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December 14, 2018 09:25
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Feedforward Neural Network Cost Function
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... | |
% Feedforward the neural network and return the cost in the variable J | |
summary = 0; | |
for j = 1:m | |
y_label = y(j,:); | |
a_one = X(j,:); | |
a_one = [1 a_one]; | |
z_one = a_one * Theta1'; | |
a_two = sigmoid(z_one); | |
m_temp = size(a_two, 1); | |
a_two = [ones(m_temp, 1) a_two]; | |
z_two = a_two * Theta2'; | |
a_three = sigmoid(z_two); | |
h = a_three; | |
sum_temp = 0; | |
sum_temp = (-y_label .* log(h)) - ((1 - y_label) .* log(1 - h)); | |
summary = summary + sum(sum_temp); | |
... | |
end | |
... | |
% Add Regularization | |
regularization = (lambda/(2*m)) * (sum(sum(Theta1(:,2:end) .^ 2, 2)) + sum(sum(Theta2(:,2:end) .^ 2, 2))); | |
J = (summary/m) + regularization; | |
... |
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