48 lines
2.1 KiB
Matlab
48 lines
2.1 KiB
Matlab
function net = cnnff(net, x)
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n = numel(net.layers);
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net.layers{1}.a{1} = x;
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inputmaps = 1;
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for l = 2 : n % for each layer
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if strcmp(net.layers{l}.type, 'c')
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% !!below can probably be handled by insane matrix operations
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for j = 1 : net.layers{l}.outputmaps % for each output map
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% create temp output map
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if(size(x,3) > 1)
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z = zeros(size(net.layers{l - 1}.a{1}) - [net.layers{l}.kernelsize - 1 net.layers{l}.kernelsize - 1 0]);
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else
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z = zeros(size(net.layers{l - 1}.a{1}) - [net.layers{l}.kernelsize - 1 net.layers{l}.kernelsize - 1]);
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end
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for i = 1 : inputmaps % for each input map
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% convolve with corresponding kernel and add to temp output map
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z = z + convn(net.layers{l - 1}.a{i}, net.layers{l}.k{i}{j}, 'valid');
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end
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% add bias, pass through nonlinearity
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net.layers{l}.a{j} = sigm(z + net.layers{l}.b{j});
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end
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% set number of input maps to this layers number of outputmaps
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inputmaps = net.layers{l}.outputmaps;
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elseif strcmp(net.layers{l}.type, 's')
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% downsample
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for j = 1 : inputmaps
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z = convn(net.layers{l - 1}.a{j}, ones(net.layers{l}.scale) / (net.layers{l}.scale ^ 2), 'valid'); % !! replace with variable
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net.layers{l}.a{j} = z(1 : net.layers{l}.scale : end, 1 : net.layers{l}.scale : end, :);
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end
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end
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end
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% concatenate all end layer feature maps into vector
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net.fv = [];
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for j = 1 : numel(net.layers{n}.a)
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sa = size(net.layers{n}.a{j});
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if(numel(sa) == 3)
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net.fv = [net.fv; reshape(net.layers{n}.a{j}, sa(1) * sa(2), sa(3))];
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else
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net.fv = [net.fv; reshape(net.layers{n}.a{j}, sa(1) * sa(2), 1)];
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end
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end
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% feedforward into output perceptrons
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net.o = sigm(net.ffW * net.fv + repmat(net.ffb, 1, size(net.fv, 2)));
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end
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