TensorFlow.js
TensorFlow.js

A WebGL accelerated, browser based JavaScript library for training and deploying ML models.

Develop ML in the Browser
Use flexible and intuitive APIs to build and train 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.

Demos

Use your phone’s camera to identify emojis in the real world. Can you find all the emojis before time expires?
Play Pac-Man using images trained in your browser.
No coding required! Teach a machine to recognize images and play sounds.
Enjoy a real-time piano performance by a neural network
Train a server-side model to classify baseball pitch types using Node.js.

Getting Started

There are two main ways to get TensorFlow.js in your JavaScript project: via script tags or by installing it from NPM and using a build tool like Parcel, WebPack, or Rollup.

via Script Tag

Add the following code to an HTML file:

          <html>
  <head>
    <!-- Load TensorFlow.js -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.11.1"> </script>

    <!-- Place your code in the script tag below. You can also use an external .js file -->
    <script>
      // Notice there is no 'import' statement. 'tf' is available on the index-page
      // because of the script tag above.

      // Define a model for linear regression.
      const model = tf.sequential();
      model.add(tf.layers.dense({units: 1, inputShape: [1]}));

      // Prepare the model for training: Specify the loss and the optimizer.
      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 using the data.
      model.fit(xs, ys).then(() => {
        // Use the model to do inference on a data point the model hasn't seen before:
        // Open the browser devtools to see the output
        model.predict(tf.tensor2d([5], [1, 1])).print();
      });
    </script>
  </head>

  <body>
  </body>
</html>
        

Open up that html file in your browser and the code should run!

via NPM

Add TensorFlow.js to your project using yarn or npm. Note: Because we use ES2017 syntax (such as `import`), this workflow assumes you are using a bundler/transpiler to convert your code to something the browser understands. See our examples to see how we use Parcel to build our code. However you are free to use any build tool that you prefer.

yarn add @tensorflow/tfjs
npm install @tensorflow/tfjs

In your main js file:

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

  // Define a model for linear regression.
  const model = tf.sequential();
  model.add(tf.layers.dense({units: 1, inputShape: [1]}));

  // Prepare the model for training: Specify the loss and the optimizer.
  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 using the data.
  model.fit(xs, ys).then(() => {
    // Use the model to do inference on a data point the model hasn't seen before:
    model.predict(tf.tensor2d([5], [1, 1])).print();
  });
  

See our tutorials, examples and documentation for more details.

Need Help? Want to connect?

Feel free to file issues on our GitHub Repository if you run into bugs using the library. We also have a community mailing list for people to ask questions, get technical help, and share what they are doing with TensorFlow.js! To keep up to date with TensorFlow.js news follow us on twitter or join the announcement only mailing list.