@tensorflow/tfjs
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@tensorflow/tfjs - npm Package Compare versions
Comparing version 0.8.0 to 0.9.0
@@ -1,2 +0,2 @@ | ||
| declare const version = "0.8.0"; | ||
| declare const version = "0.9.0"; | ||
| export { version }; |
| "use strict"; | ||
| Object.defineProperty(exports, "__esModule", { value: true }); | ||
| var version = '0.8.0'; | ||
| var version = '0.9.0'; | ||
| exports.version = version; |
| { | ||
| "name": "@tensorflow/tfjs", | ||
| "version": "0.8.0", | ||
| "version": "0.9.0", | ||
| "description": "An open-source machine learning framework.", | ||
@@ -40,5 +40,5 @@ "private": false, | ||
| "dependencies": { | ||
| "@tensorflow/tfjs-core": "0.6.1", | ||
| "@tensorflow/tfjs-layers": "0.3.0" | ||
| "@tensorflow/tfjs-core": "0.7.1", | ||
| "@tensorflow/tfjs-layers": "0.4.0" | ||
| } | ||
| } |
109
README.md
@@ -0,1 +1,2 @@ | ||
| [](https://travis-ci.org/tensorflow/tfjs) | ||
| # TensorFlow.js | ||
@@ -18,19 +19,2 @@ | ||
| ## Importing | ||
| You can import TensorFlow.js directly via yarn or npm: | ||
| `yarn add @tensorflow/tfjs` or `npm install @tensorflow/tfjs`. | ||
| Alternatively you can use a script tag. The library will be available as | ||
| a global variable named `tf`: | ||
| ```html | ||
| <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script> | ||
| <!-- or --> | ||
| <script src="https://unpkg.com/@tensorflow/tfjs@latest"></script> | ||
| ``` | ||
| You can also specify which version to load replacing `@latest` | ||
| with a specific version string (e.g. `0.6.0`). | ||
| ## About this repo | ||
@@ -60,51 +44,83 @@ | ||
| Let's add a scalar value to a vector. TensorFlow.js supports _broadcasting_ | ||
| the value of scalar over all the elements in the tensor. | ||
| 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>. | ||
| ```js | ||
| import * as tf from '@tensorflow/tfjs'; // If not loading the script as a global | ||
| ### via Script Tag | ||
| Add the following code to an HTML file: | ||
| const a = tf.tensor1d([1, 2, 3]); | ||
| const b = tf.scalar(2); | ||
| ```html | ||
| <html> | ||
| <head> | ||
| <!-- Load TensorFlow.js --> | ||
| <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.8.0"> </script> | ||
| const result = a.add(b); // a is not modified, result is a new tensor | ||
| result.data().then(data => console.log(data)); // Float32Array([3, 4, 5] | ||
| <!-- 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. | ||
| // Alternatively you can use a blocking call to get the data. | ||
| // However this might slow your program down if called repeatedly. | ||
| console.log(result.dataSync()); // Float32Array([3, 4, 5] | ||
| // 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> | ||
| ``` | ||
| See the | ||
| [core-concepts tutorial](https://js.tensorflow.org/tutorials/core-concepts.html) | ||
| for more. | ||
| Open up that html file in your browser and the code should run! | ||
| Now, let's build a toy model to perform linear regression. | ||
| ### 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 bundler/transpiler | ||
| to convert your code to something the browser understands. 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'; | ||
| // A sequential model is a container which you can add layers to. | ||
| // Define a model for linear regression. | ||
| const model = tf.sequential(); | ||
| // Add a dense layer with 1 output unit. | ||
| model.add(tf.layers.dense({units: 1, inputShape: [1]})); | ||
| // Specify the loss type and optimizer for training. | ||
| // 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]); | ||
| const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]); | ||
| const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]); | ||
| // Train the model. | ||
| await model.fit(xs, ys, {epochs: 500}); | ||
| // After the training, perform inference. | ||
| const output = model.predict(tf.tensor2d([[5]], [1, 1])); | ||
| output.print(); | ||
| // 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(); | ||
| }); | ||
| ``` | ||
| For a deeper dive into building models, see the | ||
| [MNIST tutorial](https://js.tensorflow.org/tutorials/mnist.html) | ||
| See our <a href='./tutorials'>tutorials</a>, <a href='https://github.com/tensorflow/tfjs-examples' target="_blank">examples</a> | ||
| and <a href="./api/{{latestVersion}}/">documentation</a> for more details. | ||
@@ -125,2 +141,1 @@ ## Importing pre-trained models | ||
| - [Help mailing list](https://groups.google.com/a/tensorflow.org/forum/#!forum/tfjs) | ||
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Improved metrics
- Total package byte prevSize
- increased by2.89%
1682352
- Lines of code
- increased by2.58%
25334
- Number of lines in readme file
- increased by12.1%
139
Dependency changes
+ Added@tensorflow/tfjs-core@0.7.1(transitive)
+ Added@tensorflow/tfjs-layers@0.4.0(transitive)
- Removed@tensorflow/tfjs-layers@0.3.0(transitive)
Updated@tensorflow/tfjs-core@0.7.1