How to implement the Softmax function in Python

The softmax function python is an activation function that turns numbers into probabilities that sum to one. The softmax function python outputs a vector that represents the probability distributions of a list of outcomes. It is also a core element used in deep learning classification tasks.

import numpy as np
# your solution:
def your_softmax(x): 
"""Compute softmax values for each sets of scores in x.""" 
e_x = np.exp(x - np.max(x)) 
return e_x / e_x.sum() 
# correct solution: 
def softmax(x): 
"""Compute softmax values for each sets of scores in x.""" 
e_x = np.exp(x - np.max(x)) 
return e_x / e_x.sum(axis=0) 
# only difference
The softmax function python is used when we have multiple classes.
It is helpful for finding out the class which has the max. Probability chances.
The Softmax function python is ideally used in the output layer, where we are actually trying to attain the probabilities to define the class of each input.
It usually ranges from 0 to 1.
Softmax function python turns logits [2.0, 1.0, 0.1] into probabilities [0.7, 0.2, 0.1], and the probabilities sum to 1. 
NOTE: Logits are the raw scores output by the last layer of a neural network. Before activation takes place. To understand the softmax function, we must look at the output of the (n-1)th layer.
The softmax function is, in fact, an arg max function. That means that it does not return the maximum value from the input, but the position of the largest values.
For example:
Before Software function python 
X = [13, 31, 5]
After softmax function python
array([1.52299795e-08, 9.99999985e-01, 5.10908895e-12]
The sum of outputs after the softmax function, can’t be 100%, because neural networks are universal function approximators. We can build a neural network that approximates the value of any mathematical function, but that is just an approximation, not an exact result. We use softmax to embrace that uncertainty and turn it into a probability interpretable by people.
Code: