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

    Kazuhiko Kakuda1,*, Tomoyuki Enomoto1, Shinichiro Miura2

    CMES-Computer Modeling in Engineering & Sciences, Vol.118, No.1, pp. 1-14, 2019, DOI: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. More >

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