public ConvolutionalNetwork Create(ConvolutionalTopology topology)
{
Utility.Verify(topology,t => t!=null,"Invalid network topology");
var l=topology.Layers.Length;
Link[][] layers=new Link[l][];
Bias bias=null;
if(topology.Bias.HasValue)
bias=new Bias(topology.Bias.Value);
for(var i=0; i<l-1;i++){
if(i==0)
layers[i]=Utility.Generate<Link>(topology.Layers[i]).ToArray();
int j=i+1;
int size=topology.Layers[j];
if(topology.Map[i] is SubSampling)
layers[j]= Utility.Generate<SubSamplingNeuron>(size).ToArray();
else
layers[j]= Utility.Generate<Neuron>(size).ToArray();
topology.Map[i].Connect(layers[i],(NeuronBase[])layers[j],bias);
}
SetIdentity(layers);
foreach(Link[] layer in layers)
foreach(Link link in layer)
if(link is NeuronBase)
((NeuronBase)link).Func=topology.Func;
return new ConvolutionalNetwork(new NetworkStructure(layers,bias));
}
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 ConvolutionalNetwork Create(ConvolutionalTopology topology)
{
Utility.Verify(topology, t => t != null, "Invalid network topology");
var l = topology.Layers.Length;
Link[][] layers = new Link[l][];
Bias bias = null;
if (topology.Bias.HasValue)
{
bias = new Bias(topology.Bias.Value);
}
for (var i = 0; i < l - 1; i++)
{
if (i == 0)
{
layers[i] = Utility.Generate <Link>(topology.Layers[i]).ToArray();
}
int j = i + 1;
int size = topology.Layers[j];
if (topology.Map[i] is SubSampling)
{
layers[j] = Utility.Generate <SubSamplingNeuron>(size).ToArray();
}
else
{
layers[j] = Utility.Generate <Neuron>(size).ToArray();
}
topology.Map[i].Connect(layers[i], (NeuronBase[])layers[j], bias);
}
SetIdentity(layers);
foreach (Link[] layer in layers)
{
foreach (Link link in layer)
{
if (link is NeuronBase)
{
((NeuronBase)link).Func = topology.Func;
}
}
}
return(new ConvolutionalNetwork(new NetworkStructure(layers, bias)));
}
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);
}
