In this paper we investigate the family of functions representable by deep neural networks (DNN) with rectified linear units (ReLU). We give the first-ever polynomial time (in the size of data) algorithm to train a ReLU DNN with one hidden layer to {\em global optimality}. This follows from our complete characterization of the ReLU DNN function class whereby we show that a $\R^n \to \R$ function is representable by a ReLU DNN {\em if and only if} it is a continuous piecewise linear function. The main tool used to prove this characterization is an elegant result from tropical geometry. Further, for the $n=1$ case, we show that a single hidden layer suffices to express all piecewise linear functions, and we give tight bounds for the size of such a ReLU DNN. We follow up with gap results showing that there is a smoothly parameterized family of $\R\to \R$ "hard" functions that lead to an exponential blow-up in size, if the number of layers is decreased by a small amount. An example consequence of our gap theorem is that for every natural number $N$, there exists a function representable by a ReLU DNN with depth $N^2+1$ and total size $N^3$, such that any ReLU DNN with depth at most $N + 1$ will require at least $\frac12N^{N+1}-1$ total nodes. #R##N#Finally, we construct a family of $\R^n\to \R$ functions for $n\geq 2$ (also smoothly parameterized), whose number of affine pieces scales exponentially with the dimension $n$ at any fixed size and depth. To the best of our knowledge, such a construction with exponential dependence on $n$ has not been achieved by previous families of "hard" functions in the neural nets literature. This construction utilizes the theory of zonotopes from polyhedral theory.
['Raman Arora', 'Amitabh Basu', 'Poorya Mianjy', 'Anirbit Mukherjee']
Understanding Deep Neural Networks with Rectified Linear Units