//Practical 2 - Foundations of Artificial Intelligence By Ayush Bhatt - Doodle Recogniser for A - Z (alphabets)
// Optimized Variable declaration changed by Ayush Bhatt
const PIXELS = 28
, PIXELSSQUARED = PIXELS * PIXELS
, NOTRAIN = 60000
, NOTEST = 10000
, noinput = PIXELSSQUARED
, nohidden = 64
, nooutput = 26
, learningrate = .1;
let do_training = !0;
const TRAINPERSTEP = 15
, TESTPERSTEP = 5
, ZOOMFACTOR = 7
, ZOOMPIXELS = 7 * PIXELS
, canvaswidth = PIXELS + ZOOMPIXELS + 70
, canvasheight = 3 * ZOOMPIXELS + 100
, DOODLE_THICK = 18
, DOODLE_BLUR = 3;
let mnist, nn, doodle, demo, trainrun = 1, train_index = 0, testrun = 1, test_index = 0, total_tests = 0, total_correct = 0, doodle_exists = !1, demo_exists = !1, mousedrag = !1;
var train_inputs, test_inputs, demo_inputs, doodle_inputs;
let alphabet = ["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"];
function randomWeight() {
return AB.randomFloatAtoB(-.5, .5)
}
var thehtml;
AB.headerCSS({
"max-height": "95vh"
}),
// Changed the HTML for better look and feel - by Ayush Bhatt
thehtml = "<hr><div style='display:flex'> <div style='float: left;flex:1'> <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></div> " +
"<div style='width:2px;height:200px;background-color:#000000'> </div>" +
"<div style='float: right;flex:1; padding-left:10px;'> <h1> 2. Training </h1> Middle row: Training image magnified (left) and original (right). <br> " +
" <button onclick='do_training = false;' class='normbutton'>Stop training</button></div></div>";
AB.msg ( thehtml, 1 );
thehtml = "<hr><span style='font-weight:bold;font-size:x-large;'> 4. Hidden tests </span><br> ",
AB.msg(thehtml, 5),
thehtml = "<hr> <h1> 5. 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> </div> </div> ",
AB.msg(thehtml, 7);
const greenspan = "<span style='font-weight:bold; font-size:x-large; color:darkgreen'> ";
// Changed the background color and imported new mnist.js - by Ayush Bhatt
function setup() {
createCanvas(canvaswidth, canvasheight),
background('#08CEFC');
(doodle = createGraphics(ZOOMPIXELS, ZOOMPIXELS)).pixelDensity(1),
AB.loadingScreen(),
$.getScript("/uploads/codingtrain/matrix.js", function() {
$.getScript("/uploads/codingtrain/nn.js", function() {
$.getScript("/uploads/maverick/ayush_mnist.js", function() {
console.log("All JS loaded"),
(nn = new NeuralNetwork(noinput,nohidden,nooutput)).setLearningRate(learningrate),
loadData()
})
})
})
}
function loadData() {
loadMNIST(function(t) {
mnist = t,
console.log("All data loaded into mnist object:"),
console.log(mnist),
AB.removeLoading()
})
}
function getImage(t) {
let e = createImage(PIXELS, PIXELS);
e.loadPixels();
for (let n = 0; n < PIXELSSQUARED; n++) {
let o = t[n]
, i = 4 * n;
e.pixels[i + 0] = o,
e.pixels[i + 1] = o,
e.pixels[i + 2] = o,
e.pixels[i + 3] = 255
}
return e.updatePixels(),
e
}
function getInputs(t) {
let e = [];
for (let n = 0; n < PIXELSSQUARED; n++) {
let o = t[n];
e[n] = o / 255
}
return e
}
function trainit(t) {
let e = mnist.train_images[train_index]
, n = mnist.train_labels[train_index];
if (t) {
var o = getImage(e);
image(o, 0, ZOOMPIXELS + 50, ZOOMPIXELS, ZOOMPIXELS),
image(o, ZOOMPIXELS + 50, ZOOMPIXELS + 50, PIXELS, PIXELS)
}
let i = getInputs(e)
, s = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
s[n] = 1,
console.log("train_index" + train_index),
train_inputs = i,
console.log("inputs" + i.length + "targets" + s.length),
26 == s.length && nn.train(i, s),
// HTML change : Ayush Bhatt
thehtml = "<hr><span style='font-weight:bold;font-size:x-large;'>3. Training Progress: </span><br> Trainrun: " + trainrun + "<br> no: " + train_index ;
AB.msg ( thehtml, 4 );
++train_index == NOTRAIN && (train_index = 0,
console.log("finished trainrun: " + trainrun),
trainrun++)
}
function testit() {
let t = mnist.test_images[test_index]
, e = mnist.test_labels[test_index]
, n = getInputs(t);
test_inputs = n;
let o = findMax(nn.predict(n));
total_tests++,
o == e && total_correct++;
let percent = total_correct / total_tests * 100;
thehtml = " Testrun: " + testrun + "<br> no: " + total_tests + " <br> " +
" Correct: " + total_correct + "<br><br>" +
" <span style='font-weight:bold;font-size:x-large;color:#FC5D08;border:1px solid'>Score : " + greenspan + percent.toFixed(2) + "</span></span>";
AB.msg ( thehtml, 6 );
++test_index == NOTEST && (console.log("finished testrun: " + testrun + " score: " + percent.toFixed(2)),
testrun++,
test_index = 0,
total_tests = 0,
total_correct = 0)
}
function find12(t) {
let e = 0
, n = 0
, o = 0
, i = 0;
for (let s = 0; s < t.length; s++)
t[s] > o ? (n = e,
i = o,
e = s,
o = t[s]) : t[s] > i && (n = s,
i = t[s]);
return [e, n]
}
function findMax(t) {
let e = 0
, n = 0;
for (let o = 0; o < t.length; o++)
t[o] > n && (e = o,
n = t[o]);
return e
}
function draw() {
if (void 0 !== mnist) {
if (background("#08CEFC"),
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 = !0,
doodle_exists = !0,
doodle.stroke("white"),
doodle.strokeWeight(DOODLE_THICK),
doodle.line(mouseX, mouseY, pmouseX, pmouseY))
} else
mousedrag && (mousedrag = !1,
doodle.filter(BLUR, DOODLE_BLUR))
}
}
function makeDemo() {
demo_exists = !0;
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: " + alphabet[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 = findMax(nn.predict(t));
thehtml = " We classify it as: " + greenspan + alphabet[e - 1] + "</span>",
AB.msg(thehtml, 9)
}
function drawDoodle() {
let t = doodle.get();
image(t, 0, 0, ZOOMPIXELS, ZOOMPIXELS),
image(t, ZOOMPIXELS + 50, 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 = find12(nn.predict(e));
thehtml = " We classify it as: " + greenspan + alphabet[n[0]] + "</span> <br> No.2 guess is: " + greenspan + alphabet[n[1]] + "</span>",
AB.msg(thehtml, 2)
}
function wipeDoodle() {
doodle_exists = !1,
doodle.background("#08CEFC")
}
function showInputs(t) {
var e = "";
for (let n = 0; n < t.length; n++) {
n % PIXELS == 0 && (e += "\n"),
e = e + " " + t[n].toFixed(2)
}
console.log(e)
}