ClassesClassesClassesClasses | | | | Operators

clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlp (Operator)

Name

clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlp — Clear the training data of a multilayer perceptron.

Signature

clear_samples_class_mlp( : : MLPHandle : )

Herror clear_samples_class_mlp(const Hlong MLPHandle)

Herror T_clear_samples_class_mlp(const Htuple MLPHandle)

Herror clear_samples_class_mlp(const HTuple& MLPHandle)

void ClearSamplesClassMlp(const HTuple& MLPHandle)

void HClassMlp::ClearSamplesClassMlp() const

void HOperatorSetX.ClearSamplesClassMlp([in] VARIANT MLPHandle)

void HClassMlpX.ClearSamplesClassMlp()

static void HOperatorSet.ClearSamplesClassMlp(HTuple MLPHandle)

void HClassMlp.ClearSamplesClassMlp()

Description

clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlp clears all training samples that have been added to the multilayer perceptron (MLP) MLPHandleMLPHandleMLPHandleMLPHandleMLPHandleMLPHandle with add_sample_class_mlpadd_sample_class_mlpAddSampleClassMlpadd_sample_class_mlpAddSampleClassMlpAddSampleClassMlp or read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlp. clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlp should only be used if the MLP is trained in the same process that uses the MLP for evaluation with evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlp or for classification with classify_class_mlpclassify_class_mlpClassifyClassMlpclassify_class_mlpClassifyClassMlpClassifyClassMlp. In this case, the memory required for the training samples can be freed with clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlp, and hence memory can be saved. In the normal usage, in which the MLP is trained offline and written to a file with write_class_mlpwrite_class_mlpWriteClassMlpwrite_class_mlpWriteClassMlpWriteClassMlp, it is typically unnecessary to call clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlp because write_class_mlpwrite_class_mlpWriteClassMlpwrite_class_mlpWriteClassMlpWriteClassMlp does not save the training samples, and hence the online process, which reads the MLP with read_class_mlpread_class_mlpReadClassMlpread_class_mlpReadClassMlpReadClassMlp, requires no memory for the training samples.

Parallelization

Parameters

MLPHandleMLPHandleMLPHandleMLPHandleMLPHandleMLPHandle (input_control)  class_mlp HClassMlp, HTupleHTupleHClassMlp, HTupleHClassMlpX, VARIANTHtuple (integer) (IntPtr) (Hlong) (Hlong) (Hlong) (Hlong)

MLP handle.

Result

If the parameters are valid, the operator clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlp returns the value 2 (H_MSG_TRUE). If necessary an exception is raised.

Possible Predecessors

train_class_mlptrain_class_mlpTrainClassMlptrain_class_mlpTrainClassMlpTrainClassMlp, write_samples_class_mlpwrite_samples_class_mlpWriteSamplesClassMlpwrite_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlp

See also

create_class_mlpcreate_class_mlpCreateClassMlpcreate_class_mlpCreateClassMlpCreateClassMlp, clear_class_mlpclear_class_mlpClearClassMlpclear_class_mlpClearClassMlpClearClassMlp, add_sample_class_mlpadd_sample_class_mlpAddSampleClassMlpadd_sample_class_mlpAddSampleClassMlpAddSampleClassMlp, read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlp

Module

Foundation


ClassesClassesClassesClasses | | | | Operators