// Cloned by Luke Scales on 4 Dec 2019 from World "Character recognition neural network (clone by Luke Scales)" by Luke Scales
// Please leave this clone trail here.
// Branching out to focus on Exercise 3 port
// Port of Character recognition neural network from here:
// https://github.com/CodingTrain/Toy-Neural-Network-JS/tree/master/examples/mnist
// with many modifications
// TODO: classifier working, pre-train model using mnist database or figure out how to train live in browser
//
// --- defined by MNIST - do not change these ---------------------------------------
const PIXELS = 28; // images in data set are tiny
const PIXELSSQUARED = PIXELS * PIXELS;
// number of training and test exemplars in the data set:
const NOTRAIN = 60000;
const NOTEST = 10000;
//--- can modify all these --------------------------------------------------
// no of nodes in network
const noinput = PIXELSSQUARED;
const nohidden = 64;
const nooutput = 10;
const learningrate = 0.1; // default 0.1
// should we train every timestep or not
let do_training = true;
// how many to train and test per timestep
const TRAINPERSTEP = 30;
const TESTPERSTEP = 5;
// multiply it by this to magnify for display
const ZOOMFACTOR = 7;
const ZOOMPIXELS = ZOOMFACTOR * PIXELS;
// 3 rows of
// large image + 50 gap + small image
// 50 gap between rows
const canvaswidth = ( PIXELS + ZOOMPIXELS ) + 50;
const canvasheight = ( ZOOMPIXELS * 3 ) + 100;
const DOODLE_THICK = 18; // thickness of doodle lines
const DOODLE_BLUR = 3; // blur factor applied to doodles
let mnist;
// all data is loaded into this
// mnist.train_images
// mnist.train_labels
// mnist.test_images
// mnist.test_labels
let featureExtractor;
let nn;
let net;
let classifier; // knn-classifier for tensorflow
let trainrun = 1;
let train_index = 0;
let testrun = 1;
let test_index = 0;
let total_tests = 0;
let total_correct = 0;
// images in LHS:
let doodle, demo;
let doodle_exists = false;
let demo_exists = false;
let mousedrag = false; // are we in the middle of a mouse drag drawing?
// save inputs to global var to inspect
// type these names in console
var train_inputs, test_inputs, demo_inputs, doodle_inputs;
// Matrix.randomize() is changed to point to this. Must be defined by user of Matrix.
function randomWeight()
{
return ( AB.randomFloatAtoB ( -0.5, 0.5 ) );
// Coding Train default is -1 to 1
}
// CSS trick
// make run header bigger
$("#runheaderbox").css ( { "max-height": "95vh" } );
//--- start of AB.msgs structure: ---------------------------------------------------------
// We output a serious of AB.msgs to put data at various places in the run header
var thehtml;
// 1 Doodle header
thehtml = "<hr> <h1> 1. Doodle </h1> Top row: Doodle (left) and shrunk (right). <br> " +
" Draw your doodle in top LHS. <button onclick='wipeDoodle();' class='normbutton' >Clear doodle</button> <br> ";
AB.msg ( thehtml, 1 );
// 2 Doodle variable data (guess)
// 3 Training header
thehtml = "<hr> <h1> 2. Training </h1> Middle row: Training image magnified (left) and original (right). <br> " +
" <button onclick='do_training = false;' class='normbutton' >Stop training</button> <br> ";
AB.msg ( thehtml, 3 );
// 4 variable training data
// 5 Testing header
thehtml = "<h3> Hidden tests </h3> " ;
AB.msg ( thehtml, 5 );
// 6 variable testing data
// 7 Demo header
thehtml = "<hr> <h1> 3. Demo </h1> Bottom row: Test image magnified (left) and original (right). <br>" +
" The network is <i>not</i> trained on any of these images. <br> " +
" <button onclick='makeDemo();' class='normbutton' >Demo test image</button> <br> ";
AB.msg ( thehtml, 7 );
// 8 Demo variable data (random demo ID)
// 9 Demo variable data (changing guess)
const greenspan = "<span style='font-weight:bold; font-size:x-large; color:darkgreen'> " ;
//--- end of AB.msgs structure: ---------------------------------------------------------
function setup()
{
createCanvas ( canvaswidth, canvasheight );
doodle = createGraphics ( ZOOMPIXELS, ZOOMPIXELS ); // doodle on larger canvas
doodle.pixelDensity(1);
// JS load other JS
// maybe have a loading screen while loading the JS and the data set
AB.loadingScreen();
// const TFurl = 'https://unpkg.com/@tensorflow/tfjs';
// const MNurl = 'https://unpkg.com/@tensorflow-models/mobilenet';
// const knnUrl = 'https://unpkg.com/@tensorflow-models/knn-classifier';
// $.getScript( knnUrl, function() {
// console.log('knn-classification model loaded');
// });
// // $.getScript( TFurl, function() {
// // console.log('tensorflow.js loaded');
// // });
// $.getScript( MNurl, function() {
// console.log('mobilenet script loaded')
// // let net;
// // net = mobilenet.load();
// // console.log('Successfully loaded model');
// console.log('Loading mobilenet..');
// net = mobilenet.load();
// console.log('Successfully loaded model');
// });
// var head = document.getElementsByTagName('head')[0];
// let script = document.createElement('script');
// script.type = 'text/javascript';
// script.onload = function() {
// console.log('tf inserted');
// console.log(tf.getBackend());
// }
// script.src = TFurl;
// head.appendChild(script);
// script = document.createElement('script');
// script.type = 'text/javascript';
// script.onload = function() {
// console.log('knn inserted');
// }
// script.src = knnUrl;
// head.appendChild(script);
var ml5url = 'https://unpkg.com/ml5@0.4.3/dist/ml5.min.js'
// $.getScript( ml5url, function() {
// console.log('ml5 script loaded');
// classifier = ml5.imageClassifier('MobileNet');
// });
$.getScript( ml5url, function() {
$.getScript ( "/uploads/codingtrain/matrix.js", function()
{
$.getScript ( "/uploads/codingtrain/nn.js", function()
{
$.getScript ( "/uploads/codingtrain/mnist.js", function()
{
console.log ("All JS loaded");
nn = new NeuralNetwork( noinput, nohidden, nooutput );
nn.setLearningRate ( learningrate );
featureExtractor = ml5.featureExtractor('MobileNet', modelLoaded);
// // classifier = knnClassifier.create();
// classifier = featureExtractor.classification();
// loadData();
});
});
});
});
}
function modelLoaded() {
console.log('ml5 featureExtractor loaded!')
// featureExtractor = ml5.featureExtractor('MobileNet', modelLoaded);
classifier = featureExtractor.classification();
// classifier = ml5.imageClassifier('MobileNet');
loadData();
}
// load data set from local file (on this server)
function loadData()
{
loadMNIST ( function(data)
{
mnist = data;
console.log ("All data loaded into mnist object:")
console.log(mnist);
AB.removeLoading(); // if no loading screen exists, this does nothing
});
}
function imagedata_to_image(imagedata) {
var canvas = document.createElement('canvas');
var ctx = canvas.getContext('2d');
canvas.width = imagedata.width;
canvas.height = imagedata.height;
ctx.putImageData(imagedata, 0, 0);
var image = new Image();
image.src = canvas.toDataURL();
return image;
}
function getImage ( img ) // make a P5 image object from a raw data array
{
let theimage = createImage (PIXELS, PIXELS); // make blank image, then populate it
theimage.loadPixels();
for (let i = 0; i < PIXELSSQUARED ; i++)
{
let bright = img[i];
let index = i * 4;
theimage.pixels[index + 0] = bright;
theimage.pixels[index + 1] = bright;
theimage.pixels[index + 2] = bright;
theimage.pixels[index + 3] = 255;
}
theimage.updatePixels();
return theimage;
}
function getInputs ( img ) // convert img array into normalised input array
{
let inputs = [];
for (let i = 0; i < PIXELSSQUARED ; i++)
{
let bright = img[i];
inputs[i] = bright / 255; // normalise to 0 to 1
}
return ( inputs );
}
function trainit (show) // train the network with a single exemplar, from global var "train_index", show visual on or off
{
// net = mobilenet.load();
let img = mnist.train_images[train_index];
let label = mnist.train_labels[train_index];
const myImg = createImg(img, '')
// classifier.addImage(img, label);
// // Get MobileNet activation of the image
// const activation = net.infer(img, 'conv_preds');
// // Pass to knn classifier for clustering
// classifier.addExample(activation, label);
// optional - show visual of the image
if (show)
{
var theimage = getImage ( img ); // get image from data array
image ( theimage, 0, ZOOMPIXELS+50, ZOOMPIXELS, ZOOMPIXELS ); // magnified
image ( theimage, ZOOMPIXELS+50, ZOOMPIXELS+50, PIXELS, PIXELS ); // original
}
let inputs = getInputs(img);
// classifier.addImage(getImage(img), label);
// // // set up the inputs
// // let inputs = getInputs ( img ); // get inputs from data array
// // // set up the outputs
// // let targets = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
// // targets[label] = 1; // change one output location to 1, the rest stay at 0
// // // console.log(train_index);
// // // console.log(inputs);
// // // console.log(targets);
// // train_inputs = inputs; // can inspect in console
// // nn.train ( inputs, targets )
thehtml = " trainrun: " + trainrun + "<br> no: " + train_index ;
AB.msg ( thehtml, 4 );
train_index++;
if (train_index % 100 === 0) {
// classifier.train(function(lossValue) {
// if (lossValue) {
// loss = lossValue;
// console.log('Loss: ' + loss);
// // select('#loss').html('Loss: ' + loss);
// } else {
// // select('#loss').html('Done Training! Final Loss: ' + loss);
// console.log('Done Training! Final Loss: ' + loss);
// }
// });
// classifier.train();
console.log('shoulda trained');
}
if ( train_index == NOTRAIN )
{
train_index = 0;
console.log( "finished trainrun: " + trainrun );
trainrun++;
}
}
function loadP5Image(image, callback) {
let p5img = getImage(image);
callback(p5img);
}
function testClassify(image) {
let guess;
classifier.classify(image, function(err, results) {
// Display any error
if (err) {
console.error(err.message)
}
if (results && results[0]) {
guess = results[0].label;
// select('#result').html(results[0].label);
// select('#confidence').html(results[0].confidence.toFixed(2) * 100 + '%');
classify();
}
});
return guess;
}
function testit() // test the network with a single exemplar, from global var "test_index"
{
let img = mnist.test_images[test_index];
let label = mnist.test_labels[test_index];
// // set up the inputs
// let inputs = getInputs ( img );
// test_inputs = inputs; // can inspect in console
// let prediction = nn.predict(inputs); // array of outputs
// let guess = findMax(prediction); // the top output
// // // Get the activation from mobilenet from the webcam.
// // const activation = net.infer(img, 'conv_preds');
// // // Get the most likely class and confidences from the classifier module.
// // const result = classifier.predictClass(activation);
// // // console.log('prediction: ' + result.label + '\nprobability: ' + result.confidence);
// // const guess = result.label;
let inputs = getInputs(img);
// let testimage = imagedata_to_image(inputs);
let testimage = getImage(img);
// classifier.classify(testimage, gotResult);
// createCanvas(PIXELS, PIXELS);
// testimage = loadImage(img);
// let guess;
// guess = loadP5Image(img, testClassify);
// total_tests++;
// // classifier.classify(testimage, function(err, results) {
// // // Display any error
// // if (err) {
// // console.error(err.message)
// // }
// // if (results && results[0]) {
// // guess = results[0].label;
// // // select('#result').html(results[0].label);
// // // select('#confidence').html(results[0].confidence.toFixed(2) * 100 + '%');
// // classify();
// // }
// // });
// if (guess == label) total_correct++;
// let percent = (total_correct / total_tests) * 100 ;
// thehtml = " testrun: " + testrun + "<br> no: " + total_tests + " <br> " +
// " correct: " + total_correct + "<br>" +
// " score: " + greenspan + percent.toFixed(2) + "</span>";
// AB.msg ( thehtml, 6 );
// test_index++;
// if ( test_index == NOTEST )
// {
// console.log( "finished testrun: " + testrun + " score: " + percent.toFixed(2) );
// testrun++;
// test_index = 0;
// total_tests = 0;
// total_correct = 0;
// }
}
//--- find no.1 (and maybe no.2) output nodes ---------------------------------------
// (restriction) assumes array values start at 0 (which is true for output nodes)
function find12 (a) // return array showing indexes of no.1 and no.2 values in array
{
let no1 = 0;
let no2 = 0;
let no1value = 0;
let no2value = 0;
for (let i = 0; i < a.length; i++)
{
if (a[i] > no1value)
{
no1 = i;
no1value = a[i];
}
else if (a[i] > no2value)
{
no2 = i;
no2value = a[i];
}
}
var b = [ no1, no2 ];
return b;
}
// just get the maximum - separate function for speed - done many times
// find our guess - the max of the output nodes array
function findMax (a)
{
let no1 = 0;
let no1value = 0;
for (let i = 0; i < a.length; i++)
{
if (a[i] > no1value)
{
no1 = i;
no1value = a[i];
}
}
return no1;
}
// --- the draw function -------------------------------------------------------------
// every step:
function draw()
{
// check if libraries and data loaded yet:
if ( typeof mnist == 'undefined' ) return;
// how can we get white doodle on black background on yellow canvas?
// background('#ffffcc'); doodle.background('black');
background ('black');
if ( do_training )
{
// do some training per step
for (let i = 0; i < TRAINPERSTEP; i++)
{
if (i == 0) trainit(true); // show only one per step - still flashes by
else trainit(false);
}
// do some testing per step
for (let i = 0; i < TESTPERSTEP; i++)
testit();
}
// keep drawing demo and doodle images
// and keep guessing - we will update our guess as time goes on
if ( demo_exists )
{
drawDemo();
guessDemo();
}
if ( doodle_exists )
{
drawDoodle();
// guessDoodle();
// classifyImage();
}
// detect doodle drawing
// (restriction) the following assumes doodle starts at 0,0
if ( mouseIsPressed ) // gets called when we click buttons, as well as if in doodle corner
{
// console.log ( mouseX + " " + mouseY + " " + pmouseX + " " + pmouseY );
var MAX = ZOOMPIXELS + 20; // can draw up to this pixels in corner
if ( (mouseX < MAX) && (mouseY < MAX) && (pmouseX < MAX) && (pmouseY < MAX) )
{
mousedrag = true; // start a mouse drag
doodle_exists = true;
doodle.stroke('white');
doodle.strokeWeight( DOODLE_THICK );
doodle.line(mouseX, mouseY, pmouseX, pmouseY);
}
}
else
{
// are we exiting a drawing
if ( mousedrag )
{
mousedrag = false;
// console.log ("Exiting draw. Now blurring.");
doodle.filter (BLUR, DOODLE_BLUR); // just blur once
// console.log (doodle);
}
}
}
//--- demo -------------------------------------------------------------
// demo some test image and predict it
// get it from test set so have not used it in training
function makeDemo()
{
demo_exists = true;
var i = AB.randomIntAtoB ( 0, NOTEST - 1 );
demo = mnist.test_images[i];
var label = mnist.test_labels[i];
thehtml = "Test image no: " + i + "<br>" +
"Classification: " + label + "<br>" ;
AB.msg ( thehtml, 8 );
// type "demo" in console to see raw data
}
function drawDemo()
{
var theimage = getImage ( demo );
// console.log (theimage);
image ( theimage, 0, canvasheight - ZOOMPIXELS, ZOOMPIXELS, ZOOMPIXELS ); // magnified
image ( theimage, ZOOMPIXELS+50, canvasheight - ZOOMPIXELS, PIXELS, PIXELS ); // original
}
function guessDemo()
{
let inputs = getInputs ( demo );
demo_inputs = inputs; // can inspect in console
// classifier.classify(demo_inputs);
// output = new Promise(resolve => classifier.predict(demo.get(), resolve))
classifier.classify(getImage(demo), gotResult);
// // let prediction = nn.predict(inputs); // array of outputs
// // let guess = findMax(prediction); // the top output
// let guess = output[0].className;
// thehtml = " We classify it as: " + greenspan + guess + "</span>" ;
// AB.msg ( thehtml, 9 );
}
//--- doodle -------------------------------------------------------------
function drawDoodle()
{
// doodle is createGraphics not createImage
let theimage = doodle.get();
// console.log (theimage);
image ( theimage, 0, 0, ZOOMPIXELS, ZOOMPIXELS ); // original
image ( theimage, ZOOMPIXELS+50, 0, PIXELS, PIXELS ); // shrunk
}
async function classifyImage() {
const canvases = document.querySelectorAll("canvas");
const imgEl = canvases[1];
// const imgEl = document.getElementById('defaultCanvas0');
// const img = doodle.get();
// img.resize ( PIXELS, PIXELS );
// img.loadPixels();
// set up inputs
// let inputs = new Uint32Array(PIXELSSQUARED);
// for (let i = 0; i < PIXELSSQUARED ; i++)
// {
// inputs[i] = img.pixels[i * 4] / 255;
// }
// const imageData = {data: inputs, width: PIXELS, height: PIXELS};
// const imageData = new ImageData(inputs, PIXELS, PIXELS);
// console.log(imageData);
// const imgEl = doodle.get();
// const result = await net.classify(imgEl);
// console.log(result);
// Get the activation from mobilenet from the webcam.
const activation = net.infer(img, 'conv_preds');
// Get the most likely class and confidences from the classifier module.
const result = classifier.predictClass(activation);
console.log('prediction: ' + result.label + '\nprobability: ' + result.confidence);
}
function guessDoodle()
{
// doodle is createGraphics not createImage
let img = doodle.get();
img.resize ( PIXELS, PIXELS );
img.loadPixels();
// set up inputs
let inputs = [];
for (let i = 0; i < PIXELSSQUARED ; i++)
{
inputs[i] = img.pixels[i * 4] / 255;
}
doodle_inputs = inputs; // can inspect in console
// feed forward to make prediction
let prediction = nn.predict(inputs); // array of outputs
let b = find12(prediction); // get no.1 and no.2 guesses
thehtml = " We classify it as: " + greenspan + b[0] + "</span> <br>" +
" No.2 guess is: " + greenspan + b[1] + "</span>";
AB.msg ( thehtml, 2 );
}
function gotResult(error, results) {
// Display error in the console
if (error) {
console.error(error);
}
// The results are in an array ordered by confidence.
console.log(results);
// Show the first label and confidence
// label.html('Label: ' + results[0].label);
// confidence.html('Confidence: ' + nf(results[0].confidence, 0, 2)); // Round the confidence to 0.01
thehtml = 'Label: ' + results[0].label +
'Confidence: ' + nf(results[0].confidence, 0, 2);
AB.msg ( thehtml, 2 );
}
function wipeDoodle()
{
// classifyImage();
// classifier.classify(doodle, gotResult);
classifier.addImage(doodle, '0');
// classifier.classify(doodle, function(err, results) {
// // Display any error
// if (err) {
// console.error(err);
// } else {
// // if (results && results[0]) {
// console.log(results);
// // select('#result').html(results[0].label);
// // select('#confidence').html(results[0].confidence.toFixed(2) * 100 + '%');
// classify();
// }
// });
setTimeout(function() {
doodle_exists = false;
doodle.background('black');
}, 3000);
}
// --- debugging --------------------------------------------------
// in console
// showInputs(demo_inputs);
// showInputs(doodle_inputs);
function showInputs ( inputs )
// display inputs row by row, corresponding to square of pixels
{
var str = "";
for (let i = 0; i < inputs.length; i++)
{
if ( i % PIXELS == 0 ) str = str + "\n"; // new line for each row of pixels
var value = inputs[i];
str = str + " " + value.toFixed(2) ;
}
console.log (str);
}