Python Deep Learning Cookbook

Getting started with activation functions

If we only use linear activation functions, a neural network would represent a large collection of linear combinations. However, the power of neural networks lies in their ability to model complex nonlinear behavior. We briefly introduced the non-linear activation functions sigmoid and ReLU in the previous recipes, and there are many more popular nonlinear activation functions, such as ELU, Leaky ReLU, TanH, and Maxout.

There is no general rule as to which activation works best for the hidden units. Deep learning is a relatively new field and most results are obtained by trial and error instead of mathematical proofs. For the output unit, we use a single output unit and a linear activation function ...