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let stars = [];let web = [];let circles = [];let lines = [];let tris = [];let n = 100;let shapeClassifier;function preload() { for (let i = 0; i < n; i++) { stars[i] = loadImage("data/stars/stars_"+i+".png"); web[i] = loadImage("data/web/web_"+i+".png"); circles[i] = loadImage("data/circles/circles_"+i+".png"); lines[i] = loadImage("data/lines/lines_"+i+".png"); tris[i] = loadImage("data/tris/tris_"+i+".png"); }}function setup() { createCanvas(128, 96); background(255); pixelDensity(1); let options = { inputs: [128, 96, 4], task: "imageClassification", debug: true, }; shapeClassifier = ml5.neuralNetwork(options); for (let i = 0; i < n; i++){ shapeClassifier.addData({ image: tris[i] }, { label: "triangle" }); shapeClassifier.addData({ image: stars[i] }, { label: "star" }); shapeClassifier.addData({ image: web[i] }, { label: "web" }); shapeClassifier.addData({ image: circles[i] }, { label: "circle" }); shapeClassifier.addData({ image: lines[i] }, { label: "line" }); } shapeClassifier.normalizeData(); shapeClassifier.train({epochs: 100}, finishedTraining);}function finishedTraining() { console.log("finishedTraining"); shapeClassifier.save();}// function draw() {// image(circles[4],0,0,width,height);// }