CNN - Convolutional neural network class

This project provides matlab class for implementation of convolutional neural networks.

Description

This project provides matlab class for implementation of convolutional neural networks. This networks was developed by Yann LeCun and have sucessfully used in many practical applications, such as handwritten digits recognition, face detection, robot navigation and others (see references for more info). Because of some architectural features of convolutional networks, such as weight sharing it is imposible to implement it using Matlab Neural Network Toolbox without it's source modifications. That's why this class works almost independently from NN toolbox (coming soon full independence).

This release includes sample of handwritten digits recognition using CNN. If you just want to try it run cnet_tool. You'll see a simple GUI. It loads pretrained convolutional neural net from cnet.mat and recognizes image of digit either painted in painting area or downloaded from MNIST database.

The significant improovement in this version is support of nVidia CUDA technology, which speeds up the training up to 44 times. You'll need a CUDA-capable graphic card and CUDA SDK (especially cudart.dll and cublas.dll). Currently only stochastic gradient is supported by CUDA-training, but Hessian approximation is going to be soon also.
IMPORTANT:since matlabcentral is not allows to include mex-files into submission, you need to download cudacnn.mex (mex32 or mex64) from https://sites.google.com/site/mihailsirotenko/projects/convolutional-neural-network-class for full functionality.
Though without cudacnn.mex this software is also functional in usual way.
To run CUDA-based training use cutrain_cnn.m
The source of cudacnn.mex is not included by now, but I plan to do It in future.
See readme.txt for more details.

• Acknowledgements: This submission has inspired the following:; myCNN
• Required Products: Neural Network Toolbox
• MATLAB release: MATLAB 7.4 (R2007a)
• Other requirements: You'll need a CUDA SDK and to download mex-files from external link (see description) in order to enable CUDA features.

Other Files

@cnn/adapt_dw.m,
@cnn/calchx.m,
@cnn/calcje.m,
@cnn/calcMCR.m,
@cnn/check_finit_dif.m,
@cnn/cnn.m,
@cnn/cnn_size.m,
@cnn/cutrain.m,
@cnn/init.m,
@cnn/sim.m,
@cnn/subsasgn.m,
@cnn/subsref.m,
@cnn/train.m,
back_conv2.m,
back_subsample.m,
changelog.txt,
cnet.mat,
cnet_tool.m,
cnn2singlestruct.m,
cnn_gui.fig,
cnn_gui.m,
cucalcMCR.m,
cudacnn.mexw32,
cudacnn.mexw64,
cutrain_cnn.m,
fastFilter2.m,
license.txt,
preproc_data.m,
preproc_image.m,
rand_std.m,
readme.txt,
readMNIST.m,
readMNIST_image.m,
rot180.m,
singlestruct2cnn.m,
subsample.m,
tansig_mod.m,
test_dgt.m,
train_cnn.m

[Mihail Sirotenko]

New version coming soon. It will include:
1. Trained network for experements.
2. Simple GUI, visualising digits recognition.
3. Improoved performance.
4. Independence from NN Toolbox.

[Nikolay Chumerin]

Well done! Good job. I think this is the first publicly available implementation of CNN training in Matlab. The source code is written in a pretty good style with extensive comments, which are really useful for such complex classes. This submission is an asset for computer vision Matlab community.

[Grigory]

22.10.2009 - in current Ver_0.72
- in file train_cnn.m Conv. Layer 4 should have 16 kernels not 6 (sinet.CLayer{4}.numKernels = 6 ) or sinet.CLayer{4}.ConMap shouldn't be 6x16 in size.
- net training result doesn't return "minimal error" net state. I.e. I can see lover error during training than final is. However I apply CNN to different task with my own image set.

[Mihail Sirotenko]

Thanks a lot for the valuable comment. First bug is a result of merging different versions of CNN used in my experements. About second bug, the problem is in the error calculation. In the new version I'll fix these bugs.