计算机视觉-BP学习MATLAB源码

function [nn, L] = nntrain(nn, train_x, train_y, opts) m = size(train_x, 1); batchsize = opts.batchsize; numepochs = opts.numepochs; numbatches = floor(m / batchsize); L = zeros(numepochs*numbatches,1); n = 1; for k = 1 : numepochs tic; kk = randperm(m); for j = 1 : numbatches batch_x = train_x(kk((j - 1) * batchsize + 1 : j * batchsize), :); batch_y = train_y(kk((j - 1) * batchsize + 1 : j * batchsize), :); nn = nnff(nn, batch_x, batch_y); nn = nnbp(nn); nn = nngrads(nn); L(n) = nn.loss; n = n + 1; end t = toc; nn = nnff(nn, train_x, train_y); str_perf = sprintf('; Full-batch train err = %f', nn.loss); disp(['NN train: epoch ' num2str(k) '/' num2str(opts.numepochs) '. Took ' num2str(t) ' seconds' '. Mini-batch mean squared error on training set is ' num2str(mean(L((n-numbatches):(n-1)))) str_perf]); end end function nn = nnff(nn, x, y) n = nn.n; m = size(x, 1); x = [ones(m,1) x]; nn.a{1} = x; %feedforward pass for i = 2 : n-1 nn.a{i} = sigm(nn.a{i - 1} * nn.W{i - 1}'); %Add the bias term nn.a{i} = [ones(m,1) nn.a{i}]; end nn.a{n} = sigm(nn.a{n - 1} * nn.W{n - 1}'); %error and loss nn.error = y - nn.a{n}; nn.loss = 1/2 * sum(sum(nn.error .^ 2)) / m; function X = sigm(P) X = 1./(1+exp(-P)); end end function nn = nnbp(nn) n = nn.n; d{n} = - nn.error .* (nn.a{n} .* (1 - nn.a{n})); for i = (n - 1) : -1 : 2 % Derivative of the activation function d_act = nn.a{i} .* (1 - nn.a{i}); % Backpropagate first derivatives if i+1==n % in this case in d{n} there is not the bias term to be removed d{i} = (d{i + 1} * nn.W{i}) .* d_act; % Bishop (5.56) else % in this case in d{i} the bias term has to be removed d{i} = (d{i + 1}(:,2:end) * nn.W{i}) .* d_act; end end for i = 1 : (n - 1) if i+1==n nn.dW{i} = (d{i + 1}' * nn.a{i}) / size(d{i + 1}, 1); else nn.dW{i} = (d{i + 1}(:,2:end)' * nn.a{i}) / size(d{i + 1}, 1); end end end function nn = nngrads(nn) for i = 1 : (nn.n - 1) dW = nn.dW{i}; dW = nn.learning_rate * dW; if(nn.momentum>0) nn.vW{i} = nn.momentum*nn.vW{i} + dW; dW = nn.vW{i}; end nn.W{i} = nn.W{i} - dW; end end