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arXiv · 1607.01691

A Modified Activation Function with Improved Run-Times For Neural Networks

Vincent Ike Anireh·Emmanuel Ndidi Osegi
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Abstract

In this paper we present a modified version of the Hyperbolic Tangent Activation Function as a learning unit generator for neural networks. The function uses an integer calibration constant as an approximation to the Euler number, e, based on a quadratic Real Number Formula (RNF) algorithm and an adaptive normalization constraint on the input activations to avoid the vanishing gradient. We demonstrate the effectiveness of the proposed modification using a hypothetical and real world dataset and show that lower run-times can be achieved by learning algorithms using this function leading to improved speed-ups and learning accuracies during training.

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Vincent Ike Anireh, Emmanuel Ndidi Osegi. 2016-07-06. A Modified Activation Function with Improved Run-Times For Neural Networks. https://arxiv.org/abs/1607.01691

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