Question

i am using neurondotnet library for ANN programing, my input layer vector is 400
and my output layer vector is 5 and my sample for learning is only 28.
my problem is that i don’t have any idea of how to choose how many hidden layer i should have and the size of their vectors. can anyone put me on the right direction ?
thank you.

Answer 1

Check out their help guide
http://neurondotnet.freehostia.com/manual/design.html

From the guide

Number of hidden layers (in Backpropagation Networks)

A Backpropagation network with no hidden layers cannot perform non-linear classification. (It cannot be used as a XOR function approximator). So, one hidden layer is a must for a backpropagation network. Further, it has been mathematically proved that a backpropagation network with single hidden layer when appropriately trained, can be used to approximate any function. So single hidden layer is the best choice in most cases.

Having multiple hidden layers speeds up the learning process and the trained network fits exactly with the training samples but fails to perform well on the test data. This effect is called overtraining where the trained network tends to memorize the training samples instead of learning them.