multiple input single output neural network

%%% I want to calulate Mean square error

clc ;

clear all;

load('input');

load('output');

% s=5 ; %neurons

x=input ;

y=output ;

S=8; %neurons

Nmax=100;

MinTraErr=0.01;

NORMG=0.0001;

umin=0.05;umax=1000;uscal=0.5;

inp= size(x,1);

out= size(y,1);

R=2;

n0=3;

k=1;

%RANDOMIZATION

relation = [x;y];

ind = 1:length(x);

a = ind(randperm(length(ind)));

shuffeled_vector = relation(:,a);

%NORMALIZATION

for i=1:size(x,1)

xn(i,:)=2*((x(i,:)-min(x(i,:)))/(max(x(i,:))-min(x(i,:)))-0.5);

xmin(i) = min(x(i,:));

xmax(i) = max(x(i,:));

end

yn=2*((y-min(y))/(max(y)-min(y))-0.5);

ymin = min(y);

ymax = max(y);

%SEPERATION

RATIO = 0.7; % Train-Test Ratio

train_data = shuffeled_vector(:,1:fix(length(x)*RATIO));

test_data = shuffeled_vector(:,fix(length(x)*RATIO)+1:end);

ti_vector = train_data(1:R,:);

y_vector = train_data(end,:);

ti_vector_test = test_data(1:R,:);

y_vector_test = test_data(end,:);

%Training

for p=1:10000 % p is the (number of data)/input

t(1,p)=x(1,p) ;

Yreal(1,p)=y(1,p);

end

Yreal=Yreal.';% transpose

t=t.'

b=size(t);

e=b(1);

R=b(2);

xmin=-50;xmax=50;detend=10^-6;

umin=0.05;umax=1000;uscal=0.5;

syms X [S*R+2*S+1,1] ; syms y;

h=(exp(y)-exp(-y))/(exp(y)+exp(-y)); %Activation Function

%Iteration number

maxiter=100; iter=1;

% X=randn(1,S*(R+2)+1);

bh=X(S*R+1:S*R+S,1);

wo=X((S*(R+1)+1):(S*(R+2)),1);

wo=transpose(wo);

bo = X(end);

bi=X(S*R+2*S+1,1);

wi=reshape(X(1:S*R),S,R);

J=vpa(ones(e,S*R+2*S+1));

%Jacobian

for i=1:e

%Model Outputs

sum=0;

for j=1:S

sum1=0;

for k=1:R

sum1=sum1+wi(j,k)*t(i,k);

end

A(j,1)=subs(h,y,sum1+bh(j));

sum=sum+wo(1,j)*A(j,1);

end

Ymodel(i,1)=sum+bi;

% jacobian matrix

for j=1:S*R

k=mod(j-1,S)+1;

m=fix((j-1)/S)+1;

J(i,j)=-(wo(1,k)*t(i,m))*(1-(A(k,1)^2));

xk(j,1)=wi(k,m);

end

for j=S*R+1:S*R+S

J(i,j)=-wo(1,j-S*R)*(1-(A(j-S*R,1)^2));

xk(j,1)=bh(j-S*R,1);

end

for j=S*R+S+1:S*R+2*S

J(i,j)=-A(j-S*R-S,1);

xk(j,1)=wo(1,j-S*R-S);

end

J(i,S*R+2*S+1)=-1;

xk(j+1,1)=bi(1,1);

end

xk=rand(S*R+2*S+1,1);

E=vpa(Yreal-Ymodel);

f=vpa(E.'*E);

loop1=1; Mu=1;

while loop1

iter=iter+1;

Jxk=double(subs(J,x,xk));

fxk=double(subs(f,x,xk));

Exk=double(subs(E,x,xk));

loop2=1;

while loop2

in=-inv(Jxk.'*Jxk+Mu*eye(size(Jxk,2)));

pk=(in*Jxk.'*Exk);

zk=xk+pk;

Ymodelzk=double(subs(Ymodel,x,zk));

% % while loop2

% % Jacob=-inv(Jxk.'*Jxk+u*eye(size(Jxk,2)));

% % pk=(Jacob*Jxk.'*Exk);

% % zk=xk+pk;

% % % Ymodelzk=double(subs(Ymodel,x,zk));

% % for i = 1:length(y_vector)

% % y_hat_initial = wo*tanh(wi*ti_vector(:,i)+bh)+bo;

% % y_hat(i) = y_hat_initial;

% % end

E=double(Yreal-Ymodelzk);

fzk=double(E.'*E);

if fzk<fxk

fx=subs(f,x,xk+x1.*pk);

xmin=-50;xmax=50;detend=10^-6;

[sk]= GoldenSectionMethodE(fx,xmax,xmin,detend)

xk=double(xk+sk.*pk)

u=u/ucale;

loop2=0;

else

u=u*uscale;

end

if u<umin && u>umax

loop1=0;

loop2=0;

end

end

fxk1=double(subs(f,x,xk));

NORMG=norm(sk*pk);

end

Answer 1

[Varun Sai Alaparthi]

Hello Ecem,

In order to better answer your query, could you provide me with some more details on what exactly you are looking for?

However, in case you are looking for a function to calculate mean squared error you can refer to this function ‘immse’.

Mse = immse(X,Y); 
% Run this code fir calculating mse between X,Y matrices 

If you have any further queries, please feel free to reply to my answer 

Sincerely,

Varun