### Neataptic
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.
```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](https://jsfiddle.net/tuet004f/11/). 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!
Visit the wiki to get started
or play around with neural networks
## 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!
Neuroevolution examples (supervised)
LSTM timeseries (supervised)
Color classification (supervised)
Agar.io-AI (unsupervised)
Target seeking AI (unsupervised)
Crossover playground
## Usage
Head over to the [wiki](https://wagenaartje.github.io/neataptic/docs/) for detailed usage. If you want to visualise your graphs, head
over to the [graph](https://github.com/wagenaartje/neataptic/tree/master/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!
## Further notices
Parts of [Synaptic](https://github.com/cazala/synaptic) where used to develop
Neataptic.
The neuro-evolution algorithm used is the [Instinct](https://medium.com/@ThomasWagenaar/neuro-evolution-on-steroids-82bd14ddc2f6) algorithm.
You made it all the way down! If you appreciate this repo and want to support the development of it, please consider donating :thumbsup:
[](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=CXS3G8NHBYEZE)