{
  "entries": [
    {"r":255, "g":0, "b":0, "color":"red-ish"},
    {"r":254, "g":0, "b":0, "color":"red-ish"},
    {"r":253, "g":0, "b":0, "color":"red-ish"},
    {"r":0, "g":255, "b":0, "color":"green-ish"},
    {"r":0, "g":254, "b":0, "color":"green-ish"},
    {"r":0, "g":253, "b":0, "color":"green-ish"},
    {"r":0, "g":0, "b":255, "color":"blue-ish"},
    {"r":0, "g":0, "b":254, "color":"blue-ish"},
    {"r":0, "g":0, "b":253, "color":"blue-ish"}
  ]
}

// Step 1: set your neural network options
const options = {
  dataUrl: "data/colorData.json",
  task: 'classification',
  inputs:['r', 'g', 'b'],
  outputs:['color'],
  debug: true
}

// Step 2: initialize your neural network
const nn = ml5.neuralNetwork(options, dataLoaded);

// Step 3: normalize data and train the model
function dataLoaded(){
  nn.normalizeData();
  trainModel();
}

// Step 4: train the model
function trainModel(){
  const trainingOptions = {
    epochs: 32,
    batchSize: 12
  }
  nn.train(trainingOptions, finishedTraining);
}

// Step 5: use the trained model
function finishedTraining(){
  classify();
}

// Step 6: make a classification
function classify(){
  const input = {
    r: 255, 
    g: 0, 
    b: 0
  }
  nn.classify(input, handleResults);
}

// Step 7: define a function to handle the results of your classification
function handleResults(error, result) {
    if(error){
      console.error(error);
      return;
    }
    console.log(result); // {label: 'red', confidence: 0.8};
}