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Classifier for TensorFlow.js","repository":{"type":"git","url":"git+https://github.com/tensorflow/tfjs-models.git"},"license":"Apache-2.0","readme":"# KNN Classifier\n\nThis package provides a utility for creating a classifier using the\n[K-Nearest Neighbors](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)\nalgorithm.\n\nThis package is different from the other packages in this repository in that it\ndoesn't provide a model with weights, but rather a utility for constructing a\nKNN model using activations from another model or any other tensors you can\nassociate with a class/label.\n\nYou can see example code [here](https://github.com/tensorflow/tfjs-models/tree/master/knn-classifier/demo).\n\n## Usage example\n\n##### via Script Tag\n\n```html\n<html>\n  <head>\n    <!-- Load TensorFlow.js -->\n    <script src=\"https://cdn.jsdelivr.net/npm/@tensorflow/tfjs\"></script>\n    <!-- Load MobileNet -->\n    <script src=\"https://cdn.jsdelivr.net/npm/@tensorflow-models/mobilenet\"></script>\n    <!-- Load KNN Classifier -->\n    <script src=\"https://cdn.jsdelivr.net/npm/@tensorflow-models/knn-classifier\"></script>\n </head>\n\n  <body>\n    <img id='class0' src='/images/class0.jpg '/>\n    <img id='class1' src='/images/class1.jpg '/>\n    <img id='test' src='/images/test.jpg '/>\n  </body>\n  <!-- Place your code in the script tag below. You can also use an external .js file -->\n  <script>\n\n    const init = async function() {\n      // Create the classifier.\n      const classifier = knnClassifier.create();\n\n      // Load mobilenet.\n      const mobilenetModule = await mobilenet.load();\n\n      // Add MobileNet activations to the model repeatedly for all classes.\n      const img0 = tf.browser.fromPixels(document.getElementById('class0'));\n      const logits0 = mobilenetModule.infer(img0, true);\n      classifier.addExample(logits0, 0);\n\n      const img1 = tf.browser.fromPixels(document.getElementById('class1'));\n      const logits1 = mobilenetModule.infer(img1, true);\n      classifier.addExample(logits1, 1);\n\n      // Make a prediction.\n      const x = tf.browser.fromPixels(document.getElementById('test'));\n      const xlogits = mobilenetModule.infer(x, true);\n      console.log('Predictions:');\n      const result = await classifier.predictClass(xlogits);\n      console.log(result);\n    }\n\n    init();\n\n  </script>\n</html>\n```\n\n###### via NPM\n\n```js\nconst tf = require('@tensorflow/tfjs');\nconst mobilenetModule = require('@tensorflow-models/mobilenet');\nconst knnClassifier = require('@tensorflow-models/knn-classifier');\n\n// Create the classifier.\nconst classifier = knnClassifier.create();\n\n// Load mobilenet.\nconst mobilenet = await mobilenetModule.load();\n\n// Add MobileNet activations to the model repeatedly for all classes.\nconst img0 = tf.browser.fromPixels(document.getElementById('class0'));\nconst logits0 = mobilenet.infer(img0, true);\nclassifier.addExample(logits0, 0);\n\nconst img1 = tf.browser.fromPixels(document.getElementById('class1'));\nconst logits1 = mobilenet.infer(img1, true);\nclassifier.addExample(logits1, 1);\n\n// Make a prediction.\nconst x = tf.browser.fromPixels(document.getElementById('test'));\nconst xlogits = mobilenet.infer(x, true);\nconsole.log('Predictions:');\nconsole.log(classifier.predictClass(xlogits));\n```\n\n## API\n\n#### Creating a classifier\n`knnClassifier` is the module name, which is automatically included when you use\nthe <script src> method.\n\n```ts\nclassifier = knnClassifier.create()\n```\n\nReturns a `KNNImageClassifier`.\n\n#### Adding examples\n\n```ts\nclassifier.addExample(\n  example: tf.Tensor,\n  label: number|string\n): void;\n```\n\nArgs:\n- **example:** An example to add to the dataset, usually an activation from\n  another model.\n- **label:** The label (class name) of the example.\n\n#### Making a prediction\n\n```ts\nclassifier.predictClass(\n  input: tf.Tensor,\n  k = 3\n): Promise<{label: string, classIndex: number, confidences: {[classId: number]: number}}>;\n```\n\nArgs:\n- **input:** An example to make a prediction on, usually an activation from\n  another model.\n- **k:** The K value to use in K-nearest neighbors. The algorithm will first\n  find the K nearest examples from those it was previously shown, and then choose\n  the class that appears the most as the final prediction for the input example.\n  Defaults to 3. If examples < k, k = examples.\n\nReturns an object where:\n - `label`: the label (class name) with the most confidence.\n - `classIndex`: the 0-based index of the class (for backwards compatibility).\n - `confidences`: maps each label to their confidence score.\n\n#### Misc\n\n##### Clear all examples for a class.\n\n```ts\nclassifier.clearClass(label: number|string)\n```\n\nArgs:\n- **label:** The label to clear all examples for.\n\n##### Clear all examples from all classes\n\n```ts\nclassifier.clearAllClasses()\n```\n\n##### Get the example count for each class\n\n```ts\nclassifier.getClassExampleCount(): {[label: string]: number}\n```\n\nReturns an object that maps label name to example count for that label.\n\n##### Get the full dataset, useful for saving state.\n\n```ts\nclassifier.getClassifierDataset(): {[label: string]: Tensor2D}\n```\n\n##### Set the full dataset, useful for restoring state.\n\n```ts\nclassifier.setClassifierDataset(dataset: {[label: string]: Tensor2D})\n```\n\nArgs:\n- **dataset:** The label dataset matrices map. Can be retrieved from\n  getClassifierDataset. Useful for restoring state.\n\n##### Get the total number of classes\n\n```ts\nclassifier.getNumClasses(): number\n```\n\n##### Dispose the classifier and all internal state\n\nClears up WebGL memory. Useful if you no longer need the classifier in your\napplication.\n\n```ts\nclassifier.dispose()\n```\n","readmeFilename":"README.md","homepage":"https://github.com/tensorflow/tfjs-models#readme","bugs":{"url":"https://github.com/tensorflow/tfjs-models/issues"}}