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, TrainingParameterScheduleDouble.UnitType.Sample);
IList <Learner> parameterLearners =
new List <Learner>()
{
Learner.SGDLearner(classifierOutput.Parameters(), learningRatePerSample)
};
var trainer = Trainer.CreateTrainer(classifierOutput, loss, evalError, parameterLearners);
int minibatchSize = 64;
int numMinibatchesToTrain = 1000;
int updatePerMinibatches = 50;
// train the model
for (int minibatchCount = 0; minibatchCount < numMinibatchesToTrain; minibatchCount++)
{
Value features, labels;
GenerateValueData(minibatchSize, inputDim, numOutputClasses, out features, out labels, device);
trainer.TrainMinibatch(
new Dictionary <Variable, Value>()
{
{ featureVariable, features }, { labelVariable, labels }
}, device);
TestHelper.PrintTrainingProgress(trainer, minibatchCount, updatePerMinibatches);
}
// 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}");
}
/// <summary>
/// Train and evaluate a image classifier for MNIST data.
/// </summary>
/// <param name="device">CPU or GPU device to run training and evaluation</param>
/// <param name="useConvolution">option to use convolution network or to use multilayer perceptron</param>
/// <param name="forceRetrain">whether to override an existing model.
/// if true, any existing model will be overridden and the new one evaluated.
/// if false and there is an existing model, the existing model is evaluated.</param>
public static void TrainAndEvaluate(DeviceDescriptor device, bool useConvolution, bool forceRetrain)
{
var featureStreamName = "features";
var labelsStreamName = "labels";
var classifierName = "classifierOutput";
Function classifierOutput;
int[] imageDim = useConvolution ? new int[] { 28, 28, 1 } : new int[] { 784 };
int imageSize = 28 * 28;
int numClasses = 10;
IList <StreamConfiguration> streamConfigurations = new StreamConfiguration[]
{ new StreamConfiguration(featureStreamName, imageSize), new StreamConfiguration(labelsStreamName, numClasses) };
string modelFile = useConvolution ? "MNISTConvolution.model" : "MNISTMLP.model";
// If a model already exists and not set to force retrain, validate the model and return.
if (File.Exists(modelFile) && !forceRetrain)
{
var minibatchSourceExistModel = MinibatchSource.TextFormatMinibatchSource(
Path.Combine(ImageDataFolder, "Test_cntk_text.txt"), streamConfigurations);
TestHelper.ValidateModelWithMinibatchSource(modelFile, minibatchSourceExistModel,
imageDim, numClasses, featureStreamName, labelsStreamName, classifierName, device);
return;
}
// build the network
var input = CNTKLib.InputVariable(imageDim, DataType.Float, featureStreamName);
if (useConvolution)
{
var scaledInput = CNTKLib.ElementTimes(Constant.Scalar <float>(0.00390625f, device), input);
classifierOutput = CreateConvolutionalNeuralNetwork(scaledInput, numClasses, device, classifierName);
}
else
{
// For MLP, we like to have the middle layer to have certain amount of states.
int hiddenLayerDim = 200;
var scaledInput = CNTKLib.ElementTimes(Constant.Scalar <float>(0.00390625f, device), input);
classifierOutput = CreateMLPClassifier(device, numClasses, hiddenLayerDim, scaledInput, classifierName);
}
var labels = CNTKLib.InputVariable(new int[] { numClasses }, DataType.Float, labelsStreamName);
var trainingLoss = CNTKLib.CrossEntropyWithSoftmax(new Variable(classifierOutput), labels, "lossFunction");
var prediction = CNTKLib.ClassificationError(new Variable(classifierOutput), labels, "classificationError");
// prepare training data
var minibatchSource = MinibatchSource.TextFormatMinibatchSource(
Path.Combine(ImageDataFolder, "Train_cntk_text.txt"), streamConfigurations, MinibatchSource.InfinitelyRepeat);
var featureStreamInfo = minibatchSource.StreamInfo(featureStreamName);
var labelStreamInfo = minibatchSource.StreamInfo(labelsStreamName);
// set per sample learning rate
CNTK.TrainingParameterScheduleDouble learningRatePerSample = new CNTK.TrainingParameterScheduleDouble(
0.003125, TrainingParameterScheduleDouble.UnitType.Sample);
IList <Learner> parameterLearners = new List <Learner>()
{
Learner.SGDLearner(classifierOutput.Parameters(), learningRatePerSample)
};
var trainer = Trainer.CreateTrainer(classifierOutput, trainingLoss, prediction, parameterLearners);
//
const uint minibatchSize = 64;
int outputFrequencyInMinibatches = 20, i = 0;
int epochs = 5;
while (epochs > 0)
{
var minibatchData = minibatchSource.GetNextMinibatch(minibatchSize, device);
var arguments = new Dictionary <Variable, MinibatchData>
{
{ input, minibatchData[featureStreamInfo] },
{ labels, minibatchData[labelStreamInfo] }
};
trainer.TrainMinibatch(arguments, device);
TestHelper.PrintTrainingProgress(trainer, i++, outputFrequencyInMinibatches);
// MinibatchSource is created with MinibatchSource.InfinitelyRepeat.
// Batching will not end. Each time minibatchSource completes an sweep (epoch),
// the last minibatch data will be marked as end of a sweep. We use this flag
// to count number of epochs.
if (TestHelper.MiniBatchDataIsSweepEnd(minibatchData.Values))
{
epochs--;
}
}
// save the trained model
classifierOutput.Save(modelFile);
// validate the model
var minibatchSourceNewModel = MinibatchSource.TextFormatMinibatchSource(
Path.Combine(ImageDataFolder, "Test_cntk_text.txt"), streamConfigurations, MinibatchSource.FullDataSweep);
TestHelper.ValidateModelWithMinibatchSource(modelFile, minibatchSourceNewModel,
imageDim, numClasses, featureStreamName, labelsStreamName, classifierName, device);
}
