TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning neural networks.
xdivy() is used to compute element wise x/y. It returns 0 if x==0.
Syntax: tensorflow.math.xdivy(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 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.xdivy(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([-5. -2.33333333 0.22222222 0. 1. ], 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, -7, 2, 5, 7], dtype = tf.float64) b = tf.constant([ 0, 3, 9, 4, np.inf], dtype = tf.float64) # Printing the input tensor print('a: ', a) print('b: ', b) # Calculating result res = tf.math.xdivy(a, b) # Printing the result print('Result: ', res) |
Output:
a: tf.Tensor([-5. -7. 2. 5. 7.], shape=(5, ), dtype=float64) b: tf.Tensor([ 0. 3. 9. 4. inf], shape=(5, ), dtype=float64) Result: tf.Tensor([ -inf -2.33333333 0.22222222 1.25 0. ], shape=(5, ), dtype=float64)
