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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

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/[email protected]/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:

.

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

• MNIST

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 💻 🤔 👀 📖

Christian Echevarria 💻 📖 🚇

Daniel Ryan 🐛 👀

IviieMtz ⚠️

Nicholas Szerman 💻

tracy collins 🐛

Manuel Raimann 🐛 💻 🤔

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

💬 Contributing

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

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