@tensorflow/tfjs

What is @tensorflow/tfjs?

@tensorflow/tfjs is an open-source hardware-accelerated JavaScript library for training and deploying machine learning models. It allows you to develop machine learning models in JavaScript and use them in the browser or on Node.js.

What are @tensorflow/tfjs's main functionalities?

Creating and Training Models

This code demonstrates how to create a simple neural network model, compile it, and train it using synthetic data. The model is then used for inference.

const tf = require('@tensorflow/tfjs');

// Define a simple model
const model = tf.sequential();
model.add(tf.layers.dense({units: 100, activation: 'relu', inputShape: [10]}));
model.add(tf.layers.dense({units: 1, activation: 'linear'}));

// Compile the model
model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});

// Generate some synthetic data for training
const xs = tf.randomNormal([100, 10]);
const ys = tf.randomNormal([100, 1]);

// Train the model
model.fit(xs, ys, {epochs: 10}).then(() => {
  // Use the model for inference
  model.predict(tf.randomNormal([1, 10])).print();
});

Loading Pre-trained Models

This code demonstrates how to load a pre-trained model from a URL and use it for inference on an image. The image is preprocessed to match the input requirements of the model.

const tf = require('@tensorflow/tfjs');

// Load a pre-trained model from a URL
const modelUrl = 'https://storage.googleapis.com/tfjs-models/tfjs/mobilenet_v1_0.25_224/model.json';
tf.loadGraphModel(modelUrl).then(model => {
  // Use the model for inference
  const img = tf.browser.fromPixels(document.getElementById('image'));
  const resizedImg = tf.image.resizeBilinear(img, [224, 224]);
  const input = resizedImg.expandDims(0).toFloat().div(tf.scalar(127)).sub(tf.scalar(1));
  model.predict(input).print();
});

Tensor Operations

This code demonstrates basic tensor operations such as addition. Tensors are the core data structure in TensorFlow.js, and you can perform various mathematical operations on them.

const tf = require('@tensorflow/tfjs');

// Create tensors
const a = tf.tensor([1, 2, 3, 4]);
const b = tf.tensor([5, 6, 7, 8]);

// Perform tensor operations
const c = a.add(b);
c.print(); // Output: [6, 8, 10, 12]

Other packages similar to @tensorflow/tfjs

brain.js

Brain.js is a JavaScript library for neural networks. It is simpler and more beginner-friendly compared to TensorFlow.js, but it is less powerful and flexible. Brain.js is suitable for smaller projects and simpler neural network tasks.

synaptic

Synaptic is a JavaScript neural network library for node.js and the browser. It provides a wide range of neural network architectures and is relatively easy to use. However, it does not offer the same level of performance and hardware acceleration as TensorFlow.js.

ml5

ml5.js is a high-level library built on top of TensorFlow.js, designed to make machine learning accessible to artists, creative coders, and students. It provides a simplified API for common machine learning tasks, making it easier to use but less flexible for advanced users.

README

TensorFlow.js

TensorFlow.js is an open-source hardware-accelerated JavaScript library for training and deploying machine learning models.

Develop ML in the Browser
Use flexible and intuitive APIs to build models from scratch using the low-level JavaScript linear algebra library or the high-level layers API.

Run Existing models
Use TensorFlow.js model converters to run pre-existing TensorFlow models right in the browser.

Retrain Existing models
Retrain pre-existing ML models using sensor data connected to the browser, or other client-side data.

About this repo

This repository contains the logic and scripts that combine two packages:

• TensorFlow.js Core, a flexible low-level API, formerly known as deeplearn.js.
• TensorFlow.js Layers, a high-level API which implements functionality similar to Keras.
• TensorFlow.js Converter, tools to import a TensorFlow SavedModel to TensorFlow.js

If you care about bundle size, you can import those packages individually.

If you are looking for Node.js support, check out the TensorFlow.js Node repository.

Examples

Check out our examples repository and our tutorials.

Gallery

Be sure to check out the gallery of all projects related to TensorFlow.js.

Pre-trained models

Be sure to also check out our models repository where we host pretrained models on NPM.

Getting started

There are two main ways to get TensorFlow.js 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

Add the following code to an HTML file:

<html>
  <head>
    <!-- Load TensorFlow.js -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"> </script>
  

    <!-- Place your code in the script tag below. You can also use an external .js file -->
    <script>
      // Notice there is no 'import' statement. 'tf' is available on the index-page
      // because of the script tag above.

      // Define a model for linear regression.
      const model = tf.sequential();
      model.add(tf.layers.dense({units: 1, inputShape: [1]}));

      // Prepare the model for training: Specify the loss and the optimizer.
      model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});

      // Generate some synthetic data for training.
      const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
      const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);

      // Train the model using the data.
      model.fit(xs, ys).then(() => {
        // Use the model to do inference on a data point the model hasn't seen before:
        // Open the browser devtools to see the output
        model.predict(tf.tensor2d([5], [1, 1])).print();
      });
    </script>
  </head>

  <body>
  </body>
</html>

Open up that html file in your browser and the code should run!

via NPM

Add TensorFlow.js to your project using yarn or npm. Note: Because we use ES2017 syntax (such as import), this workflow assumes you are using a modern browser or a bundler/transpiler to convert your code to something older browsers understand. See our examples to see how we use Parcel to build our code. However you are free to use any build tool that you prefer.

import * as tf from '@tensorflow/tfjs';

// Define a model for linear regression.
const model = tf.sequential();
model.add(tf.layers.dense({units: 1, inputShape: [1]}));

// Prepare the model for training: Specify the loss and the optimizer.
model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});

// Generate some synthetic data for training.
const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);

// Train the model using the data.
model.fit(xs, ys).then(() => {
  // Use the model to do inference on a data point the model hasn't seen before:
  model.predict(tf.tensor2d([5], [1, 1])).print();
});

See our tutorials, examples and documentation for more details.

Importing pre-trained models

We support porting pre-trained models from:

• TensorFlow SavedModel
• Keras

Find out more

TensorFlow.js is a part of the TensorFlow ecosystem. For more info:

• For help from the community, use tensorflow.js tag on Stack Overflow.
• js.tensorflow.org
• Tutorials
• API reference
• Discussion mailing list

Thanks BrowserStack for providing testing support.