Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about **How to implement the Softmax function in Python** **in Python**. So Here I am Explain to you all the possible Methods here.

Without wasting your time, Let’s start This Article.

Table of Contents

## How to implement the Softmax function in Python?

**How to implement the Softmax function in Python?**They're both correct, but yours is preferred from the point of view of numerical stability.

You start with`e ^ (x - max(x)) / sum(e^(x - max(x))`

**implement the Softmax function in Python**They're both correct, but yours is preferred from the point of view of numerical stability.

You start with`e ^ (x - max(x)) / sum(e^(x - max(x))`

## Method 1

They’re both correct, but yours is preferred from the point of view of numerical stability.

You start with

e ^ (x - max(x)) / sum(e^(x - max(x))

By using the fact that a^(b – c) = (a^b)/(a^c) we have

= e ^ x / (e ^ max(x) * sum(e ^ x / e ^ max(x))) = e ^ x / sum(e ^ x)

Which is what the other answer says. You could replace max(x) with any variable and it would cancel out.

## Method 2

sklearn also offers implementation of softmax

from sklearn.utils.extmath import softmax import numpy as np x = np.array([[ 0.50839931, 0.49767588, 0.51260159]]) softmax(x) # output array([[ 0.3340521 , 0.33048906, 0.33545884]])

**Summery**

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.

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