  Artificial neural networks are often very complex and too deep for a human to
understand. As a result, they are usually referred to as black boxes. For a lot
of real-world problems, the underlying pattern itself is very complicated, such
that an analytic solution does not exist. However, in some cases, laws of
physics, for example, the pattern can be described by relatively simple
mathematical expressions. In that case, we want to get a readable equation
rather than a black box. In this paper, we try to find a way to explain a
network and extract a human-readable equation that describes the model.
