// Cloned by Itisha on 18 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 ---------------------------------------
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 = 200 ; //5 ;//64;
const nooutput = 3;
const learningrate = 0.2; // default 0.1
// should we train every timestep or not
let do_training = false ; //true;
let do_testing = false ; //true;
// how many to train and test per timestep
const TRAINPERSTEP = 1 ; //30;
const TESTPERSTEP = 3000; //5;
// multiply it by this to magnify for display
const ZOOMFACTOR = 10;
const ZOOMPIXELS = ZOOMFACTOR * PIXELS + 50;
// 3 rows of
// large image + 50 gap + small image
// 50 gap between rows
const canvaswidth = ( PIXELS + ZOOMPIXELS) + 100;
const canvasheight = ( ZOOMPIXELS) + 100;
const DOODLE_THICK = 5; // thickness of doodle lines
const DOODLE_BLUR = 3; // blur factor applied to doodles
//New variables added -- Itisha
const len = 784;
const totalData = 1000;
const CAT = 0;
const RAINBOW = 1;
const TRAIN = 2;
let catsData;
let trainsData;
let rainbowsData;
let cats = {};
let trains = {};
let rainbows = {};
let save_flag = false;
// end of new variables added -- Itisha
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 0 ;
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> Doodle </h1>" +
" Draw doodle of a Train, Rainbow or a Cat in the top LHS. <br><br> <button onclick='wipeDoodle();' class='normbutton' > Clear</button>" +" "+
"<button onclick='guessDoodle();' class='normbutton' > Guess</button>" +" "+
"<button onclick='do_training = true;' class='normbutton' >Train</button>" + " "+
"<button onclick='do_testing = true;' class='normbutton' >Test</button>" + " "+
"<button onclick='do_training = false; do_testing = false;' class='normbutton' >Stop</button>" + "<br><br>"+
"<button onclick='save_data();' class='normbutton' >Save</button>" + " "+
"<button onclick='restore_data();' class='normbutton' >Restore</button>" ;
AB.msg ( thehtml, 1 );
const greenspan = "<span style='font-weight:bold; font-size:x-large; color:darkgreen'> " ;
//--- end of AB.msgs structure: ---------------------------------------------------------
// load data set from local file (on this server)
function preload()
{
catsData = loadBytes('/uploads/itisha312/cats1000.bin');
trainsData = loadBytes('/uploads/itisha312/trains1000.bin');
rainbowsData = loadBytes('/uploads/itisha312/rainbows1000.bin');
}
function prepareData(category, data, label) {
category.training = [];
category.testing = [];
for (let i = 0; i < totalData; i++) {
let offset = i * len;
let threshold = floor(0.8 * totalData);
if (i < threshold) {
category.training[i] = data.bytes.subarray(offset, offset + len);
category.training[i].label = label;
} else {
category.testing[i - threshold] = data.bytes.subarray(offset, offset + len);
category.testing[i - threshold].label = label;
}
}
}
function save_data(){
AB.saveData ( nn );
}
function restore_data(){
if ( AB.runloggedin ){
// Check if any data exists, if so make restore button
AB.queryDataExists ( function ( exists ) // asynchronous - need callback function
{
if ( exists ){
AB.restoreData( function (nn){
console.log('Restoring data from server');
console.log(nn);
// nn.setLearningRate ( learningrate );
console.log('calling saved nn obj');
nn = new NeuralNetwork(nn);
nn.setLearningRate(learningrate);
redraw();
});
}
});
}
}
function setup()
{
createCanvas ( canvaswidth, canvasheight );
background(0);
doodle = createGraphics ( ZOOMPIXELS, ZOOMPIXELS ); // doodle on larger canvas
doodle.pixelDensity(1);
// JS load other JS
$.getScript ( "/uploads/itisha312/matrix.js", function(){
$.getScript ( "/uploads/itisha312/neural_nw.js", function(){
console.log ("All JS loaded");
AB.removeLoading();
// Making the neural network
console.log('creating a new nn obj');
nn = new NeuralNetwork( noinput, nohidden, nooutput );
nn.setLearningRate ( learningrate );
});
});
// maybe have a loading screen while loading the JS and the data set
AB.loadingScreen();
}
function trainEpoch(training,nn) {
shuffle(training, true);
console.log('Begin Training');
//console.log(training);
// Train for one epoch
let train_nbr = 0;
//console.log('training.length: '+training.length);
for (let i = 0; i < training.length; i++) {
let data = training[i];
let inputs = Array.from(data).map(x => x / 255);
let label = training[i].label;
let targets = [0, 0, 0];
targets[label] = 1;
train_nbr = i+1;
// console.log(data);
thehtml = " Trained dataset: " + train_nbr ;
AB.msg ( thehtml, 4 );
nn.train(inputs, targets);
}
}
function testAllDoodles(testing,nn) {
console.log('TestAllDoodles');
let correct = 0;
// Train for one epoch
//console.log('testing.length: '+testing.length);
for (let i = 0; i < testing.length; i++) {
let data = testing[i];
let inputs = Array.from(data).map(x => x / 255);
let label = testing[i].label;
let guess = nn.predict(inputs);
let m = max(guess);
let classification = guess.indexOf(m);
if (classification === label) {
correct++;
}
}
let percent = 100 * correct / testing.length;
return percent;
}
function find_doodle_labels (guess)
{
var doodle_guess = [];
let m = max(guess);
let classification = guess.indexOf(m);
if (classification === CAT) {
doodle_guess[0] = 'CAT';
//console.log("cat");
} else if (classification === RAINBOW) {
doodle_guess[0] = 'RAINBOW';
//console.log("rainbow");
} else if (classification === TRAIN) {
doodle_guess[0] = 'TRAIN';
// console.log("train");
}
return doodle_guess;
}
// --- the draw function -------------------------------------------------------------
// every step:
function draw()
{
background ('black');
// Preparing the data
prepareData(cats, catsData, CAT);
prepareData(rainbows, rainbowsData, RAINBOW);
prepareData(trains, trainsData, TRAIN);
// Randomizing the data
let training = [];
training = training.concat(cats.training);
training = training.concat(rainbows.training);
training = training.concat(trains.training);
let testing = [];
testing = testing.concat(cats.testing);
testing = testing.concat(rainbows.testing);
testing = testing.concat(trains.testing);
let epochCounter = 0;
if ( do_training ){
trainEpoch(training,nn);
epochCounter++;
}
else{
thehtml = "";
AB.msg ( thehtml, 4 );
}
if(do_testing){
let percent = testAllDoodles(testing,nn);
thehtml = " <br> Percentage : " + greenspan + nf(percent, 2, 2) + "%" + "</span>" ;
AB.msg ( thehtml, 3 );
}
else{
thehtml = " " ;
AB.msg ( thehtml, 3 );
// console.log('Testing stopped');
}
if ( doodle_exists )
{
drawDoodle();
}
// 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 + 50; // 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);
}
}
}
//--- 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+70, 0, PIXELS, PIXELS ); // shrunk
}
function guessDoodle()
{
// doodle is createGraphics not createImage
let img = doodle.get();
//console.log('img inside guessDoodle: ');
//console.log(img);
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
if(doodle_exists){
let prediction = nn.predict(inputs); // array of outputs
let doodle_guess = find_doodle_labels(prediction); // get no.1 and no.2 guesses
thehtml = " <br> It looks like you drew a : " + greenspan + doodle_guess[0] + "</span>" ;
AB.msg ( thehtml, 2 );
}
else{
thehtml = " <br> Please draw something !!" ;
AB.msg ( thehtml, 2 );
}
}
function wipeDoodle()
{
doodle_exists = false;
doodle.background('black');
thehtml = "" ;
AB.msg ( thehtml, 2 );
}
// --- 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);
}
*/