brain.js is a JavaScript neural network library.
💡 Note: This is a continuation of the harthur/brain repository (which is not maintained anymore). For more details, check out this issue.
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
If you have node you can install with npm:
npm install brain.js
Alternatively, you can install with bower:
bower install brain.js
For debian based Linux distribution, canvas dependency installation requires install of cairo package, like so:
apt-get install libcairo2-dev libjpeg-dev libgif-dev
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.
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 probably take to train, but the better the network will be at classifiying new patterns.
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 contrast 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 }Another variation of the example above. Note that input objects do not need to be similar.
net.train([{input: { r: 0.03, g: 0.7 }, output: { black: 1 }},
{input: { r: 0.16, 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: 0.81, black: 0.18 }train() takes a hash of options as its second argument:
net.train(data, {
errorThresh: 0.005, // error threshold to reach
iterations: 20000, // maximum training iterations
log: true, // console.log() progress periodically
logPeriod: 10, // number of iterations between logging
learningRate: 0.3 // learning rate
})The network will train until the training error has gone below the threshold (default 0.005) or the max number of iterations (default 20000) has been reached, whichever comes first.
By default training won't let you know how its doing until the end, but set log to true to get periodic updates on the current training error of the network. The training error should decrease every time. The updates will be printed to console. If you set log to a function, this function will be called with the updates instead of printing to the console.
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 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.
The output of train() is a hash of information about how the training went:
{
error: 0.0039139985510105032, // training error
iterations: 406 // training iterations
}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.
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.jsNeuralNetwork() takes a hash of options:
var net = new brain.NeuralNetwork({
hiddenLayers: [4],
learningRate: 0.6 // global learning rate, useful when training using streams
});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.js uses one hidden layer with size proportionate to the size of the input array.
The network now has a WriteStream. You can train the network by using pipe() to send the training data to the network.
Refer to stream-example.js for an example on how to train the network with a stream.
To train the network using a stream you must first create the stream by calling net.createTrainStream() which takes the following options:
floodCallback()- the callback function to re-populate the stream. This gets called on every training iteration.doneTrainingCallback(info)- the callback function to execute when the network is done training. Theinfoparam will contain a hash of information about how the training went:
{
error: 0.0039139985510105032, // training error
iterations: 406 // training iterations
}Use a Transform to coerce the data into the correct format. You might also use a Transform stream to normalize your data on the fly.
var likely = require('brain/likely');
var key = likely(input, net);See: https://github.com/harthur-org/brain/blob/master/test/unit/likely.js