80 lines
3.3 KiB
Mathematica
80 lines
3.3 KiB
Mathematica
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function cnnnumgradcheck(net, x, y)
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epsilon = 1e-4;
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er = 1e-8;
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n = numel(net.layers);
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for j = 1 : numel(net.ffb)
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net_m = net; net_p = net;
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net_p.ffb(j) = net_m.ffb(j) + epsilon;
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net_m.ffb(j) = net_m.ffb(j) - epsilon;
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net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y);
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net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y);
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d = (net_p.L - net_m.L) / (2 * epsilon);
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e = abs(d - net.dffb(j));
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if e > er
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error('numerical gradient checking failed');
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end
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end
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for i = 1 : size(net.ffW, 1)
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for u = 1 : size(net.ffW, 2)
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net_m = net; net_p = net;
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net_p.ffW(i, u) = net_m.ffW(i, u) + epsilon;
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net_m.ffW(i, u) = net_m.ffW(i, u) - epsilon;
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net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y);
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net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y);
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d = (net_p.L - net_m.L) / (2 * epsilon);
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e = abs(d - net.dffW(i, u));
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if e > er
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error('numerical gradient checking failed');
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end
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end
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end
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for l = n : -1 : 2
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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_m = net; net_p = net;
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net_p.layers{l}.b{j} = net_m.layers{l}.b{j} + epsilon;
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net_m.layers{l}.b{j} = net_m.layers{l}.b{j} - epsilon;
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net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y);
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net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y);
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d = (net_p.L - net_m.L) / (2 * epsilon);
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e = abs(d - net.layers{l}.db{j});
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if e > er
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error('numerical gradient checking failed');
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end
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for i = 1 : numel(net.layers{l - 1}.a)
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for u = 1 : size(net.layers{l}.k{i}{j}, 1)
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for v = 1 : size(net.layers{l}.k{i}{j}, 2)
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net_m = net; net_p = net;
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net_p.layers{l}.k{i}{j}(u, v) = net_p.layers{l}.k{i}{j}(u, v) + epsilon;
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net_m.layers{l}.k{i}{j}(u, v) = net_m.layers{l}.k{i}{j}(u, v) - epsilon;
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net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y);
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net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y);
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d = (net_p.L - net_m.L) / (2 * epsilon);
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e = abs(d - net.layers{l}.dk{i}{j}(u, v));
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if e > er
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error('numerical gradient checking failed');
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end
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end
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end
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end
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end
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elseif strcmp(net.layers{l}.type, 's')
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% for j = 1 : numel(net.layers{l}.a)
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% net_m = net; net_p = net;
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% net_p.layers{l}.b{j} = net_m.layers{l}.b{j} + epsilon;
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% net_m.layers{l}.b{j} = net_m.layers{l}.b{j} - epsilon;
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% net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y);
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% net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y);
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% d = (net_p.L - net_m.L) / (2 * epsilon);
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% e = abs(d - net.layers{l}.db{j});
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% if e > er
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% error('numerical gradient checking failed');
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% end
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% end
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end
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end
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% keyboard
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end
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