The purpose of an activation function is to introduce non-linearity into a neural network. Non-linearity helps a neural network to learn more complex patterns. We will discuss some important activation functions, and their respective DL4J implementations.

The following are the activation functions that we will consider:

• Tanh
• Sigmoid
• ReLU (short for Rectified Linear Unit)
• Leaky ReLU
• Softmax

In this recipe, we will walk through the key steps to decide the right activation functions for a neural network.