Code not running and showing busy

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I have written a piece of code. Please check after providing convergence criteria with while loop the code is not running and only showing as busy. Please help.

input= xlsread('Input');output = xlsread('Output');bias = ones(1,3)*0.0005;      %bias values a=-1;
b=1;
rng('default')
V= a+(b-a)*rand(6,5); %weights between the input(6 nodes) and hidden(5 nodes) layers
rng('default')
W = a+(b-a)*rand(5,1); %weights between the hidden(5 nodes) and output(1 node) layers
transposed_input = input'; %inputs
d = output'; %target output
%initialization of del[W] and del[V]del_W1=0;
del_W2=0;
del_W3=0;
del_W4=0;
del_W5=0;
del_V1=0;
del_V2=0;
del_V3=0;
del_V4=0;
del_V5=0;
del_V6=0;
del_V7=0;
del_V8=0;
del_V9=0;
del_V10=0;
del_V11=0;
del_V12=0;
del_V13=0;
del_V14=0;
del_V15=0;
del_V16=0;
del_V17=0;
del_V18=0;
del_V19=0;
del_V20=0;
del_V21=0;
del_V22=0;
del_V23=0;
del_V24=0;
del_V25=0;
del_V26=0;
del_V27=0;
del_V28=0;
del_V29=0;
del_V30=0;
for epoch=1:6000 %number of iterationsalpha=0.1; %learning rateMu=0.1; %Momentum Constant
N=80; %Number of training casesfor k=1:N

%Feed-Forward Network starts

%Output to input neurons

OI(1,k)=1./(1+exp(-(transposed_input(1,k)+bias(1,1))));
OI(2,k)=1./(1+exp(-(transposed_input(2,k)+bias(1,1))));
OI(3,k)=1./(1+exp(-(transposed_input(3,k)+bias(1,1))));
OI(4,k)=1./(1+exp(-(transposed_input(4,k)+bias(1,1))));
OI(5,k)=1./(1+exp(-(transposed_input(5,k)+bias(1,1))));
OI(6,k)=1./(1+exp(-(transposed_input(6,k)+bias(1,1))));

%Input to hidden neurons

IH1(1,k)= (OI(1,k)*V(1,1))+(OI(2,k)*V(2,1))+ (OI(3,k)*V(3,1))+ (OI(4,k)*V(4,1))+ (OI(5,k)*V(5,1))+(OI(6,k)*V(6,1))+bias(1,2);
IH2(1,k)= (OI(1,k)*V(1,2))+(OI(2,k)*V(2,2))+ (OI(3,k)*V(3,2))+ (OI(4,k)*V(4,2))+ (OI(5,k)*V(5,2))+(OI(6,k)*V(6,2))+bias(1,2);
IH3(1,k)= (OI(1,k)*V(1,3))+(OI(2,k)*V(2,3))+ (OI(3,k)*V(3,3))+ (OI(4,k)*V(4,3))+ (OI(5,k)*V(5,3))+(OI(6,k)*V(6,3))+bias(1,2);
IH4(1,k)= (OI(1,k)*V(1,4))+(OI(2,k)*V(2,4))+ (OI(3,k)*V(3,4))+ (OI(4,k)*V(4,4))+ (OI(5,k)*V(5,4))+(OI(6,k)*V(6,4))+bias(1,2);
IH5(1,k)= (OI(1,k)*V(1,5))+(OI(2,k)*V(2,5))+ (OI(3,k)*V(3,5))+ (OI(4,k)*V(4,5))+ (OI(5,k)*V(5,5))+(OI(6,k)*V(6,5))+bias(1,2);

%Output to hidden neurons
OH1(1,k) = (exp(IH1(1,k)+bias(1,2)) - exp(-(IH1(1,k)+bias(1,2))))./(exp(IH1(1,k)+bias(1,2)) + exp(-(IH1(1,k)+bias(1,2))));
OH2(1,k) = (exp(IH2(1,k)+bias(1,2)) - exp(-(IH2(1,k)+bias(1,2))))./(exp(IH2(1,k)+bias(1,2)) + exp(-(IH2(1,k)+bias(1,2))));
OH3(1,k) = (exp(IH3(1,k)+bias(1,2)) - exp(-(IH3(1,k)+bias(1,2))))./(exp(IH3(1,k)+bias(1,2)) + exp(-(IH3(1,k)+bias(1,2))));
OH4(1,k) = (exp(IH4(1,k)+bias(1,2)) - exp(-(IH4(1,k)+bias(1,2))))./(exp(IH4(1,k)+bias(1,2)) + exp(-(IH4(1,k)+bias(1,2))));
OH5(1,k) = (exp(IH5(1,k)+bias(1,2)) - exp(-(IH5(1,k)+bias(1,2))))./(exp(IH5(1,k)+bias(1,2)) + exp(-(IH5(1,k)+bias(1,2))));
%Input to Output neuron

IO(1,k)=OH1(1,k)*W(1,1)+OH2(1,k)*W(2,1)+OH3(1,k)*W(3,1)+OH4(1,k)*W(4,1)+OH5(1,k)*W(5,1)+bias(1,3);
%Output to Output neuron Out(1,k)=IO(1,k)+bias(1,3); %forward step calculation corresponding to each training case of a batch run terminates here
%BackPropagation of [V] and [W] starts
%Finding Mean Squared Error(E)
error(1,k)=d(1,k)-Out(1,k);
diff_Out(1,k)=[error(1,k).^2];
E= sum(diff_Out(1,k))./(2*N);
end while (E >= 0.0004) %convergence criteria

for p=1:N

delta(1,p)=Out(1,p)*(1-Out(1,p))*error(1,p);
del=sum(delta(1,p));
%summation of Output to hidden neurons for 'k' training cases
OHS1=sum(OH1(1,p));
OHS2=sum(OH2(1,p));
OHS3=sum(OH3(1,p));
OHS4=sum(OH4(1,p));
OHS5=sum(OH5(1,p));


%summation of Output to input neurons for 'k' training cases

OI1=sum(OI(1,p));
OI2=sum(OI(2,p));
OI3=sum(OI(3,p));
OI4=sum(OI(4,p));
OI5=sum(OI(5,p));
OI6=sum(OI(6,p));

end
delta2_1=OHS1*(1-OHS1)*W(1,1)*del;
delta2_2=OHS2*(1-OHS2)*W(2,1)*del;
delta2_3=OHS3*(1-OHS3)*W(3,1)*del;
delta2_4=OHS4*(1-OHS4)*W(4,1)*del;
delta2_5=OHS5*(1-OHS5)*W(5,1)*del;

%delta[W] del_W1=-alpha*OHS1*del+Mu*del_W1;

del_W2=-alpha*OHS2*del+Mu*del_W2;


del_W3=-alpha*OHS3*del+Mu*del_W3;


del_W4=-alpha*OHS4*del+Mu*del_W4;


del_W5=-alpha*OHS5*del+Mu*del_W5;

%delta[V]
del_V1=-alpha*OI1*delta2_1+Mu*del_V1;
del_V2=-alpha*OI2*delta2_1+Mu*del_V2;
del_V3=-alpha*OI3*delta2_1+Mu*del_V3;
del_V4=-alpha*OI4*delta2_1+Mu*del_V4;
del_V5=-alpha*OI5*delta2_1+Mu*del_V5;
del_V6=-alpha*OI6*delta2_1+Mu*del_V6;


del_V7=-alpha*OI1*delta2_2+Mu*del_V7;
del_V8=-alpha*OI2*delta2_2+Mu*del_V8;
del_V9=-alpha*OI3*delta2_2+Mu*del_V9;
del_V10=-alpha*OI4*delta2_2+Mu*del_V10;
del_V11=-alpha*OI5*delta2_2+Mu*del_V11;
del_V12=-alpha*OI6*delta2_2+Mu*del_V12;

del_V13=-alpha*OI1*delta2_3+Mu*del_V13;
del_V14=-alpha*OI2*delta2_3+Mu*del_V14;
del_V15=-alpha*OI3*delta2_3+Mu*del_V15;
del_V16=-alpha*OI4*delta2_3+Mu*del_V16;
del_V17=-alpha*OI5*delta2_3+Mu*del_V17;
del_V18=-alpha*OI6*delta2_3+Mu*del_V18;

del_V19=-alpha*OI1*delta2_4+Mu*del_V19;
del_V20=-alpha*OI2*delta2_4+Mu*del_V20;
del_V21=-alpha*OI3*delta2_4+Mu*del_V21;
del_V22=-alpha*OI4*delta2_4+Mu*del_V22;
del_V23=-alpha*OI5*delta2_4+Mu*del_V23;
del_V24=-alpha*OI6*delta2_4+Mu*del_V24;

del_V25=-alpha*OI1*delta2_5+Mu*del_V25;
del_V26=-alpha*OI2*delta2_5+Mu*del_V26;
del_V27=-alpha*OI3*delta2_5+Mu*del_V27;
del_V28=-alpha*OI4*delta2_5+Mu*del_V28;
del_V29=-alpha*OI5*delta2_5+Mu*del_V29;
del_V30=-alpha*OI6*delta2_5+Mu*del_V30;



end


%updated [W] and [V] matrix
W(1,1)=W(1,1)+del_W1;
W(2,1)=W(2,1)+del_W2;
W(3,1)=W(3,1)+del_W3;
W(4,1)=W(4,1)+del_W4;
W(5,1)=W(5,1)+del_W5;
V(1,1)= V(1,1)+del_V1;
V(2,1)= V(2,1)+del_V2;
V(3,1)= V(3,1)+del_V3;
V(4,1)= V(4,1)+del_V4;
V(5,1)= V(5,1)+del_V5;
V(6,1)= V(6,1)+del_V6;
V(1,2)= V(1,2)+del_V7;
V(2,2)= V(2,2)+del_V8;
V(3,2)= V(3,2)+del_V9;
V(4,2)= V(4,2)+del_V10;
V(5,2)= V(5,2)+del_V11;
V(6,2)= V(6,2)+del_V12;
V(1,3)= V(1,3)+del_V13;
V(2,3)= V(2,3)+del_V14;
V(3,3)= V(3,3)+del_V15;
V(4,3)= V(4,3)+del_V16;
V(5,3)= V(5,3)+del_V17;
V(6,3)= V(6,3)+del_V18;
V(1,4)= V(1,4)+del_V19;
V(2,4)= V(2,4)+del_V20;
V(3,4)= V(3,4)+del_V21;
V(4,4)= V(4,4)+del_V22;
V(5,4)= V(5,4)+del_V23;
V(6,4)= V(6,4)+del_V24;
V(1,5)= V(1,5)+del_V25;
V(2,5)= V(2,5)+del_V26;
V(3,5)= V(3,5)+del_V27;
V(4,5)= V(4,5)+del_V28;
V(5,5)= V(5,5)+del_V29;
V(6,5)= V(6,5)+del_V30;
end

ANSWER

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Hi ,

From the code I see you are using first for loop inside the main for loop for updating the value of E. And the while loop condition is based on E only. So, you are not updating the variable on which the while loop is dependent inside the while loop, the problem it will create is let’s say the value of E from first for loop is >= 0.0004, then the while loop will not terminate because its value is not changing at all in that loop or any subsequent loop.

I think there is something missing in the implementation about updating E within the while loop. You may check again what are equation mathematically for writing the backpropagation and the termination condition.

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