@tensorflow/tfjs - npm Package Compare versions

Comparing version 0.0.7 to 0.6.0-alpha7

dist/version.d.ts

@@ -1,2 +0,2 @@
-declare const version = "0.0.7";
+declare const version = "0.6.0-alpha7";
 export { version };

dist/version.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
-var version = '0.0.7';
+var version = '0.6.0-alpha7';
 exports.version = version;

package.json

 {
   "name": "@tensorflow/tfjs",
-  "version": "0.0.7",
+  "version": "0.6.0-alpha7",
   "description": "An open-source machine learning framework.",
@@ -27,5 +27,5 @@ "private": false,
   "dependencies": {
-    "@tensorflow/tfjs-core": "0.0.2",
-    "@tensorflow/tfjs-layers": "0.0.7"
+    "@tensorflow/tfjs-core": "0.6.0-alpha7",
+    "@tensorflow/tfjs-layers": "0.1.0"
   }
 }

README.md

@@ -1,6 +0,161 @@
-# TensorFlow.js: Union Package
+# TensorFlow.js
 
-TensorFlow.js is a JavaScript library for building, training and serving
-machine learning models. When running in the browser, it utilizes WebGL
-acceleration. TensorFlow.js is a part of the
+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.
+
+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
+
+- [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/).
+
+
+## Importing
+
+You can import TensorFlow.js Union 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`.
+
+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>
+<!-- or -->
+<script src="https://unpkg.com/tensorflow/tfjs@latest"></script>
+```
+
+
+## Usage Examples
+
+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/)
+
+
+### Direct tensor manipulation
+
+Let's add a scalar value to a 1D Tensor. TensorFlow.js supports _broadcasting_
+the value of scalar over all the elements in the tensor.
+
+```js
+import * as tf from '@tensorflow/tfjs'; // If not loading the script as a global
+
+const a = tf.tensor1d([1, 2, 3]);
+const b = tf.scalar(2);
+
+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]
+
+// 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]
+```
+
+See the
+[core-concepts tutorial](https://js.tensorflow.org/tutorials/core-concepts.html)
+ for more.
+
+### Building, training, and executing a model using Layers
+
+The following example shows how to build a toy model with only one `dense` layer
+to perform linear regression.
+
+```js
+import * as tf from '@tensorflow/tfjs';
+
+// A sequential model is a container which you can add layers to.
+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.
+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.
+await model.fit(xs, ys, {epochs: 500});
+
+// Ater the training, perform inference.
+const output = model.predict(tf.tensor2d([[5]], [1, 1]));
+output.print();
+```
+
+For a deeper dive into building a layers classifier, see the
+[MNIST tutorial](https://js.tensorflow.org/tutorials/mnist.html)
+
+
+### Loading a pretrained Keras model using Layers
+
+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)
+
+For example, in Python, save your Keras model using
+[tensorflowjs](https://pypi.org/project/tensorflowjs/),
+which can be installed using `pip install tensorflowjs`.
+
+
+```python
+import tensorflowjs as tfjs
+
+# ... Create and train your Keras model.
+
+# Save your Keras model in TensorFlow.js format.
+tfjs.converter.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.
@@ -15,8 +170,2 @@ You can import pre-trained TensorFlow
 
-This repository contains the logic and scripts to form a **union** package,
-[@tensorflowjs/tfjs](https://www.npmjs.com/package/@tensorflow/tfjs), from
 
-- [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/).

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Dependency changes

• + Added@tensorflow/tfjs-core@0.6.0-alpha7(transitive)

• + Added@tensorflow/tfjs-layers@0.1.0(transitive)

• - Removed@tensorflow/tfjs-core@0.0.2(transitive)

• - Removed@tensorflow/tfjs-layers@0.0.7(transitive)

• Updated@tensorflow/tfjs-core@0.6.0-alpha7

• Updated@tensorflow/tfjs-layers@0.1.0