Universal approximation theorem suggests that a shallow neural network can approximate any function. The input to neurons at each layer is a weighted sum of previous layer neurons and then an activation is applied. These activation functions perform very well when the output is a linear combination of input data. When trying to learn a function which involves product of input data, the neural networks tend to overfit the data to approximate the function. In this paper we will use properties of logarithmic functions to propose a pair of custom activation functions which can translate products into linear expression and learn using backpropagation. We will try to generalize this approach for some complex arithmetic functions and test the accuracy on a disjoint distribution with the training set.
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