// Cloned by BIBEK PRASAD GUPTA on 7 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;
// Bibek- Image cropped to make it a 24*24 sized image
const CROPPED_DOODLE_PIXELS = 24;
// 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*2;
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
// Bibek - Removing blur as it is resulting in better accuracy
const DOODLE_BLUR = 0; // 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 cnn;
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();
// Bibek - Added files related to webCNN
$.getScript("/uploads/codingtrain/matrix.js", function () {
$.getScript("/uploads/codingtrain/nn.js", function () {
$.getScript("/uploads/codingtrain/mnist.js", function () {
$.getScript("/uploads/bibek20210617/webcnn.js", function () {
$.getScript("uploads/bibek20210617/mathutils.js", function () {
$.ajax({
url: "/uploads/bibek20210617/cnn_mnist_10_20_98accuracy.json",
dataType: "json",
success: onJSONLoaded
});
});
});
});
});
});
}
// 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
});
}
// Bibek - Added extra size parameter to make this function reusable and also to work for getting cropped image
function getImage(img, size = PIXELS) // 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 < size * size; 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)
// Bibek - updated the funcion to work as per object returned by cnn classfier
function find12(a) // return array showing indexes of no.1 and no.2 values in array
{
let firstGuess = 0;
let secondGuess = 0;
let firstValue = 0;
let secondValue = 0;
for (let i = 0; i < 10; i++) {
let predictedVal = a[0].getValue(0, 0, i);
if (predictedVal > firstValue) {
firstGuess = i;
firstValue = predictedVal;
}
}
// Bibek - Corrected the logic to get the second guess
for (let i = 0; i < 10; i++) {
let predictedVal = a[0].getValue(0, 0, i);
if ((firstGuess != i) && (predictedVal > secondValue)) {
secondGuess = i;
secondValue = predictedVal;
}
}
return [firstGuess, secondGuess];
}
// 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
// Commented old predictor
// let prediction = nn.predict(inputs); // array of outputs
// Bibek - Used CNN classifier to get the prediction
let prediction = cnn.classifyImages([getInObjectFormat(preprocessingImage(img.pixels, PIXELS), CROPPED_DOODLE_PIXELS)]);
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 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);
}
/*
Bibek - CNN code begins here
Reference - https://github.com/DenseInL2/webcnn
*/
// Utility function to trigger CNN and old network initilisation
function onJSONLoaded(response) {
console.log("All JS loaded");
console.log("JSON Loaded!");
// Bibek - CNN Network initilisation is done by this function
loadNetworkFromJSON(response);
// Bibek - Continue training the old Network
nn = new NeuralNetwork(noinput, nohidden, nooutput);
nn.setLearningRate(learningrate);
loadData();
}
// Bibek - Initialise CNN with pretrained weights and other parameters from the JSON file response
function loadNetworkFromJSON(networkJSON) {
cnn = new WebCNN();
if (networkJSON.momentum != undefined) cnn.setMomentum(networkJSON.momentum);
if (networkJSON.lambda != undefined) cnn.setLambda(networkJSON.lambda);
if (networkJSON.learningRate != undefined) cnn.setLearningRate(networkJSON.learningRate);
for (var layerIndex = 0; layerIndex < networkJSON.layers.length; ++layerIndex) {
let layerDesc = networkJSON.layers[layerIndex];
console.log(layerDesc);
cnn.newLayer(layerDesc);
}
for (var layerIndex = 0; layerIndex < networkJSON.layers.length; ++layerIndex) {
let layerDesc = networkJSON.layers[layerIndex];
switch (networkJSON.layers[layerIndex].type) {
case LAYER_TYPE_CONV:
case LAYER_TYPE_FULLY_CONNECTED: {
if (layerDesc.weights != undefined && layerDesc.biases != undefined) {
cnn.layers[layerIndex].setWeightsAndBiases(layerDesc.weights, layerDesc.biases);
}
break;
}
}
}
cnn.initialize();
}
// Bibek - Utility function to prepare object for CNN classifier
function getInObjectFormat(image, size) {
return {
"width": size,
"height": size,
"data": getImage(randCropUtil(image, size), size).pixels
};
}
/* Bibek - Utility function to preprocess the image before passing it to the classifier
Cropping the image to 24*24 for better acccuracy
*/
function preprocessingImage(pixels, size) {
// Bibek - Creating a 2-D array from 1-D pixels array
let imgTemp = [];
for (let i = 0; i < size; i++) {
imgTemp[i] = [];
for (let j = 0; j < size; j++) {
imgTemp[i][j] = pixels[(i * size + j) * 4];
}
}
// Bibek - Centering the image
var tmost = Number.MAX_VALUE;
var lmost = Number.MAX_VALUE;
var bmost = -1;
var rmost = -1;
for (var y = 0; y < imgTemp.length; y++) {
var l = imgTemp[y].indexOf(255);
var r = imgTemp[y].lastIndexOf(255);
if (l >= 0 && l < lmost) lmost = l;
if (r >= 0 && r > rmost) rmost = r;
if (l >= 0 && y < tmost) tmost = y;
if (l >= 0 && y > bmost) bmost = y;
}
let transX = Math.floor((size - rmost - lmost) / 2);
let transY = Math.floor((size - bmost - tmost) / 2);
let result = Array(size).fill().map(() => Array(size).fill(0));
for (i = tmost; i <= bmost; i++) {
for (j = lmost; j <= rmost; j++) {
result[i + transY][j + transX] = imgTemp[i][j];
}
}
// Bibek - Pushing the values from result the 2D array to make it a 1D array
let final = [];
for (let i = 0; i < size; i++) {
for (let j = 0; j < size; j++) {
final[i * size + j] = result[i][j];
}
}
return final;
}
// Bibek - Utility function for cropping the image starting at random (x,y)
function randCropUtil(image, size) {
const maxStartInd = PIXELS - size;
let xrandind = Math.floor(Math.random() * maxStartInd);
let yrandind = Math.floor(Math.random() * maxStartInd);
return cropImage(image, size, xrandind, yrandind);
}
// Bibek - function to crop the image starting from (x,y) and returns 1-D array
function cropImage(image, size, x = 2, y = 2) {
let result = [];
let xLastInd = x + size;
let yLastInd = y + size;
for (let i = x; i < xLastInd; i++) {
for (let j = y; j < yLastInd; j++) {
result.push(image[i * PIXELS + j])
}
}
return (result);
}