public static ActiveModelData GetDefaults2(NetworkTypes networkType = NetworkTypes.LSTMRecurrent)
{
ActiveModelData data = new ActiveModelData();
//
data.IterType = 0;
data.IterValue = 10000;
data.MinibatchSize = 125;
//
data.LearnerType = 0;
data.LearningRate = 0.000005f;
data.Momentum = 5f;
data.L1Regularizer = 0;
data.L2Regularizer = 0;
//
data.NetworkType = (int)networkType;
data.Neurons = 2500;
data.HLayers = 10;
data.Embeding = 100;
data.DropRate = 10;
data.UseStabilisation = false;
data.UseDropRate = false;
//
data.ActivationHidden = Activation.Softmax;
data.ActivationOutput = Activation.None;
//Los and Evaluation functions
data.LossFunction = LossFunctions.SquaredError;
data.EvaluationFunction = LossFunctions.SquaredError;
return(data);
}
public ActiveModelData(ActiveModelData data)
{
//
IterType = data.IterType;
IterValue = data.IterValue;
MinibatchSize = data.MinibatchSize;
//
LearnerType = data.LearnerType;
LearningRate = data.LearningRate;
Momentum = data.Momentum;
L1Regularizer = data.L1Regularizer;
L2Regularizer = data.L2Regularizer;
//
NetworkType = data.NetworkType;
Neurons = data.Neurons;
HLayers = data.HLayers;
Embeding = data.Embeding;
DropRate = data.DropRate;
UseStabilisation = data.UseStabilisation;
UseDropRate = data.UseDropRate;
//
ActivationHidden = data.ActivationHidden;
ActivationOutput = data.ActivationOutput;
//Los and Evaluation functions
LossFunction = data.LossFunction;
EvaluationFunction = data.EvaluationFunction;
}
