// Cloned by Sunil Jagtap on 19 Nov 2022 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 ---------------------------------------
AB.runloggedin; // Boolean. Are we running logged in.
AB.myuserid;
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 = 124800;
const NOTEST = 20800;
const ALPHABETS = ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z"];
//--- 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 = 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
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 trainer;
let mycnnTrain, mycnnModel;
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 ( -1, 1 ) );
// 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: ---------------------------------------------------------
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
wipeDoodle();
AB.loadingScreen();
$.getScript ( "/uploads/codingtrain/matrix.js", function()
{
$.getScript ( "/uploads/jagtaps2/convnet-min.js", function()
{
console.log ("All JS loaded");
mycnnModel = new convnetjs.Net()
AB.queryDataExists ( function ( exists ) // asynchronous - need callback function
{ var json="";
if ( exists ){
console.log ( "MH --- restoring data");
AB.restoreData ( function (json_obj )
{
json=JSON.parse(json_obj);
mycnnModel.fromJSON(json);
});
}
else{
var layerDefs = [];
// input layer of size 24x24x1
layerDefs.push({type : "input", out_sx : 24, out_sy : 24, out_depth : 1});
// 5x5 filters will be convolved with the input with tanh activation funtion
layerDefs.push({type : "conv", sx : 5, filters : 16, stride : 1, pad : 2, activation : "relu"});
// perform max pooling in 2x2 non-overlapping neighborhoods
layerDefs.push({type : "pool", sx : 2, stride : 2});
// 5x5 filters will be convolved with the input with relu activation funtion
layerDefs.push({type : "conv", sx : 5, filters : 16, stride : 1, pad : 2, activation : "tanh"});
layerDefs.push({type : "pool", sx : 3, stride : 3});
// a softmax classifier predicting probabilities for 26 classes: which is alphabets
layerDefs.push({type : "softmax", num_classes : 26});
//layerDefs.push({type:'lrn', k:1, n:26, alpha:0.1, beta:0.75});
mycnnModel.makeLayers(layerDefs);
//Converting to model to json
json = mycnnModel.toJSON();
console.log ( "MH --- saving data");
//saving the model, please make sure you need to login and the program is running under the session
AB.saveData(JSON.stringify(json));
}
trainer = new convnetjs.SGDTrainer(mycnnModel, {method : "adadelta", momentum : .9, batch_size : 20, l2_decay : 0.001 });
});
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:");
for (var i=0; i < NOTRAIN; i++) {
rotateImageBy90(mnist.train_images[i]);
}
for(i=0; i < NOTEST; i++) {
rotateImageBy90(mnist.test_images[i]);
}
AB.removeLoading(); // if no loading screen exists, this does nothing
});
}
function rotateImageBy90(img) {
for (var a = 0; a < PIXELS; a++) {
for (var b = a; b < PIXELS; b++) {
var key = a * PIXELS + b;
var s = b * PIXELS + a;
var val = img[key];
img[key] = img[s];
img[s] = val;
}
}
}
function get_activation_input(obj) {
// 24x24x1 volume of input activations
var e = new convnetjs.Vol(24, 24, 1);
var i = 0;
for (; i < PIXELSSQUARED; i++) {
e.w[i] = obj[i];
}
return e;
}
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
// console.log(train_index);
// console.log(inputs);
// console.log(targets);
train_inputs = get_activation_input(inputs); // can inspect in console
trainer.train ( train_inputs, label );
thehtml = " trainrun: " + trainrun + "<br> no: " + train_index ;
AB.msg ( thehtml, 4 );
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 = get_activation_input(inputs); // can inspect in console
let prediction = mycnnModel.forward(test_inputs).w // 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:
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');
strokeWeight(1);
stroke("yellow");
rect(0, 0, ZOOMPIXELS, ZOOMPIXELS);
textSize(10);
textAlign(CENTER);
text("You can draw DOODLE here", ZOOMPIXELS / 2, ZOOMPIXELS / 2)
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 labelNo = mnist.test_labels[i];
thehtml = "Test image no: " + i + "<br>" +
"Classification: " + ALPHABETS[labelNo-1] + "<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 = mycnnModel.forward(demo_inputs).w; // array of outputs
let guess = findMax(prediction); // the top output
thehtml = " We classify it as: " + greenspan + ALPHABETS[guess-1] + "</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 = get_activation_input(inputs); // can inspect in console
// feed forward to make prediction
let prediction = mycnnModel.forward(doodle_inputs).w; // array of outputs
let b = find12(prediction); // get no.1 and no.2 guesses
thehtml = " We classify it as: " + greenspan + ALPHABETS[b[0]-1] + "</span> <br>" +
" No.2 guess is: " + greenspan + ALPHABETS[b[1]-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);
}
function loadMNIST(callback) {
let mnist = {};
let files = {
train_images: '/uploads/dheera0704/emnist-letters-train-images-idx3-ubyte.bin',
train_labels: '/uploads/jagtaps2/emnist-letters-train-labels-idx1-ubyte.bin',
test_images: '/uploads/jagtaps2/emnist-letters-test-images-idx3-ubyte.bin',
test_labels: '/uploads/jagtaps2/emnist-letters-test-labels-idx1-ubyte.bin',
};
return Promise.all(Object.keys(files).map(async file => {
mnist[file] = await loadFile(files[file]);
})).then(() => callback(mnist));
}
async function loadFile(file) {
let buffer = await fetch(file).then(r => r.arrayBuffer());
let headerCount = 4;
let headerView = new DataView(buffer, 0, 4 * headerCount);
let headers = new Array(headerCount).fill().map((_, i) => headerView.getUint32(4 * i, false));
// Get file type from the magic number
let type, dataLength;
if(headers[0] == 2049) {
type = 'label';
dataLength = 1;
headerCount = 2;
} else if(headers[0] == 2051) {
type = 'image';
dataLength = headers[2] * headers[3];
} else {
throw new Error("Unknown file type " + headers[0])
}
let data = new Uint8Array(buffer, headerCount * 4);
if(type == 'image') {
dataArr = [];
for(let i = 0; i < headers[1]; i++) {
dataArr.push(data.subarray(dataLength * i, dataLength * (i + 1)));
}
return dataArr;
}
return data;
}