protected NeuralNetworkStore CreateNeuralNetworkStore()
{
List <float[]> matrixDataList = new List <float[]>();
List <float[]> biasDataList = new List <float[]>();
this.neuralLayers.ForEach(l =>
{
biasDataList.Add(l.Bias.GetValue());
if (!l.IsOutputLayer)
{
matrixDataList.Add(l.NeuralLink.Weights.GetValue());
}
});
NeuralNetworkStore nnStore = new NeuralNetworkStore(this.configuration, matrixDataList.AsEnumerable(), biasDataList.AsEnumerable());
return(nnStore);
}
public NeuralNetwork(MathOperationManager mathManager, NeuralNetworkStore nnStore)
{
this.CreateNeuralNetwork(mathManager, nnStore.Configuration);
this.LoadFromNeuralNetworkStore(nnStore);
}
public NeuralNetwork(MathOperationManager mathManager, NeuralNetworkStore nnStore)
{
this.CreateNeuralNetwork(mathManager, nnStore.Configuration);
this.LoadFromNeuralNetworkStore(nnStore);
}
internal void LoadFromNeuralNetworkStore(NeuralNetworkStore nnStore)
{
// First layer is the input layer, last layer is the output layer and the rest are hidden layers.
Debug.Assert(this.neuralLayers.Count == nnStore.BiasDataList.Count());
for (int i = 0; i < this.neuralLayers.Count; i++)
{
var layer = this.neuralLayers.ElementAt(i);
layer.Bias.SetValue(nnStore.BiasDataList.ElementAt(i));
if (!layer.IsOutputLayer)
{
layer.NeuralLink.Weights.SetValue(nnStore.MatrixDataList.ElementAt(i));
}
}
}
protected NeuralNetworkStore CreateNeuralNetworkStore()
{
List<float[]> matrixDataList = new List<float[]>();
List<float[]> biasDataList = new List<float[]>();
this.neuralLayers.ForEach(l =>
{
biasDataList.Add(l.Bias.GetValue());
if (!l.IsOutputLayer)
{
matrixDataList.Add(l.NeuralLink.Weights.GetValue());
}
});
NeuralNetworkStore nnStore = new NeuralNetworkStore(this.configuration, matrixDataList.AsEnumerable(), biasDataList.AsEnumerable());
return nnStore;
}
