Neuro/Net.cpp

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#include "Net.h"
#include <string>
#include <iostream>
#include <fstream>
Net::Net()
{
}
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Net::Net(std::initializer_list<size_t> layerSizes)
{
initialize(layerSizes);
}
Net::Net(const std::string &filename)
{
load(filename);
}
void Net::initialize(std::initializer_list<size_t> layerSizes)
{
clear();
if (layerSizes.size() < 2)
{
throw std::exception("A net needs at least 2 layers");
}
for (size_t numNeurons : layerSizes)
{
push_back(Layer(numNeurons));
}
for (auto layerIt = begin(); layerIt != end() - 1; ++layerIt)
{
Layer &currentLayer = *layerIt;
const Layer &nextLayer = *(layerIt + 1);
currentLayer.addBiasNeuron();
currentLayer.connectTo(nextLayer);
}
}
void Net::feedForward(const std::vector<double> &inputValues)
{
Layer &inputLayer = front();
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if (inputLayer.size() - 1 != inputValues.size())
{
throw std::exception("The number of input values has to match the input layer size");
}
inputLayer.setOutputValues(inputValues);
for (auto layerIt = begin(); layerIt != end() - 1; ++layerIt)
{
const Layer &currentLayer = *layerIt;
Layer &nextLayer = *(layerIt + 1);
nextLayer.feedForward(currentLayer);
}
}
std::vector<double> Net::getOutput()
{
std::vector<double> result;
const Layer &outputLayer = back();
for (const Neuron &neuron : outputLayer)
{
result.push_back(neuron.getOutputValue());
}
return result;
}
void Net::backProp(const std::vector<double> &targetValues)
{
Layer &outputLayer = back();
if (targetValues.size() != outputLayer.size())
{
throw std::exception("The number of target values has to match the output layer size");
}
std::vector<double> resultValues = getOutput();
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size_t numResultValues = resultValues.size();
// calculate rms error
double rmsError = 0.0;
for (unsigned int i = 0; i < numResultValues; ++i)
{
rmsError += std::pow(resultValues[i] - targetValues[i], 2);
}
rmsError = std::sqrt(rmsError / numResultValues);
// calculate output neuron gradients
for (unsigned int i = 0; i < numResultValues; ++i)
{
outputLayer[i].calcOutputGradients(targetValues[i]);
}
// calculate hidden neuron gradients
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for (auto it = end() - 1; (it - 1) != begin(); --it)
{
Layer &hiddenLayer = *(it - 1);
Layer &nextLayer = *it;
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for (Neuron &neuron : hiddenLayer)
{
neuron.calcHiddenGradients(nextLayer);
}
}
// update the input weights
for (auto it = end() - 1; it != begin(); --it)
{
Layer &currentLayer = *it;
Layer &prevLayer = *(it - 1);
currentLayer.updateInputWeights(prevLayer);
}
}
void Net::save(const std::string &filename)
{
std::ofstream outFile;
outFile.open(filename);
if (!outFile.is_open())
{
throw std::exception("unable to open output file");
}
outFile << size() << std::endl;
for (const Layer &layer : *this)
{
outFile << layer.size() << std::endl;
outFile << layer.hasBiasNeuron() << std::endl;
for (const Neuron &neuron : layer)
{
size_t numOutputWeights = neuron.getNumOutputWeights();
outFile << numOutputWeights << std::endl;
for (size_t outputWeightIndex = 0; outputWeightIndex < numOutputWeights; ++outputWeightIndex)
{
outFile << neuron.getOutputWeight(outputWeightIndex) << std::endl;
}
}
}
outFile.close();
}
void Net::load(const std::string &filename)
{
std::ifstream inFile;
inFile.open(filename, std::ios::binary);
if (!inFile.is_open())
{
throw std::exception("unable to open input file");
}
clear();
std::string buffer;
getline(inFile, buffer);
size_t numLayers = std::stol(buffer);
for (size_t layerIndex = 0; layerIndex < numLayers; ++layerIndex)
{
getline(inFile, buffer);
size_t numNeurons = std::stol(buffer);
getline(inFile, buffer);
bool hasBiasNeuron = std::stol(buffer) != 0;
size_t numNeuronsWithoutBiasNeuron = hasBiasNeuron ? numNeurons - 1 : numNeurons;
Layer newLayer(numNeuronsWithoutBiasNeuron);
if (hasBiasNeuron)
{
newLayer.addBiasNeuron();
}
for (size_t neuronIndex = 0; neuronIndex < numNeurons; ++neuronIndex)
{
getline(inFile, buffer);
size_t numWeights = std::stol(buffer);
std::list<double> outputWeights;
for (size_t weightIndex = 0; weightIndex < numWeights; ++weightIndex)
{
getline(inFile, buffer);
outputWeights.push_back(std::stod(buffer));
}
newLayer.at(neuronIndex).createOutputWeights(outputWeights);
}
push_back(newLayer);
}
inFile.close();
}