Compute cost function for the neural network

ex4/nnCostFunction.m

@@ -39,6 +39,24 @@ Theta2_grad = zeros(size(Theta2));
 %         cost function computation is correct by verifying the cost
 %         computed in ex4.m
 %
+
+X = [ones(m, 1), X];  % add a first colum of ones (bias term)
+A_2 = sigmoid(X*Theta1');
+A_2 = [ones(m, 1), A_2];  % (bias term)
+A_3 = sigmoid(A_2*Theta2');
+h_0 = A_3;
+%disp(round(h_0));
+
+% y is 1x5000 and holds the labels as numbers, turn it into 5000x10,
+% each row holding the label as vectors, e.g.  [0 1 0 0 0 ... ] for 2.
+y = eye(num_labels)(y,:);  % y is used as an index, it gets a row,
+                           % e.g. [0 0 0 1 ... 0 0]
+assert(size(y) == [m num_labels]);
+
+
+J = 1/m * sum(sum(-y.*log(h_0) - (1-y).*log(1-h_0)));
+assert(size(J) == [1 1]);
+
 % Part 2: Implement the backpropagation algorithm to compute the gradients
 %         Theta1_grad and Theta2_grad. You should return the partial derivatives of
 %         the cost function with respect to Theta1 and Theta2 in Theta1_grad and