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- function [J grad] = nnCostFunction(nn_params, ...
- input_layer_size, ...
- hidden_layer_size, ...
- num_labels, ...
- X, y, lambda)
- %NNCOSTFUNCTION Implements the neural network cost function for a two layer
- %neural network which performs classification
- % [J grad] = NNCOSTFUNCTON(nn_params, hidden_layer_size, num_labels, ...
- % X, y, lambda) computes the cost and gradient of the neural network. The
- % parameters for the neural network are "unrolled" into the vector
- % nn_params and need to be converted back into the weight matrices.
- %
- % The returned parameter grad should be a "unrolled" vector of the
- % partial derivatives of the neural network.
- %
- % Reshape nn_params back into the parameters Theta1 and Theta2, the weight matrices
- % for our 2 layer neural network
- 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));
- % Setup some useful variables
- m = size(X, 1);
- n = size(X, 2);
- % You need to return the following variables correctly
- J = 0;
- Theta1_grad = zeros(size(Theta1));
- Theta2_grad = zeros(size(Theta2));
- % ====================== YOUR CODE HERE ======================
- % Instructions: You should complete the code by working through the
- % following parts.
- %
- % Part 1: Feedforward the neural network and return the cost in the
- % variable J. After implementing Part 1, you can verify that your
- % cost function computation is correct by verifying the cost
- % computed in ex4.m
- %
- % 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
- % Theta2_grad, respectively. After implementing Part 2, you can check
- % that your implementation is correct by running checkNNGradients
- %
- % Note: The vector y passed into the function is a vector of labels
- % containing values from 1..K. You need to map this vector into a
- % binary vector of 1's and 0's to be used with the neural network
- % cost function.
- %
- % Hint: We recommend implementing backpropagation using a for-loop
- % over the training examples if you are implementing it for the
- % first time.
- %
- % Part 3: Implement regularization with the cost function and gradients.
- %
- % Hint: You can implement this around the code for
- % backpropagation. That is, you can compute the gradients for
- % the regularization separately and then add them to Theta1_grad
- % and Theta2_grad from Part 2.
- %
- %X=[ones(m, 1) X];
- Theta1SIN = Theta1(:,2:end);
- Theta2SIN = Theta2(:,2:end);
- DELTA1 = zeros(hidden_layer_size,input_layer_size+1);
- DELTA2 = zeros(num_labels,hidden_layer_size+1);
- suma = 0;
- for i=1:m
- a1 = X(i,:)';
- a1 = [1; a1];
- z2 = Theta1 * a1;
- a2 = sigmoid(z2);
- a2 = [1; a2];
- z3 = Theta2 * a2;
- a3 = sigmoid(z3);
- aux = (1:num_labels == y(i));
- h = a3;
- suma += aux*log(h)+(1-aux)*log(1-h);
- delta3 = a3-aux';
- delta2 = (Theta2'*delta3).*((1-a2).*a2);
- delta2 = delta2(2:end);
- DELTA1 = DELTA1 + delta2 * a1';
- DELTA2 = DELTA2 + delta3 * a2';
- end
- J = (-1/m)*suma;
- % -------------------------------------------------------------
- Theta1_grad(:,1) = (1/m) * DELTA1(:,1);
- Theta2_grad(:,1) = (1/m) * DELTA2(:,1);
- Theta1_grad(:,2:end) = (1/m) * DELTA1(:,2:end) + (lambda/m) * Theta1SIN;
- Theta2_grad(:,2:end) = (1/m) * DELTA2(:,2:end) + (lambda/m) * Theta2SIN;
- % =========================================================================
- % Unroll gradients
- grad = [Theta1_grad(:) ; Theta2_grad(:)];
- end
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