Implement sigmoid function using Numpy Last Updated : 03 Oct, 2019 Improve Improve Like Article Like Save Share Report 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 : Like Article Suggest improvement Previous Class and Instance Attributes in Python Next Top Machine Learning Applications in 2019 Share your thoughts in the comments Add Your Comment Please Login to comment...