Compute cost function for the neural network
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1 changed files with 24 additions and 6 deletions
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@ -8,8 +8,8 @@ function [J grad] = nnCostFunction(nn_params, ...
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% [J grad] = NNCOSTFUNCTON(nn_params, hidden_layer_size, num_labels, ...
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% X, y, lambda) computes the cost and gradient of the neural network. The
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% parameters for the neural network are "unrolled" into the vector
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% nn_params and need to be converted back into the weight matrices.
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%
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% nn_params and need to be converted back into the weight matrices.
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%
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% The returned parameter grad should be a "unrolled" vector of the
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% partial derivatives of the neural network.
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%
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@ -24,8 +24,8 @@ Theta2 = reshape(nn_params((1 + (hidden_layer_size * (input_layer_size + 1))):en
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% Setup some useful variables
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m = size(X, 1);
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% You need to return the following variables correctly
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% You need to return the following variables correctly
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J = 0;
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Theta1_grad = zeros(size(Theta1));
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Theta2_grad = zeros(size(Theta2));
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@ -39,6 +39,24 @@ Theta2_grad = zeros(size(Theta2));
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% cost function computation is correct by verifying the cost
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% computed in ex4.m
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%
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X = [ones(m, 1), X]; % add a first colum of ones (bias term)
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A_2 = sigmoid(X*Theta1');
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A_2 = [ones(m, 1), A_2]; % (bias term)
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A_3 = sigmoid(A_2*Theta2');
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h_0 = A_3;
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%disp(round(h_0));
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% y is 1x5000 and holds the labels as numbers, turn it into 5000x10,
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% each row holding the label as vectors, e.g. [0 1 0 0 0 ... ] for 2.
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y = eye(num_labels)(y,:); % y is used as an index, it gets a row,
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% e.g. [0 0 0 1 ... 0 0]
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assert(size(y) == [m num_labels]);
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J = 1/m * sum(sum(-y.*log(h_0) - (1-y).*log(1-h_0)));
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assert(size(J) == [1 1]);
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% Part 2: Implement the backpropagation algorithm to compute the gradients
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% Theta1_grad and Theta2_grad. You should return the partial derivatives of
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% the cost function with respect to Theta1 and Theta2 in Theta1_grad and
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@ -46,12 +64,12 @@ Theta2_grad = zeros(size(Theta2));
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% that your implementation is correct by running checkNNGradients
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%
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% Note: The vector y passed into the function is a vector of labels
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% containing values from 1..K. You need to map this vector into a
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% containing values from 1..K. You need to map this vector into a
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% binary vector of 1's and 0's to be used with the neural network
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% cost function.
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%
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% Hint: We recommend implementing backpropagation using a for-loop
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% over the training examples if you are implementing it for the
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% over the training examples if you are implementing it for the
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% first time.
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%
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% Part 3: Implement regularization with the cost function and gradients.
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