brain

brain is a JavaScript neural network library. Here's an example of using it to approximate the XOR function:

var net = new brain.NeuralNetwork();

net.train([{input: [0, 0], output: [0]},
           {input: [0, 1], output: [1]},
           {input: [1, 0], output: [1]},
           {input: [1, 1], output: [0]}]);

var output = net.run([1, 0]);  // [0.987]

There's no reason to use a neural network to figure out XOR however (-: so here's a more involved, realistic example: Demo: training a neural network to recognize color contrast

Using in node

If you have node you can install with npm:

npm install brain

Using in the browser

Download the latest brain.js. Training is computationally expensive, so you should try to train the network offline (or on a Worker) and use the toFunction() or toJSON() options to plug the pre-trained network in to your website.

Training

Use train() to train the network with an array of training data. Each piece of training data should have an input and an output, both of which can be either an array of numbers from 0 to 1 or a hash of numbers from 0 to 1. For the color constrast demo it looks something like this:

var net = new brain.NeuralNetwork();

net.train([{input: { r: 0.03, g: 0.7, b: 0.5 }, output: { black: 1 }},
           {input: { r: 0.16, g: 0.09, b: 0.2 }, output: { white: 1 }},
           {input: { r: 0.5, g: 0.5, b: 1.0 }, output: { white: 1 }}]);

var output = net.run({ r: 1, g: 0.4, b: 0 });  // { white: 0.99, black: 0.002 }

The network has to be trained with all the data in bulk in one call to train(). The more training data, the longer it will take to train, but the better the network will be at classifiying new data.

JSON

Serialize or load in the state of a trained network with JSON:

var json = net.toJSON();

net.fromJSON(json);

You can also get a custom standalone function from a trained network that acts just like run():

var run = net.toFunction();

var output = run({ r: 1, g: 0.4, b: 0 });

Bayesian classifier