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; }