Neural network Cost function in Andrew Ng's Lecture - matlab

Question

I am having trouble with my code that is meant to provide a cost function for my neural network. The cost function (J) is defined as the cost function. Given sample inputs, the cost function returns a negative value that is about 5 times less than the expected value. I have worked on this issue for a few hours but still cannot get the desired value.
Thanks in advance.
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
one=ones(1,10);
temp=one
one=transpose(temp);
sizeTheta1=size(Theta1);
sizeTheta2=size(Theta2);
for i=1:m
if y(i)==1
yVec=[1,0,0,0,0,0,0,0,0,0];
end
if y(i)==2
yVec=[0,1,0,0,0,0,0,0,0,0];
end
if y(i)==3
yVec=[0,0,1,0,0,0,0,0,0,0];
end
if y(i)==4
yVec=[0,0,0,1,0,0,0,0,0,0];
end
if y(i)==5
yVec=[0,0,0,0,1,0,0,0,0,0];
end
if y(i)==6
yVec=[0,0,0,0,0,1,0,0,0,0];
end
if y(i)==7
yVec=[1,0,0,0,0,0,1,0,0,0];
end
if y(i)==8
yVec=[1,0,0,0,0,0,0,1,0,0];
end
if y(i)==9
yVec=[0,0,0,0,0,0,0,0,1,0];
end
if y(i)==10
yVec=[0,0,0,0,0,0,0,0,0,1];
end
xVec=transpose(X(i,:));
term1=transpose(-yVec).*(log(sigmoid(Theta2(1:10,1:sizeTheta2(2)-1))*(sigmoid(Theta1(1:25,1:sizeTheta1(2)-1)*xVec))));
term2=(one-transpose(yVec)).*(log(one-(sigmoid(Theta2(1:10,1:sizeTheta2(2)-1)*(sigmoid(Theta1(1:25,1:sizeTheta1(2)-1)*xVec))))));
J=J+(term1-term2);
end
regTheta1=0;
regTheta2=0;
J=sum(sum(J))*(1/m);
regTheta1=(sum(sum(Theta1.*Theta1)));
regTheta2=(sum(sum(Theta2.*Theta2)));
J=J+((lambda)*(regTheta1+regTheta2))/(2*m);
% -------------------------------------------------------------
% =========================================================================
% Unroll gradients
grad = [Theta1_grad(:) ; Theta2_grad(:)];
end