// Cloned by Tony Forde on 26 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;
//--- can modify all these --------------------------------------------------
// No. of nodes in network
const noinput = PIXELSSQUARED;
const nohidden = 64;
const nooutput = 10;
// Learning rate:
const learningrate = 0.2; // default 0.1
// Weoght range:
const minWeight = -0.9;
const maxWeight = 0.9;
// Input differential:
let inputDiffRate = 0.1;
let inputDiffFactor = 1 + inputDiffRate;
console.log("inputDiffFactor = " + inputDiffFactor);
const ACTIVATION_FUNCTION = "Sigmoid";
// should we train every timestep or not
let do_training = true;
// how many to train and test per timestep. Default 30, 5.
// If you set this to TRAINPERSTEP 0 then the training will not occur so the result will be 10% (1 in 10 random).
// If you set this to small number e.g. 1 it flies through too quickly.
// But you can slow it down by increasing TESTPERSTEP to e.g. 200.
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 = 20; //18; // thickness of doodle lines
const DOODLE_BLUR = 3; // blur factor applied to doodles: default 3
// For convolution and maxpooling
let dest;
let maxpooling;
// Where to process the pixels
let xstart = 0;
let ystart = 0;
// Convolution matrix
let kernel = [
[-2, -1, 0],
[-1, 1, 1],
[0, 1, 2]
];
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;
// Used for tracking count of correct / incorrect doodle guesses
let countCorrectDoodleGuess = 0;
let countIncorrectDoodleGuess = 0;
let countAllDoodleGuess = 0;
let rateDoodleSuccess = 0;
// Matrix.randomize() is changed to point to this. Must be defined by user of Matrix.
function randomWeight()
{
return ( AB.randomFloatAtoB ( minWeight, maxWeight ) );
// 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. <br> " +
" <button onclick='wipeDoodle();' class='normbutton' >Clear doodle</button> " +
" <button onclick='clearScore();' class='normbutton' >Clear score</button> " +
" <button onclick='markCorrect();' class='normbutton' >Correct</button> " +
" <button onclick='secondCorrect();' class='normbutton' >Nearly!</button> " +
" <button onclick='markIncorrect();' class='normbutton' >Incorrect</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)
// 10. Run settings
thehtml = "<hr> <h1> 4. Settings </h1> Activation function: "+ ACTIVATION_FUNCTION + " <br> " +
" No. Hidden nodes: " + nohidden + " Learning rate: " + learningrate + "<br> " +
" Min Weight: " + minWeight + " Max Weight: " + maxWeight + "<br> " +
" Input differential: " + inputDiffRate;
AB.msg ( thehtml, 10 );
const greenspan = "<span style='font-weight:bold; font-size:x-large; color:darkgreen'> " ;
//--- end of AB.msgs structure: ---------------------------------------------------------
AB.runloggedin; // Boolean. Are we running logged in.
AB.myuserid; // The userid of the run, if running logged in.
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/fordea23/matrix.js", function()
{
$.getScript ( "/uploads/fordea23/nn.js", function()
{
$.getScript ( "/uploads/fordea23/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++) // Each image is made up of PIXELSSQUARED number of pixels
{
// Each pixel is an array of size 4 with values for R, G, B, A
let bright = img[i];
let index = i * 4;
theimage.pixels[index + 0] = bright; // R - Red
theimage.pixels[index + 1] = bright; // G - Green
theimage.pixels[index + 2] = bright; // B - Blue
theimage.pixels[index + 3] = 255; // A - Alpha
}
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
noFill();
stroke (255);
rect (0, ZOOMPIXELS+50, ZOOMPIXELS, ZOOMPIXELS ); //
}
// 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];
// 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:
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();
}
// 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
noFill();
stroke (255);
rect (0, canvasheight - ZOOMPIXELS, ZOOMPIXELS, ZOOMPIXELS ); //
}
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);
dest = theimage;
let xend = xstart + ZOOMPIXELS;
let yend = ystart + ZOOMPIXELS;
// Load all the pixels
dest.loadPixels();
theimage.loadPixels();
// Begin our loop for every pixel
for (let x = 0; x < dest.width; x++) {
for (let y = 0; y < dest.height; y++) {
// Each pixel location (x,y) gets passed into a function called convolution()
// The convolution() function returns a new color to be displayed.
let kernelsize = 3;
let result = convolution(theimage, x + xstart, y + ystart, kernel, kernelsize);
let index = (x + y * dest.width) * 4;
dest.pixels[index + 0] = result[0];
dest.pixels[index + 1] = result[1];
dest.pixels[index + 2] = result[2];
dest.pixels[index + 3] = 255;
}
}
dest.updatePixels();
image(dest, xstart, ystart);
maxpool(dest, 5, xstart, ystart);
// Top-left corner of the img is at (10, 10)
// Width and height are 50 x 50
// image(img, 10, 10, 50, 50);
// image(imagename, TOPROW, TOPCOLUMN, WIDTH, HEIGHT);
image ( theimage, 0, 0, ZOOMPIXELS, ZOOMPIXELS ); // original
image ( theimage, ZOOMPIXELS+50, 0, PIXELS, PIXELS ); // shrunken image top right
noFill();
stroke (255);
rect ( 0, 0, ZOOMPIXELS, ZOOMPIXELS ); //
// NOTE: I had to put these functions - convolution, maxpool and findMax - within drawDoolde()
// as they were causing an error outside (messing up the training score). Don't know why!
function convolution(img, x, y, kernel, kernelsize) {
// Going to sum the RGB values of all the pixels
let rsum = 0.0;
let gsum = 0.0;
let bsum = 0.0;
// Offset around the center pixel
let offset = floor(kernelsize / 2);
// Loop through convolution kernel
for (let i = 0; i < kernelsize; i++) {
for (let j = 0; j < kernelsize; j++) {
// What pixel are we testing
let xpos = x + i - offset;
let ypos = y + j - offset;
// Find the 1D location in the array
let index = (xpos + img.width * ypos) * 4;
// Make sure we haven't walked off the edge of the pixel array
// It is often good when looking at neighboring pixels to make sure we have not gone off the edge of the pixel array by accident.
index = constrain(index, 0, img.pixels.length - 1);
// Calculate the convolution
// We sum all the neighboring pixels
// multiplied by the weights in the convolution kernel.
rsum += img.pixels[index + 0] * kernel[i][j];
gsum += img.pixels[index + 1] * kernel[i][j];
bsum += img.pixels[index + 2] * kernel[i][j];
}
}
// Return an array with the three color values
return [rsum, gsum, bsum];
}
// This reluing function will iterate over all the
// "pooled" areas and draw a rectangle showing the
// brightest pixel
function maxpool(img, skip, xoff, yoff) {
// Check all the pixels
for (let x = 0; x < img.width; x += skip) {
for (let y = 0; y < img.height; y += skip) {
// Find the brightest pixel
let brightest = findMax(img, x, y, skip);
// Draw the rectangle
fill(brightest[0], brightest[1], brightest[2]);
noStroke();
rectMode(CORNER);
rect(x + xoff, y + yoff, skip, skip);
}
}
}
// This function finds the brightest pixel in a smaller area
function findMax(img, xstart, ystart, skip) {
// Brightest so far
let record = 0;
let brightest = [0, 0, 0];
for (let x = 0; x < skip; x++) {
for (let y = 0; y < skip; y++) {
// Find the 1D location in the array
let index = ((x + xstart) + (y + ystart) * img.width) * 4;
// Look at RGB
let r = img.pixels[index + 0];
let g = img.pixels[index + 1];
let b = img.pixels[index + 2];
// Add it up
let sum = r + g + b;
// Is this the new brightest pixel?
if (sum > record) {
record = sum;
brightest = [r, g, b];
}
}
}
// Return the result
return brightest;
}
// here
}
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; // Original calc
// TEST:
//console.log("inputs[i] = " + inputs[i]);
//console.log("inputs[i] * factor = " + inputs[i] * inputDiffFactor);
if ((inputs[i] * inputDiffFactor) < 1) {
// Use factored value
inputs[i] = (img.pixels[i * 4] / 255) * inputDiffFactor;
}
else {
// Go with original value
inputs[i] = img.pixels[i * 4] / 255; // Original calc
}
}
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
rateDoodleSuccess = (countCorrectDoodleGuess * 100) / countAllDoodleGuess;
rateDoodleSuccess = round(rateDoodleSuccess);
thehtml = " Correct: " + countCorrectDoodleGuess + " Incorrect: " + countIncorrectDoodleGuess +
" Success Rate: " + rateDoodleSuccess + "%<br>" +
" 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');
}
function markCorrect()
{
countCorrectDoodleGuess = countCorrectDoodleGuess + 1;
countAllDoodleGuess = countAllDoodleGuess + 1;
}
function secondCorrect()
{
countCorrectDoodleGuess = countCorrectDoodleGuess + 0.5;
countAllDoodleGuess = countAllDoodleGuess + 1;
}
function markIncorrect()
{
countIncorrectDoodleGuess = countIncorrectDoodleGuess + 1;
countAllDoodleGuess = countAllDoodleGuess + 1;
}
function clearScore()
{
countCorrectDoodleGuess = 0;
countIncorrectDoodleGuess = 0;
countAllDoodleGuess = 0;
}
// --- 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);
}