JavaScripting

A flexible neural network library for Node.js and the browser. Check out a live demo of a movie recommendation engine built with Mind.

Features

• Vectorized - uses a matrix implementation to process training data
• Configurable - allows you to customize the network topology
• Pluggable - download/upload minds that have already learned

Installation

$ yarn add node-mind

Usage

const Mind = require('node-mind');

/**
 * Letters.
 *
 * - Imagine these # and . represent black and white pixels.
 */

const a = character(
  '.#####.' +
  '#.....#' +
  '#.....#' +
  '#######' +
  '#.....#' +
  '#.....#' +
  '#.....#'
)

const b = character(
  '######.' +
  '#.....#' +
  '#.....#' +
  '######.' +
  '#.....#' +
  '#.....#' +
  '######.'
)

const c = character(
  '#######' +
  '#......' +
  '#......' +
  '#......' +
  '#......' +
  '#......' +
  '#######'
)

/**
 * Learn the letters A through C.
 */

const mind = new Mind({ activator: 'sigmoid' })
  .learn([
    { input: a, output: map('a') },
    { input: b, output: map('b') },
    { input: c, output: map('c') }
  ])

/**
 * Predict the letter C, even with a pixel off.
 */

const result = mind.predict(character(
  '#######' +
  '#......' +
  '#......' +
  '#......' +
  '#......' +
  '##.....' +
  '#######'
))

console.log(result) // ~ 0.5

/**
 * Turn the # into 1s and . into 0s.
 */

function character(string) {
  return string
    .trim()
    .split('')
    .map(integer)

  function integer(symbol) {
    if ('#' === symbol) return 1
    if ('.' === symbol) return 0
  }
}

/**
 * Map letter to a number.
 */

function map(letter) {
  if (letter === 'a') return [ 0.1 ]
  if (letter === 'b') return [ 0.3 ]
  if (letter === 'c') return [ 0.5 ]
  return 0
}

Plugins

Use plugins created by the Mind community to configure pre-trained networks that can go straight to making predictions.

Here's a cool example of the way you could use a hypothetical mind-ocr plugin:

const Mind = require('node-mind')
const ocr = require('mind-ocr')

const mind = Mind()
  .upload(ocr)
  .predict(
    '.#####.' +
    '#.....#' +
    '#.....#' +
    '#######' +
    '#.....#' +
    '#.....#' +
    '#.....#'
  )

To create a plugin, simply call download on your trained mind:

const Mind = require('node-mind')

const mind = Mind()
  .learn([
    { input: [0, 0], output: [ 0 ] },
    { input: [0, 1], output: [ 1 ] },
    { input: [1, 0], output: [ 1 ] },
    { input: [1, 1], output: [ 0 ] }
  ]);

const xor = mind.download()

Here's a list of available plugins:

• xor

API

Mind(options)

Create a new instance of Mind that can learn to make predictions.

The available options are:

• activator: the activation function to use, sigmoid or htan
• learningRate: the speed at which the network will learn
• hiddenUnits: the number of units in the hidden layer/s
• iterations: the number of iterations to run
• hiddenLayers: the number of hidden layers

.learn()

Learn from training data:

mind.learn([
  { input: [0, 0], output: [ 0 ] },
  { input: [0, 1], output: [ 1 ] },
  { input: [1, 0], output: [ 1 ] },
  { input: [1, 1], output: [ 0 ] }
])

.predict()

Make a prediction:

mind.predict([0, 1])

.download()

Download a mind:

const xor = mind.download()

.upload()

Upload a mind:

mind.upload(xor)

.on()

Listen for the 'data' event, which is fired with each iteration:

mind.on('data', (iteration, errors, results) => {
  // ...
})

Releasing / Publishing

CircleCI will handle publishing to npm. To cut a new release, just do:

$ git changelog --tag <version>
$ vim package.json # enter <version>
$ git release <version>

Where <version> follows the semver spec.

Note

If you're interested in learning more, I wrote a blog post on how to build your own neural network:

• How to Build a Neural Network

Also, here are some fantastic libraries you can check out:

• convnetjs
• synaptic
• brain

License

MIT

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stevenmiller888.github.io  ·  GitHub @stevenmiller888  ·  Twitter @stevenmiller888