Python – tensorflow.math.multiply_no_nan()
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 if 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 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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