Stanford 机器学习 Week5 作业: Neural Networks: Learning

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randInitializeWeight

epsilon_init = 0.12;W = rand(L_out, 1 + L_in) * 2 * epsilon_init - epsilon_init;

sigmoidGradient

g = sigmoid(z) .* (1 - sigmoid(z));

nnCostFunction

Theta1 = reshape(nn_params(1:hidden_layer_size * (input_layer_size + 1)), ...hidden_layer_size, (input_layer_size + 1));Theta2 = reshape(nn_params((1 + (hidden_layer_size * (input_layer_size + 1))):end), ...                 num_labels, (hidden_layer_size + 1));J = 0;Theta1_grad = zeros(size(Theta1));Theta2_grad = zeros(size(Theta2));a1 = [ones(m,1) X];z2 = a1 * Theta1';a2 = sigmoid(z2);a2 = [ones(m,1) a2];z3 = a2 * Theta2';preOut = sigmoid(z3);trueOut = zeros(size(preOut));for i = 1:m    trueOut(i,y(i)) = 1;endtmp = trueOut .* log(preOut) + (1 - trueOut) .* log(1 - preOut);J = -1.0/m * sum(tmp(:));t1 = Theta1(:,2:end) .* Theta1(:,2:end);t2 = Theta2(:,2:end) .* Theta2(:,2:end);J = J + lambda / 2 / m * (sum(t1(:)) + sum(t2(:)));epi3 = (preOut - trueOut)';epi2 = Theta2(:,2:end)' * epi3 .* sigmoidGradient(z2)';Theta2_grad  =  (Theta2_grad + epi3 * a2) / m;Theta1_grad  =  (Theta1_grad + epi2 * a1) / m;Theta2_grad(:,2:end) = Theta2_grad(:,2:end) + lambda / m * Theta2(:,2:end);Theta1_grad(:,2:end) = Theta1_grad(:,2:end) + lambda / m * Theta1(:,2:end);

这段代码调了一段时间，虽然题目建议写成loop的，我还是用向量化实现了。
几个重点：
1.ai是包含bias unit的，epi，zi是不包含bias unit的
2.epi是unitnum * m 的，而不是相反

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