function cnnnumgradcheck(net, x, y)
epsilon = 1e-4;
er = 1e-8;
n = numel(net.layers);
for j = 1 : numel(net.ffb)
net_m = net; net_p = net;
net_p.ffb(j) = net_m.ffb(j) + epsilon;
net_m.ffb(j) = net_m.ffb(j) - epsilon;
net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y);
net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y);
d = (net_p.L - net_m.L) / (2 * epsilon);
e = abs(d - net.dffb(j));
if e > er
error('numerical gradient checking failed');
end
end
for i = 1 : size(net.ffW, 1)
for u = 1 : size(net.ffW, 2)
net_m = net; net_p = net;
net_p.ffW(i, u) = net_m.ffW(i, u) + epsilon;
net_m.ffW(i, u) = net_m.ffW(i, u) - epsilon;
net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y);
net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y);
d = (net_p.L - net_m.L) / (2 * epsilon);
e = abs(d - net.dffW(i, u));
if e > er
error('numerical gradient checking failed');
end
end
end
for l = n : -1 : 2
if strcmp(net.layers{l}.type, 'c')
for j = 1 : numel(net.layers{l}.a)
net_m = net; net_p = net;
net_p.layers{l}.b{j} = net_m.layers{l}.b{j} + epsilon;
net_m.layers{l}.b{j} = net_m.layers{l}.b{j} - epsilon;
net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y);
net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y);
d = (net_p.L - net_m.L) / (2 * epsilon);
e = abs(d - net.layers{l}.db{j});
if e > er
error('numerical gradient checking failed');
end
for i = 1 : numel(net.layers{l - 1}.a)
for u = 1 : size(net.layers{l}.k{i}{j}, 1)
for v = 1 : size(net.layers{l}.k{i}{j}, 2)
net_m = net; net_p = net;
net_p.layers{l}.k{i}{j}(u, v) = net_p.layers{l}.k{i}{j}(u, v) + epsilon;
net_m.layers{l}.k{i}{j}(u, v) = net_m.layers{l}.k{i}{j}(u, v) - epsilon;
net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y);
net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y);
d = (net_p.L - net_m.L) / (2 * epsilon);
e = abs(d - net.layers{l}.dk{i}{j}(u, v));
if e > er
error('numerical gradient checking failed');
end
end
end
end
end
elseif strcmp(net.layers{l}.type, 's')
% for j = 1 : numel(net.layers{l}.a)
% net_m = net; net_p = net;
% net_p.layers{l}.b{j} = net_m.layers{l}.b{j} + epsilon;
% net_m.layers{l}.b{j} = net_m.layers{l}.b{j} - epsilon;
% net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y);
% net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y);
% d = (net_p.L - net_m.L) / (2 * epsilon);
% e = abs(d - net.layers{l}.db{j});
% if e > er
% error('numerical gradient checking failed');
% end
% end
end
end
% keyboard
end
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CNN图像分类.zip
共18个文件
m:16个
mat:2个
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2020-01-13
21:15:59
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卷积神经网络图像识别,matlab。包含了卷积神经网络matlab必备的代码部分,可直接运行,无需改变,
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CNN图像分类.zip (18个子文件)
CNN图像分类
卷积神经网络cnn
extrc_pca.m 285B
cnnsetup.m 2KB
datafet.mat 20.89MB
flipall.m 86B
cnn_start.m 902B
datalab.mat 20.89MB
cnnapplygrads.m 575B
cnntest.m 220B
NewMain.m 499B
cnnbp.m 2KB
cnntrain.m 819B
sigm.m 54B
printConMat.m 2KB
cnnff.m 2KB
TrainTest.m 2KB
cnnnumgradcheck.m 3KB
expand.m 2KB
accuracy.m 370B
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