Skip to content

Instantly share code, notes, and snippets.

@denzilc
Created November 12, 2011 15:44
Show Gist options
  • Save denzilc/1360709 to your computer and use it in GitHub Desktop.
Save denzilc/1360709 to your computer and use it in GitHub Desktop.
Neural Network Cost Function
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.
%
X = [ones(m, 1) X];
y = eye(num_labels)(y,:);
a1 = X;
z2 = a1 * Theta1';
a2 = sigmoid(z2);
n = size(a2, 1);
a2 = [ones(n,1) a2];
z3 = a2 * Theta2';
a3 = sigmoid(z3);
regularization = (lambda/(2*m)) * (sum(sum((Theta1(:,2:end)).^2)) + sum(sum((Theta2(:,2:end)).^2)));
J = ((1/m) * sum(sum((-y .* log(a3))-((1-y) .* log(1-a3))))) + regularization;
delta_3 = a3 - y;
delta_2 = (delta_3 * Theta2(:,2:end)) .* sigmoidGradient(z2);
delta_cap2 = delta_3' * a2;
delta_cap1 = delta_2' * a1;
Theta1_grad = ((1/m) * delta_cap1) + ((lambda/m) * (Theta1));
Theta2_grad = ((1/m) * delta_cap2) + ((lambda/m) * (Theta2));
Theta1_grad(:,1) -= ((lambda/m) * (Theta1(:,1)));
Theta2_grad(:,1) -= ((lambda/m) * (Theta2(:,1)));
% -------------------------------------------------------------
% =========================================================================
% Unroll gradients
grad = [Theta1_grad(:) ; Theta2_grad(:)];
end
@rock177486
Copy link

if y = [1;1;0;0;0;0;0;0;1;1]

ey = eye(num_labels);
y = ey(y,:);

will wrong

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment