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 nbDimensionsInput = trainImages[0].Length;
int nbLabels = 10; // de 0 à 10
Variable inputVariables = Variable.InputVariable(NDShape.CreateNDShape(new[] { nbDimensionsInput }), 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_103C(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}");
}
}
private Function DefineModel_103B(Example_103_Util util, int nbLabels, Variable inputVariables)
{
return(util.DenseNode(inputVariables, nbLabels, null));
}