How to calculate a logistic sigmoid function in Python?

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Today, We want to share with you sigmoid function python.In this post we will show you Activation Functions in Neural Networks (relu function python), hear for A beginner’s guide to NumPy with Sigmoid, ReLu and Softmax activation functions we will give you demo and example for implement.In this post, we will learn about How To Negate A Boolean Value In Python? with an example.

Implement sigmoid function using Numpy

The logistic sigmoid simple function defined as eq (1/(1 + e^-x)) takes an input x of any real number as well as data returns an output value in the main range of -1 and 1.

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DEFINE A LOGISTIC SIGMOID FUNCTION

Define a fresh logistic sigmoid python function that takes input x as well as returns 1/(1 + math.exp(-x)).

Example :

def sigmoid(x):
  return 1 / (1 + math.exp(-x))

print(sigmoid(0.5))

RESULTS

0.6224593312018546

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Example :

import math

def sigmoid(x):
  return 1 / (1 + math.exp(-x))

And lasy now you can simple test it by calling:

>>> sigmoid(0.458)
0.61253961344091512

REFER : It is also available in scipy : http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.logistic.html

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