65 lines
1.5 KiB
PHP
65 lines
1.5 KiB
PHP
<?php
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namespace Rubix\ML\Benchmarks\Classifiers;
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use Rubix\ML\NeuralNet\Layers\Dense;
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use Rubix\ML\Datasets\Generators\Blob;
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use Rubix\ML\NeuralNet\Layers\Activation;
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use Rubix\ML\Datasets\Generators\Agglomerate;
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use Rubix\ML\Classifiers\MultilayerPerceptron;
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use Rubix\ML\NeuralNet\ActivationFunctions\ReLU;
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/**
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* @Groups({"Classifiers"})
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* @BeforeMethods({"setUp"})
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*/
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class MultilayerPerceptronBench
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{
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protected const TRAINING_SIZE = 2500;
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protected const TESTING_SIZE = 10000;
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/**
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* @var \Rubix\ML\Datasets\Labeled;
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*/
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protected $training;
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/**
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* @var \Rubix\ML\Datasets\Labeled;
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*/
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protected $testing;
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/**
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* @var \Rubix\ML\Classifiers\MultilayerPerceptron
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*/
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protected $estimator;
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public function setUp() : void
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{
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$generator = new Agglomerate([
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'Iris-setosa' => new Blob([5.0, 3.42, 1.46, 0.24], [0.35, 0.38, 0.17, 0.1]),
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'Iris-virginica' => new Blob([6.59, 2.97, 5.55, 2.03], [0.63, 0.32, 0.55, 0.27]),
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]);
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$this->training = $generator->generate(self::TRAINING_SIZE);
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$this->testing = $generator->generate(self::TESTING_SIZE);
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$this->estimator = new MultilayerPerceptron([
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new Dense(100),
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new Activation(new ReLU()),
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]);
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}
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/**
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* @Subject
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* @Iterations(3)
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* @OutputTimeUnit("seconds", precision=3)
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*/
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public function trainPredict() : void
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{
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$this->estimator->train($this->training);
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$this->estimator->predict($this->testing);
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}
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}
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