There are really two decisions that must be made regarding the
hidden layers: how many hidden layers to actually have in the
neural network and how many neurons will be in each of these
layers. We will first examine how to determine the number of
hidden layers to use with the neural network. Problems that
require two hidden layers are rarely encountered. However,
neural networks with two hidden layers can represent functions
with any kind of shape. There is currently no theoretical reason
to use neural networks with any more than two hidden layers. In
fact, for many practical problems, there is no reason to use any
more than one hidden layer. Table 5.1 summarizes the
capabilities of neural network architectures with various hidden
layers. Table 5.1: Determining the Number of Hidden Layers
|Number of Hidden Layers||Result| |none||Only capable of
representing linear separable functions or decisions.| |1||Can
approximate any function that contains a continuous mapping from
one finite space to another.| |2||Can r