STL: A Signed and Truncated Logarithm Activation Function for Neural Networks

• URL: http://arxiv.org/abs/2307.16389v1
• Date: Mon, 31 Jul 2023 03:41:14 GMT
• Title: STL: A Signed and Truncated Logarithm Activation Function for Neural Networks
• Authors: Yuanhao Gong
• Abstract summary: Activation functions play an essential role in neural networks. We present a novel signed and truncated logarithm function as activation function. The suggested activation function can be applied in a large range of neural networks.
• Score: 5.9622541907827875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Activation functions play an essential role in neural networks. They provide the non-linearity for the networks. Therefore, their properties are important for neural networks' accuracy and running performance. In this paper, we present a novel signed and truncated logarithm function as activation function. The proposed activation function has significantly better mathematical properties, such as being odd function, monotone, differentiable, having unbounded value range, and a continuous nonzero gradient. These properties make it an excellent choice as an activation function. We compare it with other well-known activation functions in several well-known neural networks. The results confirm that it is the state-of-the-art. The suggested activation function can be applied in a large range of neural networks where activation functions are necessary.

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