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\n\n\n# Getting started\n\n**deeplearn.js** is an open source hardware-accelerated JavaScript library for\nmachine intelligence. **deeplearn.js** brings performant machine learning\nbuilding blocks to the web, allowing you to train neural networks in a browser\nor run pre-trained models in inference mode.\n\nWe provide an API that closely mirrors the TensorFlow eager API.\n**deeplearn.js** was originally developed by the Google Brain PAIR team to build\npowerful interactive machine learning tools for the browser, but it can be used\nfor everything from education, to model understanding, to art projects.\n\n## Usage\n\nYou can install this library via yarn/npm: `yarn add deeplearn`\nor `npm install deeplearn`\n\nAlternatively you can use a script tag. Here we load it from a CDN.\nIn this case it will be available as a global variable named `dl`.\n\nYou can replace also specify which version to load replacing `@latest` with a specific\nversion string (e.g. `0.5.0`).\n\n```html\n\n\n\n```\n\n#### TypeScript / ES6 JavaScript\n\nLet's add a scalar value to a 1D Tensor. Deeplearn js supports _broadcasting_\nthe value of scalar over all the elements in the tensor.\n\n```js\nimport * as dl from 'deeplearn'; // If not loading the script as a global\n\nconst a = dl.tensor1d([1, 2, 3]);\nconst b = dl.scalar(2);\n\nconst result = a.add(b); // a is not modified, result is a new tensor\nresult.data().then(data => console.log(data)); // Float32Array([3, 4, 5]\n\n// Alternatively you can use a blocking call to get the data.\n// However this might slow your program down if called repeatedly.\nconsole.log(result.dataSync()); // Float32Array([3, 4, 5]\n```\n\nSee the [TypeScript starter project](https://github.com/PAIR-code/deeplearnjs/tree/master/starter/typescript/) and the\n[ES6 starter project](https://github.com/PAIR-code/deeplearnjs/tree/master/starter/es6/) to get you quickly started.\n\n#### ES5 JavaScript\n\nLet's do the same thing in ES5 Javascript. Remember to include the script tag to\nload deeplearn.js\n\n```js\nvar a = dl.tensor1d([1, 2, 3]);\nvar b = dl.scalar(2);\n\nvar result = a.add(b); // a is not modified, result is a new tensor\n\n// Option 1: With a Promise.\nresult.data().then(data => console.log(data)); // Float32Array([3, 4, 5])\n\n// Option 2: Synchronous download of data. Blocks the UI.\nconsole.log(result.dataSync());\n```\n\n## Development\n\nTo build **deeplearn.js** from source, we need to clone the project and prepare\nthe dev environment:\n\n```bash\n$ git clone https://github.com/PAIR-code/deeplearnjs.git\n$ cd deeplearnjs\n$ yarn prep # Installs dependencies.\n```\n\n#### Yarn vs NPM\nGenerally speaking it's up to you. Yarn is fully interoperable with npm. You can either do `yarn` or `npm install`. However we use yarn, and if you are adding or removing dependencies you should use yarn to keep the `yarn.lock` file up to date.\n\n#### Code editor\nWe recommend using [Visual Studio Code](https://code.visualstudio.com/) for\ndevelopment. Make sure to install\n[TSLint VSCode extension](https://marketplace.visualstudio.com/items?itemName=eg2.tslint)\nand the npm [clang-format](https://github.com/angular/clang-format) `1.2.2` or later\nwith the\n[Clang-Format VSCode extension](https://marketplace.visualstudio.com/items?itemName=xaver.clang-format)\nfor auto-formatting.\n\n#### Interactive development\nTo interactively develop any of the demos (e.g. `demos/nn-art/`):\n\n```bash\n$ ./scripts/watch-demo demos/nn-art\n>> Starting up http-server, serving ./\n>> Available on:\n>> http://127.0.0.1:8080\n>> Hit CTRL-C to stop the server\n>> 1357589 bytes written to dist/demos/nn-art/bundle.js (0.85 seconds) at 10:34:45 AM\n```\n\nThen visit `http://localhost:8080/demos/nn-art/`. The\n`watch-demo` script monitors for changes of typescript code and does\nincremental compilation (~200-400ms), so users can have a fast edit-refresh\ncycle when developing apps.\n\n#### Testing\nBefore submitting a pull request, make sure the code passes all the tests and is clean of lint errors:\n\n```bash\n$ yarn test\n$ yarn lint\n```\n\nTo run a subset of tests and/or on a specific browser:\n\n```bash\n$ yarn test --browsers=Chrome --grep='multinomial'\n \n> ...\n> Chrome 62.0.3202 (Mac OS X 10.12.6): Executed 28 of 1891 (skipped 1863) SUCCESS (6.914 secs / 0.634 secs)\n```\n\nTo run the tests once and exit the karma process (helpful on Windows):\n\n```bash\n$ yarn test --single-run\n```\n\n#### Packaging (browser and npm)\nTo build a standalone ES5 library that can be imported in the browser with a\n`\n\n\n```\n\n#### TypeScript / ES6 JavaScript\n\nLet's add a scalar value to a 1D Tensor. Deeplearn js supports _broadcasting_\nthe value of scalar over all the elements in the tensor.\n\n```js\nimport * as dl from 'deeplearn'; // If not loading the script as a global\n\nconst a = dl.tensor1d([1, 2, 3]);\nconst b = dl.scalar(2);\n\nconst result = a.add(b); // a is not modified, result is a new tensor\nresult.data().then(data => console.log(data)); // Float32Array([3, 4, 5]\n\n// Alternatively you can use a blocking call to get the data.\n// However this might slow your program down if called repeatedly.\nconsole.log(result.dataSync()); // Float32Array([3, 4, 5]\n```\n\nSee the [TypeScript starter project](https://github.com/PAIR-code/deeplearnjs/tree/master/starter/typescript/) and the\n[ES6 starter project](https://github.com/PAIR-code/deeplearnjs/tree/master/starter/es6/) to get you quickly started.\n\n#### ES5 JavaScript\n\nLet's do the same thing in ES5 Javascript. Remember to include the script tag to\nload deeplearn.js\n\n```js\nvar a = dl.tensor1d([1, 2, 3]);\nvar b = dl.scalar(2);\n\nvar result = a.add(b); // a is not modified, result is a new tensor\n\n// Option 1: With a Promise.\nresult.data().then(data => console.log(data)); // Float32Array([3, 4, 5])\n\n// Option 2: Synchronous download of data. Blocks the UI.\nconsole.log(result.dataSync());\n```\n\n## Development\n\nTo build **deeplearn.js** from source, we need to clone the project and prepare\nthe dev environment:\n\n```bash\n$ git clone https://github.com/PAIR-code/deeplearnjs.git\n$ cd deeplearnjs\n$ yarn prep # Installs dependencies.\n```\n\n#### Yarn vs NPM\nGenerally speaking it's up to you. Yarn is fully interoperable with npm. You can either do `yarn` or `npm install`. However we use yarn, and if you are adding or removing dependencies you should use yarn to keep the `yarn.lock` file up to date.\n\n#### Code editor\nWe recommend using [Visual Studio Code](https://code.visualstudio.com/) for\ndevelopment. Make sure to install\n[TSLint VSCode extension](https://marketplace.visualstudio.com/items?itemName=eg2.tslint)\nand the npm [clang-format](https://github.com/angular/clang-format) `1.2.2` or later\nwith the\n[Clang-Format VSCode extension](https://marketplace.visualstudio.com/items?itemName=xaver.clang-format)\nfor auto-formatting.\n\n#### Interactive development\nTo interactively develop any of the demos (e.g. `demos/nn-art/`):\n\n```bash\n$ ./scripts/watch-demo demos/nn-art\n>> Starting up http-server, serving ./\n>> Available on:\n>> http://127.0.0.1:8080\n>> Hit CTRL-C to stop the server\n>> 1357589 bytes written to dist/demos/nn-art/bundle.js (0.85 seconds) at 10:34:45 AM\n```\n\nThen visit `http://localhost:8080/demos/nn-art/`. The\n`watch-demo` script monitors for changes of typescript code and does\nincremental compilation (~200-400ms), so users can have a fast edit-refresh\ncycle when developing apps.\n\n#### Testing\nBefore submitting a pull request, make sure the code passes all the tests and is clean of lint errors:\n\n```bash\n$ yarn test\n$ yarn lint\n```\n\nTo run a subset of tests and/or on a specific browser:\n\n```bash\n$ yarn test --browsers=Chrome --grep='multinomial'\n \n> ...\n> Chrome 62.0.3202 (Mac OS X 10.12.6): Executed 28 of 1891 (skipped 1863) SUCCESS (6.914 secs / 0.634 secs)\n```\n\nTo run the tests once and exit the karma process (helpful on Windows):\n\n```bash\n$ yarn test --single-run\n```\n\n#### Packaging (browser and npm)\nTo build a standalone ES5 library that can be imported in the browser with a\n`\n\n\n```\n\n#### TypeScript / ES6 JavaScript\n\nLet's add a scalar value to a 1D Tensor. Deeplearn js supports _broadcasting_\nthe value of scalar over all the elements in the tensor.\n\n```js\nimport * as dl from 'deeplearn'; // If not loading the script as a global\n\nconst a = dl.tensor1d([1, 2, 3]);\nconst b = dl.scalar(2);\n\nconst result = a.add(b); // a is not modified, result is a new tensor\nresult.data().then(data => console.log(data)); // Float32Array([3, 4, 5]\n\n// Alternatively you can use a blocking call to get the data.\n// However this might slow your program down if called repeatedly.\nconsole.log(result.dataSync()); // Float32Array([3, 4, 5]\n```\n\nSee the [TypeScript starter project](https://github.com/PAIR-code/deeplearnjs/tree/master/starter/typescript/) and the\n[ES6 starter project](https://github.com/PAIR-code/deeplearnjs/tree/master/starter/es6/) to get you quickly started.\n\n#### ES5 JavaScript\n\nLet's do the same thing in ES5 Javascript. Remember to include the script tag to\nload deeplearn.js\n\n```js\nvar a = dl.tensor1d([1, 2, 3]);\nvar b = dl.scalar(2);\n\nvar result = a.add(b); // a is not modified, result is a new tensor\n\n// Option 1: With a Promise.\nresult.data().then(data => console.log(data)); // Float32Array([3, 4, 5])\n\n// Option 2: Synchronous download of data. Blocks the UI.\nconsole.log(result.dataSync());\n```\n\n## Development\n\nTo build **deeplearn.js** from source, we need to clone the project and prepare\nthe dev environment:\n\n```bash\n$ git clone https://github.com/PAIR-code/deeplearnjs.git\n$ cd deeplearnjs\n$ yarn prep # Installs dependencies.\n```\n\n#### Yarn vs NPM\nGenerally speaking it's up to you. Yarn is fully interoperable with npm. You can either do `yarn` or `npm install`. However we use yarn, and if you are adding or removing dependencies you should use yarn to keep the `yarn.lock` file up to date.\n\n#### Code editor\nWe recommend using [Visual Studio Code](https://code.visualstudio.com/) for\ndevelopment. Make sure to install\n[TSLint VSCode extension](https://marketplace.visualstudio.com/items?itemName=eg2.tslint)\nand the npm [clang-format](https://github.com/angular/clang-format) `1.2.2` or later\nwith the\n[Clang-Format VSCode extension](https://marketplace.visualstudio.com/items?itemName=xaver.clang-format)\nfor auto-formatting.\n\n#### Interactive development\nTo interactively develop any of the demos (e.g. `demos/nn-art/`):\n\n```bash\n$ ./scripts/watch-demo demos/nn-art\n>> Starting up http-server, serving ./\n>> Available on:\n>> http://127.0.0.1:8080\n>> Hit CTRL-C to stop the server\n>> 1357589 bytes written to dist/demos/nn-art/bundle.js (0.85 seconds) at 10:34:45 AM\n```\n\nThen visit `http://localhost:8080/demos/nn-art/`. The\n`watch-demo` script monitors for changes of typescript code and does\nincremental compilation (~200-400ms), so users can have a fast edit-refresh\ncycle when developing apps.\n\n#### Testing\nBefore submitting a pull request, make sure the code passes all the tests and is clean of lint errors:\n\n```bash\n$ yarn test\n$ yarn lint\n```\n\nTo run a subset of tests and/or on a specific browser:\n\n```bash\n$ yarn test --browsers=Chrome --grep='multinomial'\n \n> ...\n> Chrome 62.0.3202 (Mac OS X 10.12.6): Executed 28 of 1891 (skipped 1863) SUCCESS (6.914 secs / 0.634 secs)\n```\n\nTo run the tests once and exit the karma process (helpful on Windows):\n\n```bash\n$ yarn test --single-run\n```\n\n#### Packaging (browser and npm)\nTo build a standalone ES5 library that can be imported in the browser with a\n`\n\n\n```\n\n#### TypeScript / ES6 JavaScript\n\nLet's add a scalar value to a 1D Tensor. Deeplearn js supports _broadcasting_\nthe value of scalar over all the elements in the tensor.\n\n```js\nimport * as dl from 'deeplearn'; // If not loading the script as a global\n\nconst a = dl.tensor1d([1, 2, 3]);\nconst b = dl.scalar(2);\n\nconst result = a.add(b); // a is not modified, result is a new tensor\nresult.data().then(data => console.log(data)); // Float32Array([3, 4, 5]\n\n// Alternatively you can use a blocking call to get the data.\n// However this might slow your program down if called repeatedly.\nconsole.log(result.dataSync()); // Float32Array([3, 4, 5]\n```\n\nSee the [TypeScript starter project](https://github.com/PAIR-code/deeplearnjs/tree/master/starter/typescript/) and the\n[ES6 starter project](https://github.com/PAIR-code/deeplearnjs/tree/master/starter/es6/) to get you quickly started.\n\n#### ES5 JavaScript\n\nLet's do the same thing in ES5 Javascript. Remember to include the script tag to\nload deeplearn.js\n\n```js\nvar a = dl.tensor1d([1, 2, 3]);\nvar b = dl.scalar(2);\n\nvar result = a.add(b); // a is not modified, result is a new tensor\n\n// Option 1: With a Promise.\nresult.data().then(data => console.log(data)); // Float32Array([3, 4, 5])\n\n// Option 2: Synchronous download of data. Blocks the UI.\nconsole.log(result.dataSync());\n```\n\n## Development\n\nTo build **deeplearn.js** from source, we need to clone the project and prepare\nthe dev environment:\n\n```bash\n$ git clone https://github.com/PAIR-code/deeplearnjs.git\n$ cd deeplearnjs\n$ yarn prep # Installs dependencies.\n```\n\n#### Yarn vs NPM\nGenerally speaking it's up to you. Yarn is fully interoperable with npm. You can either do `yarn` or `npm install`. However we use yarn, and if you are adding or removing dependencies you should use yarn to keep the `yarn.lock` file up to date.\n\n#### Code editor\nWe recommend using [Visual Studio Code](https://code.visualstudio.com/) for\ndevelopment. Make sure to install\n[TSLint VSCode extension](https://marketplace.visualstudio.com/items?itemName=eg2.tslint)\nand the npm [clang-format](https://github.com/angular/clang-format) `1.2.2` or later\nwith the\n[Clang-Format VSCode extension](https://marketplace.visualstudio.com/items?itemName=xaver.clang-format)\nfor auto-formatting.\n\n#### Interactive development\nTo interactively develop any of the demos (e.g. `demos/nn-art/`):\n\n```bash\n$ ./scripts/watch-demo demos/nn-art\n>> Starting up http-server, serving ./\n>> Available on:\n>> http://127.0.0.1:8080\n>> Hit CTRL-C to stop the server\n>> 1357589 bytes written to dist/demos/nn-art/bundle.js (0.85 seconds) at 10:34:45 AM\n```\n\nThen visit `http://localhost:8080/demos/nn-art/`. The\n`watch-demo` script monitors for changes of typescript code and does\nincremental compilation (~200-400ms), so users can have a fast edit-refresh\ncycle when developing apps.\n\n#### Testing\nBefore submitting a pull request, make sure the code passes all the tests and is clean of lint errors:\n\n```bash\n$ yarn test\n$ yarn lint\n```\n\nTo run a subset of tests and/or on a specific browser:\n\n```bash\n$ yarn test --browsers=Chrome --grep='multinomial'\n \n> ...\n> Chrome 62.0.3202 (Mac OS X 10.12.6): Executed 28 of 1891 (skipped 1863) SUCCESS (6.914 secs / 0.634 secs)\n```\n\nTo run the tests once and exit the karma process (helpful on Windows):\n\n```bash\n$ yarn test --single-run\n```\n\n#### Packaging (browser and npm)\nTo build a standalone ES5 library that can be imported in the browser with a\n`\n\n\n```\n\n#### TypeScript / ES6 JavaScript\n\nLet's add a scalar value to a 1D Tensor. Deeplearn js supports _broadcasting_\nthe value of scalar over all the elements in the tensor.\n\n```js\nimport * as dl from 'deeplearn'; // If not loading the script as a global\n\nconst a = dl.tensor1d([1, 2, 3]);\nconst b = dl.scalar(2);\n\nconst result = a.add(b); // a is not modified, result is a new tensor\nresult.data().then(data => console.log(data)); // Float32Array([3, 4, 5]\n\n// Alternatively you can use a blocking call to get the data.\n// However this might slow your program down if called repeatedly.\nconsole.log(result.dataSync()); // Float32Array([3, 4, 5]\n```\n\nSee the [TypeScript starter project](https://github.com/PAIR-code/deeplearnjs/tree/master/starter/typescript/) and the\n[ES6 starter project](https://github.com/PAIR-code/deeplearnjs/tree/master/starter/es6/) to get you quickly started.\n\n#### ES5 JavaScript\n\nLet's do the same thing in ES5 Javascript. Remember to include the script tag to\nload deeplearn.js\n\n```js\nvar a = dl.tensor1d([1, 2, 3]);\nvar b = dl.scalar(2);\n\nvar result = a.add(b); // a is not modified, result is a new tensor\n\n// Option 1: With a Promise.\nresult.data().then(data => console.log(data)); // Float32Array([3, 4, 5])\n\n// Option 2: Synchronous download of data. Blocks the UI.\nconsole.log(result.dataSync());\n```\n\n## Development\n\nTo build **deeplearn.js** from source, we need to clone the project and prepare\nthe dev environment:\n\n```bash\n$ git clone https://github.com/PAIR-code/deeplearnjs.git\n$ cd deeplearnjs\n$ yarn prep # Installs dependencies.\n```\n\n#### Yarn vs NPM\nGenerally speaking it's up to you. Yarn is fully interoperable with npm. You can either do `yarn` or `npm install`. However we use yarn, and if you are adding or removing dependencies you should use yarn to keep the `yarn.lock` file up to date.\n\n#### Code editor\nWe recommend using [Visual Studio Code](https://code.visualstudio.com/) for\ndevelopment. Make sure to install\n[TSLint VSCode extension](https://marketplace.visualstudio.com/items?itemName=eg2.tslint)\nand the npm [clang-format](https://github.com/angular/clang-format) `1.2.2` or later\nwith the\n[Clang-Format VSCode extension](https://marketplace.visualstudio.com/items?itemName=xaver.clang-format)\nfor auto-formatting.\n\n#### Interactive development\nTo interactively develop any of the demos (e.g. `demos/nn-art/`):\n\n```bash\n$ ./scripts/watch-demo demos/nn-art\n>> Starting up http-server, serving ./\n>> Available on:\n>> http://127.0.0.1:8080\n>> Hit CTRL-C to stop the server\n>> 1357589 bytes written to dist/demos/nn-art/bundle.js (0.85 seconds) at 10:34:45 AM\n```\n\nThen visit `http://localhost:8080/demos/nn-art/`. The\n`watch-demo` script monitors for changes of typescript code and does\nincremental compilation (~200-400ms), so users can have a fast edit-refresh\ncycle when developing apps.\n\n#### Testing\nBefore submitting a pull request, make sure the code passes all the tests and is clean of lint errors:\n\n```bash\n$ yarn test\n$ yarn lint\n```\n\nTo run a subset of tests and/or on a specific browser:\n\n```bash\n$ yarn test --browsers=Chrome --grep='multinomial'\n \n> ...\n> Chrome 62.0.3202 (Mac OS X 10.12.6): Executed 28 of 1891 (skipped 1863) SUCCESS (6.914 secs / 0.634 secs)\n```\n\nTo run the tests once and exit the karma process (helpful on Windows):\n\n```bash\n$ yarn test --single-run\n```\n\n#### Packaging (browser and npm)\nTo build a standalone ES5 library that can be imported in the browser with a\n`\n\n\n```\n\n#### TypeScript / ES6 JavaScript\n\nLet's add a scalar value to a 1D Tensor. Deeplearn js supports _broadcasting_\nthe value of scalar over all the elements in the tensor.\n\n```js\nimport * as dl from 'deeplearn'; // If not loading the script as a global\n\nconst a = dl.tensor1d([1, 2, 3]);\nconst b = dl.scalar(2);\n\nconst result = a.add(b); // a is not modified, result is a new tensor\nresult.data().then(data => console.log(data)); // Float32Array([3, 4, 5]\n\n// Alternatively you can use a blocking call to get the data.\n// However this might slow your program down if called repeatedly.\nconsole.log(result.dataSync()); // Float32Array([3, 4, 5]\n```\n\nSee the [TypeScript starter project](https://github.com/PAIR-code/deeplearnjs/tree/master/starter/typescript/) and the\n[ES6 starter project](https://github.com/PAIR-code/deeplearnjs/tree/master/starter/es6/) to get you quickly started.\n\n#### ES5 JavaScript\n\nLet's do the same thing in ES5 Javascript. Remember to include the script tag to\nload deeplearn.js\n\n```js\nvar a = dl.tensor1d([1, 2, 3]);\nvar b = dl.scalar(2);\n\nvar result = a.add(b); // a is not modified, result is a new tensor\n\n// Option 1: With a Promise.\nresult.data().then(data => console.log(data)); // Float32Array([3, 4, 5])\n\n// Option 2: Synchronous download of data. Blocks the UI.\nconsole.log(result.dataSync());\n```\n\n## Development\n\nTo build **deeplearn.js** from source, we need to clone the project and prepare\nthe dev environment:\n\n```bash\n$ git clone https://github.com/PAIR-code/deeplearnjs.git\n$ cd deeplearnjs\n$ yarn prep # Installs dependencies.\n```\n\n#### Yarn vs NPM\nGenerally speaking it's up to you. Yarn is fully interoperable with npm. You can either do `yarn` or `npm install`. However we use yarn, and if you are adding or removing dependencies you should use yarn to keep the `yarn.lock` file up to date.\n\n#### Code editor\nWe recommend using [Visual Studio Code](https://code.visualstudio.com/) for\ndevelopment. Make sure to install\n[TSLint VSCode extension](https://marketplace.visualstudio.com/items?itemName=eg2.tslint)\nand the npm [clang-format](https://github.com/angular/clang-format) `1.2.2` or later\nwith the\n[Clang-Format VSCode extension](https://marketplace.visualstudio.com/items?itemName=xaver.clang-format)\nfor auto-formatting.\n\n#### Interactive development\nTo interactively develop any of the demos (e.g. `demos/nn-art/`):\n\n```bash\n$ ./scripts/watch-demo demos/nn-art\n>> Starting up http-server, serving ./\n>> Available on:\n>> http://127.0.0.1:8080\n>> Hit CTRL-C to stop the server\n>> 1357589 bytes written to dist/demos/nn-art/bundle.js (0.85 seconds) at 10:34:45 AM\n```\n\nThen visit `http://localhost:8080/demos/nn-art/`. The\n`watch-demo` script monitors for changes of typescript code and does\nincremental compilation (~200-400ms), so users can have a fast edit-refresh\ncycle when developing apps.\n\n#### Testing\nBefore submitting a pull request, make sure the code passes all the tests and is clean of lint errors:\n\n```bash\n$ yarn test\n$ yarn lint\n```\n\nTo run a subset of tests and/or on a specific browser:\n\n```bash\n$ yarn test --browsers=Chrome --grep='multinomial'\n \n> ...\n> Chrome 62.0.3202 (Mac OS X 10.12.6): Executed 28 of 1891 (skipped 1863) SUCCESS (6.914 secs / 0.634 secs)\n```\n\nTo run the tests once and exit the karma process (helpful on Windows):\n\n```bash\n$ yarn test --single-run\n```\n\n#### Packaging (browser and npm)\nTo build a standalone ES5 library that can be imported in the browser with a\n`\n\n\n```\n\n#### TypeScript / ES6 JavaScript\n\nLet's add a scalar value to a 1D Tensor. Deeplearn js supports _broadcasting_\nthe value of scalar over all the elements in the tensor.\n\n```js\nimport * as dl from 'deeplearn'; // If not loading the script as a global\n\nconst a = dl.tensor1d([1, 2, 3]);\nconst b = dl.scalar(2);\n\nconst result = a.add(b); // a is not modified, result is a new tensor\nresult.data().then(data => console.log(data)); // Float32Array([3, 4, 5]\n\n// Alternatively you can use a blocking call to get the data.\n// However this might slow your program down if called repeatedly.\nconsole.log(result.dataSync()); // Float32Array([3, 4, 5]\n```\n\nSee the [TypeScript starter project](https://github.com/PAIR-code/deeplearnjs/tree/master/starter/typescript/) and the\n[ES6 starter project](https://github.com/PAIR-code/deeplearnjs/tree/master/starter/es6/) to get you quickly started.\n\n#### ES5 JavaScript\n\nLet's do the same thing in ES5 Javascript. Remember to include the script tag to\nload deeplearn.js\n\n```js\nvar a = dl.tensor1d([1, 2, 3]);\nvar b = dl.scalar(2);\n\nvar result = a.add(b); // a is not modified, result is a new tensor\n\n// Option 1: With a Promise.\nresult.data().then(data => console.log(data)); // Float32Array([3, 4, 5])\n\n// Option 2: Synchronous download of data. Blocks the UI.\nconsole.log(result.dataSync());\n```\n\n## Development\n\nTo build **deeplearn.js** from source, we need to clone the project and prepare\nthe dev environment:\n\n```bash\n$ git clone https://github.com/PAIR-code/deeplearnjs.git\n$ cd deeplearnjs\n$ yarn prep # Installs dependencies.\n```\n\n#### Yarn vs NPM\nGenerally speaking it's up to you. Yarn is fully interoperable with npm. You can either do `yarn` or `npm install`. However we use yarn, and if you are adding or removing dependencies you should use yarn to keep the `yarn.lock` file up to date.\n\n#### Code editor\nWe recommend using [Visual Studio Code](https://code.visualstudio.com/) for\ndevelopment. Make sure to install\n[TSLint VSCode extension](https://marketplace.visualstudio.com/items?itemName=eg2.tslint)\nand the npm [clang-format](https://github.com/angular/clang-format) `1.2.2` or later\nwith the\n[Clang-Format VSCode extension](https://marketplace.visualstudio.com/items?itemName=xaver.clang-format)\nfor auto-formatting.\n\n#### Interactive development\nTo interactively develop any of the demos (e.g. `demos/nn-art/`):\n\n```bash\n$ ./scripts/watch-demo demos/nn-art\n>> Starting up http-server, serving ./\n>> Available on:\n>> http://127.0.0.1:8080\n>> Hit CTRL-C to stop the server\n>> 1357589 bytes written to dist/demos/nn-art/bundle.js (0.85 seconds) at 10:34:45 AM\n```\n\nThen visit `http://localhost:8080/demos/nn-art/`. The\n`watch-demo` script monitors for changes of typescript code and does\nincremental compilation (~200-400ms), so users can have a fast edit-refresh\ncycle when developing apps.\n\n#### Testing\nBefore submitting a pull request, make sure the code passes all the tests and is clean of lint errors:\n\n```bash\n$ yarn test\n$ yarn lint\n```\n\nTo run a subset of tests and/or on a specific browser:\n\n```bash\n$ yarn test --browsers=Chrome --grep='multinomial'\n \n> ...\n> Chrome 62.0.3202 (Mac OS X 10.12.6): Executed 28 of 1891 (skipped 1863) SUCCESS (6.914 secs / 0.634 secs)\n```\n\nTo run the tests once and exit the karma process (helpful on Windows):\n\n```bash\n$ yarn test --single-run\n```\n\n#### Packaging (browser and npm)\nTo build a standalone ES5 library that can be imported in the browser with a\n`