function test_example_CNN

%{

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');

%}

train = load('train_data.mat');

train_x = reshape(train.x(2:785, :), 28, 28, 60000);

train_x = train_x;

train_y = train.labels';

train_y = train_y;

test = load('test_data.mat');

test_x = reshape(test.x(2:785, :), 28, 28, 10000);

test_x = test_x;

test_y = test.labels';

test_y = test_y;

%% ex1 Train a 6c-2s-12c-2s Convolutional neural network

%will run 1 epoch in about 200 second and get around 11% error.

%With 100 epochs you'll get around 1.2% error

rand('state',0)

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 = 100;

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

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

%plot mean squared error

figure; plot(cnn.rL);

%assert(er<0.12, 'Too big error');

end