CNN.zip_CNN_CNN 训练_卷积网络_深度神经网络_神经网络检测

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