static void Main(string[] args)
{
var device = DeviceDescriptor.GPUDevice(0);
Console.WriteLine($"======== running LogisticRegression.TrainAndEvaluate using {device.Type} ========");
LogisticRegression.TrainAndEvaluate(device);
Console.WriteLine($"======== running MNISTClassifier.TrainAndEvaluate with multilayer perceptron (MLP) classifier using {device.Type} ========");
MNISTClassifier.TrainAndEvaluate(device, false, true);
Console.WriteLine($"======== running MNISTClassifier.TrainAndEvaluate with convolutional neural network using {device.Type} ========");
MNISTClassifier.TrainAndEvaluate(device, true, true);
Console.WriteLine($"======== running CifarResNet.TrainAndEvaluate using {device.Type} ========");
CifarResNetClassifier.TrainAndEvaluate(device, true);
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($"======== running LSTMSequenceClassifier.Train using {device.Type} ========");
LSTMSequenceClassifier.Train(device);
}
static void Main(string[] args)
{
var device = DeviceDescriptor.GPUDevice(0);
Console.WriteLine($"======== runing MNISTClassifierTest.TrainAndEvaluate using {device.Type} with logistic classifier ========");
MNISTClassifier.TrainAndEvaluate(device, false, true);
Console.WriteLine($"======== runing MNISTClassifierTest.TrainAndEvaluate using {device.Type} with convolution classifier ========");
MNISTClassifier.TrainAndEvaluate(device, true, true);
Console.WriteLine($"======== runing CifarResNet.TrainAndEvaluate using {device.Type} ========");
CifarResNetClassifier.TrainAndEvaluate(device, true);
Console.WriteLine($"======== runing TransferLearning.TrainAndEvaluateWithFlowerData using {device.Type} ========");
TransferLearning.TrainAndEvaluateWithFlowerData(device, true);
Console.WriteLine($"======== runing TransferLearning.TrainAndEvaluateWithAnimalData using {device.Type} ========");
TransferLearning.TrainAndEvaluateWithAnimalData(device, true);
Console.WriteLine($"======== runing LSTMSequenceClassifier.Train using {device.Type} ========");
LSTMSequenceClassifier.Train(device, true);
}
static void Main(string[] args)
{
TestCommon.TestDataDirPrefix = "../../";
var device = DeviceDescriptor.GPUDevice(0);
Console.WriteLine($"======== running LogisticRegression.TrainAndEvaluate using {device.Type} ========");
LogisticRegression.TrainAndEvaluate(device);
Console.WriteLine($"======== running MNISTClassifier.TrainAndEvaluate with multilayer perceptron (MLP) classifier using {device.Type} ========");
MNISTClassifier.TrainAndEvaluate(device, false, true);
Console.WriteLine($"======== running MNISTClassifier.TrainAndEvaluate with convolutional neural network using {device.Type} ========");
MNISTClassifier.TrainAndEvaluate(device, true, true);
Console.WriteLine($"======== running CifarResNet.TrainAndEvaluate using {device.Type} ========");
CifarResNetClassifier.CifarDataFolder = "../../Examples/Image/DataSets/CIFAR-10";
CifarResNetClassifier.TrainAndEvaluate(device, true);
TestCommon.TestDataDirPrefix = "../../Examples/Image/DataSets/";
string modelFileSourceDir = "../../PretrainedModels/ResNet_18.model";
if (!File.Exists(modelFileSourceDir))
{
Console.WriteLine("Model file doesn't exist. Please run download_model.py in CNTK/CNTK/PretrainedModels");
Console.ReadKey();
return;
}
TransferLearning.BaseResnetModelFile = "ResNet_18.model";
File.Copy(modelFileSourceDir, TransferLearning.ExampleImageFolder + TransferLearning.BaseResnetModelFile, /*overwrite*/ true);
Console.WriteLine($"======== running TransferLearning.TrainAndEvaluateWithAnimalData using {device.Type} ========");
TransferLearning.TrainAndEvaluateWithAnimalData(device, true);
TestCommon.TestDataDirPrefix = "../../";
Console.WriteLine($"======== running LSTMSequenceClassifier.Train using {device.Type} ========");
LSTMSequenceClassifier.Train(device);
}