Sunday, October 26, 2025
HomeLanguagesImplement sigmoid function using Numpy

Implement sigmoid function using Numpy

With the help of Sigmoid activation function, we are able to reduce the loss during the time of training because it eliminates the gradient problem in machine learning model while training.




# Import matplotlib, numpy and math
import matplotlib.pyplot as plt
import numpy as np
import math
  
x = np.linspace(-10, 10, 100)
z = 1/(1 + np.exp(-x))
  
plt.plot(x, z)
plt.xlabel("x")
plt.ylabel("Sigmoid(X)")
  
plt.show()


Output :

Example #1 :




# Import matplotlib, numpy and math
import matplotlib.pyplot as plt
import numpy as np
import math
  
x = np.linspace(-100, 100, 200)
z = 1/(1 + np.exp(-x))
  
plt.plot(x, z)
plt.xlabel("x")
plt.ylabel("Sigmoid(X)")
  
plt.show()


Output :

RELATED ARTICLES

Most Popular

Dominic
32361 POSTS0 COMMENTS
Milvus
88 POSTS0 COMMENTS
Nango Kala
6728 POSTS0 COMMENTS
Nicole Veronica
11892 POSTS0 COMMENTS
Nokonwaba Nkukhwana
11954 POSTS0 COMMENTS
Shaida Kate Naidoo
6852 POSTS0 COMMENTS
Ted Musemwa
7113 POSTS0 COMMENTS
Thapelo Manthata
6805 POSTS0 COMMENTS
Umr Jansen
6801 POSTS0 COMMENTS