static void Main(string[] args)
{
// Todo: move to a separate unit test.
Console.WriteLine("Test CNTKLibraryCSTrainingExamples");
#if CPUONLY
Console.WriteLine("======== Train model on CPU using CPUOnly build ========");
#else
Console.WriteLine("======== Train model on CPU using GPU build ========");
#endif
if (ShouldRunOnCpu())
{
var device = DeviceDescriptor.CPUDevice;
SimpleFeedForwardClassifierTest.TrainSimpleFeedForwardClassifier(device);
}
if (ShouldRunOnGpu())
{
Console.WriteLine(" ====== Train model on GPU =====");
var device = DeviceDescriptor.GPUDevice(0);
SimpleFeedForwardClassifierTest.TrainSimpleFeedForwardClassifier(device);
}
Console.WriteLine("======== Train completes. ========");
}
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)
{
// 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. ========");
}