Implementing activation functions
Activation functions are the key for neural networks to approximate non-linear outputs and adapt to non-linear features. They introduce non-linear operations into neural networks. If we're careful as to which activation functions are selected and where we put them, they're very powerful operations that we can tell TensorFlow to fit and optimize.
Getting ready
When we start to use neural networks, we'll use activation functions regularly because activation functions are an essential part of any neural network. The goal of an activation function is just to adjust weight and bias. In TensorFlow, activation functions are non-linear operations that act on tensors. They are functions that operate in a similar way to the previous mathematical operations. Activation functions serve many purposes, but the main concept is that they introduce a non-linearity into the graph while normalizing the outputs.
How to do it…
The...
