Implement sigmoid function using Numpy
Last Updated :
03 Oct, 2019
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.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()
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Output :
Example #1 :
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 :
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