/// <summary>
/// Checks if new given network is better than current best. If it is, stores it as best.
/// </summary>
/// <param name="newNetwork"></param>
/// <returns>True if best network was changed. Otherwise, false</returns>
private bool UpdateBestNetwork(NeuralNetwork newNetwork)
{
var tester = new NetworkTester(ErrorCalculator);
if (BestNetwork is null) //save new network as best
{
BestNetwork = newNetwork;
BestTestError = tester.TestNetwork(BestNetwork, DataProvider);
return(true);
}
else // test new network and compare
{
var newNetworkError = tester.TestNetwork(newNetwork, DataProvider);
if (newNetworkError < BestTestError)
{
BestNetwork = newNetwork;
BestTestError = newNetworkError;
return(true);
}
else
{
return(false);
}
}
}
public void TrainNetwork(ref NeuralNetwork networkToTrain, int maxEpochs, double desiredErrorRate = 0)
{
var learnSet = DataProvider.LearnSet; //shorter
var TempTestErrorHistory = Vector <double> .Build.Dense(maxEpochs + 1);
Vector <double> TemporaryEpochErrorHistory = Vector <double> .Build.Dense(maxEpochs, 0); //place to temporary store epoch errors for all epochs
TempTestErrorHistory[0] = Double.MaxValue; // assume error at beginning is maximal
TemporaryEpochErrorHistory[0] = Double.MaxValue; // assume error at beginning is maximal
int shuffleAmount = (int)Math.Round(Math.Log(learnSet.Length, 2)); // calculate how much should shuffle learn set depending on its size
Vector <double> output;
Vector <double> errorVector;
int EpochIndex = 1; // epochs are counted starting from 1
while (EpochIndex < maxEpochs)
{
DataProvider.ShuffleDataSet(learnSet, shuffleAmount); // shuffle some data in learn set
CurrentEpochErrorVector = Vector <double> .Build.Dense(learnSet.Length, 0); // init with 0s
#region calculate epoch
for (int dataIndex = 0; dataIndex < learnSet.Length; dataIndex++)
{
output = networkToTrain.CalculateOutput(learnSet[dataIndex].X, CalculateMode.OutputsAndDerivatives);
errorVector = ErrorCalculator.CalculateErrorVector(output, learnSet[dataIndex].D);
CurrentEpochErrorVector[dataIndex] = ErrorCalculator.CalculateErrorSum(output, learnSet[dataIndex].D);
#region adapt weights
LearningAlgorithm.AdaptWeights(networkToTrain, errorVector, CurrentEpochErrorVector[dataIndex], CurrentEpochErrorVector[dataIndex.Previous()]);
#endregion
}
#endregion
#region epoch error
TemporaryEpochErrorHistory[EpochIndex] = ErrorCalculator.CalculateEpochError(CurrentEpochErrorVector);
if (TemporaryEpochErrorHistory[EpochIndex] <= desiredErrorRate) //learning is done
{
return;
}
#endregion
#region Adapt Learning Rate
LearningAlgorithm.AdaptLearningRate(TemporaryEpochErrorHistory[EpochIndex], TemporaryEpochErrorHistory[EpochIndex.Previous()]);
#endregion
#region create and store test results
var testError = tester.TestNetwork(networkToTrain, DataProvider);
TempTestErrorHistory[EpochIndex] = testError;
#endregion
#region update best network state
if (TempTestErrorHistory[EpochIndex] < BestError)
{
BestNetworkState = networkToTrain.DeepCopy();
BestError = TempTestErrorHistory[EpochIndex];
}
#endregion
EpochIndex++;
}
#region save errors for all epochs that actually were calculated
TestErrorHistory = Vector <double> .Build.Dense(EpochIndex);
EpochErrorHistory = Vector <double> .Build.Dense(EpochIndex);
TemporaryEpochErrorHistory.CopySubVectorTo(EpochErrorHistory, 0, 0, EpochIndex);
TempTestErrorHistory.CopySubVectorTo(TestErrorHistory, 0, 0, EpochIndex);
#endregion
//restore best network state ( on set for verifying)
networkToTrain = BestNetworkState;
}