  Activation functions have a notorious impact on neural networks on both
training and testing the models against the desired problem. Currently, the
most used activation function is the Rectified Linear Unit (ReLU). This paper
introduces a new and novel activation function, closely related with the new
activation $Swish = x * sigmoid(x)$ (Ramachandran et al., 2017) which
generalizes it. We call the new activation $E-swish = \beta x * sigmoid(x)$. We
show that E-swish outperforms many other well-known activations including both
ReLU and Swish. For example, using E-swish provided 1.5% and 4.6% accuracy
improvements on Cifar10 and Cifar100 respectively for the WRN 10-2 when
compared to ReLU and 0.35% and 0.6% respectively when compared to Swish. The
code to reproduce all our experiments can be found at
https://github.com/EricAlcaide/E-swish
