/// <summary>
/// Construct a BackpropagationLayer object that corresponds to a specific neuron layer.
/// </summary>
/// <param name="backpropagation">The back propagation training object.</param>
/// <param name="layer">The layer that this object corresponds to.</param>
public BackpropagationLayer(Backpropagation backpropagation,
FeedforwardLayer layer)
{
this.backpropagation = backpropagation;
this.layer = layer;
int neuronCount = layer.NeuronCount;
this.error = new double[neuronCount];
this.errorDelta = new double[neuronCount];
if (layer.Next != null)
{
this.accMatrixDelta = new Matrix.Matrix(layer.NeuronCount + 1, layer.Next.NeuronCount);
this.matrixDelta = new Matrix.Matrix(layer.NeuronCount + 1, layer.Next.NeuronCount);
this.biasRow = layer.NeuronCount;
}
}
/// <summary>
/// Calculate the error for the recognition just done.
/// </summary>
/// <param name="ideal">What the output neurons should have yielded.</param>
public void CalcError(double[] ideal)
{
if (ideal.Length != this.network.OutputLayer.NeuronCount)
{
throw new NeuralNetworkError(
"Size mismatch: Can't calcError for ideal input size="
+ ideal.Length + " for output layer size="
+ this.network.OutputLayer.NeuronCount);
}
// clear out all previous error data
foreach (FeedforwardLayer layer in this.network.Layers)
{
GetBackpropagationLayer(layer).ClearError();
}
for (int i = this.network.Layers.Count - 1; i >= 0; i--)
{
FeedforwardLayer layer = this.network.Layers[i];
if (layer.IsOutput())
{
GetBackpropagationLayer(layer).CalcError(ideal);
}
else
{
GetBackpropagationLayer(layer).CalcError();
}
}
}
/// <summary>
/// Get the BackpropagationLayer that corresponds to the specified layer.
/// </summary>
/// <param name="layer">The specified layer.</param>
/// <returns>The BackpropagationLayer that corresponds to the specified layer.</returns>
public BackpropagationLayer GetBackpropagationLayer(
FeedforwardLayer layer)
{
BackpropagationLayer result = this.layerMap[layer];
if (result == null)
{
throw new NeuralNetworkError(
"Layer unknown to backpropagation trainer, was a layer added after training begain?");
}
return(result);
}
public BackpropagationLayer(Backpropagation parent, FeedforwardLayer layer)
{
this.parent = parent;
this.layer = layer;
error = new double[layer.NeuronCount];
errorDelta = new double[layer.NeuronCount];
if (layer.Next != null)
{
accumulatedMatrixDelta = new Matrix(layer.NeuronCount + 1, layer.Next.NeuronCount);
previousMatrixDelta = new Matrix(layer.NeuronCount + 1, layer.Next.NeuronCount);
biasRow = layer.NeuronCount;
}
}
public BackpropagationLayer(Backpropagation parent, FeedforwardLayer layer)
{
_parent = parent;
_layer = layer;
_error = new double[layer.NeuronCount];
_errorDelta = new double[layer.NeuronCount];
if (layer.Next != null)
{
_accumulatedMatrixDelta = new Matrix(layer.NeuronCount + 1, layer.Next.NeuronCount);
_previousMatrixDelta = new Matrix(layer.NeuronCount + 1, layer.Next.NeuronCount);
_biasRow = layer.NeuronCount;
}
}
/// <summary>
/// Construct a BackpropagationLayer object that corresponds to a specific neuron layer.
/// </summary>
/// <param name="backpropagation">The back propagation training object.</param>
/// <param name="layer">The layer that this object corresponds to.</param>
public BackpropagationLayer(Backpropagation backpropagation,
FeedforwardLayer layer)
{
this.backpropagation = backpropagation;
this.layer = layer;
int neuronCount = layer.NeuronCount;
this.error = new double[neuronCount];
this.errorDelta = new double[neuronCount];
if (layer.Next != null)
{
this.accMatrixDelta = new Matrix.Matrix(layer.NeuronCount + 1, layer.Next.NeuronCount);
this.matrixDelta = new Matrix.Matrix(layer.NeuronCount + 1, layer.Next.NeuronCount);
this.biasRow = layer.NeuronCount;
}
}
/// <summary>
/// Get the BackpropagationLayer that corresponds to the specified layer.
/// </summary>
/// <param name="layer">The specified layer.</param>
/// <returns>The BackpropagationLayer that corresponds to the specified layer.</returns>
public BackpropagationLayer GetBackpropagationLayer(
FeedforwardLayer layer)
{
BackpropagationLayer result = this.layerMap[layer];
if (result == null)
{
throw new NeuralNetworkError(
"Layer unknown to backpropagation trainer, was a layer added after training begain?");
}
return result;
}
public BackpropagationLayer GetBackpropagationLayer(FeedforwardLayer layer)
{
return(_layerMapping[layer]);
}
public BackpropagationLayer GetBackpropagationLayer(FeedforwardLayer layer)
{
return _layerMapping[layer];
}
