// Cloned by Dheera on 16 Nov 2021 from World "Character recognition neural network" by "Coding Train" project
// Please leave this clone trail here.
// Ported the CNN implementation in javascript from:
//https://github.com/karpathy/convnetjs
//Ported the EMNIST data from here:
//https://github.com/acl21/Alphabet_Recognition_Gestures/tree/master/data
// 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 = 124800;
const NOTEST = 20800;
//--- 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 = 15;
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
//Dheera says - my global variables mycnn, mycnnTrain, mycnnModel and alphabets defined below:
let mycnn;
let mycnnTrain;
let mycnnModel;
// Dheera says - alphabets array defined for the possible outputs in charater recognition.
let 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"];
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
//Dheera says - Renamed the button to clear and redraw the doodle.
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 and Re-draw</button> <br> " ;
AB.msg ( thehtml, 1 );
// 2 Doodle variable data (guess)
// 3 Training header
// Dheera says - Added buttons to pause and re-start training.
thehtml = "<hr> <h1> 2. Training </h1> Middle row: Training image magnified (left) and original (right). <br> " +
" <button onclick='do_training = false;' class='normbutton' >Pause training</button> " +
" <button onclick='do_training = true;' class='normbutton' >Resume 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 bluespan = "<span style='font-weight:bold; font-size:large; color:darkblue'> " ;
//--- end of AB.msgs structure: ---------------------------------------------------------
function setup()
{
createCanvas ( canvaswidth, canvasheight );
doodle = createGraphics ( ZOOMPIXELS, ZOOMPIXELS ); // doodle on larger canvas
doodle.pixelDensity(1);
//Dheera says - wiped the screen before drawing the doodle so that it drwas on black screen.
wipeDoodle();
// JS load other JS
// maybe have a loading screen while loading the JS and the data set
AB.loadingScreen();
$.getScript ( "/uploads/codingtrain/matrix.js", function()
{
//Dheera says - uploaded the plug-n-play library of convnetJS here imported from https://github.com/karpathy/convnetjs
$.getScript ( "/uploads/dheera0704/convnet3.js", function()
{
//Dheera says - fetching the EMNIST files from yhe links specified in this file.
$.getScript ( "/uploads/dheera0704/mnistDhe.js", function()
{
console.log ("All JS Files loaded");
//Dheera says - Defining the CNN layers below
let layer_defs = [];
//Dheera says - This is the first input layer. The input is a 28x28 Character image in 1 dimention.
layer_defs.push({
type : "input",
out_sx : 28,
out_sy : 28,
out_depth : 1
});
//Dheera says - 1st convulution layer with 8 filters of filter size 5x5, with stride =1 and padding = 2 and "relu" activation.
layer_defs.push({
type : "conv",
sx : 5,
filters : 8,
stride : 1,
pad : 2,
activation : "relu"
});
//Dheera says - 1st pooling layer of the network of size 2x2
layer_defs.push({
type : "pool",
sx : 2,
stride : 2
});
//Dheera says - 2nd convolution layer with with 16 filters and size 5x5, with stride = 1 and padding = 2 and "relu" activation.
layer_defs.push({
type : "conv",
sx : 5,
filters : 16,
stride : 1,
pad : 2,
activation : "relu"
});
//Dheera says - 2nd pooling layer of size 3x3
layer_defs.push({
type : "pool",
sx : 3,
stride : 3
});
//Dheera says - last layer which is also called the loss layer.
//Here softmax activation has been chosen since there are more than 2 labels in output.
layer_defs.push({
type : "softmax",
num_classes : 26
});
//Dheera says - creating an empty network called mycnnModel here
mycnnModel = new convnetjs.Net();
//Dheera says - Defining the layers of my model according to the layer_defs defined above.
mycnnModel.makeLayers(layer_defs);
//Dheera says - Taken the SGD trainer model here with momentum 0.9 and batch_size of 10. L2_decay has been kept small because the size of training set is not very small.
mycnnTrain = new convnetjs.SGDTrainer(mycnnModel, {method : "adadelta", momentum: 0.9, batch_size : 10, l2_decay : .001});
loadData();
});
});
});
}
// load data set from local file (on this server)
function loadData()
{
loadMNIST (function(data){
mnist = data;
//Dheera says - Rotating the training and testing dataset images as they are sizeways.
let loc = 0;
for (; loc < NOTRAIN; loc++) {
rotateImage(mnist.train_images[loc]);
}
for (loc = 0; loc < NOTEST; loc++) {
rotateImage(mnist.test_images[loc]);
}
console.log ("All data loaded into Emnist 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 );
}
//Dheera says - new function defined to rotate the images
function rotateImage(img) {
for (let e = 0; e < PIXELS; e++) {
for (let f = e; f < PIXELS; f++) {
let a = e * PIXELS + f;
let b = f * PIXELS + e;
let c = img[a];
img[a] = img[b];
img[b] = c;
}
}
}
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
train_inputs = inputs;
{
//Dheera says - calling function getmycnnInputs below to create a volume of inputs activations required for the trainer.
let set = getmycnnInputs(inputs);
//Dheera says - trainer is passed the volume of exemplars from training images plus their labels.
mycnnTrain.train(set, label);
}
// can inspect in console
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++;
}
}
//Dheera says - New function defined to create a volume of input activations of size 28x28X1 initialized by zeros.
function getmycnnInputs(t) {
var x = new convnetjs.Vol(28, 28, 1, 0);
for (var j = 0; j < PIXELSSQUARED; j++) {
x.w[j] = t[j];
}
return x;
}
function testit() // test the network with a single exemplar, from global var "test_index"
{
let id = mnist.test_images[test_index];
let label = mnist.test_labels[test_index];
// set up the inputs
let inputs = getInputs ( id );
//Dheera says - getting inputs of test image and finding the value with the maximum weight for prediction.
let myinput = getmycnnInputs(inputs);
test_inputs = inputs; // can inspect in console
let place = findMax(mycnnModel.forward(myinput).w);
var img = getImage(id);
image(img, 0, ZOOMPIXELS + 50, ZOOMPIXELS, ZOOMPIXELS);
image(img, ZOOMPIXELS + 50, ZOOMPIXELS + 50, PIXELS, PIXELS);
total_tests++;
if (place == label) total_correct++;
let percent = (total_correct / total_tests) * 100 ;
thehtml = " testrun: " + testrun + "<br> no: " + total_tests + " <br> " +
" correct: " + total_correct + "<br>" +
" score: " + bluespan + 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;
background ('black');
strokeWeight(1);
stroke('green');
rect(0,0,ZOOMPIXELS,ZOOMPIXELS); //Dheera says - draws a rectangle with source 0,0 and length and bredth defined by Zoompixels variable.
textSize(10);
textAlign(CENTER);
text("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('red');
strokeJoin(ROUND);
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: " + alphabets[label - 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
//Dheera says - fetching the imput and finding the one with mx weight for demo prediction.
let myinput = getmycnnInputs(inputs);
let j = findMax(mycnnModel.forward(myinput).w);
thehtml = " We classify it as: " + bluespan + alphabets[j-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+20, 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
//Dheera says- fetching the inputs and displaying the 1st two predictions for the doodle.
let myinput = getmycnnInputs(inputs);
let a = find12(mycnnModel.forward(myinput).w);
thehtml = " Our 1st Guess is: " + bluespan + alphabets[a[0] - 1] + "</span> <br>" +
" Our 2nd Guess is: " + bluespan + alphabets[a[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);
}