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}");
}
}
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();
var util = new Example_103_Util();
// data
string trainImagesPath = "./Example_103/train-images-idx3-ubyte.gz";
string trainLabelsPath = "./Example_103/train-labels-idx1-ubyte.gz";
List <byte[]> trainImages = util.LoadImages(trainImagesPath);
List <int> trainLabels = util.LoadLabels(trainLabelsPath);
List <int[]> trainLabels1Hot = trainLabels.Select(x => util.ConvertTo1Hot(x)).ToList();
string evelImagesPath = "./Example_103/t10k-images-idx3-ubyte.gz";
string evalLabelsPath = "./Example_103/t10k-labels-idx1-ubyte.gz";
List <byte[]> evalImages = util.LoadImages(evelImagesPath);
List <int> evalLabels = util.LoadLabels(evalLabelsPath);
List <int[]> evalLabels1Hot = evalLabels.Select(x => util.ConvertTo1Hot(x)).ToList();
// model
int sampleSize = trainImages.Count;
int nbLabels = 10; // de 0 à 10
Variable inputVariables = Variable.InputVariable(NDShape.CreateNDShape(new[] { 28, 28, 1 }), DataType.Double, "input");
Variable expectedOutput = Variable.InputVariable(NDShape.CreateNDShape(new int[] { nbLabels }), DataType.Double, "output");
var scaledInput = CNTKLib.ElementTimes(Constant.Scalar <double>(1.0 / 255.0, device), inputVariables);
Function lastLayer = DefineModel_103D(util, nbLabels, scaledInput);
Function lossFunction = CNTKLib.CrossEntropyWithSoftmax(lastLayer, expectedOutput);
Function evalErrorFunction = CNTKLib.ClassificationError(lastLayer, expectedOutput);
// training
Trainer trainer;
{
// define training
uint minibatchSize = 64;
double learningRate = 0.2;
TrainingParameterScheduleDouble learningRatePerSample = new TrainingParameterScheduleDouble(learningRate, minibatchSize);
List <Learner> parameterLearners = new List <Learner>()
{
Learner.SGDLearner(lastLayer.Parameters(), learningRatePerSample)
};
trainer = Trainer.CreateTrainer(lastLayer, lossFunction, evalErrorFunction, parameterLearners);
// run training
int nbSamplesToUseForTraining = trainImages.Count;
int numSweepsToTrainWith = 10; // traduction de sweep ?
int numMinibatchesToTrain = nbSamplesToUseForTraining * numSweepsToTrainWith / (int)minibatchSize;
var minibatchSource = new Example_103_MinibatchSource(inputVariables, trainImages, expectedOutput, trainLabels1Hot, nbSamplesToUseForTraining, numSweepsToTrainWith, minibatchSize, device);
for (int minibatchCount = 0; minibatchCount < numMinibatchesToTrain; minibatchCount++)
{
IDictionary <Variable, MinibatchData> data = minibatchSource.GetNextRandomMinibatch();
trainer.TrainMinibatch(data, device);
util.PrintTrainingProgress(trainer, minibatchCount);
}
}
// evaluate
{
uint testMinibatchSize = 512;
int nbSamplesToTest = evalImages.Count;
int numMinibatchesToTrain = nbSamplesToTest / (int)testMinibatchSize;
double testResult = 0;
var minibatchSource = new Example_103_MinibatchSource(inputVariables, evalImages, expectedOutput, evalLabels1Hot, nbSamplesToTest, 1, testMinibatchSize, device);
for (int minibatchCount = 0; minibatchCount < numMinibatchesToTrain; minibatchCount++)
{
IDictionary <Variable, MinibatchData> data = minibatchSource.GetNextRandomMinibatch();
UnorderedMapVariableMinibatchData evalInput = new UnorderedMapVariableMinibatchData();
foreach (var row in data)
{
evalInput[row.Key] = row.Value;
}
double error = trainer.TestMinibatch(evalInput, device);
testResult += error;
//var z = CNTKLib.Softmax(lastLayer);
//var tOut = new Dictionary<Variable, Value>() { { z.Output, null } };
//z.Evaluate(
// new Dictionary<Variable, Value>() { { inputVariables, data[inputVariables].data } },
// tOut,
// device
// );
//Value outputValue = tOut[z.Output];
//IList<IList<double>> actualLabelSoftMax = outputValue.GetDenseData<double>(z.Output);
//var actualLabels = actualLabelSoftMax.Select((IList<double> l) => l.IndexOf(l.Max())).ToList();
//Value expectedOutputValue = data[expectedOutput].data;
//IList<IList<double>> expectedLabelsSoftmax = expectedOutputValue.GetDenseData<double>(z.Output);
//var expectedLabels = expectedLabelsSoftmax.Select((IList<double> l) => l.IndexOf(l.Max())).ToList();
//for(int i = 0; i < expectedLabels.Count; i++)
//{
// if (actualLabels[i] != expectedLabels[i])
// {
// Debug.WriteLine($"{actualLabels[i]} {expectedLabels[i]}");
// }
//}
//int misMatches = actualLabels.Zip(expectedLabels, (a, b) => a.Equals(b) ? 0 : 1).Sum();
Debug.WriteLine($"Average test error: {(testResult / (minibatchCount + 1)):p2}");
}
Debug.WriteLine($"Average test error: {(testResult / numMinibatchesToTrain):p2}");
}
}
