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

- The Math Module:
`1 / (1 + math.exp(-x))`

- The Numpy Library:
`1 / (1 + np.exp(-x))`

- The Scipy Library:
`scipy.special.expit(x)`

**Problem: **Given a logistic sigmoid function:

If the value of ** x** is given, how will you calculate

**in Python? Let’s say**

`F(x)`

`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.

## Method 1: Sigmoid Function in Python Using Math Module

**💡 Approach: **Define a function that accepts `x`

as an input and returns `F(x)`

as `1/(1 + math.exp(-x))`

.

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 a perfect and numerically sound implementation.

If you need a more robust implementation, some of the solutions to follow might be more instrumental in solving your case.

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

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:** `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).

## Method 2: Sigmoid Function in Python Using Numpy

The sigmoid function can also be implemented using the `exp()`

method of the Numpy module. `numpy.exp()`

works just like the `math.exp()`

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

**Example 1**: Let’s have a look at an example to visualize how to implement the sigmoid function using `numpy.exp()`

:

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:

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 look at an implementation of the sigmoid function upon an array of evenly spaced values with the help of a graph.

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
`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.

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## Method 3: Sigmoid Function in Python Using the Scipy Library

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

function.

**Example 1: Calculating logistic sigmoid for a given value**

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

**Example 2: Calculating logistic sigmoid for multiple values**

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

**Output:**

0.11920292202211755 0.2689414213699951 0.5 0.7310585786300049 0.8807970779778823

**Recommended Read: Logistic Regression in Python Scikit-Learn**

## Method 4: Transform the tanh() Function

Another workaround to compute the sigmoid function is to transform the `tanh`

function of the `math`

module as shown below:

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

Since, mathematically `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.

## How to Calculate the Sigmoid for Arrays with Size Bigger Than 1 in Python?

You can calculate the sigmoid function for 2D arrays (and even higher dimensional arrays) using NumPy. The NumPy library applies operations element-wise, so the shape of the array does not affect the ability to apply the sigmoid function.

Here’s an example of a 2D array:

import numpy as np # Define the sigmoid function def sigmoid(x): return 1 / (1 + np.exp(-x)) # Create a 2D array arr = np.array([[0, 1, 2], [3, 4, 5]]) sigmoid_arr = sigmoid(arr) print(sigmoid_arr)

In this example, `sigmoid_arr`

will be a 2D array with the same shape as `arr`

, but with the sigmoid function applied to each element.

The output will be:

```
[[0.5 0.73105858 0.88079708]
[0.95257413 0.98201379 0.99330715]]
```

These numbers are the sigmoid values of the corresponding elements in the input array. Each number is between 0 and 1, inclusive, as is the property of the sigmoid function.

**Conclusion**

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!

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