static void RunAllExamples(DeviceDescriptor device)
{
Console.WriteLine($"======== running LogisticRegression.TrainAndEvaluate using {device.Type} ========");
LogisticRegression.TrainAndEvaluate(device);
Console.WriteLine($"======== running SimpleFeedForwardClassifier.TrainSimpleFeedForwardClassifier using {device.Type} ========");
SimpleFeedForwardClassifierTest.TrainSimpleFeedForwardClassifier(device);
Console.WriteLine($"======== running MNISTClassifier.TrainAndEvaluate using {device.Type} with MLP classifier ========");
MNISTClassifier.TrainAndEvaluate(device, false, true);
Console.WriteLine($"======== running MNISTClassifier.TrainAndEvaluate using {device.Type} with convolution neural network ========");
MNISTClassifier.TrainAndEvaluate(device, true, true);
if (device.Type == DeviceKind.GPU)
{
Console.WriteLine($"======== running CifarResNet.TrainAndEvaluate using {device.Type} ========");
CifarResNetClassifier.TrainAndEvaluate(device, true);
}
if (device.Type == DeviceKind.GPU)
{
Console.WriteLine($"======== running TransferLearning.TrainAndEvaluateWithFlowerData using {device.Type} ========");
TransferLearning.TrainAndEvaluateWithFlowerData(device, true);
Console.WriteLine($"======== running TransferLearning.TrainAndEvaluateWithAnimalData using {device.Type} ========");
TransferLearning.TrainAndEvaluateWithAnimalData(device, true);
}
Console.WriteLine($"======== running LSTMSequenceClassifier.Train using {device.Type} ========");
LSTMSequenceClassifier.Train(device);
}
static void Main(string[] args)
{
try
{
//Logging.OnWriteLog += Logging_OnWriteLog;
//Setting global device
GlobalParameters.Device = DeviceDescriptor.CPUDevice;
//XOR Example
//XORExample.LoadData();
//XORExample.BuildModel();
//XORExample.Train();
//Housing regression example
//HousingRegression.LoadData();
//HousingRegression.BuildModel();
//HousingRegression.Train();
//MNIST Classification example
MNISTClassifier.LoadData();
MNISTClassifier.BuildModel();
MNISTClassifier.Train();
//Time series prediction
//TimeSeriesPrediction.LoadData();
//TimeSeriesPrediction.BuildModel();
//TimeSeriesPrediction.Train();
//Multi variate time series prediction
//MiltiVariateTimeSeriesPrediction.LoadData();
//MiltiVariateTimeSeriesPrediction.BuildModel();
//MiltiVariateTimeSeriesPrediction.Train();
//Cifar-10 Classification example
//Cifar10Classification.LoadData();
//Cifar10Classification.BuildModel();
//Cifar10Classification.Train();
//Image classification example
//Console.WriteLine("ResNet50 Prediction: " + ImageClassification.ImagenetTest(Common.ImageNetModel.ResNet50)[0].Name);
//Console.WriteLine("Cifar 10 Prediction: " + ImageClassification.Cifar10Test(Common.Cifar10Model.ResNet110)[0].Name);
//Object Detection
//ObjectDetection.PascalDetection();
//ObjectDetection.GroceryDetection();
Console.ReadLine();
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
Console.ReadLine();
}
}
static void Main(string[] args)
{
try
{
//Setting global device
Logging.OnWriteLog += Logging_OnWriteLog;
//XOR Example
XORExample.LoadData();
XORExample.BuildModel();
XORExample.Train();
//Housing regression example
HousingRegression.LoadData();
HousingRegression.BuildModel();
HousingRegression.Train();
//MNIST Classification example
MNISTClassifier.LoadData();
MNISTClassifier.BuildModel();
MNISTClassifier.Train();
//LSTM Time series example
TimeSeriesPrediction.LoadData();
TimeSeriesPrediction.BuildModel();
TimeSeriesPrediction.Train();
//Cifar - 10 Classification example
//Cifar10Classification.LoadData();
//Cifar10Classification.BuildModel();
//Cifar10Classification.Train();
//Image classification example
//Console.WriteLine("ResNet50 Prediction: " + ImageClassification.ImagenetTest(SiaNet.Common.ImageNetModel.ResNet50)[0].Name);
//Console.WriteLine("Cifar 10 Prediction: " + ImageClassification.Cifar10Test(SiaNet.Common.Cifar10Model.ResNet110)[0].Name);
//Object Detection
//ObjectDetection.PascalDetection();
//ObjectDetection.GroceryDetection();
Console.ReadLine();
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
Console.ReadLine();
}
}
static void Main(string[] args)
{
try
{
//Setting global device
Logging.OnWriteLog += Logging_OnWriteLog;
ImageDataFrame frame = new ImageDataFrame(Variable.InputVariable(new int[] { 28, 28, 1 }, DataType.Float), Variable.InputVariable(new int[] { 10 }, DataType.Float));
//frame.ExtractCifar10();
frame.Load(@"C:\BDK\Dataset\Cifar10");
//Housing regression example
HousingRegression.LoadData();
HousingRegression.BuildModel();
HousingRegression.Train();
//MNIST Classification example
MNISTClassifier.LoadData();
MNISTClassifier.BuildModel();
MNISTClassifier.Train();
//Cifar - 10 Classification example
//Cifar10Classification.LoadData();
//Cifar10Classification.BuildModel();
//Cifar10Classification.Train();
//Image classification example
Console.WriteLine("ResNet50 Prediction: " + ImageClassification.ImagenetTest(SiaNet.Common.ImageNetModel.ResNet50)[0].Name);
Console.WriteLine("Cifar 10 Prediction: " + ImageClassification.Cifar10Test(SiaNet.Common.Cifar10Model.ResNet110)[0].Name);
//Object Detection
//ObjectDetection.PascalDetection();
//ObjectDetection.GroceryDetection();
Console.ReadLine();
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
Console.ReadLine();
}
}
static void Main(string[] args)
{
// Todo: move to a separate unit test.
Console.WriteLine("Test CNTKLibraryCSTrainingExamples");
#if CPUONLY
Console.WriteLine("======== Train model using CPUOnly build ========");
#else
Console.WriteLine("======== Train model using GPU build ========");
#endif
List <DeviceDescriptor> devices = new List <DeviceDescriptor>();
if (ShouldRunOnCpu())
{
devices.Add(DeviceDescriptor.CPUDevice);
}
if (ShouldRunOnGpu())
{
devices.Add(DeviceDescriptor.GPUDevice(0));
}
string runTest = args.Length == 0 ? string.Empty : args[0];
if (args.Length > 1)
{
Console.WriteLine($"-------- running with test data prefix : {args[1]} --------");
TestCommon.TestDataDirPrefix = args[1];
}
else
{
Console.WriteLine("-------- No data folder path found in input, using default paths.");
TestCommon.TestDataDirPrefix = "../../";
}
foreach (var device in devices)
{
// Data folders of example classes are set for non-CNTK test runs.
// In case of CNTK test runs (runTest is set to a test name) data folders need to be set accordingly.
switch (runTest)
{
case "LogisticRegressionTest":
Console.WriteLine($"======== running LogisticRegression.TrainAndEvaluate using {device.Type} ========");
LogisticRegression.TrainAndEvaluate(device);
break;
case "SimpleFeedForwardClassifierTest":
Console.WriteLine($"======== running SimpleFeedForwardClassifierTest.TrainSimpleFeedForwardClassifier using {device.Type} ========");
SimpleFeedForwardClassifierTest.TrainSimpleFeedForwardClassifier(device);
break;
case "CifarResNetClassifierTest":
Console.WriteLine($"======== running CifarResNet.TrainAndEvaluate using {device.Type} ========");
if (args.Length > 1)
{
Console.WriteLine($"-------- running with test data in {args[1]} --------");
// this test uses data from external folder, we execute this test with full data dir.
CifarResNetClassifier.CifarDataFolder = TestCommon.TestDataDirPrefix;
}
CifarResNetClassifier.TrainAndEvaluate(device, true);
break;
case "LSTMSequenceClassifierTest":
Console.WriteLine($"======== running LSTMSequenceClassifier.Train using {device.Type} ========");
LSTMSequenceClassifier.Train(device);
break;
case "MNISTClassifierTest":
Console.WriteLine($"======== running MNISTClassifier.TrainAndEvaluate with Convnet using {device.Type} ========");
MNISTClassifier.TrainAndEvaluate(device, true, true);
break;
case "TransferLearningTest":
TransferLearning.BaseResnetModelFile = "ResNet_18.model";
Console.WriteLine($"======== running TransferLearning.TrainAndEvaluate with animal data using {device.Type} ========");
TransferLearning.TrainAndEvaluateWithAnimalData(device, true);
break;
case "":
RunAllExamples(device);
break;
default:
Console.WriteLine("'{0}' is not a valid test name.", runTest);
break;
}
}
Console.WriteLine("======== Train completes. ========");
}
static void Main(string[] args)
{
// Todo: move to a separate unit test.
Console.WriteLine("Test CNTKLibraryCSTrainingExamples");
#if CPUONLY
Console.WriteLine("======== Train model using CPUOnly build ========");
#else
Console.WriteLine("======== Train model using GPU build ========");
#endif
List <DeviceDescriptor> devices = new List <DeviceDescriptor>();
if (ShouldRunOnCpu())
{
devices.Add(DeviceDescriptor.CPUDevice);
}
if (ShouldRunOnGpu())
{
devices.Add(DeviceDescriptor.GPUDevice(0));
}
string runTest = args.Length == 0 ? string.Empty : args[0];
foreach (var device in devices)
{
/// Data folders of example classes are set for non-CNTK test runs.
/// In case of CNTK test runs (runTest is set to a test name) data folders need to be set accordingly.
switch (runTest)
{
case "SimpleFeedForwardClassifierTest":
SimpleFeedForwardClassifierTest.DataFolder = ".";
Console.WriteLine($"======== running SimpleFeedForwardClassifierTest.TrainSimpleFeedForwardClassifier using {device.Type} ========");
SimpleFeedForwardClassifierTest.TrainSimpleFeedForwardClassifier(device);
break;
case "CifarResNetClassifierTest":
CifarResNetClassifier.CifarDataFolder = "./cifar-10-batches-py";
Console.WriteLine($"======== running CifarResNet.TrainAndEvaluate using {device.Type} ========");
CifarResNetClassifier.TrainAndEvaluate(device, true);
break;
case "LSTMSequenceClassifierTest":
LSTMSequenceClassifier.DataFolder = "../../../Text/SequenceClassification/Data";
Console.WriteLine($"======== running LSTMSequenceClassifier.Train using {device.Type} ========");
LSTMSequenceClassifier.Train(device);
break;
case "MNISTClassifierTest":
MNISTClassifier.ImageDataFolder = "../../../Image/Data/";
Console.WriteLine($"======== running MNISTClassifierTest.TrainAndEvaluate with Convnet using {device.Type} ========");
MNISTClassifier.TrainAndEvaluate(device, true, true);
break;
case "TransferLearningTest":
TransferLearning.ExampleImageFoler = ".";
TransferLearning.BaseResnetModelFile = "ResNet_18.model";
Console.WriteLine($"======== running TransferLearning.TrainAndEvaluate with animal data using {device.Type} ========");
TransferLearning.TrainAndEvaluateWithAnimalData(device, true);
break;
default:
RunAllExamples(device);
break;
}
}
Console.WriteLine("======== Train completes. ========");
}
