DeepLearnToolbox

A Matlab toolbox for Deep Learning.

Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data. It is inspired by the human brain's apparent deep (layered, hierarchical) architecture. A good overview of the theory of Deep Learning theory is Learning Deep Architectures for AI

For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.

The Next Generation of Neural Networks (Hinton, 2007)
Recent Developments in Deep Learning (Hinton, 2010)
Unsupervised Feature Learning and Deep Learning (Ng, 2011)

If you use this toolbox in your research please cite Prediction as a candidate for learning deep hierarchical models of data

@MASTERSTHESIS\{IMM2012-06284,
    author       = "R. B. Palm",
    title        = "Prediction as a candidate for learning deep hierarchical models of data",
    year         = "2012",
}

Contact: rasmusbergpalm at gmail dot com

Directories included in the toolbox

NN/ - A library for Feedforward Backpropagation Neural Networks

CNN/ - A library for Convolutional Neural Networks

DBN/ - A library for Deep Belief Networks

SAE/ - A library for Stacked Auto-Encoders

CAE/ - A library for Convolutional Auto-Encoders

util/ - Utility functions used by the libraries

data/ - Data used by the examples

tests/ - unit tests to verify toolbox is working

For references on each library check REFS.md

Setup

Download.
addpath(genpath('DeepLearnToolbox'));

Known errors

test_cnn_gradients_are_numerically_correct fails on Octave because of a bug in Octave's convn implementation. See http://savannah.gnu.org/bugs/?39314

`test_example_CNN` fails in Octave for the same reason. Example: Deep Belief Network


function test_example_DBN
load mnist_uint8;

train_x = double(train_x) / 255;
test_x  = double(test_x)  / 255;
train_y = double(train_y);
test_y  = double(test_y);

%%  ex1 train a 100 hidden unit RBM and visualize its weights
rand('state',0)
dbn.sizes = [100];
opts.numepochs =   1;
opts.batchsize = 100;
opts.momentum  =   0;
opts.alpha     =   1;
dbn = dbnsetup(dbn, train_x, opts);
dbn = dbntrain(dbn, train_x, opts);
figure; visualize(dbn.rbm{1}.W');   %  Visualize the RBM weights

%%  ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NN
rand('state',0)
%train dbn
dbn.sizes = [100 100];
opts.numepochs =   1;
opts.batchsize = 100;
opts.momentum  =   0;
opts.alpha     =   1;
dbn = dbnsetup(dbn, train_x, opts);
dbn = dbntrain(dbn, train_x, opts);

%unfold dbn to nn
nn = dbnunfoldtonn(dbn, 10);
nn.activation_function = 'sigm';

%train nn
opts.numepochs =  1;
opts.batchsize = 100;
nn = nntrain(nn, train_x, train_y, opts);
[er, bad] = nntest(nn, test_x, test_y);

assert(er < 0.10, 'Too big error');

Example: Stacked Auto-Encoders


function test_example_SAE
load mnist_uint8;

train_x = double(train_x)/255;
test_x  = double(test_x)/255;
train_y = double(train_y);
test_y  = double(test_y);

%%  ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN
%  Setup and train a stacked denoising autoencoder (SDAE)
rand('state',0)
sae = saesetup([784 100]);
sae.ae{1}.activation_function       = 'sigm';
sae.ae{1}.learningRate              = 1;
sae.ae{1}.inputZeroMaskedFraction   = 0.5;
opts.numepochs =   1;
opts.batchsize = 100;
sae = saetrain(sae, train_x, opts);
visualize(sae.ae{1}.W{1}(:,2:end)')

% Use the SDAE to initialize a FFNN
nn = nnsetup([784 100 10]);
nn.activation_function              = 'sigm';
nn.learningRate                     = 1;
nn.W{1} = sae.ae{1}.W{1};

% Train the FFNN
opts.numepochs =   1;
opts.batchsize = 100;
nn = nntrain(nn, train_x, train_y, opts);
[er, bad] = nntest(nn, test_x, test_y);
assert(er < 0.16, 'Too big error');

Example: Convolutional Neural Nets


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');

%% 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 = 1;

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

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

%plot mean squared error
figure; plot(cnn.rL);

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

Example: Neural Networks


function test_example_NN
load mnist_uint8;

train_x = double(train_x) / 255;
test_x  = double(test_x)  / 255;
train_y = double(train_y);
test_y  = double(test_y);

% normalize
[train_x, mu, sigma] = zscore(train_x);
test_x = normalize(test_x, mu, sigma);

%% ex1 vanilla neural net
rand('state',0)
nn = nnsetup([784 100 10]);
opts.numepochs =  1;   %  Number of full sweeps through data
opts.batchsize = 100;  %  Take a mean gradient step over this many samples
[nn, L] = nntrain(nn, train_x, train_y, opts);

[er, bad] = nntest(nn, test_x, test_y);

assert(er < 0.08, 'Too big error');

%% ex2 neural net with L2 weight decay
rand('state',0)
nn = nnsetup([784 100 10]);

nn.weightPenaltyL2 = 1e-4;  %  L2 weight decay
opts.numepochs =  1;        %  Number of full sweeps through data
opts.batchsize = 100;       %  Take a mean gradient step over this many samples

nn = nntrain(nn, train_x, train_y, opts);

[er, bad] = nntest(nn, test_x, test_y);
assert(er < 0.1, 'Too big error');


%% ex3 neural net with dropout
rand('state',0)
nn = nnsetup([784 100 10]);

nn.dropoutFraction = 0.5;   %  Dropout fraction 
opts.numepochs =  1;        %  Number of full sweeps through data
opts.batchsize = 100;       %  Take a mean gradient step over this many samples

nn = nntrain(nn, train_x, train_y, opts);

[er, bad] = nntest(nn, test_x, test_y);
assert(er < 0.1, 'Too big error');

%% ex4 neural net with sigmoid activation function
rand('state',0)
nn = nnsetup([784 100 10]);

nn.activation_function = 'sigm';    %  Sigmoid activation function
nn.learningRate = 1;                %  Sigm require a lower learning rate
opts.numepochs =  1;                %  Number of full sweeps through data
opts.batchsize = 100;               %  Take a mean gradient step over this many samples

nn = nntrain(nn, train_x, train_y, opts);

[er, bad] = nntest(nn, test_x, test_y);
assert(er < 0.1, 'Too big error');

%% ex5 plotting functionality
rand('state',0)
nn = nnsetup([784 20 10]);
opts.numepochs         = 5;            %  Number of full sweeps through data
nn.output              = 'softmax';    %  use softmax output
opts.batchsize         = 1000;         %  Take a mean gradient step over this many samples
opts.plot              = 1;            %  enable plotting

nn = nntrain(nn, train_x, train_y, opts);

[er, bad] = nntest(nn, test_x, test_y);
assert(er < 0.1, 'Too big error');

%% ex6 neural net with sigmoid activation and plotting of validation and training error
% split training data into training and validation data
vx   = train_x(1:10000,:);
tx = train_x(10001:end,:);
vy   = train_y(1:10000,:);
ty = train_y(10001:end,:);

rand('state',0)
nn                      = nnsetup([784 20 10]);     
nn.output               = 'softmax';                   %  use softmax output
opts.numepochs          = 5;                           %  Number of full sweeps through data
opts.batchsize          = 1000;                        %  Take a mean gradient step over this many samples
opts.plot               = 1;                           %  enable plotting
nn = nntrain(nn, tx, ty, opts, vx, vy);                %  nntrain takes validation set as last two arguments (optionally)

[er, bad] = nntest(nn, test_x, test_y);
assert(er < 0.1, 'Too big error');

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