train.php

<?php

include __DIR__ . '/vendor/autoload.php';

use Rubix\ML\Loggers\Screen;
use Rubix\ML\Datasets\Labeled;
use Rubix\ML\PersistentModel;
use Rubix\ML\Pipeline;
use Rubix\ML\Transformers\ImageResizer;
use Rubix\ML\Transformers\ImageVectorizer;
use Rubix\ML\Transformers\ZScaleStandardizer;
use Rubix\ML\Classifiers\MultilayerPerceptron;
use Rubix\ML\NeuralNet\Layers\Dense;
use Rubix\ML\NeuralNet\Layers\Dropout;
use Rubix\ML\NeuralNet\Layers\Activation;
use Rubix\ML\NeuralNet\ActivationFunctions\ReLU;
use Rubix\ML\NeuralNet\Optimizers\Adam;
use Rubix\ML\Persisters\Filesystem;
use Rubix\ML\Extractors\CSV;

ini_set('memory_limit', '-1');

$logger = new Screen();

$logger->info('Loading data into memory');

$samples = $labels = [];

$folders = [ '1', 'B', 'b', 'K', 'k', 'N', 'n', 'P', 'p', 'Q', 'q', 'R', 'r', ];

foreach ($folders as $folder) {
    foreach (glob("training/$folder/*.jpg") as $file) {
        $samples[] = [imagecreatefromjpeg($file)];
        $labels[] = $folder;
    }
}

$dataset = new Labeled($samples, $labels);

$estimator = new PersistentModel(
    new Pipeline([
        new ImageResizer(28, 28),
        new ImageVectorizer(true),
        new ZScaleStandardizer(),
    ], new MultilayerPerceptron([
        new Dense(128),
        new Activation(new ReLU()),
        new Dropout(0.2),
        new Dense(128),
        new Activation(new ReLU()),
        new Dropout(0.2),
        new Dense(128),
        new Activation(new ReLU()),
        new Dropout(0.2),
    ], 256, new Adam(0.0001))),
    new Filesystem('piece.rbx', true)
);

$estimator->setLogger($logger);

$estimator->train($dataset);

$extractor = new CSV('progress.csv', true);

$extractor->export($estimator->steps());

$logger->info('Progress saved to progress.csv');

if (strtolower(trim(readline('Save this model? (y|[n]): '))) === 'y') {
    $estimator->save();
}