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https://github.com/cazala/synaptic

architecture-free neural network library for node.js and the browser
https://github.com/cazala/synaptic

Last synced: 4 days ago
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architecture-free neural network library for node.js and the browser

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README 

        
Synaptic [![Build Status](https://travis-ci.org/cazala/synaptic.svg?branch=master)](https://travis-ci.org/cazala/synaptic) [![Join the chat at https://synapticjs.slack.com](https://synaptic-slack.now.sh/badge.svg)](https://synaptic-slack.now.sh/)

========



## Important: [Synaptic 2.x](https://github.com/cazala/synaptic/issues/140) is in stage of discussion now! Feel free to participate



Synaptic is a javascript neural network library for **node.js** and the **browser**, its generalized algorithm is architecture-free, so you can build and train basically any type of first order or even [second order neural network](http://en.wikipedia.org/wiki/Recurrent_neural_network#Second_Order_Recurrent_Neural_Network) architectures.



This library includes a few built-in architectures like [multilayer perceptrons](http://en.wikipedia.org/wiki/Multilayer_perceptron), [multilayer long-short term memory](http://en.wikipedia.org/wiki/Long_short_term_memory) networks (LSTM), [liquid state machines](http://en.wikipedia.org/wiki/Liquid_state_machine) or [Hopfield](http://en.wikipedia.org/wiki/Hopfield_network) networks, and a trainer capable of training any given network, which includes built-in training tasks/tests like solving an XOR, completing a Distracted Sequence Recall task or an [Embedded Reber Grammar](http://www.willamette.edu/~gorr/classes/cs449/reber.html) test, so you can easily test and compare the performance of different architectures.



The algorithm implemented by this library has been taken from Derek D. Monner's paper:



[A generalized LSTM-like training algorithm for second-order recurrent neural networks](http://www.overcomplete.net/papers/nn2012.pdf)



There are references to the equations in that paper commented through the source code.



#### Introduction



If you have no prior knowledge about Neural Networks, you should start by [reading this guide](https://github.com/cazala/synaptic/wiki/Neural-Networks-101).



If you want a practical example on how to feed data to a neural network, then take a look at [this article](https://github.com/cazala/synaptic/wiki/Normalization-101).



You may also want to take a look at [this article](http://blog.webkid.io/neural-networks-in-javascript/).



#### Demos



- [Solve an XOR](http://caza.la/synaptic/#/xor)

- [Discrete Sequence Recall Task](http://caza.la/synaptic/#/dsr)

- [Learn Image Filters](http://caza.la/synaptic/#/image-filters)

- [Paint an Image](http://caza.la/synaptic/#/paint-an-image)

- [Self Organizing Map](http://caza.la/synaptic/#/self-organizing-map)

- [Read from Wikipedia](http://caza.la/synaptic/#/wikipedia)

- [Creating a Simple Neural Network (Video)](https://scrimba.com/casts/cast-1980)

- [Learn how to shoot](https://sta-ger.bitbucket.io/apps/bot/index.html)

- [Beer glass classifier](https://sta-ger.bitbucket.io/apps/beer/index.html)



The source code of these demos can be found in [this branch](https://github.com/cazala/synaptic/tree/gh-pages/scripts).



#### Getting started



- [Neurons](https://github.com/cazala/synaptic/wiki/Neurons/)

- [Layers](https://github.com/cazala/synaptic/wiki/Layers/)

- [Networks](https://github.com/cazala/synaptic/wiki/Networks/)

- [Trainer](https://github.com/cazala/synaptic/wiki/Trainer/)

- [Architect](https://github.com/cazala/synaptic/wiki/Architect/)



To try out the examples, checkout the [gh-pages](https://github.com/cazala/synaptic/tree/gh-pages) branch.



`git checkout gh-pages`



#### Other languages



This README is also available in other languages.



- [Chinese Simplified | 中文文档](https://github.com/cazala/synaptic/blob/master/README_Zh-CN.md), thanks to [@noraincode](https://github.com/noraincode).

- [Chinese Traditional | 繁體中文](https://github.com/cazala/synaptic/blob/master/README_Zh-TW.md), by [@NoobTW](https://github.com/noobtw).

- [Japanese | 日本語](https://github.com/cazala/synaptic/blob/master/README_Ja-JP.md), thanks to [@oshirogo](https://github.com/dscripps).  



## Overview



### Installation



##### In node



You can install synaptic with [npm](http://npmjs.org):



```cmd

npm install synaptic --save

```



##### In the browser



You can install synaptic with [bower](http://bower.io):



```cmd

bower install synaptic

```



Or you can simply use the CDN link, kindly provided by [CDNjs](https://cdnjs.com/)



```html



```



### Usage



```javascript

var synaptic = require('synaptic'); // this line is not needed in the browser

var Neuron = synaptic.Neuron,

	Layer = synaptic.Layer,

	Network = synaptic.Network,

	Trainer = synaptic.Trainer,

	Architect = synaptic.Architect;

```



Now you can start to create networks, train them, or use built-in networks from the [Architect](https://github.com/cazala/synaptic/wiki/Architect/).



### Examples



##### Perceptron



This is how you can create a simple **perceptron**:



![perceptron](http://www.codeproject.com/KB/dotnet/predictor/network.jpg).



```javascript

function Perceptron(input, hidden, output)

{

	// create the layers

	var inputLayer = new Layer(input);

	var hiddenLayer = new Layer(hidden);

	var outputLayer = new Layer(output);



	// connect the layers

	inputLayer.project(hiddenLayer);

	hiddenLayer.project(outputLayer);



	// set the layers

	this.set({

		input: inputLayer,

		hidden: [hiddenLayer],

		output: outputLayer

	});

}



// extend the prototype chain

Perceptron.prototype = new Network();

Perceptron.prototype.constructor = Perceptron;

```



Now you can test your new network by creating a trainer and teaching the perceptron to learn an XOR



```javascript

var myPerceptron = new Perceptron(2,3,1);

var myTrainer = new Trainer(myPerceptron);



myTrainer.XOR(); // { error: 0.004998819355993572, iterations: 21871, time: 356 }



myPerceptron.activate([0,0]); // 0.0268581547421616

myPerceptron.activate([1,0]); // 0.9829673642853368

myPerceptron.activate([0,1]); // 0.9831714267395621

myPerceptron.activate([1,1]); // 0.02128894618097928

```



##### Long Short-Term Memory



This is how you can create a simple **long short-term memory** network with input gate, forget gate, output gate, and peephole connections:



![long short-term memory](http://people.idsia.ch/~juergen/lstmcell4.jpg)



```javascript

function LSTM(input, blocks, output)

{

	// create the layers

	var inputLayer = new Layer(input);

	var inputGate = new Layer(blocks);

	var forgetGate = new Layer(blocks);

	var memoryCell = new Layer(blocks);

	var outputGate = new Layer(blocks);

	var outputLayer = new Layer(output);



	// connections from input layer

	var input = inputLayer.project(memoryCell);

	inputLayer.project(inputGate);

	inputLayer.project(forgetGate);

	inputLayer.project(outputGate);



	// connections from memory cell

	var output = memoryCell.project(outputLayer);



	// self-connection

	var self = memoryCell.project(memoryCell);



	// peepholes

	memoryCell.project(inputGate);

	memoryCell.project(forgetGate);

	memoryCell.project(outputGate);



	// gates

	inputGate.gate(input, Layer.gateType.INPUT);

	forgetGate.gate(self, Layer.gateType.ONE_TO_ONE);

	outputGate.gate(output, Layer.gateType.OUTPUT);



	// input to output direct connection

	inputLayer.project(outputLayer);



	// set the layers of the neural network

	this.set({

		input: inputLayer,

		hidden: [inputGate, forgetGate, memoryCell, outputGate],

		output: outputLayer

	});

}



// extend the prototype chain

LSTM.prototype = new Network();

LSTM.prototype.constructor = LSTM;

```



These are examples for explanatory purposes, the [Architect](https://github.com/cazala/synaptic/wiki/Architect/) already includes Multilayer Perceptrons and

Multilayer LSTM network architectures.



## Contribute



**Synaptic** is an Open Source project that started in Buenos Aires, Argentina. Anybody in the world is welcome to contribute to the development of the project.



If you want to contribute feel free to send PR's, just make sure to run **npm run test** and **npm run build** before submitting it. This way you'll run all the test specs and build the web distribution files.



## Support



If you like this project and you want to show your support, you can buy me a beer with [magic internet money](https://i.imgur.com/mScSiOo.jpg):



```

BTC: 16ePagGBbHfm2d6esjMXcUBTNgqpnLWNeK

ETH: 0xa423bfe9db2dc125dd3b56f215e09658491cc556

LTC: LeeemeZj6YL6pkTTtEGHFD6idDxHBF2HXa

XMR: 46WNbmwXpYxiBpkbHjAgjC65cyzAxtaaBQjcGpAZquhBKw2r8NtPQniEgMJcwFMCZzSBrEJtmPsTR54MoGBDbjTi2W1XmgM

```



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