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@tensorflow/tfjs
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@tensorflow/tfjs - npm Package Compare versions
Comparing version 0.6.1 to 0.7.0
@@ -1,2 +0,2 @@ | ||
| declare const version = "0.6.1"; | ||
| declare const version = "0.7.0"; | ||
| export { version }; |
| "use strict"; | ||
| Object.defineProperty(exports, "__esModule", { value: true }); | ||
| var version = '0.6.1'; | ||
| var version = '0.7.0'; | ||
| exports.version = version; |
| { | ||
| "name": "@tensorflow/tfjs", | ||
| "version": "0.6.1", | ||
| "version": "0.7.0", | ||
| "description": "An open-source machine learning framework.", | ||
@@ -16,5 +16,15 @@ "private": false, | ||
| "devDependencies": { | ||
| "@types/jasmine": "~2.8.6", | ||
| "@types/node": "~9.6.1", | ||
| "browserify": "~16.1.1", | ||
| "jasmine-core": "~3.1.0", | ||
| "karma": "~2.0.0", | ||
| "karma-browserstack-launcher": "~1.3.0", | ||
| "karma-chrome-launcher": "~2.2.0", | ||
| "karma-firefox-launcher": "~1.1.0", | ||
| "karma-jasmine": "~1.1.1", | ||
| "karma-typescript": "~3.0.12", | ||
| "rimraf": "~2.6.2", | ||
| "tsify": "~3.0.4", | ||
| "tslint": "~5.9.1", | ||
| "typescript": "2.7.2", | ||
@@ -25,8 +35,11 @@ "uglify-js": "~3.0.28" | ||
| "build": "tsc", | ||
| "build-npm": "./scripts/build-npm.sh" | ||
| "build-npm": "./scripts/build-npm.sh", | ||
| "lint": "tslint -p . -t verbose", | ||
| "test": "karma start", | ||
| "test-travis": "karma start --browsers='bs_firefox_mac,bs_chrome_mac' --singleRun" | ||
| }, | ||
| "dependencies": { | ||
| "@tensorflow/tfjs-core": "0.6.0", | ||
| "@tensorflow/tfjs-layers": "0.1.2" | ||
| "@tensorflow/tfjs-layers": "0.2.0" | ||
| } | ||
| } |
153
README.md
| # TensorFlow.js | ||
| TensorFlow.js is an open-source hardware-accelerated JavaScript library for | ||
| building, training and serving machine learning models. When running in the | ||
| browser, it utilizes WebGL acceleration. TensorFlow.js is also convenient and | ||
| intuitive, modeled after | ||
| [Keras](https://keras.io/) and | ||
| [tf.layers](https://www.tensorflow.org/api_docs/python/tf/layers) and can | ||
| load models saved from those libraries. | ||
| training and deploying machine learning models. | ||
| This repository conveniently contains the logic and scripts to form | ||
| a version-matched **union** package, | ||
| [@tensorflowjs/tfjs](https://www.npmjs.com/package/@tensorflow/tfjs), from | ||
| **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. | ||
| - [TensorFlow.js Core](https://github.com/tensorflow/tfjs-core), | ||
| a flexible low-level API, formerly known as *deeplearn.js*. | ||
| - [TensorFlow.js Layers](https://github.com/tensorflow/tfjs-layers), | ||
| a high-level API modeled after [Keras](https://keras.io/). | ||
| **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. | ||
| ## Importing | ||
| You can import TensorFlow.js Union directly via yarn or npm. | ||
| You can import TensorFlow.js directly via yarn or npm: | ||
| `yarn add @tensorflow/tfjs` or `npm install @tensorflow/tfjs`. | ||
| See snippets below for examples. | ||
| Alternatively you can use a script tag. Here we load it from a CDN. | ||
| In this case it will be available as a global variable named `tf`. | ||
| Alternatively you can use a script tag. The library will be available as | ||
| a global variable named `tf`: | ||
| You can replace also specify which version to load replacing `@latest` | ||
| with a specific | ||
| version string (e.g. `0.6.0`). | ||
| ```html | ||
| <script src="https://cdn.jsdelivr.net/npm/tensorflow/tfjs@latest"></script> | ||
| <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script> | ||
| <!-- or --> | ||
| <script src="https://unpkg.com/tensorflow/tfjs@latest"></script> | ||
| <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`). | ||
| ## Usage Examples | ||
| ## About this repo | ||
| Many examples illustrating how to use TensorFlow.js in ES5, ES6 and | ||
| TypeScript are available from the | ||
| [Examples repository](https://github.com/tensorflow/tfjs-examples) | ||
| and the | ||
| [TensorFlow.js Tutorials](https://js.tensorflow.org/tutorials/) | ||
| This repository contains the logic and scripts that combine | ||
| two packages: | ||
| - [TensorFlow.js Core](https://github.com/tensorflow/tfjs-core), | ||
| a flexible low-level API, formerly known as *deeplearn.js*. | ||
| - [TensorFlow.js Layers](https://github.com/tensorflow/tfjs-layers), | ||
| a high-level API which implements functionality similar to | ||
| [Keras](https://keras.io/). | ||
| If you care about bundle size, you can import those packages individually. | ||
| ### Direct tensor manipulation | ||
| ## Examples | ||
| Let's add a scalar value to a 1D Tensor. TensorFlow.js supports _broadcasting_ | ||
| Check out our | ||
| [examples repository](https://github.com/tensorflow/tfjs-examples) | ||
| and our [tutorials](https://js.tensorflow.org/tutorials/). | ||
| ## Migrating from deeplearn.js | ||
| See [these release notes](https://github.com/tensorflow/tfjs-core/releases/tag/v0.6.0) | ||
| for how to migrate from deeplearn.js to TensorFlow.js. | ||
| ## Getting started | ||
| Let's add a scalar value to a vector. TensorFlow.js supports _broadcasting_ | ||
| the value of scalar over all the elements in the tensor. | ||
@@ -73,7 +80,4 @@ | ||
| ### Building, training, and executing a model using Layers | ||
| Now, let's build a toy model to perform linear regression. | ||
| The following example shows how to build a toy model with only one `dense` layer | ||
| to perform linear regression. | ||
| ```js | ||
@@ -89,3 +93,3 @@ import * as tf from '@tensorflow/tfjs'; | ||
| // Specify the loss type and optimizer for training. | ||
| model.compile({loss: 'meanSquaredError', optimizer: 'SGD'}); | ||
| model.compile({loss: 'meanSquaredError', optimizer: 'sgd'}); | ||
@@ -99,3 +103,3 @@ // Generate some synthetic data for training. | ||
| // Ater the training, perform inference. | ||
| // After the training, perform inference. | ||
| const output = model.predict(tf.tensor2d([[5]], [1, 1])); | ||
@@ -105,70 +109,19 @@ output.print(); | ||
| For a deeper dive into building a layers classifier, see the | ||
| For a deeper dive into building models, see the | ||
| [MNIST tutorial](https://js.tensorflow.org/tutorials/mnist.html) | ||
| ## Importing pre-trained models | ||
| ### Loading a pretrained Keras model using Layers | ||
| We support porting pre-trained models from: | ||
| - [TensorFlow SavedModel](https://github.com/tensorflow/tfjs-converter) | ||
| - [Keras](https://js.tensorflow.org/tutorials/import-keras.html) | ||
| You can also load a model previously trained and saved from elsewhere (e.g., | ||
| from Python Keras) and use it for inference or transfer learning in the browser. | ||
| More details in the | ||
| [import-keras tutorial](https://js.tensorflow.org/tutorials/import-keras.html) | ||
| ## Find out more | ||
| For example, in Python, save your Keras model using | ||
| [tensorflowjs](https://pypi.org/project/tensorflowjs/), | ||
| which can be installed using `pip install tensorflowjs`. | ||
| [TensorFlow.js](https://js.tensorflow.org) is a part of the | ||
| [TensorFlow](https://www.tensorflow.org) ecosystem. For more info: | ||
| - [js.tensorflow.org](https://js.tensorflow.org) | ||
| - [Tutorials](https://js.tensorflow.org/tutorials) | ||
| - [API reference](https://js.tensorflow.org/api/latest/) | ||
| - [Help mailing list](https://groups.google.com/a/tensorflow.org/forum/#!forum/tfjs) | ||
| ```python | ||
| import tensorflowjs as tfjs | ||
| # ... Create and train your Keras model. | ||
| # Save your Keras model in TensorFlow.js format. | ||
| tfjs.converters.save_keras_model(model, '/path/to/tfjs_artifacts/') | ||
| # Then use your favorite web server to serve the directory at a URL, say | ||
| # http://foo.bar/tfjs_artifacts/model.json | ||
| ``` | ||
| To load the model with TensorFlow.js Layers: | ||
| ```js | ||
| import * as tf from '@tensorflow/tfjs'; | ||
| const model = await tf.loadModel('http://foo.bar/tfjs_artifacts/model.json'); | ||
| // Now the model is ready for inference, evaluation or re-training. | ||
| ``` | ||
| ## How to find more! | ||
| Again, see the | ||
| [Examples repository](https://github.com/tensorflow/tfjs-examples) and the | ||
| [TensorFlow.js Tutorials](https://js.tensorflow.org/tutorials/) | ||
| for many more examples of how to build models and manipulate tensors. | ||
| ## Supported Environments | ||
| **TensorFlow.js** targets environments with WebGL 1.0 or WebGL 2.0. For devices | ||
| without the `OES_texture_float` extension, we fall back to fixed precision | ||
| floats backed by a `gl.UNSIGNED_BYTE` texture. For platforms without WebGL, | ||
| we provide CPU fallbacks. | ||
| ## Additional Resources | ||
| TensorFlow.js is a part of the | ||
| [TensorFlow](https://www.tensorflow.org) ecosystem. | ||
| You can import pre-trained TensorFlow | ||
| [SavedModels](https://www.tensorflow.org/programmers_guide/saved_model) and | ||
| [Keras models](https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model), | ||
| for execution and retraining. | ||
| For more information on the API, follow the links to their Core and Layers | ||
| repositories below, or visit [js.tensorflow.org](https://js.tensorflow.org). | ||
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Improved metrics
- Total package byte prevSize
- increased by0.55%
1614734
- Number of package files
- increased by11.11%
10
- Lines of code
- increased by0.73%
24393
Worsened metrics
- Dev dependency count
- increased by200%
15
- Number of lines in readme file
- decreased by-27.49%
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Dependency changes
+ Added@tensorflow/tfjs-layers@0.2.0(transitive)
- Removed@tensorflow/tfjs-layers@0.1.2(transitive)