/// <summary>
/// Обучает переданную модель искусственной нейронной сети.
/// </summary>
/// <param name="neuralNerwork">Модель обучаемой искусственной нейронной сети.</param>
/// <param name="trainingSet">Тренировочная выборка вида (входной вектор, желаемый выходной вектор).</param>
/// <param name="validationSet">Проверочная выборка вида (входной вектор, желаемый выходной вектор).</param>
public INeuralNetwork Learn(INeuralNetwork neuralNetwork, IEnumerable <Tuple <IList <double>, IList <double> > > trainingSet, IEnumerable <Tuple <IList <double>, IList <double> > > validationSet)
{
Validate(neuralNetwork, trainingSet, validationSet);
var normalizedTrainingSet = NormilizeSet(neuralNetwork, trainingSet);
var normalizedValidationSet = NormilizeSet(neuralNetwork, validationSet);
var currentNetworkErrorValue = CalculateNetworkErrorValue(neuralNetwork, normalizedValidationSet);
var minNetworkErrorValue = currentNetworkErrorValue;
var bestModel = (INeuralNetwork)neuralNetwork.Clone();
for (var i = 0; i < LearningEpochMaxAmount; i++)
{
PerformEpoch(neuralNetwork, normalizedTrainingSet);
currentNetworkErrorValue = CalculateNetworkErrorValue(neuralNetwork, normalizedValidationSet);
if (currentNetworkErrorValue < minNetworkErrorValue && i + 1 >= _learningEpochMinAmount)
{
minNetworkErrorValue = currentNetworkErrorValue;
bestModel = (INeuralNetwork)neuralNetwork.Clone();
}
else
{
break;
}
}
return(bestModel);
}
public void TrainOnMemory(IConcurrentMemory <TData> memory)
{
var trainingData = _dataBuilder.BuildDataset(memory);
if (trainingData == null || trainingData.Count == 0)
{
return;
}
var clonedInstance = _network.Clone();
// Train the network
var result = NetworkManager.TrainNetwork(clonedInstance,
trainingData,
TrainingAlgorithms.AdaDelta(),
_configuration.Epochs, 0.5f,
TrackBatchProgress,
TrainingProgress);
Console.WriteLine("\nTraining session completed, moving to next one");
var backupName = $"backup-network-{DateTime.Now:yyyyMMdd-HH-mm-ss-fff}.modl";
_network.Save(File.Create(backupName));
Console.WriteLine($"Backup model {backupName} saved");
_network = clonedInstance;
}
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()));
}