DBN爬坑记之RBM

epsilonw      = 0.01;   % 权值学习效率
epsilonvb     = 0.01;   % 可见层偏置
epsilonhb     = 0.01;   % 隐含层偏置
weightcost  = 0.001;   
initialmomentum  = 0.5;
finalmomentum    = 0.9;

[numcases numdims numbatches]=size(batchdata);

% 初始化权值和偏置
  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);
  batchposhidprobs=zeros(numcases,numhid,numbatches);


for epoch = epoch:maxepoch, %重建次数
 errsum=0;
 for batch = 1:numbatches,  %对数据进行分批在处理

  %从可见层向隐含层运算
  data = batchdata(:,:,batch);
  poshidprobs = 1./(1 + exp(-data*vishid - repmat(hidbiases,numcases,1)));    %生成隐含成的概率
  batchposhidprobs(:,:,batch)=poshidprobs;
  posprods    = data' * poshidprobs;
  poshidact   = sum(poshidprobs);
  posvisact = sum(data);

  
  poshidstates = poshidprobs > rand(numcases,numhid);   %可见层到隐含层的激活状态

   %从隐含层到可见层的解码过程，调整权值
  negdata = 1./(1 + exp(-poshidstates*vishid' - repmat(visbiases,numcases,1)));
  neghidprobs = 1./(1 + exp(-negdata*vishid - repmat(hidbiases,numcases,1)));    %生成可见层的概率  
  negprods  = negdata'*neghidprobs;
  neghidact = sum(neghidprobs);
  negvisact = sum(negdata); 

  err= sum(sum( (data-negdata).^2 )); %计算误差
  errsum = err + errsum;

  %调整学习效率
   if epoch>5,
     momentum=finalmomentum;
   else
     momentum=initialmomentum;
   end;

%更新权值和偏置
    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;
end;