  Deep learning at its core, contains functions that are composition of a
linear transformation with a non-linear function known as activation function.
In past few years, there is an increasing interest in construction of novel
activation functions resulting in better learning. In this work, we propose a
family of novel activation functions, namely TanhSoft, with four undetermined
hyper-parameters of the form
tanh({\alpha}x+{\beta}e^{{\gamma}x})ln({\delta}+e^x) and tune these
hyper-parameters to obtain activation functions which are shown to outperform
several well known activation functions. For instance, replacing ReLU with
xtanh(0.6e^x)improves top-1 classification accuracy on CIFAR-10 by 0.46% for
DenseNet-169 and 0.7% for Inception-v3 while with tanh(0.87x)ln(1 +e^x) top-1
classification accuracy on CIFAR-100 improves by 1.24% for DenseNet-169 and
2.57% for SimpleNet model.
