K-cross validation ANN

Check my following code, if there is any error (may be logical) let me know. Thank You.

clc;clear all; close all;
%training data of size (1024x3600) It has 1024 features and 3600 samples
x=dlmread('train_data.txt'); 
%targets data labels of size (15x3600) The data has 15 classes.
y=dlmread('targets_train.txt'); 
%test data has 1024 features and 900 samples.
test_data=dlmread('test_data.txt');
%target test data is of size (15x900); 
test_target=dlmread('test_target.txt');
% Take note that, the train and test data is already divided. These two datasets are independant. 
best_acc=0; %this is the best cross-validation accuracy, initialized as zero.
nr_fold=5; % number of cross-validation required, in my case, I took number 5
%number hidden layers iterates over the loop, to calculate the best best hidden layer. 
hiddenLayerSize=10:1:20; 
for i=1:length(hiddenLayerSize)
net=patternnet(hiddenLayerSize(i));
net.divideFcn='';
acc=get_cv_ac(y,x,nr_fold,net); %get_cv_ac function is written below. 
if acc>best_acc
best_acc=acc; best_hiddenLayer=hiddenLayerSize(i);
end
fprintf('%g %g (best acc=%g, best hidden Layer=%g)\n)', acc,hiddenLayerSize(i), best_acc, best_hiddenLayer);
end
end
%now retrain the whole training data with best hiddenLayer number.
net=patternnet(best_hiddenLayer);
net.divideFcn='';
best_model=ovrtrain(net,x,y); %ovrtrain function is written below.
%now check the performance of test dataset (this independant) on this best_model
[pred,pred_ind,actual_ind,acc]=ovrpredict(best_model,test_data,test_target); %ovrpredict function is written below.
fprintf('Accuracy on test dataset=%g\n',acc)

Now get_cv_ac function code

function [ac]=get_cv_ac(y,x,nr_fold,net)
%y is train label 
%x is train data
 len=size(y,2);
ac=0;
rand_ind=randperm(len);
for i=1:nr_fold
val_ind=rand_ind([floor((i-1)*len/nr_fold)+1:floor(i*len/nr_fold)])';
train_ind=rand_ind[1:len]';
train_ind(test_ind)=[];
[model,tr]=ovrtrain(net,x(:,train_ind),y(:,train_ind)); %ovrtrain function is written below
[pred,pred_ind,actual_ind,acc]=ovrpredict(model,x(:,val_ind),y(:,val_ind)); %ovrpredict function is written below
ac=ac+sum(pred_ind==actual_ind);
end
ac=ac/len*100;
fprintf('cross-val accuracy=%g\n',ac);
end

Now ovrtrain function is written below

function [model,tr] = ovrtrain(net,x,y)
net=configure(net,x,y);
[model,tr]=train(net,x,y);
end

Now ovrpredict function is written below

function [pred,pred_ind,actual_ind,acc]=ovrpredict(model,x,test_ind)
 pred=model(x);
[values,pred_ind]=max(pred,[],1);
[~,actual_ind]=max(test_ind,[],1);
acc=(sum(pred_ind==actual_ind)/size(x,2))*100
end

[Greg Heath]

Run your code. If there are any errors, let us know.

Greg