raimannma_testing

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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)
• Complete customizable Networks with various types of layers
• Mutable Neurons, Connections, Layers, and Networks

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/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:

.

How to build it with Carrot:

const architect = new Architect();
 
architect.addLayer(new InputLayer(4));
architect.addLayer(new DenseLayer(5, { activationType: RELUActivation }));
architect.addLayer(new OutputLayer(1));
 
const network = architect.buildModel();

Building networks is easy with 17 built-in layers You can combine them as you need.

const architect = new Architect();
 
architect.addLayer(new InputLayer(10));
architect.addLayer(new DenseLayer(10, { activationType: RELUActivation }));
architect.addLayer(new MaxPooling1DLayer(5, { activation: IdentityActivation }));
architect.addLayer(new OutputLayer(2, { activation: RELUActivation }));
 
const network = architect.buildModel();

Networks also shape themselves with neuro-evolution

const XOR = [
  { input: [0, 0], output: [0] },
  { input: [0, 1], output: [1] },
  { input: [1, 0], output: [1] },
  { input: [1, 1], output: [0] },
];
 
// this network learns the XOR gate (through neuro-evolution)
async function execute(): Promise<void> {
  this.timeout(20000);
 
  const network: Network = new Network(2, 1);
 
  const initial: number = network.test(XOR);
  await network.evolve({ iterations: 50, dataset: XOR });
  const final: number = network.test(XOR);
 
  expect(final).to.be.at.most(initial);
}
 
execute();

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

Silver Patrons
Solinfra

Bronze Patrons
Kappaxbeta

Patrons
DollarBizClub

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