public void Train(Dataset dataset, ILoss loss, IOptimizer optimizer, int batchSize, int nEpoches, double minError, Dataset valDataset, IMetric metric, bool shuffle = true)
{
if (dataset.InputShape != InputShape)
{
throw new ShapeMismatchException($"{nameof(dataset)} {nameof(dataset.InputShape)}");
}
if (dataset.TargetShape != OutputShape)
{
throw new ShapeMismatchException($"{nameof(dataset)} {nameof(dataset.TargetShape)}");
}
if (valDataset.InputShape != InputShape)
{
throw new ShapeMismatchException($"{nameof(valDataset)} {nameof(dataset.InputShape)}");
}
if (valDataset.TargetShape != OutputShape)
{
throw new ShapeMismatchException($"{nameof(valDataset)} {nameof(dataset.TargetShape)}");
}
ClearCache();
double valError = Validate(valDataset, metric);
int epoch;
OnStart?.Invoke(valError);
for (epoch = 0; epoch < nEpoches; epoch++)
{
if (valError <= minError)
{
break;
}
if (shuffle)
{
dataset.Shuffle();
}
dataset.ForEach((D, index) =>
{
Forward(D.Inputs, true);
CalcGrads(loss, D.Targets);
optimizer.UpdateEpoch(epoch);
Optimize(optimizer);
if (index % batchSize == 0)
{
Update();
OnBatch?.Invoke(index / batchSize);
}
});
valError = Validate(valDataset, metric);
OnEpoch?.Invoke(epoch, valError);
}
OnFinish?.Invoke(epoch, valError);
}