TensorFlow.js

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

Machine LearningWebGL
tensorflow/tfjsjs.tensorflow.org@tensorflow/tfjs

README

TensorFlow.js


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.

**Develop ML in Node.js**
Execute native TensorFlow with the same TensorFlow.js API under the Node.js
runtime.

**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.

About this repo


This repository contains the logic and scripts that combine
several packages.

APIs:
- TensorFlow.js Core,
  a flexible low-level API for neural networks and numerical computation.
- TensorFlow.js Layers,
  a high-level API which implements functionality similar to
  Keras.
- TensorFlow.js Data,
  a simple API to load and prepare data analogous to
  tf.data.
- TensorFlow.js Converter,
  tools to import a TensorFlow SavedModel to TensorFlow.js
- TensorFlow.js Vis,
  in-browser visualization for TensorFlow.js models
- TensorFlow.js AutoML,
  Set of APIs to load and run models produced by
  AutoML Edge.


Backends/Platforms:
- TensorFlow.js CPU Backend, pure-JS backend for Node.js and the browser.
- TensorFlow.js WebGL Backend, WebGL backend for the browser.
- TensorFlow.js WASM Backend, WebAssembly backend for the browser.
- TensorFlow.js WebGPU, WebGPU backend for the browser.
- TensorFlow.js Node, Node.js platform via TensorFlow C++ adapter.
- TensorFlow.js React Native, React Native platform via expo-gl adapter.

If you care about bundle size, you can import those packages individually.

If you are looking for Node.js support, check out the TensorFlow.js Node directory.

Examples


Check out our
examples repository
and our tutorials.

Gallery


Be sure to check out the gallery of all projects related to TensorFlow.js.

Pre-trained models


Be sure to also check out our models repository where we host pre-trained models
on NPM.

Benchmarks


Local benchmark tool. Use this webpage tool to collect the performance related metrics (speed, memory, etc) of TensorFlow.js models and kernelson your local device with CPU, WebGL or WASM backends. You can benchmark custom models by following this guide.
Multi-device benchmark tool. Use this tool to collect the same performance related metricson a collection of remote devices.

Getting started


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

via Script Tag


Add the following code to an HTML file:

  1. ``` html
  2. <html>
  3.   <head>
  4.     
  5.     <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"> </script>
  6. <script>
  7.       // Notice there is no 'import' statement. 'tf' is available on the index-page
  8.       // because of the script tag above.
  10.       // Define a model for linear regression.
  11.       const model = tf.sequential();
  12.       model.add(tf.layers.dense({units: 1, inputShape: [1]}));
  14.       // Prepare the model for training: Specify the loss and the optimizer.
  15.       model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
  17.       // Generate some synthetic data for training.
  18.       const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
  19.       const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);
  21.       // Train the model using the data.
  22.       model.fit(xs, ys).then(() => {
  23.         // Use the model to do inference on a data point the model hasn't seen before:
  24.         // Open the browser devtools to see the output
  25.         model.predict(tf.tensor2d([5], [1, 1])).print();
  26.       });
  27.     </script>
  28.   </head>
  29. <body>
  30.   </body>
  31. </html>
  32. ```

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 modern browser or a bundler/transpiler
to convert your code to something older browsers understand. See our
examplesto see how we use Parcel to build
our code. However, you are free to use any build tool that you prefer.



  1. ``` js
  2. import * as tf from '@tensorflow/tfjs';

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

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

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

  15. // Train the model using the data.
  16. model.fit(xs, ys).then(() => {
  17.   // Use the model to do inference on a data point the model hasn't seen before:
  18.   model.predict(tf.tensor2d([5], [1, 1])).print();
  19. });
  20. ```

See our tutorials, examplesand documentation for more details.

Importing pre-trained models


We support porting pre-trained models from:
- TensorFlow SavedModel
- Keras

Various ops supported in different backends


Please refer below :
- TFJS Ops Matrix

Find out more


TensorFlow.js is a part of the
TensorFlow ecosystem. For more info:
- For help from the community, use the tfjs tag on the TensorFlow Forum.
- TensorFlow.js Website
- Tutorials
- API reference
- TensorFlow.js Blog

Thanks, BrowserStack, for providing testing support.