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Multilayer perceptron architecture optimization using parallel computing techniques

Author

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  • Wilson Castro
  • Jimy Oblitas
  • Roberto Santa-Cruz
  • Himer Avila-George

Abstract

The objective of this research was to develop a methodology for optimizing multilayer-perceptron-type neural networks by evaluating the effects of three neural architecture parameters, namely, number of hidden layers (HL), neurons per hidden layer (NHL), and activation function type (AF), on the sum of squares error (SSE). The data for the study were obtained from quality parameters (physicochemical and microbiological) of milk samples. Architectures or combinations were organized in groups (G1, G2, and G3) generated upon interspersing one, two, and three layers. Within each group, the networks had three neurons in the input layer, six neurons in the output layer, three to twenty-seven NHL, and three AF (tan-sig, log-sig, and linear) types. The number of architectures was determined using three factorial-type experimental designs, which reached 63, 2 187, and 50 049 combinations for G1, G2 and G3, respectively. Using MATLAB 2015a, a logical sequence was designed and implemented for constructing, training, and evaluating multilayer-perceptron-type neural networks using parallel computing techniques. The results show that HL and NHL have a statistically relevant effect on SSE, and from two hidden layers, AF also has a significant effect; thus, both AF and NHL can be evaluated to determine the optimal combination per group. Moreover, in the three study groups, it is observed that there is an inverse relationship between the number of processors and the total optimization time.

Suggested Citation

  • Wilson Castro & Jimy Oblitas & Roberto Santa-Cruz & Himer Avila-George, 2017. "Multilayer perceptron architecture optimization using parallel computing techniques," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-17, December.
  • Handle: RePEc:plo:pone00:0189369
    DOI: 10.1371/journal.pone.0189369
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    References listed on IDEAS

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    1. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
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    1. J. R. S. Iruela & L. G. B. Ruiz & M. I. Capel & M. C. Pegalajar, 2021. "A TensorFlow Approach to Data Analysis for Time Series Forecasting in the Energy-Efficiency Realm," Energies, MDPI, vol. 14(13), pages 1-22, July.
    2. Md. Alamin Talukder & Rakib Hossen & Md Ashraf Uddin & Mohammed Nasir Uddin & Uzzal Kumar Acharjee, 2024. "Securing Transactions: A Hybrid Dependable Ensemble Machine Learning Model using IHT-LR and Grid Search," Papers 2402.14389, arXiv.org.

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