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.rmsprop() function is used to create a tf.RMSPropOptimizer that uses RMSProp gradient decent algorithm. The implementation of RMSProp optimizer is not the centered version of RMSProp and it uses plain momentum.Â
Syntax:
tf.train.rmsprop(learningRate, decay, momentum, epsilon, centered)
Parameters:
- learningRate (number): It specifies the learning rate which will be used by adadelta gradient descent algorithm.
- decay (number): It specifies the decay rate of each gradient.
- momentum (number): It specifies the momentum which will be used by rmsprop gradient descent algorithm.
- epsilon:Â It specifies a constant small value which is used to avoid zero denominator.
- centered (boolean): It specifies whether the gradients are normalised by the estimated gradient variance or not.
Return value: It returns a tf.RMSPropOptimizer
Example 1: Fit a function f=(a*x+y) using RMSProp optimizer, by learning coefficients a and b.
Javascript
// Importing tensorflowimport * as tf from "@tensorflow/tfjs"Â
const xs = tf.tensor1d([0, 1, 2]);const ys = tf.tensor1d([1.1, 5.9, 16.8]);Â
// Choosing random coefficients.const a = tf.scalar(Math.random()).variable();const b = tf.scalar(Math.random()).variable();Â
// Defining function f = (a*x + b). We will use// optimizer to fit fconst f = x => a.mul(x).add(b);const loss = (pred, label) => pred.sub(label).square().mean();Â
// Define rate which will be used by rmsprop algorithmconst learningRate = 0.01;Â
// Create optimizerconst optimizer = tf.train.rmsprop(learningRate);Â
// Train the model.for (let i = 0; i < 8; i++) {Â Â Â optimizer.minimize(() => loss(f(xs), ys));}Â
// Make predictions.console.log(Â Â Â Â Â `a: ${a.dataSync()}, b: ${b.dataSync()}}`);const preds = f(xs).dataSync();preds.forEach((pred, i) => {Â Â Â console.log(`x: ${i}, pred: ${pred}`);}); |
Output:
a:0.9164762496948242, b: 1.0887205600738525} x: 0, pred: 1.0887205600738525 x: 1, pred: 2.0051968097686768 x: 2, pred: 2.921673059463501
Example 2:Â Fit a quadratic equation using RMSProp optimizer, by learning coefficients a, b and c. Optimizer will have following configuration:
- learningRate = 0.01
- decay = 0.1
- momentum = 1
- epsilon = 0.5
- centered = true
Javascript
// Importing tensorflowimport * as tf from "@tensorflow/tfjs"Â
const xs = tf.tensor1d([0, 1, 2, 3]);const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);Â
// Choosing random coefficientsconst a = tf.scalar(Math.random()).variable();const b = tf.scalar(Math.random()).variable();const c = tf.scalar(Math.random()).variable();Â
// Defining function f = (a*x^2 + b*x + c)const f = x => a.mul(x.square()).add(b.mul(x)).add(c);const loss = (pred, label) => pred.sub(label).square().mean();Â
// Setting configurations for our optimizerconst learningRate = 0.01;const decay = 0.1;const momentum = 1;const epsilon = 0.5;const centered = true;Â
// Create the optimizerconst optimizer = tf.train.rmsprop(learningRate, Â Â Â Â Â Â Â Â decay, momentum, epsilon, centered);Â
// Train the model.for (let i = 0; i < 8; i++) {Â Â Â optimizer.minimize(() => loss(f(xs), ys));}Â
// Make predictions.console.log(`a: ${a.dataSync()}, Â Â Â Â b: ${b.dataSync()}, c: ${c.dataSync()}`);const preds = f(xs).dataSync();preds.forEach((pred, i) => {Â Â Â console.log(`x: ${i}, pred: ${pred}`);}); |
Output:
a: 3.918823003768921, b: 3.333444833755493, c: 6.297145843505859 x: 0, pred: 6.297145843505859 x: 1, pred: 13.549413681030273 x: 2, pred: 28.639328002929688 x: 3, pred: 51.56688690185547
Reference: https://js.tensorflow.org/api/1.0.0/#train.rmsprop
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