• Difficulty Level : Expert
• Last Updated : 20 Jul, 2022

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.

Syntax:

`tf.train.adamax(learningRate, beta1, beta2, epsilon, decay)`

Parameters:

• learningRate: It specifies the learning rate which will be used by adamax gradient descent algorithm.
• beta1: It specifies the estimated exponential decay rate for the 1st moment.
• beta2: It specifies the estimated exponential decay rate for the 2nd moment.
• epsilon: It specifies a small constant for numerical stability.
• decay: It specifies the decay rate for each update.

Return value: It returns a tf.adamaxOptimizer.

Example 1: Fit a function f = (a*x + y) using adamax optimiser by learning the coefficients a and b.

## 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]);` `// Choosing random coefficients``const a = tf.scalar(Math.random()).variable();``const b = tf.scalar(Math.random()).variable();` `// Defining function f = (a*x + b).``const f = x => a.mul(x).add(b);``const loss = (pred, label) => pred.sub(label).square().mean();` `// Defining learning rate of adamax algorithm``const learningRate = 0.01;` `// Creating our optimizer.``const optimizer = tf.train.adamax(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()}}`);``const preds = f(xs).dataSync();``preds.forEach((pred, i) => {``   ``console.log(`x: \${i}, pred: \${pred}`);``});`

Output:

```a: 0.4271160364151001, b: 0.21284617483615875}
x: 0, pred: 0.21284617483615875
x: 1, pred: 0.6399621963500977
x: 2, pred: 1.0670782327651978
x: 3, pred: 1.4941942691802979```

Example 2: Fit a quadratic equation using adamax optimizer, by learning coefficients a, b and c. The configurations of our optimiser are as follows:

• learningRate = 0.01;
• beta1 = 0.1;
• beta2 = 0.1;
• epsilon = 0.3;
• decay = 0.5;

## 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]);` `// Choosing random coefficients``const a = tf.scalar(Math.random()).variable();``const b = tf.scalar(Math.random()).variable();` `// Defining function f = (a*x^2 + b*x + c).``const f = x => a.mul(x).add(b);``const loss = (pred, label) => pred.sub(label).square().mean();` `// Defining configurations of adamax algorithm``const learningRate = 0.01;``const beta1 = 0.1;``const beta2 = 0.1;``const epsilon = 0.3;``const decay = 0.5;` `// Creating our optimizer.``const optimizer = tf.train.adamax(``    ``learningRate, beta1, beta2, epsilon, decay);` `// 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: 0.8346626162528992, b: 0.5925931334495544}
x: 0, pred: 0.21284617483615875
x: 1, pred: 1.4272557497024536
x: 2, pred: 2.261918306350708
x: 3, pred: 3.096580982208252```

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