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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);
% 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.
%
ylable = eye(num_labels)(y,:);
a1 = [ones(m,1) X];
z2 = a1 * Theta1';
a2 = sigmoid(z2);
a2 = [ones(m,1) a2];
a3 = sigmoid(a2 * Theta2');
% 这里不知道为什么用向量的形式写出来是不对的?
%J = 1 / m * (-ylable' * log(a3) - (1 - ylable') * log(1 - a3));
J = 1 / m * sum( sum( -ylable.* log(a3) - (1-ylable).*log(1-a3) ));
% pay attention :" Theta1(:,2:end) " , no "Theta1" .
regularized = lambda/(2*m) * (sum(sum(Theta1(:,2:end).^2)) + sum(sum(Theta2(:,2:end).^2)) );
J = J + regularized;
delta3 = a3-ylable; %5000x10
delta2 = delta3 * Theta2 ;
delta2 = delta2(:,2:end);
delta2 = delta2 .* sigmoidGradient(z2); %5000x25
Delta_1 = zeros(size(Theta1));
Delta_2 = zeros(size(Theta2));
Delta_1 = Delta_1 + delta2' * a1 ;
Delta_2 = Delta_2 + delta3' * a2 ;
Theta1_grad = 1/m * Delta_1;
Theta2_grad = 1/m * Delta_2;
regularized_1 = lambda/m * Theta1;
regularized_2 = lambda/m * Theta2;
% j = 0是不需要正则化的
regularized_1(:,1) = zeros(size(regularized_1,1),1);
regularized_2(:,1) = zeros(size(regularized_2,1),1);
Theta1_grad = Theta1_grad + regularized_1;
Theta2_grad = Theta2_grad + regularized_2;
% =========================================================================
% Unroll gradients
grad = [Theta1_grad(:) ; Theta2_grad(:)];
end
|
