brain

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

Data format

Each training pattern 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 }

Threshold

The optional second argument to train() is the error threshold (default 0.004), the third is the maximum training iterations (default 20000).

The network will train until the training error has gone below the threshold or the max number of iterations has been reached, whichever comes first.

Output

The ouput of train() is a hash of information about how the training went:

{
  error: 0.0039139985510105032,  // training error
  iterations: 406                // training iterations
}

Failing

If the network failed to train, the error will be above the error threshold. This could happen because the training data is too noisy (most likely), the network doesn't have enough hidden layers or nodes to handle the complexity of the data, or it hasn't trained for enough iterations.

If the training error is still something huge like 0.4 after 20000 iterations, it's a good sign that the network can't make sense of the data you're giving it.

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 });

console.log(run.toString()); // copy and paste! no need to import brain.js

Options

NeuralNetwork() takes a hash of options:

var net = new NeuralNetwork({
   hiddenLayers: [4],
   learningRate: 0.6
});

hiddenLayers

Specify the number of hidden layers in the network and the size of each layer. For example, if you want two hidden layers - the first with 3 nodes and the second with 4 nodes, you'd give:

hiddenLayers: [3, 4]

By default brain uses one hidden layer with size proportionate to the size of the input array.

learningRate

The learning rate is a parameter that influences how quickly the network trains. It's a number from 0 to 1. If the learning rate is close to 0 it will take a lot longer to train. If the learning rate is closer to 1 it will train faster but it's in danger of training to a local minimum and performing badly on new data. The default learning rate is 0.3.

Bayesian classifier

The Bayesian classifier that used to be here has moved to its own library, classifier.