NNET Procedure

Note: Input data must be in a CAS table that is accessible in your CAS session. You must refer to this table by using a two-level name. The first level must be a CAS engine libref, and the second level must be the table name. For more information, see the sections Using CAS Sessions and CAS Engine Librefs and Loading a SAS Data Set onto a CAS Server 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 CAS session libref by naming your CAS engine libref in the first statement of the following DATA step:

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

These statements assume that your CAS engine libref is named mylib, but you can substitute any appropriately defined CAS engine libref.

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.