// Cloned by Deborah Djon on 3 Dec 2022 from World "Sumbission Practical 2" by Deborah Djon
// Please leave this clone trail here.
// Cloned by Deborah Djon on 3 Dec 2022 from World "Working Google Thing" by Deborah Djon
// Please leave this clone trail here.
// Cloned by Deborah Djon on 1 Dec 2022 from World "Letter recognition neural network (clone by Deborah Djon)" by Deborah Djon
// Please leave this clone trail here.
// Cloned by Deborah Djon on 29 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 = 26; //altereed by Deborah Djon
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 = canvaswidth//( ZOOMPIXELS * 3 ) + 100;
const DOODLE_THICK = 18; // thickness of doodle lines
const DOODLE_BLUR = 3; // blur factor applied to doodles
/*altered by Deborah Djon
Added some letters.
*/
const 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 mnist;
// all data is loaded into this
// mnist.train_images
// mnist.train_labels
// mnist.test_images
// mnist.test_labels
let nn;
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;
//altered by Deborah Djon
let data;
let model;
// 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='showExamples();' class='normbutton' >Show Visor</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/tharealog/matrix_tharealog.js", function()
{
$.getScript ( "/uploads/tharealog/cnn_tharealog.js", function()
{
$.getScript ( "/uploads/tharealog/emnist2_tharealog.js", function() // todo: alter this
{
$.getScript("https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-vis@1.0.2/dist/tfjs-vis.umd.min.js", async function(){ // todo outsource into cnn
console.log ("All JS loaded");
//nn = new NeuralNetwork( noinput, nohidden, nooutput );
//nn.setLearningRate ( learningrate );
//loadData();
data = new MnistData();
await data.load()
//await showExamples(data);
model = getModel();
tfvis.show.modelSummary({name: 'Model Architecture', tab: 'Model'}, model);
await train(model, data);
await showAccuracy(model, data);
await showConfusion(model, data);
// AB.removeLoading();
console.log(data)
});
});
});
});
}
function getImage ( img ) // make a P5 image object from a raw data array
{
let theimage = createImage (PIXELS, PIXELS); // make blank image, then populate it
theimage.loadPixels();
for (let i = 0; i < PIXELSSQUARED ; i++)
{
let bright = img[i];
let index = i * 4;
theimage.pixels[index + 0] = bright;
theimage.pixels[index + 1] = bright;
theimage.pixels[index + 2] = bright;
theimage.pixels[index + 3] = 255;
}
theimage.updatePixels();
return theimage;
}
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 );
}
// --- the draw function -------------------------------------------------------------
// every step:
function draw()
{
background ('black');
//if ( demo_exists )
{
//drawDemo();
}
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
}
}
}
//--- demo -------------------------------------------------------------
// demo some test image and predict it
// get it from test set so have not used it in training
//todo
function makeDemo()
{
demo_exists = true;
var i = AB.randomIntAtoB ( 0, NOTEST - 1 );
demo = mnist.test_images[i];
var label = mnist.test_labels[i];
guessDemo()
thehtml = "Test image no: " + i + "<br>" +
"Classification: " + label + "<br>" ;
AB.msg ( thehtml, 8 );
// type "demo" in console to see raw data
}
//todo
function drawDemo()
{
var theimage = getImage ( demo );
// console.log (theimage);
image ( theimage, 0, canvasheight - ZOOMPIXELS, ZOOMPIXELS, ZOOMPIXELS ); // magnified
image ( theimage, ZOOMPIXELS+50, canvasheight - ZOOMPIXELS, PIXELS, PIXELS ); // original
}
//todo
/*
altered by Deborah Djon
change guess from number to letter
*/
function guessDemo()
{
console.log("Demo")
let inputs = getInputs ( demo );
demo_inputs = inputs; // can inspect in console
console.log("inputs")
console.log(inputs)
let prediction = nn.predict(inputs); // array of outputs
console.log("prediction")
console.log(prediction)
let guess = findMax(prediction); // the top output
console.log("guess")
console.log(guess)
thehtml = " We classify it as: " + greenspan + LETTERS[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
}
async 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] ;
}
prediction = await predict(model, inputs)
doodle_inputs = inputs; // can inspect in consolen
thehtml = " We classify it as: " + greenspan +LETTERS[prediction[0]] + "</span> <br>" +
" No.2 guess is: " + greenspan + LETTERS[prediction[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);
}
//------------- altered by Deborah Djon ----------------------
function showExamples(data) {
// Create a container in the visor
const surface =
tfvis.visor().surface({ name: 'Input Data Examples', tab: 'Input Data'});
// Get the examples
const examples = data.nextTestBatch(20);
const numExamples = examples.xs.shape[0];
// Create a canvas element to render each example
for (let i = 0; i < numExamples; i++) {
const imageTensor = tf.tidy(() => {
// Reshape the image to 28x28 px
return examples.xs
.slice([i, 0], [1, examples.xs.shape[1]])
.reshape([28, 28, 1]);
});
const canvas = document.createElement('canvas');
canvas.width = 28;
canvas.height = 28;
canvas.style = 'margin: 4px;';
tf.browser.toPixels(imageTensor, canvas);
surface.drawArea.appendChild(canvas);
imageTensor.dispose();
}
}
async function showVisor(){
console.log("VPBP")
await tfvis.show.modelSummary({name: 'Model Architecture', tab: 'Model'}, model);
await showAccuracy(model, data);
await showConfusion(model, data);
}