Stochastic Normalized Gradient Descent with Momentum for Large-Batch Training

• URL: http://arxiv.org/abs/2007.13985v2
• Date: Mon, 15 Apr 2024 03:27:58 GMT
• Title: Stochastic Normalized Gradient Descent with Momentum for Large-Batch Training
• Authors: Shen-Yi Zhao, Chang-Wei Shi, Yin-Peng Xie, Wu-Jun Li,
• Abstract summary: gradient descent(SGD) and its variants have been the dominating optimization methods in machine learning. In this paper, we propose a simple yet effective method, called normalized gradient descent with momentum(SNGM) for largebatch training.
• Score: 9.964630991617764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stochastic gradient descent~(SGD) and its variants have been the dominating optimization methods in machine learning. Compared to SGD with small-batch training, SGD with large-batch training can better utilize the computational power of current multi-core systems such as graphics processing units~(GPUs) and can reduce the number of communication rounds in distributed training settings. Thus, SGD with large-batch training has attracted considerable attention. However, existing empirical results showed that large-batch training typically leads to a drop in generalization accuracy. Hence, how to guarantee the generalization ability in large-batch training becomes a challenging task. In this paper, we propose a simple yet effective method, called stochastic normalized gradient descent with momentum~(SNGM), for large-batch training. We prove that with the same number of gradient computations, SNGM can adopt a larger batch size than momentum SGD~(MSGD), which is one of the most widely used variants of SGD, to converge to an $\epsilon$-stationary point. Empirical results on deep learning verify that when adopting the same large batch size, SNGM can achieve better test accuracy than MSGD and other state-of-the-art large-batch training methods.

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