TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning neural networks.
divide_no_nan() is used to compute element wise safe division of x by y i.e it returns 0 if y is zero
Syntax: tensorflow.math.divide_no_nan( x, y, name)
Parameters:
- x: It is a tensor.
- y: It is a tensor.
- name(optional): It defines the name of the operation
Returns: It returns a tensor.
Example 1:
Python3
# importing the library import tensorflow as tf # Initializing the input tensor a = tf.constant([6, 8, 12, 15], dtype = tf.float64) b = tf.constant([2, 3, 4, 5], dtype = tf.float64) # Printing the input tensor print('a: ', a) print('b: ', b) # Calculating safe division res = tf.math.divide_no_nan(x = a, y = b) # Printing the result print('Result: ', res) |
Output:
a: tf.Tensor([ 6. 8. 12. 15.], shape=(4, ), dtype=float64) b: tf.Tensor([2. 3. 4. 5.], shape=(4, ), dtype=float64) Result: tf.Tensor([3. 2.66666667 3. 3. ], shape=(4, ), dtype=float64)
Example 2: In this example one of the value in second tensor is taken 0.
Python3
# importing the library import tensorflow as tf # Initializing the input tensor a = tf.constant([6, 8, 12, 15], dtype = tf.float64) b = tf.constant([2, 3, 4, 0], dtype = tf.float64) # Printing the input tensor print('a: ', a) print('b: ', b) # Calculating safe division res = tf.math.divide_no_nan(x = a, y = b) # Printing the result print('Result: ', res) |
Output:
a: tf.Tensor([ 6. 8. 12. 15.], shape=(4, ), dtype=float64) b: tf.Tensor([2. 3. 4. 0.], shape=(4, ), dtype=float64) Result: tf.Tensor([3. 2.66666667 3. 0], shape=(4, ), dtype=float64)
