  This paper presents the 'hyper-sinh', a variation of the m-arcsinh activation
function suitable for Deep Learning (DL)-based algorithms for supervised
learning, such as Convolutional Neural Networks (CNN). 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 both shallow and
deep neural networks. Improvements in accuracy and reliability in image and
text classification tasks on five (N = 5) benchmark data sets available from
Keras are discussed. Experimental results demonstrate the overall competitive
classification performance of both shallow and deep neural networks, obtained
via this novel function. This function is evaluated with respect to gold
standard activation functions, demonstrating its overall competitive accuracy
and reliability for both image and text classification.
