On the convergence and improvement of stochastic normalized gradient descent
Non-convex models, like deep neural networks, have been widely used in machine learning applications. Training non-convex models is a difficult task owing to the saddle points of models. Recently, stochastic normalized gradient descent (SNGD), which updates the model parameter by a normalized gradie...
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| Vydané v: | Science China. Information sciences Ročník 64; číslo 3; s. 132103 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Beijing
Science China Press
01.03.2021
Springer Nature B.V |
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| ISSN: | 1674-733X, 1869-1919 |
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| Abstract | Non-convex models, like deep neural networks, have been widely used in machine learning applications. Training non-convex models is a difficult task owing to the saddle points of models. Recently, stochastic normalized gradient descent (SNGD), which updates the model parameter by a normalized gradient in each iteration, has attracted much attention. Existing results show that SNGD can achieve better performance on escaping saddle points than classical training methods like stochastic gradient descent (SGD). However, none of the existing studies has provided theoretical proof about the convergence of SNGD for non-convex problems. In this paper, we firstly prove the convergence of SNGD for non-convex problems. Particularly, we prove that SNGD can achieve the same computation complexity as SGD. In addition, based on our convergence proof of SNGD, we find that SNGD needs to adopt a small constant learning rate for convergence guarantee. This makes SNGD do not perform well on training large non-convex models in practice. Hence, we propose a new method, called stagewise SNGD (S-SNGD), to improve the performance of SNGD. Different from SNGD in which a small constant learning rate is necessary for convergence guarantee, S-SNGD can adopt a large initial learning rate and reduce the learning rate by stage. The convergence of S-SNGD can also be theoretically proved for non-convex problems. Empirical results on deep neural networks show that S-SNGD achieves better performance than SNGD in terms of both training loss and test accuracy. |
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| AbstractList | Non-convex models, like deep neural networks, have been widely used in machine learning applications. Training non-convex models is a difficult task owing to the saddle points of models. Recently, stochastic normalized gradient descent (SNGD), which updates the model parameter by a normalized gradient in each iteration, has attracted much attention. Existing results show that SNGD can achieve better performance on escaping saddle points than classical training methods like stochastic gradient descent (SGD). However, none of the existing studies has provided theoretical proof about the convergence of SNGD for non-convex problems. In this paper, we firstly prove the convergence of SNGD for non-convex problems. Particularly, we prove that SNGD can achieve the same computation complexity as SGD. In addition, based on our convergence proof of SNGD, we find that SNGD needs to adopt a small constant learning rate for convergence guarantee. This makes SNGD do not perform well on training large non-convex models in practice. Hence, we propose a new method, called stagewise SNGD (S-SNGD), to improve the performance of SNGD. Different from SNGD in which a small constant learning rate is necessary for convergence guarantee, S-SNGD can adopt a large initial learning rate and reduce the learning rate by stage. The convergence of S-SNGD can also be theoretically proved for non-convex problems. Empirical results on deep neural networks show that S-SNGD achieves better performance than SNGD in terms of both training loss and test accuracy. |
| ArticleNumber | 132103 |
| Author | Zhao, Shen-Yi Li, Wu-Jun Xie, Yin-Peng |
| Author_xml | – sequence: 1 givenname: Shen-Yi surname: Zhao fullname: Zhao, Shen-Yi organization: National Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University – sequence: 2 givenname: Yin-Peng surname: Xie fullname: Xie, Yin-Peng organization: National Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University – sequence: 3 givenname: Wu-Jun surname: Li fullname: Li, Wu-Jun email: liwujun@nju.edu.cn organization: National Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University |
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| Cites_doi | 10.1137/070704277 10.1007/s10107-015-0871-8 10.1007/978-1-4419-8853-9 10.1137/S1052623495294797 10.1007/s11432-018-9656-y 10.1214/aoms/1177729586 10.1007/s11432-008-0117-y 10.1007/978-3-642-35289-8_3 10.1109/CVPR.2016.90 |
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| DOI | 10.1007/s11432-020-3023-7 |
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| SubjectTerms | Artificial neural networks Computer Science Convergence Information Systems and Communication Service Iterative methods Machine learning Neural networks Performance enhancement Research Paper Saddle points |
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| Title | On the convergence and improvement of stochastic normalized gradient descent |
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