You may create the iris example set with the Create iris example... command that you will find under the Neural nets option in the New menu. Three new objects will appear in the List of Objects: a FFNet, a Categories and a Pattern.

The Pattern contains the iris data set in a 150 rows by 4 columns matrix. To guarantee that every cell in the Pattern is in the [0,1] interval, all measurement values were divided by 10. In the Categories the three iris species setosa, versicolor, and virginica were categorized with the numbers 1, 2 and 3, respectively. Because there are 4 data columns in the Pattern and 3 different iris species in the Categories, the newly created FFNet has 4 inputs and 3 outputs. If you have entered a positive number in one of the fields in the form, the FFNet will have this number of units in a hidden layer. The name of the newly created FFNet will reflect its topology. If you did opt for the default, 0 hidden units, the FFNet will be named 4-3.

Learning the iris data

The first thing you probably might want to do is to let the FFNet learn the association in each pattern-category pair. To do this select all three objects together and choose Learn.... A form will appear, asking you to supply some settings for the learning algorithm. Learning starts after you have clicked the OK-button. Since the example network does not have too many weights that need to be adjusted and the learning data set is very small and computers nowadays are very fast, this will only take a very short time.

Classify

Now, if you are curious how well the FFNet has learned the iris data, you may select the FFNet and the Pattern together and choose To Categories.... A new Categories appears in the List of Objects with the name 4-3_iris (if 4-3 was the name of the FFNet and iris the name of the Pattern). We have two different Categories in the list of objects, the topmost one has the original categories, the other the categories as were assigned by the FFNet classifier. The obvious thing to do now is to compare the original categories with the assigned categories by making a confusion table. Select the two Categories and choose To Confusion and a newly created Confusion appears. Pressing the Info button will show you an info window with, among others, the fraction correct.

You might also want to compare the FFNet classifier with a discriminant classifier.

Create other neural net topologies

With a Pattern and a Categories selected together, you can for example create a new FFNet of a different topology.

Links to this page

• Feedforward neural networks