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Python – tensorflow.math.multiply_no_nan()

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  • Last Updated : 10 Jun, 2020
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TensorFlow is open-source python library designed by Google to develop Machine Learning models and deep learning  neural networks. multiply_no_nan() is used to find element wise x*y. It supports broadcasting and returns 0 if y is 0 even is x is infinite or NaN.

Syntax: tf.math.multiply_no_nan(x, y, name)

Parameter:

  • x: It’s the input tensor. Allowed dtype for this tensor are bfloat16, half, float32, float64, uint8, int8, uint16, int16, int32, int64, complex64, complex128.
  • y: It’s the input tensor of same dtype as x.
  • name(optional): It’s 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([.2, .5, .7, 1], dtype = tf.float64)
b = tf.constant([.1, .3, 1, 5], dtype = tf.float64)
  
# Printing the input tensor
print('a: ', a)
print('b: ', b)
  
# Calculating result
res = tf.math.multiply_no_nan(x = a, y = b)
  
# Printing the result
print('Result: ', res)


Output:

a:  tf.Tensor([0.2 0.5 0.7 1. ], shape=(4, ), dtype=float64)
b:  tf.Tensor([0.1 0.3 1.  5. ], shape=(4, ), dtype=float64)
Result:  tf.Tensor([0.02 0.15 0.7  5.  ], shape=(4, ), dtype=float64)


Example 2: Complex number multiplication

Python3




# importing the library
import tensorflow as tf
import numpy as np
  
# Initializing the input tensor
a = tf.constant([-2, -5, np.inf, np.nan], dtype = tf.float64)
b = tf.constant([-1, -6, 0, 0], dtype = tf.float64)
  
# Printing the input tensor
print('a: ', a)
print('b: ', b)
  
# Calculating result
res = tf.math.multiply_no_nan(x = a, y = b)
  
# Printing the result
print('Result: ', res)


Output:

a:  tf.Tensor([-2. -5. inf nan], shape=(4, ), dtype=float64)
b:  tf.Tensor([-1. -6.  0.  0.], shape=(4, ), dtype=float64)
Result:  tf.Tensor([ 2. 30.  0.  0.], shape=(4, ), dtype=float64)

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