PHP code example of rubix / mnist

1. Go to this page and download the library: Download rubix/mnist library. Choose the download type require.

2. Extract the ZIP file and open the index.php.

3. Add this code to the index.php.

    
        
<?php
require_once('vendor/autoload.php');

/* Start to develop here. Best regards https://php-download.com/ */

rubix / mnist example snippets


$samples = $labels = [];

for ($label = 0; $label < 10; $label++) {
    foreach (glob("training/$label/*.png") as $file) {
        $samples[] = [imagecreatefrompng($file)];
        $labels[] = "#$label";
    }
}


use Rubix\ML\Datasets\Labeled;

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


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;

$estimator = new PersistentModel(
    new Pipeline([
        new ImageResizer(28, 28),
        new ImageVectorizer(true),
        new ZScaleStandardizer(),
    ], new MultiLayerPerceptron([
        new Dense(100),
        new Activation(new ReLU()),
        new Dropout(0.2),
        new Dense(100),
        new Activation(new ReLU()),
        new Dropout(0.2),
        new Dense(100),
        new Activation(new ReLU()),
        new Dropout(0.2),
    ], 256, new Adam(0.0001))),
    new Filesystem('mnist.rbx', true)
);


$estimator->train($dataset);


use Rubix\ML\Extractors\CSV;

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

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


$estimator->save();


use Rubix\ML\Datasets\Labeled;

$samples = $labels = [];

for ($label = 0; $label < 10; $label++) {
    foreach (glob("testing/$label/*.png") as $file) {
        $samples[] = [imagecreatefrompng($file)];
        $labels[] = "#$label";
    }
}

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


use Rubix\ML\PersistentModel;
use Rubix\ML\Persisters\Filesystem;

$estimator = PersistentModel::load(new Filesystem('mnist.rbx'));


$predictions = $estimator->predict($dataset);


use Rubix\ML\CrossValidation\Reports\AggregateReport;
use Rubix\ML\CrossValidation\Reports\ConfusionMatrix;
use Rubix\ML\CrossValidation\Reports\MulticlassBreakdown;

$report = new AggregateReport([
    'breakdown' => new MulticlassBreakdown(),
    'matrix' => new ConfusionMatrix(),
]);


$results = $report->generate($predictions, $dataset->labels());

echo $results;

sh
$ php train.php

sh
$ php validate.php