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.
The Tensorflow.js tf.losses.hingeLoss() function calculates the hinge loss between two given tensors.
Syntax:
tf.losses.hingeLoss (labels, predictions, weights, reduction)
Parameters:
- labels: It specifies the truth output tensor. The absolute difference is predicted based on this tensor.
- predictions: It specifies the predicted output tensor with the same dimensions as labels.
- weights: It specifies a tensor of rank either equal to that of labels so that it can be broadcastable or 0. It is an optional parameter.
- reduction: It specifies the type of reduction to the loss. It is an optional parameter.
Return Value: It returns a tf.Tensor which is calculated by hingeLoss() function.
Example 1: In this example we will take two 2d tensors as labels and predictions. Then we will find the estimated hinge loss between these two tensors.
Javascript
// Importing the tensorflow.js library const tf = require("@tensorflow/tfjs"); // Defining label tensor const x_label = tf.tensor2d([ [0., 1., 0.], [1., 0., 1.] ]); // Defining prediction tensor const x_pred = tf.tensor2d([ [1., 1., 1.], [0., 0., 0. ] ]); // Calculating hinge lossconst hinge_loss = tf.losses.hingeLoss(x_label,x_pred) // Printing the output hinge_loss.print() |
Output:
Tensor
1.1666667461395264
Example 2:
Javascript
// Importing the tensorflow.Js libraryimport * as tf from "@tensorflow/tfjs"// Computing hinge loss between two // tensors and printing the resulttf.losses.hingeLoss( tf.tensor4d([[[[0], [4]], [[5], [1]]]]), tf.tensor4d([[[[1], [2]], [[3], [4]]]])).print(); |
Output:
Tensor
0.5
Reference: https://js.tensorflow.org/api/1.0.0/#losses.hingeLoss
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