/// <summary>
/// Trains a backpropagation network.
/// </summary>
/// <param name="backpropagationNetwork">The backpropagation network to train.</param>
/// <param name="maxIterationCount">The maximum number of iterations.</param>
/// <param name="usedIterationCount">The number of used iterations.</param>
/// <param name="maxTolerableNetoworError">The maximum tolerable network error.</param>
/// <param name="minAchievedNetworkError">The minimum achieved network error.</param>
/// <param name="networkErrorUpdateInterval">The interaval of network error update.</param>
/// <param name="accumulatedLearning">The accumulated learning flag.</param>
private void Train(BackpropagationNetwork backpropagationNetwork,
int maxIterationCount, out int usedIterationCount,
double maxTolerableNetworkError, out double minAchievedNetworkError,
int networkErrorUpdateInterval, bool accumulatedLearning
)
{
// 1.
// Put the discrete time of adaptation t := 0.
usedIterationCount = 0;
// 2.
// Choose randomly w_ji^(0) e <-1, 1>.
backpropagationNetwork.Initialize();
// Calculate the error of the network.
minAchievedNetworkError = backpropagationNetwork.CalculateError(trainingSet);
while ((usedIterationCount < maxIterationCount) && (minAchievedNetworkError > maxTolerableNetworkError))
{
// 3.
// Increment t := 1 + 1.
usedIterationCount++;
// 4.
// Put E'_ji := 0 for each synapse from i to j.
if (accumulatedLearning)
{
backpropagationNetwork.ResetSynapseErrors();
}
// 5.
// For each training pattern k = 1, ..., p (taken from the training set in random order) do:
foreach (TrainingPattern trainingPattern in trainingSet.TrainingPatternsRandomOrder)
{
if (!accumulatedLearning)
{
backpropagationNetwork.ResetSynapseErrors();
}
// (a)
// Calculate the output of the network y(w^(t - 1), x_k), i.e. the states (outputs) and the inner potentials (inputs)
// of all neurons (active regime) for the input x_k of the k-th training pattern using formulas (2.6) and (2.8).
backpropagationNetwork.Evaluate(trainingPattern.InputVector);
// (b)
// By error back-propagation calculate for each non-input neuron j /e X the partial derivation dE_k / dy_j (w^(t - 1))
// of the partial error function of the k-th training pattern according to the state (output) of the neuron j in point w^(t - 1)
// using formulas (2.18) and (2.19) (use the values of the states and the inner potentials calculated in step 5a).
//backpropagationNetwork.CalculateNeuronGradients( trainingPattern.OutputVector );
// (c)
// Calculate gradient of the partial error function dE_k / dw_ji (w^(t - 1)) in point w^(t - 1) using formula (2.17)
// using the partial derivations (errors) dE_k / dy_j (w^(t - 1)) calculated in step 5b.
//backpropagationNetwork.CalculateSynapseGradients();
// (d)
// Add E'_ji := E'_ji + dE_k / dw_ji (w^(t - 1)).
//backpropagationNetwork.UpdateSynapseErrors();
// Replaces three steps - (b), (c) and (d) - with one.
backpropagationNetwork.Backpropagate(trainingPattern.OutputVector);
if (!accumulatedLearning)
{
backpropagationNetwork.UpdateSynapseWeights();
}
}
// 6.
// {The following is valid E'_ji = dE / dw_ji (w^(t - 1)).}
// According to (2.12) put delta w_ji^(t) := -learning_rate * E'_ji.
// 7.
// According to (2.11) put w_ji^(t) := w_ji^(t - 1) + delta w_ji^(t).
if (accumulatedLearning)
{
backpropagationNetwork.UpdateSynapseWeights();
}
// Update the error of the network.
if (usedIterationCount % networkErrorUpdateInterval == 0)
{
minAchievedNetworkError = backpropagationNetwork.CalculateError(trainingSet);
}
}
}
