public void Train_Multilayer_SaveThenLoad()
{
var network = new MultilayerNetwork(4, new[] { 4, 2 });
var bp = new BackPropagationLearning(network);
var input = ColumnVector1D.Create(0, 0.2, 0.66, 0.28);
var output = ColumnVector1D.Create(0, 0, 0, 0.999);
var err = bp.Train(input, output);
using (var ms = new MemoryStream())
{
network.Save(ms);
ms.Position = 0;
var network2 = MultilayerNetwork.LoadData(ms);
Assert.That(network2 != null);
var output2 = network2.Evaluate(ColumnVector1D.Create(0.1, 0.3, 0.2, 0.1));
Assert.That(output2 != null);
}
}
public void Train_Multilayer_IsTrained()
{
var network = new MultilayerNetwork(4, new[] { 4, 2 });
var bp = new BackPropagationLearning(network);
var input = ColumnVector1D.Create(0, 0.2, 0.66, 0.28);
var output = ColumnVector1D.Create(0, 0, 0, 0.999);
var err = bp.Train(input, output);
Assert.That(err > 0);
}
public void InitialiseAndTrain_ReturnsErrorGt0(int layer1Size, int layer2Size)
{
var parameters = new NetworkParameters(new int[] { 4, layer1Size, layer2Size, 4 });
var network = new MultilayerNetwork(parameters);
var bp = new BackPropagationLearning(network);
var input = ColumnVector1D.Create(0, 0.2, 0.66, 0.28);
var output = ColumnVector1D.Create(0, 0, 0, 0.999);
var err = bp.Train(input, output);
Assert.That(err > 0);
}
