Python – tensorflow.math.xlog1py()

0 0 1 minute read

TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning neural networks.

xlog1py() is used to compute element wise x * log1p(y).

Syntax: tensorflow.math.xlog1py(x, y, name)

Parameters:

x: It’s a tensor. Allowed dtypes are bfloat16, half, float32, float64, complex64, complex128.

y: It’s a tensor. ALlowed dtypes are bfloat16, half, float32, float64, complex64, complex128.

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

Returns: It returns a tensor.

Example 1:

Python3

# importing the library 
import tensorflow as tf 
  
# Initializing the input tensor 
a = tf.constant([ -5, -7, 2, 0, 7], dtype = tf.float64) 
b = tf.constant([ 1, 3, 9, 4, 7], dtype = tf.float64) 
  
# Printing the input tensor 
print('a: ', a) 
print('b: ', b) 
  
# Calculating result 
res = tf.math.xlog1py(a, b) 
  
# Printing the result 
print('Result: ', res) 

Output:

a:  tf.Tensor([-5. -7.  2.  0.  7.], shape=(5, ), dtype=float64)
b:  tf.Tensor([1. 3. 9. 4. 7.], shape=(5, ), dtype=float64)
Result:  tf.Tensor([-3.4657359  -9.70406053  4.60517019  0.         14.55609079], shape=(5, ), dtype=float64)

Example 2:

Python3

# importing the library 
import tensorflow as tf 
import numpy as np 
  
# Initializing the input tensor 
a = tf.constant([ -5 + 2j, -7-5j, 2 + 2j, 5-3j, 7 + 6j], dtype = tf.complex128) 
b = tf.constant([ 0 + 0j, 3-1j, 9 + 5j, 4-3j, -6-8j], dtype = tf.complex128) 
  
# Printing the input tensor 
print('a: ', a) 
print('b: ', b) 
  
# Calculating result 
res = tf.math.xlog1py(a, b) 
  
# Printing the result 
print('Result: ', res) 

Output:

a:  tf.Tensor([-5.+2.j -7.-5.j  2.+2.j  5.-3.j  7.+6.j], shape=(5, ), dtype=complex128)
b:  tf.Tensor([ 0.+0.j  3.-1.j  9.+5.j  4.-3.j -6.-8.j], shape=(5, ), dtype=complex128)
Result:  tf.Tensor(
[ -0.        +0.j         -11.14114002-5.36818272j
   3.90101852+5.75560896j   7.19464281-7.99163829j
  28.48660115-1.43986039j], shape=(5, ), dtype=complex128)