39 lines
1.1 KiB
Mathematica
39 lines
1.1 KiB
Mathematica
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function cae = caetrain(cae, x, opts)
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n = cae.inputkernel(1);
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cae.rL = [];
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for m = 1 : opts.rounds
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tic;
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disp([num2str(m) '/' num2str(opts.rounds) ' rounds']);
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i1 = randi(numel(x));
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l = randi(size(x{i1}{1},1) - opts.batchsize - n + 1);
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x1{1} = double(x{i1}{1}(l : l + opts.batchsize - 1, :, :)) / 255;
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if n == 1 %Auto Encoder
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x2{1} = x1{1};
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else %Predictive Encoder
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x2{1} = double(x{i1}{1}(l + n : l + n + opts.batchsize - 1, :, :)) / 255;
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end
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% Add noise to input, for denoising stacked autoenoder
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x1{1} = x1{1} .* (rand(size(x1{1})) > cae.noise);
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cae = caeup(cae, x1);
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cae = caedown(cae);
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cae = caebp(cae, x2);
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cae = caesdlm(cae, opts, m);
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% caenumgradcheck(cae,x1,x2);
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cae = caeapplygrads(cae);
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if m == 1
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cae.rL(1) = cae.L;
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end
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% cae.rL(m + 1) = 0.99 * cae.rL(m) + 0.01 * cae.L;
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cae.rL(m + 1) = cae.L;
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% if cae.sv < 1e-10
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% disp('Converged');
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% break;
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% end
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toc;
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end
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end
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