Python – tensorflow.math.xlog1py()
Last Updated :
16 Jun, 2020
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
import tensorflow as tf
a = tf.constant([ - 5 , - 7 , 2 , 0 , 7 ], dtype = tf.float64)
b = tf.constant([ 1 , 3 , 9 , 4 , 7 ], dtype = tf.float64)
print ( 'a: ' , a)
print ( 'b: ' , b)
res = tf.math.xlog1py(a, b)
print ( 'Result: ' , res)
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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
import tensorflow as tf
import numpy as np
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)
print ( 'a: ' , a)
print ( 'b: ' , b)
res = tf.math.xlog1py(a, b)
print ( 'Result: ' , res)
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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)
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