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A Neural Network in JavaScript. This now has it's own Github repo: https://github.com/ianchanning/neural-network-js
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| { | |
| "env": { | |
| "browser": true, | |
| "es6": true | |
| }, | |
| "extends": "eslint:recommended", | |
| "parserOptions": { | |
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| }, | |
| "ecmaVersion": 2018, | |
| "sourceType": "module" | |
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| 2 | |
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| node_modules |
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| <!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> |
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| /* 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 | |
| } | |
| })() |
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| "resolved": "https://registry.npmjs.org/write/-/write-0.2.1.tgz", | |
| "integrity": "sha1-X8A4KOJkzqP+kUVUdvejxWbLB1c=", | |
| "dev": true, | |
| "requires": { | |
| "mkdirp": "0.5.1" | |
| } | |
| } | |
| } | |
| } |
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| { | |
| "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://git@gist.github.com/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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