This paper studies learning rate policies for deep neural networks, offering metrics and tools for better tuning.
problem Effective tuning of learning rates for deep neural networks is challenging and crucial for achieving high accuracy.
method Comprehensive study of 13 learning rate functions, proposing metrics for evaluation, and developing LRBench for benchmarking and selection.
result Identification of good learning rate policies with effective ranges and step sizes for various LR update schedules.