gave up on saving the NL, moving onto poseClassification

projects/Wint24/ML/nauralNetwork/js/script.js

@@ -8,23 +8,25 @@ let model;
 let targetLabel = "C";
 //let trainingData = [];
 
-let state = "collection";
+let state = "collection"; //begins by collecting data, might have to be changed
 let env;
 let wave;
 
 let notes = {
+  //notes used by the sound stuff
   C: 261.6256,
   D: 293.6648,
   E: 329.6276,
   F: 349.2282,
   G: 391.9954,
-  A: 440.0000,
-  B: 493.8833
+  A: 440.0,
+  B: 493.8833,
 };
 
 function setup() {
   createCanvas(400, 400);
 
+  //sound stuff
   env = new p5.Envelope();
   env.setADSR(0.05, 0.1, 0.5, 1);
   env.setRange(1.2, 0);
@@ -35,41 +37,76 @@ function setup() {
   wave.start();
   wave.freq(440);
   wave.amp(env);
+  //end sound stuff
 
   let options = {
+    //parameters for network. 2 inputs, and outputs are label.
+    //network will use the classify task, and debug is true to
+    //show the graph when training.
     inputs: ["x", "y"],
     outputs: ["label"],
     task: "classification",
     debug: "true",
   };
 
-  model = ml5.neuralNetwork(options);
+  model = ml5.neuralNetwork(options); //this begins the model with the above params.
+
+  model.loadData("./mouse-notes.json", dataLoaded); //loads training data
+
+  background(255);
+}
+
+function dataLoaded() {   
+  //THIS IS BROKEN, I CANT FIND OUT WHY XS' X AND Y VALS ARE NORMALIZED AND HOW TO REVERT THAT
+  //ALSO, THE JSON RETURNS AS EMPTY IF TRAINING DOES NOT START
+
+
+  // console.log(model.data);
+
+  let data = model.data.training;
+
+  for (let i = 0; i < data.length; i++) {
+    let inputs = data[i].xs;
+    let target = data[i].ys;
+
+    stroke(0);
+    noFill();
+    ellipse(inputs.x, inputs.y, 24);
+    fill(0);
+    noStroke();
+    textAlign(CENTER, CENTER);
+    text(target.label, inputs.x, inputs.y);
+  }
+  state = "training";
+  console.log("starting training!");
+  model.normalizeData(); //this makes all numbers relative to 0 and 1
+  let options = {
+    epochs: 100, //200 cycles, takes longer but is more accurate
+  };
+  model.train(options, whileTraining, finishedTraining); //using options params, calls whileTraining during training, and calls finishedTraining when done training,
 }
 
 function keyPressed() {
   if (key == "t") {
-    state = "training";
-    console.log("starting training!");
-    model.normalizeData();
-    let options = {
-      epochs: 200,
-    };
-    model.train(options, whileTraining, finishedTraining);
-  } else if (key == 's'){
-    model.saveData('mouse-notes');
+    //assign a train button
+  } else if (key == "s") {
+    //assign a save button
+    model.saveData("mouse-notes"); //call it mouse-notes
   } else {
-    targetLabel = key.toUpperCase();
+    targetLabel = key.toUpperCase(); //letter pressed to show network
   }
 }
 
 function whileTraining(epoch, loss) {
   //loss is error of incorrect guesses
-  console.log(epoch);
+  console.log(epoch); //for each cycle, print the epoch to show progress
 }
 
 function finishedTraining() {
+  //self explanatory
   console.log("finished training!");
   state = "prediction";
+  //dataLoaded();
 }
 
 function mousePressed() {
@@ -79,12 +116,14 @@ function mousePressed() {
   };
 
   if (state == "collection") {
+    //when in learning state
     let target = {
       label: targetLabel,
     };
 
-    model.addData(inputs, target);
+    model.addData(inputs, target); //add data that shows what each letter should be.
 
+    //all this is user made.
     stroke(0);
     noFill();
     ellipse(mouseX, mouseY, 24);
@@ -112,9 +151,9 @@ function gotResults(error, results) {
   fill(0);
   noStroke();
   textAlign(CENTER, CENTER);
-  let label = results[0].label;
+  let label = results[0].label; //whatever is the most confident, make it that letter
   text(label, mouseX, mouseY);
 
-  wave.freq(notes[label]);
+  wave.freq(notes[label]); //then using that confident letter, play that note
   env.play();
 }

projects/Wint24/ML/nauralNetwork/mouse-notes.json

@@ -0,0 +1 @@
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