jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
76 lines
1.6 KiB
Plaintext
76 lines
1.6 KiB
Plaintext
function rbm_eval(nh, batch, numEpochs)
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[nv batchsize] = size(batch);
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numGibbs = 1;
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muW = 0.1;
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muBV = 0.1;
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muBH = 0.1;
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momentum = 0.5;
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momentum_final = 0.9;
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weightDecay = 0.000;
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w = 0.1*randn(nv, nh);
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bv = zeros(nv, 1);
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bh = zeros(1, nh);
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v = zeros(batchsize, nv);
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h = zeros(batchsize, nh);
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diffW = zeros(nv, nh);
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diffBV = zeros(1, nv);
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diffBH = zeros(1, nh);
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diffErr = zeros(nv, batchsize);
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dW = zeros(nv, nh);
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dBV = zeros(nv, 1);
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dBH = zeros(nh, 1);
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for k=1:numEpochs
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errsum = 0;
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% Positive phase
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v = batch;
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h = 1./(1 + exp(-(v' * w + repmat(bh, batchsize, 1))));
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% Update difference
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diffW = v * h;
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diffBV = sum(v, 2);
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diffBH = sum(h')';
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diffErr = v;
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% Negative phase
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for n=1:numGibbs
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% h is sampled
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h = h > rand(batchsize, nh);
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% reconstruct
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v = 1./(1 + exp(-(h *w'+ repmat(bv, batchsize, 1))));
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% Get new h probabilities from reconstruction
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h = 1./(1 + exp(-(v' * w + repmat(bh, batchsize, 1))))';
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end
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% Update difference
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diffW = diffW - v * h';
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diffBV = diffBV - sum(v, 2);
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diffBH = diffBH - sum(h');
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diffErr = diffErr - v;
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err = sum(sum((diffErr).^2));
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errsum = errsum + err;
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if k > 5,
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momentum = momentum_final;
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end;
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% Update parameter gradients
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dW = momentum*dW + muW*(diffW/batchsize - weightDecay*w);
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dBV = momentum*dBV + muBV*diffBV/batchsize;
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dBH = momentum*dBH + muBH*diffBH/batchsize;
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% Update parameters
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w = w + dW;
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bv = bv + dBV;
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bh = bh + dBH;
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end
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v = v
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err = err
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errsum = errsum |