Deep neural network framework (C++/OpenCV).
C++ CMake
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README.md

conv-net-version-3.1.0

Deep neural network frame (C++ / OpenCV).

To run this code, you should have

  • a cifar-10 dataset( put "cifar-10-batches-bin" where this .md file is, you can get it from HERE, make sure to download the binary version which suitable for C programs);
  • OpenCV 3.0.
  • cmake

Compile & Run

  • Compile:
cmake .
make
  • Run:
./cnn3

Updates

  • 3-channels images supported.
  • Add Dropout;
  • Local Response Normalization supported.
  • Use log files dig deeper.
  • Use second order derivative back-prop to alter learning rate.
  • Jul 1: version 3.1.0 released
  • NEW: Jul.13 OpenCV 3.0 supported, use dft to accelerate convolution

Layer Config Description

  • For each layer, there is a layer_name, a layer_type, and a output_format.
  • There are currently 2 output formats: matrix (cv::Mat, CV_64FC1), and image (vector of cv::Mat, CV_64FC3).

Input Layer

  • batch size: the training process is using mini-batch stochastic gradient descent.

Convolutional Layer

  • kernel size: size of kernels for convolution calculation.
  • kernel amount: amount of kernels for convolution calculation.
  • combine map: amount of combine feature map, details can be found in Notes on Convolutional Neural Networks.
  • weight decay: weight decay for convolutional kernels.
  • padding: padding before doing convolution.
  • stride: stride when doing convolution (For "VALID" type of convolution, result size = (image_size + 2 * padding - kernel_size) / stride + 1).

Fully Connected Layer

  • num hidden neurons: size of fully connected layer.
  • weight decay: weight decay for fully connected layer.

Softmax Layer

  • num classes: output size of softmax layer.
  • weight decay: weight decay for softmax layer.

Non-linearity Layer

  • method: sigmoid/tanh/relu/leaky_relu.

Pooling Layer

  • method: max/mean/stochastic.
  • overlap: if use overlap pooling.
  • window size: window size when using overlap pooling.
  • stride: pooling stride.

Local Response Normalization Layer

Dropout Layer

  • dropout rate: percentage of zeros when generating Bernoulli matrix.

Combine Layer

  • for implementing GoogLeNet, TODO...

Branch Layer

  • for implementing GoogLeNet, TODO...

Structure and Algorithm

See my several posts about CNNs at my tech-blog.

TODO

*combine layer *branch layer

GPU Version

There's also a GPU version of this code which I used nVidia CUDA..

The MIT License (MIT)

Copyright (c) 2014 Xingdi (Eric) Yuan

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.