DBN.rar_dbn 分类_deep classifier_immediatelyeyg_分类器_深度信念

function rbm = rbmtrain(rbm, x, opts) assert(isfloat(x), 'x must be a float'); assert(all(x(:)>=0) && all(x(:)<=1), 'all data in x must be in [0:1]'); m = size(x, 1); numbatches = m / opts.batchsize; assert(rem(numbatches, 1) == 0, 'numbatches not integer'); for i = 1 : opts.numepochs kk = randperm(m); err = 0; for l = 1 : numbatches batch = x(kk((l - 1) * opts.batchsize + 1 : l * opts.batchsize), :); v1 = batch; h1 = sigmrnd(repmat(rbm.c', opts.batchsize, 1) + v1 * rbm.W'); v2 = sigmrnd(repmat(rbm.b', opts.batchsize, 1) + h1 * rbm.W); h2 = sigm(repmat(rbm.c', opts.batchsize, 1) + v2 * rbm.W'); c1 = h1' * v1; c2 = h2' * v2; rbm.vW = rbm.momentum * rbm.vW + rbm.alpha * (c1 - c2) / opts.batchsize; rbm.vb = rbm.momentum * rbm.vb + rbm.alpha * sum(v1 - v2)' / opts.batchsize; rbm.vc = rbm.momentum * rbm.vc + rbm.alpha * sum(h1 - h2)' / opts.batchsize; rbm.W = rbm.W + rbm.vW; rbm.b = rbm.b + rbm.vb; rbm.c = rbm.c + rbm.vc; err = err + sum(sum((v1 - v2) .^ 2)) / opts.batchsize; end disp(['epoch ' num2str(i) '/' num2str(opts.numepochs) '. Average reconstruction error is: ' num2str(err / numbatches)]); end end