87 lines
1.5 KiB
C++
87 lines
1.5 KiB
C++
#pragma once
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#include "Neuron.h"
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#include "Layer.h"
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Neuron::Neuron(double value)
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: outputValue(value)
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, gradient(0)
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{
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}
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void Neuron::setOutputValue(double value)
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{
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outputValue = value;
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}
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double Neuron::transferFunction(double inputValue)
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{
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return std::tanh(inputValue);
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}
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double Neuron::transferFunctionDerivative(double inputValue)
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{
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return 1.0 - (inputValue * inputValue);
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}
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void Neuron::feedForward(double inputValue)
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{
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outputValue = transferFunction(inputValue);
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}
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double Neuron::getWeightedOutputValue(unsigned int outputNeuron) const
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{
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if (outputNeuron < outputWeights.size())
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{
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return outputValue * outputWeights[outputNeuron];
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}
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return 0.0;
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}
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void Neuron::createRandomOutputWeights(size_t numberOfWeights)
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{
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outputWeights.clear();
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for (unsigned int i = 0; i < numberOfWeights; ++i)
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{
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outputWeights.push_back(std::rand() / (double)RAND_MAX);
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}
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}
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double Neuron::getOutputValue() const
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{
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return outputValue;
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}
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void Neuron::calcOutputGradients(double targetValue)
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{
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double delta = targetValue - outputValue;
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gradient = delta * transferFunctionDerivative(outputValue);
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}
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double Neuron::sumDOW(const Layer & nextLayer) const
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{
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double sum = 0;
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for (size_t i = 0; i < nextLayer.size() - 1; ++i)
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{
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sum += outputWeights[i] * nextLayer[i].getGradient();
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}
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return sum;
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}
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void Neuron::calcHiddenGradients(const Layer &nextLayer)
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{
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double dow = sumDOW(nextLayer);
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gradient = dow * transferFunctionDerivative(outputValue);
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}
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double Neuron::getGradient() const
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{
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return gradient;
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}
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