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Highlights from
Deep Learning Toolbox

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Deep Learning Toolbox

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Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets and more. With examples.

cnnexamples.m
clear all; close all; clc;
addpath('../data');
load mnist_uint8;

train_x = double(reshape(train_x',28,28,60000))/255;
test_x = double(reshape(test_x',28,28,10000))/255;
train_y = double(train_y');
test_y = double(test_y');

%% ex1 
%will run 1 epoch in about 200 second and get around 11% error. 
%With 100 epochs you'll get around 1.2% error

cnn.layers = {
    struct('type', 'i') %input layer
    struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer
    struct('type', 's', 'scale', 2) %sub sampling layer
    struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer
    struct('type', 's', 'scale', 2) %subsampling layer
};
cnn = cnnsetup(cnn, train_x, train_y);

opts.alpha = 1;
opts.batchsize = 50;
opts.numepochs = 1;

cnn = cnntrain(cnn, train_x, train_y, opts);

[er, bad] = cnntest(cnn, test_x, test_y);

%plot mean squared error
plot(cnn.rL);
%show test error
disp([num2str(er*100) '% error']);

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