@Article{cmes.2019.04676,
AUTHOR = {Kazuhiko Kakuda, Tomoyuki Enomoto, Shinichiro Miura},
TITLE = {Nonlinear Activation Functions in CNN Based on Fluid Dynamics and Its Applications},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {118},
YEAR = {2019},
NUMBER = {1},
PAGES = {1--14},
URL = {http://www.techscience.com/CMES/v118n1/33890},
ISSN = {1526-1506},
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.},
DOI = {10.31614/cmes.2019.04676}
}