TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning neural networks.
zeta() is used to compute the Hurwitz zeta function. It is defined as:
Syntax: tensorflow.math.zeta( x, q, name)
Parameter:
- x: It’s a Tensor. Allowed dtypes are float32 and float64.
- q: It’s a Tensor of same dtype as x.
- name(optional): It defines the name for the operation.
Returns:
It returns a Tensor of same dtype as x.
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.zeta(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( [ nan nan 1.17512015e-01 nan 2.12260976e-06], shape=(5, ), dtype=float64)
Example 2:
Python3
# importing the libraryimport tensorflow as tf# Initializing the input tensora = tf.constant([ [-5, -7], [ 2, 0]], dtype = tf.float64)b = tf.constant([ [1, 3], [9, 4]], dtype = tf.float64)# Printing the input tensorprint('a: ', a)print('b: ', b)# Calculating resultres = tf.math.zeta(a, b)# Printing the resultprint('Result: ', res) |
Output:
a: tf.Tensor( [[-5. -7.] [ 2. 0.]], shape=(2, 2), dtype=float64) b: tf.Tensor( [[1. 3.] [9. 4.]], shape=(2, 2), dtype=float64) Result: tf.Tensor( [[ nan nan] [0.11751201 nan]], shape=(2, 2), dtype=float64)
