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)
