  In exchange for large quantities of data and processing power, deep neural
networks have yielded models that provide state of the art predication
capabilities in many fields. However, a lack of strong guarantees on their
behaviour have raised concerns over their use in safety-critical applications.
A first step to understanding these networks is to develop alternate
representations that allow for further analysis. It has been shown that neural
networks with piecewise affine activation functions are themselves piecewise
affine, with their domains consisting of a vast number of linear regions. So
far, the research on this topic has focused on counting the number of linear
regions, rather than obtaining explicit piecewise affine representations. This
work presents a novel algorithm that can compute the piecewise affine form of
any fully connected neural network with rectified linear unit activations.
