57 lines
2.1 KiB
Mathematica
57 lines
2.1 KiB
Mathematica
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function net = cnnbp(net, y)
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n = numel(net.layers);
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% error
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net.e = net.o - y;
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% loss function
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net.L = 1/2* sum(net.e(:) .^ 2) / size(net.e, 2);
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%% backprop deltas
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net.od = net.e .* (net.o .* (1 - net.o)); % output delta
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net.fvd = (net.ffW' * net.od); % feature vector delta
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if strcmp(net.layers{n}.type, 'c') % only conv layers has sigm function
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net.fvd = net.fvd .* (net.fv .* (1 - net.fv));
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end
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% reshape feature vector deltas into output map style
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sa = size(net.layers{n}.a{1});
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fvnum = sa(1) * sa(2);
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for j = 1 : numel(net.layers{n}.a)
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net.layers{n}.d{j} = reshape(net.fvd(((j - 1) * fvnum + 1) : j * fvnum, :), sa(1), sa(2), sa(3));
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end
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for l = (n - 1) : -1 : 1
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if strcmp(net.layers{l}.type, 'c')
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for j = 1 : numel(net.layers{l}.a)
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net.layers{l}.d{j} = net.layers{l}.a{j} .* (1 - net.layers{l}.a{j}) .* (expand(net.layers{l + 1}.d{j}, [net.layers{l + 1}.scale net.layers{l + 1}.scale 1]) / net.layers{l + 1}.scale ^ 2);
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end
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elseif strcmp(net.layers{l}.type, 's')
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for i = 1 : numel(net.layers{l}.a)
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z = zeros(size(net.layers{l}.a{1}));
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for j = 1 : numel(net.layers{l + 1}.a)
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z = z + convn(net.layers{l + 1}.d{j}, rot180(net.layers{l + 1}.k{i}{j}), 'full');
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end
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net.layers{l}.d{i} = z;
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end
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end
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end
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%% calc gradients
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for l = 2 : n
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if strcmp(net.layers{l}.type, 'c')
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for j = 1 : numel(net.layers{l}.a)
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for i = 1 : numel(net.layers{l - 1}.a)
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net.layers{l}.dk{i}{j} = convn(flipall(net.layers{l - 1}.a{i}), net.layers{l}.d{j}, 'valid') / size(net.layers{l}.d{j}, 3);
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end
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net.layers{l}.db{j} = sum(net.layers{l}.d{j}(:)) / size(net.layers{l}.d{j}, 3);
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end
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end
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end
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net.dffW = net.od * (net.fv)' / size(net.od, 2);
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net.dffb = mean(net.od, 2);
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function X = rot180(X)
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X = flipdim(flipdim(X, 1), 2);
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end
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end
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