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Activation functions play a key role in neural networks so it becomesfundamental to understand their advantages and disadvantages in order toachieve better performances. This paper will first introduce common types ofnon linear activation functions that are alternative to the well known sigmoidfunction and then evaluate their characteristics. Moreover deeper neuralnetworks will be analysed because they positively influence the finalperformances compared to shallower networks. They also strictly depend on theweight initialisation hence the effect of drawing weights from Gaussian anduniform distribution will be analysed making particular attention on how thenumber of incoming and outgoing connection to a node influence the wholenetwork.

Comparison of non-linear activation functions for deep neural networks on MNIST classification task