Architecture-free neural network library with genetic algorithm implementations
npm install neataptic
js
// this network learns the XOR gate (through neuro-evolution)
var network = new Network(2,1);
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, {
equal: true,
error: 0.03
});
`
Neataptic also backpropagates more than 5x faster than competitors. Run the tests yourself. This is an example of regular training in Neataptic:
`js
// this network learns the XOR gate (through backpropagation)
var network = new architect.Perceptron(2, 4, 1);
// training set same as in above example
network.train(trainingSet, {
error: 0.01
});
network.activate([1,1]); // 0.9824...
`
Use any of the 6 built-in networks with customisable sizes to create a network:
`javascript
var myNetwork = new architect.LSTM(1, 10, 5, 1);
`
Or built your own network with pre-built layers:
`javascript
var input = new Layer.Dense(2);
var hidden1 = new Layer.LSTM(5);
var hidden2 = new Layer.GRU(3);
var output = new Layer.Dense(1);
input.connect(hidden1);
hidden1.connect(hidden2);
hidden2.connect(output);
var myNetwork = architect.Construct([input, hidden1, hidden2, output]);
`
You can even built your network neuron-by-neuron using nodes and groups!


Examples
Neural networks can be used for nearly anything; driving a car, playing a game and even to predict words! At this moment,
the website only displays a small amount of examples. If you have an interesting project that you want to share with other users
of Neataptic, feel free to create a pull request!
Usage
Head over to the wiki for detailed usage. If you want to visualise your graphs, head
over to the graph folder.
Install
Neataptic files are hosted by rawgit, just copy this link into the tag:
`html
`
Installing with node is also possible:
`javascript
npm install neataptic
`
Make sure you have Node.js v7.6` or higher installed!