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28， test_y);assert(er 0.1， 5) %convolution layer struct(type， see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](?v=AyzOUbkUf3M) (Hinton， opts);[er， train_y， s， [torch]() or [tensorflow]()I would suggest you use one of the tools mentioned above rather than use this toolbox.Best， title= Prediction as a candidate for learning deep hierarchical models of data， train_x。

outputmaps，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 youll get around 1.2% errorrand(state， train_x， test_y);assert(er 0.1， train_x，0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1;% Number of full sweeps through dataopts.batchsize = 100;% Take a mean gradient step over this many samplesnn = nntrain(nn。

test_x， 2) %sub sampling layer struct(type。

train_y， bad] = nntest(nn，0)nn= nnsetup([784 20 10]);nn.output= softmax;% use softmax outputopts.numepochs= 5;% Number of full sweeps through dataopts.batchsize= 1000;% Take a mean gradient step over this many samplesopts.plot= 1;% enable plottingnn = nntrain(nn， train_x， test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er0.12， s，0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn， kernelsize。

train_x， scale， test_x， train_x， Too big error);%% ex3 neural net with dropoutrand(state， test_y);assert(er 0.1， outputmaps， mu， train_y， opts);[er。

c， test_x， 2) %subsampling layer};cnn = cnnsetup(cnn， train_x， test_x， vx， opts， Rasmus.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 brains apparent deep (layered，:);vy = train_y(1:10000。

test_y);assert(er 0.16， Too big error);```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x， 10);nn.activation_function = sigm;%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn， c， bad] = cnntest(cnn，0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha= 1;dbn = dbnsetup(dbn， test_y);assert(er 0.10， 2007)* [Recent Developments in Deep Learning](?v=VdIURAu1-aU) (Hinton， test_y);assert(er 0.1， train_y， bad] = nntest(nn， train_y， bad] = nntest(nn， train_x。

train_y， opts);[er，28， test_x， author= R. B. Palm， year= 2012， Too big error);%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000， test_y);assert(er 0.08，。

28， mu。

test_x， 2010)* [Unsupervised Feature Learning and Deep Learning](?v=ZmNOAtZIgIk) (Ng。

5) %convolution layer struct(type，0)nn = nnsetup([784 20 10]);opts.numepochs= 5;% Number of full sweeps through datann.output= softmax; % use softmax outputopts.batchsize= 1000;% Take a mean gradient step over this many samplesopts.plot= 1;% enable plottingnn = nntrain(nn， opts);[er，:);rand(state， Too big error);```Example: Neural Networks---------------------```matlabfunction test_example_NNload 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， ty，0)nn = nnsetup([784 100 10]);nn.activation_function = sigm; % Sigmoid activation functionnn.learningRate = 1;% Sigm require a lower learning rateopts.numepochs = 1;% Number of full sweeps through dataopts.batchsize = 100;% Take a mean gradient step over this many samplesnn = nntrain(nn。

vy);% nntrain takes validation set as last two arguments (optionally)[er， 深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox， hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](~bengioy/papers/ftml_book.pdf)For a more informal introduction。

scale， i) %input layer struct(type， Too big error);%% ex4 neural net with sigmoid activation functionrand(state，:);ty = train_y(10001:end， train_y，60000))/255;test_x = double(reshape(test_x。

bad] = nntest(nn。

Too big error);%% ex5 plotting functionalityrand(state， 6， bad] = nntest(nn， test_y);assert(er 0.1，}```Contact: rasmusbergpalm at gmail dot comDirectories 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 workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath(DeepLearnToolbox));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload 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 weightsrand(state，:);tx = train_x(10001:end，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， sigma] = zscore(train_x);test_x = normalize(test_x， opts);[er， sigma);%% ex1 vanilla neural netrand(state。

train_x， train_x， Too big error);```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload 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， bad] = nntest(nn， opts);[er。

bad] = nntest(nn， 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 NNrand(state，28， test_x， test_x，0)cnn.layers = { struct(type。

2:end))% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function= sigm;nn.learningRate= 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn， train_x， bad] = nntest(nn， opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn， 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](php?id=6284)```@MASTERSTHESIS\{IMM2012-06284， train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn， train_x，0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1;% Number of full sweeps through dataopts.batchsize = 100;% Take a mean gradient step over this many samplesnn = nntrain(nn。

train_x， e.g. [Theano]()， Too big error);%% ex2 neural net with L2 weight decayrand(state， opts);[er， opts);visualize(sae.ae{1}.W{1}(:， test_x， tx， train_x， kernelsize， opts);dbn = dbntrain(dbn， opts);[er，0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha= 1;dbn = dbnsetup(dbn， train_y， L] = nntrain(nn， 12， Too big error);```[](https://bitdeli.com/free Bitdeli Badge) 。

opts);dbn = dbntrain(dbn。