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 mathimport matplotlib.pyplot as pltimport numpy as npimport 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 mathimport matplotlib.pyplot as pltimport numpy as npimport 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 :
