[Tut] How to Calculate a Logistic Sigmoid Function in Python?

[Tut] How to Calculate a Logistic Sigmoid Function in Python? - Printable Version

+- Sick Gaming (https://sickgaming.net)
+-- Forum: Programming (https://sickgaming.net/forum-76.html)
+--- Forum: Python (https://sickgaming.net/forum-83.html)
+--- Thread: [Tut] How to Calculate a Logistic Sigmoid Function in Python? (/thread-99483.html)

[Tut] How to Calculate a Logistic Sigmoid Function in Python? - xSicKxBot - 05-31-2022

How to Calculate a Logistic Sigmoid Function in Python?

Rate this post

Summary: You can caculate the logistic sigmoid function in Python using:

The Math Module:
Code:
1 / (1 + math.exp(-x))
The Numpy Library:
Code:
1 / (1 + np.exp(-x))
The Scipy Library:
Code:
scipy.special.expit(x)

Problem: Given a logistic sigmoid function:

[Image: SUuRi.png]

If the value of x is given, how will you calculate F(x) in Python? Let’s say

Code:
x=0.458

.

Note: Logistic sigmoid function is defined as (1/(1 + e^-x)) where x is the input variable and represents any real number. The function returns a value that lies within the range -1 and 1. It forms an S-shaped curve when plotted on a graph.

❒[b]Method 1: Sigmoid Function in Python Using Math Module[/b]

Approach: Define a function that accepts x as an input and returns F(x) as 1/(1 + math.exp(-x)).

Code:

Code:
import math def sigmoid(x): return 1 / (1 + math.exp(-x)) print(sigmoid(0.458)) # OUTPUT: 0.6125396134409151

Caution: The above solution is mainly intended as a simple one-to-one translation of the given sigmoid expression into Python code. It is not strictly tested or considered to be a perfect and numerically sound implementation. In case you need a more robust implementation, some of the solutions to follow might prove to be more instrumental in solving your case.

Here’s a more stable implementation of the above solution:

Code:
import math def sigmoid(x): if x >= 0: k = math.exp(-x) res = 1 / (1 + k) return res else: k = math.exp(x) res = k / (1 + k) return res print(sigmoid(0.458))

Note:

Code:
exp()

is a method of the math module in Python that returns the value of E raised to the power of x. Here, x is the input value passed to the exp() function, while E represents the base of the natural system of the logarithm (approximately 2.718282).

❒[b]Method 2: Sigmoid Function in Python Using Numpy[/b]

The sigmoid function can also be implemented using the

Code:
exp()

method of the Numpy module.

Code:
numpy.exp()

works just like the

Code:
math.exp()

method, with the additional advantage of being able to handle arrays along with integers and float values.

Let’s have a look at an example to visualize how to implement the sigmoid function using

Code:
numpy.exp()

Code:
import numpy as np def sigmoid(x): return 1 / (1 + np.exp(-x)) print(sigmoid(0.458)) # OUTPUT: 0.6125396134409151

Probably a more numerically stable version of the above implementation is as follows:

Code:
import numpy as np def sigmoid(x): return np.where(x < 0, np.exp(x) / (1 + np.exp(x)), 1 / (1 + np.exp(-x))) print(sigmoid(0.458)) # OUTPUT: 0.6125396134409151

#Example 2: Let’s have a look at an implementation of the sigmoid function upon an array of evenly spaced values with the help of a graph in the following example.

Code:
import numpy as np
import matplotlib.pyplot as plt def sigmoid(x): return np.where(x < 0, np.exp(x) / (1 + np.exp(x)), 1 / (1 + np.exp(-x))) val = np.linspace(start=-10, stop=10, num=200)
sigmoid_values = sigmoid(val)
plt.plot(val, sigmoid_values)
plt.xlabel("x")
plt.ylabel("sigmoid(X)")
plt.show()

Output:

Explanation:

Initially, we created an array of evenly spaced values within the range of -10 and 10 with the help of the
Code:
linspace
method of the Numpy module, i.e., val.
We then used the sigmoid function on these values. If you print them out, you will find that they are either extremely close to 0 or very close to 1. This can also be visualized once the graph is plotted.
Finally, we plotted the sigmoid function graph that we previously computed with the help of the function. The x-axis maps the values contained in val, while the y-axis maps the values returned by the sigmoid function.

Do you want to become a NumPy master? Check out our interactive puzzle book Coffee Break NumPy and boost your data science skills! (Amazon link opens in new tab.)

[Image: Cover_Coffee_Break_NumPy-200x300.jpg]

❒[b]Method 3: Sigmoid Function in Python Using the Scipy Library[/b]

Another efficient way to calculate the sigmoid function in Python is to use the Scipy libraries

Code:
expit

function.

Example 1: Calculating logistic sigmoid for a given value

Code:
from scipy.special import expit
print(expit(0.458)) # OUTPUT: 0.6125396134409151

Example 2: Calculating logistic sigmoid for multiple values

Code:
from scipy.special import expit
x = [-2, -1, 0, 1, 2]
for value in expit(x): print(value)

Output:

Code:
11920292202211755
2689414213699951
5
7310585786300049
8807970779778823

Recommended Read: Logistic Regression in Python Scikit-Learn

❒[b]Method 4: Transform the tanh function [/b]

Another workaround to compute the sigmoid function is to transform the tanh function of the math module as shown below:

Code:
import math sigmoid = lambda x: .5 * (math.tanh(.5 * x) + 1)
print(sigmoid(0.458)) # OUTPUT: 0.6125396134409151

Since, mathematically

Code:
sigmoid(x) == (1 + tanh(x/2))/2

. Hence, the above implementation should work and is a valid solution. However, the methods mentioned earlier are undoubtedly more stable numerically and superior to this solution.

[b]Conclusion[/b]

Well, that’s it for this tutorial. We have discussed as many as four ways of calculating the logistic sigmoid function in Python. Feel free to use the one that suits your requirements.

I hope this article has helped you. Please subscribe and stay tuned for more interesting solutions and tutorials. Happy learning!

TensorFlow – A Hands-On Introduction to Deep Learning and Neural Networks for Beginners

This course gives you a charming introduction into deep learning and neural networks using Google’s TensorFlow library for Python beginners.