Neuro/Layer.cpp

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#include "Layer.h"
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Layer::Layer(size_t numNeurons)
{
for (unsigned int i = 0; i < numNeurons; ++i)
{
push_back(Neuron());
}
}
void Layer::setOutputValues(const std::vector<double> & outputValues)
{
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if (size() - 1 != outputValues.size())
{
throw std::exception("The number of output values has to match the layer size");
}
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auto neuronIt = begin();
for (const double &value : outputValues)
{
(neuronIt++)->setOutputValue(value);
}
}
void Layer::feedForward(const Layer &inputLayer)
{
int neuronNumber = 0;
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for (auto neuronIt = begin(); neuronIt != end(); ++neuronIt)
{
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neuronIt->feedForward(inputLayer.getWeightedSum(neuronNumber++));
}
}
double Layer::getWeightedSum(int outputNeuron) const
{
double sum = 0.0;
for (const Neuron &neuron : *this)
{
sum += neuron.getWeightedOutputValue(outputNeuron);
}
return sum;
}
void Layer::connectTo(const Layer & nextLayer)
{
for (Neuron &neuron : *this)
{
neuron.createRandomOutputWeights(nextLayer.size());
}
}
void Layer::updateInputWeights(Layer & prevLayer)
{
static const double trainingRate = 0.8;
for (size_t currentLayerIndex = 0; currentLayerIndex < size() - 1; ++currentLayerIndex)
{
Neuron &targetNeuron = at(currentLayerIndex);
for (size_t prevLayerIndex = 0; prevLayerIndex < prevLayer.size(); ++prevLayerIndex)
{
Neuron &sourceNeuron = prevLayer.at(prevLayerIndex);
sourceNeuron.setOutputWeight(currentLayerIndex,
sourceNeuron.getOutputWeight(currentLayerIndex) +
sourceNeuron.getOutputValue() * targetNeuron.getGradient() * trainingRate);
}
}
}