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KNN Classifier

This package provides a utility for creating a classifier using the K-Nearest Neighbors algorithm.

This package is different from the other packages in this repository in that it doesn't provide a model with weights, but rather a utility for constructing a KNN model using activations from another model or any other tensors you can associate with a class/label.

You can see example code here.

Usage example

via Script Tag
<html>
  <head>
    <!-- Load TensorFlow.js -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
    <!-- Load MobileNet -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/mobilenet"></script>
    <!-- Load KNN Classifier -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/knn-classifier"></script>
 </head>

  <body>
    <img id='class0' src='/images/class0.jpg '/>
    <img id='class1' src='/images/class1.jpg '/>
    <img id='test' src='/images/test.jpg '/>
  </body>
  <!-- Place your code in the script tag below. You can also use an external .js file -->
  <script>

    const init = async function() {
      // Create the classifier.
      const classifier = knnClassifier.create();

      // Load mobilenet.
      const mobilenetModule = await mobilenet.load();

      // Add MobileNet activations to the model repeatedly for all classes.
      const img0 = tf.browser.fromPixels(document.getElementById('class0'));
      const logits0 = mobilenetModule.infer(img0, 'conv_preds');
      classifier.addExample(logits0, 0);

      const img1 = tf.browser.fromPixels(document.getElementById('class1'));
      const logits1 = mobilenetModule.infer(img1, 'conv_preds');
      classifier.addExample(logits1, 1);

      // Make a prediction.
      const x = tf.browser.fromPixels(document.getElementById('test'));
      const xlogits = mobilenetModule.infer(x, 'conv_preds');
      console.log('Predictions:');
      const result = await classifier.predictClass(xlogits);
      console.log(result);
    }

    init();

  </script>
</html>
via NPM
const tf = require('@tensorflow/tfjs');
const mobilenetModule = require('@tensorflow-models/mobilenet');
const knnClassifier = require('@tensorflow-models/knn-classifier');

// Create the classifier.
const classifier = knnClassifier.create();

// Load mobilenet.
const mobilenet = await mobilenetModule.load();

// Add MobileNet activations to the model repeatedly for all classes.
const img0 = tf.browser.fromPixels(document.getElementById('class0'));
const logits0 = mobilenet.infer(img0, 'conv_preds');
classifier.addExample(logits0, 0);

const img1 = tf.browser.fromPixels(document.getElementById('class1'));
const logits1 = mobilenet.infer(img1, 'conv_preds');
classifier.addExample(logits1, 1);

// Make a prediction.
const x = tf.browser.fromPixels(document.getElementById('test'));
const xlogits = mobilenet.infer(x, 'conv_preds');
console.log('Predictions:');
console.log(classifier.predictClass(xlogits));

API

Creating a classifier

knnClassifier is the module name, which is automatically included when you use the <script src> method.

classifier = knnClassifier.create()

Returns a KNNImageClassifier.

Adding examples

classifier.addExample(
  example: tf.Tensor,
  label: number|string
): void;

Args:

  • example: An example to add to the dataset, usually an activation from another model.
  • label: The label (class name) of the example.

Making a prediction

classifier.predictClass(
  input: tf.Tensor,
  k = 3
): Promise<{label: string, classIndex: number, confidences: {[classId: number]: number}}>;

Args:

  • input: An example to make a prediction on, usually an activation from another model.
  • k: The K value to use in K-nearest neighbors. The algorithm will first find the K nearest examples from those it was previously shown, and then choose the class that appears the most as the final prediction for the input example. Defaults to 3. If examples < k, k = examples.

Returns an object where:

  • label: the label (class name) with the most confidence.
  • classIndex: the 0-based index of the class (for backwards compatibility).
  • confidences: maps each label to their confidence score.

Misc

Clear all examples for a class.
classifier.clearClass(label: number|string)

Args:

  • label: The label to clear all examples for.
Clear all examples from all classes
classifier.clearAllClasses()
Get the example count for each class
classifier.getClassExampleCount(): {[label: string]: number}

Returns an object that maps label name to example count for that label.

Get the full dataset, useful for saving state.
classifier.getClassifierDataset(): {[label: string]: Tensor2D}
Set the full dataset, useful for restoring state.
classifier.setClassifierDataset(dataset: {[label: string]: Tensor2D})

Args:

  • dataset: The label dataset matrices map. Can be retrieved from getClassDatsetMatrices. Useful for restoring state.
Get the total number of classes
classifier.getNumClasses(): number
Dispose the classifier and all internal state

Clears up WebGL memory. Useful if you no longer need the classifier in your application.

classifier.dispose()