  We have proposed orthogonal-Pad\'e activation functions, which are trainable
activation functions and show that they have faster learning capability and
improves the accuracy in standard deep learning datasets and models. Based on
our experiments, we have found two best candidates out of six orthogonal-Pad\'e
activations, which we call safe Hermite-Pade (HP) activation functions, namely
HP-1 and HP-2. When compared to ReLU, HP-1 and HP-2 has an increment in top-1
accuracy by 5.06% and 4.63% respectively in PreActResNet-34, by 3.02% and 2.75%
respectively in MobileNet V2 model on CIFAR100 dataset while on CIFAR10 dataset
top-1 accuracy increases by 2.02% and 1.78% respectively in PreActResNet-34, by
2.24% and 2.06% respectively in LeNet, by 2.15% and 2.03% respectively in
Efficientnet B0.
