cppnn.cpp

#include <vector>
#include <iostream>
#include <cstdlib>
#include <cassert>
#include <cmath>
#include <fstream>
#include <sstream>

using namespace std;

struct Connection
{
    double weight;
    double deltaWeight;
};


class Neuron;

typedef vector<Neuron> Layer;

class Neuron
{
public:
    Neuron(unsigned numOutputs, unsigned myIndex);
    void setOutputVal(double val) { m_outputVal = val; }
    double getOutputVal(void) const { return m_outputVal; }
    void feedForward(const Layer &prevLayer);
    void calcOutputGradients(double targetVal);
    void calcHiddenGradients(const Layer &nextLayer);
    void updateInputWeights(Layer &prevLayer);

private:
    static double eta;  
    static double alpha; 
    static double transferFunction(double x);
    static double transferFunctionDerivative(double x);
    static double randomWeight(void) { return rand() / double(RAND_MAX); }
    double sumDOW(const Layer &nextLayer) const;
    double m_outputVal;
    vector<Connection> m_outputWeights;
    unsigned m_myIndex;
    double m_gradient;
};

double Neuron::eta = 0.15;  
double Neuron::alpha = 0.5;   


void Neuron::updateInputWeights(Layer &prevLayer)
{

    for (unsigned n = 0; n < prevLayer.size(); ++n) {
        Neuron &neuron = prevLayer[n];
        double oldDeltaWeight = neuron.m_outputWeights[m_myIndex].deltaWeight;

        double newDeltaWeight =

        neuron.m_outputWeights[m_myIndex].deltaWeight = newDeltaWeight;
        neuron.m_outputWeights[m_myIndex].weight += newDeltaWeight;
    }
}

double Neuron::sumDOW(const Layer &nextLayer) const
{
    double sum = 0.0;


    for (unsigned n = 0; n < nextLayer.size() - 1; ++n) {
        sum += m_outputWeights[n].weight * nextLayer[n].m_gradient;
    }

    return sum;
}

void Neuron::calcHiddenGradients(const Layer &nextLayer)
{
    double dow = sumDOW(nextLayer);
    m_gradient = dow * Neuron::transferFunctionDerivative(m_outputVal);
}

void Neuron::calcOutputGradients(double targetVal)
{
    double delta = targetVal - m_outputVal;
    m_gradient = delta * Neuron::transferFunctionDerivative(m_outputVal);
}

double Neuron::transferFunction(double x)
{

    return tanh(x);
}

double Neuron::transferFunctionDerivative(double x)
{
    return 1.0 - x * x;
}

void Neuron::feedForward(const Layer &prevLayer)
{
    double sum = 0.0;

    for (unsigned n = 0; n < prevLayer.size(); ++n) {
        sum += prevLayer[n].getOutputVal() *
                prevLayer[n].m_outputWeights[m_myIndex].weight;
    }

    m_outputVal = Neuron::transferFunction(sum);
}

Neuron::Neuron(unsigned numOutputs, unsigned myIndex)
{
    for (unsigned c = 0; c < numOutputs; ++c) {
        m_outputWeights.push_back(Connection());
        m_outputWeights.back().weight = randomWeight();
    }

    m_myIndex = myIndex;
}


class Net
{
public:
    Net(const vector<unsigned> &topology);
    void feedForward(const vector<double> &inputVals);
    void backProp(const vector<double> &targetVals);
    void getResults(vector<double> &resultVals) const;
    double getRecentAverageError(void) const { return m_recentAverageError; }

private:
    vector<Layer> m_layers; // m_layers[layerNum][neuronNum]
    double m_error;
    double m_recentAverageError;
    static double m_recentAverageSmoothingFactor;
};


double Net::m_recentAverageSmoothingFactor = 100.0;


void Net::getResults(vector<double> &resultVals) const
{
    resultVals.clear();

    for (unsigned n = 0; n < m_layers.back().size() - 1; ++n) {
        resultVals.push_back(m_layers.back()[n].getOutputVal());
    }
}

void Net::backProp(const vector<double> &targetVals)
{

    Layer &outputLayer = m_layers.back();
    m_error = 0.0;

    for (unsigned n = 0; n < outputLayer.size() - 1; ++n) {
        double delta = targetVals[n] - outputLayer[n].getOutputVal();
        m_error += delta * delta;
    }
    m_error /= outputLayer.size() - 1; // get average error squared
    m_error = sqrt(m_error); // RMS

    m_recentAverageError =
            (m_recentAverageError * m_recentAverageSmoothingFactor + m_error)
            / (m_recentAverageSmoothingFactor + 1.0);

    for (unsigned n = 0; n < outputLayer.size() - 1; ++n) {
        outputLayer[n].calcOutputGradients(targetVals[n]);
    }

    for (unsigned layerNum = m_layers.size() - 2; layerNum > 0; --layerNum) {
        Layer &hiddenLayer = m_layers[layerNum];
        Layer &nextLayer = m_layers[layerNum + 1];

        for (unsigned n = 0; n < hiddenLayer.size(); ++n) {
            hiddenLayer[n].calcHiddenGradients(nextLayer);
        }
    }

    for (unsigned layerNum = m_layers.size() - 1; layerNum > 0; --layerNum) {
        Layer &layer = m_layers[layerNum];
        Layer &prevLayer = m_layers[layerNum - 1];

        for (unsigned n = 0; n < layer.size() - 1; ++n) {
            layer[n].updateInputWeights(prevLayer);
        }
    }
}

void Net::feedForward(const vector<double> &inputVals)
{
    assert(inputVals.size() == m_layers[0].size() - 1);

    for (unsigned i = 0; i < inputVals.size(); ++i) {
        m_layers[0][i].setOutputVal(inputVals[i]);
    }

    for (unsigned layerNum = 1; layerNum < m_layers.size(); ++layerNum) {
        Layer &prevLayer = m_layers[layerNum - 1];
        for (unsigned n = 0; n < m_layers[layerNum].size() - 1; ++n) {
            m_layers[layerNum][n].feedForward(prevLayer);
        }
    }
}

Net::Net(const vector<unsigned> &topology)
{
    unsigned numLayers = topology.size();
    for (unsigned layerNum = 0; layerNum < numLayers; ++layerNum) {
        m_layers.push_back(Layer());
        unsigned numOutputs = layerNum == topology.size() - 1 ? 0 : topology[layerNum + 1];

        for (unsigned neuronNum = 0; neuronNum <= topology[layerNum]; ++neuronNum) {
            m_layers.back().push_back(Neuron(numOutputs, neuronNum));
            cout << "Made a Neuron!" << endl;
        }

        m_layers.back().back().setOutputVal(1.0);
    }
}


void showVectorVals(string label, vector<double> &v)
{
    cout << label << " ";
    for (unsigned i = 0; i < v.size(); ++i) {
        cout << v[i] << " ";
    }

    cout << endl;
}