// Cloned by Akash Gupta 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;
var classifier;
//--- 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 = 1;
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 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 );
// // 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: ---------------------------------------------------------
var pixelBrain;
function finishedTraining() {
console.log('training complete');
classifyImage();
}
function classifyImage(){
let inputImage = {
image: getImage(mnist.train_images[100]).pixels,
}
pixelBrain.classify(inputImage, gotResults);
}
function gotResults(error, results){
console.log("Inside Got results");
if(results == null){
console.log("results is null");
}
if (error) {
console.log("ERROR " + error.message);
return;
}
//label = results[0].label;
}
// function keyPressed() {
// if (key == 't') {
// //pixelBrain.normalizeData();
// pixelBrain.train({epochs: 5},finishedTraining);
// }
// else {
// addExample();
// }
// }
function addExample(){
console.log("In add Example");
for(var i=0;i<100;i++){
var img = getImage(mnist.train_images[i]);
var label = mnist.train_labels[i];
train_index++;
let inputLabel = {
image: img,
};
let target = {
label,
};
//console.log(img);
//console.log(label);
pixelBrain.addData(inputLabel, target);
}
}
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();
let options = {
task: 'imageClassification',
inputs: [28, 28, 4],
debug: true
};
$.getScript("/uploads/akash037/ml5.min.js", function()
{
console.log("Load Success");
$.getScript ( "/uploads/codingtrain/matrix.js", function()
{
$.getScript ( "/uploads/codingtrain/nn.js", function()
{
$.getScript ("/uploads/codingtrain/mnist.js", function()
{
console.log ("All JS loaded");
pixelBrain = ml5.neuralNetwork(options);
nn = new NeuralNetwork(noinput, nohidden, nooutput);
nn.setLearningRate ( learningrate );
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 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 arrayToImage(img){
var width = 28;
var height = 28;
var buffer = new Uint8ClampedArray(width * height * 4); // have enough bytes
for(var y = 0; y < height; y++) {
for(var x = 0; x < width; x++) {
var pos = (y * width + x) * 4; // position in buffer based on x and y
buffer[pos ] = 45; // some R value [0, 255]
buffer[pos+1] = 23; // some G value
buffer[pos+2] = 155; // some B value
buffer[pos+3] = 255; // set alpha channel
}
}
return buffer;
}
function oneDimensionTo2D(arr, size){
var matrix = [];
for (var i = 0; i < size; i++) {
matrix[i] = [];
for (var j = 0; j < size; j++) {
matrix[i][j] = arr[(i * size + j) * 4];
}
}
return matrix;
}
// GetImage Data
function getImageData(img) {
var imageData = {
"data": getImage(img).pixels
}
return imageData;
}
function trainit (show) // train the network with a single exemplar, from global var "train_index", show visual on or off
{
for(var i = 0; i <= 50; i++) {
let img = mnist.train_images[i];
let label = mnist.train_labels[i];
let inputImage = {
image: getImage(img),
};
let target = {
label,
};
//console.log(getImage(img));
pixelBrain.addData(inputImage, target);
}
//pixelBrain.normalizeData();
pixelBrain.train({epochs:2}, finishedTraining);
//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];
//classifyImage(getImage(getInputs(img)));
// if(result == label)
// total_correct++;
// 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) // new no1
{
// old no1 becomes no2
no2 = no1;
no2value = no1value;
// now put in the new no1
no1 = i;
no1value = a[i];
}
else if (a[i] > no2value) // new no2
{
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:
// Call train only once
var val = true;
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 && val)
{
//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);
}
val = 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();
//console.log("Insider guess doodle function")
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;
}*/
//classifyImage(getImage(getInputs(img)));
/*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 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);
}