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Tensorflow.js tf.losses.computeWeightedLoss() Function

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  • Last Updated : 21 Jul, 2021
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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.computeWeightedLoss() function calculates the weighted loss between two given tensors.

Syntax:

tf.losses.computeWeightedLoss(losses, weights, reduction)

Parameters:

  • losses: It is a tensor of shape.
  • weights: These are those tensors whose rank is either 0 or 1, and they must be broad castable to loss of shape.
  • reduction: It is the type of reduction to apply to loss. It must be of Reduction type.

Note: The weight, and reduction are optional parameters.

Return value: It returns tf.Tensor.

Example 1:

Javascript




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


Output:

Tensor
    32.333335876464844

Example 2:

Javascript




// Importing the tensorflow.Js library
import * as tf from "@tensorflow/tfjs"
 
// Computing weight loss between 3D and
// 4D tensors and printing the result.
tf.losses.computeWeightedLoss(
    tf.tensor3d([[[1], [2]], [[3], [4]]]),
    tf.tensor4d([[[[1], [2]], [[3], [4]]]])
).print();


Output:

Tensor
    7.5

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

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