Modeling and Multivariate Methods - SAS

Chapter 10 Creating Neural Networks 283
Overview of Neural Networks
Table 10.3 Validation Methods (Continued)
Validation Column
Uses the column’s values to divide the data into parts. The column is
assigned using the Validation role on the Neural launch window. See
Figure 10.3.
The column’s values determine how the data is split, and what method
is used for validation:
• If the column has three unique values, then:
– the smallest value is used for the Training set.
– the middle value is used for the Validation set.
– the largest value is used for the Test set.
• If the column has two unique values, then only Training and
Validation sets are used.
• If the column has more than three unique values, then KFold
validation is performed.
Hidden Layer Structure
Note: The standard edition of JMP uses only the TanH activation function, and can fit only neural
networks with one hidden layer.
The Neural platform can fit one or two-layer neural networks. Increasing the number of nodes in the first
layer, or adding a second layer, makes the neural network more flexible. You can add an unlimited number
of nodes to either layer. The second layer nodes are functions of the X variables. The first layer nodes are
functions of the second layer nodes. The Y variables are functions of the first layer nodes.
The functions applied at the nodes of the hidden layers are called activation functions. An activation
function is a transformation of a linear combination of the X variables. Table 10.4 describes the three types
of activation functions.
Table 10.4 Activation Functions
TanH
The hyperbolic tangent function is a sigmoid function. TanH transforms
values to be between -1 and 1, and is the centered and scaled version of the
logistic function. The hyperbolic tangent function is:
e 2x – 1
----------------
e 2x + 1
where x is a linear combination of the X variables.