On Empirical Comparisons of Optimizers for Deep Learning

Anonymous

Sep 25, 2019 ICLR 2020 Conference Blind Submission readers: everyone Show Bibtex
  • TL;DR: Optimizer comparisons depend more than you would think on metaparameter tuning details and our prior should be that more general update rules (e.g. adaptive gradient methods) are better.
  • Abstract: Selecting an optimizer is a central step in the contemporary deep learning pipeline. In this paper we demonstrate the sensitivity of optimizer comparisons to the metaparameter tuning protocol. Our findings suggest that the metaparameter search space may be the single most important factor explaining the rankings obtained by recent empirical comparisons in the literature. In fact, we show that these results can be contradicted when metaparameter search spaces are changed. As tuning effort grows without bound, more general update rules should never underperform the ones they can approximate (i.e., Adam should never perform worse than momentum), but the recent attempts to compare optimizers either assume these inclusion relationships are not relevant in practice or restrict the metaparameters they tune to break the inclusions. In our experiments, we find that the inclusion relationships between optimizers matter in practice and always predict optimizer comparisons. In particular, we find that the popular adative gradient methods never underperform momentum or gradient descent. We also report practical tips around tuning rarely-tuned metaparameters of adaptive gradient methods and raise concerns about fairly benchmarking optimizers for neural network training.
  • Keywords: Deep learning, optimization, adaptive gradient methods, Adam, hyperparameter tuning
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