PID controller‐based adaptive gradient optimizer for deep neural networks

Abstract

Abstract Due to improper selection of gradient update direction or learning rate, SGD optimization algorithms for deep learning suffer from oscillation and slow convergence. Although Adam algorithm can adaptively adjust the update direction and learning rate at the same time, it still has the overshoot phenomenon, and hence suffers from wasting computing resources and slow convergence. In this work, the PID controller from the feedback control area is borrowed to re‐express the adaptive optimization algorithm (the Adam optimization algorithm is derived into the integral I component form) of deep learning. In order to alleviate the overshoot phenomenon and hence speed up the convergence of Adam, a complete adaptive PID optimizer (adaptive‐PID) is proposed by incorporating the proportional P and derivative D component. Extensive experiments on standard data sets verify that the proposed adaptive‐PID algorithm significantly outperforms Adam algorithm in terms of convergence rate and accuracy.

Keywords

• Stochastic gradient descent (SGD)
• adaptive optimizer
• Adam
• proportional‐integral‐derivative (PID)
• feedback control