public static async Task Main()
{
// Create the network
INeuralNetwork network = NetworkManager.NewSequential(TensorInfo.Image <Alpha8>(28, 28),
NetworkLayers.Convolutional((5, 5), 20, ActivationType.Identity),
NetworkLayers.Pooling(ActivationType.LeakyReLU),
NetworkLayers.FullyConnected(100, ActivationType.LeCunTanh),
NetworkLayers.Softmax(10));
// Prepare the dataset
ITrainingDataset trainingData = await Mnist.GetTrainingDatasetAsync(100); // Batches of 100 samples
ITestDataset testData = await Mnist.GetTestDatasetAsync(p => Printf($"Epoch {p.Iteration}, cost: {p.Result.Cost}, accuracy: {p.Result.Accuracy}"));
if (trainingData == null || testData == null)
{
Printf("Error downloading the datasets");
Console.ReadKey();
return;
}
// Train the network
TrainingSessionResult result = await NetworkManager.TrainNetworkAsync(network,
trainingData,
TrainingAlgorithms.AdaDelta(),
60, 0.5f,
TrackBatchProgress,
testDataset : testData);
Printf($"Stop reason: {result.StopReason}, elapsed time: {result.TrainingTime}");
Console.ReadKey();
}
public void Initialization1()
{
INeuralNetwork network = NetworkManager.NewGraph(TensorInfo.Image <Alpha8>(60, 60), root =>
{
var conv1 = root.Layer(NetworkLayers.Convolutional((5, 5), 10, ActivationType.Identity));
var pool1 = conv1.Layer(NetworkLayers.Pooling(ActivationType.LeakyReLU));
var conv2 = pool1.Layer(NetworkLayers.Convolutional((3, 3), 10, ActivationType.Identity));
var pool2 = conv2.Layer(NetworkLayers.Pooling(ActivationType.ReLU));
var fc = pool2.Layer(NetworkLayers.FullyConnected(64, ActivationType.LeCunTanh));
_ = fc.Layer(NetworkLayers.Softmax(10));
});
public void JsonMetadataSerialization()
{
INeuralNetwork network = NetworkManager.NewSequential(TensorInfo.Image <Rgb24>(120, 120),
NetworkLayers.Convolutional((10, 10), 20, ActivationType.AbsoluteReLU),
NetworkLayers.Convolutional((5, 5), 20, ActivationType.ELU),
NetworkLayers.Convolutional((10, 10), 20, ActivationType.Identity),
NetworkLayers.Pooling(ActivationType.ReLU),
NetworkLayers.Convolutional((10, 10), 20, ActivationType.Identity),
NetworkLayers.Pooling(ActivationType.Identity),
NetworkLayers.BatchNormalization(NormalizationMode.Spatial, ActivationType.ReLU),
NetworkLayers.FullyConnected(125, ActivationType.Tanh),
NetworkLayers.Softmax(133));
string metadata1 = network.SerializeMetadataAsJson();
Assert.IsTrue(metadata1.Length > 0);
Assert.IsTrue(metadata1.Equals(network.Clone().SerializeMetadataAsJson()));
network.Layers.First().To <INetworkLayer, ConvolutionalLayer>().Weights[0] += 0.1f;
Assert.IsFalse(metadata1.Equals(network.SerializeMetadataAsJson()));
}
public void NetworkSerialization()
{
INeuralNetwork network = NetworkManager.NewSequential(TensorInfo.Image <Rgb24>(120, 120),
NetworkLayers.Convolutional((10, 10), 20, ActivationFunctionType.AbsoluteReLU),
NetworkLayers.Convolutional((5, 5), 20, ActivationFunctionType.ELU),
NetworkLayers.Convolutional((10, 10), 20, ActivationFunctionType.Identity),
NetworkLayers.Pooling(ActivationFunctionType.ReLU),
NetworkLayers.Convolutional((10, 10), 20, ActivationFunctionType.Identity),
NetworkLayers.Pooling(ActivationFunctionType.ReLU),
NetworkLayers.FullyConnected(125, ActivationFunctionType.Tanh),
NetworkLayers.Softmax(133));
using (MemoryStream stream = new MemoryStream())
{
network.Save(stream);
stream.Seek(0, SeekOrigin.Begin);
INeuralNetwork copy = NetworkLoader.TryLoad(stream, LayersLoadingPreference.Cpu);
Assert.IsTrue(network.Equals(copy));
}
}