Add mobilenet. (#4)

Adds a pre-trained MobileNet model module which lets you use a pretrained MobileNet model. It completely hides Tensors so users don't need to know anything about machine learning.

You can load mobilenet like this:
```
const model = await mobilenet.load();
```

You can classify an image element like this:
```
const classification = await model.classify(document.getElementById('img'));
```

You can get intermediate activations like this:
```
const activation = await model.infer(document.getElementById('img'), 'conv_w_1');
```

Readme link for more details:
https://github.com/tensorflow/tfjs-models/blob/2576a5132de5dc5d328ed297d37c3595b28c62ca/mobilenet/README.md

Author: Nikhil Thorat

mobilenet/README.md

@@ -0,0 +1,139 @@
+# MobileNet
+
+MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used.
+
+MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.
+
+This TensorFlow.js model does not require you to know about machine learning.
+It can take as input any browser-based image elements (`<img>`, `<video>`, `<canvas>`
+elements, for example) and returns an array of most likely predictions and
+their confidences.
+
+For more information about MobileNet, check out this readme in
+[tensorflow/models](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md).
+
+## Usage
+
+There are two main ways to get this model in your JavaScript project: via script tags or by installing it from NPM and using a build tool like Parcel, WebPack, or Rollup.
+
+### via Script Tag
+
+```html
+<!-- Load TensorFlow.js. This is required to use MobileNet. -->
+<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.10.3"> </script>
+<!-- Load the MobileNet model. -->
+<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/mobilenet@0.1.0"> </script>
+
+<!-- Replace this with your image. Make sure CORS settings allow reading the image! -->
+<img id="img" src="cat.jpg"></img>
+
+<!-- Place your code in the script tag below. You can also use an external .js file -->
+<script>
+  // Notice there is no 'import' statement. 'mobilenet' and 'tf' is
+  // available on the index-page because of the script tag above.
+
+  const img = document.getElementById('img');
+
+  // Load the model.
+  mobilenet.load().then(model => {
+    // Classify the image.
+    model.classify(img).then(predictions => {
+      console.log('Predictions: ');
+      console.log(predictions);
+    });
+  });
+</script>
+```
+
+### via NPM
+
+```js
+// Note: you do not need to import @tensorflow/tfjs here.
+
+import * as mobilenet from '@tensorflow-models/mobilenet';
+
+const img = document.getElementById('img');
+
+// Load the model.
+const model = await mobilenet.load();
+
+// Classify the image.
+const predictions = await model.classify(img);
+
+console.log('Predictions: ');
+console.log(predictions);
+```
+
+## API
+
+#### Loading the model
+`mobilenet` is the module name, which is automatically included when you use
+the <script src> method. When using ES6 imports, mobilenet is the module.
+
+```ts
+mobilenet.load(
+  version?: 1,
+  alpha?: 0.25 | .50 | .75 | 1.0
+)
+```
+
+Args:
+- **version:** The MobileNet version number. Currently only accepts and defaults to version 1. In the future we will support MobileNet V2.
+- **alpha:** Controls the width of the network, trading accuracy for performance. A smaller alpha decreases accuracy and increases performance. Defaults to 1.0.
+
+Returns a `model` object.
+
+#### Making a classification
+
+You can make a classification with mobilenet without needing to create a Tensor
+with `MobileNet.classify`, which takes an input image element and returns an
+array with top classes and their probabilities.
+
+If you want to use this for transfer learning, see the `infer` method.
+
+This method exists on the model that is loaded from `mobilenet.load`.
+
+```ts
+model.classify(
+  img: tf.Tensor3D | ImageData | HTMLImageElement |
+      HTMLCanvasElement | HTMLVideoElement,
+  topk?: number
+)
+```
+
+Args:
+- **img:** A Tensor or an image element to make a classification on.
+- **topk:** How many of the top probabilities to return. Defaults to 3.
+
+Returns an array of classes and probabilities that looks like:
+
+```js
+[{
+  className: "Egyptian cat",
+  probability: 0.8380282521247864
+}, {
+  className: "tabby, tabby cat",
+  probability: 0.04644153267145157
+}, {
+  className: "Siamese cat, Siamese",
+  probability: 0.024488523602485657
+}]
+```
+
+#### Getting activations
+
+You can also use this model to get intermediate activations or logits as
+TensorFlow.js tensors.
+
+This method exists on the model that is loaded from `mobilenet.load`.
+
+```ts
+model.infer(
+  img: tf.Tensor3D | ImageData | HTMLImageElement |
+      HTMLCanvasElement | HTMLVideoElement,
+  endpoint?: string
+)
+```
+
+- **img:** A Tensor or an image element to make a classification on.
+- **endpoint:** The optional endpoint to predict through. You can list all the endpoints with `model.endpoint`. These correspond to layers of the MobileNet model. If undefined, will return 1000D unnormalized logits.