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Code viewer for World: ConvNet Port (clone by Jac...

// Cloned by Jack O'Brien on 5 Dec 2020 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 nohidden1 = 64;
const nohidden2 = 64;
const nooutput = 10;

const learningrate = 0.01;   // default 0.1  

// should we train every timestep or not 
let do_training = true;

// how many to train and test per timestep 
const TRAINPERSTEP = 32;
const TESTPERSTEP  = 16;

// multiply it by this to magnify for display 
const ZOOMFACTOR    = 10;                        
const ZOOMPIXELS    = ZOOMFACTOR * PIXELS; 

// 3 rows of
// large image + 50 gap + small image    
// 50 gap between rows 

const canvaswidth = ( PIXELS + ZOOMPIXELS ) + 415;
const canvasheight = ( ZOOMPIXELS * 3 ) + 100;


const DOODLE_THICK = 16;    // 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 nn;

let trainrun = 1;
let train_index = 0;

let testrun = 1;
let test_index = 0;
let total_tests = 0;
let total_correct = 0;
let globalScore = 0;
let learningRateFlag = false;
// 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?  

// ConfusionMatrix
let confusionM;
let canvas;
// let draw;
let ctxdraw;
var thedoodleimage;
var doodleImageData;

var convnet;
let trainer;
const inputSize = 24;
// save inputs to 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 ( -0.9, 0.9 ) );
            // Coding Train default is -1 to 1
}    

// CSS trick 
// make run header bigger 
 $("#runheaderbox").css ( { "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> " +
        "<div></div>";
        // <canvas id='doodleCanvas' style='width:358px;height:400px'></canvas>
   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 == false?do_training = true: do_training = false;' class='normbutton' >Toggle training</button> " +
        " <button class='normbutton' onclick='saveModel()'>Save Model</button><button class='normbutton' onclick='loadModel()'>Load Model</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);
  
//   draw = document.getElementById("defaultCanvas0");
//   ctxdraw = draw.getContext("2d");
//   painting = false,
//   lastX = 0,
//   lastY = 0,
//   lineThickness = 1;
  
// JS load other JS 
// maybe have a loading screen while loading the JS and the data set 

      AB.loadingScreen();
 
 $.getScript ( "/uploads/jobrien14/webcnn.js", function()
 {
   $.getScript ( "/uploads/jobrien14/mathutils.js", function()
   {
        $.getScript ( "/uploads/jobrien14/mnist.js", function()
        {
            $.getScript ( "/uploads/jobrien14/matrix.js", function()
            {
                console.log ("All JS loaded");
                createModel();
                confusionM = new Matrix(10, 10);
                loadData();
            });
        });
   });
 });
}

//Code to create initial model architecture
function createModel(){
    convnet = new WebCNN;
	convnet.newLayer( { name: "image", type: LAYER_TYPE_INPUT_IMAGE, width: inputSize, height: inputSize, depth: 1 } );
	convnet.newLayer( { name: "conv1", type: LAYER_TYPE_CONV, units: 10, kernelWidth: 5, kernelHeight: 5, strideX: 1, strideY: 1, padding: false, activation: ACTIVATION_RELU } );
	convnet.newLayer( { name: "pool1", type: LAYER_TYPE_MAX_POOL, poolWidth: 2, poolHeight: 2, strideX: 2, strideY: 2 } );
	convnet.newLayer( { name: "conv2", type: LAYER_TYPE_CONV, units: 20, kernelWidth: 5, kernelHeight: 5, strideX: 1, strideY: 1, padding: false, activation: ACTIVATION_RELU } );
	convnet.newLayer( { name: "pool2", type: LAYER_TYPE_MAX_POOL, poolWidth: 2, poolHeight: 2, strideX: 2, strideY: 2 } );
	convnet.newLayer( { name: "out", type: LAYER_TYPE_FULLY_CONNECTED, units: 10, activation: ACTIVATION_SOFTMAX } );
	convnet.initialize();

	convnet.setLearningRate( 0.01 );
	convnet.setMomentum( 0.9 );
	convnet.setLambda( 0.0 );
	return convnet;
}

function saveModel(){
    console.log('Saving Model to server')
    AB.saveData ( nn.serialize()); 
}

function loadModel()
{
	 AB.restoreData ( function ( a )            
	 {
	    console.log('Restoring')
// 		nn.deserialize(a);
	 });
} 
// load data set from local file (on this server)

function loadData()    
{
  loadMNIST ( function(data)    
  {
    mnist = data;
    console.log ("All data loaded into mnist object:")
    console.log(mnist);
    AB.removeLoading();     // if no loading screen exists, this does nothing 
  });
}



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 );
}

// Image processing
// returns the image in a suitable format that the model expects
//  expects a 3 dimensional object with a height, width and pixel data, must also match model input
function imagePreProcessing(img, Dimensions) {
    return {
        width: Dimensions,
        height: Dimensions,
        data: getImage(randomCrop(img, Dimensions), Dimensions).pixels
 }
} 


// Randomly crops the input image so as to reduce overfitting and imporve generalisation
// Mentioned on the associate website and github
function randomCrop(img, Dimensions) {
    const cropVal = PIXELS - Dimensions;
    var randVal1 = Math.floor(Math.random() * cropVal);
    var randVal2 = Math.floor(Math.random() * cropVal);
    let newWidth = randVal1 + Dimensions;
    let newHeight = randVal2 + Dimensions;
    let outputImg = [];
    for (let i = randVal1; i < newWidth; i++)
        for (let j = randVal2; j < newHeight; j++)
            outputImg.push(img[i  * PIXELS + j]);
    return outputImg
}


function trainit (show)        // train the network with a single exemplar, from global var "train_index", show visual on or off 
{
  if (train_index % TRAINPERSTEP !== 0)
      return void train_index++;
  let img   = mnist.train_images[train_index];
  let label = mnist.train_labels[train_index];
  let trainImages = [];
  let trainLabels = [];

    //  For this implementation the model is trained in batches as opposed to on single images at a time like the original implementation
    //  Starting from the current train_index, a batch of images are loaded into an array as this is what the model expects as an input
    for(var i =0;i< TRAINPERSTEP;i++){
        trainImages.push(imagePreProcessing(mnist.train_images[train_index+i], inputSize));
        trainLabels.push(mnist.train_labels[train_index+i]);
    }
  var theimage = getImage ( img );    // get image from data array 
    image ( theimage,   0,                ZOOMPIXELS+50,    ZOOMPIXELS,     ZOOMPIXELS  );      // magnified 
    image ( theimage,   ZOOMPIXELS+50,    ZOOMPIXELS+50,    PIXELS,         PIXELS      );      // original
      

// The model is trained on the current batch with the given labels
  convnet.trainCNNClassifier(trainImages, trainLabels);

  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++;
  }
}

let img1;
function testit()    // test the network with a single exemplar, from global var "test_index"
{ 
  let img   = mnist.test_images[test_index];
  let label = mnist.test_labels[test_index];

 img1=img;
  // set up the inputs
  let inputs = getInputs ( img ); 
  
  test_inputs = inputs;        // can inspect in console 
//   similar to above, the image needs to be put through the same process as the training data to have the best possibility for a match
  let prediction    = convnet.classifyImages([imagePreProcessing(img, inputSize)]);       // array of outputs 
  let guess         = findMax(prediction);      // the top output 
//   let confidence         = 100; 
  
//   
  confusionM.data[guess][label]++;
  total_tests++;
  if (guess == label)  total_correct++;

  let percent = (total_correct / total_tests) * 100 ;
  globalScore = percent
  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) );
    confusionM.print();
    confusionM = new Matrix(10, 10);
    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) 

let no1value;
let no2value;
let no1;
let no2;

// function was editied to work with the convnet model and to find the top 2 predictions
function find12 (a)         // return array showing indexes of no.1 and no.2 values in array 
{
   no1 = 0;
   no2 = 0;
  
 
//   Find no1
  for(var i=0;i<a[0].size;i++){
    a[0].getValue(0, 0, i)>a[0].getValue(0, 0, no1)?no1=i:no1=no1;
  }
  
  //   Find no2
  for(var j=0;j<a[0].size;j++){
    (a[0].getValue(0, 0, j)>a[0].getValue(0, 0, no2) && a[0].getValue(0, 0, j)<a[0].getValue(0, 0, no1))?no2=j:no2=no2;
  }
  
  no1value = a[0].getValue(0, 0, no1);     
  no2value = a[0].getValue(0, 0, no2);
  var b = [[ no1, no1value ], [no2, no2value]];
  return b;
}


// just get the maximum - separate function for speed - done many times 
// find our guess - the max of the output nodes array

// Altered similar to above, uses built in .get Value to find the score for each of the nodes and returns the highest score

function findMax (a)        
{
    let test= a[0];
    var guess =0;
    for(var i=0;i<test.size;i++){
        test.getValue(0, 0, i)>test.getValue(0, 0, guess)?guess=i:guess=guess
    }

  return guess;
}




// --- the draw function -------------------------------------------------------------
// every step:
 
function draw() 
{
  // check if libraries and data loaded yet:
  if ( typeof mnist == 'undefined' ) return;
    

// how can we get white doodle on black background on yellow canvas?
//        background('#ffffcc');    doodle.background('black');

    //   background ('black');
    stroke(0);
    fill(0);
    rect(0, 0, ( PIXELS + ZOOMPIXELS ) + 50, ( ZOOMPIXELS * 3 ) + 100);
    fill(255);
    rect(( PIXELS + ZOOMPIXELS ) + 58, ( PIXELS + ZOOMPIXELS ) + 58, ( PIXELS + ZOOMPIXELS ) + 50, ( ZOOMPIXELS * 3 ) + 100);
if ( do_training )    
{

    for (let i = 0; i < TRAINPERSTEP; i++) 
    {
      if (i == 0){
        trainit(true);
      }        // show only one per step - still flashes by  
      else           
        trainit(false);
    }
    
  // do some testing per step 
    for (let i = 0; i < TESTPERSTEP; i++) 
      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 = 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
            // doodle.filter (ERODE);
            // doodle.filter (ERODE);
            // doodle.filter (ERODE);
            // doodle.filter (ERODE);
            // doodle.filter (ERODE);
            
            console.log (doodle);
      }
  }
    textSize(22);
    fill(255);
    text("Confusion Matrix", ( PIXELS + ZOOMPIXELS ) + 140, ( PIXELS + ZOOMPIXELS)+50);
    for(var i = 0;i<10;i++){
        for(var j = 0;j<10;j++){
            var innerValue = confusionM.data[i][j];
            var x = (( PIXELS + ZOOMPIXELS ) + 58)+(i*35);
            var y = (( PIXELS + ZOOMPIXELS ) + 58)+(j*35);
            // stroke(0);
            squareColor = color(80, 20, 255);
            squareColor.setAlpha(255*(innerValue/1000));
            fill(squareColor);
            rect(x, y, 35, 35);
            textSize(12);
            fill(0);
            text(innerValue, x+10, y+25);
            
        }
    }
}


//--- 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  i = AB.randomIntAtoB ( 0, NOTEST - 1 );  
    
    demo        = mnist.test_images[i];     
    var label   = mnist.test_labels[i];
    
   thehtml =  "Test image no: " + i + "<br>" + 
            "Classification: " + label + "<br>" ;
   AB.msg ( thehtml, 8 );
   
   // type "demo" in console to see raw data 
}


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
}


function guessDemo()
{
   let inputs = getInputs ( demo ); 
   
  demo_inputs = inputs;  // can inspect in console 
  
  
  let prediction = convnet.classifyImages([imagePreProcessing(demo, inputSize)]); 
  let guess             = findMax(prediction); 
  

   thehtml =   " We classify it as: " + greenspan + guess + "</span>" ;
   AB.msg ( thehtml, 9 );
}



let doodle1;
//--- 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
    
}
      

function formatDoodle(img, Dimensions){
    let outputImg = [];
    for (let i = 0; i < Dimensions; i++)
        for (let j = 0; j < Dimensions; j++)
            outputImg.push(img[i  * PIXELS + j]);
    // return outputImg
    return {
        width: Dimensions,
        height: Dimensions,
        data: getImage(outputImg).pixels
 }
}
function guessDoodle() 
{
    if(mousedrag===false)return;
   // doodle is createGraphics not createImage
   let img = doodle.get();
  
  img.resize ( PIXELS, PIXELS );     
  img.loadPixels();
  doodle1=img;

  
  let inputs = [];
  for (let j = 0; j < PIXELS; j++) {
    /** @type {!Array} */
    inputs[j] = [];
    for (let i = 0; i < PIXELS; i++) {
      inputs[j][i] = img.pixels[4 * (j * PIXELS + i)];
    }
  }

//   doodle_inputs = inputs;     // can inspect in console 
//   thedoodleimage = getImage ( doodle_inputs );
//   doodleImageData = thedoodleimage.imageData;


  // feed forward to make prediction 
  let prediction = convnet.classifyImages([formatDoodle(inputs, inputSize)]); 
  let b             = find12(prediction);  


  thehtml =   " We classify it as: " + greenspan + b[0][0] + "</span> with a confidence of: " + (b[0][1]*100).toFixed(2) + "%<br>" +
            " No.2 guess is: " + greenspan + b[1][0] + "</span> with a confidence of: " + (b[1][1]*100).toFixed(2) + "%";
  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);
}


// Penicl Drawing