How to unable data division in net training?

I want to classify 11 different hand motions using patternnet and validate the result using 10-fold cross validation (9 for train and 1 for test). How can I unable data division in the training process? Is it ok to do that? My code is as follow:

load FeatureSet;
p=input;  %[16 by 1342 ]
t=target;  %[11 by 1342]
Nk=1342/11;
labels=[ones(Nk,1); 2.*ones(Nk,1); 3.*ones(Nk,1); 4.*ones(Nk,1); 5.*ones(Nk,1); 6.*ones(Nk,1); 7.*ones(Nk,1); 8.*ones(Nk,1); 9.*ones(Nk,1); 10.*ones(Nk,1); 11.*ones(Nk,1)];
kfold=10;
indices = crossvalind('Kfold',labels,kfold);
for i = 1:kfold
  ts = (indices == i); tr = ~ts;
  Ptr = p(tr,:); Pts = p(ts,:);
  Ttr=t(tr,:); Tts = t(ts,:);
  trainFcn = 'trainbr';
  net = patternnet([32 32], trainFcn);
  net.layers{1}.transferFcn = 'tansig';    % hidden layer 1
  net.layers{2}.transferFcn = 'tansig';    % hidden layer 2
  % %------------------------parameter
  net.trainParam.lr = 0.1;   %learning rate
  net.trainParam.mc = 0.1;   %momentum
  %------------------------ train
  [net,tr]= train(net,Ptr,Ttr);
  %------------------------ test
  outputs= sim(net,Pts);
  [c,cm] = confusion(Tts,outputs);
  pct(i)=100*(1-c);   %correction rate
end