// Cloned by Vishu Bhatnagar on 10 Dec 2020 from World "Character recognition neural network" by "Coding Train" project
// Please leave this clone trail here.
// Port of Character recognition neural network from here:
// https://github.com/CodingTrain/Toy-Neural-Network-JS/tree/master/examples/mnist
// with many modifications
// --- 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 = 300;
const nooutput = 10;
const learningrate = 0.2; // 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 = 12; // 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 nn;
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, doodle_label;
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;
// variable used
let doodleTestData = [];
let correct = 0;
let incorrect = 0;
var numberList;
var doodle_guess;
// 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 = `<div><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>
Select your classification and click on button to get score:
<span>
<select id='numberlist'>
<option value=''></option>
<option value='0'>0</option>
<option value='1'>1</option>
<option value='2'>2</option>
<option value='3'>3</option>
<option value='4'>4</option>
<option value='5'>5</option>
<option value='6'>6</option>
<option value='7'>7</option>
<option value='8'>8</option>
<option value='9'>9</option>
</select>
</span>
<button onclick='saveDoodleDataAndScore();' class='normbutton'>Get Score</button><br/>
<p id="showAccuracy" style="display:none">
<span>Correct Predictions:</span><span id="correctCount"></span><br/>
<span>Incorrect Predictions:</span><span id="incorrectCount"></span><br/>
<span>
Accuracy is : <span id="predictionScore"></span>
</span>
</p>
<span>
For future better accuracy lets save the helpul data and use next time.
</span>
<button onclick='sendDatatoServer();' class='normbutton'>Save</button></div>`;
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);
//vishu- hide accuracy span
// $('showAccuracy').css('display:none');
// JS load other JS
// maybe have a loading screen while loading the JS and the data set
AB.loadingScreen();
$.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);
loadData();
getDataFromServer();
});
});
});
}
// 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 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
{
let img = mnist.train_images[train_index];
let label = mnist.train_labels[train_index];
// 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
}
// 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 == NOTRAIN) {
train_index = 0;
console.log("finished trainrun: " + trainrun);
trainrun++;
}
}
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
total_tests++;
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();
}
// 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
let prediction = nn.predict(inputs); // array of outputs
let guess = findMax(prediction); // the top output
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
}
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
doodle_guess = find12(nn.predict(reduceInput(inputs))); // get no.1 and no.2 guesses
thehtml = " We classify it as: " + greenspan + doodle_guess[0] + "</span> <br>" +
" No.2 guess is: " + greenspan + doodle_guess[1] + "</span>";
AB.msg(thehtml, 2);
}
function wipeDoodle() {
doodle_exists = false;
doodle.background('black');
}
// --- 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);
}
// vishu- for restoring data from server if user uploads some test data;
function getDataFromServer() {
console.log("Restoring doodle test data from server")
AB.restoreData((e) => {
doodleTestData = e;
console.log('Data restored successfully')
});
}
function sendDatatoServer() {
if (doodleTestData) {
console.log('Sending data to server')
AB.saveData(doodleTestData)
console.log('transimission complete')
} else {
console.log('transmission failed...')
}
}
// vishu - for updating doodle score and saving the clasification result in doodle trainig set
function saveDoodleDataAndScore() {
if (doodleTestData) {
console.log('Saving data with label and doodle to test set');
//doodle = doodle_inputs;
doodle_label = $("#numberlist").val();
let y = new InitializeDoodleAndLabel(doodle_inputs, doodle_label)
doodleTestData.push(y)
console.log('local test data' + doodleTestData);
updateDoodlePercent();
wipeDoodle();
$("#numberlist").val('');
}
}
function updateDoodlePercent() {
// checking logic and updating score
let correct = 0;
let incorrect = 0;
for (let t = 0; t < doodleTestData.length; t++) {
let o;
let n = doodleTestData[t].doodle;
let r = find12((nn.predict(reduceInput(n))));
if (r[0] == doodleTestData[t].label) {
correct++;
console.log("Predicted values are: " + r[0] +" --- Correct Label: " + doodleTestData[t].label);
}
else if(r[1] == doodleTestData[t].label){
correct++;
console.log("Predicted values are: " + r[1] +" --- Correct Label: " + doodleTestData[t].label);
}
else {
incorrect++;
console.log('Incorrect Predicted values are: '+r[0]+' ,'+ r[1]+' Correct Label is: '+ doodleTestData[t].label)
}
}
console.log("Got " + correct + " correct outputs");
console.log("Got " + incorrect + " incorrect outputs");
let t = ((correct / doodleTestData.length) * 100).toFixed(2);
$('#showAccuracy').css('display','block');
$('#predictionScore').text(t + '%');
$('#correctCount').text(correct);
$('#correctCount').css('color', 'green');
$('#incorrectCount').text(incorrect);
$('#incorrectCount').css('color', 'red');
$('#predictionScore').css('display', 'inline-block');
if(t>50){
$('#predictionScore').css('color', 'green');
} else{
$('#predictionScore').css('color', 'red');
}
console.log('accuracy score is ' + t + '%');
}
class InitializeDoodleAndLabel {
constructor(e, t) {
this.doodle = e;
this.label = t;
}
}
// vishu -- a convolution approach Classifier
function reduceInput(e) {
var t = []
for (var o = 0; o < 784; o++) {
t[o] = 0;
}
var n, r, s;
var l = 0;
var i = 27;
var d = 0;
var a = 27;
for (o = 0; o < 28; o++) {
found = false;
for (n = 0; n < 28; n++)
if (e[28 * o + n] != 0) {
found = true;
};
if (found) {
l = o;
break;
}
}
for (o = 27; o >= 0; o--) {
found = false;
for (n = 0; n < 28; n++)
if (e[28 * o + n] != 0)
found = true;
if (found) {
i = o;
break;
}
}
for (n = 0; n < 28; n++) {
found = false;
for (o = 0; o < 28; o++)
if (e[28 * o + n] != 0) {
found = true
};
if (found) {
d = n;
break;
}
}
for (n = 27; n >= 0; n--) {
found = false;
for (o = 0; o < 28; o++)
if (e[28 * o + n] != 0) {
found = true
};
if (found) {
a = n;
break;
}
}
var c = i - l + 1;
var u = a - d + 1;
var g = 20 / c;
if (20 / u < g) {
g = 20 / u;
}
var m = Math.floor(u * g);
var f = Math.round((28 - m) / 2);
var h = Math.floor(c * g);
var D = Math.round((28 - h) / 2);
for (o = 0; o < 784; o++){
t[o] = 0;
}
for (n = 0; n < m; n++) {
for (o = 0; o < h; o++) {
s = Math.floor(n / g) + d;
r = Math.floor(o / g) + l;
t[28 * (o + D) + n + f] = e[28 * r + s];
}
}
return t;
}