Tensorflow.js tf.layers addLoss() Method

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Tensorflow.js is an open-source library that is developed by Google for running machine learning models as well as deep learning neural networks in the browser or node environment.

The .addLoss() function is used to attach losses to the stated layer. Moreover, the loss might be probably conditional on a few input tensors, for example operation losses are dependent on the inputs of the stated layers.

Syntax:

addLoss(losses)

Parameters:

losses: It is the stated losses. It can be of type RegularizerFn or RegularizerFn[].

Return Value: It returns void.

Example 1:

Javascript

// Importing the tensorflow.js library 
import * as tf from "@tensorflow/tfjs"
  
// Creating a model 
const model = tf.sequential(); 
  
// Adding a layer 
model.add(tf.layers.dense({units: 1, inputShape: [3]})); 
  
// Defining input 
const input = tf.tensor1d([1, 2, 3, 4]); 
  
// Calling addLoss() method with its  
// parameter 
const res = model.layers[0].addLoss([tf.abs(input)]); 
  
// Printing output 
console.log(JSON.stringify(input)); 
model.layers[0].getWeights()[0].print();

Output:

{"kept":false,"isDisposedInternal":false,"shape":[4],"dtype":"float32",
"size":4,"strides":[],"dataId":{"id":82},"id":124,"rankType":"1","scopeId":61}
Tensor
    [[0.143441  ],
     [-0.58002  ],
     [-0.5836995]]

Example 2:

Javascript

// Importing the tensorflow.js library 
import * as tf from "@tensorflow/tfjs"
  
// Creating a model 
const model = tf.sequential(); 
  
// Adding layers 
model.add(tf.layers.dense({units: 1, inputShape: [3]})); 
model.add(tf.layers.dense({units: 4})); 
model.add(tf.layers.dense({units: 9, inputShape: [11]})); 
  
// Defining inputs 
const input1 = tf.tensor1d([0.5, 0.2, -33, null]); 
const input2 = tf.tensor1d([0.33, 0.5, -1]); 
const input3 = tf.tensor1d([1, 0.44]); 
  
// Calling addLoss() method with its  
// parameter 
const res1 = model.layers[0].addLoss([tf.cos(input1)]); 
const res2 = model.layers[0].addLoss([tf.sin(input2)]); 
const res3 = model.layers[0].addLoss([tf.tan(input3)]); 
  
// Printing outputs 
console.log(JSON.stringify(input1)); 
console.log(JSON.stringify(input2)); 
console.log(JSON.stringify(input3)); 
model.layers[0].getWeights()[0].print();

Output:

 {"kept":false,"isDisposedInternal":false,"shape":[4],"dtype":"float32",
 "size":4,"strides":[],"dataId":{"id":169},"id":261,"rankType":"1","scopeId":112}
{"kept":false,"isDisposedInternal":false,"shape":[3],"dtype":"float32",
"size":3,"strides":[],"dataId":{"id":170},"id":262,"rankType":"1","scopeId":112}
{"kept":false,"isDisposedInternal":false,"shape":[2],"dtype":"float32",
"size":2,"strides":[],"dataId":{"id":171},"id":263,"rankType":"1","scopeId":112}
Tensor
    [[-0.0062826],
     [0.0883235 ],
     [-1.0633234]]

Reference: https://js.tensorflow.org/api/latest/#tf.layers.Layer.addLoss

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