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Improving performance of deep learning models and reducing their training times are ongoing challenges in deep neural networks. There are several approaches proposed to address these challenges, one of which is to increase the depth of the neural networks. Such deeper networks not only increase training times, but also suffer from vanishing gradients problem while training. In this work, we propose gradient amplification approach for training deep learning models to prevent vanishing gradients and also develop a training strategy to enable or disable gradient amplification method across several epochs with different learning rates. We perform experiments on VGG-19 and Resnet models (Resnet-18 and Resnet-34) , and study the impact of amplification parameters on these models in detail. Our proposed approach improves performance of these deep learning models even at higher learning rates, thereby allowing these models to achieve higher performance with reduced training time.


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Gradient Amplification: An Efficient Way to Train Deep Neural Networks

Show Author's information Sunitha BasodiChunyan JiHaiping ZhangYi Pan( )
Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA.
Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

Abstract

Improving performance of deep learning models and reducing their training times are ongoing challenges in deep neural networks. There are several approaches proposed to address these challenges, one of which is to increase the depth of the neural networks. Such deeper networks not only increase training times, but also suffer from vanishing gradients problem while training. In this work, we propose gradient amplification approach for training deep learning models to prevent vanishing gradients and also develop a training strategy to enable or disable gradient amplification method across several epochs with different learning rates. We perform experiments on VGG-19 and Resnet models (Resnet-18 and Resnet-34) , and study the impact of amplification parameters on these models in detail. Our proposed approach improves performance of these deep learning models even at higher learning rates, thereby allowing these models to achieve higher performance with reduced training time.

Keywords:

deep learning, gradient amplification, learning rate, backpropagation, vanishing gradients
Received: 01 April 2020 Accepted: 16 April 2020 Published: 16 July 2020 Issue date: September 2020
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Publication history

Received: 01 April 2020
Accepted: 16 April 2020
Published: 16 July 2020
Issue date: September 2020

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© The author(s) 2020

Acknowledgements

The authors would like to thank Georgia State University (GSU) for providing us access to High Performance Computing (HPC) cluster on which most of the experiments are performed. This research was supported in part by an NVIDIA Academic Hardware Grant.

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