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Nonlinear Activation Functions in CNN Based on Fluid Dynamics and Its Applications

Kazuhiko Kakuda1,*, Tomoyuki Enomoto1, Shinichiro Miura2

Department of Mathematical Information Engineering, College of Industrial Technology, Nihon University, Chiba 275-8575, Japan.
Department of Liberal Arts and Basic Sciences, College of Industrial Technology, Nihon University, Chiba 275-8576, Japan.

* Corresponding Author: Kazuhiko Kakuda. Email: email.

Computer Modeling in Engineering & Sciences 2019, 118(1), 1-14. https://doi.org/10.31614/cmes.2019.04676

Abstract

The nonlinear activation functions in the deep CNN (Convolutional Neural Network) based on fluid dynamics are presented. We propose two types of activation functions by applying the so-called parametric softsign to the negative region. We use significantly the well-known TensorFlow as the deep learning framework. The CNN architecture consists of three convolutional layers with the max-pooling and one fully-connected softmax layer. The CNN approaches are applied to three benchmark datasets, namely, MNIST, CIFAR-10, and CIFAR-100. Numerical results demonstrate the workability and the validity of the present approach through comparison with other numerical performances.

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APA Style
Kakuda, K., Enomoto, T., Miura, S. (2019). Nonlinear activation functions in CNN based on fluid dynamics and its applications. Computer Modeling in Engineering & Sciences, 118(1), 1-14. https://doi.org/10.31614/cmes.2019.04676
Vancouver Style
Kakuda K, Enomoto T, Miura S. Nonlinear activation functions in CNN based on fluid dynamics and its applications. Comput Model Eng Sci. 2019;118(1):1-14 https://doi.org/10.31614/cmes.2019.04676
IEEE Style
K. Kakuda, T. Enomoto, and S. Miura "Nonlinear Activation Functions in CNN Based on Fluid Dynamics and Its Applications," Comput. Model. Eng. Sci., vol. 118, no. 1, pp. 1-14. 2019. https://doi.org/10.31614/cmes.2019.04676



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