matlab - (RBM)的实值输入深度置信网络存在的问题

我正在尝试使用MNIST数字Reducing the dimensionality of data with neural networks的改编版本来重新生成olivetti face dataset中自动编码matlab code的结果，但是遇到了一些困难。看来，无论我对历时，速率或动量进行多少微调，堆叠的RBM都将进入微调阶段，并且会产生大量误差，因此在微调阶段无法做出很大的改进。我还在另一个实值数据集上遇到了类似的问题。

对于第一层，我使用的是学习率较低的RBM(如本文所述)，并且

negdata = poshidstates*vishid' + repmat(visbiases,numcases,1);

我完全有信心按照supporting material中的说明进行操作，但是无法实现正确的错误。

我有什么想念的吗？请参阅下面用于实值可见单位RBM的代码以及整个深度培训。其余代码可以在here中找到。

rbmvislinear.m:

epsilonw      = 0.001; % Learning rate for weights
epsilonvb     = 0.001; % Learning rate for biases of visible units
epsilonhb     = 0.001; % Learning rate for biases of hidden units
weightcost  = 0.0002;
initialmomentum  = 0.5;
finalmomentum    = 0.9;


[numcases numdims numbatches]=size(batchdata);

if restart ==1,
  restart=0;
  epoch=1;

% Initializing symmetric weights and biases.
  vishid     = 0.1*randn(numdims, numhid);
  hidbiases  = zeros(1,numhid);
  visbiases  = zeros(1,numdims);


  poshidprobs = zeros(numcases,numhid);
  neghidprobs = zeros(numcases,numhid);
  posprods    = zeros(numdims,numhid);
  negprods    = zeros(numdims,numhid);
  vishidinc  = zeros(numdims,numhid);
  hidbiasinc = zeros(1,numhid);
  visbiasinc = zeros(1,numdims);
  sigmainc = zeros(1,numhid);
  batchposhidprobs=zeros(numcases,numhid,numbatches);
end

for epoch = epoch:maxepoch,
 fprintf(1,'epoch %d\r',epoch);
 errsum=0;
 for batch = 1:numbatches,
 if (mod(batch,100)==0)
     fprintf(1,' %d ',batch);
 end


%%%%%%%%% START POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  data = batchdata(:,:,batch);
  poshidprobs = 1./(1 + exp(-data*vishid - repmat(hidbiases,numcases,1)));
  batchposhidprobs(:,:,batch)=poshidprobs;
  posprods    = data' * poshidprobs;
  poshidact   = sum(poshidprobs);
  posvisact = sum(data);

%%%%%%%%% END OF POSITIVE PHASE  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  poshidstates = poshidprobs > rand(numcases,numhid);

%%%%%%%%% START NEGATIVE PHASE  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  negdata = poshidstates*vishid' + repmat(visbiases,numcases,1);% + randn(numcases,numdims) if not using mean
  neghidprobs = 1./(1 + exp(-negdata*vishid - repmat(hidbiases,numcases,1)));
  negprods  = negdata'*neghidprobs;
  neghidact = sum(neghidprobs);
  negvisact = sum(negdata);

%%%%%%%%% END OF NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  err= sum(sum( (data-negdata).^2 ));
  errsum = err + errsum;

   if epoch>5,
     momentum=finalmomentum;
   else
     momentum=initialmomentum;
   end;

%%%%%%%%% UPDATE WEIGHTS AND BIASES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    vishidinc = momentum*vishidinc + ...
                epsilonw*( (posprods-negprods)/numcases - weightcost*vishid);
    visbiasinc = momentum*visbiasinc + (epsilonvb/numcases)*(posvisact-negvisact);
    hidbiasinc = momentum*hidbiasinc + (epsilonhb/numcases)*(poshidact-neghidact);

    vishid = vishid + vishidinc;
    visbiases = visbiases + visbiasinc;
    hidbiases = hidbiases + hidbiasinc;

%%%%%%%%%%%%%%%% END OF UPDATES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

 end
fprintf(1, '\nepoch %4i error %f \n', epoch, errsum);

end

dofacedeepauto.m:

clear all
close all

maxepoch=200; %In the Science paper we use maxepoch=50, but it works just fine.
numhid=2000; numpen=1000; numpen2=500; numopen=30;

fprintf(1,'Pretraining a deep autoencoder. \n');
fprintf(1,'The Science paper used 50 epochs. This uses %3i \n', maxepoch);

load fdata
%makeFaceData;

[numcases numdims numbatches]=size(batchdata);

fprintf(1,'Pretraining Layer 1 with RBM: %d-%d \n',numdims,numhid);
restart=1;
rbmvislinear;
hidrecbiases=hidbiases;
save mnistvh vishid hidrecbiases visbiases;

maxepoch=50;
fprintf(1,'\nPretraining Layer 2 with RBM: %d-%d \n',numhid,numpen);
batchdata=batchposhidprobs;
numhid=numpen;
restart=1;
rbm;
hidpen=vishid; penrecbiases=hidbiases; hidgenbiases=visbiases;
save mnisthp hidpen penrecbiases hidgenbiases;

fprintf(1,'\nPretraining Layer 3 with RBM: %d-%d \n',numpen,numpen2);
batchdata=batchposhidprobs;
numhid=numpen2;
restart=1;
rbm;
hidpen2=vishid; penrecbiases2=hidbiases; hidgenbiases2=visbiases;
save mnisthp2 hidpen2 penrecbiases2 hidgenbiases2;

fprintf(1,'\nPretraining Layer 4 with RBM: %d-%d \n',numpen2,numopen);
batchdata=batchposhidprobs;
numhid=numopen;
restart=1;
rbmhidlinear;
hidtop=vishid; toprecbiases=hidbiases; topgenbiases=visbiases;
save mnistpo hidtop toprecbiases topgenbiases;

backpropface;

谢谢你的时间

最佳答案

愚蠢的我，我忘了更改反向传播微调脚本(backprop.m)。必须将输出层(重构面的位置)更改为实值单位。 IE。

dataout = w7probs*w8;

关于matlab - (RBM)的实值输入深度置信网络存在的问题，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/3048170/