//Name: Raj Vibhute
// Cloned by Raj Vibhute on 2 Dec 2022 from World "CharRecognition_UsingCNN (clone by Dheera)" by "Dheera"
// Please leave this clone trail here.
// --- defined by MNIST - do not change these ---------------------------------------
const PIXELS = 28, // images in data set are tiny
PIXELSSQUARED = PIXELS * PIXELS,
// number of training and test exemplars in the data set:
NOTRAIN = 124800,
NOTEST = 20800,
//--- can modify all these --------------------------------------------------
// no of nodes in network
noinput = PIXELSSQUARED,
nohidden = 64,
nooutput = 10,
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,
TESTPERSTEP = 5,
// multiply it by this to magnify for display
ZOOMFACTOR = 7,
ZOOMPIXELS = 7 * PIXELS,
// 3 rows of
// large image + 50 gap + small image
// 50 gap between rows
canvaswidth = PIXELS + ZOOMPIXELS + 100,
canvasheight = 3 * ZOOMPIXELS + 60,
DOODLE_THICK = 18, // thickness of doodle lines
DOODLE_BLUR = 3; // blur factor applied to doodles
let mnist, mycnn, mycnnTrain, mycnnModel, doodle, demo,
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"],
trainrun = 1,
train_index = 0,
testrun = 1,
test_index = 0,
total_tests = 0,
total_correct = 0,
// images in LHS:
doodle_exists = false,
demo_exists = false,
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, thehtml;
// 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
// 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</button> <br> ",
AB.msg(thehtml, 1),
// 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> <button onclick='do_training = true;' class='normbutton' >Resume training</button> <br> ",
AB.msg(thehtml, 3),
// 5 Testing header
thehtml = "<h3> Hidden tests </h3> ",
AB.msg(thehtml, 5),
// 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);
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)).pixelDensity(1),
wipeDoodle(), AB.loadingScreen(),
$.getScript("/uploads/codingtrain/matrix.js",
function() {
$.getScript("/uploads/rajv261/convnet.js",
function() {
$.getScript("/uploads/rajv261/mnistDhe.js",
function() {
setup
console.log("All JS Files loaded");
let t = [];
t.push({
type: "input",
out_sx: 28,
out_sy: 28,
out_depth: 1
}),
t.push({
type: "conv",
sx: 5,
filters: 8,
stride: 1,
pad: 2,
activation: "relu"
}),
t.push({
type: "pool",
sx: 2,
stride: 2
}),
t.push({
type: "conv",
sx: 5,
filters: 16,
stride: 1,
pad: 2,
activation: "relu"
}),
t.push({
type: "pool",
sx: 3,
stride: 3
}),
t.push({
type: "softmax",
num_classes: 26
}),
(mycnnModel = new convnetjs.Net).makeLayers(t),
mycnnTrain = new convnetjs.SGDTrainer(mycnnModel, {
method: "adadelta",
momentum: 0.9,
batch_size: 10,
l2_decay: 0.001
}),
lData();
});
});
});
}
// load data set from local file (on this server)
function lData() {
loadMNIST(function(img) {
mnist = img;
let e = 0;
for (; e < NOTRAIN; e++) rotateImage(mnist.train_images[e]);
for (e = 0; e < NOTEST; e++) rotateImage(mnist.test_images[e]);
console.log("All data loaded into Emnist object."),
console.log(mnist),
AB.removeLoading();
});
}
function getImage(img) {
let e = createImage(PIXELS, PIXELS);
e.loadPixels();
for (let n = 0; n < PIXELSSQUARED; n++) {
let o = img[n],
s = 4 * n;
e.pixels[s + 0] = o, e.pixels[s + 1] = o, e.pixels[s + 2] = o, e.pixels[s + 3] = 255;
}
return e.updatePixels(), e;
}
function getInputs(img) {
let e = [];
for (let n = 0; n < PIXELSSQUARED; n++) {
let o = img[n];
e[n] = o / 255;
}
return e;
}
function rotateImage(img) {
for (let e = 0; e < PIXELS; e++)
for (let n = e; n < PIXELS; n++) {
let o = e * PIXELS + n,
s = n * PIXELS + e,
i = img[o];
img[o] = img[s], img[s] = i;
}
}
function trainit(img) {
let e = mnist.train_images[train_index],
n = mnist.train_labels[train_index];
if (img) {
var o = getImage(e);
image(o, 0, ZOOMPIXELS + 50, ZOOMPIXELS, ZOOMPIXELS), image(o, ZOOMPIXELS + 50, ZOOMPIXELS + 50, PIXELS, PIXELS);
}
let s = getInputs(e);
train_inputs = s; {
let img = getmycnnInputs(s);
mycnnTrain.train(img, n);
}
thehtml = " trainrun: " + trainrun + "<br> no: " + train_index, AB.msg(thehtml, 4), ++train_index == NOTRAIN && (train_index = 0, console.log("finished trainrun: " + trainrun), trainrun++);
}
function getmycnnInputs(img) {
for (var e = new convnetjs.Vol(28, 28, 1, 0), n = 0; n < PIXELSSQUARED; n++) e.w[n] = img[n];
return e;
}
function testit() {
let img = mnist.test_images[test_index],
e = mnist.test_labels[test_index],
n = getInputs(img),
o = getmycnnInputs(n);
test_inputs = n;
let s = findMax(mycnnModel.forward(o).w);
var i = getImage(img);
image(i, 0, ZOOMPIXELS + 50, ZOOMPIXELS, ZOOMPIXELS),
image(i, ZOOMPIXELS + 50, ZOOMPIXELS + 50, PIXELS, PIXELS),
total_tests++, s == e && total_correct++;
let a = total_correct / total_tests * 100;
thehtml = " testrun: " + testrun + "<br> no: " + total_tests + " <br> correct: " + total_correct + "<br> score: " + bluespan + a.toFixed(2) + "</span>", AB.msg(thehtml, 6),
++test_index == NOTEST && (console.log("finished testrun: " + testrun + " score: " + a.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 e = 0,
n = 0,
o = 0,
s = 0;
for (let i = 0; i < a.length; i++)
a[i] > o ? (n = e, s = o, e = i, o = a[i]) : a[i] > s && (n = i, s = a[i]);
return [e, n];
}
// 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 e = 0,
n = 0;
for (let o = 0; o < a.length; o++) a[o] > n && (e = o, n = a[o]);
return e;
}
// --- the draw function -------------------------------------------------------------
// every step:
function draw() {
if (void 0 !== mnist) {
if (background("black"), strokeWeight(1), rect(0, 0, ZOOMPIXELS, ZOOMPIXELS), textSize(10), textAlign(CENTER), do_training) {
for (let t = 0; t < TRAINPERSTEP; t++) trainit(0 === t);
for (let t = 0; t < TESTPERSTEP; t++) testit();
}
if (demo_exists && (drawDemo(), guessDemo()), doodle_exists && (drawDoodle(), guessDoodle()), mouseIsPressed) {
var t = ZOOMPIXELS + 20;
mouseX < t && mouseY < t && pmouseX < t && pmouseY < t && (mousedrag = true, doodle_exists = true, doodle.stroke("white"), strokeJoin(ROUND), doodle.strokeWeight(DOODLE_THICK), doodle.line(mouseX, mouseY, pmouseX, pmouseY));
} else mousedrag && (mousedrag = false, doodle.filter(BLUR, DOODLE_BLUR));
}
}
//--- 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 t = AB.randomIntAtoB(0, NOTEST - 1);
demo = mnist.test_images[t];
var e = mnist.test_labels[t];
thehtml = "Test image no: " + t + "<br>Classification: " + alphabets[e - 1] + "<br>", AB.msg(thehtml, 8);
}
function drawDemo() {
var t = getImage(demo);
image(t, 0, canvasheight - ZOOMPIXELS, ZOOMPIXELS, ZOOMPIXELS), image(t, ZOOMPIXELS + 50, canvasheight - ZOOMPIXELS, PIXELS, PIXELS);
}
function guessDemo() {
let t = getInputs(demo);
demo_inputs = t;
let e = getmycnnInputs(t),
n = findMax(mycnnModel.forward(e).w);
thehtml = " We classify it as: " + bluespan + alphabets[n - 1] + "</span>", AB.msg(thehtml, 9);
}
//--- doodle -------------------------------------------------------------
function drawDoodle() {
let t = doodle.get();
image(t, 0, 0, ZOOMPIXELS, ZOOMPIXELS),
image(t, ZOOMPIXELS + 20, 0, PIXELS, PIXELS);
}
function guessDoodle() {
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 = getmycnnInputs(e),
o = find12(mycnnModel.forward(n).w);
thehtml = " Our 1st Guess is: " + bluespan + alphabets[o[0] - 1] + "</span> <br> Our 2nd Guess is: " + bluespan + alphabets[o[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) {
var e = "";
for (let n = 0; n < inputs.length; n++) {
n % PIXELS == 0 && (e += "\n"),
e = e + " " + inputs[n].toFixed(2);
}
console.log(e);
}