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Method that determining the Hyperparameter of CNN using HS algorithm

HS 알고리즘을 이용한 CNN의 Hyperparameter 결정 기법

  • Lee, Woo-Young (Department of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Ko, Kwang-Eun (Department of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Geem, Zong-Woo (Department of Energy IT, Gachon University) ;
  • Sim, Kwee-Bo (Department of Electrical and Electronics Engineering, Chung-Ang University)
  • 이우영 (중앙대학교 전자전기공학부) ;
  • 고광은 (중앙대학교 전자전기공학부) ;
  • 김종우 (가천대학교 에너지IT학과) ;
  • 심귀보 (중앙대학교 전자전기공학부)
  • Received : 2017.01.04
  • Accepted : 2017.02.17
  • Published : 2017.02.25

Abstract

The Convolutional Neural Network(CNN) can be divided into two stages: feature extraction and classification. The hyperparameters such as kernel size, number of channels, and stride in the feature extraction step affect the overall performance of CNN as well as determining the structure of CNN. In this paper, we propose a method to optimize the hyperparameter in CNN feature extraction stage using Parameter-Setting-Free Harmony Search (PSF-HS) algorithm. After setting the overall structure of CNN, hyperparameter was set as a variable and the hyperparameter was optimized by applying PSF-HS algorithm. The simulation was conducted using MATLAB, and CNN learned and tested using mnist data. We update the parameters for a total of 500 times, and it is confirmed that the structure with the highest accuracy among the CNN structures obtained by the proposed method classifies the mnist data with an accuracy of 99.28%.

Acknowledgement

Supported by : 한국연구재단

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