protected override void ProcessRecord()
{
IList <Value> results;
if (ParameterSetName == "Hashtable")
{
results = FunctionInvoke.Invoke(Function, Arguments, Device, false);
}
else if (ParameterSetName == "DataSourceSet")
{
results = FunctionInvoke.Invoke(Function, DataSourceSet, Device, false);
}
else
{
DataNameToInputMap map = new DataNameToInputMap(new Function[] { Function }, DataNameToInputMap);
Minibatch batch = null;
var values = new List <Value>();
do
{
batch = Sampler.GetNextMinibatch(Device);
map.InitializeByMinibatch(batch);
values.AddRange(FunctionInvoke.Invoke(Function, batch, map, Device));
}while (!batch.SweepEnd);
results = values;
}
foreach (var r in results)
{
WriteObject(r);
}
}
public IEnumerable <TrainingSession> GetIterator(int maxIteration = int.MaxValue)
{
_stopwatch = Stopwatch.StartNew();
_stop = false;
Epoch = 1;
EpochIncremented = false;
if (LearningRateScheduler != null)
{
Learner.ResetLearningRate(new TrainingParameterScheduleDouble(LearningRateScheduler.LearningRate));
}
for (Iteration = 1; Iteration <= maxIteration; ++Iteration)
{
var minibatch = Sampler.GetNextMinibatch(TrainingDevice);
if (minibatch == null)
{
break;
}
DataNameToInputMap.InitializeByMinibatch(minibatch);
var arguments = DataNameToInputMap.GetVariableValueMap(minibatch);
Trainer.TrainMinibatch(arguments, minibatch.SweepEnd, TrainingDevice);
SampleCount = (int)Trainer.PreviousMinibatchSampleCount();
Loss = Trainer.PreviousMinibatchLossAverage();
if (Trainer.EvaluationFunction() != null)
{
Metric = Trainer.PreviousMinibatchEvaluationAverage();
}
foreach (var cb in Callbacks)
{
cb.Run(this);
}
if (_stop)
{
break;
}
yield return(this);
if (LearningRateScheduler != null)
{
bool update = LearningRateScheduler.UpdateLearningRate(Epoch, Iteration, Loss);
if (update)
{
Learner.ResetLearningRate(new TrainingParameterScheduleDouble(LearningRateScheduler.LearningRate));
}
}
EpochIncremented = false;
if (minibatch.SweepEnd)
{
++Epoch;
EpochIncremented = true;
}
}
}