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The Effect of Hyperparameter Choice on ReLU and SELU Activation Function

  • Kevin, Pratama (Department of Ubiquitous IT, Graduate School, Dongseo University) ;
  • Kang, Dae-Ki (Department of Computer Engineering, Dongseo University)
  • Received : 2017.11.10
  • Accepted : 2017.12.07
  • Published : 2017.12.31

Abstract

The Convolutional Neural Network (CNN) has shown an excellent performance in computer vision task. Applications of CNN include image classification, object detection in images, autonomous driving, etc. This paper will evaluate the performance of CNN model with ReLU and SELU as activation function. The evaluation will be performed on four different choices of hyperparameter which are initialization method, network configuration, optimization technique, and regularization. We did experiment on each choice of hyperparameter and show how it influences the network convergence and test accuracy. In this experiment, we also discover performance improvement when using SELU as activation function over ReLU.

Acknowledgement

Supported by : National Research Foundation of Korea (NRF)

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