Initial forward-feeding net

This commit is contained in:
mandlm 2015-03-23 19:28:29 +01:00
parent 341a88ee2e
commit c8e08193f1
5 changed files with 330 additions and 0 deletions

159
Net.h Normal file
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#pragma once
#include <vector>
class Neuron
{
private:
double outputValue;
std::vector<double> outputWeights;
public:
void setOutputValue(double value)
{
outputValue = value;
}
static double transferFunction(double inputValue)
{
return std::tanh(inputValue);
}
void feedForward(double inputValue)
{
outputValue = Neuron::transferFunction(inputValue);
}
double getWeightedOutputValue(int outputNeuron) const
{
return outputValue * outputWeights[outputNeuron];
}
void createOutputWeights(unsigned int number)
{
outputWeights.clear();
for (unsigned int i = 0; i < number; ++i)
{
outputWeights.push_back(std::rand() / (double)RAND_MAX);
}
}
double getOutputValue() const
{
return outputValue;
}
};
class Layer : public std::vector < Neuron >
{
public:
Layer(unsigned int numNeurons)
{
for (unsigned int i = 0; i < numNeurons; ++i)
{
push_back(Neuron());
}
}
void setOutputValues(const std::vector<double> & outputValues)
{
if (size() != outputValues.size())
{
throw std::exception("The number of output values has to match the layer size");
}
auto valueIt = outputValues.begin();
for (Neuron &neuron : *this)
{
neuron.setOutputValue(*valueIt++);
}
}
void feedForward(const Layer &inputLayer)
{
int neuronNumber = 0;
for (Neuron &neuron : *this)
{
neuron.feedForward(inputLayer.getWeightedSum(neuronNumber));
}
}
double getWeightedSum(int outputNeuron) const
{
double sum = 0.0;
for (const Neuron &neuron : *this)
{
sum += neuron.getWeightedOutputValue(outputNeuron);
}
return sum;
}
void connectTo(const Layer & nextLayer)
{
for (Neuron &neuron : *this)
{
neuron.createOutputWeights(nextLayer.size());
}
}
};
class Net : public std::vector < Layer >
{
public:
Net(std::initializer_list<unsigned int> layerSizes)
{
if (layerSizes.size() < 3)
{
throw std::exception("A net needs at least 3 layers");
}
for (unsigned int numNeurons : layerSizes)
{
push_back(Layer(numNeurons));
}
for (auto layerIt = begin(); layerIt != end() - 1; ++layerIt)
{
Layer &currentLayer = *layerIt;
const Layer &nextLayer = *(layerIt + 1);
currentLayer.connectTo(nextLayer);
}
}
void feedForward(const std::vector<double> &inputValues)
{
Layer &inputLayer = front();
if (inputLayer.size() != 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> getResult()
{
std::vector<double> result;
const Layer &outputLayer = back();
for (const Neuron &neuron : outputLayer)
{
result.push_back(neuron.getOutputValue());
}
return result;
}
};

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Neuro.cpp Normal file
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#include <iostream>
#include <exception>
#include "Net.h"
int main()
{
try
{
std::cout << "Neuro running" << std::endl;
Net myNet({ 2, 3, 1 });
myNet.feedForward({ 1.0, 0.0 });
std::vector<double> result = myNet.getResult();
std::cout << "Result: ";
for (double &value : result)
{
std::cout << value << " ";
}
std::cout << std::endl;
}
catch (std::exception &ex)
{
std::cerr << "Error: " << ex.what() << std::endl;
return 1;
}
return 0;
}

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Neuro.sln Normal file
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