// Cloned by Abdelshafa Abdala on 1 Dec 2022 from World "CA 2 Character recognition neural network " by HaoIoCheong
// Please leave this clone trail here.
const PIXELS=28,
PIXELSSQUARED=PIXELS*PIXELS,
NOTRAIN=6e4,
NOTEST=1e4,
noinput=PIXELSSQUARED,
nohidden=123,
nooutput=10,
learningrate=.1;
let do_training=!0;
const TRAINPERSTEP=30,
TESTPERSTEP=5,
ZOOMFACTOR=7,
ZOOMPIXELS=7*PIXELS,
canvaswidth=PIXELS+ZOOMPIXELS+50,
canvasheight=3*ZOOMPIXELS+100,
DOODLE_THICK=18,
DOODLE_BLUR=3;
let mnist,nn,doodle,demo,
nnClassifer=1,
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,thehtml;
function randomWeight()
{return AB.randomFloatAtoB(-.5,.5)}AB.headerCSS({"max-height":"95vh"}),
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),
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),
thehtml="<h3> Hidden tests </h3> ",
AB.msg(thehtml,5),
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),
thehtml=" <button id='save' onclick='saveData();' class='normbutton mybutton' >Save weight for better learning </button> ",
AB.msg(thehtml,2);
const greenspan="<span style='font-weight:bold; font-size:x-large; color:purple'> ";
function setup(){createCanvas(canvaswidth,canvasheight),(doodle=createGraphics(ZOOMPIXELS,
ZOOMPIXELS)).pixelDensity(1),
AB.loadingScreen(),
$.getScript("/uploads/codingtrain/matrix.js",
function(){$.getScript("/uploads/karinmeow321/nn.js",
function(){$.getScript("/uploads/codingtrain/mnist.js",
function(){$.getJSON("/uploads/karinmeow321/mnist_intoJason.json",
function(t){console.log("All JS loaded"),
1===nnClassifer?nn=NeuralNetwork.deserialize(t):2===nnClassifer?nn=new NeuralNetwork(noinput,
nohidden,nooutput,learningrate):
console.log("Not falling into any NN classifer.."),
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 saveData(){AB.saveData(nn)}function restoreData(){AB.restoreData(function(t){nn=NeuralNetwork.deserialize(t),
loadData(),
console.log("AB.restoredata"+NeuralNetwork.deserialize(t).serialize())})}
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];s[n]=1,
console.log(i),console.log(s),train_inputs=i,nn.train(i,s),
console.log("Trained for the images"),
thehtml=" 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 i=total_correct/total_tests*100;
console.log("% Correct"+i),
thehtml=" testrun: "+testrun+"<br> no: "+total_tests+" <br> correct: "+total_correct+"<br> score: "+greenspan+i.toFixed(2)+"</span>",
AB.msg(thehtml,6),
++test_index==NOTEST&&(console.log("finished testrun: "+testrun+" score: "+i.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?(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("pink"),
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("purple"),doodle.smooth(),
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];
hehtml="Test image no: "+t+"<br>Classification: "+e+"<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+e+"</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+n[0]+"</span> <br> No.2 guess is: "+greenspan+n[1]+"</span>",AB.msg(thehtml,2)}function wipeDoodle(){doodle_exists=!1,
doodle.background("orange")}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)}