Continuing the argument in the previous subsection, we can also visualize the effect by superimposing all the hidden unit boundaries and coming up with the separation done at the output layer. This way, one can see how the network behaves to classify the inputs patterns in a visual space by using multiple boundaries (see files in Section 4.2.1 for details about implementation).