/// <summary>
///
/// </summary>
/// <param name="desired"></param>
/// <returns></returns>
public LayerResult[] CalculateLoss(Vector desired)
{
Layer[] layers = AllLayers;
Vector LossL1 = NNOperations.OutputLoss(Output, desired, Properties.LossFunction);
LayerResult[] results = new LayerResult[LayerCount];
for (int i = layers.Length - 1; i >= 0; i--)
{
if (i == 0)
{
break;
}
Layer L1 = layers[i];
Layer L0 = layers[i - 1];
Vector LossL0 = NNOperations.PropagateLoss(LossL1, L0);
LayerResult ResultL1 = new LayerResult(L1, LossL1);
LayerResult ResultL0 = new LayerResult(L0, LossL0);
results[i] = ResultL1;
results[i - 1] = ResultL0;
LossL1 = LossL0;
}
return(results);
}
/// <summary>
/// <para>
/// Perform one pass of training. Adjust the weights based on the current state of the <see cref="Output"/> layer and the desired values.
/// Use <see cref="FeedForward"/> to calculate the output values.
/// </para>
///
/// <para>
/// Calculate the errors/losses of each layer (using <see cref="CalculateLoss(Vector)"/>)
/// and then adjust the weights accordingly (using <see cref="NNOperations.CalculateDeltas(Layer, Layer, Vector, ActivationFunction, float)"/>).
/// </para>
/// </summary>
/// <param name="desiredOutput">the desired output value of the network</param>
/// <returns>the results</returns>
public LayerResult[] AdjustWeights(Vector desiredOutput)
{
LayerResult[] results = CalculateLoss(desiredOutput);
for (int i = results.Length - 1; i >= 0; i--) // Iterate over results backwards
{
if (i == 0)
{
break;
}
LayerResult L1R = results[i];
LayerResult L0R = results[i - 1];
// Get the values to adjust weights and biases
Deltas L0deltas = NNOperations.CalculateDeltas(L0R.Layer, L1R.Layer, L1R.Loss, Properties.DerivativeActivation, Properties.LearningRate);
// create new adjusted weights and biases
Matrix nw = L0R.Layer.Weights + L0deltas.Weights;
Vector nb = L0R.Layer.Biases + L0deltas.Biases;
// Apply adjustments
L0R.Layer.Weights.Values = nw.Values;
L0R.Layer.Biases.Values = nb.Values;
results[i - 1].Deltas = L0deltas;
}
return(results);
}
