  A ReLU neural network determines/is a continuous piecewise linear map from an
input space to an output space. The weights in the neural network determine a
decomposition of the input space into convex polytopes and on each of these
polytopes the network can be described by a single affine mapping. The
structure of the decomposition, together with the affine map attached to each
polytope, can be analyzed to investigate the behavior of the associated neural
network.
