Python – tensorflow.math.zeta()

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 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.zeta(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(
[           nan            nan 1.17512015e-01            nan
 2.12260976e-06], shape=(5, ), dtype=float64)
 
 
 
 

Example 2:

Python3

# importing the library
import tensorflow as tf

# Initializing the input tensor
a = tf.constant([ [-5, -7], [ 2, 0]], dtype = tf.float64)
b = tf.constant([ [1, 3], [9, 4]], dtype = tf.float64)

# Printing the input tensor
print('a: ', a)
print('b: ', b)

# Calculating result
res = tf.math.zeta(a, b)

# Printing the result
print('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)