//

// This software is released under the MIT License.

// https://opensource.org/licenses/MIT

​

/* ===

ml5 Example

Image classification using MobileNet and p5.js

This example uses a callback pattern to create the classifier

=== */

let nn;

​

const options = {

  inputs: 1,

  outputs: 2,

  task: 'classification',

  debug: true

}

​

function setup(){

  createCanvas(400, 400);

  nn = ml5.neuralNetwork(options);

​

​

  console.log(nn)

  createTrainingData();

  nn.normalizeData();

​

  const trainingOptions={

    batchSize: 24,

    epochs: 32

  }

  nn.train(trainingOptions,finishedTraining); // if you want to change the training options

  // nn.train(finishedTraining); // use the default training options

}

​

function finishedTraining(){

​

  nn.classify([300], function(err, result){

    console.log(result);

  })

}

​

function createTrainingData(){

    for(let i = 0; i < 400; i++){

      if(i%2 === 0){

        const x = random(0, width/2);

        nn.addData( [x],  ['left'])

      } else {

        const x = random(width/2, width);

        nn.addData( [x],  ['right'])

      }

    }

}

​