MATLAB: MLP Neural network and k-fold cross validation

mlpneural network

I want to train and test MLP Neural network by using k-fold cross validation and train the network by using differential evolution algorithm traindiffevol . this network to predict breast cancer.
first is my code is correct regarding train and test using k-fold cross validation ?
Second I couldn't figure out how to Set NET.trainFcn to 'traindiffevol' could anyone help me ?
close all, clear all, clc, plt=0;
tic
k = 10 % k-fold 
%%%%%%%%%%%%%%%%%%%%%%%%%%

[x,t] = iris_dataset;
if iscell( iris_dataset) ;
    x = cell2mat(x); % array 
    t = cell2mat(t);
end
[ I N ] = size(x) %[ 1 94 ]get row size with size() function  get 2 dimension
[O N ] = size(t) % [ 1 94 ]
MSE00 = mean(var(t',1)) % 8.34 Biased Reference MSE00a is the MSE "a"djusted for the loss in estimation degrees of freedom caused by the bias of evaluating the MSE with the same data that was used to build the model. 
MSE00a = mean(var(t',0)) % 8.43 Unbiased Reference
whos
%%%%%%%%%%%%%%%%%%%%%%%%%%%
  rng('default') % Or substitute your lucky number
  ind0 = randperm(N);
   % ind0 = 1:N; % For debugging
   M = floor(N/k) % 

   Ntrn = N-2*M % length(trnind)
   Ntrneq = Ntrn*O %  No. of training equations
   H = 10;
   Nw = (I+1)*H+(H+1)*O %  No. of unknown weights
   Ndof = Ntrneq-Nw %  No. of estimation degrees of freedom
   MSEgoal = 0.01*MSE00 %  
   MinGrad = MSEgoal/100 % 
  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



  % Create a Pattern Recognition Network
    net = patternnet(H);
    net.trainParam.goal = MSEgoal;
    net.trainParam.min_grad = MinGrad;
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    % Input and Output Pre/Post-Processing Functions
    %net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
    % net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    k=10;
    net.divideFcn = 'divideind'; 
    cvFolds = crossvalind('Kfold', size(t,2), k);   %# get indices of 10-fold CV
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    for i = 1:k                                  %# for each fold
        rngstate(i) = rng;
        net = configure(net,x,t);
        testIdx = (cvFolds == i);               %# get indices of test instances
        trainIdx = ~testIdx  ;                  %# get indices training instances
        trInd=find(trainIdx)
        tstInd=find(testIdx)
        net.trainParam.epochs = 100;
        net.divideParam.trainInd=trInd
        net.divideParam.testInd=tstInd
     %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%  
    % Choose a Performance Function
    net.performFcn = 'mse';  % Mean squared error
    % Train the Network
    [net,tr] = train( net, x, t );
     %# test using test instances
    outputs = net(x);
    errors = gsubtract(t,outputs);
    performance = perform(net,t,outputs)
    trainTargets = t .* tr.trainMask{1};
    testTargets = t .* tr.testMask{1};
    trainPerformance = perform(net,trainTargets,outputs)
    testPerformance = perform(net,testTargets,outputs)
    test(k)=testPerformance;
    %%%%%%%%%%%%%%%%%%%%%%%%%%
    save net
      stopcrit{i,1} = tr.stop;
      bestepoch(i,1) = tr.best_epoch;
      R2trn(i,1) = 1 - tr.best_perf/MSE00;
      R2trna(i,1) = 1 - (Ntrneq/Ndof)* tr.best_perf/MSE00a;
      R2val(i,1) = 1 - tr.best_vperf/MSE00;
      R2tst(i,1) = 1 - tr.best_tperf/MSE00;
    end
      figure, plotconfusion(t,outputs)
    accuracy=mean(test);
    % View the Network
    view(net)
  stopcrit = stopcrit
  result = [ bestepoch R2trn R2trna R2val R2tst]
  minresult = min(result)
  meanresult = mean(result)
  medresult = median(result)
  stdresult = std(result)
  maxresult = max(result)
  Elapsedtime = toc %3.87 sec

Best Answer

No. Your code is not correct. You have chosen a HIGH-DIMENSIONAL-CLASSIFICATION DATASET for which that version of my code is innapropriate.
Go to
 help nndatasets
 doc nndatasets
and choose a regression/curvefitting dataset with much lower dimensions.
After that you might want to consider a low dimensional classification/patternrecognition dataset.
Hope this helps.
Thank you for formally accepting my answer
Greg
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