89 lines
1.8 KiB
C++
89 lines
1.8 KiB
C++
#include "Layer.h"
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Layer::Layer(size_t numNeurons)
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{
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for (unsigned int i = 0; i < numNeurons; ++i)
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{
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push_back(Neuron());
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}
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}
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void Layer::setOutputValues(const std::vector<double> & outputValues)
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{
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if (size() - 1 != outputValues.size())
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{
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throw std::exception("The number of output values has to match the layer size");
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}
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auto neuronIt = begin();
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for (const double &value : outputValues)
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{
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(neuronIt++)->setOutputValue(value);
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}
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}
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void Layer::feedForward(const Layer &inputLayer)
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{
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for (int neuronNumber = 0; neuronNumber < sizeWithoutBiasNeuron(); ++neuronNumber)
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{
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at(neuronNumber).feedForward(inputLayer.getWeightedSum(neuronNumber));
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}
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}
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double Layer::getWeightedSum(int outputNeuron) const
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{
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double sum = 0.0;
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for (const Neuron &neuron : *this)
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{
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sum += neuron.getWeightedOutputValue(outputNeuron);
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}
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return sum;
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}
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void Layer::connectTo(const Layer & nextLayer)
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{
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for (Neuron &neuron : *this)
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{
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neuron.createOutputWeights(nextLayer.sizeWithoutBiasNeuron(), 0.5);
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}
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}
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void Layer::updateInputWeights(Layer & prevLayer)
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{
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static const double trainingRate = 0.2;
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for (size_t currentLayerIndex = 0; currentLayerIndex < sizeWithoutBiasNeuron(); ++currentLayerIndex)
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{
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Neuron &targetNeuron = at(currentLayerIndex);
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for (size_t prevLayerIndex = 0; prevLayerIndex < prevLayer.size(); ++prevLayerIndex)
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{
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Neuron &sourceNeuron = prevLayer.at(prevLayerIndex);
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sourceNeuron.setOutputWeight(currentLayerIndex,
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sourceNeuron.getOutputWeight(currentLayerIndex) +
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sourceNeuron.getOutputValue() * targetNeuron.getGradient() * trainingRate);
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}
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}
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}
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void Layer::addBiasNeuron()
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{
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push_back(Neuron(1.0));
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hasBiasNeuron = true;
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}
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size_t Layer::sizeWithoutBiasNeuron() const
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{
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if (hasBiasNeuron)
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{
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return size() - 1;
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}
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else
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{
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return size();
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}
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}
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