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);
}
public void lstm_test01()
{
//define values, and variables
Variable x = Variable.InputVariable(new int[] { 4 }, DataType.Float, "input");
var xValues = Value.CreateBatchOfSequences <float>(new int[] { 4 }, mData, device);
//
var lstm00 = RNN.RecurrenceLSTM(x, 3, 3, DataType.Float, device, false, Activation.TanH, true, true, 1);
//
LSTMReccurentNN lstmNN = new LSTMReccurentNN(1, 1, device);
//lstm implme reference 01
var lstmCell = lstmNN.CreateLSTM(x, "output1");
var lstm01 = CNTKLib.SequenceLast(lstmCell.h);
//lstme implementation refe 02
var lstm02 = LSTMSequenceClassifier.LSTMNet(x, 1, device, "output1");
//
var wParams00 = lstm00.Inputs.Where(p => p.Uid.Contains("Parameter")).ToList();
var wParams01 = lstm00.Inputs.Where(p => p.Uid.Contains("Parameter")).ToList();
var wParams02 = lstm00.Inputs.Where(p => p.Uid.Contains("Parameter")).ToList();
//parameter count
Assert.Equal(wParams00.Count, wParams01.Count);
Assert.Equal(wParams00.Count, wParams02.Count);
//structure of parameters test
Assert.Equal(wParams00.Where(p => p.Name.Contains("_b")).Count(), wParams01.Where(p => p.Name.Contains("_b")).Count());
Assert.Equal(wParams00.Where(p => p.Name.Contains("_w")).Count(), wParams01.Where(p => p.Name.Contains("_w")).Count());
Assert.Equal(wParams00.Where(p => p.Name.Contains("_u")).Count(), wParams01.Where(p => p.Name.Contains("_u")).Count());
Assert.Equal(wParams00.Where(p => p.Name.Contains("peep")).Count(), wParams01.Where(p => p.Name.Contains("peep")).Count());
Assert.Equal(wParams00.Where(p => p.Name.Contains("stabilize")).Count(), wParams01.Where(p => p.Name.Contains("stabilize")).Count());
//check structure of parameters with originaly developed lstm
//chech for arguments
Assert.True(lstm01.Arguments.Count == lstm02.Arguments.Count);
for (int i = 0; i < lstm01.Arguments.Count; i++)
{
testVariable(lstm01.Arguments[i], lstm01.Arguments[i]);
}
///
Assert.True(lstm01.Inputs.Count == lstm02.Inputs.Count);
for (int i = 0; i < lstm01.Inputs.Count; i++)
{
testVariable(lstm01.Inputs[i], lstm02.Inputs[i]);
}
///
Assert.True(lstm01.Outputs.Count == lstm02.Outputs.Count);
for (int i = 0; i < lstm01.Outputs.Count; i++)
{
testVariable(lstm01.Outputs[i], lstm02.Outputs[i]);
}
}
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. ========");
}
public void LSTM_Test_Params_Count_with_peep_selfstabilize()
{
//define values, and variables
Variable x = Variable.InputVariable(new int[] { 2 }, DataType.Float, "input");
Variable y = Variable.InputVariable(new int[] { 3 }, DataType.Float, "output");
#region lstm org implemented in cntk for reference
//lstme implementation refe 02
var lstmTest02 = LSTMSequenceClassifier.LSTMNet(x, 3, device, "output1");
var ft2 = lstmTest02.Inputs.Where(l => l.Uid.StartsWith("Parameter")).ToList();
var totalSize = ft2.Sum(p => p.Shape.TotalSize);
//bias params
var bs2 = ft2.Where(p => p.Name.Contains("_b")).ToList();
var totalBs2 = bs2.Sum(v => v.Shape.TotalSize);
//weights
var ws2 = ft2.Where(p => p.Name.Contains("_w")).ToList();
var totalWs2 = ws2.Sum(v => v.Shape.TotalSize);
//update
var us2 = ft2.Where(p => p.Name.Contains("_u")).ToList();
var totalUs2 = us2.Sum(v => v.Shape.TotalSize);
//peephole
var ph2 = ft2.Where(p => p.Name.Contains("_peep")).ToList();
var totalph2 = ph2.Sum(v => v.Shape.TotalSize);
//stabilize
var st2 = ft2.Where(p => p.Name.Contains("_stabilize")).ToList();
var totalst2 = st2.Sum(v => v.Shape.TotalSize);
#endregion
#region anndotnet old implementation
//
//LSTMReccurentNN lstmNN = new LSTMReccurentNN(3, 3, device);
////lstm implme reference 01
//var lstmCell11 = lstmNN.CreateLSTM(x, "output1");
//var lstmTest01 = CNTKLib.SequenceLast(lstmCell11.h);
//var ft1 = lstmTest01.Inputs.Where(l => l.Uid.StartsWith("Parameter")).ToList();
//var consts1 = lstmTest01.Inputs.Where(l => l.Uid.StartsWith("Constant")).ToList();
//var inp1 = lstmTest01.Inputs.Where(l => l.Uid.StartsWith("Input")).ToList();
//var pparams1 = ft1.Sum(v => v.Shape.TotalSize);
#endregion
//Number of LSTM parameters
var lstm1 = RNN.RecurrenceLSTM(x, 3, 3, DataType.Float, device, false, Activation.TanH, true, true, 1);
var ft = lstm1.Inputs.Where(l => l.Uid.StartsWith("Parameter")).ToList();
var consts = lstm1.Inputs.Where(l => l.Uid.StartsWith("Constant")).ToList();
var inp = lstm1.Inputs.Where(l => l.Uid.StartsWith("Input")).ToList();
//bias params
var bs = ft.Where(p => p.Name.Contains("_b")).ToList();
var totalBs = bs.Sum(v => v.Shape.TotalSize);
Assert.Equal(12, totalBs);
//weights
var ws = ft.Where(p => p.Name.Contains("_w")).ToList();
var totalWs = ws.Sum(v => v.Shape.TotalSize);
Assert.Equal(24, totalWs);
//update
var us = ft.Where(p => p.Name.Contains("_u")).ToList();
var totalUs = us.Sum(v => v.Shape.TotalSize);
Assert.Equal(36, totalUs);
//peephole
var ph = ft.Where(p => p.Name.Contains("_peep")).ToList();
var totalPh = ph.Sum(v => v.Shape.TotalSize);
Assert.Equal(9, totalPh);
//stabilize
var st = ft.Where(p => p.Name.Contains("_stabilize")).ToList();
var totalst = st.Sum(v => v.Shape.TotalSize);
Assert.Equal(6, totalst);
var totalOnly = totalBs + totalWs + totalUs;
var totalWithSTabilize = totalOnly + totalst;
var totalWithPeep = totalOnly + totalPh;
var totalP = totalOnly + totalst + totalPh;
var totalParams = ft.Sum(v => v.Shape.TotalSize);
Assert.Equal(totalP, totalParams);
//72- without peep and stab
//75 - witout peep with stabil +3xm =
//81 - with peephole and without stabil
//87 - with peep ans stab 3+9
}
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. ========");
}
