchNorm"
+bottom: "conv1"
+top: "conv1_pcs_arm_sim"
+  batch_norm_param {
+ }       
+}
+layer {
+name: "conv1_relu"
+type: "ReLU"
+bottom: "conv1_pcs_arm_sim"
+top: "conv1_pcs_arm_sim"
+
+}
+layer {
+name: "conv1_pool"
+type: "Pooling"
+bottom: "conv1_pcs_arm_sim"
+top: "conv1_pool"
+pooling_param {
+  kernel_size: 3
+  stride: 2
+}
+
+}
+layer {
+name: "layer_64_1_conv1"
+type: "Convolution"
+bottom: "conv1_pool"
+top: "layer_64_1_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_1_bn2"
+type: "BatchNorm"
+bottom: "layer_64_1_conv1"
+top: "layer_64_1_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }     
+}
+layer {
+name: "layer_64_1_relu2"
+type: "ReLU"
+bottom: "layer_64_1_conv1_pcs_arm_sim"
+top: "layer_64_1_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_1_conv2"
+type: "Convolution"
+bottom: "layer_64_1_conv1_pcs_arm_sim"
+top: "layer_64_1_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_1_bn3"
+type: "BatchNorm"
+bottom: "layer_64_1_conv2"
+top: "layer_64_1_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }        
+}
+layer {
+name: "layer_64_1_relu3"
+type: "ReLU"
+bottom: "layer_64_1_conv2_pcs_arm_sim"
+top: "layer_64_1_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_1_conv3"
+type: "Convolution"
+bottom: "layer_64_1_conv2_pcs_arm_sim"
+top: "layer_64_1_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_1_conv_expand"
+type: "Convolution"
+bottom: "layer_64_1_conv1_pcs_arm_sim"
+top: "layer_64_1_conv_expand"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_1_sum"
+type: "Eltwise"
+bottom: "layer_64_1_conv3"
+bottom: "layer_64_1_conv_expand"
+top: "layer_64_1_sum"
+
+}
+layer {
+name: "layer_64_2_bn1"
+type: "BatchNorm"
+bottom: "layer_64_1_sum"
+top: "layer_64_2_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }       
+}
+layer {
+name: "layer_64_2_relu1"
+type: "ReLU"
+bottom: "layer_64_2_bn1_pcs_arm_sim"
+top: "layer_64_2_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_2_conv1"
+type: "Convolution"
+bottom: "layer_64_2_bn1_pcs_arm_sim"
+top: "layer_64_2_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_2_bn2"
+type: "BatchNorm"
+bottom: "layer_64_2_conv1"
+top: "layer_64_2_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }        
+}
+layer {
+name: "layer_64_2_relu2"
+type: "ReLU"
+bottom: "layer_64_2_conv1_pcs_arm_sim"
+top: "layer_64_2_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_2_conv2"
+type: "Convolution"
+bottom: "layer_64_2_conv1_pcs_arm_sim"
+top: "layer_64_2_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_2_bn3"
+type: "BatchNorm"
+bottom: "layer_64_2_conv2"
+top: "layer_64_2_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }        
+}
+layer {
+name: "layer_64_2_relu3"
+type: "ReLU"
+bottom: "layer_64_2_conv2_pcs_arm_sim"
+top: "layer_64_2_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_2_conv3"
+type: "Convolution"
+bottom: "layer_64_2_conv2_pcs_arm_sim"
+top: "layer_64_2_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_2_sum"
+type: "Eltwise"
+bottom: "layer_64_2_conv3"
+bottom: "layer_64_1_sum"
+top: "layer_64_2_sum"
+
+}
+layer {
+name: "layer_64_3_bn1"
+type: "BatchNorm"
+bottom: "layer_64_2_sum"
+top: "layer_64_3_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }       
+}
+layer {
+name: "layer_64_3_relu1"
+type: "ReLU"
+bottom: "layer_64_3_bn1_pcs_arm_sim"
+top: "layer_64_3_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_3_conv1"
+type: "Convolution"
+bottom: "layer_64_3_bn1_pcs_arm_sim"
+top: "layer_64_3_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_3_bn2"
+type: "BatchNorm"
+bottom: "layer_64_3_conv1"
+top: "layer_64_3_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_64_3_relu2"
+type: "ReLU"
+bottom: "layer_64_3_conv1_pcs_arm_sim"
+top: "layer_64_3_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_3_conv2"
+type: "Convolution"
+bottom: "layer_64_3_conv1_pcs_arm_sim"
+top: "layer_64_3_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_3_bn3"
+type: "BatchNorm"
+bottom: "layer_64_3_conv2"
+top: "layer_64_3_conv2_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_64_3_relu3"
+type: "ReLU"
+bottom: "layer_64_3_conv2_pcs_arm_sim"
+top: "layer_64_3_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_3_conv3"
+type: "Convolution"
+bottom: "layer_64_3_conv2_pcs_arm_sim"
+top: "layer_64_3_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_3_sum"
+type: "Eltwise"
+bottom: "layer_64_3_conv3"
+bottom: "layer_64_2_sum"
+top: "layer_64_3_sum"
+
+}
+layer {
+name: "layer_128_1_bn1"
+type: "BatchNorm"
+bottom: "layer_64_3_sum"
+top: "layer_128_1_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_128_1_relu1"
+type: "ReLU"
+bottom: "layer_128_1_bn1_pcs_arm_sim"
+top: "layer_128_1_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_1_conv1"
+type: "Convolution"
+bottom: "layer_128_1_bn1_pcs_arm_sim"
+top: "layer_128_1_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_1_bn2"
+type: "BatchNorm"
+bottom: "layer_128_1_conv1"
+top: "layer_128_1_conv1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_128_1_relu2"
+type: "ReLU"
+bottom: "layer_128_1_conv1_pcs_arm_sim"
+top: "layer_128_1_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_1_conv2"
+type: "Convolution"
+bottom: "layer_128_1_conv1_pcs_arm_sim"
+top: "layer_128_1_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 2
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_1_bn3"
+type: "BatchNorm"
+bottom: "layer_128_1_conv2"
+top: "layer_128_1_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_128_1_relu3"
+type: "ReLU"
+bottom: "layer_128_1_conv2_pcs_arm_sim"
+top: "layer_128_1_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_1_conv3"
+type: "Convolution"
+bottom: "layer_128_1_conv2_pcs_arm_sim"
+top: "layer_128_1_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_1_conv_expand"
+type: "Convolution"
+bottom: "layer_128_1_bn1_pcs_arm_sim"
+top: "layer_128_1_conv_expand"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 2
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_1_sum"
+type: "Eltwise"
+bottom: "layer_128_1_conv3"
+bottom: "layer_128_1_conv_expand"
+top: "layer_128_1_sum"
+
+}
+layer {
+name: "layer_128_2_bn1"
+type: "BatchNorm"
+bottom: "layer_128_1_sum"
+top: "layer_128_2_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_2_relu1"
+type: "ReLU"
+bottom: "layer_128_2_bn1_pcs_arm_sim"
+top: "layer_128_2_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_2_conv1"
+type: "Convolution"
+bottom: "layer_128_2_bn1_pcs_arm_sim"
+top: "layer_128_2_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_2_bn2"
+type: "BatchNorm"
+bottom: "layer_128_2_conv1"
+top: "layer_128_2_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_2_relu2"
+type: "ReLU"
+bottom: "layer_128_2_conv1_pcs_arm_sim"
+top: "layer_128_2_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_2_conv2"
+type: "Convolution"
+bottom: "layer_128_2_conv1_pcs_arm_sim"
+top: "layer_128_2_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_2_bn3"
+type: "BatchNorm"
+bottom: "layer_128_2_conv2"
+top: "layer_128_2_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_2_relu3"
+type: "ReLU"
+bottom: "layer_128_2_conv2_pcs_arm_sim"
+top: "layer_128_2_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_2_conv3"
+type: "Convolution"
+bottom: "layer_128_2_conv2_pcs_arm_sim"
+top: "layer_128_2_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_2_sum"
+type: "Eltwise"
+bottom: "layer_128_2_conv3"
+bottom: "layer_128_1_sum"
+top: "layer_128_2_sum"
+
+}
+layer {
+name: "layer_128_3_bn1"
+type: "BatchNorm"
+bottom: "layer_128_2_sum"
+top: "layer_128_3_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_3_relu1"
+type: "ReLU"
+bottom: "layer_128_3_bn1_pcs_arm_sim"
+top: "layer_128_3_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_3_conv1"
+type: "Convolution"
+bottom: "layer_128_3_bn1_pcs_arm_sim"
+top: "layer_128_3_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_3_bn2"
+type: "BatchNorm"
+bottom: "layer_128_3_conv1"
+top: "layer_128_3_conv1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_128_3_relu2"
+type: "ReLU"
+bottom: "layer_128_3_conv1_pcs_arm_sim"
+top: "layer_128_3_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_3_conv2"
+type: "Convolution"
+bottom: "layer_128_3_conv1_pcs_arm_sim"
+top: "layer_128_3_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_3_bn3"
+type: "BatchNorm"
+bottom: "layer_128_3_conv2"
+top: "layer_128_3_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_3_relu3"
+type: "ReLU"
+bottom: "layer_128_3_conv2_pcs_arm_sim"
+top: "layer_128_3_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_3_conv3"
+type: "Convolution"
+bottom: "layer_128_3_conv2_pcs_arm_sim"
+top: "layer_128_3_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_3_sum"
+type: "Eltwise"
+bottom: "layer_128_3_conv3"
+bottom: "layer_128_2_sum"
+top: "layer_128_3_sum"
+
+}
+layer {
+name: "layer_128_4_bn1"
+type: "BatchNorm"
+bottom: "layer_128_3_sum"
+top: "layer_128_4_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_4_relu1"
+type: "ReLU"
+bottom: "layer_128_4_bn1_pcs_arm_sim"
+top: "layer_128_4_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_4_conv1"
+type: "Convolution"
+bottom: "layer_128_4_bn1_pcs_arm_sim"
+top: "layer_128_4_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_4_bn2"
+type: "BatchNorm"
+bottom: "layer_128_4_conv1"
+top: "layer_128_4_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_4_relu2"
+type: "ReLU"
+bottom: "layer_128_4_conv1_pcs_arm_sim"
+top: "layer_128_4_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_4_conv2"
+type: "Convolution"
+bottom: "layer_128_4_conv1_pcs_arm_sim"
+top: "layer_128_4_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_4_bn3"
+type: "BatchNorm"
+bottom: "layer_128_4_conv2"
+top: "layer_128_4_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }       
+}
+layer {
+name: "layer_128_4_relu3"
+type: "ReLU"
+bottom: "layer_128_4_conv2_pcs_arm_sim"
+top: "layer_128_4_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_4_conv3"
+type: "Convolution"
+bottom: "layer_128_4_conv2_pcs_arm_sim"
+top: "layer_128_4_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_4_sum"
+type: "Eltwise"
+bottom: "layer_128_4_conv3"
+bottom: "layer_128_3_sum"
+top: "layer_128_4_sum"
+
+}
+layer {
+name: "layer_256_1_bn1"
+type: "BatchNorm"
+bottom: "layer_128_4_sum"
+top: "layer_256_1_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_1_relu1"
+type: "ReLU"
+bottom: "layer_256_1_bn1_pcs_arm_sim"
+top: "layer_256_1_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_1_conv1"
+type: "Convolution"
+bottom: "layer_256_1_bn1_pcs_arm_sim"
+top: "layer_256_1_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_1_bn2"
+type: "BatchNorm"
+bottom: "layer_256_1_conv1"
+top: "layer_256_1_conv1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_1_relu2"
+type: "ReLU"
+bottom: "layer_256_1_conv1_pcs_arm_sim"
+top: "layer_256_1_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_1_conv2"
+type: "Convolution"
+bottom: "layer_256_1_conv1_pcs_arm_sim"
+top: "layer_256_1_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 2
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_1_bn3"
+type: "BatchNorm"
+bottom: "layer_256_1_conv2"
+top: "layer_256_1_conv2_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_1_relu3"
+type: "ReLU"
+bottom: "layer_256_1_conv2_pcs_arm_sim"
+top: "layer_256_1_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_1_conv3"
+type: "Convolution"
+bottom: "layer_256_1_conv2_pcs_arm_sim"
+top: "layer_256_1_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_1_conv_expand"
+type: "Convolution"
+bottom: "layer_256_1_bn1_pcs_arm_sim"
+top: "layer_256_1_conv_expand"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 2
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_1_sum"
+type: "Eltwise"
+bottom: "layer_256_1_conv3"
+bottom: "layer_256_1_conv_expand"
+top: "layer_256_1_sum"
+
+}
+layer {
+name: "layer_256_2_bn1"
+type: "BatchNorm"
+bottom: "layer_256_1_sum"
+top: "layer_256_2_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_2_relu1"
+type: "ReLU"
+bottom: "layer_256_2_bn1_pcs_arm_sim"
+top: "layer_256_2_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_2_conv1"
+type: "Convolution"
+bottom: "layer_256_2_bn1_pcs_arm_sim"
+top: "layer_256_2_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_2_bn2"
+type: "BatchNorm"
+bottom: "layer_256_2_conv1"
+top: "layer_256_2_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_2_relu2"
+type: "ReLU"
+bottom: "layer_256_2_conv1_pcs_arm_sim"
+top: "layer_256_2_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_2_conv2"
+type: "Convolution"
+bottom: "layer_256_2_conv1_pcs_arm_sim"
+top: "layer_256_2_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_2_bn3"
+type: "BatchNorm"
+bottom: "layer_256_2_conv2"
+top: "layer_256_2_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_2_relu3"
+type: "ReLU"
+bottom: "layer_256_2_conv2_pcs_arm_sim"
+top: "layer_256_2_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_2_conv3"
+type: "Convolution"
+bottom: "layer_256_2_conv2_pcs_arm_sim"
+top: "layer_256_2_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_2_sum"
+type: "Eltwise"
+bottom: "layer_256_2_conv3"
+bottom: "layer_256_1_sum"
+top: "layer_256_2_sum"
+
+}
+layer {
+name: "layer_256_3_bn1"
+type: "BatchNorm"
+bottom: "layer_256_2_sum"
+top: "layer_256_3_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_3_relu1"
+type: "ReLU"
+bottom: "layer_256_3_bn1_pcs_arm_sim"
+top: "layer_256_3_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_3_conv1"
+type: "Convolution"
+bottom: "layer_256_3_bn1_pcs_arm_sim"
+top: "layer_256_3_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_3_bn2"
+type: "BatchNorm"
+bottom: "layer_256_3_conv1"
+top: "layer_256_3_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_3_relu2"
+type: "ReLU"
+bottom: "layer_256_3_conv1_pcs_arm_sim"
+top: "layer_256_3_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_3_conv2"
+type: "Convolution"
+bottom: "layer_256_3_conv1_pcs_arm_sim"
+top: "layer_256_3_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_3_bn3"
+type: "BatchNorm"
+bottom: "layer_256_3_conv2"
+top: "layer_256_3_conv2_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_3_relu3"
+type: "ReLU"
+bottom: "layer_256_3_conv2_pcs_arm_sim"
+top: "layer_256_3_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_3_conv3"
+type: "Convolution"
+bottom: "layer_256_3_conv2_pcs_arm_sim"
+top: "layer_256_3_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_3_sum"
+type: "Eltwise"
+bottom: "layer_256_3_conv3"
+bottom: "layer_256_2_sum"
+top: "layer_256_3_sum"
+
+}
+layer {
+name: "layer_256_4_bn1"
+type: "BatchNorm"
+bottom: "layer_256_3_sum"
+top: "layer_256_4_bn1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_4_relu1"
+type: "ReLU"
+bottom: "layer_256_4_bn1_pcs_arm_sim"
+top: "layer_256_4_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_4_conv1"
+type: "Convolution"
+bottom: "layer_256_4_bn1_pcs_arm_sim"
+top: "layer_256_4_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_4_bn2"
+type: "BatchNorm"
+bottom: "layer_256_4_conv1"
+top: "layer_256_4_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_256_4_relu2"
+type: "ReLU"
+bottom: "layer_256_4_conv1_pcs_arm_sim"
+top: "layer_256_4_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_4_conv2"
+type: "Convolution"
+bottom: "layer_256_4_conv1_pcs_arm_sim"
+top: "layer_256_4_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_4_bn3"
+type: "BatchNorm"
+bottom: "layer_256_4_conv2"
+top: "layer_256_4_conv2_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_4_relu3"
+type: "ReLU"
+bottom: "layer_256_4_conv2_pcs_arm_sim"
+top: "layer_256_4_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_4_conv3"
+type: "Convolution"
+bottom: "layer_256_4_conv2_pcs_arm_sim"
+top: "layer_256_4_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_4_sum"
+type: "Eltwise"
+bottom: "layer_256_4_conv3"
+bottom: "layer_256_3_sum"
+top: "layer_256_4_sum"
+
+}
+layer {
+name: "layer_256_5_bn1"
+type: "BatchNorm"
+bottom: "layer_256_4_sum"
+top: "layer_256_5_bn1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_5_relu1"
+type: "ReLU"
+bottom: "layer_256_5_bn1_pcs_arm_sim"
+top: "layer_256_5_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_5_conv1"
+type: "Convolution"
+bottom: "layer_256_5_bn1_pcs_arm_sim"
+top: "layer_256_5_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_5_bn2"
+type: "BatchNorm"
+bottom: "layer_256_5_conv1"
+top: "layer_256_5_conv1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_5_relu2"
+type: "ReLU"
+bottom: "layer_256_5_conv1_pcs_arm_sim"
+top: "layer_256_5_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_5_conv2"
+type: "Convolution"
+bottom: "layer_256_5_conv1_pcs_arm_sim"
+top: "layer_256_5_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_5_bn3"
+type: "BatchNorm"
+bottom: "layer_256_5_conv2"
+top: "layer_256_5_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_256_5_relu3"
+type: "ReLU"
+bottom: "layer_256_5_conv2_pcs_arm_sim"
+top: "layer_256_5_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_5_conv3"
+type: "Convolution"
+bottom: "layer_256_5_conv2_pcs_arm_sim"
+top: "layer_256_5_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_5_sum"
+type: "Eltwise"
+bottom: "layer_256_5_conv3"
+bottom: "layer_256_4_sum"
+top: "layer_256_5_sum"
+
+}
+layer {
+name: "layer_256_6_bn1"
+type: "BatchNorm"
+bottom: "layer_256_5_sum"
+top: "layer_256_6_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_6_relu1"
+type: "ReLU"
+bottom: "layer_256_6_bn1_pcs_arm_sim"
+top: "layer_256_6_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_6_conv1"
+type: "Convolution"
+bottom: "layer_256_6_bn1_pcs_arm_sim"
+top: "layer_256_6_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_6_bn2"
+type: "BatchNorm"
+bottom: "layer_256_6_conv1"
+top: "layer_256_6_conv1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_6_relu2"
+type: "ReLU"
+bottom: "layer_256_6_conv1_pcs_arm_sim"
+top: "layer_256_6_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_6_conv2"
+type: "Convolution"
+bottom: "layer_256_6_conv1_pcs_arm_sim"
+top: "layer_256_6_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_6_bn3"
+type: "BatchNorm"
+bottom: "layer_256_6_conv2"
+top: "layer_256_6_conv2_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_6_relu3"
+type: "ReLU"
+bottom: "layer_256_6_conv2_pcs_arm_sim"
+top: "layer_256_6_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_6_conv3"
+type: "Convolution"
+bottom: "layer_256_6_conv2_pcs_arm_sim"
+top: "layer_256_6_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_6_sum"
+type: "Eltwise"
+bottom: "layer_256_6_conv3"
+bottom: "layer_256_5_sum"
+top: "layer_256_6_sum"
+
+}
+layer {
+name: "layer_512_1_bn1"
+type: "BatchNorm"
+bottom: "layer_256_6_sum"
+top: "layer_512_1_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_512_1_relu1"
+type: "ReLU"
+bottom: "layer_512_1_bn1_pcs_arm_sim"
+top: "layer_512_1_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_1_conv1"
+type: "Convolution"
+bottom: "layer_512_1_bn1_pcs_arm_sim"
+top: "layer_512_1_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_1_bn2"
+type: "BatchNorm"
+bottom: "layer_512_1_conv1"
+top: "layer_512_1_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_512_1_relu2"
+type: "ReLU"
+bottom: "layer_512_1_conv1_pcs_arm_sim"
+top: "layer_512_1_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_1_conv2"
+type: "Convolution"
+bottom: "layer_512_1_conv1_pcs_arm_sim"
+top: "layer_512_1_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 2
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_1_bn3"
+type: "BatchNorm"
+bottom: "layer_512_1_conv2"
+top: "layer_512_1_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_512_1_relu3"
+type: "ReLU"
+bottom: "layer_512_1_conv2_pcs_arm_sim"
+top: "layer_512_1_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_1_conv3"
+type: "Convolution"
+bottom: "layer_512_1_conv2_pcs_arm_sim"
+top: "layer_512_1_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 2048
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_1_conv_expand"
+type: "Convolution"
+bottom: "layer_512_1_bn1_pcs_arm_sim"
+top: "layer_512_1_conv_expand"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 2048
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 2
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_1_sum"
+type: "Eltwise"
+bottom: "layer_512_1_conv3"
+bottom: "layer_512_1_conv_expand"
+top: "layer_512_1_sum"
+
+}
+layer {
+name: "layer_512_2_bn1"
+type: "BatchNorm"
+bottom: "layer_512_1_sum"
+top: "layer_512_2_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_512_2_relu1"
+type: "ReLU"
+bottom: "layer_512_2_bn1_pcs_arm_sim"
+top: "layer_512_2_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_2_conv1"
+type: "Convolution"
+bottom: "layer_512_2_bn1_pcs_arm_sim"
+top: "layer_512_2_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_2_bn2"
+type: "BatchNorm"
+bottom: "layer_512_2_conv1"
+top: "layer_512_2_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_512_2_relu2"
+type: "ReLU"
+bottom: "layer_512_2_conv1_pcs_arm_sim"
+top: "layer_512_2_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_2_conv2"
+type: "Convolution"
+bottom: "layer_512_2_conv1_pcs_arm_sim"
+top: "layer_512_2_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_2_bn3"
+type: "BatchNorm"
+bottom: "layer_512_2_conv2"
+top: "layer_512_2_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }   
+}
+layer {
+name: "layer_512_2_relu3"
+type: "ReLU"
+bottom: "layer_512_2_conv2_pcs_arm_sim"
+top: "layer_512_2_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_2_conv3"
+type: "Convolution"
+bottom: "layer_512_2_conv2_pcs_arm_sim"
+top: "layer_512_2_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 2048
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_2_sum"
+type: "Eltwise"
+bottom: "layer_512_2_conv3"
+bottom: "layer_512_1_sum"
+top: "layer_512_2_sum"
+
+}
+layer {
+name: "layer_512_3_bn1"
+type: "BatchNorm"
+bottom: "layer_512_2_sum"
+top: "layer_512_3_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }    
+}
+layer {
+name: "layer_512_3_relu1"
+type: "ReLU"
+bottom: "layer_512_3_bn1_pcs_arm_sim"
+top: "layer_512_3_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_3_conv1"
+type: "Convolution"
+bottom: "layer_512_3_bn1_pcs_arm_sim"
+top: "layer_512_3_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_3_bn2"
+type: "BatchNorm"
+bottom: "layer_512_3_conv1"
+top: "layer_512_3_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_512_3_relu2"
+type: "ReLU"
+bottom: "layer_512_3_conv1_pcs_arm_sim"
+top: "layer_512_3_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_3_conv2"
+type: "Convolution"
+bottom: "layer_512_3_conv1_pcs_arm_sim"
+top: "layer_512_3_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_3_bn3"
+type: "BatchNorm"
+bottom: "layer_512_3_conv2"
+top: "layer_512_3_conv2_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_512_3_relu3"
+type: "ReLU"
+bottom: "layer_512_3_conv2_pcs_arm_sim"
+top: "layer_512_3_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_3_conv3"
+type: "Convolution"
+bottom: "layer_512_3_conv2_pcs_arm_sim"
+top: "layer_512_3_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 2048
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_3_sum"
+type: "Eltwise"
+bottom: "layer_512_3_conv3"
+bottom: "layer_512_2_sum"
+top: "layer_512_3_sum"
+
+}
+layer {
+name: "last_bn"
+type: "BatchNorm"
+bottom: "layer_512_3_sum"
+top: "layer_512_3_sum_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "last_relu"
+type: "ReLU"
+bottom: "layer_512_3_sum_pcs_arm_sim"
+top: "layer_512_3_sum_pcs_arm_sim"
+
+}
+layer {
+name: "global_pool"
+type: "Pooling"
+bottom: "layer_512_3_sum_pcs_arm_sim"
+top: "global_pool"
+pooling_param {
+  pool: AVE
+  global_pooling: true
+}
+
+}
+layer {
+name: "score"
+type: "InnerProduct"
+bottom: "global_pool"
+top: "score"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+param {
+  lr_mult: 2.0
+  decay_mult: 1.0
+}
+inner_product_param {
+  num_output: 1000
+}
+
+}
+layer {
+name: "loss"
+type: "SoftmaxWithLoss"
+bottom: "score"
+bottom: "label"
+top: "loss"
+
+}
+layer {
+name: "accuracy"
+type: "Accuracy"
+bottom: "score"
+bottom: "label"
+top: "accuracy"
+include {
+  phase: TEST
+}
+
+}
diff --git a/models/intel_optimized_models/resnet_50/knl/train_val_dummydata.prototxt b/models/intel_optimized_models/resnet_50/knl/train_val_dummydata.prototxt
new file mode 100644
index 000000000..f0f4ffd60
--- /dev/null
+++ b/models/intel_optimized_models/resnet_50/knl/train_val_dummydata.prototxt
@@ -0,0 +1,2294 @@
+#This is Intel(R) optimized (in terms of time to train) version of topology described in the [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) publication.
+#
+#Top-5 and Top-1 results achieved with this topology:
+#Top-5: 92%
+#Top-1: 73.9%
+#Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor.
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  dummy_data_param {
+    data_filler {
+        type: "constant"
+        value: 0.01
+    }
+    shape: { dim: 64 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 64 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.01
+    }
+    shape: { dim: 128 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 128 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+
+layer {
+name: "conv1"
+type: "Convolution"
+bottom: "data"
+top: "conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+param {
+  lr_mult: 2.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  pad: 3
+  kernel_size: 7
+  stride: 2
+  weight_filler {
+    type: "msra"
+    variance_norm: FAN_OUT
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "conv1_bn"
+type: "BatchNorm"
+bottom: "conv1"
+top: "conv1_pcs_arm_sim"
+  batch_norm_param {
+ }       
+}
+layer {
+name: "conv1_relu"
+type: "ReLU"
+bottom: "conv1_pcs_arm_sim"
+top: "conv1_pcs_arm_sim"
+
+}
+layer {
+name: "conv1_pool"
+type: "Pooling"
+bottom: "conv1_pcs_arm_sim"
+top: "conv1_pool"
+pooling_param {
+  kernel_size: 3
+  stride: 2
+}
+
+}
+layer {
+name: "layer_64_1_conv1"
+type: "Convolution"
+bottom: "conv1_pool"
+top: "layer_64_1_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_1_bn2"
+type: "BatchNorm"
+bottom: "layer_64_1_conv1"
+top: "layer_64_1_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }     
+}
+layer {
+name: "layer_64_1_relu2"
+type: "ReLU"
+bottom: "layer_64_1_conv1_pcs_arm_sim"
+top: "layer_64_1_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_1_conv2"
+type: "Convolution"
+bottom: "layer_64_1_conv1_pcs_arm_sim"
+top: "layer_64_1_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_1_bn3"
+type: "BatchNorm"
+bottom: "layer_64_1_conv2"
+top: "layer_64_1_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }        
+}
+layer {
+name: "layer_64_1_relu3"
+type: "ReLU"
+bottom: "layer_64_1_conv2_pcs_arm_sim"
+top: "layer_64_1_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_1_conv3"
+type: "Convolution"
+bottom: "layer_64_1_conv2_pcs_arm_sim"
+top: "layer_64_1_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_1_conv_expand"
+type: "Convolution"
+bottom: "layer_64_1_conv1_pcs_arm_sim"
+top: "layer_64_1_conv_expand"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_1_sum"
+type: "Eltwise"
+bottom: "layer_64_1_conv3"
+bottom: "layer_64_1_conv_expand"
+top: "layer_64_1_sum"
+
+}
+layer {
+name: "layer_64_2_bn1"
+type: "BatchNorm"
+bottom: "layer_64_1_sum"
+top: "layer_64_2_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }       
+}
+layer {
+name: "layer_64_2_relu1"
+type: "ReLU"
+bottom: "layer_64_2_bn1_pcs_arm_sim"
+top: "layer_64_2_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_2_conv1"
+type: "Convolution"
+bottom: "layer_64_2_bn1_pcs_arm_sim"
+top: "layer_64_2_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_2_bn2"
+type: "BatchNorm"
+bottom: "layer_64_2_conv1"
+top: "layer_64_2_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }        
+}
+layer {
+name: "layer_64_2_relu2"
+type: "ReLU"
+bottom: "layer_64_2_conv1_pcs_arm_sim"
+top: "layer_64_2_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_2_conv2"
+type: "Convolution"
+bottom: "layer_64_2_conv1_pcs_arm_sim"
+top: "layer_64_2_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_2_bn3"
+type: "BatchNorm"
+bottom: "layer_64_2_conv2"
+top: "layer_64_2_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }        
+}
+layer {
+name: "layer_64_2_relu3"
+type: "ReLU"
+bottom: "layer_64_2_conv2_pcs_arm_sim"
+top: "layer_64_2_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_2_conv3"
+type: "Convolution"
+bottom: "layer_64_2_conv2_pcs_arm_sim"
+top: "layer_64_2_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_2_sum"
+type: "Eltwise"
+bottom: "layer_64_2_conv3"
+bottom: "layer_64_1_sum"
+top: "layer_64_2_sum"
+
+}
+layer {
+name: "layer_64_3_bn1"
+type: "BatchNorm"
+bottom: "layer_64_2_sum"
+top: "layer_64_3_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }       
+}
+layer {
+name: "layer_64_3_relu1"
+type: "ReLU"
+bottom: "layer_64_3_bn1_pcs_arm_sim"
+top: "layer_64_3_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_3_conv1"
+type: "Convolution"
+bottom: "layer_64_3_bn1_pcs_arm_sim"
+top: "layer_64_3_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_3_bn2"
+type: "BatchNorm"
+bottom: "layer_64_3_conv1"
+top: "layer_64_3_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_64_3_relu2"
+type: "ReLU"
+bottom: "layer_64_3_conv1_pcs_arm_sim"
+top: "layer_64_3_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_3_conv2"
+type: "Convolution"
+bottom: "layer_64_3_conv1_pcs_arm_sim"
+top: "layer_64_3_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_3_bn3"
+type: "BatchNorm"
+bottom: "layer_64_3_conv2"
+top: "layer_64_3_conv2_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_64_3_relu3"
+type: "ReLU"
+bottom: "layer_64_3_conv2_pcs_arm_sim"
+top: "layer_64_3_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_3_conv3"
+type: "Convolution"
+bottom: "layer_64_3_conv2_pcs_arm_sim"
+top: "layer_64_3_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_3_sum"
+type: "Eltwise"
+bottom: "layer_64_3_conv3"
+bottom: "layer_64_2_sum"
+top: "layer_64_3_sum"
+
+}
+layer {
+name: "layer_128_1_bn1"
+type: "BatchNorm"
+bottom: "layer_64_3_sum"
+top: "layer_128_1_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_128_1_relu1"
+type: "ReLU"
+bottom: "layer_128_1_bn1_pcs_arm_sim"
+top: "layer_128_1_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_1_conv1"
+type: "Convolution"
+bottom: "layer_128_1_bn1_pcs_arm_sim"
+top: "layer_128_1_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_1_bn2"
+type: "BatchNorm"
+bottom: "layer_128_1_conv1"
+top: "layer_128_1_conv1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_128_1_relu2"
+type: "ReLU"
+bottom: "layer_128_1_conv1_pcs_arm_sim"
+top: "layer_128_1_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_1_conv2"
+type: "Convolution"
+bottom: "layer_128_1_conv1_pcs_arm_sim"
+top: "layer_128_1_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 2
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_1_bn3"
+type: "BatchNorm"
+bottom: "layer_128_1_conv2"
+top: "layer_128_1_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_128_1_relu3"
+type: "ReLU"
+bottom: "layer_128_1_conv2_pcs_arm_sim"
+top: "layer_128_1_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_1_conv3"
+type: "Convolution"
+bottom: "layer_128_1_conv2_pcs_arm_sim"
+top: "layer_128_1_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_1_conv_expand"
+type: "Convolution"
+bottom: "layer_128_1_bn1_pcs_arm_sim"
+top: "layer_128_1_conv_expand"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 2
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_1_sum"
+type: "Eltwise"
+bottom: "layer_128_1_conv3"
+bottom: "layer_128_1_conv_expand"
+top: "layer_128_1_sum"
+
+}
+layer {
+name: "layer_128_2_bn1"
+type: "BatchNorm"
+bottom: "layer_128_1_sum"
+top: "layer_128_2_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_2_relu1"
+type: "ReLU"
+bottom: "layer_128_2_bn1_pcs_arm_sim"
+top: "layer_128_2_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_2_conv1"
+type: "Convolution"
+bottom: "layer_128_2_bn1_pcs_arm_sim"
+top: "layer_128_2_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_2_bn2"
+type: "BatchNorm"
+bottom: "layer_128_2_conv1"
+top: "layer_128_2_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_2_relu2"
+type: "ReLU"
+bottom: "layer_128_2_conv1_pcs_arm_sim"
+top: "layer_128_2_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_2_conv2"
+type: "Convolution"
+bottom: "layer_128_2_conv1_pcs_arm_sim"
+top: "layer_128_2_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_2_bn3"
+type: "BatchNorm"
+bottom: "layer_128_2_conv2"
+top: "layer_128_2_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_2_relu3"
+type: "ReLU"
+bottom: "layer_128_2_conv2_pcs_arm_sim"
+top: "layer_128_2_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_2_conv3"
+type: "Convolution"
+bottom: "layer_128_2_conv2_pcs_arm_sim"
+top: "layer_128_2_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_2_sum"
+type: "Eltwise"
+bottom: "layer_128_2_conv3"
+bottom: "layer_128_1_sum"
+top: "layer_128_2_sum"
+
+}
+layer {
+name: "layer_128_3_bn1"
+type: "BatchNorm"
+bottom: "layer_128_2_sum"
+top: "layer_128_3_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_3_relu1"
+type: "ReLU"
+bottom: "layer_128_3_bn1_pcs_arm_sim"
+top: "layer_128_3_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_3_conv1"
+type: "Convolution"
+bottom: "layer_128_3_bn1_pcs_arm_sim"
+top: "layer_128_3_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_3_bn2"
+type: "BatchNorm"
+bottom: "layer_128_3_conv1"
+top: "layer_128_3_conv1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_128_3_relu2"
+type: "ReLU"
+bottom: "layer_128_3_conv1_pcs_arm_sim"
+top: "layer_128_3_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_3_conv2"
+type: "Convolution"
+bottom: "layer_128_3_conv1_pcs_arm_sim"
+top: "layer_128_3_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_3_bn3"
+type: "BatchNorm"
+bottom: "layer_128_3_conv2"
+top: "layer_128_3_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_3_relu3"
+type: "ReLU"
+bottom: "layer_128_3_conv2_pcs_arm_sim"
+top: "layer_128_3_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_3_conv3"
+type: "Convolution"
+bottom: "layer_128_3_conv2_pcs_arm_sim"
+top: "layer_128_3_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_3_sum"
+type: "Eltwise"
+bottom: "layer_128_3_conv3"
+bottom: "layer_128_2_sum"
+top: "layer_128_3_sum"
+
+}
+layer {
+name: "layer_128_4_bn1"
+type: "BatchNorm"
+bottom: "layer_128_3_sum"
+top: "layer_128_4_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_4_relu1"
+type: "ReLU"
+bottom: "layer_128_4_bn1_pcs_arm_sim"
+top: "layer_128_4_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_4_conv1"
+type: "Convolution"
+bottom: "layer_128_4_bn1_pcs_arm_sim"
+top: "layer_128_4_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_4_bn2"
+type: "BatchNorm"
+bottom: "layer_128_4_conv1"
+top: "layer_128_4_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_4_relu2"
+type: "ReLU"
+bottom: "layer_128_4_conv1_pcs_arm_sim"
+top: "layer_128_4_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_4_conv2"
+type: "Convolution"
+bottom: "layer_128_4_conv1_pcs_arm_sim"
+top: "layer_128_4_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_4_bn3"
+type: "BatchNorm"
+bottom: "layer_128_4_conv2"
+top: "layer_128_4_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }       
+}
+layer {
+name: "layer_128_4_relu3"
+type: "ReLU"
+bottom: "layer_128_4_conv2_pcs_arm_sim"
+top: "layer_128_4_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_4_conv3"
+type: "Convolution"
+bottom: "layer_128_4_conv2_pcs_arm_sim"
+top: "layer_128_4_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_4_sum"
+type: "Eltwise"
+bottom: "layer_128_4_conv3"
+bottom: "layer_128_3_sum"
+top: "layer_128_4_sum"
+
+}
+layer {
+name: "layer_256_1_bn1"
+type: "BatchNorm"
+bottom: "layer_128_4_sum"
+top: "layer_256_1_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_1_relu1"
+type: "ReLU"
+bottom: "layer_256_1_bn1_pcs_arm_sim"
+top: "layer_256_1_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_1_conv1"
+type: "Convolution"
+bottom: "layer_256_1_bn1_pcs_arm_sim"
+top: "layer_256_1_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_1_bn2"
+type: "BatchNorm"
+bottom: "layer_256_1_conv1"
+top: "layer_256_1_conv1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_1_relu2"
+type: "ReLU"
+bottom: "layer_256_1_conv1_pcs_arm_sim"
+top: "layer_256_1_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_1_conv2"
+type: "Convolution"
+bottom: "layer_256_1_conv1_pcs_arm_sim"
+top: "layer_256_1_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 2
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_1_bn3"
+type: "BatchNorm"
+bottom: "layer_256_1_conv2"
+top: "layer_256_1_conv2_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_1_relu3"
+type: "ReLU"
+bottom: "layer_256_1_conv2_pcs_arm_sim"
+top: "layer_256_1_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_1_conv3"
+type: "Convolution"
+bottom: "layer_256_1_conv2_pcs_arm_sim"
+top: "layer_256_1_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_1_conv_expand"
+type: "Convolution"
+bottom: "layer_256_1_bn1_pcs_arm_sim"
+top: "layer_256_1_conv_expand"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 2
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_1_sum"
+type: "Eltwise"
+bottom: "layer_256_1_conv3"
+bottom: "layer_256_1_conv_expand"
+top: "layer_256_1_sum"
+
+}
+layer {
+name: "layer_256_2_bn1"
+type: "BatchNorm"
+bottom: "layer_256_1_sum"
+top: "layer_256_2_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_2_relu1"
+type: "ReLU"
+bottom: "layer_256_2_bn1_pcs_arm_sim"
+top: "layer_256_2_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_2_conv1"
+type: "Convolution"
+bottom: "layer_256_2_bn1_pcs_arm_sim"
+top: "layer_256_2_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_2_bn2"
+type: "BatchNorm"
+bottom: "layer_256_2_conv1"
+top: "layer_256_2_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_2_relu2"
+type: "ReLU"
+bottom: "layer_256_2_conv1_pcs_arm_sim"
+top: "layer_256_2_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_2_conv2"
+type: "Convolution"
+bottom: "layer_256_2_conv1_pcs_arm_sim"
+top: "layer_256_2_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_2_bn3"
+type: "BatchNorm"
+bottom: "layer_256_2_conv2"
+top: "layer_256_2_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_2_relu3"
+type: "ReLU"
+bottom: "layer_256_2_conv2_pcs_arm_sim"
+top: "layer_256_2_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_2_conv3"
+type: "Convolution"
+bottom: "layer_256_2_conv2_pcs_arm_sim"
+top: "layer_256_2_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_2_sum"
+type: "Eltwise"
+bottom: "layer_256_2_conv3"
+bottom: "layer_256_1_sum"
+top: "layer_256_2_sum"
+
+}
+layer {
+name: "layer_256_3_bn1"
+type: "BatchNorm"
+bottom: "layer_256_2_sum"
+top: "layer_256_3_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_3_relu1"
+type: "ReLU"
+bottom: "layer_256_3_bn1_pcs_arm_sim"
+top: "layer_256_3_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_3_conv1"
+type: "Convolution"
+bottom: "layer_256_3_bn1_pcs_arm_sim"
+top: "layer_256_3_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_3_bn2"
+type: "BatchNorm"
+bottom: "layer_256_3_conv1"
+top: "layer_256_3_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_3_relu2"
+type: "ReLU"
+bottom: "layer_256_3_conv1_pcs_arm_sim"
+top: "layer_256_3_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_3_conv2"
+type: "Convolution"
+bottom: "layer_256_3_conv1_pcs_arm_sim"
+top: "layer_256_3_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_3_bn3"
+type: "BatchNorm"
+bottom: "layer_256_3_conv2"
+top: "layer_256_3_conv2_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_3_relu3"
+type: "ReLU"
+bottom: "layer_256_3_conv2_pcs_arm_sim"
+top: "layer_256_3_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_3_conv3"
+type: "Convolution"
+bottom: "layer_256_3_conv2_pcs_arm_sim"
+top: "layer_256_3_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_3_sum"
+type: "Eltwise"
+bottom: "layer_256_3_conv3"
+bottom: "layer_256_2_sum"
+top: "layer_256_3_sum"
+
+}
+layer {
+name: "layer_256_4_bn1"
+type: "BatchNorm"
+bottom: "layer_256_3_sum"
+top: "layer_256_4_bn1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_4_relu1"
+type: "ReLU"
+bottom: "layer_256_4_bn1_pcs_arm_sim"
+top: "layer_256_4_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_4_conv1"
+type: "Convolution"
+bottom: "layer_256_4_bn1_pcs_arm_sim"
+top: "layer_256_4_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_4_bn2"
+type: "BatchNorm"
+bottom: "layer_256_4_conv1"
+top: "layer_256_4_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_256_4_relu2"
+type: "ReLU"
+bottom: "layer_256_4_conv1_pcs_arm_sim"
+top: "layer_256_4_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_4_conv2"
+type: "Convolution"
+bottom: "layer_256_4_conv1_pcs_arm_sim"
+top: "layer_256_4_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_4_bn3"
+type: "BatchNorm"
+bottom: "layer_256_4_conv2"
+top: "layer_256_4_conv2_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_4_relu3"
+type: "ReLU"
+bottom: "layer_256_4_conv2_pcs_arm_sim"
+top: "layer_256_4_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_4_conv3"
+type: "Convolution"
+bottom: "layer_256_4_conv2_pcs_arm_sim"
+top: "layer_256_4_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_4_sum"
+type: "Eltwise"
+bottom: "layer_256_4_conv3"
+bottom: "layer_256_3_sum"
+top: "layer_256_4_sum"
+
+}
+layer {
+name: "layer_256_5_bn1"
+type: "BatchNorm"
+bottom: "layer_256_4_sum"
+top: "layer_256_5_bn1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_5_relu1"
+type: "ReLU"
+bottom: "layer_256_5_bn1_pcs_arm_sim"
+top: "layer_256_5_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_5_conv1"
+type: "Convolution"
+bottom: "layer_256_5_bn1_pcs_arm_sim"
+top: "layer_256_5_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_5_bn2"
+type: "BatchNorm"
+bottom: "layer_256_5_conv1"
+top: "layer_256_5_conv1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_5_relu2"
+type: "ReLU"
+bottom: "layer_256_5_conv1_pcs_arm_sim"
+top: "layer_256_5_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_5_conv2"
+type: "Convolution"
+bottom: "layer_256_5_conv1_pcs_arm_sim"
+top: "layer_256_5_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_5_bn3"
+type: "BatchNorm"
+bottom: "layer_256_5_conv2"
+top: "layer_256_5_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_256_5_relu3"
+type: "ReLU"
+bottom: "layer_256_5_conv2_pcs_arm_sim"
+top: "layer_256_5_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_5_conv3"
+type: "Convolution"
+bottom: "layer_256_5_conv2_pcs_arm_sim"
+top: "layer_256_5_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_5_sum"
+type: "Eltwise"
+bottom: "layer_256_5_conv3"
+bottom: "layer_256_4_sum"
+top: "layer_256_5_sum"
+
+}
+layer {
+name: "layer_256_6_bn1"
+type: "BatchNorm"
+bottom: "layer_256_5_sum"
+top: "layer_256_6_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_6_relu1"
+type: "ReLU"
+bottom: "layer_256_6_bn1_pcs_arm_sim"
+top: "layer_256_6_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_6_conv1"
+type: "Convolution"
+bottom: "layer_256_6_bn1_pcs_arm_sim"
+top: "layer_256_6_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_6_bn2"
+type: "BatchNorm"
+bottom: "layer_256_6_conv1"
+top: "layer_256_6_conv1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_6_relu2"
+type: "ReLU"
+bottom: "layer_256_6_conv1_pcs_arm_sim"
+top: "layer_256_6_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_6_conv2"
+type: "Convolution"
+bottom: "layer_256_6_conv1_pcs_arm_sim"
+top: "layer_256_6_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_6_bn3"
+type: "BatchNorm"
+bottom: "layer_256_6_conv2"
+top: "layer_256_6_conv2_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_6_relu3"
+type: "ReLU"
+bottom: "layer_256_6_conv2_pcs_arm_sim"
+top: "layer_256_6_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_6_conv3"
+type: "Convolution"
+bottom: "layer_256_6_conv2_pcs_arm_sim"
+top: "layer_256_6_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_6_sum"
+type: "Eltwise"
+bottom: "layer_256_6_conv3"
+bottom: "layer_256_5_sum"
+top: "layer_256_6_sum"
+
+}
+layer {
+name: "layer_512_1_bn1"
+type: "BatchNorm"
+bottom: "layer_256_6_sum"
+top: "layer_512_1_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_512_1_relu1"
+type: "ReLU"
+bottom: "layer_512_1_bn1_pcs_arm_sim"
+top: "layer_512_1_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_1_conv1"
+type: "Convolution"
+bottom: "layer_512_1_bn1_pcs_arm_sim"
+top: "layer_512_1_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_1_bn2"
+type: "BatchNorm"
+bottom: "layer_512_1_conv1"
+top: "layer_512_1_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_512_1_relu2"
+type: "ReLU"
+bottom: "layer_512_1_conv1_pcs_arm_sim"
+top: "layer_512_1_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_1_conv2"
+type: "Convolution"
+bottom: "layer_512_1_conv1_pcs_arm_sim"
+top: "layer_512_1_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 2
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_1_bn3"
+type: "BatchNorm"
+bottom: "layer_512_1_conv2"
+top: "layer_512_1_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_512_1_relu3"
+type: "ReLU"
+bottom: "layer_512_1_conv2_pcs_arm_sim"
+top: "layer_512_1_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_1_conv3"
+type: "Convolution"
+bottom: "layer_512_1_conv2_pcs_arm_sim"
+top: "layer_512_1_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 2048
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_1_conv_expand"
+type: "Convolution"
+bottom: "layer_512_1_bn1_pcs_arm_sim"
+top: "layer_512_1_conv_expand"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 2048
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 2
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_1_sum"
+type: "Eltwise"
+bottom: "layer_512_1_conv3"
+bottom: "layer_512_1_conv_expand"
+top: "layer_512_1_sum"
+
+}
+layer {
+name: "layer_512_2_bn1"
+type: "BatchNorm"
+bottom: "layer_512_1_sum"
+top: "layer_512_2_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_512_2_relu1"
+type: "ReLU"
+bottom: "layer_512_2_bn1_pcs_arm_sim"
+top: "layer_512_2_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_2_conv1"
+type: "Convolution"
+bottom: "layer_512_2_bn1_pcs_arm_sim"
+top: "layer_512_2_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_2_bn2"
+type: "BatchNorm"
+bottom: "layer_512_2_conv1"
+top: "layer_512_2_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_512_2_relu2"
+type: "ReLU"
+bottom: "layer_512_2_conv1_pcs_arm_sim"
+top: "layer_512_2_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_2_conv2"
+type: "Convolution"
+bottom: "layer_512_2_conv1_pcs_arm_sim"
+top: "layer_512_2_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_2_bn3"
+type: "BatchNorm"
+bottom: "layer_512_2_conv2"
+top: "layer_512_2_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }   
+}
+layer {
+name: "layer_512_2_relu3"
+type: "ReLU"
+bottom: "layer_512_2_conv2_pcs_arm_sim"
+top: "layer_512_2_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_2_conv3"
+type: "Convolution"
+bottom: "layer_512_2_conv2_pcs_arm_sim"
+top: "layer_512_2_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 2048
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_2_sum"
+type: "Eltwise"
+bottom: "layer_512_2_conv3"
+bottom: "layer_512_1_sum"
+top: "layer_512_2_sum"
+
+}
+layer {
+name: "layer_512_3_bn1"
+type: "BatchNorm"
+bottom: "layer_512_2_sum"
+top: "layer_512_3_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }    
+}
+layer {
+name: "layer_512_3_relu1"
+type: "ReLU"
+bottom: "layer_512_3_bn1_pcs_arm_sim"
+top: "layer_512_3_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_3_conv1"
+type: "Convolution"
+bottom: "layer_512_3_bn1_pcs_arm_sim"
+top: "layer_512_3_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_3_bn2"
+type: "BatchNorm"
+bottom: "layer_512_3_conv1"
+top: "layer_512_3_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_512_3_relu2"
+type: "ReLU"
+bottom: "layer_512_3_conv1_pcs_arm_sim"
+top: "layer_512_3_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_3_conv2"
+type: "Convolution"
+bottom: "layer_512_3_conv1_pcs_arm_sim"
+top: "layer_512_3_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_3_bn3"
+type: "BatchNorm"
+bottom: "layer_512_3_conv2"
+top: "layer_512_3_conv2_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_512_3_relu3"
+type: "ReLU"
+bottom: "layer_512_3_conv2_pcs_arm_sim"
+top: "layer_512_3_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_3_conv3"
+type: "Convolution"
+bottom: "layer_512_3_conv2_pcs_arm_sim"
+top: "layer_512_3_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 2048
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_3_sum"
+type: "Eltwise"
+bottom: "layer_512_3_conv3"
+bottom: "layer_512_2_sum"
+top: "layer_512_3_sum"
+
+}
+layer {
+name: "last_bn"
+type: "BatchNorm"
+bottom: "layer_512_3_sum"
+top: "layer_512_3_sum_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "last_relu"
+type: "ReLU"
+bottom: "layer_512_3_sum_pcs_arm_sim"
+top: "layer_512_3_sum_pcs_arm_sim"
+
+}
+layer {
+name: "global_pool"
+type: "Pooling"
+bottom: "layer_512_3_sum_pcs_arm_sim"
+top: "global_pool"
+pooling_param {
+  pool: AVE
+  global_pooling: true
+}
+
+}
+layer {
+name: "score"
+type: "InnerProduct"
+bottom: "global_pool"
+top: "score"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+param {
+  lr_mult: 2.0
+  decay_mult: 1.0
+}
+inner_product_param {
+  num_output: 1000
+}
+
+}
+layer {
+name: "loss"
+type: "SoftmaxWithLoss"
+bottom: "score"
+bottom: "label"
+top: "loss"
+
+}
+layer {
+name: "accuracy"
+type: "Accuracy"
+bottom: "score"
+bottom: "label"
+top: "accuracy"
+include {
+  phase: TEST
+}
+
+}
diff --git a/models/intel_optimized_models/resnet_50/skx/train_val_dummydata.prototxt b/models/intel_optimized_models/resnet_50/skx/train_val_dummydata.prototxt
new file mode 100644
index 000000000..f0f4ffd60
--- /dev/null
+++ b/models/intel_optimized_models/resnet_50/skx/train_val_dummydata.prototxt
@@ -0,0 +1,2294 @@
+#This is Intel(R) optimized (in terms of time to train) version of topology described in the [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) publication.
+#
+#Top-5 and Top-1 results achieved with this topology:
+#Top-5: 92%
+#Top-1: 73.9%
+#Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor.
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  dummy_data_param {
+    data_filler {
+        type: "constant"
+        value: 0.01
+    }
+    shape: { dim: 64 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 64 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.01
+    }
+    shape: { dim: 128 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 128 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+
+layer {
+name: "conv1"
+type: "Convolution"
+bottom: "data"
+top: "conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+param {
+  lr_mult: 2.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  pad: 3
+  kernel_size: 7
+  stride: 2
+  weight_filler {
+    type: "msra"
+    variance_norm: FAN_OUT
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "conv1_bn"
+type: "BatchNorm"
+bottom: "conv1"
+top: "conv1_pcs_arm_sim"
+  batch_norm_param {
+ }       
+}
+layer {
+name: "conv1_relu"
+type: "ReLU"
+bottom: "conv1_pcs_arm_sim"
+top: "conv1_pcs_arm_sim"
+
+}
+layer {
+name: "conv1_pool"
+type: "Pooling"
+bottom: "conv1_pcs_arm_sim"
+top: "conv1_pool"
+pooling_param {
+  kernel_size: 3
+  stride: 2
+}
+
+}
+layer {
+name: "layer_64_1_conv1"
+type: "Convolution"
+bottom: "conv1_pool"
+top: "layer_64_1_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_1_bn2"
+type: "BatchNorm"
+bottom: "layer_64_1_conv1"
+top: "layer_64_1_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }     
+}
+layer {
+name: "layer_64_1_relu2"
+type: "ReLU"
+bottom: "layer_64_1_conv1_pcs_arm_sim"
+top: "layer_64_1_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_1_conv2"
+type: "Convolution"
+bottom: "layer_64_1_conv1_pcs_arm_sim"
+top: "layer_64_1_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_1_bn3"
+type: "BatchNorm"
+bottom: "layer_64_1_conv2"
+top: "layer_64_1_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }        
+}
+layer {
+name: "layer_64_1_relu3"
+type: "ReLU"
+bottom: "layer_64_1_conv2_pcs_arm_sim"
+top: "layer_64_1_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_1_conv3"
+type: "Convolution"
+bottom: "layer_64_1_conv2_pcs_arm_sim"
+top: "layer_64_1_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_1_conv_expand"
+type: "Convolution"
+bottom: "layer_64_1_conv1_pcs_arm_sim"
+top: "layer_64_1_conv_expand"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_1_sum"
+type: "Eltwise"
+bottom: "layer_64_1_conv3"
+bottom: "layer_64_1_conv_expand"
+top: "layer_64_1_sum"
+
+}
+layer {
+name: "layer_64_2_bn1"
+type: "BatchNorm"
+bottom: "layer_64_1_sum"
+top: "layer_64_2_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }       
+}
+layer {
+name: "layer_64_2_relu1"
+type: "ReLU"
+bottom: "layer_64_2_bn1_pcs_arm_sim"
+top: "layer_64_2_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_2_conv1"
+type: "Convolution"
+bottom: "layer_64_2_bn1_pcs_arm_sim"
+top: "layer_64_2_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_2_bn2"
+type: "BatchNorm"
+bottom: "layer_64_2_conv1"
+top: "layer_64_2_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }        
+}
+layer {
+name: "layer_64_2_relu2"
+type: "ReLU"
+bottom: "layer_64_2_conv1_pcs_arm_sim"
+top: "layer_64_2_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_2_conv2"
+type: "Convolution"
+bottom: "layer_64_2_conv1_pcs_arm_sim"
+top: "layer_64_2_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_2_bn3"
+type: "BatchNorm"
+bottom: "layer_64_2_conv2"
+top: "layer_64_2_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }        
+}
+layer {
+name: "layer_64_2_relu3"
+type: "ReLU"
+bottom: "layer_64_2_conv2_pcs_arm_sim"
+top: "layer_64_2_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_2_conv3"
+type: "Convolution"
+bottom: "layer_64_2_conv2_pcs_arm_sim"
+top: "layer_64_2_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_2_sum"
+type: "Eltwise"
+bottom: "layer_64_2_conv3"
+bottom: "layer_64_1_sum"
+top: "layer_64_2_sum"
+
+}
+layer {
+name: "layer_64_3_bn1"
+type: "BatchNorm"
+bottom: "layer_64_2_sum"
+top: "layer_64_3_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }       
+}
+layer {
+name: "layer_64_3_relu1"
+type: "ReLU"
+bottom: "layer_64_3_bn1_pcs_arm_sim"
+top: "layer_64_3_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_3_conv1"
+type: "Convolution"
+bottom: "layer_64_3_bn1_pcs_arm_sim"
+top: "layer_64_3_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_3_bn2"
+type: "BatchNorm"
+bottom: "layer_64_3_conv1"
+top: "layer_64_3_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_64_3_relu2"
+type: "ReLU"
+bottom: "layer_64_3_conv1_pcs_arm_sim"
+top: "layer_64_3_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_3_conv2"
+type: "Convolution"
+bottom: "layer_64_3_conv1_pcs_arm_sim"
+top: "layer_64_3_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 64
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_3_bn3"
+type: "BatchNorm"
+bottom: "layer_64_3_conv2"
+top: "layer_64_3_conv2_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_64_3_relu3"
+type: "ReLU"
+bottom: "layer_64_3_conv2_pcs_arm_sim"
+top: "layer_64_3_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_64_3_conv3"
+type: "Convolution"
+bottom: "layer_64_3_conv2_pcs_arm_sim"
+top: "layer_64_3_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_64_3_sum"
+type: "Eltwise"
+bottom: "layer_64_3_conv3"
+bottom: "layer_64_2_sum"
+top: "layer_64_3_sum"
+
+}
+layer {
+name: "layer_128_1_bn1"
+type: "BatchNorm"
+bottom: "layer_64_3_sum"
+top: "layer_128_1_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_128_1_relu1"
+type: "ReLU"
+bottom: "layer_128_1_bn1_pcs_arm_sim"
+top: "layer_128_1_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_1_conv1"
+type: "Convolution"
+bottom: "layer_128_1_bn1_pcs_arm_sim"
+top: "layer_128_1_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_1_bn2"
+type: "BatchNorm"
+bottom: "layer_128_1_conv1"
+top: "layer_128_1_conv1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_128_1_relu2"
+type: "ReLU"
+bottom: "layer_128_1_conv1_pcs_arm_sim"
+top: "layer_128_1_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_1_conv2"
+type: "Convolution"
+bottom: "layer_128_1_conv1_pcs_arm_sim"
+top: "layer_128_1_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 2
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_1_bn3"
+type: "BatchNorm"
+bottom: "layer_128_1_conv2"
+top: "layer_128_1_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_128_1_relu3"
+type: "ReLU"
+bottom: "layer_128_1_conv2_pcs_arm_sim"
+top: "layer_128_1_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_1_conv3"
+type: "Convolution"
+bottom: "layer_128_1_conv2_pcs_arm_sim"
+top: "layer_128_1_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_1_conv_expand"
+type: "Convolution"
+bottom: "layer_128_1_bn1_pcs_arm_sim"
+top: "layer_128_1_conv_expand"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 2
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_1_sum"
+type: "Eltwise"
+bottom: "layer_128_1_conv3"
+bottom: "layer_128_1_conv_expand"
+top: "layer_128_1_sum"
+
+}
+layer {
+name: "layer_128_2_bn1"
+type: "BatchNorm"
+bottom: "layer_128_1_sum"
+top: "layer_128_2_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_2_relu1"
+type: "ReLU"
+bottom: "layer_128_2_bn1_pcs_arm_sim"
+top: "layer_128_2_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_2_conv1"
+type: "Convolution"
+bottom: "layer_128_2_bn1_pcs_arm_sim"
+top: "layer_128_2_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_2_bn2"
+type: "BatchNorm"
+bottom: "layer_128_2_conv1"
+top: "layer_128_2_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_2_relu2"
+type: "ReLU"
+bottom: "layer_128_2_conv1_pcs_arm_sim"
+top: "layer_128_2_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_2_conv2"
+type: "Convolution"
+bottom: "layer_128_2_conv1_pcs_arm_sim"
+top: "layer_128_2_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_2_bn3"
+type: "BatchNorm"
+bottom: "layer_128_2_conv2"
+top: "layer_128_2_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_2_relu3"
+type: "ReLU"
+bottom: "layer_128_2_conv2_pcs_arm_sim"
+top: "layer_128_2_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_2_conv3"
+type: "Convolution"
+bottom: "layer_128_2_conv2_pcs_arm_sim"
+top: "layer_128_2_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_2_sum"
+type: "Eltwise"
+bottom: "layer_128_2_conv3"
+bottom: "layer_128_1_sum"
+top: "layer_128_2_sum"
+
+}
+layer {
+name: "layer_128_3_bn1"
+type: "BatchNorm"
+bottom: "layer_128_2_sum"
+top: "layer_128_3_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_3_relu1"
+type: "ReLU"
+bottom: "layer_128_3_bn1_pcs_arm_sim"
+top: "layer_128_3_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_3_conv1"
+type: "Convolution"
+bottom: "layer_128_3_bn1_pcs_arm_sim"
+top: "layer_128_3_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_3_bn2"
+type: "BatchNorm"
+bottom: "layer_128_3_conv1"
+top: "layer_128_3_conv1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_128_3_relu2"
+type: "ReLU"
+bottom: "layer_128_3_conv1_pcs_arm_sim"
+top: "layer_128_3_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_3_conv2"
+type: "Convolution"
+bottom: "layer_128_3_conv1_pcs_arm_sim"
+top: "layer_128_3_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_3_bn3"
+type: "BatchNorm"
+bottom: "layer_128_3_conv2"
+top: "layer_128_3_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_3_relu3"
+type: "ReLU"
+bottom: "layer_128_3_conv2_pcs_arm_sim"
+top: "layer_128_3_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_3_conv3"
+type: "Convolution"
+bottom: "layer_128_3_conv2_pcs_arm_sim"
+top: "layer_128_3_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_3_sum"
+type: "Eltwise"
+bottom: "layer_128_3_conv3"
+bottom: "layer_128_2_sum"
+top: "layer_128_3_sum"
+
+}
+layer {
+name: "layer_128_4_bn1"
+type: "BatchNorm"
+bottom: "layer_128_3_sum"
+top: "layer_128_4_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_4_relu1"
+type: "ReLU"
+bottom: "layer_128_4_bn1_pcs_arm_sim"
+top: "layer_128_4_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_4_conv1"
+type: "Convolution"
+bottom: "layer_128_4_bn1_pcs_arm_sim"
+top: "layer_128_4_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_4_bn2"
+type: "BatchNorm"
+bottom: "layer_128_4_conv1"
+top: "layer_128_4_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_128_4_relu2"
+type: "ReLU"
+bottom: "layer_128_4_conv1_pcs_arm_sim"
+top: "layer_128_4_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_4_conv2"
+type: "Convolution"
+bottom: "layer_128_4_conv1_pcs_arm_sim"
+top: "layer_128_4_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 128
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_4_bn3"
+type: "BatchNorm"
+bottom: "layer_128_4_conv2"
+top: "layer_128_4_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }       
+}
+layer {
+name: "layer_128_4_relu3"
+type: "ReLU"
+bottom: "layer_128_4_conv2_pcs_arm_sim"
+top: "layer_128_4_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_128_4_conv3"
+type: "Convolution"
+bottom: "layer_128_4_conv2_pcs_arm_sim"
+top: "layer_128_4_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_128_4_sum"
+type: "Eltwise"
+bottom: "layer_128_4_conv3"
+bottom: "layer_128_3_sum"
+top: "layer_128_4_sum"
+
+}
+layer {
+name: "layer_256_1_bn1"
+type: "BatchNorm"
+bottom: "layer_128_4_sum"
+top: "layer_256_1_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_1_relu1"
+type: "ReLU"
+bottom: "layer_256_1_bn1_pcs_arm_sim"
+top: "layer_256_1_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_1_conv1"
+type: "Convolution"
+bottom: "layer_256_1_bn1_pcs_arm_sim"
+top: "layer_256_1_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_1_bn2"
+type: "BatchNorm"
+bottom: "layer_256_1_conv1"
+top: "layer_256_1_conv1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_1_relu2"
+type: "ReLU"
+bottom: "layer_256_1_conv1_pcs_arm_sim"
+top: "layer_256_1_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_1_conv2"
+type: "Convolution"
+bottom: "layer_256_1_conv1_pcs_arm_sim"
+top: "layer_256_1_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 2
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_1_bn3"
+type: "BatchNorm"
+bottom: "layer_256_1_conv2"
+top: "layer_256_1_conv2_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_1_relu3"
+type: "ReLU"
+bottom: "layer_256_1_conv2_pcs_arm_sim"
+top: "layer_256_1_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_1_conv3"
+type: "Convolution"
+bottom: "layer_256_1_conv2_pcs_arm_sim"
+top: "layer_256_1_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_1_conv_expand"
+type: "Convolution"
+bottom: "layer_256_1_bn1_pcs_arm_sim"
+top: "layer_256_1_conv_expand"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 2
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_1_sum"
+type: "Eltwise"
+bottom: "layer_256_1_conv3"
+bottom: "layer_256_1_conv_expand"
+top: "layer_256_1_sum"
+
+}
+layer {
+name: "layer_256_2_bn1"
+type: "BatchNorm"
+bottom: "layer_256_1_sum"
+top: "layer_256_2_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_2_relu1"
+type: "ReLU"
+bottom: "layer_256_2_bn1_pcs_arm_sim"
+top: "layer_256_2_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_2_conv1"
+type: "Convolution"
+bottom: "layer_256_2_bn1_pcs_arm_sim"
+top: "layer_256_2_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_2_bn2"
+type: "BatchNorm"
+bottom: "layer_256_2_conv1"
+top: "layer_256_2_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_2_relu2"
+type: "ReLU"
+bottom: "layer_256_2_conv1_pcs_arm_sim"
+top: "layer_256_2_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_2_conv2"
+type: "Convolution"
+bottom: "layer_256_2_conv1_pcs_arm_sim"
+top: "layer_256_2_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_2_bn3"
+type: "BatchNorm"
+bottom: "layer_256_2_conv2"
+top: "layer_256_2_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_2_relu3"
+type: "ReLU"
+bottom: "layer_256_2_conv2_pcs_arm_sim"
+top: "layer_256_2_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_2_conv3"
+type: "Convolution"
+bottom: "layer_256_2_conv2_pcs_arm_sim"
+top: "layer_256_2_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_2_sum"
+type: "Eltwise"
+bottom: "layer_256_2_conv3"
+bottom: "layer_256_1_sum"
+top: "layer_256_2_sum"
+
+}
+layer {
+name: "layer_256_3_bn1"
+type: "BatchNorm"
+bottom: "layer_256_2_sum"
+top: "layer_256_3_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_3_relu1"
+type: "ReLU"
+bottom: "layer_256_3_bn1_pcs_arm_sim"
+top: "layer_256_3_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_3_conv1"
+type: "Convolution"
+bottom: "layer_256_3_bn1_pcs_arm_sim"
+top: "layer_256_3_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_3_bn2"
+type: "BatchNorm"
+bottom: "layer_256_3_conv1"
+top: "layer_256_3_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_3_relu2"
+type: "ReLU"
+bottom: "layer_256_3_conv1_pcs_arm_sim"
+top: "layer_256_3_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_3_conv2"
+type: "Convolution"
+bottom: "layer_256_3_conv1_pcs_arm_sim"
+top: "layer_256_3_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_3_bn3"
+type: "BatchNorm"
+bottom: "layer_256_3_conv2"
+top: "layer_256_3_conv2_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_3_relu3"
+type: "ReLU"
+bottom: "layer_256_3_conv2_pcs_arm_sim"
+top: "layer_256_3_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_3_conv3"
+type: "Convolution"
+bottom: "layer_256_3_conv2_pcs_arm_sim"
+top: "layer_256_3_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_3_sum"
+type: "Eltwise"
+bottom: "layer_256_3_conv3"
+bottom: "layer_256_2_sum"
+top: "layer_256_3_sum"
+
+}
+layer {
+name: "layer_256_4_bn1"
+type: "BatchNorm"
+bottom: "layer_256_3_sum"
+top: "layer_256_4_bn1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_4_relu1"
+type: "ReLU"
+bottom: "layer_256_4_bn1_pcs_arm_sim"
+top: "layer_256_4_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_4_conv1"
+type: "Convolution"
+bottom: "layer_256_4_bn1_pcs_arm_sim"
+top: "layer_256_4_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_4_bn2"
+type: "BatchNorm"
+bottom: "layer_256_4_conv1"
+top: "layer_256_4_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_256_4_relu2"
+type: "ReLU"
+bottom: "layer_256_4_conv1_pcs_arm_sim"
+top: "layer_256_4_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_4_conv2"
+type: "Convolution"
+bottom: "layer_256_4_conv1_pcs_arm_sim"
+top: "layer_256_4_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_4_bn3"
+type: "BatchNorm"
+bottom: "layer_256_4_conv2"
+top: "layer_256_4_conv2_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_4_relu3"
+type: "ReLU"
+bottom: "layer_256_4_conv2_pcs_arm_sim"
+top: "layer_256_4_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_4_conv3"
+type: "Convolution"
+bottom: "layer_256_4_conv2_pcs_arm_sim"
+top: "layer_256_4_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_4_sum"
+type: "Eltwise"
+bottom: "layer_256_4_conv3"
+bottom: "layer_256_3_sum"
+top: "layer_256_4_sum"
+
+}
+layer {
+name: "layer_256_5_bn1"
+type: "BatchNorm"
+bottom: "layer_256_4_sum"
+top: "layer_256_5_bn1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_5_relu1"
+type: "ReLU"
+bottom: "layer_256_5_bn1_pcs_arm_sim"
+top: "layer_256_5_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_5_conv1"
+type: "Convolution"
+bottom: "layer_256_5_bn1_pcs_arm_sim"
+top: "layer_256_5_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_5_bn2"
+type: "BatchNorm"
+bottom: "layer_256_5_conv1"
+top: "layer_256_5_conv1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_5_relu2"
+type: "ReLU"
+bottom: "layer_256_5_conv1_pcs_arm_sim"
+top: "layer_256_5_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_5_conv2"
+type: "Convolution"
+bottom: "layer_256_5_conv1_pcs_arm_sim"
+top: "layer_256_5_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_5_bn3"
+type: "BatchNorm"
+bottom: "layer_256_5_conv2"
+top: "layer_256_5_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_256_5_relu3"
+type: "ReLU"
+bottom: "layer_256_5_conv2_pcs_arm_sim"
+top: "layer_256_5_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_5_conv3"
+type: "Convolution"
+bottom: "layer_256_5_conv2_pcs_arm_sim"
+top: "layer_256_5_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_5_sum"
+type: "Eltwise"
+bottom: "layer_256_5_conv3"
+bottom: "layer_256_4_sum"
+top: "layer_256_5_sum"
+
+}
+layer {
+name: "layer_256_6_bn1"
+type: "BatchNorm"
+bottom: "layer_256_5_sum"
+top: "layer_256_6_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_256_6_relu1"
+type: "ReLU"
+bottom: "layer_256_6_bn1_pcs_arm_sim"
+top: "layer_256_6_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_6_conv1"
+type: "Convolution"
+bottom: "layer_256_6_bn1_pcs_arm_sim"
+top: "layer_256_6_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_6_bn2"
+type: "BatchNorm"
+bottom: "layer_256_6_conv1"
+top: "layer_256_6_conv1_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_6_relu2"
+type: "ReLU"
+bottom: "layer_256_6_conv1_pcs_arm_sim"
+top: "layer_256_6_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_6_conv2"
+type: "Convolution"
+bottom: "layer_256_6_conv1_pcs_arm_sim"
+top: "layer_256_6_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 256
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_6_bn3"
+type: "BatchNorm"
+bottom: "layer_256_6_conv2"
+top: "layer_256_6_conv2_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_256_6_relu3"
+type: "ReLU"
+bottom: "layer_256_6_conv2_pcs_arm_sim"
+top: "layer_256_6_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_256_6_conv3"
+type: "Convolution"
+bottom: "layer_256_6_conv2_pcs_arm_sim"
+top: "layer_256_6_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 1024
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_256_6_sum"
+type: "Eltwise"
+bottom: "layer_256_6_conv3"
+bottom: "layer_256_5_sum"
+top: "layer_256_6_sum"
+
+}
+layer {
+name: "layer_512_1_bn1"
+type: "BatchNorm"
+bottom: "layer_256_6_sum"
+top: "layer_512_1_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_512_1_relu1"
+type: "ReLU"
+bottom: "layer_512_1_bn1_pcs_arm_sim"
+top: "layer_512_1_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_1_conv1"
+type: "Convolution"
+bottom: "layer_512_1_bn1_pcs_arm_sim"
+top: "layer_512_1_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_1_bn2"
+type: "BatchNorm"
+bottom: "layer_512_1_conv1"
+top: "layer_512_1_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_512_1_relu2"
+type: "ReLU"
+bottom: "layer_512_1_conv1_pcs_arm_sim"
+top: "layer_512_1_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_1_conv2"
+type: "Convolution"
+bottom: "layer_512_1_conv1_pcs_arm_sim"
+top: "layer_512_1_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 2
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_1_bn3"
+type: "BatchNorm"
+bottom: "layer_512_1_conv2"
+top: "layer_512_1_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_512_1_relu3"
+type: "ReLU"
+bottom: "layer_512_1_conv2_pcs_arm_sim"
+top: "layer_512_1_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_1_conv3"
+type: "Convolution"
+bottom: "layer_512_1_conv2_pcs_arm_sim"
+top: "layer_512_1_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 2048
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_1_conv_expand"
+type: "Convolution"
+bottom: "layer_512_1_bn1_pcs_arm_sim"
+top: "layer_512_1_conv_expand"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 2048
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 2
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_1_sum"
+type: "Eltwise"
+bottom: "layer_512_1_conv3"
+bottom: "layer_512_1_conv_expand"
+top: "layer_512_1_sum"
+
+}
+layer {
+name: "layer_512_2_bn1"
+type: "BatchNorm"
+bottom: "layer_512_1_sum"
+top: "layer_512_2_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }  
+}
+layer {
+name: "layer_512_2_relu1"
+type: "ReLU"
+bottom: "layer_512_2_bn1_pcs_arm_sim"
+top: "layer_512_2_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_2_conv1"
+type: "Convolution"
+bottom: "layer_512_2_bn1_pcs_arm_sim"
+top: "layer_512_2_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_2_bn2"
+type: "BatchNorm"
+bottom: "layer_512_2_conv1"
+top: "layer_512_2_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_512_2_relu2"
+type: "ReLU"
+bottom: "layer_512_2_conv1_pcs_arm_sim"
+top: "layer_512_2_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_2_conv2"
+type: "Convolution"
+bottom: "layer_512_2_conv1_pcs_arm_sim"
+top: "layer_512_2_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_2_bn3"
+type: "BatchNorm"
+bottom: "layer_512_2_conv2"
+top: "layer_512_2_conv2_pcs_arm_sim"
+  batch_norm_param {
+ }   
+}
+layer {
+name: "layer_512_2_relu3"
+type: "ReLU"
+bottom: "layer_512_2_conv2_pcs_arm_sim"
+top: "layer_512_2_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_2_conv3"
+type: "Convolution"
+bottom: "layer_512_2_conv2_pcs_arm_sim"
+top: "layer_512_2_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 2048
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_2_sum"
+type: "Eltwise"
+bottom: "layer_512_2_conv3"
+bottom: "layer_512_1_sum"
+top: "layer_512_2_sum"
+
+}
+layer {
+name: "layer_512_3_bn1"
+type: "BatchNorm"
+bottom: "layer_512_2_sum"
+top: "layer_512_3_bn1_pcs_arm_sim"
+  batch_norm_param {
+ }    
+}
+layer {
+name: "layer_512_3_relu1"
+type: "ReLU"
+bottom: "layer_512_3_bn1_pcs_arm_sim"
+top: "layer_512_3_bn1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_3_conv1"
+type: "Convolution"
+bottom: "layer_512_3_bn1_pcs_arm_sim"
+top: "layer_512_3_conv1"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_3_bn2"
+type: "BatchNorm"
+bottom: "layer_512_3_conv1"
+top: "layer_512_3_conv1_pcs_arm_sim"
+  batch_norm_param {
+ }
+}
+layer {
+name: "layer_512_3_relu2"
+type: "ReLU"
+bottom: "layer_512_3_conv1_pcs_arm_sim"
+top: "layer_512_3_conv1_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_3_conv2"
+type: "Convolution"
+bottom: "layer_512_3_conv1_pcs_arm_sim"
+top: "layer_512_3_conv2"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 512
+  bias_term: false
+  pad: 1
+  kernel_size: 3
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_3_bn3"
+type: "BatchNorm"
+bottom: "layer_512_3_conv2"
+top: "layer_512_3_conv2_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "layer_512_3_relu3"
+type: "ReLU"
+bottom: "layer_512_3_conv2_pcs_arm_sim"
+top: "layer_512_3_conv2_pcs_arm_sim"
+
+}
+layer {
+name: "layer_512_3_conv3"
+type: "Convolution"
+bottom: "layer_512_3_conv2_pcs_arm_sim"
+top: "layer_512_3_conv3"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+convolution_param {
+  num_output: 2048
+  bias_term: false
+  pad: 0
+  kernel_size: 1
+  stride: 1
+  weight_filler {
+    type: "msra"
+  }
+  bias_filler {
+    type: "constant"
+    value: 0.0
+  }
+}
+
+}
+layer {
+name: "layer_512_3_sum"
+type: "Eltwise"
+bottom: "layer_512_3_conv3"
+bottom: "layer_512_2_sum"
+top: "layer_512_3_sum"
+
+}
+layer {
+name: "last_bn"
+type: "BatchNorm"
+bottom: "layer_512_3_sum"
+top: "layer_512_3_sum_pcs_arm_sim"
+  batch_norm_param {
+ } 
+}
+layer {
+name: "last_relu"
+type: "ReLU"
+bottom: "layer_512_3_sum_pcs_arm_sim"
+top: "layer_512_3_sum_pcs_arm_sim"
+
+}
+layer {
+name: "global_pool"
+type: "Pooling"
+bottom: "layer_512_3_sum_pcs_arm_sim"
+top: "global_pool"
+pooling_param {
+  pool: AVE
+  global_pooling: true
+}
+
+}
+layer {
+name: "score"
+type: "InnerProduct"
+bottom: "global_pool"
+top: "score"
+param {
+  lr_mult: 1.0
+  decay_mult: 1.0
+}
+param {
+  lr_mult: 2.0
+  decay_mult: 1.0
+}
+inner_product_param {
+  num_output: 1000
+}
+
+}
+layer {
+name: "loss"
+type: "SoftmaxWithLoss"
+bottom: "score"
+bottom: "label"
+top: "loss"
+
+}
+layer {
+name: "accuracy"
+type: "Accuracy"
+bottom: "score"
+bottom: "label"
+top: "accuracy"
+include {
+  phase: TEST
+}
+
+}
diff --git a/scripts/prepare_env.sh b/scripts/prepare_env.sh
new file mode 100755
index 000000000..76e39b471
--- /dev/null
+++ b/scripts/prepare_env.sh
@@ -0,0 +1,75 @@
+#!/bin/bash
+
+os="centos"
+
+username=`whoami`
+if [ "$username" != "root" ];
+then
+    package_installer="sudo -E"
+fi
+
+# centos: yum; ubuntu: apt-get
+package_installer+=" yum -y"
+
+function install_deps
+{
+    echo "Install dependencies..."
+    if [ "$os" == "centos" ]; then
+        $package_installer clean all
+    	$package_installer upgrade
+        $package_installer install epel-release
+        $package_installer groupinstall "Development Tools"
+    fi
+    
+    $package_installer install python-devel boost boost-devel cmake numpy \
+        numpy-devel gflags gflags-devel glog glog-devel protobuf protobuf-devel hdf5 \
+        hdf5-devel lmdb lmdb-devel leveldb leveldb-devel snappy-devel opencv \
+        opencv-devel wget bc numactl
+}
+
+function check_os
+{
+    echo "Check OS and the version..."
+}
+
+
+function checkout_source
+{
+    echo "Checkout source code of Intel Caffe..."
+    git clone https://github.com/intel/caffe.git
+    if [ $? -eq 128 ]; then
+        echo "Error during checking out source code. Please set proxy as below:"
+        echo "    export https_proxy=https://username:password@proxy.com:port"
+    fi
+}
+function build_caffe
+{
+    echo "Build Intel Caffe..."
+    cp Makefile.config.example Makefile.config
+    make -j 8
+}
+
+function is_sudoer
+{
+    sudo -v >/dev/null
+    if [ $? -eq 1 ]; then
+        echo "User $(whoami) is not sudoer, and cannot install dependencies."
+        return 1
+    fi
+    return 0
+}
+
+check_os
+if [ "$os" == "ubuntu" ]; then
+    package_installer="apt-get"
+fi
+
+is_sudoer
+if [ $? -eq 0 ]; then
+    install_deps
+fi
+
+
+build_caffe
+
+echo "Done."
diff --git a/scripts/run_benchmark.sh b/scripts/run_benchmark.sh
new file mode 100755
index 000000000..ac9a702a9
--- /dev/null
+++ b/scripts/run_benchmark.sh
@@ -0,0 +1,125 @@
+#!/bin/sh
+
+# model path
+model_path="models/intel_optimized_models"
+
+# network topology, support alexnet, googlenet, googlenet v2, resnet50
+# if you set it as 'all', then it will benchmark all supported topologies.
+topology=""
+topology_list="alexnet googlenet googlenet_v2 resnet_50 all"
+declare -a model_list=("alexnet" "googlenet" "googlenet_v2" "resnet_50")
+
+# it's assigned by detect_cpu
+cpu_model="skx"
+
+# specify default engine for running caffe benchmarks
+engine="MKL2017"
+
+# directory path to save results
+result_dir=""
+
+# specific script used to run intelcaffe 
+caffe_bin="./scripts/run_intelcaffe.sh"
+
+# Iterations to run benchmark
+iterations=100
+
+function usage
+{
+    script_name=$0
+    echo "Usage:"
+    echo "   $script_name --topology network_topology"
+    echo ""
+    echo "   Parameters:"
+    echo "     topology: network topology used to benchmark, support alexnet, googlenet, googlenet_v2, resnet_50"
+    echo "               , by specifying it as 'all', we run all supported topologies."
+    echo ""
+}
+
+function is_supported_topology
+{
+    echo ""
+    if [[ "$topology_list" =~ (^|[[:space:]])"$topology"($|[[:space:]]) ]]; then
+        echo "Topology: ${topology}"
+    else
+        echo "$topology is not supported, please check the usage." 
+        usage 
+        exit 1
+    fi
+}
+
+function detect_cpu
+{
+    # detect cpu model
+    model_string=`lscpu | grep "Model name" | awk -F ':' '{print $2}'`
+    if [[ $model_string == *"72"* ]]; then
+        cpu_model="knl"
+    elif [[ $model_string == *"8180"* ]]; then
+        cpu_model="skx"
+    elif [[ $model_string == *"6148"* ]]; then
+        cpu_model="skx"
+    elif [[ $model_string == *"E5-26"* ]]; then
+        cpu_model="bdw"
+    else
+        echo "Will use default settings, which may not be the optimal one."
+    fi
+}
+
+function run_specific_model
+{
+    model_file="models/intel_optimized_models/${model}/${cpu_model}/train_val_dummydata.prototxt"
+    exec_command="${caffe_bin} --model_file ${model_file} --mode time --iteration ${iterations} --benchmark none"
+    $exec_command
+}
+
+function run_benchmark
+{
+    detect_cpu
+    echo "Cpu model : $model_string"
+    if [[ $topology = "all" ]]; then
+       for ((i=0; i<${#model_list[@]}; i++))
+       do
+          echo "    ${model_list[$i]}"
+          model=${model_list[$i]}
+          run_specific_model
+       done
+    else
+       model=$topology
+       run_specific_model
+    fi 
+}
+
+if [[ $# -le 1 ]]; then
+    usage
+    exit 0
+fi
+
+root_dir=$(cd $(dirname $(dirname $0)); pwd)
+while [[ $# -gt 1 ]]
+do
+    key="$1"
+    case $key in 
+        --topology)
+            topology="$2"
+            shift
+            ;;
+        *)
+            echo "Unknown option: $key"
+            usage
+            exit 1
+            ;;
+    esac
+    shift
+done
+
+# check parameters
+if [[ $topology = "" ]]; then
+    echo "Error: topology is not specified."
+    exit 1
+fi
+
+# check if input topology is supported
+is_supported_topology
+
+# start running benchmark
+run_benchmark
diff --git a/scripts/run_intelcaffe.sh b/scripts/run_intelcaffe.sh
index 29a5309ab..1e357db65 100755
--- a/scripts/run_intelcaffe.sh
+++ b/scripts/run_intelcaffe.sh
@@ -1,5 +1,4 @@
 #!/bin/sh
- set -x
 
 benchmark_mode="all"
 
@@ -7,7 +6,7 @@ benchmark_mode="all"
 mode="train"
 
 # it's assigned by detect_cpu
-cpu_model=skx
+cpu_model="skx"
 
 # a list of nodes
 host_file=""
@@ -30,30 +29,37 @@ solver_file=""
 # specify engine for running caffe
 engine="MKL2017"
 
+#default numa node if needed
+numanode=0
+
 result_dir=""
 debug="off"
+mpibench_bin="IMB-MPI1"
+mpibench_param="allreduce"
 
 function usage
 {
     script_name=$0
     echo "Usage:"
-    echo "  $script_name --host host_file [--solver solver_file]"
+    echo "  $script_name [--host host_file] [--solver solver_file]"
     echo "               [--network opa/tcp] [--netmask tcp_netmask] [--debug on/off]"
     echo "               [--mode train/resume_train/time/none] [--benchmark all/qperf/mpi/none]"
     echo "               [--iteration iter] [--model_file deploy.prototxt]"
     echo "               [--snapshot snapshot.caffemodel]"
     echo "               [--num_mlsl_servers num_mlsl_servers]"
     echo "               [--output output_folder]"
+    echo "               [--mpibench_bin mpibench_bin]"
+    echo "               [--mpibench_param mpibench_param]"
     echo ""
     echo "  Parameters:"
-    echo "    host: host file includes list of nodes."
+    echo "    mode: train(default), resume_train, time, none(not to run caffe test)"
     echo ""
     echo "  Optional parameters:"
-    echo "    solver: specify solver file if mode is train/resume_train"
+    echo "    host: host file includes list of nodes. Only used when you're running multinodes mode"
+    echo "    solver: need to be specified a solver file if mode is train/resume_train"
     echo "    network: opa(default), tcp"
     echo "    netmask: only used if network is tcp"
     echo "    debug: off(default). MLSL debug information is outputed if it's on"
-    echo "    mode: train(default), resume_train, time, none(not to run caffe test)"
     echo "    benchmark: all(default). Includes qperf, all-reduce performance"
     echo "      Dependency: user needs to install qperf, IMB-MPI1;"
     echo "                  and add them in system path."
@@ -61,6 +67,8 @@ function usage
     echo "    snapshot: only used if mode is resume_train"
     echo "    num_mlsl_servers: number of MLSL ep servers"
     echo "    output_folder: output folder for storing results"
+    echo "    mpibench_bin: IMB-MPI1 (default). relative path of binary of mpi benchmark."
+    echo "    mpibench_param: allreduce (default). parameter of mpi benchmark."
 }
 
 declare -a cpu_list=("Intel Xeon E5-26xx (Broadwell)" "Intel Xeon Phi 72xx (Knight Landing)" 
@@ -71,16 +79,17 @@ function detect_cpu
     # detect cpu model
     model_string=`lscpu | grep "Model name" | awk -F ':' '{print $2}'`
     if [[ $model_string == *"72"* ]]; then
-        cpu_model=knl
+        cpu_model="knl"
     elif [[ $model_string == *"8180"* ]]; then
-        cpu_model=skx
+        cpu_model="skx"
     elif [[ $model_string == *"6148"* ]]; then
-        cpu_model=skx
+        cpu_model="skx"
     elif [[ $model_string == *"E5-26"* ]]; then
-        cpu_model=bdw
+        cpu_model="bdw"
     else
-        echo "CPU model: $model_string"
-        echo "  Use default settings, which may not be optimal ones."
+        cpu_model="unknown"
+        echo "CPU model :$model_string is unknown."
+        echo "Will use default settings, which may not be the optimal one."
     fi
 }
 
@@ -143,8 +152,8 @@ function clear_shm
     clear_command="rm -rf /dev/shm/*"
     check_shm_command="df -h | grep shm"
 
-    # TODO: check if 50G is the minimum shm size?
-    min_shm_size=50
+    # TODO: check if 40G is the minimum shm size?
+    min_shm_size=40
     shm_unit="G"
 
     for node in "${nodenames[@]}"
@@ -157,10 +166,9 @@ function clear_shm
         if [ "$unit" == "$shm_unit" ] && [ $shm_size -ge ${min_shm_size} ]; then
             continue
         else
-            echo "Error: /dev/shm size = ${shm_size}${unit}, on node: ${node}."
-            echo "       It's less than minimum size: ${min_shm_size}${shm_unit}."
-            echo "       Please clean or enlarge it."
-            exit 1
+            echo "Warning: /dev/shm free size = ${shm_size}${unit}, on node: ${node}."
+            echo "       Better to larger than ${min_shm_size}${shm_unit}."
+            echo "       Please clean or enlarge the partition."
         fi
     done
 }
@@ -183,13 +191,13 @@ function clear_envs
 function set_mlsl_vars
 {
     if [ "${num_mlsl_servers}" -eq -1 ]; then
-        if [ ${numnodes} -eq 1 ]; then
+        if [ "${numnodes}" -eq 1 ]; then
             numservers=0
         else
-            if [ ${cpu_model} == knl ]; then
-                numservers=4
-            else
+            if [ "${cpu_model}" == "bdw" ] || [ "${cpu_model}" == "skx" ]; then
                 numservers=2
+            else
+                numservers=4
             fi
         fi
     else
@@ -200,10 +208,10 @@ function set_mlsl_vars
     export MLSL_NUM_SERVERS=${numservers}
 
     if [ ${numservers} -gt 0 ]; then
-        if [ ${cpu_model} == knl ]; then
-            listep=6,7,8,9,10,11,12,13
-        else
+        if [ "${cpu_model}" == "bdw" ] || [ "${cpu_model}" == "skx" ]; then
             listep=6,7,8,9
+        else
+            listep=6,7,8,9,10,11,12,13
         fi
         export MLSL_SERVER_AFFINITY="${listep}"
         echo "MLSL_SERVER_AFFINITY: ${listep}"
@@ -220,6 +228,7 @@ function set_mlsl_vars
 function set_env_vars
 {
     set_mlsl_vars
+    init_mpi_envs
 
     ppncpu=1
     threadspercore=1
@@ -245,13 +254,14 @@ function execute_command
     local xeonbin_=$1
     local result_dir_=$2
 
-    if [ ${cpu_model} == knl ]; then
-        exec_command="numactl --preferred=$numanode $xeonbin_"
-    else
+    if [ "${cpu_model}" == "bdw" ] || [ "${cpu_model}" == "skx" ]; then
         exec_command="$xeonbin_"
+    else
+        
+        exec_command="numactl --preferred=$numanode $xeonbin_"
     fi
 
-    if [ ${numnodes} -gt 1 ]; then
+    if [ "${numnodes}" -gt 1 ]; then
         # Produce the configuration file for mpiexec. 
         # Each line of the config file contains a # host, environment, binary name.
         cfile_=nodeconfig-${cpu_model}-${numnodes}.txt
@@ -262,9 +272,9 @@ function execute_command
             echo "-host ${node} -n $ppncpu $exec_command" >> $cfile_
         done
     fi
+    log_file=outputCluster-${cpu_model}-${numnodes}.txt
 
     clear_envs
-    log_file=outputCluster-${cpu_model}-${numnodes}.txt
 
     sensors_bin="sensors"
     check_dependency $sensors_bin
@@ -275,10 +285,9 @@ function execute_command
         mv $sensor_log_file $result_dir_/
     fi
     
-    if [ ${numnodes} -eq 1 ]; then
+    if [ "${numnodes}" -eq 1 ]; then
         time GLOG_minloglevel=0 $exec_command >${log_file} 2>&1
     else
-        init_mpi_envs
         exec_command="-l -configfile $cfile_"
         time GLOG_minloglevel=0 mpiexec.hydra $exec_command >${log_file} 2>&1 
     fi
@@ -291,6 +300,35 @@ function execute_command
     mv $log_file $cfile_ $result_dir_/
 }
 
+# used to calculate images / s
+function obtain_average_fwd_bwd_time
+{
+    result_file="${result_dir}/${log_file}"
+    if [ -f $result_file ]; then
+        average_time_line=`cat $result_file | grep "Average Forward-Backward"`
+        average_time=`echo $average_time_line | awk -F ' ' '{print $(NF-1)}'`
+        echo "average time : ${average_time} ms"
+    else
+        echo "Error: result file $result_file does not exist..."
+        exit 1
+    fi
+}
+
+# used to calculate images / s
+function obtain_batch_size
+{
+    batch_size=`cat $model_file | grep shape | sed -n "3, 1p" | awk '{print $4}'`
+    echo "batch size : $batch_size"
+}
+
+function calculate_images_per_second 
+{
+    obtain_batch_size
+    obtain_average_fwd_bwd_time
+    speed=`echo "$batch_size*1000/$average_time" | bc`
+    echo "benchmark speed : $speed images/s"
+}
+
 function run_qperf_bench
 {
     qperf_bin="qperf"
@@ -335,14 +373,15 @@ function run_qperf_bench
 function run_mpi_bench
 {
     # MPI benchmark
-    mpibench_bin="IMB-MPI1"
     check_dependency $mpibench_bin
     if [ $? -ne 0 ]; then
         echo "Skip MPI benchmark..."
         return
     fi
 
-    xeonbin="$mpibench_bin allreduce"
+    xeonbin="$mpibench_bin $mpibench_param"
+
+    mpibench_bin_bname=`basename $mpibench_bin`
 
     declare -a adjust_values=(1 2 3 5 7 8 9 0)
     declare -a collective_values=('tmi' 'none')
@@ -367,7 +406,7 @@ function run_mpi_bench
             echo "I_MPI_ADJUST_ALLREDUCE=$I_MPI_ADJUST_ALLREDUCE"
             echo "I_MPI_COLLECTIVE_DEFAULTS=$I_MPI_COLLECTIVE_DEFAULTS"
 
-            test_result_dir=$result_dir/mpibench-${adjust_values[$i]}-${collective_values[$j]}
+            test_result_dir=$result_dir/mpibench-${mpibench_bin_bname}-${mpibench_param}-${adjust_values[$i]}-${collective_values[$j]}
             mkdir -p $test_result_dir
             execute_command "$xeonbin" $test_result_dir
         done
@@ -397,7 +436,9 @@ function run_benchmark
 
 function run_caffe
 {
-    echo "Run caffe with ${numnodes} nodes..."
+    if [[ $host_file != "" ]]; then
+        echo "Run caffe with ${numnodes} nodes..."
+    fi
 
     if [ ${mode} == "time" ]; then
         xeonbin="$caffe_bin time --iterations $iteration --model $model_file  -engine=$engine"
@@ -408,8 +449,13 @@ function run_caffe
         fi
     fi
 
-    set_env_vars
+    if [[ $host_file != "" ]]; then 
+        set_env_vars
+    fi
     execute_command "$xeonbin" $result_dir
+    if [ ${mode} == "time" ]; then
+        calculate_images_per_second 
+    fi
 }
 
 
@@ -420,7 +466,6 @@ fi
 
 root_dir=$(cd $(dirname $(dirname $0)); pwd)
 result_dir=${root_dir}/"result-`date +%Y%m%d%H%M%S`"
-
 while [[ $# -gt 1 ]]
 do
     key="$1"
@@ -477,6 +522,14 @@ do
             result_dir=$2
             shift
             ;;
+        --mpibench_bin)
+            mpibench_bin=$2
+            shift
+            ;;
+        --mpibench_param)
+            mpibench_param=$2
+            shift
+            ;;
         *)
             echo "Unknown option: $key"
             usage
@@ -486,16 +539,6 @@ do
     shift
 done
 
-# check parameters
-if [ "$host_file" == "" ]; then
-    echo "Error: host file is NOT specified."
-    exit 1
-fi
-if [ ! -f $host_file ]; then
-    echo "Error: host file does NOT exist."
-    exit 1
-fi
-
 echo ""
 echo "CPUs with optimal settings:"
 for ((i=0; i<${#cpu_list[@]}; i++))
@@ -567,22 +610,27 @@ if [ "$network" == "tcp" ]; then
 fi
 
 # Names to configfile, binary (executable) files #
-nodenames=( `cat $host_file | sort | uniq ` )
-if [ ${#nodenames[@]} -eq 0 ]; then
-    echo "Error: empty host file! Exit."
-    exit 0
+# Add check for host_file's existence to support single node
+if [[ $host_file != "" ]]; then
+    nodenames=( `cat $host_file | sort | uniq ` )
+    if [ ${#nodenames[@]} -eq 0 ]; then
+        echo "Error: empty host file! Exit."
+        exit 0
+    fi
+    numnodes=${#nodenames[@]}
+else
+    numnodes=1
 fi
-numnodes=${#nodenames[@]}
-echo "Number of nodes: $numnodes"
+echo "    Number of nodes: $numnodes"
 
 detect_cpu
 
-if [ $cpu_model == knl ]; then
+if [ "$cpu_model" == "knl" ]; then
     set_numa_node
 fi
 
 if [ ! -d $result_dir ]; then
-    echo "Create result directory: $result_dir"
+    #echo "Create result directory: $result_dir"
     mkdir -p $result_dir
 fi