//------------------------------createNet()------------------------------
//
// this method builds the ANN. The weights are all initially set to
// random values -1 < w < 1
//------------------------------------------------------------------------
void CNeuralNet::CreateNet()
{
//create the layers of the network
if (m_NumHiddenLayers > 0)
{
//create first hidden layer
m_vecLayers.push_back(SNeuronLayer(m_NeuronsPerHiddenLyr, m_NumInputs));
for (int i=0; i<m_NumHiddenLayers-1; ++i)
{
m_vecLayers.push_back(SNeuronLayer(m_NeuronsPerHiddenLyr,
m_NeuronsPerHiddenLyr));
}
//create output layer
m_vecLayers.push_back(SNeuronLayer(m_NumOutputs, m_NeuronsPerHiddenLyr));
}
else
{
//create output layer
m_vecLayers.push_back(SNeuronLayer(m_NumOutputs, m_NumInputs));
}
}
CNeuralNet::CNeuralNet(int numInputs, int numOutputs, int numHiddenLayers, int neuronsPerHiddenLayer):
m_numInputs(numInputs),
m_numOutputs(numOutputs),
m_numHiddenLayers(numHiddenLayers),
m_neuronsPerHiddenLayer(neuronsPerHiddenLayer)
{
for(int i = 0; i < numHiddenLayers; ++i)
{
m_vecLayers.push_back(SNeuronLayer(neuronsPerHiddenLayer,
i > 0 ? neuronsPerHiddenLayer : numInputs));
}
}
//------------------------------createNet()------------------------------
//
// this method builds the ANN. The weights are all initially set to
// random values -1 < w < 1
//------------------------------------------------------------------------
void CNeuralNet::CreateNet()
{
numOperacion = 0;
//create the layers of the network
if (m_NumHiddenLayers > 0)
{
//create first hidden layer
m_vecLayers.push_back(SNeuronLayer(m_NeuronsPerHiddenLyr, m_NumInputs));
for (int i=0; i<m_NumHiddenLayers-1; ++i)
{
m_vecLayers.push_back(SNeuronLayer(m_NeuronsPerHiddenLyr,
m_NeuronsPerHiddenLyr));
}
//create output layer
m_vecLayers.push_back(SNeuronLayer(m_NumOutputs, m_NeuronsPerHiddenLyr));
for(int ik = 0; ik<4; ik++){
for (int i=0; i<m_NumHiddenLayers + 1; ++i)
{
vector<double> auxOut;
//for each neuron
for (int j=0; j<m_vecLayers[i].m_NumNeurons; ++j)
{
auxOut.push_back(0);
}
outputs[ik].push_back(auxOut);
}
}
}
else
{
//create output layer
m_vecLayers.push_back(SNeuronLayer(m_NumOutputs, m_NumInputs));
}
}