//

// 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;

​

function setup() {

  const options = {

    task: 'classification'

  };

  nn = ml5.neuralNetwork(options);

​

  addData();

​

  nn.normalizeData();

​

  // train the model

  const training_options = {

    batchSize: 32,

    epochs: 10

  };

  nn.train(training_options, finishedTraining);

}

​

function addData() {

  for (let i = 0; i < 500; i++) {

    let xVal, labelVal;

    if (i < 250) {

      xVal = i;

      labelVal = 'a';

    } else {

      xVal = i;

      labelVal = 'b';

    }

    const yVal = floor(random(500));

​

    nn.addData({ x: xVal, y: yVal }, { label: labelVal });

  }

}

​

function finishedTraining() {

  console.log('done');

​

  nn.classify({ x: 0, y: 0.5 }, function(err, result) {

    if (err) {

      console.log(err);

      return;

    }

    console.log('hi from callback', result);

  });

}

​