Incremental PID Controller-Based Learning Rate Scheduler for Stochastic Gradient Descent.

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Abstract

As we all know, the learning rate plays a vital role in deep neural network (DNN) training. This study introduces an incremental proportional-integral-derivative (PID) controller widely used in automatic control as a learning rate scheduler for stochastic gradient descent (SGD). To automatically calculate the current learning rate, we utilize feedback control to determine the relationship between training losses and learning rates, named incremental PID learning rates, which include PID-Base and PID-Warmup. The new schedulers reduce the dependence on the initial learning rate and achieve higher accuracy. Compared with multistep learning rates (MSLR), cyclical learning rates (CLR), and SGD with warm restarts (SGDR), incremental PID learning rates based on feedback control obtain higher accuracy on CIFAR-10, CIFAR-100, and Tiny-ImageNet-200. We believe that our methods can improve the performance of SGD.