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Activation functions play an essential role in neural networks. They providethe non-linearity for the networks. Therefore, their properties are importantfor neural networks' accuracy and running performance. In this paper, wepresent a novel signed and truncated logarithm function as activation function.The proposed activation function has significantly better mathematicalproperties, such as being odd function, monotone, differentiable, havingunbounded value range, and a continuous nonzero gradient. These properties makeit an excellent choice as an activation function. We compare it with otherwell-known activation functions in several well-known neural networks. Theresults confirm that it is the state-of-the-art. The suggested activationfunction can be applied in a large range of neural networks where activationfunctions are necessary.

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