| public IsLayerBiased ( int l ) : bool | ||
| l | int | The layer number. |
| return | bool | |
/// <summary>
/// Craete a freeform network from a basic network.
/// </summary>
/// <param name="network">The basic network to use.</param>
public FreeformNetwork(BasicNetwork network)
{
if (network.LayerCount < 2)
{
throw new FreeformNetworkError(
"The BasicNetwork must have at least two layers to be converted.");
}
// handle each layer
IFreeformLayer previousLayer = null;
for (int currentLayerIndex = 0;
currentLayerIndex < network
.LayerCount;
currentLayerIndex++)
{
// create the layer
IFreeformLayer currentLayer = _layerFactory.Factor();
// Is this the input layer?
if (_inputLayer == null)
{
_inputLayer = currentLayer;
}
// Add the neurons for this layer
for (int i = 0; i < network.GetLayerNeuronCount(currentLayerIndex); i++)
{
// obtain the summation object.
IInputSummation summation = null;
if (previousLayer != null)
{
summation = _summationFactory.Factor(network
.GetActivation(currentLayerIndex));
}
// add the new neuron
currentLayer.Add(_neuronFactory.FactorRegular(summation));
}
// Fully connect this layer to previous
if (previousLayer != null)
{
ConnectLayersFromBasic(network, currentLayerIndex - 1,
previousLayer, currentLayer);
}
// Add the bias neuron
// The bias is added after connections so it has no inputs
if (network.IsLayerBiased(currentLayerIndex))
{
IFreeformNeuron biasNeuron = _neuronFactory
.FactorRegular(null);
biasNeuron.IsBias = true;
biasNeuron.Activation = network
.GetLayerBiasActivation(currentLayerIndex);
currentLayer.Add(biasNeuron);
}
// update previous layer
previousLayer = currentLayer;
}
// finally, set the output layer.
_outputLayer = previousLayer;
}