资源简介

代码片段和文件信息
% Version 1.000
%
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
%
% Permission is granted for anyone to copy use modify or distribute this
% program and accompanying programs and documents for any purpose provided
% this copyright notice is retained and prominently displayed along with
% a note saying that the original programs are available from our
% web page.
% The programs and documents are distributed without any warranty express or
% implied. As the programs were written for research purposes only they have
% not been tested to the degree that would be advisable in any important
% application. All use of these programs is entirely at the user‘s own risk.
% This program fine-tunes an autoencoder with backpropagation.
% Weights of the autoencoder are going to be saved in mnist_weights.mat
% and trainig and test reconstruction errors in mnist_error.mat
% You can also set maxepoch default value is 200 as in our paper.
maxepoch=200;
fprintf(1‘\nTraining discriminative model on MNIST by minimizing cross entropy error. \n‘);
fprintf(1‘60 batches of 1000 cases each. \n‘);
load mnistvhclassify
load mnisthpclassify
load mnisthp2classify
makebatches;
[numcases numdims numbatches]=size(batchdata);
N=numcases;
%%%% PREINITIALIZE WEIGHTS OF THE DISCRIMINATIVE MODEL%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
w1=[vishid; hidrecbiases];
w2=[hidpen; penrecbiases];
w3=[hidpen2; penrecbiases2];
w_class = 0.1*randn(size(w32)+110);
%%%%%%%%%% END OF PREINITIALIZATIO OF WEIGHTS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
l1=size(w11)-1;
l2=size(w21)-1;
l3=size(w31)-1;
l4=size(w_class1)-1;
l5=10;
test_err=[];
train_err=[];
for epoch = 1:maxepoch
%%%%%%%%%%%%%%%%%%%% COMPUTE TRAINING MISCLASSIFICATION ERROR %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
err=0;
err_cr=0;
counter=0;
[numcases numdims numbatches]=size(batchdata);
N=numcases;
for batch = 1:numbatches
data = [batchdata(::batch)];
target = [batchtargets(::batch)];
data = [data ones(N1)];
w1probs = 1./(1 + exp(-data*w1)); w1probs = [w1probs ones(N1)];
w2probs = 1./(1 + exp(-w1probs*w2)); w2probs = [w2probs ones(N1)];
w3probs = 1./(1 + exp(-w2probs*w3)); w3probs = [w3probs ones(N1)];
targetout = exp(w3probs*w_class);%?
targetout = targetout./repmat(sum(targetout2)110);%?
[I J]=max(targetout[]2);
[I1 J1]=max(target[]2);
counter=counter+length(find(J==J1));
err_cr = err_cr- sum(sum( target(:1:end).*log(targetout))) ;%?
end
train_err(epoch)=(numcases*numbatches-counter);
train_crerr(epoch)=err_cr/numbatches;
%%%%%%%%%%%%%% END OF COMPUTING TRAINING MISCLASSIFICATION ERROR %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%% COMPUTE TEST MISCLASSIFICATION ERROR %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
err=0;
err_cr=0;
counter=0;
[testnumcases testnumdims testnumbatches]=size(testbatchdata);
N=testnumcases;
for batch = 1:testnumbatches
data = [testbatchdata(::batch)];
target = [testbatchtargets(::batch)];
data = [data ones(N1)
属性 大小 日期 时间 名称
----------- --------- ---------- ----- ----
目录 0 2012-12-04 14:37 dbn\
文件 5483 2012-05-07 09:30 dbn\backpropclassify.m
文件 904 2012-05-18 19:21 dbn\bp.asv
文件 935 2012-05-21 12:36 dbn\bp.m
文件 2001 2012-05-10 21:07 dbn\dbnFit.m
文件 495 2010-10-31 13:01 dbn\dbnPredict.m
文件 1799 2012-04-27 15:58 dbn\examplecode.m
文件 409 2010-10-31 13:01 dbn\interweave.m
文件 65 2010-10-31 13:01 dbn\logistic.m
文件 977 2010-10-31 13:01 dbn\nunique.m
文件 690 2010-10-31 13:01 dbn\prepareArgs.m
文件 3819 2010-10-31 13:01 dbn\process_options.m
文件 5293 2012-05-14 16:48 dbn\rbmBB.m
文件 6149 2012-05-14 16:48 dbn\rbmFit.m
文件 3806 2012-05-14 16:47 dbn\rbmGB.m
文件 358 2010-10-31 13:01 dbn\rbmHtoV.m
文件 877 2010-10-31 13:22 dbn\rbmPredict.m
文件 355 2010-10-31 13:01 dbn\rbmVtoH.m
文件 286 2010-10-31 13:01 dbn\softmaxPmtk.m
文件 371 2010-10-31 13:01 dbn\softmax_sample.m
文件 976 2012-05-18 10:21 dbn\train.asv
文件 913 2012-05-18 14:46 dbn\train.m
文件 3205929 2011-11-22 08:33 dbn\traindata.mat
文件 750 2010-10-31 13:01 dbn\visualize.m
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