Adaptive hybrid activation function for deep neural networks




adaptive hybrid activation function, double-stage parameter turning process, deep neural networks


The adaptive hybrid activation function (AHAF) is proposed that combines the properties of the rectifier units and the squashing functions. The proposed function can be used as a drop-in replacement for ReLU, SiL and Swish activations for deep neural networks and can evolve to one of such functions during the training. The effectiveness of the function was evaluated on the image classification task using the Fashion-MNIST and CIFAR-10 datasets. The evaluation shows that the neural networks with AHAF activations achieve better classification accuracy comparing to their base implementations that use ReLU and SiL. A double-stage parameter tuning process for training the neural networks with AHAF is proposed. The proposed approach is sufficiently simple from the implementation standpoint and provides high performance for the neural network training process.

Author Biographies

Yevgeniy Bodyanskiy, Kharkiv National University of Radio Electronics, Kharkiv

Yevgeniy V. Bodyanskiy,

Doctor of Technical Sciences, a professor at the Department of Artificial Intelligence of Kharkiv National University of Radio Electronics, Kharkiv, Ukraine.

Serhii Kostiuk, Kharkiv National University of Radio Electronics, Kharkiv

Serhii O. Kostiuk,

a Ph.D. student at the Department of Artificial Intelligence of Kharkiv National University of Radio Electronics, Kharkiv, Ukraine.


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