Cross-validation improvement

Hi, All

I want to improve cross-validation results.

so, I fellow this suggestion from @Greg Heath use multiple nets to improve the cross-validation results.

https://www.mathworks.com/matlabcentral/answers/130702-neural-network-k-fold-cross-validation

a. For each i of i =1:k design multiple nets differing by the assignment of random initial weights. Discard those with poor performance and average the performance of the rest

is this code right?

clear all; 
close all; 
clc
counter=0
for h= 5:20
    for j= 5:20
    

%%input file
xp = predictors;
xt = table2array(predictors);
x= xt'  % input 
t = response'  % target


trainFcn = 'trainbr';  % Scaled conjugate gradient backpropagation.


hiddenLayerSize = [h,j]
net = fitnet(hiddenLayerSize, trainFcn)


sum_rmse =0 
fid=fopen('sum-r.txt','w')
KFolds = 10  % K value 
cvp = cvpartition(size(response, 1), 'KFold', KFolds) %gernater cvp 
for i= 1:KFolds
     trainIdx =cvp.training(i);  % index of training data
    testIdx = cvp.test(i);        % index of validation data
    trInd=find(trainIdx)
    testInd=find(testIdx)
    
     xtrain=x(:,trainIdx); 
    ytrain=t(:,trainIdx);
     xtest=x(:,testIdx); 
     ytest=t(:,testIdx);
     
      
  net.layers{1}.transferFcn ='tansig'; % logsig
  net.layers{2}.transferFcn ='tansig';
  net.layers{3}.transferFcn ='purelin';
         net.divideFcn = 'divideind'; 
      net.divideParam.trainInd=trInd;
      net.divideParam.testInd=tstInd;
   net.trainParam.showWindow = 0
     net.performFcn = 'mse'  % MSE
   %rng('default')
   nets{i} = train(net,xtrain,ytrain)     % trains a network net according to net.trainFcn and net.trainParam. tr:  returns a training record
  
end
%% multiple nets
for i= 1: KFolds
    neti=nets{i}
    yPred = neti(xtest)
    ypt=neti(xtrain)
    
    y = neti(x)   % test the molde net % Estimate the targets using the trained network.
    Train_RMSE = sqrt(sum(( ypt - ytrain ).^2) / numel(ytrain))
     %da = y-t; 
     vrmse = sqrt(sum((yPred-ytest).^2) / numel(ytest))
  
     train_t(i)=Train_RMSE
     ttrr(i)=vrmse
      R= corrcoef(ytest,yPred)
   Rv(i)=R(1,2)
%avrg_rmse = mean(testrmse)
  trmse= mse(neti,ytest,yPred)

 
 sum_rmse = sum_rmse +vrmse
 counter=counter+1
  figname = strcat(num2str(counter),'.mat')
 save(figname);
 fprintf(fid,'%6.2f %12.2f\n\n',sum_rmse/10);
%plotregression(yTest,yPred,'Test')

end
fclose(fid);
average_rmse(h,j)=sum_rmse/10;
%accuracy=mean(tata)
cvrmse(h,j)=mean(ttrr)
ttrmse(h,j)=mean(train_t)
R_value(h,j) = mean(Rv)
    end
end
R_value=R_value'
ttrmse= ttrmse'
cvrmse= cvrmse'
save ('test.txt','cvrmse','-ascii')
save('train.txt','ttrmse','-ascii')
save('r.txt','R_value','-ascii')