一個JavaScript神經網絡庫：Synaptic

Synaptic是一個JavaScript神經網絡庫，可用于node.js 和瀏覽器環境。 its generalized algorithm is architecture-free, so you can build and train basically any type of first order or even second order neural network architectures.

This library includes a few built-in architectures like multilayer perceptrons, multilayer long-short term memory networks (LSTM), liquid state machines or Hopfield 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 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

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.

Demos

• Solve an XOR
• Discrete Sequence Recall Task
• Learn Image Filters
• Paint an Image
• Read from Wikipedia

Getting started

• Neurons
• Layers
• Networks
• Trainer
• Architect

Overview

Installation

In node

You can install synaptic with npm:

npm install synaptic --save

In the browser

Just include the file synaptic.js from/distdirectory with a script tag in your HTML:

<script src="synaptic.js"></script>

Usage

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.

Gulp Tasks

• gulp or gulp build: builds the source code from/srcinto the/distdirectory (bundled and minified).
• gulp debug: builds the source code from/srcinto the/distdirectory (not minifed and with source maps for debugging).
• gulp dev: same as debug but it watches for changes in the source files and rebuilds when any change is detected.
• gulp test: runs all the tests.

Examples

Perceptron

This is how you can create a simple perceptron:

.

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

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:

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,  Layer.connectionType.ONE_TO_ONE);
    memoryCell.project(forgetGate, Layer.connectionType.ONE_TO_ONE);
    memoryCell.project(outputGate, Layer.connectionType.ONE_TO_ONE);

    // 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 already includes Multilayer Perceptrons and Multilayer LSTM network architectures.

項目主頁：http://www.baiduhome.net/lib/view/home/1427085874512