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A Neural Network in JavaScript. This now has it's own Github repo: https://github.com/ianchanning/neural-network-js
Raw
.eslintrc.json
{
"env": {
"browser": true,
"es6": true
},
"extends": "eslint:recommended",
"parserOptions": {
"ecmaFeatures": {
"jsx": true
},
"ecmaVersion": 2018,
"sourceType": "module"
},
"plugins": [
"react"
],
"rules": {
"indent": [
"error",
2
],
"linebreak-style": [
"error",
"unix"
],
"quotes": [
"error",
"double"
],
"semi": [
"error",
"never"
]
}
}
Raw
.gitignore
node_modules
Raw
index.html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>Neural Networks 101</title>
<style>
h1 {font: normal 2em Georgia;}
</style>
</head>
<body>
<h1>Bring on the machines</h1>
<div id="root"></div>
<script src="neural-network.js"></script>
</body>
</html>
Raw
neural-network.js
/* eslint no-unused-vars: "off" */
/**
* This is an experiment to see if I can recreate funfunfunctions machine-learning / back-propagation code
* @link https://www.youtube.com/watch?v=anN2Ey37s-o
*
* Step 1 is to recreate the JS code
* Step 2 is to port to ReasonML
* @return object
*/
const fff = (() => {
"use strict"
const X_MAX = 400
const Y_MAX = 400
const EXAMPLE_COUNT = 100000
/**
* Generate the required data specific for this network
*
* @returns {object} {weights,points} the initial weights and training/test data
*/
const generator = () => {
/**
* Generate random number between min and max
*
* @param {float} min
* @param {float} max
* @returns {float} Random number
*/
const rand = (min, max) => Math.random() * (max - min) + min
// random set of data points
const points = length => Array(length).fill().map(() => ({
x: rand(0, X_MAX),
y: rand(0, Y_MAX)
}))
// initial random weights
const weights = {
x: rand(-1, 1),
y: rand(-1, 1)
}
// we happen to know that this will classify out points correctly
// so we can use it to generate actual values for training examples
const team = point => point.x > point.y ? 1 : 0
const labeller = examples => examples.map(
point => ({point, actual: team(point)})
)
// generate labelled training data
const examples = length => labeller(points(length))
return {
weights,
points,
examples
}
}
/**
* Neural Network
*
* @param {object}
* @param {object} weights {x,y} Initial weights
* @param {array} examples [{x,y}] Training set examples
* @returns {object} {trainedWeights,prediction} Required data to classify the chart points
*/
const gym = (weights, examples) => {
/**
* Binary classifier
*
* TODO: Is this our activation function too?
* I think so - effectively for a ReLU we want to return output not one
* But we want a binary classifier
* Ah perhaps this is not the activation function
* It just happens that we're assuming that the dot product will never be less zero?
* Or maybe this is a combination of the activation function and the classifier
* For a binary classifier we should be using a sigmoid I think
* But this appears to be using a ReLU and classifying as true if > 0
*
* @param {integer} output Neuron value before activation function
* @returns {integer} Class of the example 0|1
*/
const classifier = output => output > 0 ? 1 : 0
/**
* Matrix dot product
*
* @param {object} a {x,y}
* @param {object} b {x,y}
* @returns {float} Dot product value
*/
const dot = (a, b) => a.x * b.x + a.y * b.y
// make a prediction given the weigts and a point
const prediction = (weights, point) => classifier(dot(weights, point))
/**
* Single training step
*
* @param {object} weights {x,y} I think this is typically {w1, w2}
* @param {object} point {x,y} Training example typically x1, x2
* @param {integer} actual 0|1 Correct label for the example
* @returns {object} {x,y} updated weights
*/
const train = (weights, point, actual) => {
// also know as... y_hat
const predict = prediction(weights, point)
// TODO: I'm not convinced this is correct
// Or at least I don't know why it's correct
// It could be by accident because of the line we've chosen
// possibilities are
// actual: 0|1
// predict: 0|1
// error: 0|-1|1|0
// Andrew Ng Deep Learning wk 2:
// This does appear that we're using Logistic Regression
// dZ = A - Y (N.B. matrices, A = activation/predict, Y = actual)
// dw = 1/m X . dZ_T (_T = matrix transpose)
// In individual loop steps (m examples):
// dw = x_1 * dz_1 + x_2 * dx_2 + ... x_m * dx_m (equivalent of {point.x, point.y} * error for all examples)
// dw /= m (it seems we miss the division here)
// w -= alpha * dw
//
// Comparing to my notes it seems like we calculate -error (or -dZ)
// As A - Y is reversed
// Then it makes sense to have a '+' when updating the weights
const error = actual - predict
// TODO: I think this is effectively the back propagation step
// w := w - alpha * dw (as per Andrew Ng python deep learning code)
// N.B. We're currently *not* using the learning rate (alpha)
return {
x: weights.x + (point.x * error),
y: weights.y + (point.y * error)
}
}
const trainer = (acc, example) => train(acc, example.point, example.actual)
// TODO: I think just just does one iteration of gradient descent
const trainedWeights = examples.reduce(trainer, weights)
// console.log(trainedWeights)
return {
train,
prediction,
trainedWeights
}
}
const chart = () => {
const el = name => document.createElementNS(
"http://www.w3.org/2000/svg",
name
)
const circle = (centre, radius, colour) => {
let c = el("circle")
c.setAttribute("cx", centre.x)
c.setAttribute("cy", centre.y)
c.setAttribute("r", radius)
c.style.fill = colour
return c
}
const clickelem = el => {
/* eslint no-console: "off" */
el.onclick = e => console.log(e.target.attributes)
return el
}
const line = (start, end, colour) => {
let l = el("line")
l.setAttribute("x1", start.x)
l.setAttribute("y1", start.y)
l.setAttribute("x2", end.x)
l.setAttribute("y2", end.y)
l.setAttribute("stroke", colour)
return l
}
const svg = () => {
let svg = el("svg")
svg.setAttribute("height", Y_MAX)
svg.setAttribute("width", X_MAX)
return svg
}
return {
clickelem,
circle,
line,
svg
}
}
const fill = (generator, gym, chart) => {
const colours = ["red", "blue"]
const graphPoints = generator.points(200)
const svg = chart.svg()
graphPoints.map(point => svg.appendChild(
chart.clickelem(chart.circle(
point,
5,
colours[gym.prediction(gym.trainedWeights, point)]
))
))
graphPoints.map(point => svg.appendChild(
chart.circle(point, 1, "white")
))
// want the line to appear above the dots
svg.appendChild(chart.line({x: 0, y: 0}, {x: X_MAX, y: Y_MAX}, "gray"))
return svg
}
const chartGen = generator()
const chartGym = gym(chartGen.weights, chartGen.examples(EXAMPLE_COUNT))
document.getElementById("root").appendChild(fill(chartGen, chartGym, chart()))
return {
chartGym
}
})()
Raw
package-lock.json
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Raw
package.json
{
"name": "fff-machine-learning",
"version": "1.0.0",
"description": "Recreation of funfunfunction machine learning video",
"main": "machine-learning.js",
"scripts": {
"test": "echo \"Error: no test specified\" && exit 1"
},
"repository": {
"type": "git",
"url": "git+ssh://[email protected]/d6791c8181fbee2bb4a83c442219f77a.git"
},
"keywords": [
"machine-learning",
"javascript"
],
"author": "Ian Channing",
"license": "MIT",
"bugs": {
"url": "https://gist.github.com/d6791c8181fbee2bb4a83c442219f77a"
},
"homepage": "https://gist.github.com/d6791c8181fbee2bb4a83c442219f77a",
"devDependencies": {
"eslint": "^5.9.0",
"eslint-plugin-react": "^7.11.1"
}
}
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