# 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](/tfjs-core), a flexible low-level API for neural networks and numerical computation. - [TensorFlow.js Layers](/tfjs-layers), a high-level API which implements functionality similar to [Keras](https://keras.io/). - [TensorFlow.js Data](/tfjs-data), a simple API to load and prepare data analogous to [tf.data](https://www.tensorflow.org/guide/datasets). - [TensorFlow.js Converter](/tfjs-converter), tools to import a TensorFlow SavedModel to TensorFlow.js - [TensorFlow.js Vis](/tfjs-vis), in-browser visualization for TensorFlow.js models - [TensorFlow.js AutoML](/tfjs-automl), Set of APIs to load and run models produced by [AutoML Edge](https://cloud.google.com/vision/automl/docs/edge-quickstart). Backends/Platforms: - [TensorFlow.js CPU Backend](/tfjs-backend-cpu), pure-JS backend for Node.js and the browser. - [TensorFlow.js WebGL Backend](/tfjs-backend-webgl), WebGL backend for the browser. - [TensorFlow.js WASM Backend](/tfjs-backend-wasm), WebAssembly backend for the browser. - [TensorFlow.js WebGPU](/tfjs-backend-webgpu), WebGPU backend for the browser. - [TensorFlow.js Node](/tfjs-node), Node.js platform via TensorFlow C++ adapter. - [TensorFlow.js React Native](/tfjs-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](/tfjs-node). ## Examples Check out our [examples repository](https://github.com/tensorflow/tfjs-examples) and our [tutorials](https://js.tensorflow.org/tutorials/). ## Gallery Be sure to check out [the gallery](GALLERY.md) of all projects related to TensorFlow.js. ## Pre-trained models Be sure to also check out our [models repository](https://github.com/tensorflow/tfjs-models) where we host pre-trained models on NPM. ## Benchmarks * [Local benchmark tool](https://tfjs-benchmarks.web.app/). Use this webpage tool to collect the performance related metrics (speed, memory, etc) of TensorFlow.js models and kernels **on your local device** with CPU, WebGL or WASM backends. You can benchmark custom models by following this [guide](https://github.com/tensorflow/tfjs/blob/master/e2e/benchmarks/local-benchmark/README.md). * [Multi-device benchmark tool](https://github.com/tensorflow/tfjs/tree/master/e2e/benchmarks/browserstack-benchmark/README.md). Use this tool to collect the same performance related metrics **on 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 NPM and using a build tool like Parcel, WebPack, or Rollup. ### via Script Tag Add the following code to an HTML file: ```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 modern browser or a bundler/transpiler to convert your code to something older browsers understand. 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. ```js 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. ## Importing pre-trained models We support porting pre-trained models from: - [TensorFlow SavedModel](https://www.tensorflow.org/js/tutorials/conversion/import_saved_model) - [Keras](https://js.tensorflow.org/tutorials/import-keras.html) ## Various ops supported in different backends Please refer below : - [TFJS Ops Matrix](https://docs.google.com/spreadsheets/d/1D25XtWaBrmUEErbGQB0QmNhH-xtwHo9LDl59w0TbxrI/edit#gid=0) ## Find out more [TensorFlow.js](https://js.tensorflow.org) is a part of the [TensorFlow](https://www.tensorflow.org) ecosystem. For more info: - For help from the community, use the `tfjs` tag on the [TensorFlow Forum](https://discuss.tensorflow.org/tag/tfjs). - [TensorFlow.js Website](https://js.tensorflow.org) - [Tutorials](https://js.tensorflow.org/tutorials) - [API reference](https://js.tensorflow.org/api/latest/) - [TensorFlow.js Blog](https://blog.tensorflow.org/search?label=TensorFlow.js) Thanks, BrowserStack, for providing testing support.