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