  Training deep neural networks using a large batch size has shown promising
results and benefits many real-world applications. However, the optimizer
converges slowly at early epochs and there is a gap between large-batch deep
learning optimization heuristics and theoretical underpinnings. In this paper,
we propose a novel Complete Layer-wise Adaptive Rate Scaling (CLARS) algorithm
for large-batch training. We also analyze the convergence rate of the proposed
method by introducing a new fine-grained analysis of gradient-based methods.
Based on our analysis, we bridge the gap and illustrate the theoretical
insights for three popular large-batch training techniques, including linear
learning rate scaling, gradual warmup, and layer-wise adaptive rate scaling.
Extensive experiments demonstrate that the proposed algorithm outperforms
gradual warmup technique by a large margin and defeats the convergence of the
state-of-the-art large-batch optimizer in training advanced deep neural
networks (ResNet, DenseNet, MobileNet) on ImageNet dataset.
