// const data = [

//   {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 2: set your neural network options

// const options = {

//   task: 'classification',

//   debug: true,

//   hiddenUnits: 3

// }

const options = {

  dataUrl: "data/entries.json",

  task: 'classification',

  inputs:["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"],

  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();

    nn.neuralNetwork.model.layers[0].getWeights()[0].print()

  nn.neuralNetwork.model.layers[0].getWeights()[1].print()

console.log(nn.neuralNetwork.model.layers)

​

}

​

// 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};

}

​