Moving course1 to course1 subdir.

machine_learning/course1/mlclass-ex4-008/mlclass-ex4/nnCostFunction.m

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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.
+%
+
+
+%
+%	Activations ....
+%
+a1 = [ones(m, 1) X];
+a2 = [ones(m,1) sigmoid(a1*Theta1')];
+a3 = sigmoid(a2*Theta2');
+h = a3;
+
+%
+%	Transform y to .....
+%
+Y = zeros(m,num_labels);
+for i=1:m,
+	Y(i,y(i))=1;
+end
+
+%
+%	This is the J without ....
+%
+J = sum(sum( Y .* log(h) .+ (1-Y) .* log(1-h) )) / (-m);
+J = J + (lambda/(2*m))*sum(sum(Theta1(:,2:size(Theta1,2)).^2));
+J = J + (lambda/(2*m))*sum(sum(Theta2(:,2:size(Theta2,2)).^2));
+
+%
+%	Gradient ....
+%
+d3 = a3 .- Y;
+d2 = d3 * Theta2 .* (a2.*(1-a2));
+
+dlt1 = zeros(size(Theta1));
+dlt2 = zeros(size(Theta2));
+for i=1:m,
+	dlt2 = dlt2 + d3(i,:)'*a2(i,:);
+	t = d2(i,:)'*a1(i,:);
+	t = t(2:size(t,1),:);
+	dlt1 = dlt1 + t;
+end
+
+Theta1_grad = dlt1/m;
+Theta2_grad = dlt2/m;
+
+%
+%	Regularization ....
+%
+r1 = lambda*Theta1/m;
+r2 = lambda*Theta2/m;
+
+t1s = size(Theta1);
+r1 = [ zeros(t1s(1),1) r1(:,2:t1s(2))];
+
+t2s = size(Theta2);
+r2 = [ zeros(t2s(1),1) r2(:,2:t2s(2))];
+
+Theta1_grad = Theta1_grad + r1;
+Theta2_grad = Theta2_grad + r2;
+
+
+% =========================================================================
+
+% Unroll gradients
+grad = [Theta1_grad(:) ; Theta2_grad(:)];
+
+
+end