JavaScripting

The definitive source of the best
JavaScript libraries, frameworks, and plugins.


  • ×

    đŸĨ• Multi-threaded Self-Assembling Neural Networks in Node.js & Browser
    Filed under  â€ē 

    • 🔾31%Overall
    • 248
    • 3.8 days
    • 🕩33
    • 👥7

    Carrot Logo

    Build Status via Travis CI Codacy Badge Coverage Status Join the chat at https://gitter.im/carrot-ai/community Carrot's License Made with love

    ℹī¸ The new TypeScript version is coming! If you would like to try the expiremental version please clone the repository and checkout the typescript branch of the project. Docs for this new version can temporarily be found here

    Carrot is an architecture-free neural network library built around neuroevolution

    Why / when should I use this? Whenever you have a problem that you: - Don't know how-to solve - Don't want to design a custom network for - Want to discover the ideal neural-network structure for You can use Carrot's ability to design networks of arbitrary complexity by itself to solve whatever problem you have. If you want to see Carrot designing a neural-network to play flappy-bird check here

    For Documentation, visit here

    Key Features

    • Simple docs & interactive examples
    • Neuro-evolution & population based training
    • Multi-threading & GPU (coming soon)
    • Preconfigured GRU, LSTM, NARX Networks
    • Mutable Neurons, Layers, Groups, and Networks
    • SVG Network Visualizations using D3.js

    Demos

    flappy bird neuro-evolution demo
    Flappy bird neuro-evolution

    Install

    $ npm i @liquid-carrot/carrot
    

    Carrot files are hosted by JSDelivr

    For prototyping or learning, use the latest version here:

    <script src="https://cdn.jsdelivr.net/npm/@liquid-carrot/carrot/dist/carrot.umd2.min.js"></script>
    

    For production, link to a specific version number to avoid unexpected breakage from newer versions:

    <script src="https://cdn.jsdelivr.net/npm/@liquid-carrot/carrot@0.3.17/dist/carrot.umd2.min.js"></script>
    

    Getting Started

    💡 Want to be super knowledgeable about neuro-evolution in a few minutes?

    Check out this article by the creator of NEAT, Kenneth Stanley

    💡 Curious about how neural-networks can understand speech and video?

    Check out this video on Recurrent Neural Networks, from @LearnedVector, on YouTube

    This is a simple perceptron:

    perceptron.

    How to build it with Carrot:

    let { architect } = require('@liquid-carrot/carrot');
    
    // The example Perceptron you see above with 4 inputs, 5 hidden, and 1 output neuron
    let simplePerceptron = new architect.Perceptron(4, 5, 1);
    

    Building networks is easy with 6 built-in networks

    let { architect } = require('@liquid-carrot/carrot');
    
    let LSTM = new architect.LSTM(4, 5, 1);
    
    // Add as many hidden layers as needed
    let Perceptron = new architect.Perceptron(4, 5, 20, 5, 10, 1);
    

    Building custom network architectures

    let architect = require('@liquid-carrot/carrot').architect
    let Layer = require('@liquid-carrot/carrot').Layer
    
    let input = new Layer.Dense(1);
    let hidden1 = new Layer.LSTM(5);
    let hidden2 = new Layer.GRU(1);
    let output = new Layer.Dense(1);
    
    // connect however you want
    input.connect(hidden1);
    hidden1.connect(hidden2);
    hidden2.connect(output);
    
    let network = architect.Construct([input, hidden1, hidden2, output]);
    

    Networks also shape themselves with neuro-evolution

    let { Network, methods } = require('@liquid-carrot/carrot');
    
    // this network learns the XOR gate (through neuro-evolution)
    async function execute () {
      // no hidden layers...
       var network = new Network(2,1);
    
       // XOR dataset
       var trainingSet = [
           { input: [0,0], output: [0] },
           { input: [0,1], output: [1] },
           { input: [1,0], output: [1] },
           { input: [1,1], output: [0] }
       ];
    
       await network.evolve(trainingSet, {
           mutation: methods.mutation.FFW,
           equal: true,
           error: 0.05,
           elitism: 5,
           mutation_rate: 0.5
       });
    
       // and it works!
       network.activate([0,0]); // 0.2413
       network.activate([0,1]); // 1.0000
       network.activate([1,0]); // 0.7663
       network.activate([1,1]); // 0.008
    }
    
    execute();
    

    Build vanilla neural networks

    let Network = require('@liquid-carrot/carrot').Network
    
    let network = new Network([2, 2, 1]) // Builds a neural network with 5 neurons: 2 + 2 + 1
    

    Or implement custom algorithms with neuron-level control

    let Node = require('@liquid-carrot/carrot').Node
    
    let A = new Node() // neuron
    let B = new Node() // neuron
    
    A.connect(B)
    A.activate(0.5)
    console.log(B.activate())
    

    Try with

    Data Sets

    Contributors ✨

    This project exists thanks to all the people who contribute. We can't do it without you! 🙇

    Thanks goes to these wonderful people (emoji key):


    Luis Carbonell

    đŸ’ģ 🤔 👀 📖

    Christian Echevarria

    đŸ’ģ 📖 🚇

    Daniel Ryan

    🐛 👀

    IviieMtz

    ⚠ī¸

    Nicholas Szerman

    đŸ’ģ

    tracy collins

    🐛

    Manuel Raimann

    🐛 đŸ’ģ 🤔

    This project follows the all-contributors specification. Contributions of any kind welcome!

    đŸ’Ŧ Contributing

    Carrot's GitHub Issues

    Your contributions are always welcome! Please have a look at the contribution guidelines first. 🎉

    To build a community welcome to all, Carrot follows the Contributor Covenant Code of Conduct.

    And finally, a big thank you to all of you for supporting! 🤗

    Planned Features [ ] Performance Enhancements [ ] GPU Acceleration [ ] Tests [ ] Benchmarks [ ] Matrix Multiplications [ ] Tests [ ] Benchmarks [ ] Clustering | Multi-Threading [ ] Tests [ ] Benchmarks [ ] Syntax Support [ ] Callbacks [ ] Promises [ ] Streaming [ ] Async/Await [ ] Math Support [ ] Big Numbers [ ] Small Numbers

    Patrons

    Carrot's Patrons

    Become a Patron

    Acknowledgements

    A special thanks to:

    @wagenaartje for Neataptic which was the starting point for this project

    @cazala for Synaptic which pioneered architecture free neural networks in javascript and was the starting point for Neataptic

    @robertleeplummerjr for GPU.js which makes using GPU in JS easy and Brain.js which has inspired Carrot's development

    Show All