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);

}

​

function keyPressed() {

  if(key === 't') {

    state = 'training';

    model.normalizeData();

    let options = {

      epochs: 200,

    }

    model.train(options, whileTraining, finishedTraining);

  } else {

    targetLabel = key.toUpperCase();

  }

}

​

function whileTraining(epoch, loss)  {

  console.log(epoch);

}

​

function finishedTraining() {

  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.error(error);

    return;

  }

  console.log(results);

  stroke(0,0,255,100);

  noFill();

  ellipse(mouseX, mouseY, 24);

  fill(0);

  noStroke();

  textAlign(CENTER, CENTER);

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

}

​