public void TestMinibatch()
{
var features = DataSourceFactory.Create(new float[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 }, new int[] { 2, 1, -1 });
var dss = new Dictionary <string, IDataSource <float> >()
{
{ "input", features }
};
var sampler = new DataSourceSampler(dss, 2, false, true);
{
var batch = sampler.GetNextMinibatch();
// GC.Collect();
// GC.Collect();
Assert.AreEqual(2, batch.SampleCount);
Assert.AreEqual(false, batch.SweepEnd);
var data = batch.Features["input"];
// var c1 = SharedPtrMethods.GetUseCountOf(data);
// var c2 = SharedPtrMethods.GetUseCountOf(data.data);
// var c3 = SharedPtrMethods.GetUseCountOf(data.data.Data);
var ds = DataSourceFactory.FromValue(data);
CollectionAssert.AreEqual(new float[] { 0, 1, 2, 3 }, ds.TypedData);
CollectionAssert.AreEqual(new int[] { 2, 1, 2 }, data.Shape.Dimensions.ToArray());
}
{
var batch = sampler.GetNextMinibatch();
// GC.Collect();
Assert.AreEqual(2, batch.SampleCount);
Assert.AreEqual(true, batch.SweepEnd);
var data = batch.Features["input"];
// var c1 = SharedPtrMethods.GetUseCountOf(data);
// var c2 = SharedPtrMethods.GetUseCountOf(data.data);
// var c3 = SharedPtrMethods.GetUseCountOf(data.data.Data);
var ds = DataSourceFactory.FromValue(data);
CollectionAssert.AreEqual(new float[] { 4, 5, 6, 7 }, ds.TypedData);
CollectionAssert.AreEqual(new int[] { 2, 1, 2 }, data.Shape.Dimensions.ToArray());
}
// When not randomized, remnant data that is smaller than the minibatch size is ignored.
{
var batch = sampler.GetNextMinibatch();
// GC.Collect();
Assert.AreEqual(2, batch.SampleCount);
Assert.AreEqual(false, batch.SweepEnd);
var data = batch.Features["input"];
var ds = DataSourceFactory.FromValue(data);
CollectionAssert.AreEqual(new float[] { 0, 1, 2, 3 }, ds.TypedData);
CollectionAssert.AreEqual(new int[] { 2, 1, 2 }, data.Shape.Dimensions.ToArray());
}
}
public void TestTrainingSession2()
{
// Data
var features = DataSourceFactory.Create(new float[] { 0, 0, 0, 1, 1, 0, 1, 1, 3, 4, 3, 5, 4, 4, 4, 5 }, new int[] { 2, 1, -1 });
var labels = DataSourceFactory.Create(new float[] { 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0 }, new int[] { 2, 1, -1 });
var sampler = new DataSourceSampler(new Dictionary <string, IDataSource <float> >()
{
{ "input", features },
{ "label", labels }
}, 2);
// Model
var input = CNTKLib.InputVariable(new int[] { 2 }, false, DataType.Float, "input");
var h = Composite.Dense(input, new int[] { 100 }, CNTKLib.HeNormalInitializer(), true, null, false, 4, "relu", DeviceDescriptor.UseDefaultDevice(), "");
h = Composite.Dense(h, new int[] { 2 }, CNTKLib.GlorotNormalInitializer(), true, null, false, 4, "sigmoid", DeviceDescriptor.UseDefaultDevice(), "");
var output = h;
var label = CNTKLib.InputVariable(new int[] { 2 }, DataType.Float, "label");
// Loss and metric functions
var loss = CNTKLib.BinaryCrossEntropy(output, label);
var error = CNTKLib.ClassificationError(output, label);
// Train
var lr = new TrainingParameterScheduleDouble(.01);
var m = new TrainingParameterScheduleDouble(.9);
var learner = Learner.MomentumSGDLearner(output.Parameters(), lr, m, true);
var session = new TrainingSession(output, loss, error, learner, null, sampler, null);
var iteration = session.GetEnumerator();
for (var i = 0; i < 1000; ++i)
{
iteration.MoveNext();
var dummy = iteration.Current;
var valid = session.GetValidationMetric();
}
Assert.IsTrue(session.Metric < 0.1);
}
public void TestSequence()
{
var features = DataSourceFactory.Create(new float[] { 0, 1, 2, 3, 4, 5, 6, 7 }, new int[] { 2, 2, -1 });
var ds = new Dictionary <string, IDataSource <float> >()
{
{ "input", features }
};
var sampler = new DataSourceSampler(ds, 2, false, true);
var batch = sampler.GetNextMinibatch();
Assert.AreEqual(2, batch.SampleCount);
Assert.AreEqual(true, batch.SweepEnd);
var data = batch.Features["input"];
CollectionAssert.AreEqual(new float[] { 0, 1, 2, 3, 4, 5, 6, 7 }, DataSourceFactory.FromValue(data).TypedData);
CollectionAssert.AreEqual(new int[] { 2, 2, 2 }, data.Shape.Dimensions.ToArray());
}
