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Activation functions have come up as one of the essential components ofneural networks. The choice of adequate activation function can impact theaccuracy of these methods. In this study, we experiment for finding an optimalactivation function by defining it as a weighted sum of existing activationfunctions and then further optimizing these weights while training the network.The study uses three activation functions, ReLU, tanh, and sin, over threepopular image datasets, MNIST, FashionMNIST, and KMNIST. We observe that theReLU activation function can easily overlook other activation functions. Also,we see that initial layers prefer to have ReLU or LeakyReLU type of activationfunctions, but deeper layers tend to prefer more convergent activationfunctions.

Activation Functions: Dive into an optimal activation function