Hyper-sinh: An accurate and reliable function from shallow to deep learning in TensorFlow and Keras

Abstract

This paper presents the ‘hyper-sinh’, a variation of the m-arcsinh activation function suit-able for Deep Learning (DL)-based algorithms for supervised learning, including Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), such as the Long Short-Term Memory (LSTM). hyper-sinh, developed in the open-source Python libraries TensorFlow and Keras, is thus described and validated as an accurate and reliable activation function for shallow and deep neural networks. Improvements in accuracy and reliability in image and text classification tasks on six (N=6) medium-to-large open-source benchmark datasets are discussed. Experimental results demonstrate that the overall competitive classification performance of the novel hyper-sinh function on shallow and deep neural networks yielded the highest performance. Furthermore, this activation is evaluated against other gold standard activation functions, demonstrating its overall competitive accuracy and reliability for both image and text classification tasks.