Update callback methods and update readme and accept alternate image formats

Author: Kevin Scott

README.md

@@ -67,14 +67,58 @@ const mlClassifier = new MLClassifier();
 
 This will begin loading the pretrained model and provide you with an object onto which to add data and train.
 
+### `constructor`
+
+`MLClassifier` accepts a number of callbacks when initialized:
+
+#### Parameters
+
+  * **onLoadStart** (`Function`) *Optional* - A callback for when `load` (loading the pre-trained model) is first called.
+  * **onLoadComplete** (`Function`) *Optional* - A callback for when `load` (loading the pre-trained model) is complete.
+  * **onAddDataStart** (`Function`) *Optional* - A callback for when `addData` is first called.
+  * **onAddDataComplete** (`Function`) *Optional* - A callback for when `addData` is complete.
+  * **onClearDataStart** (`Function`) *Optional* - A callback for when `clearData` is first called.
+  * **onClearDataComplete** (`Function`) *Optional* - A callback for when `clearData` is complete.
+  * **onTrainStart** (`Function`) *Optional* - A callback for when `train` is first called.
+  * **onTrainComplete** (`Function`) *Optional* - A callback for when `train` is complete.
+  * **onEvaluateStart** (`Function`) *Optional* - A callback for when `evaluate` is first called.
+  * **onEvaluateComplete** (`Function`) *Optional* - A callback for when `evaluate` is complete.
+  * **onPredictStart** (`Function`) *Optional* - A callback for when `predict` is first called.
+  * **onPredictComplete** (`Function`) *Optional* - A callback for when `predict` is complete.
+  * **onSaveStart** (`Function`) *Optional* - A callback for when `save` is first called.
+  * **onSaveComplete** (`Function`) *Optional* - A callback for when `save` is complete.
+
+
+#### Example
+```
+import MLClassifier from 'ml-classifier';
+const mlClassifier = new MLClassifier({
+  onLoadStart: () => console.log('onLoadStart'),
+  onLoadComplete: () => console.log('onLoadComplete'),
+  onAddDataStart: () => console.log('onAddDataStart'),
+  onAddDataComplete: () => console.log('onAddDataComplete'),
+  onClearDataStart: () => console.log('onClearDataStart'),
+  onClearDataComplete: () => console.log('onClearDataComplete'),
+  onTrainStart: () => console.log('onTrainStart'),
+  onTrainComplete: () => console.log('onTrainComplete'),
+  onEvaluateStart: () => console.log('onEvaluateStart'),
+  onEvaluateComplete: () => console.log('onEvaluateComplete'),
+  onPredictStart: () => console.log('onPredictStart'),
+  onPredictComplete: () => console.log('onPredictComplete'),
+  onSaveStart: () => console.log('onSaveStart'),
+  onSaveComplete: () => console.log('onSaveComplete'),
+});
+```
+
 ### `addData`
 
 This method takes an array of incoming images, an optional array of labels, and an optional dataType.
 
 #### Example
 
 ```
-import MLClassifier, { DataType } from 'ml-classifier';
+import MLClassifier from 'ml-classifier';
+const mlClassifier = new MLClassifier();
 mlClassifier.addData(images, labels, 'train');
 ```
 
@@ -95,7 +139,8 @@ Nothing.
 #### Example
 
 ```
-import MLClassifier, { DataType } from 'ml-classifier';
+import MLClassifier from 'ml-classifier';
+const mlClassifier = new MLClassifier();
 mlClassifier.addData(images, labels, DataType.TRAIN);
 mlClassifier.train({
   callbacks: {
@@ -121,7 +166,8 @@ mlClassifier.train({
 #### Example
 
 ```
-import MLClassifier, { DataType } from 'ml-classifier';
+import MLClassifier from 'ml-classifier';
+const mlClassifier = new MLClassifier();
 mlClassifier.addData(images, labels, DataType.TRAIN);
 mlClassifier.train();
 mlClassifier.addData(evaluationImages, labels, DataType.EVALUATE);
@@ -143,7 +189,8 @@ mlClassifier.evaluate();
 #### Example
 
 ```
-import MLClassifier, { DataType } from 'ml-classifier';
+import MLClassifier from 'ml-classifier';
+const mlClassifier = new MLClassifier();
 mlClassifier.addData(images, labels, DataType.TRAIN);
 mlClassifier.train();
 mlClassifier.predict(imageToPredict);
@@ -164,7 +211,8 @@ mlClassifier.predict(imageToPredict);
 #### Example
 
 ```
-import MLClassifier, { DataType } from 'ml-classifier';
+import MLClassifier from 'ml-classifier';
+const mlClassifier = new MLClassifier();
 mlClassifier.addData(images, labels, DataType.TRAIN);
 mlClassifier.train();
 mlClassifier.save(('path-to-save');
@@ -183,7 +231,8 @@ mlClassifier.save(('path-to-save');
 #### Example
 
 ```
-import MLClassifier, { DataType } from 'ml-classifier';
+import MLClassifier from 'ml-classifier';
+const mlClassifier = new MLClassifier();
 mlClassifier.addData(images, labels, DataType.TRAIN);
 mlClassifier.train();
 mlClassifier.getModel();
@@ -203,7 +252,8 @@ The saved Tensorflow.js model.
 
 #### Example
 ```
-import MLClassifier, { DataType } from 'ml-classifier';
+import MLClassifier from 'ml-classifier';
+const mlClassifier = new MLClassifier();
 mlClassifier.addData(images, labels, DataType.TRAIN);
 mlClassifier.clearData(DataType.TRAIN);
 ```

package.json

@@ -1,14 +1,15 @@
 {
   "name": "ml-classifier",
-  "version": "0.3.8",
+  "version": "0.3.9",
   "description": "A machine learning engine for quickly training image classification models in your browser",
   "main": "dist/index.js",
   "typings": "dist/index.d.ts",
   "repository": "https://github.com/thekevinscott/ml-classifier",
   "scripts": {
     "clean": "rimraf dist/*",
     "watch": "npm run build -- --watch",
-    "build": "npm run clean && rollup -c",
+    "clean-build": "npm run clean && npm run build",
+    "build": "rollup -c",
     "test": "jest"
   },
   "keywords": [

src/cropAndResizeImage.ts

@@ -10,7 +10,7 @@ const crop = (img: tf.Tensor3D) => {
 }
 
 // convert pixel data into a tensor
-const cropAndResizeImage = async (img: tf.Tensor3D, dims: [number, number] = [224, 224]): Promise<tf.Tensor3D> => {
+const cropAndResizeImage = async (img: tf.Tensor3D, dims: [number, number]): Promise<tf.Tensor3D> => {
   return tf.tidy(() => {
     const croppedImage = crop(tf.image.resizeBilinear(img, dims));
     return croppedImage.expandDims(0).toFloat().div(tf.scalar(127)).sub(tf.scalar(1));