script.js

import {MnistData} from './data.js';

const trainingExamples = [
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];

var canvas;
var context;
var model;
var modelReady = false;
var hiddenLayerHelperModels = [];
const hiddenLayerOutputScaling1 = 3;
const hiddenLayerOutputScaling2 = 6;
const layerWeightScaling = 12;
const trainingExamplesScaling = 4;



async function run() {  
	canvas = document.getElementById('canvas');
	context = canvas.getContext("2d");
	
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");
	model = await tf.loadLayersModel('localstorage://model01');
	
	showNetworkParameters();
	
	showTrainingExamples();
	
	
	modelReady = true;
	recalculate();
}

document.addEventListener('DOMContentLoaded', run);



function arrayToCanvas(wrapperElement, data, width, height, canvasWidthScale, canvasHeightScale){
	if(data.length != width*height){
		console.error("arrayToCanvas: data mismatch to width and height, expected length: " + width +" x " + height + " = " + (width*height) + ", but got: " + data.length);
		return;
	}
	wrapperElement.innerHTML = '<canvas width="'+(canvasWidthScale*width)+'" height="'+(canvasHeightScale*height)+'"></canvas>';
	var ctx = wrapperElement.children[0].getContext("2d");
	var imgData = ctx.createImageData(canvasWidthScale*width, canvasHeightScale*height);
	for(var i=0; i<imgData.data.length; i+=4){
		imgData.data[i+3] = 255*(data[Math.floor(i/4/canvasWidthScale)%width + Math.floor(Math.floor(i/4/canvasWidthScale)/(width*canvasHeightScale))*width]);
	}
	ctx.putImageData(imgData, 0, 0);
}

function arrayToCanvas2Color(wrapperElement, data, width, height, canvasWidthScale, canvasHeightScale){
	if(data.length != width*height){
		console.error("arrayToCanvas: data mismatch to width and height, expected length: " + width +" x " + height + " = " + (width*height) + ", but got: " + data.length);
		return;
	}
	wrapperElement.innerHTML = '<canvas width="'+(canvasWidthScale*width)+'" height="'+(canvasHeightScale*height)+'"></canvas>';
	var ctx = wrapperElement.children[0].getContext("2d");
	var imgData = ctx.createImageData(canvasWidthScale*width, canvasHeightScale*height);
	for(var i=0; i<imgData.data.length; i+=4){
		if(data[Math.floor(i/4/canvasWidthScale)%width + Math.floor(Math.floor(i/4/canvasWidthScale)/(width*canvasHeightScale))*width]>0){
			imgData.data[i]   = 255*(1-data[Math.floor(i/4/canvasWidthScale)%width + Math.floor(Math.floor(i/4/canvasWidthScale)/(width*canvasHeightScale))*width]); //red
			imgData.data[i+1] = 255; //green
			imgData.data[i+2] = 255*(1-data[Math.floor(i/4/canvasWidthScale)%width + Math.floor(Math.floor(i/4/canvasWidthScale)/(width*canvasHeightScale))*width]); //blue
		}
		else{
			imgData.data[i]   = 255; //red
			imgData.data[i+1] = 255*(1+data[Math.floor(i/4/canvasWidthScale)%width + Math.floor(Math.floor(i/4/canvasWidthScale)/(width*canvasHeightScale))*width]); //green
			imgData.data[i+2] = 255*(1+data[Math.floor(i/4/canvasWidthScale)%width + Math.floor(Math.floor(i/4/canvasWidthScale)/(width*canvasHeightScale))*width]); //blue
		}
		imgData.data[i+3] = 255; //alpha
	}
	ctx.putImageData(imgData, 0, 0);
}

function showNetworkParameters(){
	hiddenLayerHelperModels[0] = tf.model({inputs: model.inputs, outputs: model.layers[0].output});
	hiddenLayerHelperModels[1] = tf.model({inputs: model.inputs, outputs: model.layers[2].output});
	
	var modelLayer = 0;
	var weights = model.layers[modelLayer].weights[0].val.dataSync();
	var canvasWrappers = document.getElementsByClassName("canvasWrappers5");
	for(var i=0;i<model.layers[modelLayer].weights[0].val.shape[3];i++){
		var kernel = [];
		for(var j=0;j<weights.length;j+=model.layers[modelLayer].weights[0].val.shape[3]){
			kernel.push(1/(1+Math.pow(Math.E,-2*weights[i+j])));
		}
		arrayToCanvas(canvasWrappers[i],kernel,model.layers[modelLayer].weights[0].val.shape[0],model.layers[modelLayer].weights[0].val.shape[1],layerWeightScaling,layerWeightScaling);
	}
}

function showTrainingExamples(){
	var wrapperElement = document.getElementById("trainingData");
	for(var i=0; i<wrapperElement.children.length && i<trainingExamples.length; i++){
		arrayToCanvas(wrapperElement.children[i],trainingExamples[i].map(item => item/255),28,28,trainingExamplesScaling,trainingExamplesScaling);
	}
}

$('#canvas').mousedown(function(e){
  paint = true;
  addClick(e.pageX - $('#canvas').offset().left, e.pageY - $('#canvas').offset().top);
  redraw();
});

$('#canvas').mousemove(function(e){
  if(paint){
    addClick(e.pageX - $('#canvas').offset().left, e.pageY - $('#canvas').offset().top, true);
    redraw();
  }
});

$('#canvas').mouseup(function(e){
  paint = false;
  recalculate();
});

$('#canvas').mouseleave(function(e){
  paint = false;
  recalculate();
});

$('#clearCanvas').mousedown(function(e)
	{
		clickX = new Array();
		clickY = new Array();
		clickDrag = new Array();
		clearCanvas(); 
		recalculate();
	});
	
function clearCanvas()
{
	context.clearRect(0, 0, canvas.width, canvas.height);
}

var clickX = new Array();
var clickY = new Array();
var clickDrag = new Array();
var paint;

function addClick(x, y, dragging)
{
  clickX.push(x);
  clickY.push(y);
  clickDrag.push(dragging);
}

function redraw(){
  context.clearRect(0, 0, context.canvas.width, context.canvas.height); // Clears the canvas
  
  context.strokeStyle = "#000000";
  context.lineJoin = "round";
  context.lineWidth = 8;
			
  for(var i=0; i < clickX.length; i++) {		
    context.beginPath();
    if(clickDrag[i] && i){
      context.moveTo(clickX[i-1], clickY[i-1]);
     }else{
       context.moveTo(clickX[i]-1, clickY[i]);
     }
     context.lineTo(clickX[i], clickY[i]);
     context.closePath();
     context.stroke();
  }
}

function recalculate(){
    var dataRGB = context.getImageData(0,0,canvas.width,canvas.height).data;
    var dataBW = [];
    for(var i=0;i<dataRGB.length;i++){
      if(i%4 == 3) dataBW.push(dataRGB[i]/255);
    }
    
    if(modelReady){
		var input = tf.pool(tf.tensor4d(dataBW, [1, 224, 224, 1]), 8, 'avg', 'valid', 1, 8);
		var prediction = model.predict(input).dataSync();
	    for(var i=0;i<prediction.length;i++){
		  document.getElementById("probabilities").children[0].children[i].children[1].innerHTML = Math.floor(Math.round(prediction[i]*1000)/10) + "," + (Math.round(prediction[i]*1000)%10) + "%";
		  document.getElementById("probabilities").children[0].children[i].children[2].children[0].style.width = Math.round(prediction[i]*100) + "px";
	      if(prediction[i]==Math.max(...prediction)){
			  document.getElementById("probabilities").children[0].children[i].children[0].style.fontWeight = "bold";
			  document.getElementById("probabilities").children[0].children[i].children[1].style.fontWeight = "bold";
		  }
		  else{
			  document.getElementById("probabilities").children[0].children[i].children[0].style.fontWeight = "";
			  document.getElementById("probabilities").children[0].children[i].children[1].style.fontWeight = "";
		  }
		}
		
		arrayToCanvas(document.getElementById("canvasWrapperInput"),input.dataSync(),input.shape[1],input.shape[2],hiddenLayerOutputScaling1,hiddenLayerOutputScaling1);
		
		for(var i=0;i<hiddenLayerHelperModels.length;i++){
			var hiddenLayerPrediction = hiddenLayerHelperModels[i].predict(input);
			var hiddenLayerOutput = hiddenLayerPrediction.dataSync();
			var hiddenLayerPredictionPooled = tf.pool(hiddenLayerPrediction,2,'max','valid',1,2);
			var hiddenLayerOutputPooled = hiddenLayerPredictionPooled.dataSync();
			var canvasWrappersConv = document.getElementsByClassName("canvasWrappers"+i*2);
			var canvasWrappersPool = document.getElementsByClassName("canvasWrappers"+(i*2+1));
			for(var j=0;j<hiddenLayerHelperModels[i].outputShape[3];j++){
				var hiddenLayerSingleFilterOutput = [];
				var hiddenLayerSingleFilterOutputPooled = [];
				for(var k=0;k<hiddenLayerOutput.length;k+=hiddenLayerPrediction.shape[3]){
					hiddenLayerSingleFilterOutput.push(hiddenLayerOutput[j+k]);
				}
				for(var k=0;k<hiddenLayerOutputPooled.length;k+=hiddenLayerPredictionPooled.shape[3]){
					hiddenLayerSingleFilterOutputPooled.push(hiddenLayerOutputPooled[j+k]);
				}
				if(i==0){
					arrayToCanvas(canvasWrappersConv[j],hiddenLayerSingleFilterOutput,hiddenLayerPrediction.shape[1],hiddenLayerPrediction.shape[2],hiddenLayerOutputScaling1,hiddenLayerOutputScaling1);
					arrayToCanvas(canvasWrappersPool[j],hiddenLayerSingleFilterOutputPooled,hiddenLayerPredictionPooled.shape[1],hiddenLayerPredictionPooled.shape[2],hiddenLayerOutputScaling1,hiddenLayerOutputScaling1);
				}
				else{
					arrayToCanvas(canvasWrappersConv[j],hiddenLayerSingleFilterOutput,hiddenLayerPrediction.shape[1],hiddenLayerPrediction.shape[2],hiddenLayerOutputScaling2,hiddenLayerOutputScaling2);
					arrayToCanvas(canvasWrappersPool[j],hiddenLayerSingleFilterOutputPooled,hiddenLayerPredictionPooled.shape[1],hiddenLayerPredictionPooled.shape[2],hiddenLayerOutputScaling2,hiddenLayerOutputScaling2);
				}
			}
		}
		
	}
}

$('#saveModel').mousedown(function(e)
	{
		model.save('localstorage://' + document.getElementById("nameModel").value);
		document.getElementById("modelState").innerHTML = "Saved Model";
	});

$('#loadModel').mousedown(async function(e)
	{
		model = await tf.loadLayersModel('localstorage://' + document.getElementById("nameModel").value);
		modelReady = true;
		showNetworkParameters();
		recalculate();
		document.getElementById("modelState").innerHTML = "Loaded Model";
	});
	
	
$('#trainModel').mousedown(async function(e)
	{
		document.getElementById("trainingProgress").innerHTML = "loading...";
		const data = new MnistData();
		await data.load();
		document.getElementById("trainingProgress").innerHTML = "training...";
		const epochs = parseInt(document.getElementById("trainingEpochs").value);
		const batchSize = parseInt(document.getElementById("trainingBatchSize").value);
		const trainDataSize = parseInt(document.getElementById("trainingTrainDataSize").value);
		await train(model, data, epochs, batchSize, trainDataSize);
		document.getElementById("trainingProgress").innerHTML = "ready";
		modelReady = true;
		showNetworkParameters();
		recalculate();
	});
	
$('#newModel').mousedown(async function(e)
	{
		model = getModel();
		modelReady = true;
		showNetworkParameters();
		recalculate();
		document.getElementById("modelState").innerHTML = "Created New Model";
	});


function getModel() {
  const model = tf.sequential();
  
  const IMAGE_WIDTH = 28;
  const IMAGE_HEIGHT = 28;
  const IMAGE_CHANNELS = 1;  
  
  // In the first layer of out convolutional neural network we have 
  // to specify the input shape. Then we specify some paramaters for 
  // the convolution operation that takes place in this layer.
  model.add(tf.layers.conv2d({
    inputShape: [IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS],
    kernelSize: 5,
    filters: 8,
    strides: 1,
    activation: 'relu',
    kernelInitializer: 'varianceScaling'
  }));

  // The MaxPooling layer acts as a sort of downsampling using max values
  // in a region instead of averaging.  
  model.add(tf.layers.maxPooling2d({poolSize: [2, 2], strides: [2, 2]}));
  
  // Repeat another conv2d + maxPooling stack. 
  // Note that we have more filters in the convolution.
  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]}));
  
  // Now we flatten the output from the 2D filters into a 1D vector to prepare
  // it for input into our last layer. This is common practice when feeding
  // higher dimensional data to a final classification output layer.
  model.add(tf.layers.flatten());

  // Our last layer is a dense layer which has 10 output units, one for each
  // output class (i.e. 0, 1, 2, 3, 4, 5, 6, 7, 8, 9).
  const NUM_OUTPUT_CLASSES = 10;
  model.add(tf.layers.dense({
    units: NUM_OUTPUT_CLASSES,
    kernelInitializer: 'varianceScaling',
    activation: 'softmax'
  }));

  
  // Choose an optimizer, loss function and accuracy metric,
  // then compile and return the model
  const optimizer = tf.train.adam();
  model.compile({
    optimizer: optimizer,
    loss: 'categoricalCrossentropy',
  });

  return model;
}

async function train(model, data, epochs, batchSize, trainDataSize) {
  
  const BATCH_SIZE = batchSize;
  const TRAIN_DATA_SIZE = trainDataSize;

  const [trainXs, trainYs] = tf.tidy(() => {
    const d = data.nextTrainBatch(TRAIN_DATA_SIZE);
    return [
      d.xs.reshape([TRAIN_DATA_SIZE, 28, 28, 1]),
      d.labels
    ];
  });

  return model.fit(trainXs, trainYs, {
    batchSize: BATCH_SIZE,
    epochs: epochs,
    shuffle: true
  });
}

$('#menuTraining').mousedown(function(e)
	{
		document.getElementById("training").style.display = "initial";
		document.getElementById("hiddenLayerOutput").style.display = "none";
		document.getElementById("trainingData").style.display = "none";
		document.getElementById("networkArchitecture").style.display = "none";
		
		document.getElementById("menuTraining").className = "menuItem activeMenuItem";
		document.getElementById("menuHiddenLayerOutput").className = "menuItem";
		document.getElementById("menuTrainingData").className = "menuItem";
		document.getElementById("menuArchitecture").className = "menuItem";
	});
	
$('#menuHiddenLayerOutput').mousedown(function(e)
	{
		document.getElementById("training").style.display = "none";
		document.getElementById("hiddenLayerOutput").style.display = "initial";
		document.getElementById("trainingData").style.display = "none";
		document.getElementById("networkArchitecture").style.display = "none";
		
		document.getElementById("menuTraining").className = "menuItem";
		document.getElementById("menuHiddenLayerOutput").className = "menuItem activeMenuItem";
		document.getElementById("menuTrainingData").className = "menuItem";
		document.getElementById("menuArchitecture").className = "menuItem";
	});
	
$('#menuTrainingData').mousedown(function(e)
	{
		document.getElementById("training").style.display = "none";
		document.getElementById("hiddenLayerOutput").style.display = "none";
		document.getElementById("trainingData").style.display = "initial";
		document.getElementById("networkArchitecture").style.display = "none";
		
		document.getElementById("menuTraining").className = "menuItem";
		document.getElementById("menuHiddenLayerOutput").className = "menuItem";
		document.getElementById("menuTrainingData").className = "menuItem activeMenuItem";
		document.getElementById("menuArchitecture").className = "menuItem";
	});
	
$('#menuArchitecture').mousedown(function(e)
	{
		document.getElementById("training").style.display = "none";
		document.getElementById("hiddenLayerOutput").style.display = "none";
		document.getElementById("trainingData").style.display = "none";
		document.getElementById("networkArchitecture").style.display = "initial";
		
		document.getElementById("menuTraining").className = "menuItem";
		document.getElementById("menuHiddenLayerOutput").className = "menuItem";
		document.getElementById("menuTrainingData").className = "menuItem";
		document.getElementById("menuArchitecture").className = "menuItem activeMenuItem";
	});