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Code viewer for World: Character recognition neur...

// Cloned by Marko on 1 Dec 2019 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;
const IMAGE_CHANNELS = 1;
const NUM_OUTPUT_CLASSES = 10;

// 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 = false;

// 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 = ( ZOOMPIXELS * 3 ) + 100;


const DOODLE_THICK = 10;    // thickness of doodle lines 
const DOODLE_BLUR = 2;      // blur factor applied to doodles 

const BATCH_SIZE = 512;

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;


// 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
}    



// 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> ";
   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;' 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: ---------------------------------------------------------

let model;

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 ( "https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.0.0/dist/tf.min.js", function()
   {
        $.getScript ( "/uploads/codingtrain/mnist.js", function()
        {
            loadMNIST ( function(data)    
            {
                mnist = data;
                imagesToInputs(mnist.train_images);
                imagesToInputs(mnist.test_images);
                mnist.train_labels = oneHotLabels(mnist.train_labels);
                mnist.test_labels = oneHotLabels(mnist.test_labels);
                console.log ("All data loaded into mnist object:");
                
                model = getModel();
                console.log ("Model built");
                
                train().then(() => {
                    console.log("Model trained");
                    AB.removeLoading();
                });
            });
            
        });
  });
}


// idea is to leave only significant pixels
function processPixel(pixel) {
    pixel /= 255;
    pixel = pixel / (2 - pixel);
    if(pixel < 0.4) {
        return 0;
    }
    
    return pixel;
}


// if image is not centered shift it as necessary
function centerImage(image) {
    let leftmost = PIXELS;
    let rightmost = 0;
    let highest = PIXELS;
    let lowest = 0;
    
    for(var row = 0; row < PIXELS; row++) {
        for(var col = 0; col < PIXELS; col++) {
            let filled = image[(row * PIXELS) + col] > 0;
            if(filled) {
                if(row < highest) {
                    highest = row;
                }
                if(row > lowest) {
                    lowest = row;
                }
                if(col < leftmost) {
                    leftmost = col;
                }
                if(col > rightmost) {
                    rightmost = col;
                }
            }
        }
    }
    
    let shiftX = Math.floor((PIXELS - rightmost - leftmost) / 2);
    let shiftY = Math.floor((PIXELS - highest - lowest) / 2);
    
    // init new image
    let newImage = [];
    for(var i = 0; i < PIXELSSQUARED; i++) {
        newImage[i] = 0;
    }
    
    for(var row = 0; row < PIXELS; row++) {
        for(var col = 0; col < PIXELS; col++) {
            let field = image[(row * PIXELS) + col];
            if(field > 0) {
                newImage[((row + shiftY) * PIXELS) + col + shiftX] = field;
            }
        }
    }
    
    return newImage;
}


// TensorFlow methods

function getModel() {
    const model = tf.sequential();
    
    model.add(tf.layers.conv2d({
        inputShape: [PIXELS, PIXELS, IMAGE_CHANNELS],
        kernelSize: 5,
        filters: 8,
        strides: 1,
        activation: 'relu',
        kernelInitializer: 'varianceScaling'
    }));
    
    model.add(tf.layers.maxPooling2d({poolSize: [2, 2], strides: [2, 2]}));
    
    model.add(tf.layers.conv2d({
        kernelSize: 5,
        filters: 16,
        strides: 1,
        activation: 'relu',
        kernelInitializer: 'varianceScaling'
    }));
    model.add(tf.layers.maxPooling2d({poolSize: [2, 2], strides: [2, 2]}));
    
    model.add(tf.layers.flatten());
    
    model.add(tf.layers.dense({
        units: NUM_OUTPUT_CLASSES,
        kernelInitializer: 'varianceScaling',
        activation: 'softmax'
    }));
    
    const optimizer = tf.train.adam();
        model.compile({
        optimizer: optimizer,
        loss: 'categoricalCrossentropy',
        metrics: ['accuracy'],
    });
    
    return model;
}

function train() {
    let td = trainData(mnist);
    
    return modelFit(td);
}


function oneHotLabels(labels) {
    let newLabels = Array();
    for(var i = 0; i < labels.length; i++) {
        let tmp = Array(NUM_OUTPUT_CLASSES).fill(0);
        tmp[labels[i]] = 1;
        newLabels.push(tmp);
    }
    
    return newLabels;
}

function imagesToInputs(images) {
    for(var i = 0; i < images.length; i++) {
        images[i] = getInputs(images[i]);
    }
}

function trainData(mnist) {
    let trainImages = tf.tensor2d(mnist.train_images, [mnist.train_images.length, PIXELSSQUARED]);
    let trainLabels = tf.tensor2d(mnist.train_labels, [mnist.train_labels.length, NUM_OUTPUT_CLASSES]);
    let testImages = tf.tensor2d(mnist.test_images, [mnist.test_images.length, PIXELSSQUARED]);
    let testLabels = tf.tensor2d(mnist.test_labels, [mnist.test_labels.length, NUM_OUTPUT_CLASSES]);
    
    return {
      trainImages: trainImages.reshape([mnist.train_images.length, PIXELS, PIXELS, IMAGE_CHANNELS]),
      trainLabels: trainLabels,
      testImages: testImages.reshape([mnist.test_images.length, PIXELS, PIXELS, IMAGE_CHANNELS]),
      testLabels: testLabels
    };
}

function modelFit(trainData) {
    return model.fit(trainData.trainImages, trainData.trainLabels, {
        batchSize: BATCH_SIZE,
        validationData: [trainData.testImages, trainData.testLabels],
        epochs: 10,
        shuffle: true
    });
}

function digitPrediction(img) {
    let imgTensor = tf.tensor2d(img, [1, PIXELSSQUARED]);
    
    return model.predict(imgTensor.reshape([1, PIXELS, PIXELS, IMAGE_CHANNELS]))
                .data()
}

function findPredictionLabels(arr) {
    let firstVal = 0;
    let secondVal = 0;
    let firstDigit = 0;
    let secondDigit = 0;
    
    console.log('extracting from labels');
    console.log(arr);
    for(var i = 0; i < 10; i++) {
        if(arr[i] > firstVal) {
            secondVal = firstVal;
            secondDigit = firstDigit;
            firstVal = arr[i];
            firstDigit = i;
        } else if(arr[i] > secondVal) {
            secondVal = arr[i];
            secondDigit = i;
        }
    }
    
    return [firstDigit, secondDigit];
}

// end TensorFlow


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] = processPixel(bright);       // normalise to 0 to 1
    }
    
    inputs = centerImage(inputs);
    return ( inputs );
}
 

function trainit (show)        // train the network with a single exemplar, from global var "train_index", show visual on or off 
{
  let img   = mnist.train_images[train_index];
  let label = mnist.train_labels[train_index];
  
  // optional - show visual of the image 
  if (show)                
  {
    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
  }

  // set up the inputs
  let inputs = getInputs ( img );       // get inputs from data array 

  // set up the outputs
  let targets = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
  targets[label] = 1;       // change one output location to 1, the rest stay at 0 

  // console.log(train_index);
  // console.log(inputs);
  // console.log(targets);

  train_inputs = inputs;        // can inspect in console 
  nn.train ( inputs, targets );

  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 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];

  // set up the inputs
  let inputs = getInputs ( img ); 
  
  test_inputs = inputs;        // can inspect in console 
  let prediction    = nn.predict(inputs);       // array of outputs 
  let guess         = findMax(prediction);      // the top output 

  total_tests++;
  if (guess == label)  total_correct++;

  let percent = (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 (a)         // return array showing indexes of no.1 and no.2 values in array 
{
  let no1 = 0;
  let no2 = 0;
  let no1value = 0;     
  let no2value = 0;
  
  for (let i = 0; i < a.length; i++) 
  {
    if (a[i] > no1value) 
    {
      no1 = i;
      no1value = a[i];
    }
    else if (a[i] > no2value) 
    {
      no2 = i;
      no2value = a[i];
    }
  }
  
  var b = [ no1, no2 ];
  return b;
}


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

function findMax (a)        
{
  let no1 = 0;
  let no1value = 0;     
  
  for (let i = 0; i < a.length; i++) 
  {
    if (a[i] > no1value) 
    {
      no1 = i;
      no1value = a[i];
    }
  }
  
  return no1;
}




// --- 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');
    
if ( do_training )    
{
  // do some training per step 
    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
            //   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 = 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    = nn.predict(inputs);       // array of outputs 
  let guess         = findMax(prediction);      // the top output 

   thehtml =   " We classify it as: " + greenspan + 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
}
      
      
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] = processPixel(img.pixels[i * 4]);
    }
    
    inputs = centerImage(inputs);
    
    doodle_inputs = inputs;     // can inspect in console 
    // console.log('doodle inputs set');
    // console.log(doodle_inputs);
    
    digitPrediction(inputs).then((arr) => {
        let [first, second] = findPredictionLabels(arr);
        
        thehtml =   " We classify it as: " + first + "</span> <br>" +
                " No.2 guess is: " + second + "</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);
}