User define function missbehave...

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Shivang Patel
Shivang Patel 2015-3-12
Hi... I m working with BPNN, And as a reference code i m using this... " ANOOP ACADEMIA – BLOG's BPNN Code "
My problem is, in the for loop after function execute... value's are not updated and result are same... Where is the problem with it... Everything looks fine... I m confuse...where is my mistake... Code is...below
% Assigning the number of hidden neurons in hidden layer
m = 2;
errorValue_theshold = 100;
errorValue = errorValue_theshold + 1; % Only for initial...
delta_V = 0;
delta_W = 0;
[l,b] = size(data);
[n,a] = size(target);
V = rand(l,m); % Weight matrix from Input to Hidden
W = rand(m,n); % Weight matrix from Hidden to Output
count = 0;
itration = 100;
for count = 1: itration
[errorValue delta_V delta_W] = trainNeuralNet(data,target,V,W,delta_V,delta_W);
count = count + 1;
fprintf('Error : %f\n', errorValue);
fprintf('#itration : %d \t', count);
Error_Mat(count)=errorValue;
W = W + delta_W;
V = V + delta_V;
if errorValue < errorValue_theshold
fprintf('\n\nFinal Error : %f\n', errorValue);
fprintf('#totalItration : %d\n', count);
Error_Mat(count)=errorValue;
end
end
Function Code is : ----------------
*function [errorValue delta_V delta_W] = trainNeuralNet(Input, Output, V, W, delta_V, delta_W)*
Output_of_InputLayer = Input;
Input_of_HiddenLayer = V' * Output_of_InputLayer; % % netj
[m n] = size(Input_of_HiddenLayer);
Output_of_HiddenLayer = 1./(1+exp(-Input_of_HiddenLayer));
Input_of_OutputLayer = W'*Output_of_HiddenLayer;
clear m n;
[m n] = size(Input_of_OutputLayer);
Output_of_OutputLayer = 1./(1+exp(-Input_of_OutputLayer));
difference = Output - Output_of_OutputLayer;
square = difference.*difference;
errorValue = sqrt(sum(square(:)));
clear m n
[n a] = size(Output);
for i = 1 : n
for j = 1 : a
d(i,j) =(Output(i,j)-Output_of_OutputLayer(i,j))*Output_of_OutputLayer(i,j)*(1-Output_of_OutputLayer(i,j));
end
end
Y = Output_of_HiddenLayer * d';
if nargin == 4
delta_W=zeros(size(W));
delta_V=zeros(size(V));
end
etta=0.6; alpha=1;
delta_W= alpha.*delta_W + etta.*Y;
error = W*d;
clear m n
[m n] = size(error);
for i = 1 : m
for j = 1 :n
d_star(i,j)= error(i,j)*Output_of_HiddenLayer(i,j)*(1-Output_of_HiddenLayer(i,j));
end
end
X = Input * d_star';
delta_V = alpha * delta_V + etta * X;
end
In Advance... Thanks :)

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