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Tensorflow.js tf.losses.huberLoss() Function

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 Tensorflow.js tf.losses.huberLoss() function calculates the Huber loss between two given tensors.



Syntax:

tf.losses.huberLoss(
    labels, predictions,
    weights, delta, reduction
);

Parameters:



Note: The weights, delta, and reduction are optional parameters.

Return value: It returns tf.Tensor.

Example 1:




// Importing the tensorflow.Js library
import * as tf from "@tensorflow/tfjs"
 
// Initializing tensor1 as geek1
let geek1 = tf.tensor2d([[1, 2, 5], [6, 7, 10]]);
 
// Initializing tensor2 as geek2
let geek2 = tf.tensor2d([[5, 7, 11], [2, 4, 8]])
 
// Computing huber loss between geek1 and geek2
// using .huberLoss() function
geek = tf.losses.huberLoss(geek1, geek2)
geek.print();

Output: 

Tensor
    3.5

Example 2: 




// Importing the tensorflow.Js library
import * as tf from "@tensorflow/tfjs"
 
// Computing huber loss between two 3D
// tensors and printing the result
tf.losses.huberLoss(
    tf.tensor4d([[[[9], [8]], [[7], [5]]]]),
    tf.tensor4d([[[[1], [2]], [[3], [4]]]])
).print();

Output: 

Tensor
    4.25

Reference: https://js.tensorflow.org/api/latest/#losses.huberLoss

 


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