let targetLabel = "C"

​

let state = 'collection';

​

function setup() {

  createCanvas(400, 400);

  let options = {

    inputs: ['x','y'], 

    outputs: ['label'],

    task: 'classification',

    debug: 'true'

  };

  model = ml5.neuralNetwork(options);

  background(255);

}

​

function keyPressed(){

  targetLabel = key.toUpperCase();

  if (key == 't'){

    state = 'training'

    console.log('Starting training');

    model.normalizeData();

    let options = {

      epochs: 200

    };

    model.train(options, whileTraining, finishTraining);

  }

}

​

function whileTraining(epoch, loss){

  console.log(epoch);

}

​

function finishTraining(){

  console.log('finished training');

  state = 'prediction';

}

​

function mousePressed(){

  let inputs = {

    x: mouseX,

    y: mouseY

  };

  if (state == 'collection'){

    let target = {

      label: targetLabel

    }

    model.addData(inputs,target);

    stroke(0);

    noFill();

    ellipse(mouseX,mouseY,24);

    fill(0)

    noStroke();

    textAlign(CENTER,CENTER);

    text(targetLabel,mouseX,mouseY);

  } else if(state == 'prediction'){

    model.classify(inputs, gotResults);

  }

}

​

function gotResults(error, results){

  if(error){

    console.log(error);

    return;

  }else {

    console.log(results);

    stroke(0);

    noFill();

    ellipse(mouseX,mouseY,24);

    fill(0,0,255);

    noStroke();

    textAlign(CENTER,CENTER);

    text(results[0].label,mouseX,mouseY);

  }

}

​

​