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.adagrad() function us used to create a tf.AdagradOptimizer that uses Adaptive Gradient Algorithm(adagrad).
Syntax:
tf.train.adagrad(learningRate).
Parameters:
- learningRate: It specifies the learning rate which will be used by adaptive gradient descent algorithm.
- initialAccumulatorValue: It specifies the initial value of accumulators. It must be positive.
Return value: It returns a tf.adagradOptimizer.
Example 1 : Fit a function f = (x + y) by learning the coefficients x, y.
Javascript
// importing tensorflowimport tensorflow as tfconst xs = tf.tensor1d([0, 1, 2]);const ys = tf.tensor1d([1.3, 2.5, 3.7]);const x = tf.scalar(Math.random()).variable();const y = tf.scalar(Math.random()).variable();// Define a function f(x, y) = x + y.const f = x => x.add(y);const loss = (pred, label) => pred.sub(label).square().mean();const learningRate = 0.05;// Create adagrad optimizerconst optimizer = tf.train.adagrad(learningRate);// Train the model.for (let i = 0; i < 5; i++) { optimizer.minimize(() => loss(f(xs), ys));}// Make predictions.console.log(`x: ${x.dataSync()}, y: ${y.dataSync()}`);const preds = f(xs).dataSync();preds.forEach((pred, i) => {console.log(`x: ${i}, pred: ${pred}`);}); |
Output
x: 0.8561810255050659, y: 0.6922483444213867 x: 0, pred: 0.6922483444213867 x: 1, pred: 1.6922483444213867 x: 2, pred: 2.6922483444213867
Example 2: Fit a quadratic function by learning the coefficients a, b, c.
Javascript
// importing tensorflowimport tensorflow as tfconst 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 optimizer = tf.train.adagrad(learningRate);// 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: 0.3611285388469696, b: 0.6980878114700317, c: 0.8787991404533386 x: 0, pred: 0.8787991404533386 x: 1, pred: 1.9380154609680176 x: 2, pred: 3.7194888591766357 x: 3, pred: 6.223219394683838
Reference: https://js.tensorflow.org/api/1.0.0/#train.adagrad
Whether you’re preparing for your first job interview or aiming to upskill in this ever-evolving tech landscape, neveropen Courses are your key to success. We provide top-quality content at affordable prices, all geared towards accelerating your growth in a time-bound manner. Join the millions we’ve already empowered, and we’re here to do the same for you. Don’t miss out – check it out now!
