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 Save_And_Load()
{
var parameters = new NetworkParameters(new int[] { 2, 8, 4 })
{
LearningRate = 0.123
};
var network = new MultilayerNetwork(parameters);
{
int li = 0;
network.ForEachLayer(l =>
{
li += 10;
l.ForEachNeuron((n, i) =>
{
n.Adjust((w, wi) => wi * li);
return 0;
}).ToList();
return 0;
}).ToList();
}
byte[] serialisedData;
using (var blobStore = new MemoryStream())
{
network.Save(blobStore);
blobStore.Flush();
serialisedData = blobStore.ToArray();
}
using (var blobStoreRead = new MemoryStream(serialisedData))
{
var network2 = MultilayerNetwork.LoadData(blobStoreRead);
Assert.That(network2.Parameters.LearningRate, Is.EqualTo(0.123));
Assert.That(network2.Parameters.InputVectorSize, Is.EqualTo(2));
Assert.That(network2.Parameters.OutputVectorSize, Is.EqualTo(4));
Assert.That(network2.Layers.Count(), Is.EqualTo(3));
int li = 0;
network2.ForEachLayer(l =>
{
li += 10;
l.ForEachNeuron((n, i) =>
{
n.Adjust((w, wi) =>
{
Assert.That(w == wi * li);
return w;
});
return 0;
}).ToList();
return 0;
}).ToList();
}
}
