Hands-On Deep Learning Algorithms with Python

The softmax function

The softmax function is basically the generalization of the sigmoid function. It is usually applied to the final layer of the network and while performing multi-class classification tasks. It gives the probabilities of each class for being output and thus, the sum of softmax values will always equal 1.

It can be represented as follows:

As shown in the following diagram, the softmax function converts their inputs to probabilities:

The softmax function can be implemented in Python as follows:

def softmax(x): return np.exp(x) ...