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();
}
}
private Guid ProcessSave(CNN network, string name, NetworkContext context)
{
var cnnModel = new CnnModel
{
CnnModelId = Guid.NewGuid()
};
var layers = new List <CnnLayer>();
var cnnWeights = new List <CnnWeights>();
for (int i = 0; i < network.LayersCount; i++)
{
if (network.Layers[i] is ConvolutionalLayer)
{
var cnnLayer = network.Layers[i] as ConvolutionalLayer;
var layer = new CnnLayer
{
CnnLayerId = Guid.NewGuid(),
PositionIn = i,
KernelHeight = cnnLayer.KernelSize,
KernelWidth = cnnLayer.KernelSize,
KernelsCount = cnnLayer.KernelsCount,
FeatureMapsCountIn = cnnLayer.KernelDepth,
LayerType = (byte)LayerType.CovolutionalLayer,
Model = cnnModel
};
var weights = new CnnWeights
{
CnnWeightsId = Guid.NewGuid(),
Layer = layer,
LayerId = layer.CnnLayerId
};
var w = string.Empty;
var builder = new StringBuilder(String.Empty);
for (int j = 0; j < cnnLayer.KernelsCount; ++j)
{
for (int k = 0; k < cnnLayer.KernelDepth; ++k)
{
for (int a = 0; a < cnnLayer.Kernels[j][k].Length; ++a)
{
for (int b = 0; b < cnnLayer.Kernels[j][k][a].Length; ++b)
{
builder.Append(cnnLayer.Kernels[j][k][a][b] + ";");
}
}
}
}
w = builder.ToString();
weights.Weights = w;
cnnWeights.Add(weights);
layer.Weights = weights;
layers.Add(layer);
}
else if (network.Layers[i] is PollingLayer)
{
var cnnLayer = network.Layers[i] as PollingLayer;
var layer = new CnnLayer
{
CnnLayerId = Guid.NewGuid(),
PositionIn = i,
KernelHeight = cnnLayer.KernelSize,
KernelWidth = cnnLayer.KernelSize,
LayerType = (byte)LayerType.PoolingLayer,
Model = cnnModel
};
layers.Add(layer);
}
else
{
var cnnLayer = network.Layers[i] as ReLuLayer;
var layer = new CnnLayer
{
CnnLayerId = Guid.NewGuid(),
PositionIn = i,
KernelHeight = cnnLayer.KernelSize,
KernelWidth = cnnLayer.KernelSize,
LayerType = (byte)LayerType.ReluLayer,
Model = cnnModel
};
layers.Add(layer);
}
}
var l = new CnnLayer
{
CnnLayerId = Guid.NewGuid(),
KernelHeight = network.FlattenLayer.KernelSize,
KernelWidth = network.FlattenLayer.KernelSize,
LayerType = (byte)LayerType.FlattenLayer,
Model = cnnModel
};
layers.Add(l);
cnnModel.Layers = layers;
var perceptronModel = new PerceptronModel
{
PerceptronModelId = Guid.NewGuid()
};
var percLayers = new List <PerceptronLayer>();
var percWeights = new List <PerceptronWeights>();
for (var i = 0; i < network.Perceptron.LayersCount; ++i)
{
var layer = network.Perceptron.Layers[i];
var perLayer = new PerceptronLayer
{
PerceptronLayerId = Guid.NewGuid(),
NeuronsCount = layer.NeuronsCount,
PositionIn = i,
Perceptron = perceptronModel
};
var weights = new PerceptronWeights
{
PerceptronWeightsId = perLayer.PerceptronLayerId,
Height = layer.WeightRowsCount,
Width = layer.WeightColumnsCount
};
string w = String.Empty;
var builder = new StringBuilder(String.Empty);
for (int a = 0; a < layer.Weights.Length; ++a)
{
for (int b = 0; b < layer.Weights[a].Length; ++b)
{
builder.Append(layer.Weights[a][b] + ";");
}
}
w = builder.ToString();
weights.Weights = w;
percWeights.Add(weights);
percLayers.Add(perLayer);
}
perceptronModel.Layers = percLayers;
//save
var networkModel = new NetworkModel
{
NetworkModelId = Guid.NewGuid(),
Perceptron = perceptronModel,
PerceptronId = perceptronModel.PerceptronModelId,
Cnn = cnnModel,
CnnId = cnnModel.CnnModelId,
Name = name
};
cnnModel.NetworkModel = networkModel;
cnnModel.NetworkModelId = networkModel.NetworkModelId;
perceptronModel.NetworkModel = networkModel;
perceptronModel.NetworkModelId = networkModel.NetworkModelId;
context.NetworkModels.Add(networkModel);
context.CnnLayers.AddRange(layers);
context.CnnWeightsSet.AddRange(cnnWeights);
context.CnnModels.Add(cnnModel);
context.PerceptronLayers.AddRange(percLayers);
context.PerceptronWeights.AddRange(percWeights);
context.PerceptronModels.Add(perceptronModel);
context.SaveChanges();
return(networkModel.NetworkModelId);
}
