An Empirical Analysis of Deep Network Loss Surfaces
Daniel Jiwoong Im, Michael W. Tao, Kristin M. Branson · arXiv (Cornell University) · 2017
The training of deep neural networks is a high-dimension optimization problem with respect to the loss function of a model. Unfortunately, these functions are of high dimension and non-convex and hence difficult to characterize. In this paper, we empirically investigate the geometry of the loss functions for state-of-the-art networks with multiple stochastic optimization methods. We do this through several experiments that are visualized on polygons to understand how and when these stochastic optimization methods find minima.