A Framework for the construction of upper bounds on the number of affine linear regions of ReLU feed-forward neural networks

06/05/2018

∙

by   Peter Hinz, et al.

∙

0

∙

share

In this work we present a new framework to derive upper bounds on the number regions of feed-forward neural nets with ReLU activation functions. We derive all existing such bounds as special cases, however in a different representation in terms of matrices. This provides new insight and allows a more detailed analysis of the corresponding bounds. In particular, we provide a Jordan-like decomposition for the involved matrices and present new tighter results for an asymptotic setting. Moreover, new even stronger bounds may be obtained from our framework.