public object Clone()
{
var clone = Of(InputLayer.ColsCount, OutputLayer.ColsCount, ActivationsFunctions);
var cloneWeights = new List <Matrix>();
foreach (var weight in Weights)
{
var currentWeight = new Matrix(weight.RowsCount, weight.ColsCount);
for (var rowI = 0; rowI < currentWeight.RowsCount; rowI++)
{
for (var colJ = 0; colJ < currentWeight.ColsCount; colJ++)
{
currentWeight[rowI, colJ] = weight[rowI, colJ];
}
}
cloneWeights.Add(currentWeight);
}
var cloneBiases = new List <double>(Biases);
clone.Weights = cloneWeights;
clone.Biases = cloneBiases;
var neuronsInHiddenLayers = HiddenLayers.Select(layer => layer.ColsCount).ToArray();
clone.InitializeHiddenLayers(neuronsInHiddenLayers);
return(clone);
}
public NeuralNetworkGene GetGenes()
{
return(new NeuralNetworkGene
{
InputGene = InputLayer.GetGenes(),
HiddenGenes = HiddenLayers.Select(l => l.GetGenes()).ToList(),
OutputGene = OutputLayer.GetGenes()
});
}
public IPredictorAlgorithmSettings Clone() => new NeuralNetworkSettingsEntity
{
Device = Device,
PredictionType = PredictionType,
HiddenLayers = HiddenLayers.Select(hl => hl.Clone()).ToMList(),
OutputActivation = OutputActivation,
OutputInitializer = OutputInitializer,
LossFunction = LossFunction,
EvalErrorFunction = EvalErrorFunction,
Optimizer = Optimizer,
LearningRate = LearningRate,
MinibatchSize = MinibatchSize,
NumMinibatches = NumMinibatches,
BestResultFromLast = BestResultFromLast,
SaveProgressEvery = SaveProgressEvery,
SaveValidationProgressEvery = SaveValidationProgressEvery,
};
