mirror of
https://github.com/Llewellynvdm/php-ml.git
synced 2024-11-25 22:37:34 +00:00
e83f7b95d5
- Backpropagation using the neuron activation functions derivative - instead of hardcoded sigmoid derivative - Added missing activation functions derivatives - Sigmoid forced for the output layer - Updated ThresholdedReLU default threshold to 0 (acts as a ReLU) - Unit tests for derivatives - Unit tests for classifiers using different activation functions - Added missing docs
54 lines
1.3 KiB
PHP
54 lines
1.3 KiB
PHP
<?php
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declare(strict_types=1);
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namespace Phpml\Tests\NeuralNetwork\ActivationFunction;
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use Phpml\NeuralNetwork\ActivationFunction\ThresholdedReLU;
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use PHPUnit\Framework\TestCase;
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class ThresholdedReLUTest extends TestCase
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{
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/**
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* @dataProvider thresholdProvider
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*/
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public function testThresholdedReLUActivationFunction($theta, $expected, $value): void
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{
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$thresholdedReLU = new ThresholdedReLU($theta);
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$this->assertEquals($expected, $thresholdedReLU->compute($value));
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}
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public function thresholdProvider(): array
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{
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return [
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[1.0, 0, 1.0],
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[0.5, 3.75, 3.75],
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[0.0, 0.5, 0.5],
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[0.9, 0, 0.1],
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];
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}
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/**
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* @dataProvider thresholdDerivativeProvider
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*/
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public function testThresholdedReLUDerivative($theta, $expected, $value): void
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{
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$thresholdedReLU = new ThresholdedReLU($theta);
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$activatedValue = $thresholdedReLU->compute($value);
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$this->assertEquals($expected, $thresholdedReLU->differentiate($value, $activatedValue));
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}
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public function thresholdDerivativeProvider(): array
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{
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return [
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[0, 1, 1],
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[0, 1, 0],
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[0.5, 1, 1],
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[0.5, 1, 1],
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[0.5, 0, 0],
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[2, 0, -1],
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];
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}
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}
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