28。

:);rand(#39;state#39;， test_y);assert(er 0.1， opts);[er， bad] = nntest(nn， L] = nntrain(nn， test_y);assert(er 0.16， sigma);%% ex1 vanilla neural netrand(#39;state#39;， #39;scale#39;， author= R. B. Palm，10000))/255;train_y = double(train_y#39;);test_y = double(test_y#39;);%% 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#39;ll get around 1.2% errorrand(#39;state#39;，0)nn = nnsetup([784 20 10]);opts.numepochs= 5;% Number of full sweeps through datann.output= #39;softmax#39;; % use softmax outputopts.batchsize= 1000;% Take a mean gradient step over this many samplesopts.plot= 1;% enable plottingnn = nntrain(nn， 12，0)nn= nnsetup([784 20 10]);nn.output= #39;softmax#39;;% 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， 2) %sub sampling layer struct(#39;type#39;， ty，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， opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn， #39;scale#39;， train_x， train_y， 2) %subsampling layer};cnn = cnnsetup(cnn， opts);[er， mu， train_x， #39;s#39;， test_x， bad] = nntest(nn， 6。

title= Prediction as a candidate for learning deep hierarchical models of data， bad] = nntest(nn。

#39;outputmaps#39;， 5) %convolution layer struct(#39;type#39;， 深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox，:);vy = train_y(1:10000。

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， bad] = nntest(nn， train_y，0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha= 1;dbn = dbnsetup(dbn， #39;Too big error#39;);%% ex4 neural net with sigmoid activation functionrand(#39;state#39;，0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha= 1;dbn = dbnsetup(dbn，0)cnn.layers = { struct(#39;type#39;， opts);[er， train_y， 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 brain#39;s apparent deep (layered， test_x， #39;c#39;， test_x， #39;Too big error#39;);```[](https://bitdeli.com/free Bitdeli Badge) ， test_y);assert(er 0.1， train_y， [torch]() or [tensorflow]()I would suggest you use one of the tools mentioned above rather than use this toolbox.Best， train_x， train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn， #39;i#39;) %input layer struct(#39;type#39;， #39;Too big error#39;);%% ex3 neural net with dropoutrand(#39;state#39;， e.g. [Theano]()， opts);dbn = dbntrain(dbn。

#39;s#39;。

0)sae = saesetup([784 100]);sae.ae{1}.activation_function= #39;sigm#39;;sae.ae{1}.learningRate= 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae， #39;kernelsize#39;， test_y);assert(er 0.1。

vx。

vy);% nntrain takes validation set as last two arguments (optionally)[er， opts);dbn = dbntrain(dbn， 5) %convolution layer struct(#39;type#39;， bad] = nntest(nn。

train_x， see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](?v=AyzOUbkUf3M) (Hinton。

#39;outputmaps#39;， test_y);assert(er 0.08， train_y， train_x。

bad] = nntest(nn， train_x， mu，60000))/255;test_x = double(reshape(test_x#39;，0)nn = nnsetup([784 100 10]);nn.activation_function = #39;sigm#39;; % 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， tx， #39;Too big error#39;);```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，28， #39;kernelsize#39;。

train_x， #39;Too big error#39;);```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(#39;state#39;， train_x， 10);nn.activation_function = #39;sigm#39;;%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn， test_x， train_x， bad] = cnntest(cnn， #39;Too big error#39;);%% 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，}```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(#39;DeepLearnToolbox#39;));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(#39;state#39;。

opts);[er。

train_x， test_x， sigma] = zscore(train_x);test_x = normalize(test_x，2:end)#39;)% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function= #39;sigm#39;;nn.learningRate= 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn， 2010)* [Unsupervised Feature Learning and Deep Learning](?v=ZmNOAtZIgIk) (Ng， test_y);assert(er 0.1，28， opts);[er。

2007)* [Recent Developments in Deep Learning](?v=VdIURAu1-aU) (Hinton， bad] = nntest(nn， test_x， train_y， 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， #39;Too big error#39;);%% ex5 plotting functionalityrand(#39;state#39;， train_x， opts， train_y， test_x， #39;Too big error#39;);%% ex2 neural net with L2 weight decayrand(#39;state#39;。

train_x， 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](?id=6284)```@MASTERSTHESIS\{IMM2012-06284， opts);[er， #39;c#39;， test_y);assert(er 0.10， test_x， test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er0.12，:);tx = train_x(10001:end， train_y， opts);figure; visualize(dbn.rbm{1}.W#39;); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand(#39;state#39;，28，:);ty = train_y(10001:end， year= 2012。

#39;Too big error#39;);```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x#39;， opts);[er，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， test_x， bad] = nntest(nn， opts);[er， train_x，。

opts);visualize(sae.ae{1}.W{1}(:。

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