//

// 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 = {

    debug: true,

    task: 'classification',

    layers: [

      {

        type: 'dense',

        units: 16,

        activation: 'relu'

      },

      {

        type: 'dense',

        units: 16,

        activation: 'sigmoid'

      },

      {

        type: 'dense',

        activation: 'sigmoid'

      }

    ]

  };

  nn = ml5.neuralNetwork(options);

​

  // add your data

  addData();

  // normalize it

  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);

    }

  );

}

​