Sigmoid Function in Numpy

Question

For fast computations, I have to implement my sigmoid function in Numpy this is the code below

   def sigmoid(Z):
    """
    Implements the sigmoid activation in bumpy

    Arguments:
    Z -- numpy array of any shape

    Returns:
    A -- output of sigmoid(z), same shape as Z
    cache -- returns Z, useful during backpropagation
    """

    cache=Z

    print(type(Z))
    print(Z)
    A=1/(1+(np.exp((-Z))))

    return A, cache

Also some relevant information:

  Z=(np.matmul(W,A)+b)

and the type of Z is:

  <class 'numpy.ndarray'>

Sadly I am getting a: "bad operand type for unary -: 'tuple' " I have tried to work around this problem without any luck.I appreciate any suggestions. Best

Answer 1

This worked for me. I think no need to use cache because you already initialized it. Try this code below.

import matplotlib.pyplot as plt 
import numpy as np 

z = np.linspace(-10, 10, 100) 
def sigmoid(z):
    return 1/(1 + np.exp(-z))

a = sigmoid(z)
plt.plot(z, a) 
plt.xlabel("z") 
plt.ylabel("sigmoid(z)")