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How to implement the Softmax function in Python

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?

  1. 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))

  2. 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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