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clear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlpclear_samples_class_mlpT_clear_samples_class_mlp🔗

Short description🔗

clear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlpclear_samples_class_mlpT_clear_samples_class_mlp — Clear the training data of a multilayer perceptron.

Signature🔗

clear_samples_class_mlp( class_mlp MLPHandle )void ClearSamplesClassMlp( const HTuple& MLPHandle )static void HOperatorSet.ClearSamplesClassMlp( HTuple MLPHandle )def clear_samples_class_mlp( mlphandle: MaybeSequence[HHandle] ) -> None

Herror T_clear_samples_class_mlp( const Htuple MLPHandle )

static void HClassMlp::ClearSamplesClassMlp( const HClassMlpArray& MLPHandle )

void HClassMlp::ClearSamplesClassMlp( ) const

static void HClassMlp.ClearSamplesClassMlp( HClassMlp[] MLPHandle )

void HClassMlp.ClearSamplesClassMlp( )

Description🔗

clear_samples_class_mlpClearSamplesClassMlp clears all training samples that have been added to the multilayer perceptron (MLP) MLPHandleMLPHandlemlphandle with add_sample_class_mlpAddSampleClassMlp or read_samples_class_mlpReadSamplesClassMlp. clear_samples_class_mlpClearSamplesClassMlp should only be used if the MLP is trained in the same process that uses the MLP for evaluation with evaluate_class_mlpEvaluateClassMlp or for classification with classify_class_mlpClassifyClassMlp. In this case, the memory required for the training samples can be freed with clear_samples_class_mlpClearSamplesClassMlp, 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_mlpWriteClassMlp, it is typically unnecessary to call clear_samples_class_mlpClearSamplesClassMlp because write_class_mlpWriteClassMlp does not save the training samples, and hence the online process, which reads the MLP with read_class_mlpReadClassMlp, requires no memory for the training samples.

Execution information🔗

Execution information
  • Multithreading type: reentrant (runs in parallel with non-exclusive operators).

  • Multithreading scope: global (may be called from any thread).

  • Processed without parallelization.

This operator modifies the state of the following input parameter:

During execution of this operator, access to the value of this parameter must be synchronized if it is used across multiple threads.

Parameters🔗

MLPHandleMLPHandlemlphandle (input_control, state is modified) class_mlp(-array) → (handle)HTuple (HHandle)HClassMlp, HTuple (IntPtr)MaybeSequence[HHandle]Htuple (handle)

MLP handle.

Result🔗

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

Combinations with other operators🔗

Combinations

Possible predecessors

train_class_mlpTrainClassMlp, write_samples_class_mlpWriteSamplesClassMlp

See also

create_class_mlpCreateClassMlp, clear_class_mlpClearClassMlp, add_sample_class_mlpAddSampleClassMlp, read_samples_class_mlpReadSamplesClassMlp

Module🔗

Foundation