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)
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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() |
Output:
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