public void TSSetUp()
{
int[] layers = new int[] {
1024, //32x32 input image
4704, //6 @ 28x28 convolution
1176, //6 @ 14x14 subsampling
1600, //16 @ 10x10 convolution
400, //16 @ 5x5 subsampling
120, // 120x1x1 convolution!
84, //full
10 //full
};
LayerConnector[] map = new LayerConnector[] {
new ConvolutionAuto(5, 6, 1, 4),
new SubSampling(6),
new Convolution(5, 16, 6, 4, GetSchema()),
new SubSampling(16),
new ConvolutionAuto(5, 120, 16, 0),
new FullLayerConnector(),
new FullLayerConnector()
};
ConvolutionalTopology topology = new ConvolutionalTopology(layers, 1, map);
ConvolutionalGenerator generator = new ConvolutionalGenerator();
Network = generator.Create(topology);
}
void BuildNetwork()
{
int[] layers = new int[] {
841, 1014, 1250, 100, 10
};
LayerConnector[] map = new LayerConnector[] {
new ConvolutionAuto(5, 6, 1, 3),
new ConvolutionAuto(5, 50, 6, 1),
new FullLayerConnector(),
new FullLayerConnector()
};
ConvolutionalTopology topology = new ConvolutionalTopology(layers, 1, map, new HyperbolicTangent());
ConvolutionalGenerator generator = new ConvolutionalGenerator();
Network = generator.Create(topology);
Network.LearnFactor = 0.0005;
Network.Reset(-0.1, 0.1);
}
public void ConvolutionSerialization()
{
int[] layers = new int[] {
1024, //32x32 input image
4704, //6 @ 28x28 convolution
1176, //6 @ 14x14 subsampling
1600, //16 @ 10x10 convolution
400, //16 @ 5x5 subsampling
120, // 120x1x1 convolution!
84, //full
10 //full
};
LayerConnector[] map = new LayerConnector[] {
new ConvolutionAuto(5, 6, 1, 4),
new SubSampling(6),
new ConvolutionAuto(5, 16, 6, 4),
new SubSampling(16),
new ConvolutionAuto(5, 120, 16, 0),
new FullLayerConnector(),
new FullLayerConnector()
};
ConvolutionalTopology topology = new ConvolutionalTopology(layers, 1, map);
ConvolutionalGenerator generator = new ConvolutionalGenerator();
ConvolutionalNetwork network = generator.Create(topology);
ConvolutionalXMLSerializer serializer = new ConvolutionalXMLSerializer();
XDocument doc = serializer.Serialize(network);
ConvolutionalNetwork network2 = serializer.Deserialize(doc);
Assert.AreEqual(network2.Structure.Elements.Length, network.Structure.Elements.Length);
Assert.AreEqual(network2.Structure.Elements[3000].GetDescription(), network.Structure.Elements[3000].GetDescription());
Assert.AreEqual(network2.Structure.Elements[0].Next[0].Weight.Value, network.Structure.Elements[0].Next[0].Weight.Value);
Assert.AreEqual(((NeuronBase)network2.Structure.Elements[4000]).Previous[0].Weight.Value, ((NeuronBase)network.Structure.Elements[4000]).Previous[0].Weight.Value);
Assert.AreEqual(((NeuronBase)network2.Structure.Elements[3004]).Func.GetType(), ((NeuronBase)network.Structure.Elements[3004]).Func.GetType());
Weight w1 = network2.Structure.Layers[0][0].Next[0].Weight;
Weight w2 = network2.Structure.Layers[0][1].Next.Select(x => x.Weight).FirstOrDefault(x => x == w1);
Assert.IsNotNull(w1);
Assert.IsTrue(w1 == w2);
Link[] l1 = network2.Structure.Layers[1];
int c = GetConnectionsCount(l1, false, true);
int w = GetWeightsCount(l1, false, true);
Assert.AreEqual(c, 122304);
Assert.AreEqual(w, 156);
for (int i = 0; i < network.Structure.Layers.Length; i++)
{
for (int j = 0; j < network.Structure.Layers[i].Length; j++)
{
Connection[] n1 = network.Structure.Layers[i][j].Next;
if (n1 != null)
{
Connection[] n2 = network2.Structure.Layers[i][j].Next;
Assert.IsNotNull(n2);
for (int z = 0; z < n1.Length; z++)
{
Assert.AreEqual(n1[z].Weight.Value, n2[z].Weight.Value);
Assert.AreEqual(n1[z].Weight.GetType(), n2[z].Weight.GetType());
}
}
if (network.Structure.Layers[i][j] is NeuronBase)
{
Connection[] p1 = ((NeuronBase)network.Structure.Layers[i][j]).Previous;
if (p1 != null)
{
Connection[] p2 = ((NeuronBase)network2.Structure.Layers[i][j]).Previous;
Assert.IsNotNull(p2);
for (int z = 0; z < p1.Length; z++)
{
Assert.AreEqual(p1[z].Weight.Value, p2[z].Weight.Value);
Assert.AreEqual(p1[z].Weight.GetType(), p2[z].Weight.GetType());
}
}
}
}
}
}