don’t decay the learning rate

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A arXiv:171100489v2 [csLG] 24 Feb 2018

When one decays the learning rate one simultaneously decays the scale of random fluctu-ations g in the SGD dynamics Decaying the learning rate is simulated annealing We propose an alternative procedure; instead of decaying the learning rate we increase the batch size during training

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How to decay your learning rate

Complex learning rate schedules have become an integral part of deep learning We find empirically that common fine-tuned schedules decay the learn-ing rate after the weight norm bounces This leads to the proposal of ABEL: an automatic scheduler which decays the learning rate by keeping track of the weight norm ABEL’s performance matches that of

Does Abel decay the learning rate after the weight norm bounces?
We find empirically that common fine-tuned schedules decay the learn-ing rate after the weight norm bounces. This leads to the proposal of ABEL: an automatic scheduler which decays the learning rate by keeping track of the weight norm. ABEL’s performance matches that of tuned schedules and is more robust with respect to its parameters.
Do complex learning rate schedules decay after weight norm bounces?
Complex learning rate schedules have become an integral part of deep learning. We find empirically that common fine-tuned schedules decay the learn-ing rate after the weight norm bounces. This leads to the proposal of ABEL: an automatic scheduler which decays the learning rate by keeping track of the weight norm.
What happens if a learning rate is decayed?
When one decays the learning rate, one simultaneously decays the scale of random fluctu-ations g in the SGD dynamics. Decaying the learning rate is simulated annealing. We propose an alternative procedure; instead of decaying the learning rate, we increase the batch size during training.
Can a learning curve be decayed?
It is common practice to decay the learning rate. Here we show one can usually obtain the same learning curve on both training and test sets by instead increasing the batch size during training. This procedure is successful for stochastic gradient descent (SGD), SGD with momentum, Nesterov momentum, and Adam.

Abstract

Complex learning rate schedules have become an integral part of deep learning. We find empirically that common fine-tuned schedules decay the learn-ing rate after the weight norm bounces. This leads to the proposal of ABEL: an automatic scheduler which decays the learning rate by keeping track of the weight norm. ABEL’s performance matches that of

Comparison of ABEL with other schedules

It is very natural to compare ABEL with step-wise decay. Step-wise decay is complicated to use in new settings be-cause on top of the base learning rate and the decay factor, one has to determine when to decay the learning rate. ABEL, takes care of the ‘when’ automatically without hurting per-formance. Because when to decay depends strongly on the

4. Understanding weight norm bouncing

In this section, we will pursue some first steps towards understanding the mechanism behind the phenomena that we found empirically in the previous sections. arxiv.org

2 gt wt + O( 2 ) (1)

be beneficial. In other setups, training with a constant learn-ing rate and decay it at the end of training should not hurt performance and might be a preferable, simpler method. ABEL’s hyperparameters. The main hyperparameters of ABEL are the base learning rate and decay factor: while our schedule is not hyperparameter free, ABEL is more robust th

What is the behaviour of the layerwise weight norm?

As discussed previously and in the SM B.2, most layers exhibit the same pattern as the total weight norm. Understanding the source of the generalization advan-tage of learning rate schedules. It would be nice to un-derstand if the bouncing of the weight norm is a proxy for some other phenomena. While we have tried tracking other simple quantities,

Acknowledgments

The authors would like to thank Anders Andreassen, Yasaman Bahri, Ethan Dyer, Orhan Firat, Pierre Foret, Guy Gur-Ari, Jaehoon Lee, Behnam Neyshabur and Vinay Ra-masesh for useful discussions. arxiv.org

Learning Rate in a Neural Network explained

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No More Pesky Learning Rates - Proceedings of Machine Learning

the learning rate (or step size) for each model and each problem, as fixed or decaying learning rates (full lines): any fixed learning rate limits the precision to which the optimum can Variants are marked in bold if they don't differ statistically