// Cloned by Pradnyesh Bhalange on 22 Nov 2021 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 ---------------------------------------
var PIXELS = 28;
var PIXELSSQUARED = PIXELS * PIXELS; // images in data set are tiny
var RESIZEDPIXELS = 24;
// // multiply it by this to magnify for display
var ZOOMFACTOR = 7;
var ZOOMPIXELS = ZOOMFACTOR * PIXELS;
const NOTRAIN = 60000;
const NOTEST = 10000;
// 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;
// // 3 rows of
// // large image + 50 gap + small image
// // 50 gap between rows
const TRAINPERSTEP = 30;
const TESTPERSTEP = 5;
// var canvaswidth = PIXELS + ZOOMPIXELS + 80;
// var canvasheight = ZOOMPIXELS + 20;
const canvaswidth = ( PIXELS + ZOOMPIXELS ) + 50;
const canvasheight = ( ZOOMPIXELS * 3 ) + 100;
var DOODLE_THICK = 15; // thickness of doodle lines
var 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 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
}
// make run header bigger
AB.headerCSS ( { "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 );
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)).pixelDensity(1);
// JS load other JS
// maybe have a loading screen while loading the JS and the data set
AB.loadingScreen();
uploadFiles();
}
function uploadFiles() {
$.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();
});
});
});
$.getScript("/uploads/pradzb/mathutils.js", function()
{
$.getScript("/uploads/pradzb/webcnn.js", function()
{
$.ajax({
url : "/uploads/pradzb/cnn_mnist.json",
dataType : "json",
success : loadNet
});
});
});
}
// WEBCNN is a Javascript library for training Deep Learning models to run them in browser directly
// i have also imported network for webcnn.js
// load data set from local file (on this server)
function loadData(netJson)
{
loadMNIST ( function(data)
{
mnist = data;
AB.removeLoading(); // if no loading screen exists, this does nothing
});
}
function loadNet(netJson){
loadNetworkFromJSON(netJson);
AB.removeLoading();
}
//cited form https://github.com/DenseInL2/webcnn/blob/master/digittraining.js#L136
// this loads network from JSON file
function loadNetworkFromJSON( networkJSON ){
cnn = new WebCNN();
for (var i = 0; i < networkJSON.layers.length; ++i) {
var item = networkJSON.layers[i];
cnn.newLayer(item);
}
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();
}
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;
}
}
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 * 5;
theimage.pixels[index + 0] = bright;
theimage.pixels[index + 1] = bright;
theimage.pixels[index + 2] = bright;
theimage.pixels[index + 3] = bright;
theimage.pixels[index + 4] = 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 );
}
//--- 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 < 10; i++)
{
var Val1 = a[0].getValue(0,0,i);
if (Val1 > no1value) // new no1
{
no1 = i;
no1value = Val1;
}
for (let j =0; j< 10; j++)
{
var Val2 =a[0].getValue(0,0,j);
if (Val2 > no2value) // new no2
{
no2 = j;
no2value = Val2;
}
}
}
var b = [ no1, no2 ];
return b;
}
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;
}
function canvasChange(a, b) {
return {
width : b,
height : b,
data : getImage(cropImage(a,b), b).pixels
};
}
// --- 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('yellow');
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);
}
}
}
//--- doodle -------------------------------------------------------------
function drawDoodle()
{
// doodle is createGraphics not createImage
let theimage = doodle.get();
image ( theimage, 0, 0, ZOOMPIXELS, ZOOMPIXELS ); // original
image ( theimage, ZOOMPIXELS+50, 0, PIXELS, PIXELS ); // shrunk
}
function cropImage(img_x, img_y)
{
var image_cord = PIXELS - img_y;
var t1 = Math.floor(Math.random() * image_cord);
var prefix = Math.floor(Math.random() * image_cord);
var t2 = t1 + img_y;
var t3 = prefix + img_y;
var result = [];
for( var i= t1; i < t2 ; i++){
for(var j = prefix; j< t3; j++){
result.push(img_x[i * PIXELS + j]);
}
}
return result;
}
function guessDoodle() {
var img = doodle.get();
img.resize ( PIXELS, PIXELS );
img.loadPixels();
var inputs = [];
for (var i = 0; i < PIXELSSQUARED; i++) {
inputs[i] = img.pixels[4 * i] / 255;
}
doodle_inputs = inputs;
var canvas = canvasChange(ImageToCenter(img.pixels, PIXELS), RESIZEDPIXELS);
var n = find12(cnn.classifyImages([canvas]));
thehtml = " CNN classify it as: " + greenspan + n[0] +
"</span> <br> No.2 guess is: " + greenspan + n[1] + "</span>";
AB.msg(thehtml, 2);
}
function convertTo2D(digit, x)
{
var obj = [];
for( var i = 0; i< x; i++){
obj[i] = [];
for( var j=0; j< x; j++){
obj[i][j] = digit[4 * (i * x+j)];
}
}
return obj;
}
function indexOfCoordinates(l, h, x, y, r) {
for (var i = 0; i < l.length; i++) {
var temp = l[i].indexOf(255);
var current = l[i].lastIndexOf(255);
if (temp >= 0 && temp < x) {
x = temp;
}
if (current >= 0 && current > r) {
r = current;
}
if (temp >= 0 && i < h) {
h = i;
}
if (temp >= 0 && i > y) {
y = i;
}
}
return [x, r, h, y];
}
function ImageToCenter(object, size) {
var result = [];
var cord = indexOfCoordinates(result = convertTo2D(object, size), Number.MAX_VALUE, Number.MAX_VALUE, -1, -1);
var x = Math.floor((size - cord[3] - cord[2]) / 2);
var y = Math.floor((size - cord[1] - cord[0]) / 2);
var img_pattern = Array(size).fill().map( function () {
return Array(size).fill(0);
});
for( var i = cord[2]; i<= cord[3]; i++){
for( var j = cord[0]; j<= cord[1]; j++){
img_pattern[i + x][j + y]= result[i][j];
}
}
var res= [];
for(var index1 =0; index1 < size; index1++){
for(var index2 = 0; index2 < size; index2++){
res[index1 * size + index2] = img_pattern[index1][index2];
}
}
return res;
}
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 );
}
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);
}