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 tf.train.momemtum() function is used to create a tf.MomentumOptimizer that uses momentum gradient decent algorithm.
Syntax:
tf.train.momentum(learningRate, momentum, useNesterov)
Parameters:
- learningRate (number): It specifies the learning rate which will be used by momentum gradient descent algorithm.
- momentum (number): It specifies the momentum which will be used by momentum gradient descent algorithm.
- useNesterov (boolean): It specifies whether to use nesterov momentum or not. It is an optional parameter.
Return value: It returns a tf.MomentumOptimizer
Example 1: Fit a function f=(a*x+b) using momentum optimizer, by learning coefficients a and b. In this example we will use nesterov momentum. So useNestrov will be true.
Javascript
// Importing tensorflow import * as tf from "@tensorflow/tfjs" const xs = tf.tensor1d([0, 1, 2]); const ys = tf.tensor1d([1.1, 5.9, 16.8]); const a = tf.scalar(Math.random()).variable(); const b = tf.scalar(Math.random()).variable(); const f = x => a.mul(x).add(b); const loss = (pred, label) => pred.sub(label).square().mean(); const learningRate = 0.01; const momentum = 10; const useNestrov = true; const optimizer = tf.train.momentum(learningRate, momentum, useNestrov); // Train the model. for (let i = 0; i < 10; i++) { optimizer.minimize(() => loss(f(xs), ys)); } // Make predictions. console.log( `a: ${a.dataSync()}, b: ${b.dataSync()}}`); const preds = f(xs).dataSync(); preds.forEach((pred, i) => { console.log(`x: ${i}, pred: ${pred}`); }); |
Output:
a: 1982014720, b:1076448384 x: 0, pred: 1076448384 x: 1, pred: 3058463232 x: 2, pred: 5040477696
Example 2: Fit a quadratic equation using momentum optimizer, by learning coefficients a and b. In this example we will not use nesterov momentum. So useNestrov will be false.
Javascript
// Importing tensorflow import * as tf from "@tensorflow/tfjs" const xs = tf.tensor1d([0, 1, 2, 3]); const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]); const a = tf.scalar(Math.random()).variable(); const b = tf.scalar(Math.random()).variable(); const c = tf.scalar(Math.random()).variable(); const f = x => a.mul(x.square()).add(b.mul(x)).add(c); const loss = (pred, label) => pred.sub(label).square().mean(); const learningRate = 0.01; const momentum = 10; const useNestrov = false; const optimizer = tf.train.momentum(learningRate, momentum, useNestrov); // Train the model. for (let i = 0; i < 10; i++) { optimizer.minimize(() => loss(f(xs), ys)); } // Make predictions. console.log( `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`); const preds = f(xs).dataSync(); preds.forEach((pred, i) => { console.log(`x: ${i}, pred: ${pred}`); }); |
Output:
a: 892235776, b: 331963616, c: 134188384 x:0, pred: 134188384 x:1, pred: 1358387840 x:2, pred: 4367058944 x:3, pred: 9160201216
Reference: https://js.tensorflow.org/api/1.0.0/#train.momentum
