Tensorflow. js – Run machine learning JS framework in the browser

TensorFlow.js is an open-source hardware-accelerated JavaScript library for training and deploying machine learning models.

Develop ML in the Browser

Use flexible and intuitive APIs to build models from scratch using the low-level JavaScript linear algebra library or the high-level layers API.

Run Existing models

Use TensorFlow.js model converters to run pre-existing TensorFlow models right in the browser.

Retrain Existing models

Retrain pre-existing ML models using sensor data connected to the browser, or other client-side data.

Importing

You can import TensorFlow.js directly via yarn or npm: yarn add @tensorflow/tfjs or npm install @tensorflow/tfjs.

Alternatively you can use a script tag. The library will be available as a global variable named tf:

<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script> <! -- or --> <script src="https://unpkg.com/@tensorflow/tfjs@latest"></script>Copy the code

You can also specify which version to load replacing @latest with a specific version string (e.g. 0.6.0).

About this repo

Examples

Check out our examples repository and our tutorials.

Getting started

Let’s add a scalar value to a vector. TensorFlow.js supports broadcasting the value of scalar over all the elements in the tensor.

import * as tf from '@tensorflow/tfjs'; // If not loading the script as a global

const a = tf.tensor1d([1, 2, 3]);
const b = tf.scalar(2);

const result = a.add(b); // a is not modified, result is a new tensor
result.data().then(data => console.log(data)); // Float32Array([3, 4, 5]

// Alternatively you can use a blocking call to get the data.
// However this might slow your program down if called repeatedly.
console.log(result.dataSync()); // Float32Array([3, 4, 5]Copy the code

See the core-concepts tutorial for more.

import * as tf from '@tensorflow/tfjs';

// A sequential model is a container which you can add layers to.
const model = tf.sequential();

// Add a dense layer with 1 output unit.
model.add(tf.layers.dense({units: 1, inputShape: [1]}));

// Specify the loss type and optimizer for training.
model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});

// Generate some synthetic data for training.
const xs = tf.tensor2d([[1], [2], [3], [4]], [4, 1]);
const ys = tf.tensor2d([[1], [3], [5], [7]], [4, 1]);

// Train the model.
await model.fit(xs, ys, {epochs: 500});

// Ater the training, perform inference.
const output = model.predict(tf.tensor2d([[5]], [1, 1]));
output.print();Copy the code

Importing pre-trained models

We support porting pre-trained models from:

• TensorFlow SavedModel
• Keras

Find out more

TensorFlow.js is a part of the TensorFlow ecosystem. For more info: