ML Classifier

ML Classifier is a machine learning engine for quickly training image classification models in your browser. Models can be saved with a single command, and the resulting models reused to make image classification predictions.

Getting Started

Installation

ml-classifier can be installed via yarn or npm:

yarn add ml-classifier

or

npm install ml-classifier

Quick Start

Start by instantiating a new MLClassifier.

import MLClassifier from 'ml-classifier';

const mlClassifier = new MLClassifier();

Then, train the model:

await mlClassifier.train(imageData, {
  callbacks: {
    onTrainBegin: () => {
      console.log('training begins');
    },
    onBatchEnd: (batch: any,logs: any) => {
      console.log('Loss is: ' + logs.loss.toFixed(5));
    }
  },
});

And get predictions:

const prediction = await mlClassifier.predict(data);

When you have a trained model you're happy with, save it with:

mlClassifier.save();

API Documentation

Start by instantiating a new instance of MLClassifier with:

const mlClassifier = new MLClassifier();

This will begin loading the pretrained model and provide you with an object onto which to add data and train.

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';
mlClassifier.addData(images, labels, DataType.TRAIN);

Parameters

• images (Tensor3D[]) - an array of 3D tensors. Images can be any sizes, but will be cropped and sized down to match the pretrained model.
• labels (string[]) - an array of strings, matching the images passed above.
• dataType (DataType) Optional - an enum specifying which data type the images match. Data types can be DataType.TRAIN for data used in model.train(), and DataType.EVAL, for data used in model.evaluate(). If no argument is supplied, dataType will default to DataType.TRAIN.

Returns

null

train

train begins training on the given dataset.

Example

import MLClassifier, { DataType } from 'ml-classifier';
mlClassifier.addData(images, labels, DataType.TRAIN);
mlClassifier.train({
  callbacks: {
    onTrainBegin: () => {
      console.log('training begins');
    },
  },
});

Parameters

• params (Object) Optional - a set of parameters that will be passed directly to model.fit. View the Tensorflow.JS docs for an up-to-date list of arguments.

Returns

train returns the resolved promise from fit, an object containing loss and accuracy.

evaluate

evaluate is used to evaluate a model's performance.

Example

import MLClassifier, { DataType } from 'ml-classifier';
mlClassifier.addData(images, labels, DataType.TRAIN);
mlClassifier.train();
mlClassifier.addData(evaluationImages, labels, DataType.EVALUATE);
mlClassifier.evaluate();

Parameters

• params (Object) Optional - a set of parameters that will be passed directly to model.evaluate. View the Tensorflow.JS docs for an up-to-date list of arguments.

Returns

evaluate returns a tf.Scalar representing the result of evaluate.

predict

predict is used to make a specific prediction using a saved model.

Example

import MLClassifier, { DataType } from 'ml-classifier';
mlClassifier.addData(images, labels, DataType.TRAIN);
mlClassifier.train();
mlClassifier.predict(imageToPredict);

Parameters

• image (tf.Tensor3D) - a single image encoded as a tf.Tensor3D. Image can be any size, but will be cropped and sized down to match the pretrained model.

Returns

predict will return a string matching the prediction.

save

save is a proxy to tf.model.save, and will initiate a download from the browser, or save to local storage.

Example

import MLClassifier, { DataType } from 'ml-classifier';
mlClassifier.addData(images, labels, DataType.TRAIN);
mlClassifier.train();
mlClassifier.save(('path-to-save');

Parameters

• handlerOrUrl (io.IOHandler | string) Optional - an argument to be passed to model.save. If omitted, the model's unique labels will be concatenated together in the form of class1-class2-class3.

getModel

getModel will return the trained Tensorflow.js model. Calling this method prior to calling mlClassifier.train will return null.

Example

import MLClassifier, { DataType } from 'ml-classifier';
mlClassifier.addData(images, labels, DataType.TRAIN);
mlClassifier.train();
mlClassifier.getModel();

Parameters

None.

Returns

The saved Tensorflow.js model.

clearData

clearData will clear out saved data.

Example

import MLClassifier, { DataType } from 'ml-classifier';
mlClassifier.addData(images, labels, DataType.TRAIN);
mlClassifier.clearData(DataType.TRAIN);

Parameters

• dataType (DataType) Optional - specifies which data to clear. If no argument is provided, all data will be cleared.

Returns

Nothing.

Contributing

Contributions are welcome!

You can start up a local copy of ml-classifier with:

yarn watch

clearData

ml-classifier is written in Typescript.

Tests

Tests are a work in progress. Currently, the test suite only consists of unit tests. Pull requests for additional tests are welcome!

Run tests with:

yarn test

Author

• Kevin Scott

License

This project is licensed under the MIT License - see the LICENSE file for details