NeurApp 1.1
NeurApp - software for exploring approximation by artificial neural networks on functions of one or two variables.
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Transfer Object (DTO) for neural network training limits. More...
Public Member Functions | |
override NeuralTrainingLimits | CreateObject () |
Creates and returns a new object of the corresponding type. | |
Public Attributes | |
double | LearningRateMin |
Minimum limit for learning rate. | |
double | LearningRateMax |
Maximum limit for learning rate. | |
int | LearningRateNum |
Number of learning rates. | |
double | MomentumMin |
Minimum limit for momentum. | |
double | MomentumMax |
Maximum limit for momentum. | |
int | MomentumNum |
Number of momentums. | |
double | AlphaMin |
Minimum limit for alpha value. | |
double | AlphaMax |
Maximum limit for alpha value. | |
int | AlphaNum |
Number of alpha values. | |
double | InputSafetyFactorMin |
Minimum limit for input safety factor value. | |
double | InputSafetyFactorMax |
Maximum limit for input safety factor value. | |
int | InputSafetyFactorNum |
Number of input safety factor values. | |
double | OutputSafetyFactorMin |
Minimum limit for output safety factor value. | |
double | OutputSafetyFactorMax |
Maximum limit for output safety factor value. | |
int | OutputSafetyFactorNum |
Number of output safety factor values. | |
int | MaxEpochs |
Maximum number of epochs performed in training. | |
int | EpochBundle |
Number of epochs in boundle. | |
bool | EnableRangeTolerance |
Flag for enabling toelrance that represent a percentage of the output range. | |
VectorDtoBase | ToleranceRms |
Tolerance over RMS error of outputs over training points. Training will continue until error becomes below tolerance or until maximal number of epochs is reached. If less or equal than 0 then this tolerance is not taken into account. | |
VectorDtoBase | ToleranceRmsRelativeToRange |
Relative tolerances on RMS errors of outputs over training points, relative to the correspoinding ranges of output data. | |
double | ToleranceRmsRelativeToRangeScalar = NeuralTrainingParameters.DefaultToleranceRmsRelativeToRangeScalar |
Scalar through which all components of the Relative tolerances on RMS errors of outputs can be set to the same value. | |
VectorDtoBase | ToleranceMax |
Tolerance on maximal error of outputs over training points. Training will continue until error becomes below tolerance or until maximal number of epochs is reached. If less or equal than 0 then this tolerance is not taken into account. | |
VectorDtoBase | ToleranceMaxRelativeToRange |
Relative tolerances on max. abs. errors of outputs over training points, relative to the correspoinding ranges of output data. | |
double | ToleranceMaxRelativeToRangeScalar = NeuralTrainingParameters.DefaultToleranceMaxRelativeToRangeScalar |
Scalar through which all components of the Relative tolerances on max. abs. errors of outputs can be set to the same value. | |
int | InputLenght |
Number of input neurons. | |
int | OutputLength |
Number of output neurons. | |
bool | EnableArchitectureTest |
Flag for enabling test in architecture of ANN. | |
int | NumHiddenLayersNum |
Number of hidden layers in neural network. | |
int | NumHiddenNeuronsFirstMin |
Minimum number of hidden neurons in first hidden layer. | |
int | NumHiddenNeuronsFirstMax |
Maximum number of hidden neurons in first hidden layer. | |
int | NumHiddenNeuronsFirstNum |
Number of numbers of hidden neurons in first hidden layer. | |
int[] | NumHiddenNeuronsFirstValues |
Values for number of hidden neurons in the first hidden layer. | |
int | NumHiddenNeuronsSecondMin |
Minimum number of hidden neurons in second hidden layer. | |
int | NumHiddenNeuronsSecondMax |
Maximum number of hidden neurons in second hidden layer. | |
int | NumHiddenNeuronsSecondNum |
Number of numbers of hidden neurons in second hidden layer. | |
int[] | NumHiddenNeuronsSecondValues |
Values for number of hidden neurons in the second hidden layer. | |
int | NumHiddenNeuronsThirdMin |
Minimum number of hidden neurons in third hidden layer. | |
int | NumHiddenNeuronsThirdMax |
Maximum number of hidden neurons in third hidden layer. | |
int | NumHiddenNeuronsThirdNum |
Number of numbers of hidden neurons in third hidden layer. | |
int[] | NumHiddenNeuronsThirdValues |
Values for number of hidden neurons in the third hidden layer. | |
Protected Member Functions | |
override void | CopyFromPlain (NeuralTrainingLimits trainingLimits) |
Copies the specified training limits to the current DTO. | |
override void | CopyToPlain (ref NeuralTrainingLimits trainingLimits) |
Copies contents of the current DTO to the specified training limits object. |
Transfer Object (DTO) for neural network training limits.
$A Tako78 Aug12; Igor Aug12;
override NeuralTrainingLimits IG::Neural::NeuralTrainingLimitsDto::CreateObject | ( | ) | [inline] |
Creates and returns a new object of the corresponding type.
override void IG::Neural::NeuralTrainingLimitsDto::CopyFromPlain | ( | NeuralTrainingLimits | trainingLimits | ) | [inline, protected] |
Copies the specified training limits to the current DTO.
trainingLimits | Object that is copied to the current DTO. |
override void IG::Neural::NeuralTrainingLimitsDto::CopyToPlain | ( | ref NeuralTrainingLimits | trainingLimits | ) | [inline, protected] |
Copies contents of the current DTO to the specified training limits object.
trainingLimits | Object that the current DTO content is copied to. |
Minimum limit for learning rate.
Maximum limit for learning rate.
Number of learning rates.
Minimum limit for momentum.
Maximum limit for momentum.
Number of momentums.
Minimum limit for alpha value.
Maximum limit for alpha value.
Number of alpha values.
Minimum limit for input safety factor value.
Maximum limit for input safety factor value.
Number of input safety factor values.
Minimum limit for output safety factor value.
Maximum limit for output safety factor value.
Number of output safety factor values.
Maximum number of epochs performed in training.
Number of epochs in boundle.
Flag for enabling toelrance that represent a percentage of the output range.
VectorDtoBase IG::Neural::NeuralTrainingLimitsDto::ToleranceRms |
Tolerance over RMS error of outputs over training points. Training will continue until error becomes below tolerance or until maximal number of epochs is reached. If less or equal than 0 then this tolerance is not taken into account.
$A Tako78 Jul12;; Igor Jul12;
Relative tolerances on RMS errors of outputs over training points, relative to the correspoinding ranges of output data.
double IG::Neural::NeuralTrainingLimitsDto::ToleranceRmsRelativeToRangeScalar = NeuralTrainingParameters.DefaultToleranceRmsRelativeToRangeScalar |
Scalar through which all components of the Relative tolerances on RMS errors of outputs can be set to the same value.
VectorDtoBase IG::Neural::NeuralTrainingLimitsDto::ToleranceMax |
Tolerance on maximal error of outputs over training points. Training will continue until error becomes below tolerance or until maximal number of epochs is reached. If less or equal than 0 then this tolerance is not taken into account.
$A Tako78 Jul12;
Relative tolerances on max. abs. errors of outputs over training points, relative to the correspoinding ranges of output data.
double IG::Neural::NeuralTrainingLimitsDto::ToleranceMaxRelativeToRangeScalar = NeuralTrainingParameters.DefaultToleranceMaxRelativeToRangeScalar |
Scalar through which all components of the Relative tolerances on max. abs. errors of outputs can be set to the same value.
Number of input neurons.
Number of output neurons.
Flag for enabling test in architecture of ANN.
Number of hidden layers in neural network.
Minimum number of hidden neurons in first hidden layer.
Maximum number of hidden neurons in first hidden layer.
Number of numbers of hidden neurons in first hidden layer.
Values for number of hidden neurons in the first hidden layer.
Minimum number of hidden neurons in second hidden layer.
Maximum number of hidden neurons in second hidden layer.
Number of numbers of hidden neurons in second hidden layer.
Values for number of hidden neurons in the second hidden layer.
Minimum number of hidden neurons in third hidden layer.
Maximum number of hidden neurons in third hidden layer.
Number of numbers of hidden neurons in third hidden layer.
Values for number of hidden neurons in the third hidden layer.