public Layer(
WeightsMatrix weights,
NetworkVector biases,
ActivationFunction activationfunction,
DerivativeFunction derivativefunction
)
: base(weights.NumberOfOutputs, weights.NumberOfInputs)
{
if (activationfunction != null && derivativefunction == null)
{
throw new ArgumentException("derivativefunction cannot be null, if activatioin is not null");
}
if (weights == null || biases == null)
{
throw new ArgumentException("Attempt to make a layer with null weights or biases.");
}
_combiner = new WeightedCombiner(weights, biases);
if (activationfunction == null)
{
_neuralFunction = null;
}
else
{
_neuralFunction = new NeuralFunction(_combiner.NumberOfOutputs, activationfunction, derivativefunction);
}
}
public Layer(double[,] weights, double[] biases, TrainingMode mode)
: base(mode)
{
int numberOfOutputs = weights.GetLength(0);
int numberOfInputs = weights.GetLength(1);
_combiner = new WeightedCombiner(weights, biases);
_neuralFunction = null;
}
public Layer(double[,] weights, ActivationFunction activationfunction, DerivativeFunction derivativefunction)
: this(weights)
{
if (activationfunction != null && derivativefunction == null)
{
throw new ArgumentException("derivativefunction cannot be null, if activatioin is not null");
}
_neuralFunction = new NeuralFunction(NumberOfOutputs, activationfunction, derivativefunction);
}
protected Layer(Layer layer)
: base(layer.Mode)
{
if (layer == null)
{
throw new ArgumentException("Attempt to create a LayerBank with a null layer.");
}
_combiner = layer._combiner;
_neuralFunction = layer._neuralFunction;
}