public WeightedCombiner(NetworkMatrix weights, NetworkVector biases, TrainingMode mode)
: base(mode)
{
if (weights == null)
{
throw new ArgumentException("Attempt to create a WeightedCombiner with null weights.");
}
if (biases == null)
{
throw new ArgumentException("Attempt to create a WeightedCombiner with null biases.");
}
if (biases.Dimension != weights.NumberOfOutputs)
{
throw new ArgumentException("Dimension of biases must the the same of the outputs.");
}
_weights = weights.Copy();
_biases = biases.Copy();
_inputs = new NetworkVector(NumberOfInputs);
_outputs = new NetworkVector(NumberOfOutputs);
_inputGradient = new NetworkVector(NumberOfInputs);
}
public static void BackgroundLearn(NetworkMatrix net)
{
while (true)
{
net.Learn();
}
}
public WeightedCombiner(WeightedCombiner combiner)
{
if (combiner == null)
{
throw new ArgumentException("Attempt to make a WeightedCombiner from null");
}
this._biases = combiner._biases.Copy();
this._weights = combiner._weights.Copy();
this.Mode = combiner.Mode;
}
public LayerState(NetworkMatrix weights, NetworkVector biases)
{
Biases = biases.ToArray();
Weights = weights.ToArray();
}
public WeightedCombiner(NetworkMatrix weights)
: this(weights, new NetworkVector(weights.NumberOfOutputs))
{
}
public WeightedCombiner(NetworkMatrix weights, NetworkVector biases)
: this(weights, biases, TrainingMode.ONLINE)
{
}
public BatchWeightedCombiner(NetworkMatrix weights, NetworkVector biases)
: base(weights, biases)
{
_biasesDelta = new NetworkVector(NumberOfOutputs);
_weightsDelta = new NetworkMatrix(NumberOfOutputs, NumberOfInputs);
}
public OnlineWeightedCombiner(NetworkMatrix weights, NetworkVector biases)
: base(weights, biases)
{
}