public void Run()
{
var device = DeviceDescriptor.UseDefaultDevice();
int nbObservationsInItem = 5;
List <Example_106_Data> allData = GenerateExampleData(10000, nbObservationsInItem, 5);
List <Example_106_Data> trainingData = allData.Take(8000).ToList();
List <Example_106_Data> evalData = allData.Skip(8000).ToList();
int[] inputDim = new int[] { 1 };
Variable input = Variable.InputVariable(NDShape.CreateNDShape(inputDim), DataType.Double, "input", dynamicAxes: new List <Axis>()
{
Axis.DefaultDynamicAxis(), Axis.DefaultBatchAxis()
}); // default dynamic axis ???
int outputDim = 1;
Function model = DefineModel_LSTM(input, nbObservationsInItem, outputDim);
Function output = model.Output;
//Function output = Variable.InputVariable(NDShape.CreateNDShape(new[] { 1 }), DataType.Float, "output", model.Output.DynamicAxes);
//IEnumerable<int> inputSequence = ...; // pas clair
//Value sequence = Value.CreateSequence(NDShape.CreateNDShape(new int[] { 1 }), inputSequence, device);
uint minibatchSize = 100;
Variable expectedOutput = Variable.InputVariable(NDShape.CreateNDShape(new int[] { outputDim }), DataType.Double, "expectedOutput", dynamicAxes: new List <Axis>()
{
Axis.DefaultBatchAxis()
}); // default dynamic axis ???
Trainer trainer = MakeTrainer(expectedOutput, output, model, minibatchSize);
{ // train
int nbSamplesToUseForTraining = trainingData.Count;
int numSweepsToTrainWith = 20;
int numMinibatchesToTrain = nbSamplesToUseForTraining * numSweepsToTrainWith / (int)minibatchSize;
var trainingInput = trainingData.Select(x => x.Observations.Select(y => y)).ToList();
var trainingOutput = trainingData.Select(x => new[] { x.ExpectedPrediction }).ToList();
var trainingMinibatchSource = new GenericMinibatchSequenceSource(input, trainingInput, expectedOutput, trainingOutput, nbSamplesToUseForTraining, numSweepsToTrainWith, minibatchSize, device);
RunTraining(trainer, trainingMinibatchSource, numMinibatchesToTrain, device);
}
// evaluate
Evaluate(model, evalData, input, device);
}
private void RunTraining(Trainer trainer, GenericMinibatchSequenceSource minibatchSource, int numMinibatchesToTrain, DeviceDescriptor device)
{
double aggregate_metric = 0;
for (int minibatchCount = 0; minibatchCount < numMinibatchesToTrain; minibatchCount++)
{
IDictionary <Variable, MinibatchData> data = minibatchSource.GetNextRandomMinibatch();
trainer.TrainMinibatch(data, device);
double samples = trainer.PreviousMinibatchSampleCount();
double avg = trainer.PreviousMinibatchEvaluationAverage();
aggregate_metric += avg * samples;
double nbSampleSeen = trainer.TotalNumberOfSamplesSeen();
double train_error = aggregate_metric / nbSampleSeen;
Debug.WriteLine($"{minibatchCount} Average training error: {train_error:p2}");
}
}
