Produce Image Classifier Confusion Matrix

This example evaluates an image classifier trained to identify apples, oranges and bananas. The diagonal values are the correctly classified images. Apples and oranges create more confusion with each other than either does with bananas, which can guide a review of the training images, lighting conditions or class definitions. A confusion matrix is valuable because a single overall accuracy can hide this pattern. Before drawing conclusions, check that the test set contains representative images and that no class is much smaller than the others. This is a teaching example, so it uses simple labels and a compact three-class layout.