129 lines
3 KiB
PHP
129 lines
3 KiB
PHP
<?php
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namespace Rubix\ML\Tests\NeuralNet\Layers;
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use Tensor\Matrix;
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use Rubix\ML\Deferred;
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use Rubix\ML\NeuralNet\Layers\PReLU;
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use Rubix\ML\NeuralNet\Layers\Layer;
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use Rubix\ML\NeuralNet\Layers\Hidden;
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use Rubix\ML\NeuralNet\Layers\Parametric;
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use Rubix\ML\NeuralNet\Optimizers\Stochastic;
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use Rubix\ML\NeuralNet\Initializers\Constant;
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use PHPUnit\Framework\TestCase;
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/**
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* @group Layers
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* @covers \Rubix\ML\NeuralNet\Layers\PReLU
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*/
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class PReLUTest extends TestCase
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{
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protected const RANDOM_SEED = 0;
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/**
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* @var int
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*/
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protected $fanIn;
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/**
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* @var \Tensor\Matrix
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*/
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protected $input;
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/**
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* @var \Rubix\ML\Deferred
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*/
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protected $prevGrad;
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/**
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* @var \Rubix\ML\NeuralNet\Optimizers\Optimizer
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*/
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protected $optimizer;
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/**
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* @var \Rubix\ML\NeuralNet\Layers\PReLU
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*/
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protected $layer;
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/**
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* @before
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*/
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protected function setUp() : void
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{
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$this->fanIn = 3;
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$this->input = Matrix::quick([
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[1., 2.5, -0.1],
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[0.1, 0., 3.],
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[0.002, -6., -0.5],
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]);
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$this->prevGrad = new Deferred(function () {
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return Matrix::quick([
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[0.25, 0.7, 0.1],
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[0.50, 0.2, 0.01],
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[0.25, 0.1, 0.89],
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]);
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});
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$this->optimizer = new Stochastic(0.001);
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$this->layer = new PReLU(new Constant(0.25));
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srand(self::RANDOM_SEED);
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}
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/**
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* @test
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*/
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public function build() : void
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{
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$this->assertInstanceOf(PReLU::class, $this->layer);
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$this->assertInstanceOf(Layer::class, $this->layer);
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$this->assertInstanceOf(Hidden::class, $this->layer);
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$this->assertInstanceOf(Parametric::class, $this->layer);
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}
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/**
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* @test
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*/
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public function initializeForwardBackInfer() : void
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{
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$this->layer->initialize($this->fanIn);
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$this->assertEquals($this->fanIn, $this->layer->width());
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$forward = $this->layer->forward($this->input);
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$expected = [
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[1.0, 2.5, -0.025],
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[0.1, 0.0, 3.0],
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[0.002, -1.5, -0.125],
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];
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$this->assertInstanceOf(Matrix::class, $forward);
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$this->assertEquals($expected, $forward->asArray());
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$gradient = $this->layer->back($this->prevGrad, $this->optimizer)->compute();
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$expected = [
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[0.25, 0.7, 0.025001000000000002],
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[0.5, 0.05, 0.01],
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[0.25, 0.025104500000000002, 0.22343005000000002],
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];
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$this->assertInstanceOf(Matrix::class, $gradient);
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$this->assertEquals($expected, $gradient->asArray());
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$expected = [
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[1.0, 2.5, -0.025001000000000002],
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[0.1, 0.0, 3.0],
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[0.002, -1.5062700000000002, -0.1255225],
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];
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$infer = $this->layer->infer($this->input);
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$this->assertInstanceOf(Matrix::class, $infer);
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$this->assertEquals($expected, $infer->asArray());
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}
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}
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