Softmax Activation Function with Python

Softmax is a mathematical function that converts a vector of numbers into a vector of probabilities, where the probabilities of each value are proportional to the relative scale of each value in the vector.

The most common use of the softmax function in applied machine learning is in its use as an activation function in a neural network model. Specifically, the network is configured to output N values, one for each class in the classification task, and the softmax function is used to normalize the outputs, converting them from weighted sum values into probabilities that sum to one. Each value in the output of the softmax function is interpreted as the probability of membership for each class.

In this tutorial, you will discover the softmax activation function used in neural network models.

After completing this tutorial, you will know:

• Linear and Sigmoid activation functions are inappropriate for multi-class classification tasks.
• Softmax can be thought of as a softened version of the argmax function that returns the index of the largest value in a list.
• How to implement the softmax function from scratch in Python and how to convert the output into a class label.

Let’s get started.