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 (Palm, 2012)
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
- Download.
- addpath(genpath('DeepLearnToolbox'));
clear all; close all; clc;
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
dbn.sizes = [100];
opts.numepochs = 5;
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', 1); % Visualize the RMB weights
%% ex2 train a 100-100-100 DBN and use its weights to initialize a NN
dbn.sizes = [100 100 100];
opts.numepochs = 5;
opts.batchsize = 100;
opts.momentum = 0;
opts.alpha = 1;
dbn = dbnsetup(dbn, train_x, opts);
dbn = dbntrain(dbn, train_x, opts);
nn = dbnunfoldtonn(dbn, 10);
nn.alpha = 1;
nn.lambda = 1e-4;
opts.numepochs = 10;
opts.batchsize = 100;
nn = nntrain(nn, train_x, train_y, opts);
[er, bad] = nntest(nn, test_x, test_y);
disp([num2str(er * 100) '% error']);
figure; visualize(nn.W{1}', 1);
clear all; close all; clc;
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)
sae = saesetup([784 100]);
sae.ae{1}.alpha = 0.5;
sae.ae{1}.inputZeroMaskedFraction = 0.5;
opts.numepochs = 5;
opts.batchsize = 100;
sae = saetrain(sae, train_x, opts);
figure; visualize(sae.ae{1}.W{1}', 1) % Visualize the weights
% use the SDAE to initialize a FFNN
nn = nnsetup([784 100 10]);
nn.W{1} = sae.ae{1}.W{1};
nn.b{1} = sae.ae{1}.b{1};
nn.lambda = 1e-5; % L2 weight decay
nn.alpha = 1e-0; % Learning rate
opts.numepochs = 5;
opts.batchsize = 100;
nn = nntrain(nn, train_x, train_y, opts);
[er, bad] = nntest(nn, test_x, test_y);
disp([num2str(er * 100) '% error']);
figure; visualize(nn.W{1}', 1) % Visualize the weights
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']);clear all; close all; clc; dbstop if error
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: Using 100 hidden units, learn to recognize handwritten digits
nn = nnsetup([784 100 10]);
nn.lambda = 1e-5; % L2 weight decay
nn.alpha = 1e-0; % Learning rate
opts.numepochs = 10; % 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);
disp([num2str(er * 100) '% error']);
figure; visualize(nn.W{1}', 1) % Visualize the weights
%% ex2: Using 100-50 hidden units, learn to recognize handwritten digits
nn = nnsetup([784 100 50 10]);
nn.lambda = 1e-5; % L2 weight decay
nn.alpha = 1e-0; % Learning rate
opts.numepochs = 10; % 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);
disp([num2str(er * 100) '% error']);
figure; visualize(nn.W{1}', 1) %Visualize the weights
%% ex3 using 800-800 hidden units w. dropout
nn = nnsetup([784 800 800 10]);
nn.dropoutFraction = 0.5;
nn.alpha = 1e1; % Learning rate
opts.numepochs = 10; % 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);
disp([num2str(er * 100) '% error']);
figure; visualize(nn.W{1}', 1) %Visualize the weights