cross validation in neural network using K-fold

Dear All;

i am using neural network for classification but i need to use instead of holdout option , K-fold.

i use cvparatition command to do that , which parameter of neural network shall i change to enable K-Fold option

the code

c = cvpartition(length(input1),'KFold',10)

net=patternnet(100)

net=train(net,input',Target_main')

Answer 1

[Greg Heath]

%i am using neural network for classification but i need to use instead of

holdout option , K-fold.

==> FALSE!. You mean you WANT to use K-fold.

% i use cvparatition command to do that , which parameter of neural

network shall i change to enable K-Fold option the code

%c = cvpartition(length(input1),'KFold',10)

% net=patternnet(100)

==> WRONG! numH = 100 is ridiculously large.

There is no excuse for this. There are numerous examples in both the

NEWSGROUP and ANSWERS on how to choose a reasonable value

for numH.

Greg

[Mustafa Al-Nasser]

Dear Greg;

i am takling about K-fold cross valdation technique for neural network

the defualt option is holdout one which hold certain perecantge for testing and valdiation

in my example, i need to use this technuque in my code

my problem is how to run code for 10 folds, shall repeat the excuation 10 time using for loop and take the average of 10 runs

for number of neourons , i know this big but the data set large, this was just trail , forget about it

i wrote the below code which can do 1 fold only, so, how can we run it for all fold and take the avergae

c = cvpartition(length(input1),'KFold',10)

trainData =input(training(c),:);

testData = input(test(c),:);

net=patternnet(50)

net=train(net,trainData,Target_main)