public static void Training(Network network, DataSet.DataSet data, Optimizer.Optimizer optimizer = null,
float limitError = 1e-5f, bool printLog = false)
{
var error = float.MaxValue;
var it = 0;
var usedOptimizer = optimizer?.Clone() ?? DefaultOptimizer.Clone();
usedOptimizer.LearningRate /= data.TrainingDataSize;
network.SetOptimizer(usedOptimizer);
while (error > limitError)
{
if (printLog)
{
WriteLine($"Epoch {it++}");
}
error = 0.0f;
network.ClearDeltaW();
foreach (var datum in data.TrainingData())
{
network.SetInputs(datum.Input);
network.ForwardPropagation();
error += network.Error(datum.Output);
network.BackPropagation(datum.Output);
}
network.UpdateWeights();
error /= data.TrainingDataSize;
if (printLog)
{
WriteLine($"Error : {error: ##0.00000;-##0.00000}");
}
}
network.FinishLearning();
}
