Python - tensorflow.math.not_equal() Last Updated : 03 Apr, 2023 Comments Improve Suggest changes Like Article Like Report TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning neural networks. not_equal() is used to find element wise truth value of x!=y. It supports broadcasting Syntax: tensorflow.math.not_equal( x, y, name) Parameters: x: It is a tensor. Allowed dtypes are float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.y: It is a tensor of same dtype as x.name(optional): It defines the name of the operation Returns: It returns a tensor of type bool. Example 1: Python3 # importing the library import tensorflow as tf # Initializing the input tensor a = tf.constant([7, 8, 13, 11], dtype = tf.float64) b = tf.constant([2, 8, 13, 5], dtype = tf.float64) # Printing the input tensor print('a: ', a) print('b: ', b) # Finding truth value res = tf.math.not_equal(x = a, y = b) # Printing the result print('Result: ', res) Output: a: tf.Tensor([ 7. 8. 13. 11.], shape=(4, ), dtype=float64) b: tf.Tensor([ 2. 8. 13. 5.], shape=(4, ), dtype=float64) Result: tf.Tensor([ True False False True], shape=(4, ), dtype=bool) Example 2: In this example broadcasting will be performed on input. Python3 # Importing the library import tensorflow as tf # Initializing the input tensor a = tf.constant([7, 9, 13, 11], dtype = tf.float64) b = (9) # Printing the input tensor print('a: ', a) print('b: ', b) # Finding truth value res = tf.math.not_equal(x = a, y = b) # Printing the result print('Result: ', res) Output: a: tf.Tensor([ 7. 9. 13. 11.], shape=(4, ), dtype=float64) b: 9 Result: tf.Tensor([ True False True True], shape=(4, ), dtype=bool) Comment More infoAdvertise with us Next Article Python - tensorflow.math.not_equal() aman neekhara Follow Improve Article Tags : Machine Learning AI-ML-DS With Python Practice Tags : Machine Learning Similar Reads Machine Learning Tutorial Machine learning is a branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data without being explicitly programmed for every task. 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