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<?php |
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declare(strict_types=1); |
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namespace Phpml\NeuralNetwork\Network; |
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use Phpml\Exception\InvalidArgumentException; |
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use Phpml\NeuralNetwork\Layer; |
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use Phpml\NeuralNetwork\Network; |
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use Phpml\NeuralNetwork\Node\Input; |
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use Phpml\NeuralNetwork\Node\Neuron; |
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abstract class LayeredNetwork implements Network |
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{ |
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/** |
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* @var Layer[] |
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*/ |
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protected $layers = []; |
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public function addLayer(Layer $layer): void |
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{ |
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$this->layers[] = $layer; |
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} |
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/** |
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* @return Layer[] |
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*/ |
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public function getLayers(): array |
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{ |
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return $this->layers; |
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} |
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public function removeLayers(): void |
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{ |
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unset($this->layers); |
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} |
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public function getOutputLayer(): Layer |
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{ |
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return $this->layers[count($this->layers) - 1]; |
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} |
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public function getOutput(): array |
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{ |
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$result = []; |
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foreach ($this->getOutputLayer()->getNodes() as $neuron) { |
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$result[] = $neuron->getOutput(); |
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} |
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return $result; |
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} |
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/** |
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* @param mixed $input |
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*/ |
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public function setInput($input): Network |
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{ |
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$firstLayer = $this->layers[0]; |
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foreach ($firstLayer->getNodes() as $key => $neuron) { |
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if ($neuron instanceof Input) { |
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$neuron->setInput($input[$key]); |
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} |
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} |
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foreach ($this->getLayers() as $layer) { |
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foreach ($layer->getNodes() as $node) { |
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if ($node instanceof Neuron) { |
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$node->reset(); |
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} |
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} |
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} |
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return $this; |
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} |
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public function getTrainedCharacteristics(): array |
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{ |
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$result = []; |
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foreach ($this->layers as $layer) { |
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$result[] = $layer->getTrainedCharacteristics(); |
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} |
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return $result; |
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} |
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public function setTrainedCharacteristics(array $characteristics): void |
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{ |
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if (count($characteristics) != count($this->layers)) { |
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throw new InvalidArgumentException('Loaded data does not match the network structure'); |
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} |
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for ($i = 0; $i < count($this->layers); $i++) { |
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$this->layers[$i]->setTrainedCharacteristics($characteristics[$i]); |
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} |
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} |
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} |
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php-ai /
php-ml
If the size of the collection does not change during the iteration, it is generally a good practice to compute it beforehand, and not on each iteration: