Python – tensorflow.math.log_sigmoid()

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TensorFlow is open-source python library designed by Google to develop Machine Learning models and deep learning neural networks. log_sigmoid() is used to find element wise log sigmoid of x. Specifically, y = log(1 / (1 + exp(-x))).

Syntax: tf.math.log_sigmoid(x, name)

Parameter:

x: It’s the input tensor. Allowed dtype for this tensor are float32, float64.

name(optional): It defines the name for the operation.

Returns: It returns a tensor of same dtype as x.

Example 1:

Python3

# Importing the library
import tensorflow as tf
 
# Initializing the input tensor
a = tf.constant([.2, .5, .7, 1], dtype = tf.float64)
 
# Printing the input tensor
print('Input: ', a)
 
# Calculating result
res = tf.math.log_sigmoid(x = a)
 
# Printing the result
print('Result: ', res)

Output:

Input:  tf.Tensor([0.2 0.5 0.7 1. ], shape=(4, ), dtype=float64)
Result:  tf.Tensor([-0.59813887 -0.47407698 -0.40318605 -0.31326169], shape=(4, ), dtype=float64)

Example 2: Visualization

Python3

# importing the library
import tensorflow as tf
import matplotlib.pyplot as plt
 
# Initializing the input tensor
a = tf.constant([.2, .5, .7, 1], dtype = tf.float64)
 
# Calculating result
res = tf.math.log_sigmoid(x = a)
 
# Plotting the graph
plt.plot(a, res, color = 'green')
plt.title('tensorflow.math.log_sigmoid')
plt.xlabel('Input')
plt.ylabel('Result')
plt.show()