Tensorflow.js is an open-source library developed by Google for running machine learning models and deep learning neural networks in the browser or node environment. It also helps the developers to develop ML models in JavaScript language and can use ML directly in the browser or in Node.js.
The tf.metrics.cosineProximity() function is defined as: -[sum(l2Normalize(tensor1)) * (l2Normalize(tensor2))], where l2Normalize() normalizes the L2 norm of the input to 1 and * represents multiplication.
Syntax:
tf.metrics.cosineProximity(yTrue, yPred)
Parameters: This function accepts the following two parameters:
- yTrue: It is a simple Truth tensor.
- yPred: It is a simple Prediction tensor.
Return Value: It returns the tf.Tensor object.
Example 1:
Javascript
// Importing the tensorflow.js libraryimport * as tf from "@tensorflow/tfjs"// Initializing truth tensor.let tensor1 = tf.tensor1d([1, 2, 3]);// Initializing Prediction tensor.let tensor2 = tf.tensor1d([ Math.atan(8 / 10), Math.atan(4 / 5), Math.acosh(2)]);// Finding the result using .cosineProximity() Functionlet result = tf.metrics.cosineProximity(tensor1, tensor2);// Printing the result.result.print(); |
Output:
Tensor
-0.9819149971008301
Example 2:
Javascript
// Importing the tensorflow.js libraryimport * as tf from "@tensorflow/tfjs"// Finding the cosime proximity between// truth and prediction tensor// using .cosineProximity() Functiontf.metrics.cosineProximity( tf.tensor1d([1, 2, 3]), tf.tensor1d([4, 5, 6])) .print(); |
Output:
Tensor
-0.9746317863464355
Reference: https://js.tensorflow.org/api/3.6.0/#metrics.cosineProximity
Whether you’re preparing for your first job interview or aiming to upskill in this ever-evolving tech landscape, neveropen Courses are your key to success. We provide top-quality content at affordable prices, all geared towards accelerating your growth in a time-bound manner. Join the millions we’ve already empowered, and we’re here to do the same for you. Don’t miss out – check it out now!
