Activation functions (AFs) are crucial components of deep neural networks
(DNNs), having a significant impact on their performance. An activation
function in a DNN is typically a smooth, nonlinear function that transforms an
input signal into an output signal for the subsequent layer. In this paper, we
propose the Parametric Leaky Tanh (PLTanh), a novel hybrid activation function
designed to combine the strengths of both the Tanh and Leaky ReLU (LReLU)
activation functions. PLTanh is differentiable at all points and addresses the
'dying ReLU' problem by ensuring a non-zero gradient for negative inputs,
consistent with the behavior of LReLU. By integrating the unique advantages of
these two diverse activation functions, PLTanh facilitates the learning of more
intricate nonlinear relationships within the network. This paper presents an
empirical evaluation of PLTanh against established activation functions, namely
ReLU, LReLU, and ALReLU utilizing five diverse datasets.

本文在深度神经网络中提出了 Parametric Leaky Tanh（PLTanh）作为一种新的混合激活函数，结合了 Tanh 和 Leaky ReLU 激活函数的优势，通过保证负输入的梯度非零，解决了 'dying ReLU' 问题，并通过与 ReLU、LReLU 和 ALReLU 在五个不同数据集上的实证评估，展示了 PLTanh 的优势。