| Description |
This project provides matlab class for implementation of convolutional neural networks. This networks was created 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. I should note that currently recognition have about 4% error, which is higher than in Yann LeCun's classifier comparison table (http://yann.lecun.com/exdb/mnist/index.html). This is because my implementation have another activation and error functions (tansig and MSE). Soon will be release with radbas and MLE functions, as stated in [2].
See readme.txt for more details. |
| Other Files |
@cnn/adapt_dw.m,
@cnn/calchx.m,
@cnn/calcje.m,
@cnn/check_finit_dif.m,
@cnn/cnn.m,
@cnn/cnn_size.m,
@cnn/init.m,
@cnn/sim.m,
@cnn/subsasgn.m,
@cnn/subsref.m,
@cnn/train.m,
back_conv2.m,
back_subsample.m,
cnet.mat,
cnet_tool.m,
eraser.gif,
fastFilter2.m,
license.txt,
preproc_data.m,
preproc_image.m,
rand_std.m,
readme.txt,
readMNIST.m,
readMNIST_image.m,
rot180.m,
subsample.m,
tansig_mod.m,
test_dgt.m,
train_cnn.m
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