public static IConvLayer ToConvLayer(this CnnLayer layer)
{
switch (layer.LayerType)
{
case (byte)LayerType.CovolutionalLayer:
var convLayer = new ConvolutionalLayer
{
Kernels = new List <double[][][]>(),
KernelPadding = 0,
KernelStride = 1,
KernelSize = layer.KernelHeight
};
var weights = layer.Weights.Weights.Split(';');
for (int i = 0; i < layer.KernelsCount; ++i)
{
var kernels = new double[layer.FeatureMapsCountIn][][];
for (int j = 0; j < layer.FeatureMapsCountIn; ++j)
{
kernels[j] = new double[layer.KernelHeight][];
for (int a = 0; a < layer.KernelHeight; ++a)
{
kernels[j][a] = new double[layer.KernelWidth];
for (int b = 0; b < layer.KernelWidth; ++b)
{
kernels[j][a][b] =
double.Parse(weights[j * layer.KernelHeight * layer.KernelWidth + a * layer.KernelWidth + b]);
}
}
}
convLayer.Kernels.Add(kernels);
}
return(convLayer);
case (byte)LayerType.PoolingLayer:
var poolingLayer = new PollingLayer(layer.KernelHeight, 0, 1);
return(poolingLayer);
case (byte)LayerType.ReluLayer:
var reluLayer = new ReLuLayer();
return(reluLayer);
default:
throw new Exception();
}
}
public static IConvLayer Create(int neuronsCount = 0, int inputMapsCount = 0, int kernelsCount = 0, byte layerType = 0, double lr = 0, int prevNeuronsCount = 0)
{
switch (layerType)
{
case (byte)LayerType.CovolutionalLayer:
var convLayer = new ConvolutionalLayer
{
Kernels = new List <double[][][]>(),
LearningRate = lr,
KernelPadding = 0,
KernelStride = 1,
KernelSize = 3
};
var kernels = new double[inputMapsCount][][];
for (int i = 0; i < kernelsCount; ++i)
{
for (int j = 0; j < inputMapsCount; ++j)
{
kernels[j] = CreateKernel(3, neuronsCount);
}
convLayer.Kernels.Add(kernels);
}
return(convLayer);
case (byte)LayerType.PoolingLayer:
var poolingLayer = new PollingLayer(2, 0, 1);
return(poolingLayer);
case (byte)LayerType.ReluLayer:
var reluLayer = new ReLuLayer();
return(reluLayer);
default:
throw new Exception();
}
}
