// Cloned by Sunil Jagtap on 1 Dec 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;
const PIXELS_DROP = 24;
const CROPPED_DOODLE_PIXELS = 24;
// 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 = 0.1; // 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 = 16; // thickness of doodle lines
const DOODLE_BLUR = 0; // 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 cnn;
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
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
AB.loadingScreen();
$.getScript("/uploads/codingtrain/matrix.js", function() {
$.getScript("/uploads/codingtrain/nn.js", function() {
$.getScript("/uploads/codingtrain/mnist.js", function() {
$.getScript("/uploads/jagtaps2/webcnn.js", function() {
$.getScript("uploads/jagtaps2/mathutils.js", function() {
$.ajax({
url: "/uploads/jagtaps2/cnn_mnist_accuracy.json",
dataType: "json",
success: JSONLoaded
})
})
})
})
})
})
}
function JSONLoaded(t) {
cnnFromJSON(t); console.log("JSON Loaded!");
console.log(t);
console.log("All JS loaded");
(nn = new NeuralNetwork(noinput, nohidden, nooutput)).setLearningRate(learningrate);
loadData();
}
// load data set from local file (on this server)
function cnnFromJSON(t) {
cnn = new WebCNN;
void 0 != t.momentum && cnn.setMomentum(t.momentum);
void 0 != t.lambda && cnn.setLambda(t.lambda);
void 0 != t.learningRate && cnn.setLearningRate(t.learningRate);
for (var e = 0; e < t.layers.length; ++e) {
let n = t.layers[e];
console.log(n), cnn.newLayer(n)
}
for (e = 0; e < t.layers.length; ++e) {
let n = t.layers[e];
switch (t.layers[e].type) {
case LAYER_TYPE_CONV:
case LAYER_TYPE_FULLY_CONNECTED:
void 0 != n.weights && void 0 != n.biases && cnn.layers[e].setWeightsAndBiases(n.weights, n.biases)
}
}
cnn.initialize()
}
function getInObjectFormat(t, e) {
return {
width: e,
height: e,
data: getImage(randCropUtil(t, e), e).pixels
}
}
function preprocessingImage(t, e) {
let n = [];
for (let o = 0; o < e; o++) {
n[o] = [];
for (let i = 0; i < e; i++) n[o][i] = t[4 * (o * e + i)]
}
for (var o = Number.MAX_VALUE, s = Number.MAX_VALUE, a = -1, r = -1, l = 0; l < n.length; l++) {
var d = n[l].indexOf(255),
m = n[l].lastIndexOf(255);
d >= 0 && d < s && (s = d), m >= 0 && m > r && (r = m), d >= 0 && l < o && (o = l), d >= 0 && l > a && (a = l)
}
let u = Math.floor((e - r - s) / 2),
c = Math.floor((e - a - o) / 2),
g = Array(e).fill().map(() => Array(e).fill(0));
for (i = o; i <= a; i++)
for (j = s; j <= r; j++) g[i + c][j + u] = n[i][j];
let h = [];
for (let t = 0; t < e; t++)
for (let n = 0; n < e; n++) h[t * e + n] = g[t][n];
return h
}
function randCropUtil(t, e) {
const n = PIXELS - e;
return cropImage(t, e, Math.floor(Math.random() * n), Math.floor(Math.random() * n))
}
function cropImage(t, e, n = 2, o = 2) {
let i = [],
s = n + e,
a = o + e;
for (let e = n; e < s; e++)
for (let n = o; n < a; n++) i.push(t[e * PIXELS + n]);
return i
}
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(t, e = PIXELS) {
let n = createImage(PIXELS, PIXELS);
n.loadPixels();
for (let o = 0; o < e * e; o++) {
let e = t[o],
i = 4 * o;
n.pixels[i + 0] = e, n.pixels[i + 1] = e, n.pixels[i + 2] = e, n.pixels[i + 3] = 255
}
return n.updatePixels(), n
}
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 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 (t) // return array showing indexes of no.1 and no.2 values in array
{
let e = 0,
n = 0,
o = 0,
i = 0;
for (let n = 0; n < 10; n++) {
let i = t[0].getValue(0, 0, n);
i > o && (e = n, o = i)
}
for (let o = 0; o < 10; o++) {
let s = t[0].getValue(0, 0, o);
e != o && s > i && (n = o, i = s)
}
return [e, n]
// 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++)
{
trainit(0 == i);// show only one per step - still flashes by
}
// 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;
let i = AB.randomIntAtoB ( 0, NOTEST - 1 );
demo = mnist.test_images[i];
let 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()
{
let 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 = 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);
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 = inputs; // can inspect in console
// console.log(inputs);
// // feed forward to make prediction
// let prediction = find12(cnn.classifyImages([requireFormat(centerImage(img.pixels, PIXELS), PIXELS_DROP)]));; // array of outputs
// let b = find12(prediction); // get no.1 and no.2 guesses
// thehtml = " We classify it as: " + greenspan + b[0] + "</span> <br>" +
// " No.2 guess is: " + greenspan + b[1] + "</span>";
// AB.msg ( thehtml, 2 );
let t = doodle.get();
t.resize(PIXELS, PIXELS), t.loadPixels();
let e = [];
for (let n = 0; n < PIXELSSQUARED; n++) e[n] = t.pixels[4 * n] / 255;
doodle_inputs = e;
let n = find12(cnn.classifyImages([getInObjectFormat(preprocessingImage(t.pixels, PIXELS), CROPPED_DOODLE_PIXELS)]));
thehtml = " We classify it as: " + greenspan + n[0] + "</span> <br> No.2 guess is: " + greenspan + n[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);
}