한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제24권1호
  • /
  • Pages.137-143
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  • 2019
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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Hyper Parameter Tuning Method based on Sampling for Optimal LSTM Model

  • Kim, Hyemee (Department of Industrial Engineering, Pusan National University) ;
  • Jeong, Ryeji (Department of Industrial Engineering, Pusan National University) ;
  • Bae, Hyerim (Department of Industrial Engineering, Pusan National University)
  • 투고 : 2018.12.04
  • 심사 : 2018.12.07
  • 발행 : 2019.01.31
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초록

As the performance of computers increases, the use of deep learning, which has faced technical limitations in the past, is becoming more diverse. In many fields, deep learning has contributed to the creation of added value and used on the bases of more data as the application become more divers. The process for obtaining a better performance model will require a longer time than before, and therefore it will be necessary to find an optimal model that shows the best performance more quickly. In the artificial neural network modeling a tuning process that changes various elements of the neural network model is used to improve the model performance. Except Gride Search and Manual Search, which are widely used as tuning methods, most methodologies have been developed focusing on heuristic algorithms. The heuristic algorithm can get the results in a short time, but the results are likely to be the local optimal solution. Obtaining a global optimal solution eliminates the possibility of a local optimal solution. Although the Brute Force Method is commonly used to find the global optimal solution, it is not applicable because of an infinite number of hyper parameter combinations. In this paper, we use a statistical technique to reduce the number of possible cases, so that we can find the global optimal solution.

키워드

CPTSCQ_2019_v24n1_137_f0001.png 이미지

Fig. 1. Basic RNN construction

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Fig. 2. Various structures of RNN

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Fig. 3. Algorithm flow chart

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Fig. 4. Distribution of sample with criterion

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Fig. 5. Example of filtering combinations

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Fig. 6. Example of setting a criterion

Table 1. Condition of experimentsa = the number of input variablesb = any integer,  

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Table 2. Mean RMSE from experiment’s first loop using gold price

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Table 3. Probability that the distribution is bigger than the criterion from experiment’s first loop using gold price

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Table 4. Experiment result – performance(RMSE)

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Table 5. Experiment result – the number of experiments

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