// Cloned by Paul Geoghegan on 13 Dec 2020 from World "Smart Doodle & Char Recognition Neural Network " by Paul Geoghegan
// Please leave this clone trail here.
// Cloned by Paul Geoghegan on 8 Dec 2020 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 --------------------------------------------------
// MYCODE: Variable for doodle and demo correct perecentage statistics
let numDoodleDraw =0;
let doodleCorrectCnt = 0;
let doodlePercentage =0;
let numDemotestCnt =0;
let demoCorrectCnt = 0;
let demoPercentage =0;
const max_doodles = 20; // const setting max saved & restored doodles to 20
var saved_doodles; // variable to store a single doodle 28x28 pixel image
var saved_d_array = []; // variable to store array of saved doodle 28x28 pixel image
var doodle_test_cntr =0;
var test_Saved_Dcount =0;
var Saved_Doodle_Ctr = -1;
var draw_saved_Doodles = false;
let stride =7;
// no of nodes in network
const noinput = PIXELSSQUARED;
const nohidden = 64;
// MYCODE : reduce no hidden nodes to 1 and 5
//const nohidden = 1; // 20% accuracy reached with 4 training runs
//const nohidden = 5; // 73% accuracy reached with 4 training runs
const nooutput = 10;
// MYCODE : reduce learning rate to 0.03 (30% of default)
//const learningrate = 0.03; // default 0.1
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
// MYCODE: Reduce ZOOMFACTOR TO 3
//const ZOOMFACTOR = 3;
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 * 4 ) + 30;
//const DOODLE_THICK = 30; // MYCODE : increase thickness of doodle line to 30
//const DOODLE_THICK = 9; // MYCODE : reduce thickness of doodle line to 9
//const DOODLE_BLUR = 7; // MYCODE : increase blur factor to 7 applied to doodles
const DOODLE_THICK = 18; // thickness of doodle lines
const DOODLE_BLUR = 9; // 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
}
// MYCODE : Change width of run header to allow for doddle correct incorect buttons
// CSS trick
// make run header bigger // MYCODE : and run headerwider
$("#runheaderbox").css ( { "max-height": "95vh", "max-width": "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;
// MYCODE : Change Doodle header colour to Purple and add two Doodle Correct /Incorrect User interface Buttons
if ( AB.onDesktop() )
{
if ( AB.runloggedin )
{
// 1 Doodle header
thehtml = "<hr> <h1> <span style= color:purple> 1. Doodle (User Input) </span></h1> Top row: Doodle (left) and shrunk (right). " +
" <span style='font-weight:bold; color:purple'>Draw your doodle (using mouse)</span> in top LHS. <br><br>" +
" <button onclick='wipeDoodle();' class='normbutton' > Clear doodle</button> "
// MYCODE: add record doodle button and restore saved doodles button
+ "<button onclick='recordDoodle();' class='normbutton' > Record doodle</button> "+
"<button onclick='deleteRecordDoodle();' class='normbutton' > Delete Recorded doodle(s)</button> " +
" <button onclick='saveData();' class='normbutton' >Save Recorded doodles</button> <br> <br>" +
" <button onclick='restoreDoodleData();' class='normbutton' >Restore Saved doodles</button> "+
"<button onclick='draw_Rec_doodles();' class='normbutton' >Draw Recorded doodles</button> " +
"<button onclick='test_Rec_doodles();' class='normbutton' >Test Recorded doodles</button><br> <br>" +
// MYCODE : Add two Doodle Correct /Incorrect User interface Buttons
" Click if doodle is: " +
" <button onclick='doodleStats(true);' class='normbutton' > Correct</button> <button onclick='doodleStats(false);'"+
" class='normbutton' > Incorrect</button> <br>";
AB.msg ( thehtml, 1 );
} // end of if AB.runloggedin
}
else
{
// 1 Doodle header
thehtml = "<hr> <h1> <span style= color:purple> 1. Doodle (User Input) </span></h1> Top row: Doodle (left) and shrunk (right). " +
" <span style='font-weight:bold; color:purple'>Draw your doodle (using mouse)</span> in top LHS.<br>" +
" <button onclick='wipeDoodle();' class='normbutton' > Clear doodle</button> "
// MYCODE: add record doodle button
+ "<button onclick='recordDoodle();' class='normbutton' > Record doodle</button> <br> " +
// MYCODE : Add two Doodle Correct /Incorrect User interface Buttons
" Click if doodle is: " +
" <button onclick='doodleStats(true);' class='normbutton' > Correct</button> <button onclick='doodleStats(false);'"+
" class='normbutton' > Incorrect</button> <br>";
AB.msg ( thehtml, 1 );
} // end of else
// 2 Doodle variable data (guess)
// 3 Training header
thehtml = "<hr> <h1> <span style= color:green>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
// MYCODE : Change Deemo header colour to Purple and add correct /Incorrect User interface Buttons
// 7 Demo header
thehtml = "<hr> <h1><span style= color:purple> 3. Demo (User Input) </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>"+
" <span style='font-weight:bold; color:purple'> Click if demo is:</span> <button onclick='demoStats(true);'"+
" class='normbutton' > Correct</button> <button onclick='demoStats(false);'"+
" class='normbutton' > Incorrect</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: ---------------------------------------------------------
// MYCODE : ============== Deleating Saving and Restoring Doodle data to/from AB world =================
function deleteRecordDoodle()
{
while (saved_d_array.length >0)
{
saved_d_array.shift();
}
console.log (saved_d_array) // check content of deleted array
}
function saveData() // save saved_d_array to server
{
console.log ( "Saving " + saved_d_array.length + " doodles to server" );
AB.saveData ( saved_d_array);
}
function restoreDoodleData()
{
AB.queryDataExists ( function ( exists ) // asynchronous - need callback function
{
AB.restoreData ( function ( a )
{
// object returned from server is an array of dooodles
saved_d_array = a;
console.log ( "Restoring " + a.length + " doodles from server" );
console.log (saved_d_array) // check content of restored array data
});
}); // end of AB.queryDataExists code blk
}
// MYCODE : Testing and Drawing Recorded Doodle set (max 20)
// MYCODE : to display Correct Doodle Guess Percentage Statistics =====
function doodleStats(arg) // boolean arg
{
// print out Percentage stats on NN system correct doodle guess
numDoodleDraw ++;
if (arg) doodleCorrectCnt ++;
doodlePercentage = (doodleCorrectCnt*100)/numDoodleDraw;
return;
}
// MYCODE : to display Correct Demo Guess Percentage Statistics =====
function demoStats(arg) // boolean arg
{
// print out Percentage stats on NN system correct demo guess
numDemotestCnt ++;
if (arg) demoCorrectCnt ++;
demoPercentage = (demoCorrectCnt*100)/numDemotestCnt;
return;
}
// MYCODE : Reording doodles function for AB Save and Restore for repeat testing
function draw_Rec_doodles() // MyCODE: Trigger the drawing of recorded doodles inside Draw function
{
draw_saved_Doodles = true;
Saved_Doodle_Ctr ++
}
// MYCODE : For drwawing images from saved doodles array =====
function drawSaveddoodles()
{
if (Saved_Doodle_Ctr < saved_d_array.length)
{
let theimage = saved_d_array [Saved_Doodle_Ctr] ;
console.log (theimage);
// MYCODE: Draw saved doodle images is the same canvas space as demo draw
image ( theimage, 0, canvasheight - ZOOMPIXELS, ZOOMPIXELS, ZOOMPIXELS ); // magnified
image ( theimage, ZOOMPIXELS+50, canvasheight - ZOOMPIXELS, PIXELS, PIXELS ); // original
}
}
function recordDoodle()
{
if (doodle_exists)
{
let theimage = doodle.get();
console.log ("record doodle image" + theimage);
if (saved_d_array.length< max_doodles) // max saved & restored doodles to 20
{
saved_d_array.push(theimage); // variable to store array of saved doodles
console.log(saved_d_array.length);
console.log(saved_d_array);
}
}// end of if doodle_exists if statement
return;
}
function test_Rec_doodles()
{
let img = saved_d_array [doodle_test_cntr] ;
console.log (img);
doodle_test_cntr ++; // increment Saved doodle Array index
console.log (doodle_test_cntr);
if (doodle_test_cntr > saved_d_array.length -1) doodle_test_cntr =0; // reset Saved doodle Array index
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;
}
let prediction = nn.predict(inputs); // array of outputs
let b = find12(prediction); // get no.1 and no.2 guesses
// MYCODE : change Doodle display to output percentage statistics
thehtml = " We classify it as: " + greenspan + b[0] + "</span> <br>" +
" No.2 guess is: " + greenspan + b[1] + "</span> <br>" + " Correct Guess: "
+ greenspan + doodlePercentage.toFixed(2) + "% </span>" +
" "+" Num of Doodles: " + greenspan+ numDoodleDraw;
AB.msg ( thehtml, 2 );
}
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();
$.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();
});
});
});
}
// 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 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 trainit END
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>" +
// MYCODE : addition of "%" symbol to score result for clarity
" 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)
{
no2value = no1value // MYCODE : suggested change to code to correct glitch
no1 = i;
no1value = a[i];
}
else if (a[i] > no2value)
{
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');
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();
}
if (draw_saved_Doodles) // If user have clicked on Test Saved Doodles button
{
drawSaveddoodles();
}
// 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);
}
}
} // MYCODE : End of Draw function bracket marker
//--- 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 ("draw demo image" + 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
// MYCODE : change Demo display to output percentage statistics
thehtml = " We classify it as: " + greenspan + guess + "</span>" + " Correct Guess: "
+ greenspan + demoPercentage.toFixed(2) + "% </span>" +
" "+" Num of Demo tests: " + greenspan+ numDemotestCnt;
AB.msg ( thehtml, 10 );
}
//--- doodle -------------------------------------------------------------
function drawDoodle()
{
// doodle is createGraphics not createImage
let theimage = doodle.get();
console.log (" the doodle image " +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("the size of the doodle is "+ img)
// MYCODE : Applying Pooling with a stride factor of 7 (196x196 => 28x28)
/// MYCODE: This code is partly ported from:
//https://github.com/CodingTrain/website/tree/main/learning/ml5/8.3-cnn-2/P5 -Author Daniel Shiffman
// stride = 7 defined as a global variable
let pooled;
pooled = createImage(196/stride, 196 / stride); // a poled image of (196 /7): 28 x 28
img.loadPixels();
// Pooling
pooled.loadPixels();
for (let x = 0; x < 196- 1; x += stride) {
for (let y = 0; y < 196 - 1; y += stride) {
let rgb = pooling(img, x, y);
let px = x / stride;
let py = y / stride;
let pix = index(px, py, pooled);
pooled.pixels[pix + 0] = rgb.r;
pooled.pixels[pix + 1] = rgb.g;
pooled.pixels[pix + 2] = rgb.b;
pooled.pixels[pix + 3] = 255;
}
}
pooled.updatePixels();
function pooling(img, x, y) {
let brightR = -Infinity;
let brightG = -Infinity;
let brightB = -Infinity;
for (let i = 0; i < stride; i++) {
for (let j = 0; j < stride; j++) {
let pix = index(x + i, y + j, img);
let r = img.pixels[pix + 0];
let g = img.pixels[pix + 1];
let b = img.pixels[pix + 2];
brightR = max(brightR, r);
brightG = max(brightG, g);
brightB = max(brightB, b);
}
}
return {
r: brightR,
g: brightG,
b: brightB
};
}
function index(x, y, img) {
return (x + y * img.width) * 4;
}
///===============
pooled.resize ( PIXELS, PIXELS ); // MYCODE : May not be meeded as poole image should already be 28 x 28 pixels
pooled.loadPixels();
// set up inputs
let inputs = [];
for (let i = 0; i < PIXELSSQUARED ; i++)
{
inputs[i] = pooled.pixels[i * 4] / 255;
}
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
// MYCODE : change Doodle display to output percentage statistics
thehtml = " We classify it as: " + greenspan + b[0] + "</span> <br>" +
" No.2 guess is: " + greenspan + b[1] + "</span> <br>" + " Correct Guess: "
+ greenspan + doodlePercentage.toFixed(2) + "% </span>" +
" "+" Num of Doodles: " + greenspan+ numDoodleDraw;
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);
}