public override void Connect(Layer inLayer)
{
if (inLayer.Shape != InputShape)
{
throw new ShapeMismatchException(nameof(inLayer));
}
ShapedArray <Neuron> padded = Padder.PadArray(inLayer.Neurons.ToShape(Shape), Paddings, () => new Neuron()
{
OutVal = PadVal
});
var inConnections = new ShapedArray <List <Synapse> >(PaddedShape, () => new List <Synapse>());
IndexGen.ByStrides(PaddedShape, Strides, KernelShape).ForEach((idxKernel, i) =>
{
Neuron outN = base.Neurons[i];
outN.InSynapses = IndexGen.ByStart(KernelShape, Array <int> .FromRef(idxKernel)).Select((idx, j) =>
{
var S = new Synapse(padded[idx], outN);
inConnections[idx].Add(S);
return(S);
});
});
padded.ForEach((N, i) => N.OutSynapses = Array <Synapse> .FromRef(inConnections[i].ToArray()));
}
public override void Connect(Layer inLayer)
{
if (inLayer.Shape != InputShape)
{
throw new ShapeMismatchException(nameof(inLayer));
}
ShapedArray <Neuron> padded = Padder.PadArray(inLayer.Neurons.ToShape(Shape), Paddings, () => new Neuron()
{
OutVal = PadVal
});
var inConnections = new ShapedArray <List <Synapse> >(PaddedShape, () => new List <Synapse>());
Kernels.ForEach((kernel, i) =>
{
int offset = i * Shape.Volume / KernelsNum;
IndexGen.ByStrides(PaddedShape, Strides, KernelShape).ForEach((idxKernel, j) =>
{
var outN = (CNeuron)Neurons[offset + j];
outN.KernelBias = kernel.Bias;
outN.InSynapses = IndexGen.ByStart(KernelShape, Array <int> .FromRef(idxKernel)).Select((idx, k) =>
{
var S = new CSynapse(padded[idx], outN)
{
KernelWeight = kernel.Weights[k]
};
inConnections[idx].Add(S);
return((Synapse)S);
});
});
});
padded.ForEach((N, i) => N.OutSynapses = Array <Synapse> .FromRef(inConnections[i].ToArray()));
}
