// Cloned by J Faj on 26 Nov 2019 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 = 128;
const nooutput = 10;
const learningrate = 0.2; // 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
//**********************************************************************************
//Changed by Justin
//Changed the width and height of the canvas to landscape
//const canvaswidth = ( PIXELS + ZOOMPIXELS ) + 50;
//const canvasheight = ( ZOOMPIXELS * 3 ) + 100;
const canvasheight = ( PIXELS + ZOOMPIXELS );
const canvaswidth = ( ZOOMPIXELS * 3 ) + 150;
//*********************************************************************************
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?
//**************************************************************
// Added by Justin - new global variables
let topTwo = [];
let correct = 0;
let incorrect = 0;
var numberlist;
let trackScore = [];
let learningDecay = [0.2, 0.1, 0.05, 0.01];
let learnIndex = 0;
const augmentImage = true;
const moveTop = true;
const moveLeft = false;
//**************************************************************
// 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
}
// CSS trick
// make run header bigger
$("#runheaderbox").css ( { "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()
{
AB.headerRHS();
createCanvas ( canvaswidth, canvasheight );
//Import charting to use for displaying a graph of the accuracy over time
$.getScript('https://cdn.jsdelivr.net/npm/chart.js@2.9.3/dist/Chart.min.js', function () {
alert('Load was performed');
});
AB.newDiv ( "doodle" );
$("#doodle").css({
"padding-top": "200",
"padding-left": "10",
"padding-bottom": "10",
"float":"left",
});
$("#doodle").html( "<h1>Doodle</h1> " +
"<button onclick='wipeDoodle();' class='normbutton' >Clear doodle</button> <br></p>" +
"<p>Draw you doodle above.<br>" +
"Select correct answer below.<br>" +
"Classify:<span id='numberlist'></span>    " +
"<button onclick='updateDoodlePercent();' class='normbutton' >OK</button>" +
"<div id='doodleData'></div><br>" +
"<div id='doodlePercent'></div><br>");
AB.newDiv ( "demo" );
$("#demo").css({
"padding-top": "200",
"padding-left": "40",
"padding-bottom": "10",
"float":"left",
});
$("#demo").html( "<h1>Demo</h1> " +
"<button onclick='makeDemo();' class='normbutton' >Demo test image</button> <br></p>" +
"<p>Test image magnified (left) <br>" + "and original (right)<br>" +
" The network is <i>not</i> trained on any " + "<br> of these images. <br> " +
"<div id='demoData'></div>");
AB.newDiv ( "train" );
$("#train").css({
"padding-top": "200",
"padding-left": "20",
"padding-bottom": "10",
"float":"left"
//"border-left": "thick solid black"
});
$("#train").html( "<h1> Training</h1> " +
"<button onclick='do_training = false;' class='normbutton' >Stop training</button></p>" +
"<p>Training image magnified (left) <br>" + "and original (right)<br>" +
"<div id='trainData'></div><br>" +
"<div id='testData'></div>");
AB.newDiv ( "chart" );
$("#chart").css({
"padding-top": "1",
"padding-left": "1",
"padding-bottom": "1",
"float":"left"
//"border-left": "thick solid black"
});
// Create dropdown
numberlist = createSelect();
numberlist.parent('numberlist');
// Add all the numbers
for (var i = 0; i < 10; i++)
numberlist.option(i);
console.log("NUMBER LIST VALUE" + numberlist.value());
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/jfajou/nn.js", function() // Updated by Justin. Copied my own version to "Uploads"
{
$.getScript ( "/uploads/codingtrain/mnist.js", function()
{
console.log ("All JS loaded");
nn = new NeuralNetwork( noinput, nohidden, nooutput );
nn.setLearningRate ( learningrate );
//nn.setActivationFunction(relu);
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
}
if (augmentImage == true)
moveImage (inputs);
return ( inputs );
}
//************************************************
//Added by Justin
function moveImage (inputs)
{
//Put the image into a 2D array of 28*28
let array2D = [];
var i = 0;
for (l = inputs.length + 1; (i + PIXELS) < l; i += PIXELS) {
array2D.push(inputs.slice(i, i + PIXELS));
}
//Move all "black" rows from the top to the bottom
//If the sum of a row is zero, then move it to the bottom
if (moveTop == true)
{
var sum= array2D.map( function(row){
return row.reduce(function(a,b){ return a + b; }, 0);
});
let zeroArr
let tempArr
for (let i = 0; i < sum.length; i++)
{
if (sum[i] == 0)
{
zeroArr = array2D.slice(0,1);
tempArr = array2D.slice(1);
array2D = tempArr.concat(zeroArr);
}
else
break;
}
}
//Now sum the columns
if (moveLeft == true)
{
var sum2= array2D.map(function(row, i) {
return array2D.map(function(row) {
return row[i]; }
).reduce(function(a, b) { return a+b; }, 0);
});
let zeroCol;
for (let i=0; i < sum2.length; i++)
{
if (sum2[i] == 0)
{
zeroCol = removeCol(array2D, 0);
//Now add the column to the end
for (let j=0; j < array2D.length; j++)
array2D[j].push(0);
}
else
break;
}
}
//console.log(sum2);
//console.log(array2D);
//noLoop();
//Flatten out the 2D array to send to the neural network
inputs = array2D.flat();
}
function removeCol(arr2d, colIndex)
{
for (var i = 0; i < arr2d.length; i++) {
var row = arr2d[i];
row.splice(colIndex, 1);
}
return arr2d;
}
//End Update - Justin
//*******************************************************
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, 0, ZOOMPIXELS, ZOOMPIXELS ); // magnified
//image ( theimage, ZOOMPIXELS, 0, PIXELS, PIXELS ); // original
//***********************************************************************************
//Changed by Justin
//Updated where the training images appear on the canvas
image ( theimage, (ZOOMPIXELS * 2) + 120, 0, ZOOMPIXELS, ZOOMPIXELS ); // magnified
image ( theimage, (ZOOMPIXELS * 3) + 120, 0, 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
train_inputs = inputs; // can inspect in console
nn.train ( inputs, targets );
thehtml = " trainrun: " + trainrun + "<br> no: " + train_index ;
//AB.msg ( thehtml, 4 );
$("#trainData").html(thehtml);
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];
// 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 );
$("#testData").html("<h4> Hidden tests </h4>" + thehtml);
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;
}
return percent.toFixed(2)
}
//--- 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)
{
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');
//*****************************************************************************
//Added by Justin - Start
//Divide the areas properly
strokeWeight(10);
stroke('white');
line(ZOOMPIXELS+50,0,ZOOMPIXELS + 50,ZOOMPIXELS + 50) ;
line(ZOOMPIXELS+ZOOMPIXELS+110,0,ZOOMPIXELS + ZOOMPIXELS + 110,ZOOMPIXELS + 50) ;
//Call the Chart function
//chartTraining();
//Added by Justin - End
//*****************************************************************************
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++)
var percent = testit();
}
//****************************************************************
// Added by Justin
//This was to help me track the training accuracy over time
if (train_index % 5000 < TRAINPERSTEP)
{
trackScore.push(percent);
}
if (train_index % 30000 < TRAINPERSTEP)
{
learnIndex += 1;
if (learnIndex <= 3)
{
console.log("New learning rate " + learningDecay[learnIndex]);
nn.setLearningRate ( learningDecay[learnIndex] );
}
}
//****************************************************************
// 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) )
{
//console.log ( mouseX + " " + mouseY + " " + pmouseX + " " + pmouseY );
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);
}
}
}
/*
//Display a graph of the training rate to visually show the results for 2-3 epochs
function chartTraining(){
$("#chart").html( "<canvas id='graph' width='200' height='200'></canvas> ");
const ctx = document.getElementById('graph').getcontext
}
*/
//--- 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 );
$("#demoData").html(thehtml);
// type "demo" in console to see raw data
}
function drawDemo()
{
var theimage = getImage ( demo );
// console.log (theimage);
//****************************************************************
//Changed by Justin
//Updated where the Demo image is displayed on the canvas
image ( theimage, ZOOMPIXELS + 60, 0, ZOOMPIXELS, ZOOMPIXELS ); // magnified
image ( theimage, ZOOMPIXELS + ZOOMPIXELS + 60, 0, 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>" ;
//Commented out by Justin
//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+10, 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;
//console.log ("Pixel is " + inputs[i]);
}
//Call this to move the Doodle to the top, to align with the training set
moveImage (inputs);
doodle_inputs = inputs; // can inspect in console
// feed forward to make prediction
let prediction = nn.predict(inputs); // array of outputs
topTwo = find12(prediction); // get no.1 and no.2 guesses
thehtml = " We classify it as: " + greenspan + topTwo[0] + "</span> <br>" +
" No.2 guess is: " + greenspan + topTwo[1] + "</span>";
//AB.msg ( thehtml, 2 );
$("#doodleData").html(thehtml);
}
function wipeDoodle()
{
doodle_exists = false;
doodle.background('black');
}
function updateDoodlePercent()
{
if (doodle_exists == false) return;
if (numberlist.value() == topTwo[0])
correct += 1;
else
incorrect += 1;
let percent = 0;
percent = (correct / (incorrect + correct)) * 100 ;
thehtml = "Correct: " + correct + "<br>Incorrect: " + incorrect + "<br>" +
"Score is: " + greenspan + percent.toFixed(2) + "</span>";
$("#doodlePercent").html(thehtml);
wipeDoodle();
}
// --- 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);
}