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## Related Articles

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.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.

Example 1 : Fit a function f = (x + y) by learning the coefficients x, y.

## Javascript

 `// importing tensorflow``import tensorflow as tf` `const 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 optimizer``const 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 tensorflow``import tensorflow as tf` `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 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```

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