Neataptic offers flexible neural networks; neurons and synapses can be removed with a single line of code. No fixed architecture is required for neural networks to function at all. This flexibility allows networks to be shaped for your dataset through neuro-evolution, which is done using multiple threads.
// this network learns the XOR gate (through neuro-evolution)var network = 21;var trainingSet =input: 00 output: 0input: 01 output: 1input: 10 output: 1input: 11 output: 0;await network;
Neataptic also backpropagates more than 5x faster than competitors. Run the tests yourself. This is an example of regular training in Neataptic:
// this network learns the XOR gate (through backpropagation)var network = 2 4 1;// training set same as in above examplenetwork;network; // 0.9824...
Use any of the 6 built-in networks with customisable sizes to create a network:
var myNetwork = 1 10 5 1;
Or built your own network with pre-built layers:
var input = 2;var hidden1 = 5;var hidden2 = 3;var output = 1;input;hidden1;hidden2;var myNetwork = architect;
You can even built your network neuron-by-neuron using nodes and groups!
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!
Neataptic files are hosted by rawgit, just copy this link into the
Installing with node is also possible:
npm install neataptic
Make sure you have Node.js
v7.6 or higher installed!
Parts of Synaptic where used to develop Neataptic.
The neuro-evolution algorithm used is the Instinct algorithm.