public static Function GetEvalFunction(NeuralNetworkEvalFunction lossFunction, Function calculatedOutputs, Variable outputVariable)
{
switch (lossFunction)
{
case NeuralNetworkEvalFunction.CrossEntropyWithSoftmax: return(CNTKLib.CrossEntropyWithSoftmax(calculatedOutputs, outputVariable));
case NeuralNetworkEvalFunction.ClassificationError: return(CNTKLib.ClassificationError(calculatedOutputs, outputVariable));
case NeuralNetworkEvalFunction.SquaredError: return(CNTKLib.SquaredError(calculatedOutputs, outputVariable));
case NeuralNetworkEvalFunction.MeanAbsoluteError: return(NetworkBuilder.MeanAbsoluteError(calculatedOutputs, outputVariable));
case NeuralNetworkEvalFunction.MeanAbsolutePercentageError: return(NetworkBuilder.MeanAbsolutePercentageError(calculatedOutputs, outputVariable));
default:
throw new InvalidOperationException("Unexpected " + lossFunction);
}
}
//Errors with CNTK: https://github.com/Microsoft/CNTK/issues/2614
public void Train(PredictorTrainingContext ctx)
{
InitialSetup();
var p = ctx.Predictor;
var nn = (NeuralNetworkSettingsEntity)p.AlgorithmSettings;
DeviceDescriptor device = GetDevice(nn);
Variable inputVariable = Variable.InputVariable(new[] { ctx.InputCodifications.Count }, DataType.Float, "input");
Variable outputVariable = Variable.InputVariable(new[] { ctx.OutputCodifications.Count }, DataType.Float, "output");
Variable currentVar = inputVariable;
nn.HiddenLayers.ForEach((layer, i) =>
{
currentVar = NetworkBuilder.DenseLayer(currentVar, layer.Size, device, layer.Activation, layer.Initializer, p.Settings.Seed ?? 0, "hidden" + i);
});
Function calculatedOutputs = NetworkBuilder.DenseLayer(currentVar, ctx.OutputCodifications.Count, device, nn.OutputActivation, nn.OutputInitializer, p.Settings.Seed ?? 0, "output");
Function loss = NetworkBuilder.GetEvalFunction(nn.LossFunction, calculatedOutputs, outputVariable);
Function evalError = NetworkBuilder.GetEvalFunction(nn.EvalErrorFunction, calculatedOutputs, outputVariable);
// prepare for training
Learner learner = NetworkBuilder.GetInitializer(calculatedOutputs.Parameters(), nn);
Trainer trainer = Trainer.CreateTrainer(calculatedOutputs, loss, evalError, new List <Learner>()
{
learner
});
Random rand = p.Settings.Seed == null ?
new Random() :
new Random(p.Settings.Seed.Value);
var(training, validation) = ctx.SplitTrainValidation(rand);
var minibachtSize = nn.MinibatchSize;
var numMinibatches = nn.NumMinibatches;
Stopwatch sw = Stopwatch.StartNew();
List <FinalCandidate> candidate = new List <FinalCandidate>();
for (int i = 0; i < numMinibatches; i++)
{
using (HeavyProfiler.Log("MiniBatch", () => i.ToString()))
{
ctx.ReportProgress("Training Minibatches", (i + 1) / (decimal)numMinibatches);
{
var trainMinibatch = 0.To(minibachtSize).Select(_ => rand.NextElement(training)).ToList();
using (Value inputValue = CreateValue(ctx, trainMinibatch, ctx.InputCodifications.Count, ctx.InputCodificationsByColumn, device))
using (Value outputValue = CreateValue(ctx, trainMinibatch, ctx.OutputCodifications.Count, ctx.OutputCodificationsByColumn, device))
{
using (HeavyProfiler.Log("TrainMinibatch", () => i.ToString()))
trainer.TrainMinibatch(new Dictionary <Variable, Value>()
{
{ inputVariable, inputValue },
{ outputVariable, outputValue },
}, false, device);
}
}
var ep = new EpochProgress
{
Ellapsed = sw.ElapsedMilliseconds,
Epoch = i,
TrainingExamples = (int)trainer.TotalNumberOfSamplesSeen(),
LossTraining = trainer.PreviousMinibatchLossAverage(),
EvaluationTraining = trainer.PreviousMinibatchEvaluationAverage(),
LossValidation = null,
EvaluationValidation = null,
};
ctx.Progresses.Enqueue(ep);
if (ctx.StopTraining)
{
p = ctx.Predictor = ctx.Predictor.ToLite().RetrieveAndRemember();
}
var isLast = numMinibatches - nn.BestResultFromLast <= i;
if (isLast || (i % nn.SaveProgressEvery) == 0 || ctx.StopTraining)
{
if (isLast || (i % nn.SaveValidationProgressEvery) == 0 || ctx.StopTraining)
{
using (HeavyProfiler.LogNoStackTrace("Validation"))
{
var validateMinibatch = 0.To(minibachtSize).Select(_ => rand.NextElement(validation)).ToList();
using (Value inputValValue = CreateValue(ctx, validateMinibatch, ctx.InputCodifications.Count, ctx.InputCodificationsByColumn, device))
using (Value outputValValue = CreateValue(ctx, validateMinibatch, ctx.OutputCodifications.Count, ctx.OutputCodificationsByColumn, device))
{
var inputs = new Dictionary <Variable, Value>()
{
{ inputVariable, inputValValue },
{ outputVariable, outputValValue },
};
ep.LossValidation = loss.EvaluateAvg(inputs, device);
ep.EvaluationValidation = evalError.EvaluateAvg(inputs, device);
}
}
}
var progress = ep.SaveEntity(ctx.Predictor);
if (isLast || ctx.StopTraining)
{
using (HeavyProfiler.LogNoStackTrace("FinalCandidate"))
{
candidate.Add(new FinalCandidate
{
Model = calculatedOutputs.Save(),
ResultTraining = new PredictorMetricsEmbedded {
Evaluation = progress.EvaluationTraining, Loss = progress.LossTraining
},
ResultValidation = new PredictorMetricsEmbedded {
Evaluation = progress.EvaluationValidation, Loss = progress.LossValidation
},
});
}
}
}
if (ctx.StopTraining)
{
break;
}
}
}
var best = candidate.WithMin(a => a.ResultValidation.Loss !.Value);
p.ResultTraining = best.ResultTraining;
p.ResultValidation = best.ResultValidation;
var fp = new Entities.Files.FilePathEmbedded(PredictorFileType.PredictorFile, "Model.cntk", best.Model);
p.Files.Add(fp);
using (OperationLogic.AllowSave <PredictorEntity>())
p.Save();
}
