// create ProgressWriterVector with attached native progress writer
public static ProgressWriterVector CreateVector(string path, Function network)
{
var progress = new ProgressWriterVector();
var getV = typeof(ProgressWriterVector).GetMethods(BindingFlags.Static | BindingFlags.NonPublic).First(m => m.Name == "getCPtr");
HandleRef vector = (HandleRef)(getV.Invoke(null, new object[] { progress }));
var getF = typeof(Function).GetMethods(BindingFlags.Static | BindingFlags.NonPublic).First(m => m.Name == "getCPtr");
HandleRef function = (HandleRef)(getF.Invoke(null, new object[] { network }));
TensorBoardFileWriter.InitVec(vector.Handle, path, function.Handle);
return(progress);
}
static public void TrainAndEvaluate(DeviceDescriptor device)
{
// build a logistic regression model
Variable featureVariable = Variable.InputVariable(new int[] { inputDim }, DataType.Float);
Variable labelVariable = Variable.InputVariable(new int[] { numOutputClasses }, DataType.Float);
var classifierOutput = CreateLinearModel(featureVariable, numOutputClasses, device);
var loss = CNTKLib.CrossEntropyWithSoftmax(classifierOutput, labelVariable);
var evalError = CNTKLib.ClassificationError(classifierOutput, labelVariable);
// prepare for training
CNTK.TrainingParameterScheduleDouble learningRatePerSample = new CNTK.TrainingParameterScheduleDouble(0.02, 1);
IList <Learner> parameterLearners =
new List <Learner>()
{
Learner.SGDLearner(classifierOutput.Parameters(), learningRatePerSample)
};
var progressWriter = new TensorBoardFileWriter("log/test");
var progressWriterVector = TensorBoardFileWriter.CreateVector("log/main", classifierOutput);
var trainer = Trainer.CreateTrainer(classifierOutput, loss, evalError, parameterLearners, progressWriterVector);
int minibatchSize = 64;
int numMinibatchesToTrain = 1000;
int updatePerMinibatches = 50;
// train the model
Random random = new Random(0);
for (int minibatchCount = 0; minibatchCount < numMinibatchesToTrain; minibatchCount++)
{
Value features, labels;
GenerateValueData(minibatchSize, inputDim, numOutputClasses, out features, out labels, device);
//TODO: sweepEnd should be set properly instead of false.
#pragma warning disable 618
trainer.TrainMinibatch(
new Dictionary <Variable, Value>()
{
{ featureVariable, features }, { labelVariable, labels }
}, device);
#pragma warning restore 618
PrintTrainingProgress(trainer, minibatchCount, updatePerMinibatches);
progressWriter.WriteValue("random1", (float)random.Next(), minibatchCount);
progressWriter.WriteValue("random2", (float)random.Next(), minibatchCount);
progressWriter.Flush();
}
// test and validate the model
int testSize = 100;
Value testFeatureValue, expectedLabelValue;
GenerateValueData(testSize, inputDim, numOutputClasses, out testFeatureValue, out expectedLabelValue, device);
// GetDenseData just needs the variable's shape
IList <IList <float> > expectedOneHot = expectedLabelValue.GetDenseData <float>(labelVariable);
IList <int> expectedLabels = expectedOneHot.Select(l => l.IndexOf(1.0F)).ToList();
var inputDataMap = new Dictionary <Variable, Value>()
{
{ featureVariable, testFeatureValue }
};
var outputDataMap = new Dictionary <Variable, Value>()
{
{ classifierOutput.Output, null }
};
classifierOutput.Evaluate(inputDataMap, outputDataMap, device);
var outputValue = outputDataMap[classifierOutput.Output];
IList <IList <float> > actualLabelSoftMax = outputValue.GetDenseData <float>(classifierOutput.Output);
var actualLabels = actualLabelSoftMax.Select((IList <float> l) => l.IndexOf(l.Max())).ToList();
int misMatches = actualLabels.Zip(expectedLabels, (a, b) => a.Equals(b) ? 0 : 1).Sum();
Console.WriteLine($"Validating Model: Total Samples = {testSize}, Misclassify Count = {misMatches}");
}