//
public NeuralNet(Topology topology, ActivationFunction activationFunction_, double alpha = 0.5, double eta = 0.15)
{
RandomSequence.Reset();
numberLayers = topology.NumberLayers();
if (numberLayers < 2)
{
throw new Exception("Number of layers should be at least 2");
}
layers = new Layer[numberLayers];
ActivationFunction activationFunction = activationFunction_;
for (int layerIdx = 0; layerIdx < numberLayers; layerIdx++)
{
int nNeurons = topology.NumberNeuronsAtLayer(layerIdx);
Neuron[] neurons = new Neuron[nNeurons + 1];
int nOutputs = (layerIdx == numberLayers - 1 ? 0 : topology.NumberNeuronsAtLayer(layerIdx + 1));
// Main neurons
for (int neuronIdx = 0; neuronIdx < nNeurons + 1; neuronIdx++)
{
neurons[neuronIdx] = new Neuron(nOutputs, neuronIdx, activationFunction, alpha, eta);
}
// Force the bias neuron's output value to 1.0
neurons.Back().OutputValue = 1.0;
layers[layerIdx] = new Layer(neurons);
}
}
//
public Connection()
{
Weight = RandomSequence.DrawDouble();
DeltaWeight = 0.0;
//DeltaWeight = Constant.MACHINE_MAX;
}
