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An Empirical Analysis of Deep Network Loss Surfaces
Daniel Jiwoong Im, Michael Tao, Kristin Branson
Nov 05, 2016 (modified: Dec 08, 2016)ICLR 2017 conference submissionreaders: everyone
Abstract: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.
TL;DR:Analyzing the loss surface of deep neural network trained with different optimization methods