TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning neural networks.
xlogy() is used to compute element wise x * log(y).
Syntax: tensorflow.math.xlogy(x, y, name)
Parameters:
- x: It’s a tensor. Allowed dtypes are half, float32, float64, complex64, complex128.
- y: It’s a tensor of same dtype as x.
- name(optional): It defines the name for the operation.
Returns: It returns a tensor.
Example 1:
Python3
# importing the libraryimport tensorflow as tf# Initializing the input tensora = tf.constant([ -5, -7, 2, 0, 7], dtype = tf.float64)b = tf.constant([ 1, 3, 9, 4, 7], dtype = tf.float64)# Printing the input tensorprint('a: ', a)print('b: ', b)# Calculating resultres = tf.math.xlogy(a, b)# Printing the resultprint('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([-0. -7.69028602 4.39444915 0. 13.62137104], shape=(5, ), dtype=float64)
Example 2:
Python3
# importing the libraryimport tensorflow as tf# Initializing the input tensora = 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 tensorprint('a: ', a)print('b: ', b)# Calculating resultres = tf.math.xlogy(a, b)# Printing the resultprint('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( [ inf -infj -9.6678006 -3.50420885j 3.64924209+5.6776361j 6.11668624-8.04581928j 29.40388026-1.68457149j], shape=(5, ), dtype=complex128)
