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<?php |
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declare(strict_types=1); |
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namespace Phpml\NeuralNetwork\Training; |
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use Phpml\NeuralNetwork\Node\Neuron; |
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use Phpml\NeuralNetwork\Training\Backpropagation\Sigma; |
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class Backpropagation |
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{ |
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/** |
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* @var int |
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*/ |
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private $theta; |
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/** |
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* @var array |
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*/ |
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private $sigmas; |
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/** |
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* @var array |
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*/ |
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private $prevSigmas; |
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/** |
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* @param int $theta |
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*/ |
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public function __construct(int $theta) |
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{ |
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$this->theta = $theta; |
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} |
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/** |
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* @param array $layers |
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* @param mixed $targetClass |
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*/ |
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public function backpropagate(array $layers, $targetClass) |
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{ |
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$layersNumber = count($layers); |
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// Backpropagation. |
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for ($i = $layersNumber; $i > 1; --$i) { |
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$this->sigmas = []; |
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foreach ($layers[$i - 1]->getNodes() as $key => $neuron) { |
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if ($neuron instanceof Neuron) { |
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$sigma = $this->getSigma($neuron, $targetClass, $key, $i == $layersNumber); |
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foreach ($neuron->getSynapses() as $synapse) { |
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$synapse->changeWeight($this->theta * $sigma * $synapse->getNode()->getOutput()); |
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} |
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} |
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} |
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$this->prevSigmas = $this->sigmas; |
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} |
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} |
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/** |
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* @param Neuron $neuron |
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* @param int $targetClass |
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* @param int $key |
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* @param bool $lastLayer |
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* |
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* @return float |
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*/ |
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private function getSigma(Neuron $neuron, int $targetClass, int $key, bool $lastLayer): float |
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{ |
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$neuronOutput = $neuron->getOutput(); |
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$sigma = $neuronOutput * (1 - $neuronOutput); |
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if ($lastLayer) { |
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$value = 0; |
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if ($targetClass === $key) { |
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$value = 1; |
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} |
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$sigma *= ($value - $neuronOutput); |
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} else { |
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$sigma *= $this->getPrevSigma($neuron); |
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} |
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$this->sigmas[] = new Sigma($neuron, $sigma); |
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return $sigma; |
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} |
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/** |
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* @param Neuron $neuron |
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* |
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* @return float |
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*/ |
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private function getPrevSigma(Neuron $neuron): float |
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{ |
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$sigma = 0.0; |
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foreach ($this->prevSigmas as $neuronSigma) { |
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$sigma += $neuronSigma->getSigmaForNeuron($neuron); |
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} |
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return $sigma; |
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} |
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} |
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php-ai /
php-ml
