public void ExecuteTest()
{
Snapshot snapshot = new Snapshot();
snapshot.Append("item1", Shape.Map0D(2, 3), new float[] { 0f, 1f, 2f, 3f, 4f, 5f });
snapshot.Append("item2", Shape.Map0D(3, 4), new float[] { 5f, 3f, 1f, 0f, 2f, 4f, 1f, 0f, 5f, 3f, 2f, 4f });
ZippedBinaryShapshotSaver saver = new ZippedBinaryShapshotSaver();
byte[] data = null;
using (var stream = new MemoryStream()) {
saver.Save(stream, snapshot);
data = stream.ToArray();
}
Snapshot snapshot2 = null;
using (var stream = new MemoryStream(data)) {
snapshot2 = saver.Load(stream);
}
CollectionAssert.AreEquivalent(new string[] { "item1", "item2" }, snapshot2.Keys.ToArray());
Assert.AreEqual(snapshot.Table["item1"].shape, snapshot2.Table["item1"].shape);
CollectionAssert.AreEqual(snapshot.Table["item1"].state, snapshot2.Table["item1"].state);
Assert.AreEqual(snapshot.Table["item2"].shape, snapshot2.Table["item2"].shape);
CollectionAssert.AreEqual(snapshot.Table["item2"].state, snapshot2.Table["item2"].state);
using (var stream = new FileStream("zipdebug.tss", FileMode.Create)) {
using (var writer = new BinaryWriter(stream)) {
writer.Write(data);
}
}
}
static void Main()
{
const string dirpath_dataset = "mnist_dataset";
const string dirpath_result = "result";
const int classes = 10;
Console.WriteLine("Download mnist...");
MnistDownloader.Download(dirpath_dataset);
Console.WriteLine("Setup loader...");
Random random = new Random(1234);
MnistLoader loader = new MnistLoader(dirpath_dataset, num_batches: 1000);
Iterator train_iterator = new ShuffleIterator(loader.NumBatches, loader.CountTrainDatas, random);
Iterator test_iterator = new ShuffleIterator(loader.NumBatches, loader.CountTestDatas, random);
Console.WriteLine("Create input tensor...");
VariableField x = new Tensor(loader.BatchShape);
VariableField t = new Tensor(Shape.Vector(loader.NumBatches));
Console.WriteLine("Build model...");
Field y = CNN.Forward(x, classes);
Field acc = Accuracy(y, t);
Field err = Sum(SoftmaxCrossEntropy(y, OneHotVector(t, classes)), axes: new int[] { Axis.Map0D.Channels });
StoreField accnode = acc.Save(), lossnode = Average(err).Save();
Console.WriteLine("Build optimize flow...");
(Flow trainflow, Parameters parameters) = Flow.Optimize(err);
Console.WriteLine("Initialize params...");
parameters
.Where((parameter) => parameter.Category == ParameterCategory.Kernel)
.InitializeTensor((tensor) => new HeNormal(tensor, random));
parameters
.Where((parameter) => parameter.Category == ParameterCategory.Bias)
.InitializeTensor((tensor) => new Zero(tensor));
Console.WriteLine("Set params updater...");
parameters.AddUpdater((parameter) => new Nadam(parameter, alpha: 0.01f));
parameters.AddUpdater((parameter) => new Ridge(parameter, decay: 1e-4f));
Console.WriteLine("Training...");
Train(train_iterator, loader, x, t, accnode, lossnode, trainflow, parameters);
Console.WriteLine("Build inference flow...");
Flow testflow = Flow.Inference(accnode);
Console.WriteLine("Testing...");
Test(test_iterator, loader, x, t, testflow, accnode);
Console.WriteLine("Saving snapshot...");
Snapshot snapshot = parameters.Save();
SnapshotSaver saver = new ZippedBinaryShapshotSaver();
if (!Directory.Exists(dirpath_result))
{
Directory.CreateDirectory(dirpath_result);
}
saver.Save($"{dirpath_result}/mnist.tss", snapshot);
Console.WriteLine("END");
Console.Read();
}
