# hyper-sinh: An Accurate and Reliable Function from Shallow to Deep Learning in TensorFlow and Keras

arXiv: https://arxiv.org/abs/2011.07661 · HTML: https://mlsanity.com/paper/2011.07661/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [M-ar-K-Fast Independent Component Analysis](https://mlsanity.com/paper/2108.07908/) (2021)

## Abstract (arXiv)

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.