AutoLR: A Method for Automatic Tuning of Learning Rate

One very important hyperparameter for training deep neural networks is the learning rate of the optimizer. The choice of learning rate schedule determines the computational cost of getting close to a minima, how close you actually get to the minima, and most importantly the kind of local minima (wide/narrow) attained. The kind of minima attained has a significant impact on the generalization accuracy of the network. Current systems employ hand tuned learning rate schedules, which are painstakingly tuned for each network and dataset. Given that the state space of schedules is huge, finding a satisfactory learning rate schedule can be very time consuming. In this paper, we present AutoLR, a method for auto-tuning the learning rate as training proceeds. Our method works with any optimizer, and we demonstrate results on SGD, Momentum, and Adam optimizers. We extensively evaluate AutoLR on multiple datasets, models, and across multiple optimizers. We compare favorably against state of the art learning rate schedules for the given dataset and models, including for ImageNet on Resnet-50, Cifar-10 on Resnet-18, and SQuAD fine-tuning on BERT. For example, AutoLR achieves an EM score of 81.2 on SQuAD v1.1 with BERT_BASE compared to 80.8 reported in (Devlin et al. (2018)) by just auto-tuning the learning rate schedule. To the best of our knowledge, this is the first automatic learning rate tuning scheme to achieve state of the art generalization accuracy on these datasets with the given models.