jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
59 lines
1.9 KiB
Matlab
59 lines
1.9 KiB
Matlab
% Version 1.000
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%
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% Code provided by Ruslan Salakhutdinov and Geoff Hinton
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%
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% Permission is granted for anyone to copy, use, modify, or distribute this
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% program and accompanying programs and documents for any purpose, provided
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% this copyright notice is retained and prominently displayed, along with
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% a note saying that the original programs are available from our
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% web page.
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% The programs and documents are distributed without any warranty, express or
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% implied. As the programs were written for research purposes only, they have
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% not been tested to the degree that would be advisable in any important
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% application. All use of these programs is entirely at the user's own risk.
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% This program pretrains a deep autoencoder for MNIST dataset
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% You can set the maximum number of epochs for pretraining each layer
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% and you can set the architecture of the multilayer net.
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clear all
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close all
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maxepoch=50;
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numhid=500; numpen=500; numpen2=2000;
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fprintf(1,'Converting Raw files into Matlab format \n');
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converter;
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fprintf(1,'Pretraining a deep autoencoder. \n');
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fprintf(1,'The Science paper used 50 epochs. This uses %3i \n', maxepoch);
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makebatches;
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[numcases numdims numbatches]=size(batchdata);
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fprintf(1,'Pretraining Layer 1 with RBM: %d-%d \n',numdims,numhid);
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restart=1;
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rbm;
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hidrecbiases=hidbiases;
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save mnistvhclassify vishid hidrecbiases visbiases;
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fprintf(1,'\nPretraining Layer 2 with RBM: %d-%d \n',numhid,numpen);
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batchdata=batchposhidprobs;
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numhid=numpen;
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restart=1;
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rbm;
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hidpen=vishid; penrecbiases=hidbiases; hidgenbiases=visbiases;
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save mnisthpclassify hidpen penrecbiases hidgenbiases;
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fprintf(1,'\nPretraining Layer 3 with RBM: %d-%d \n',numpen,numpen2);
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batchdata=batchposhidprobs;
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numhid=numpen2;
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restart=1;
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rbm;
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hidpen2=vishid; penrecbiases2=hidbiases; hidgenbiases2=visbiases;
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save mnisthp2classify hidpen2 penrecbiases2 hidgenbiases2;
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backpropclassify;
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