How do I cross-validate in recurrent neural networks?

There doesn't seem to be any facility to use cross-validation in recurrent neural networks in the NN Toolbox.

The following script is taken directly from the documentation on the 'layrecnet' function;

[X,T] = simpleseries_dataset; net = layrecnet(1:2,10); [Xs,Xi,Ai,Ts] = preparets(net,X,T); net = train(net,Xs,Ts,Xi,Ai); view(net) Y = net(Xs,Xi,Ai); perf = perform(net,Y,Ts)

Running this script starts the window, but no validation checks are made during training, and the process is stopped either by reaching the maximum epochs or mu value.

I've tried dividerand and divideind, but I cannot get validation checks to occur in any simulation.

Any suggestions?

Also, the example given in the documentation on Design Layer-Recurrent Neural Networks has the same problem. Is it a trait of recurrent neural networks that I was unaware of that validation checks cannot occur, am I doing something wrong, or is this a bug in the program?

Cheers,

Alex

Answer 1

[Greg Heath]

http://www.mathworks.com/matlabcentral/answers/225901-cross-validation-in-recurrent-neural-network