The softmax function is a mathematical function that takes a vector of real numbers as input and returns a vector of probability scores as output. The probability scores sum to 1, which means that they can be interpreted as the probabilities of the different outcomes in a classification task.

The softmax function is often used in machine learning, especially in neural networks. It is used to convert the outputs of a neural network into probabilities, which can then be used to make predictions.

**Here is a simple explanation of how to calculate the softmax function in steps:**

**Take the exponential of each input value.**This means raising each input value to the power of e, which is a mathematical constant.**Sum the exponentials of all the input values.**This gives us a denominator for the softmax function.**Divide each exponential by the denominator.**This gives us the softmax output values.

**Here is an example of how to use the softmax function:**

```
import numpy as np
def softmax(x):
"""Calculates the softmax of a vector.
Args:
x: A vector of real numbers.
Returns:
A vector of softmax output values.
"""
# Calculate the exponentials of all the input values.
exps = np.exp(x)
# Sum the exponentials of all the input values.
sum_of_exps = np.sum(exps)
# Divide each exponential by the sum of all the exponentials.
softmax_output = exps / sum_of_exps
return softmax_output
# Example usage:
x = np.array([1.0, 2.0, 3.0])
softmax_output = softmax(x)
print(softmax_output)
```

Output:

`[0.09003057 0.24472847 0.66524096]`

As you can see, the softmax output values sum to 1. This means that they can be interpreted as the probabilities of the different outcomes in a classification task.

In this example, the softmax output values represent the probabilities of three different classes. The first class has a probability of 0.09, the second class has a probability of 0.24, and the third class has a probability of 0.66.

The softmax function is a powerful tool that can be used to convert the outputs of a neural network into probabilities. This makes it possible to use neural networks for classification tasks, such as image classification and natural language processing.