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Activation functions play a pivotal role in the function learning usingneural networks. The non-linearity in the learned function is achieved byrepeated use of the activation function. Over the years, numerous activationfunctions have been proposed to improve accuracy in several tasks. Basicfunctions like ReLU, Sigmoid, Tanh, or Softplus have been favorite among thedeep learning community because of their simplicity. In recent years, severalnovel activation functions arising from these basic functions have beenproposed, which have improved accuracy in some challenging datasets. We proposea five hyper-parameters family of activation functions, namely EIS, defined as,\[ \frac{x(\ln(1+e^x))^\alpha}{\sqrt{\beta+\gamma x^2}+\delta e^{-\theta x}}.\] We show examples of activation functions from the EIS family whichoutperform widely used activation functions on some well known datasets andmodels. For example, $\frac{x\ln(1+e^x)}{x+1.16e^{-x}}$ beats ReLU by 0.89\% inDenseNet-169, 0.24\% in Inception V3 in CIFAR100 dataset while 1.13\% inInception V3, 0.13\% in DenseNet-169, 0.94\% in SimpleNet model in CIFAR10dataset. Also, $\frac{x\ln(1+e^x)}{\sqrt{1+x^2}}$ beats ReLU by 1.68\% inDenseNet-169, 0.30\% in Inception V3 in CIFAR100 dataset while 1.0\% inInception V3, 0.15\% in DenseNet-169, 1.13\% in SimpleNet model in CIFAR10dataset.

EIS -- a family of activation functions combining Exponential, ISRU, and Softplus