NNET Procedure

Note: Input data must be in a table that is accessible in your session. You can refer to this table by using a two-level name. The first level is a libref, and the second level is the table name. For more information, see the section Using SAS Viya Workbench in Chapter 2, Shared Concepts.

This example shows how to use the NNET procedure to train a neural network to predict the type of iris plant. The iris data published by Fisher (1936) have been widely used for examples in discriminant and cluster analyses. The sepal length, sepal width, petal length, and petal width are measured in millimeters on 50 iris specimens from each of three species: Iris setosa, I. versicolor, and I. virginica. The data set is available in the Sashelp library.

You can load the sashelp.iris data into your SAS library by naming your SAS library in the first statement of the following DATA step:

data mylib.iris;
   set sashelp.iris;
run;

These statements assume that your SAS library is named mylib, but you can substitute any appropriately defined SAS library.

The following statements run PROC NNET and output the results to ODS tables:

proc nnet data=mylib.iris;
   input SepalLength SepalWidth PetalLength PetalWidth;
   target Species / level=nominal;
   hidden 2;
   train outmodel=mylib.nnetModel_gs seed=635117188;
   partition fraction(validate=0.3 seed=103873735);
run;

Figure 1 shows the model information for the neural network.

Figure 2 shows the misclassification rate for the training sample.

Figure 3 shows the misclassification rate for the validation sample.