101 lines
2.4 KiB
PHP
101 lines
2.4 KiB
PHP
<?php
|
|
|
|
declare(strict_types=1);
|
|
|
|
namespace Phpml\NeuralNetwork\Training;
|
|
|
|
use Phpml\NeuralNetwork\Node\Neuron;
|
|
use Phpml\NeuralNetwork\Training\Backpropagation\Sigma;
|
|
|
|
class Backpropagation
|
|
{
|
|
/**
|
|
* @var float
|
|
*/
|
|
private $learningRate;
|
|
|
|
/**
|
|
* @var array
|
|
*/
|
|
private $sigmas = [];
|
|
|
|
/**
|
|
* @var array
|
|
*/
|
|
private $prevSigmas = [];
|
|
|
|
public function __construct(float $learningRate)
|
|
{
|
|
$this->setLearningRate($learningRate);
|
|
}
|
|
|
|
public function setLearningRate(float $learningRate): void
|
|
{
|
|
$this->learningRate = $learningRate;
|
|
}
|
|
|
|
public function getLearningRate(): float
|
|
{
|
|
return $this->learningRate;
|
|
}
|
|
|
|
/**
|
|
* @param mixed $targetClass
|
|
*/
|
|
public function backpropagate(array $layers, $targetClass): void
|
|
{
|
|
$layersNumber = count($layers);
|
|
|
|
// Backpropagation.
|
|
for ($i = $layersNumber; $i > 1; --$i) {
|
|
$this->sigmas = [];
|
|
foreach ($layers[$i - 1]->getNodes() as $key => $neuron) {
|
|
if ($neuron instanceof Neuron) {
|
|
$sigma = $this->getSigma($neuron, $targetClass, $key, $i == $layersNumber);
|
|
foreach ($neuron->getSynapses() as $synapse) {
|
|
$synapse->changeWeight($this->learningRate * $sigma * $synapse->getNode()->getOutput());
|
|
}
|
|
}
|
|
}
|
|
|
|
$this->prevSigmas = $this->sigmas;
|
|
}
|
|
|
|
// Clean some memory (also it helps make MLP persistency & children more maintainable).
|
|
$this->sigmas = [];
|
|
$this->prevSigmas = [];
|
|
}
|
|
|
|
private function getSigma(Neuron $neuron, int $targetClass, int $key, bool $lastLayer): float
|
|
{
|
|
$neuronOutput = $neuron->getOutput();
|
|
$sigma = $neuron->getDerivative();
|
|
|
|
if ($lastLayer) {
|
|
$value = 0;
|
|
if ($targetClass === $key) {
|
|
$value = 1;
|
|
}
|
|
|
|
$sigma *= ($value - $neuronOutput);
|
|
} else {
|
|
$sigma *= $this->getPrevSigma($neuron);
|
|
}
|
|
|
|
$this->sigmas[] = new Sigma($neuron, $sigma);
|
|
|
|
return $sigma;
|
|
}
|
|
|
|
private function getPrevSigma(Neuron $neuron): float
|
|
{
|
|
$sigma = 0.0;
|
|
|
|
foreach ($this->prevSigmas as $neuronSigma) {
|
|
$sigma += $neuronSigma->getSigmaForNeuron($neuron);
|
|
}
|
|
|
|
return $sigma;
|
|
}
|
|
}
|