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Compute cost function for the neural network

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neingeist 2014-11-01 20:30:58 +01:00
parent be6f3cbdef
commit f2154a8cc1
1 changed files with 24 additions and 6 deletions
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18
ex4/nnCostFunction.m
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@ -39,6 +39,24 @@ Theta2_grad = zeros(size(Theta2));
% cost function computation is correct by verifying the cost % cost function computation is correct by verifying the cost
% computed in ex4.m % 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 % Part 2: Implement the backpropagation algorithm to compute the gradients
% Theta1_grad and Theta2_grad. You should return the partial derivatives of % 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 % the cost function with respect to Theta1 and Theta2 in Theta1_grad and