public void BinaryNetSerializerTest()
{
var net = new Net();
net.AddLayer(new InputLayer(5, 5, 3));
var conv = new ConvLayer(2, 2, 16);
net.AddLayer(conv);
var fullycon = new FullyConnLayer(3);
net.AddLayer(fullycon);
net.AddLayer(new SoftmaxLayer(3));
// Serialize (binary)
using (var ms = new MemoryStream())
{
net.SaveBinary(ms);
ms.Position = 0;
// Deserialize (binary)
Net deserialized = SerializationExtensions.LoadBinary(ms) as Net;
// Make sure deserialized is identical to serialized
Assert.IsNotNull(deserialized.Layers);
Assert.AreEqual(net.Layers.Count, deserialized.Layers.Count);
Assert.IsTrue(net.Layers[0] is InputLayer);
var deserializedConv = net.Layers[1] as ConvLayer;
Assert.NotNull(deserializedConv);
Assert.NotNull(deserializedConv.Filters);
Assert.AreEqual(16, deserializedConv.Filters.Count);
for (int i = 0; i < deserializedConv.Filters.Count; i++)
{
for (int k = 0; k < deserializedConv.Filters[i].Length; k++)
{
Assert.AreEqual(conv.Filters[i].Get(k), deserializedConv.Filters[i].Get(k));
Assert.AreEqual(conv.Filters[i].GetGradient(k), deserializedConv.Filters[i].GetGradient(k));
}
}
var deserializedFullyCon = net.Layers[2] as FullyConnLayer;
Assert.NotNull(deserializedFullyCon);
Assert.NotNull(deserializedFullyCon.Filters);
Assert.AreEqual(3, deserializedFullyCon.Filters.Count);
for (int i = 0; i < deserializedFullyCon.Filters.Count; i++)
{
for (int k = 0; k < deserializedFullyCon.Filters[i].Length; k++)
{
Assert.AreEqual(fullycon.Filters[i].Get(k), deserializedFullyCon.Filters[i].Get(k));
Assert.AreEqual(fullycon.Filters[i].GetGradient(k), deserializedFullyCon.Filters[i].GetGradient(k));
}
}
Assert.IsTrue(deserialized.Layers[3] is SoftmaxLayer);
Assert.AreEqual(3, ((SoftmaxLayer)deserialized.Layers[3]).ClassCount);
}
}
private void LoadNeuralNetwork()
{
//FileStream networkFileStream = File.OpenRead(String.Format(@"{0}\neural-network-40-180-2.txt", dataFolder));
//network = Network.Load(networkFileStream);
//
//String convNetJson = File.ReadAllText(String.Format(@"{0}\conv-neural-network.json", dataFolder));
//convNet = SerializationExtensions.FromJSON(convNetJson);
FileStream convNetBin = File.OpenRead(String.Format(@"{0}\conv-neural-network-bin", dataFolder));
convNet = (Net)SerializationExtensions.LoadBinary(convNetBin);
}
public static INet LoadNet(string filename)
{
INet result = null;
if (File.Exists(filename))
{
using (var fs = new FileStream(filename, FileMode.Open))
{
result = SerializationExtensions.LoadBinary(fs);
}
}
return(result);
}
public void FluentBinaryNetSerializerTest()
{
var net = FluentNet.Create(5, 5, 3)
.Conv(2, 2, 16)
.FullyConn(3)
.Softmax(3)
.Build();
// Serialize (binary)
using (var ms = new MemoryStream())
{
net.SaveBinary(ms);
ms.Position = 0;
// Deserialize (binary)
FluentNet deserialized = SerializationExtensions.LoadBinary(ms) as FluentNet;
Assert.IsNotNull(deserialized);
Assert.AreEqual(net.InputLayers.Count, deserialized.InputLayers.Count);
// TODO: improve test
}
}