// Cloned by Laura Campbell on 25 Nov 2022 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;
const learningrate = 0.1; // default 0.1
// should we train every timestep or not
let do_training =!0;
// how many to train and test per timestep
const TRAINPERSTEP = 30;
const TESTPERSTEP = 10;
// 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 = 18; // thickness of doodle lines
const DOODLE_BLUR = 3; // 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 cnn;
let cnnTrain;
let cnnModel;
let letters=["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"];
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 =!0;
let demo_exists =!0;
let mousedrag =!0; // are we in the middle of a mouse drag drawing?
// saveeto 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 ) );
//return ( AB.randomFloatAtoB ( -1, 1 ) );
// 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 =!1;' 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
wipeDoodle();
AB.loadingScreen();
//$.getScript("/uploads/codingtrain/matrix.js",function()
$.getScript("/uploads/lauracampbell26/matrix.js",function()
{
$.getScript ( "/uploads/lauracampbell26/convnet.js", function()
{
$.getScript ( "/uploads/lauracampbell26/mnist.js", function()
{
//https://cs.stanford.edu/people/karpathy/convnetjs/demo/mnist.html
console.log ("All JS loaded");
let layer_defs = [];
layer_defs.push({type:'input', out_sx:28, out_sy:28, out_depth:1});
layer_defs.push({type:'conv', sx:5, filters:8, stride:1, pad:2, activation:'relu'});
layer_defs.push({type:'pool', sx:2, stride:2});
layer_defs.push({type:'conv', sx:5, filters:16, stride:1, pad:2, activation:'relu'});
layer_defs.push({type:'pool', sx:3, stride:3});
layer_defs.push({type:'softmax', num_classes:10});
cnnMode = new convnetjs.Net();
cnnMode.makeLayers(layer_defs);
cnnTrain= new convnetjs.SGDTrainer(cnnMode, {method:'adadelta', batch_size:20, l2_decay:0.001});
loadData();
});
});
});
}
// load data set from local file (on this server)
function loadData() {
loadMNIST(function(t) {
mnist = t;
for (e = 0; 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(); // if no loading screen exists, this does nothing
})
}
function getImage( t ) // make a P5 image object from a raw data array
{
let e = createImage (PIXELS, PIXELS); // make blank image, then populate it
e.loadPixels();
for (let n = 0; n < PIXELSSQUARED ; n++)
{
let o = t[n];
s = n * 4;
e.pixels[s + 0] = o;
e.pixels[s + 1] = o;
e.pixels[s + 2] = o;
e.pixels[s + 3] = 255;
}
//e.updatePixels();
return e.updatePixels,e;
}
function getInputs( t ) // convert t array into normalised input array
{
let e= [];
for (let n = 0; n < PIXELSSQUARED ; n++)
{
let o = t[n];
e[n] = o / 255; // normalise to 0 to 1
}
return (e);
}
function rotateImage(t) {
for (let e = 0; e < PIXELS; e++)
for (let n = e; n < PIXELS; n++) {
let o = e * PIXELS + n
, s = n * PIXELS + e
, i = t[o];
t[o] = t[s],
t[s] = i
}
}
function trainit (t) // train the network with a single exemplar, from global var "train_s", show visual on or off
{
let e = mnist.train_images[train_index];
let n = mnist.train_labels[train_index];
// optional - show visual of the image
if (t)
{
var o = getImage ( e ); // get image from data array
image ( o, 0, ZOOMPIXELS+50, ZOOMPIXELS, ZOOMPIXELS ); // magnified
image ( o, ZOOMPIXELS+50, ZOOMPIXELS+50, PIXELS, PIXELS ); // original
}
// set up the inputs
let s= getInputs( e ); // getefrom data array
train_inputs = s;
{
let t = getcnnInputs(s);
cnnTrain.train(t, n);
}
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 getcnnInputs(t)
{
for (var e = new convnetjs.Vol(28,28,1,0),n=0;n<PIXELSSQUARED;n++)
e.w[n]=t[n];
return e;
}
function testit() // test the network with a single exemplar, from global var "test_s"
{
let t = mnist.test_images[test_index];
let e = mnist.test_labels[test_index];
// set up the inputs
let n = getInputs(t);
let o = getcnnInputs(n);
test_inputs = n; // can inspect in console
//let prediction = nn.predict(inputs); // array of outputs
//let guess = findMax(prediction); // the top output
let s = findMax(cnnModel.forward(o).w);
var i = getImage(t);
image(i, 0, ZOOMPIXELS+50, ZOOMPIXELS, ZOOMPIXELS);
image(i, ZOOMPIXELS+50, ZOOMPIXELS+50, PIXELS, PIXELS);
total_tests++;
if (s == e)
{
total_correct++;
}
let a = (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 ses of no.1 and no.2 values in array
{
let e=0;
let n=0;
let o=0;
let s=0;
for (let i = 0; i < t.length; i++)
{
if (t[i] > o) // new no1
{
// old no1 becomes no2
n = e;
s = o;
// now put in the new no1
e = i;
o = t[i];
}
else if (t[i] > s) // new no2
{
n = i;
s = t[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 (t)
{
let e = 0;
let n = 0;
for (let o = 0; o < t.length; o++)
{
if (t[o] > n)
{
e = o;
n = t[o];
}
}
return e;
}
// --- the draw function -------------------------------------------------------------
// every step:
function draw()
{
// check if libraries and data loaded yet:
if(void 0!==mnist) {
if (background("black"),
// how can we get white doodle on black background on yellow canvas?
// background('#ffffcc'); doodle.background('black');
strokeWeight(1),
stroke("pink"),
rect( 0, 0, ZOOMPIXELS, ZOOMPIXELS ),
textSize(10),
textAlign(CENTER),
do_training)
{
// do some training per step
for (let t = 0; t < TRAINPERSTEP; t++)
{
//if (t == 0) trainit(true); // show only one per step - still flashes by
// else trainit(!0);
trainit(0===t);
}
// do some testing per step
for (let t = 0; t < TESTPERSTEP; t++)
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 =!0; // start a mouse drag
doodle_exists =!0;
doodle.stroke('white');
doodle.strokeWeight( DOODLE_THICK );
doodle.line(mouseX, mouseY, pmouseX, pmouseY);
}
}
else
{
// are we exiting a drawing
if ( mousedrag )
{
mousedrag =!1;
// 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 =!0;
var t = AB.randomIntAtoB ( 0, NOTEST - 1 );
demo = mnist.test_images[t];
var label = mnist.test_labels[t];
thehtml="Test image no: "+t+"<br>Classification: "+ letters[e-1]+"<br>";
AB.msg ( thehtml, 8 );
// type "demo" in console to see raw data
}
function drawDemo()
{
var t = getImage( demo );
// console.log (e);
image ( t, 0, canvasheight - ZOOMPIXELS, ZOOMPIXELS, ZOOMPIXELS ); // magnified
image ( t, ZOOMPIXELS+50, canvasheight - ZOOMPIXELS, PIXELS, PIXELS ); // original
}
function guessDemo()
{
let t= getInputs( demo );
demo_inputs = t; // can inspect in console
let e = getcnnInputs(t); // array of outputs
let n = findMax(cnnModel.forward(e).w); // the top output
thehtml = " We classify it as: " + greenspan + letters[n-1] + "</span>" ;
AB.msg ( thehtml, 9 );
}
//--- doodle -------------------------------------------------------------
function drawDoodle()
{
// doodle is createGraphics not createImage
let t = doodle.get();
// console.log (e);
image ( t, 0, 0, ZOOMPIXELS, ZOOMPIXELS ); // original
image ( t, ZOOMPIXELS+50, 0, PIXELS, PIXELS ); // shrunk
}
function guessDoodle()
{
// doodle is createGraphics not createImage
let t = doodle.get();
t.resize ( PIXELS, PIXELS );
t.loadPixels();
// set upe
let e= [];
for (let n = 0; n < PIXELSSQUARED ; n++)
{
e[n] = t.pixels[n * 4] / 255;
}
doodle_inputs = e; // can inspect in console
// feed forward to make prediction
let n = getcnnInputs(e); // array of outputs
let o = find12(cnnModel.forward(n).w); // get no.1 and no.2 guesses
thehtml = " We classify it as: " + greenspan +letters[o[0]-1]+ "</span> <br>" +
" No.2 guess is: " + greenspan +letters[o[1]-1]+ "</span>";
AB.msg ( thehtml, 2 );
}
function wipeDoodle()
{
doodle_exists =!1;
doodle.background('black');
}
// --- debugging --------------------------------------------------
// in console
// showInputs(demo_inputs);
// showInputs(doodle_inputs);
function showInputs (t)
// displayerow by row, corresponding to square of pixels
{
var e = "";
for (let n = 0; n < t.length; n++)
{
if ( n % PIXELS === 0 ) e+="\n"; // new line for each row of pixels
e=e+" "+t[n].toFixed(2);
}
console.log (e);
}