  We use graphical methods to probe neural nets that classify images. Plots of
t-SNE outputs at successive layers in a network reveal increasingly organized
arrangement of the data points. They can also reveal how a network can diminish
or even forget about within-class structure as the data proceeds through
layers. We use class-specific analogues of principal components to visualize
how succeeding layers separate the classes. These allow us to sort images from
a given class from most typical to least typical (in the data) and they also
serve as very useful projection coordinates for data visualization. We find
them especially useful when defining versions guided tours for animated data
visualization.

Machine Learning;Artificial Intelligence