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. In simple words, ML teaches the systems to think and understand like humans by learning from the data.It can 5 min read Linear Regression in Machine learning Linear regression is a type of supervised machine-learning algorithm that learns from the labelled datasets and maps the data points with most optimized linear functions which can be used for prediction on new datasets. It assumes that there is a linear relationship between the input and output, mea 15+ min read Support Vector Machine (SVM) Algorithm Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It tries to find the best boundary known as hyperplane that separates different classes in the data. It is useful when you want to do binary classification like spam vs. not spam or 9 min read Logistic Regression in Machine Learning Logistic Regression is a supervised machine learning algorithm used for classification problems. Unlike linear regression which predicts continuous values it predicts the probability that an input belongs to a specific class. It is used for binary classification where the output can be one of two po 11 min read K means Clustering – Introduction K-Means Clustering is an Unsupervised Machine Learning algorithm which groups unlabeled dataset into different clusters. It is used to organize data into groups based on their similarity. Understanding K-means ClusteringFor example online store uses K-Means to group customers based on purchase frequ 4 min read K-Nearest Neighbor(KNN) Algorithm K-Nearest Neighbors (KNN) is a supervised machine learning algorithm generally used for classification but can also be used for regression tasks. It works by finding the "k" closest data points (neighbors) to a given input and makesa predictions based on the majority class (for classification) or th 8 min read Backpropagation in Neural Network Back Propagation is also known as "Backward Propagation of Errors" is a method used to train neural network . Its goal is to reduce the difference between the model’s predicted output and the actual output by adjusting the weights and biases in the network.It works iteratively to adjust weights and 9 min read 100+ Machine Learning Projects with Source Code [2025] This article provides over 100 Machine Learning projects and ideas to provide hands-on experience for both beginners and professionals. Whether you're a student enhancing your resume or a professional advancing your career these projects offer practical insights into the world of Machine Learning an 5 min read Introduction to Convolution Neural Network Convolutional Neural Network (CNN) is an advanced version of artificial neural networks (ANNs), primarily designed to extract features from grid-like matrix datasets. This is particularly useful for visual datasets such as images or videos, where data patterns play a crucial role. CNNs are widely us 8 min read Naive Bayes Classifiers Naive Bayes is a classification algorithm that uses probability to predict which category a data point belongs to, assuming that all features are unrelated. This article will give you an overview as well as more advanced use and implementation of Naive Bayes in machine learning. Illustration behind 7 min read Like