private void RunTraining(Trainer trainer, GenericMinibatchSource minibatchSource, int numMinibatchesToTrain, DeviceDescriptor device)
{
Debug.WriteLine($"Minibatch;CrossEntropyLoss;EvaluationCriterion;");
double aggregate_metric = 0;
for (int minibatchCount = 0; minibatchCount < numMinibatchesToTrain; minibatchCount++)
{
IDictionary <Variable, MinibatchData> data = minibatchSource.GetNextRandomMinibatch();
trainer.TrainMinibatch(data, device);
PrintTrainingProgress(trainer, minibatchCount);
}
}
private void RunTraining(Trainer trainer, GenericMinibatchSequenceSource minibatchSource, int numMinibatchesToTrain, DeviceDescriptor device)
{
double aggregate_metric = 0;
for (int minibatchCount = 0; minibatchCount < numMinibatchesToTrain; minibatchCount++)
{
IDictionary <Variable, MinibatchData> data = minibatchSource.GetNextRandomMinibatch();
trainer.TrainMinibatch(data, device);
double samples = trainer.PreviousMinibatchSampleCount();
double avg = trainer.PreviousMinibatchEvaluationAverage();
aggregate_metric += avg * samples;
double nbSampleSeen = trainer.TotalNumberOfSamplesSeen();
double train_error = aggregate_metric / nbSampleSeen;
Debug.WriteLine($"{minibatchCount} Average training error: {train_error:p2}");
}
}
public void Run()
{
var device = DeviceDescriptor.UseDefaultDevice();
// 1. Generate Data
int sampleSize = 32;
int nbDimensionsInput = 2; // 2 dimensions (age&tumorsize)
int nbLabels = 2; // l'output est un vecteur de probabilités qui doit sommer à 1. Si on ne met qu'une seule dimension de sortie, l'output sera toujours de 1.
// on met donc deux dimension, une dimension 'vrai' et une dimension 'faux'. L'output sera du genre 0.25 vrai et 0.75 faux => total des poids = 1;
// premier label = faux, second = vrai
IEnumerable <DataPoint> data = GenerateData(sampleSize);
//foreach (var pt in data)
// Debug.WriteLine($"{pt.Age};{pt.TumorSize};{(pt.HasCancer ? 1 : 0)}");
Variable inputVariables = Variable.InputVariable(NDShape.CreateNDShape(new[] { nbDimensionsInput }), DataType.Double, "input");
Variable expectedOutput = Variable.InputVariable(new int[] { nbLabels }, DataType.Double, "output");
Parameter bias = new Parameter(NDShape.CreateNDShape(new[] { nbLabels }), DataType.Double, 0); // une abscisse pour chaque dimension
Parameter weights = new Parameter(NDShape.CreateNDShape(new[] { nbDimensionsInput, nbLabels }), DataType.Double, 0); // les coefficients à trouver
// 2 variable d'input, 2 estimations en sortie (proba vrai et proba faux)
Function predictionFunction = CNTKLib.Plus(CNTKLib.Times(weights, inputVariables), bias);
Function lossFunction = CNTKLib.CrossEntropyWithSoftmax(predictionFunction, expectedOutput);
Function evalErrorFunction = CNTKLib.ClassificationError(predictionFunction, expectedOutput);
//Function logisticClassifier = CNTKLib.Sigmoid(evaluationFunction, "LogisticClassifier");
uint minibatchSize = 25;
//double learningRate = 0.5;
//TrainingParameterScheduleDouble learningRatePerSample = new TrainingParameterScheduleDouble(learningRate, minibatchSize);
TrainingParameterScheduleDouble learningRatePerSample = new TrainingParameterScheduleDouble(0.3, (uint)(data.Count() / 1.0));
TrainingParameterScheduleDouble momentumSchedule = new TrainingParameterScheduleDouble(0.9126265014311797, minibatchSize);
var parameters = new ParameterVector();
foreach (var p in predictionFunction.Parameters())
{
parameters.Add(p);
}
List <Learner> parameterLearners = new List <Learner>()
{
CNTKLib.FSAdaGradLearner(parameters, learningRatePerSample, momentumSchedule, true)
};
Trainer trainer = Trainer.CreateTrainer(predictionFunction, lossFunction, evalErrorFunction, parameterLearners);
double nbSamplesToUseForTraining = 20000;
int numMinibatchesToTrain = (int)(nbSamplesToUseForTraining / (int)minibatchSize);
// train the model
for (int minibatchCount = 0; minibatchCount < numMinibatchesToTrain; minibatchCount++)
{
IEnumerable <DataPoint> trainingData = GenerateData((int)minibatchSize);
List <double> minibatchInput = new List <double>();
List <double> minibatchOutput = new List <double>();
foreach (DataPoint row in trainingData)
{
minibatchInput.Add(row.Age);
minibatchInput.Add(row.TumorSize);
minibatchOutput.Add(row.HasCancer ? 0d : 1d);
minibatchOutput.Add(row.HasCancer ? 1d : 0d);
}
Value inputData = Value.CreateBatch <double>(NDShape.CreateNDShape(new int[] { nbDimensionsInput }), minibatchInput, device);
Value outputData = Value.CreateBatch <double>(NDShape.CreateNDShape(new int[] { nbLabels }), minibatchOutput, device);
trainer.TrainMinibatch(new Dictionary <Variable, Value>()
{
{ inputVariables, inputData }, { expectedOutput, outputData }
}, device);
PrintTrainingProgress(trainer, minibatchCount);
}
// test
{
int testSize = 100;
IEnumerable <DataPoint> trainingData = GenerateData(testSize);
List <double> minibatchInput = new List <double>();
List <double> minibatchOutput = new List <double>();
foreach (DataPoint row in trainingData)
{
minibatchInput.Add(row.Age);
minibatchInput.Add(row.TumorSize);
minibatchOutput.Add(row.HasCancer ? 0d : 1d);
minibatchOutput.Add(row.HasCancer ? 1d : 0d);
}
Value inputData = Value.CreateBatch <double>(NDShape.CreateNDShape(new int[] { nbDimensionsInput }), minibatchInput, device);
Value outputData = Value.CreateBatch <double>(NDShape.CreateNDShape(new int[] { nbLabels }), minibatchOutput, device);
IList <IList <double> > expectedOneHot = outputData.GetDenseData <double>(predictionFunction.Output);
IList <int> expectedLabels = expectedOneHot.Select(l => l.IndexOf(1.0d)).ToList();
var outputDataMap = new Dictionary <Variable, Value>()
{
{ predictionFunction.Output, null }
};
predictionFunction.Evaluate(
new Dictionary <Variable, Value>()
{
{ inputVariables, inputData }
},
outputDataMap,
device);
Value outputValue = outputDataMap[predictionFunction.Output];
IList <IList <double> > actualLabelSoftMax = outputValue.GetDenseData <double>(predictionFunction.Output);
var actualLabels = actualLabelSoftMax.Select((IList <double> l) => l.IndexOf(l.Max())).ToList();
int misMatches = actualLabels.Zip(expectedLabels, (a, b) => a.Equals(b) ? 0 : 1).Sum();
Debug.WriteLine($"Validating Model: Total Samples = {testSize}, Misclassify Count = {misMatches}");
}
}
public void Run()
{
var device = DeviceDescriptor.UseDefaultDevice();
// 1. Generate Data
int sampleSize = 32;
int nbDimensionsInput = 2; // 2 dimensions (age&tumorsize)
int nbLabels = 2; // l'output est un vecteur de probabilités qui doit sommer à 1. Si on ne met qu'une seule dimension de sortie, l'output sera toujours de 1.
// on met donc deux dimension, une dimension 'vrai' et une dimension 'faux'. L'output sera du genre 0.25 vrai et 0.75 faux => total des poids = 1;
// premier label = faux, second = vrai
IEnumerable <DataPoint> data = GenerateData(sampleSize);
//foreach (var pt in data)
// Debug.WriteLine($"{pt.Age};{pt.TumorSize};{(pt.HasCancer ? 1 : 0)}");
Variable inputVariables = Variable.InputVariable(NDShape.CreateNDShape(new[] { nbDimensionsInput }), DataType.Double, "input");
Variable expectedOutput = Variable.InputVariable(new int[] { nbLabels }, DataType.Double, "output");
int nbHiddenLayers = 1;
Function lastLayer = DefineNetwork(inputVariables, nbLabels, nbHiddenLayers, CNTKLib.Sigmoid);
Function lossFunction = CNTKLib.CrossEntropyWithSoftmax(lastLayer, expectedOutput);
Function evalErrorFunction = CNTKLib.ClassificationError(lastLayer, expectedOutput);
uint minibatchSize = 25;
double learningRate = 0.5;
TrainingParameterScheduleDouble learningRatePerSample = new TrainingParameterScheduleDouble(learningRate, minibatchSize);
IList <Learner> parameterLearners = new List <Learner>()
{
Learner.SGDLearner(lastLayer.Parameters(), learningRatePerSample)
};
Trainer trainer = Trainer.CreateTrainer(lastLayer, lossFunction, evalErrorFunction, parameterLearners);
double nbSamplesToUseForTraining = 20000;
int numMinibatchesToTrain = (int)(nbSamplesToUseForTraining / (int)minibatchSize);
// train the model
for (int minibatchCount = 0; minibatchCount < numMinibatchesToTrain; minibatchCount++)
{
IEnumerable <DataPoint> trainingData = GenerateData((int)minibatchSize);
List <double> minibatchInput = new List <double>();
List <double> minibatchOutput = new List <double>();
foreach (DataPoint row in trainingData)
{
minibatchInput.Add(row.Age);
minibatchInput.Add(row.TumorSize);
minibatchOutput.Add(row.HasCancer ? 0d : 1d);
minibatchOutput.Add(row.HasCancer ? 1d : 0d);
}
Value inputData = Value.CreateBatch <double>(NDShape.CreateNDShape(new int[] { nbDimensionsInput }), minibatchInput, device);
Value outputData = Value.CreateBatch <double>(NDShape.CreateNDShape(new int[] { nbLabels }), minibatchOutput, device);
trainer.TrainMinibatch(new Dictionary <Variable, Value>()
{
{ inputVariables, inputData }, { expectedOutput, outputData }
}, false, device);
PrintTrainingProgress(trainer, minibatchCount);
}
// test
{
int testSize = 100;
IEnumerable <DataPoint> trainingData = GenerateData(testSize);
List <double> minibatchInput = new List <double>();
List <double> minibatchOutput = new List <double>();
foreach (DataPoint row in trainingData)
{
minibatchInput.Add(row.Age);
minibatchInput.Add(row.TumorSize);
minibatchOutput.Add(row.HasCancer ? 0d : 1d);
minibatchOutput.Add(row.HasCancer ? 1d : 0d);
}
Value inputData = Value.CreateBatch <double>(NDShape.CreateNDShape(new int[] { nbDimensionsInput }), minibatchInput, device);
Value outputData = Value.CreateBatch <double>(NDShape.CreateNDShape(new int[] { nbLabels }), minibatchOutput, device);
IList <IList <double> > expectedOneHot = outputData.GetDenseData <double>(lastLayer.Output);
IList <int> expectedLabels = expectedOneHot.Select(l => l.IndexOf(1.0d)).ToList();
var outputDataMap = new Dictionary <Variable, Value>()
{
{ lastLayer.Output, null }
};
lastLayer.Evaluate(
new Dictionary <Variable, Value>()
{
{ inputVariables, inputData }
},
outputDataMap,
device);
Value outputValue = outputDataMap[lastLayer.Output];
IList <IList <double> > actualLabelSoftMax = outputValue.GetDenseData <double>(lastLayer.Output);
var actualLabels = actualLabelSoftMax.Select((IList <double> l) => l.IndexOf(l.Max())).ToList();
int misMatches = actualLabels.Zip(expectedLabels, (a, b) => a.Equals(b) ? 0 : 1).Sum();
Debug.WriteLine($"Validating Model: Total Samples = {testSize}, Misclassify Count = {misMatches}");
}
}