@tensorflow/tfjs

# TensorFlow.js TensorFlow.js is an open-source hardware-accelerated JavaScript library for training and deploying machine learning models. **Develop ML in the Browser** <br/> 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** <br/> Execute native TensorFlow with the same TensorFlow.js API under the Node.js runtime. **Run Existing models** <br/> Use TensorFlow.js model converters to run pre-existing TensorFlow models right in the browser. **Retrain Existing models** <br/> 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 <a href="https://developer.mozilla.org/en-US/docs/Learn/HTML/Howto/Use_JavaScript_within_a_webpage" target="_blank">script tags</a> <strong>or</strong> by installing it from <a href="https://www.npmjs.com/" target="_blank">NPM</a> and using a build tool like <a href="https://parceljs.org/" target="_blank">Parcel</a>, <a href="https://webpack.js.org/" target="_blank">WebPack</a>, or <a href="https://rollupjs.org/guide/en" target="_blank">Rollup</a>. ### via Script Tag Add the following code to an HTML file: ```html <html> <head>  <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"> </script>  <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 <a href="https://yarnpkg.com/en/" target="_blank">yarn</a> <em>or</em> <a href="https://docs.npmjs.com/cli/npm" target="_blank">npm</a>. <b>Note:</b> 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 <a href='https://github.com/tensorflow/tfjs-examples' target="_blank">examples</a> to see how we use <a href="https://parceljs.org/" target="_blank">Parcel</a> 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 <a href="https://js.tensorflow.org/tutorials/" target="_blank">tutorials</a>, <a href="https://github.com/tensorflow/tfjs-examples" target="_blank">examples</a> and <a href="https://js.tensorflow.org/api/latest/">documentation</a> 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, <a href="https://www.browserstack.com/">BrowserStack</a>, for providing testing support.