mirror of
https://github.com/Llewellynvdm/php-ml.git
synced 2024-12-02 01:48:22 +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
58 lines
1.4 KiB
PHP
58 lines
1.4 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\HyperbolicTangent;
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use PHPUnit\Framework\TestCase;
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class HyperboliTangentTest extends TestCase
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{
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/**
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* @dataProvider tanhProvider
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*/
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public function testHyperbolicTangentActivationFunction($beta, $expected, $value): void
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{
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$tanh = new HyperbolicTangent($beta);
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$this->assertEquals($expected, $tanh->compute($value), '', 0.001);
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}
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public function tanhProvider(): array
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{
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return [
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[1.0, 0.761, 1],
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[1.0, 0, 0],
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[1.0, 1, 4],
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[1.0, -1, -4],
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[0.5, 0.462, 1],
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[0.3, 0, 0],
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];
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}
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/**
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* @dataProvider tanhDerivativeProvider
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*/
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public function testHyperbolicTangentDerivative($beta, $expected, $value): void
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{
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$tanh = new HyperbolicTangent($beta);
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$activatedValue = $tanh->compute($value);
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$this->assertEquals($expected, $tanh->differentiate($value, $activatedValue), '', 0.001);
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}
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public function tanhDerivativeProvider(): array
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{
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return [
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[1.0, 0, -6],
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[1.0, 0.419, -1],
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[1.0, 1, 0],
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[1.0, 0.419, 1],
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[1.0, 0, 6],
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[0.5, 0.786, 1],
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[0.5, 0.786, -1],
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[0.3, 1, 0],
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];
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}
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}
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