/// <summary>
/// Add Hidden Layer, starting from index 0
/// Do not add Output (idxLayer=numberOfLayers) Layers
///
/// Output Layer will be determined automatically based
/// on where Neural Net is running in classification of Regression mode
/// </summary>
/// <param name="idxLayer"></param>
/// <param name="numberOfUnits"></param>
/// <param name="activationFunction"></param>
public void AddHiddenLayer(int idxLayer,
int numberOfUnits,
IActivationFunction activationFunction)
{
if (_model.GetNumberOfLayers() == 0)
{
throw new InvalidNeuralNetworkLayer();
}
//Input layer is already added by now
if (idxLayer + 1 >= _model.GetNumberOfLayers() - 1) //throw Error
{
throw new InvalidNeuralNetworkLayer();
}
_model.AddLayer(idxLayer + 1, numberOfUnits, //Hidden Lalyer
_weightBaseValue,
activationFunction,
_maxParallelThreads);
}
protected bool VerifyLayers(ModelBackPropagationBase model)
{
bool exceptionFlag = false;
Parallel.For(0, model.GetNumberOfLayers() - 1,
new ParallelOptions {
MaxDegreeOfParallelism = _maxParallelThreads
},
idxLayer =>
{
if (model.GetLayer(idxLayer) == null)
{
exceptionFlag = true;
}
});
return(exceptionFlag);
}
protected bool VerifyUpstreamLayers(ModelBackPropagationBase model)
{
bool exceptionFlag = false;
Parallel.For(1, model.GetNumberOfLayers(),
new ParallelOptions {
MaxDegreeOfParallelism = _maxParallelThreads
},
idxLayer =>
{
if (model.GetLayer(idxLayer).GetNumberOfUpstreamUnits() < 0) //Check if Upstream Layer is setup, this will set an exception otherwise
{
exceptionFlag = true;
}
});
return(exceptionFlag);
}