ASLR: An Adaptive Scheduler for Learning Rate
Alireza Khodamoradi, Kristof Denolf, Kees Vissers, Ryan Kastner · 2021
Training a neural network is a complicated and time-consuming task that involves adjusting and testing different combinations of hyperparameters. One of the essential hyperparameters is the learning rate, which balances the magnitude of changes at each training step. We introduce an Adaptive Scheduler for Learning Rate (ASLR) that significantly lowers the tuning effort since it only has a single hyperparameter. ASLR produces competitive results compared to the state-of-the-art for both hand-optimized learning rate schedulers and line search methods while requiring significantly less tuning effort. Our algorithm's computational cost is trivial and can be used to train various network topologies included quantized networks.