Python|Tensorflownn.sigmoid()

# Importing the Tensorflow library
import tensorflow as tf
# A constant vector of size 6
a = tf.constant([1.0, -0.5, 3.4, -2.1, 0.0, -6.5], dtype = tf.float32)
# Applying the sigmoid function and
# storing the result in 'b'
b = tf.nn.sigmoid(a, name ='sigmoid')
# Initiating a Tensorflow session
with tf.Session() as sess:
    print('Input type:', a)
    print('Input:', sess.run(a))
    print('Return type:', b)
    print('Output:', sess.run(b))


Input type: Tensor("Const_1:0", shape=(6, ), dtype=float32)
Input: [ 1\. -0.5 3.4000001 -2.0999999 0\. -6.5 ]
Return type: Tensor("sigmoid:0", shape=(6, ), dtype=float32)
Output: [ 0.7310586 0.37754068 0.96770459 0.10909683 0.5 0.00150118]


# Importing the Tensorflow library
import tensorflow as tf
# Importing the NumPy library
import numpy as np
# Importing the matplotlib.pyplot function
import matplotlib.pyplot as plt
# A vector of size 15 with values from -5 to 5
a = np.linspace(-5, 5, 15)
# Applying the sigmoid function and
# storing the result in 'b'
b = tf.nn.sigmoid(a, name ='sigmoid')
# Initiating a Tensorflow session
with tf.Session() as sess:
    print('Input:', a)
    print('Output:', sess.run(b))
    plt.plot(a, sess.run(b), color = 'red', marker = "o")
    plt.title("tensorflow.nn.sigmoid")
    plt.xlabel("X")
    plt.ylabel("Y")
    plt.show()


Input: Input: [-5\. -4.28571429 -3.57142857 -2.85714286 -2.14285714 -1.42857143
-0.71428571 0\. 0.71428571 1.42857143 2.14285714 2.85714286
3.57142857 4.28571429 5\. ]
Output: [ 0.00669285 0.01357692 0.02734679 0.05431327 0.10500059 0.19332137
0.32865255 0.5 0.67134745 0.80667863 0.89499941 0.94568673
0.97265321 0.98642308 0.99330715]