private void _setInputGradient(List <NetworkVector> outputGradientParts)
{
List <NetworkVector> inputGradientParts = new List <NetworkVector>();
foreach (NetworkVector outputGradientPart in outputGradientParts)
{
inputGradientParts.Add(_weights.DotWithWeightsPerInput(outputGradientPart));
}
_inputGradient = NetworkVector.Concatenate(inputGradientParts);
}
public override NetworkVector InputGradient(NetworkVector outputgradient)
{
if (outputgradient == null || outputgradient.Dimension != NumberOfOutputs)
{
throw new ArgumentException("outputgradient may not be null and must have dimension equal to NumberOfNeurons.");
}
List <NetworkVector> inputGradientParts = new List <NetworkVector>();
foreach (NetworkVector outputGradientPart in _segment(outputgradient))
{
inputGradientParts.Add(Weights.DotWithWeightsPerInput(outputGradientPart));
}
return(NetworkVector.Concatenate(inputGradientParts));
}
public override void Run(NetworkVector inputvalues)
{
if (inputvalues.Dimension != NumberOfInputs)
{
throw new ArgumentException("Input dimension does not match this Layer.");
}
_inputs = inputvalues;
_inputParts = inputvalues.Segment(_repetitions);
List <NetworkVector> outputParts = new List <NetworkVector>();
foreach (NetworkVector inputPart in _inputParts)
{
outputParts.Add(_biases.SumWith(_weights.LeftMultiply(inputPart)));
}
_output = NetworkVector.Concatenate(outputParts);
}
public override void BackPropagate(NetworkVector outputgradient)
{
if (outputgradient.Dimension != NumberOfOutputs)
{
throw new ArgumentException("The dimension of outputgradient does not match this WeightedCombiner.");
}
List <NetworkVector> inputGradients = new List <NetworkVector>();
List <NetworkVector> outputGradientParts = outputgradient.Segment(_bankWidth);
foreach (NetworkVector outputGradientPart in outputGradientParts)
{
_layer.BackPropagate(outputGradientPart);
inputGradients.Add(_layer.InputGradient);
}
_inputGradient = NetworkVector.Concatenate(inputGradients);
}
protected override NetworkVector _run(NetworkVector inputvalues)
{
if (inputvalues.Dimension != NumberOfInputs)
{
throw new ArgumentException("Input dimension does not match this Layer.");
}
VectorInput = inputvalues;
BatchInput = null;
List <NetworkVector> outputParts = new List <NetworkVector>();
foreach (NetworkVector inputPart in _segment(inputvalues))
{
outputParts.Add(Biases.SumWith(Weights.LeftMultiply(inputPart)));
}
return(NetworkVector.Concatenate(outputParts));
}
public override void Run(NetworkVector input)
{
if (input.Dimension != NumberOfInputs)
{
throw new ArgumentException("The dimension of the input does not match this WeightedCombiner.");
}
_input = input;
List <NetworkVector> inputParts = input.Segment(_bankWidth);
List <NetworkVector> outputParts = new List <NetworkVector>();
foreach (NetworkVector inputPart in inputParts)
{
_layer.Run(inputPart);
outputParts.Add(_layer.Output);
}
_output = NetworkVector.Concatenate(outputParts);
}
