HALCON Operator reference

HALCON Operator Reference / Classification / Neural Nets

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write_samples_class_mlpT_write_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlpwrite_samples_class_mlp (Operator)

write_samples_class_mlpT_write_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlpwrite_samples_class_mlp — Write the training data of a multilayer perceptron to a file.

Signature

write_samples_class_mlp( : : MLPHandle, FileName : )

Herror T_write_samples_class_mlp(const Htuple MLPHandle, const Htuple FileName)

void WriteSamplesClassMlp(const HTuple& MLPHandle, const HTuple& FileName)

void HClassMlp::WriteSamplesClassMlp(const HString& FileName) const

void HClassMlp::WriteSamplesClassMlp(const char* FileName) const

void HClassMlp::WriteSamplesClassMlp(const wchar_t* FileName) const   ( Windows only)

static void HOperatorSet.WriteSamplesClassMlp(HTuple MLPHandle, HTuple fileName)

void HClassMlp.WriteSamplesClassMlp(string fileName)

def write_samples_class_mlp(mlphandle: HHandle, file_name: str) -> None

Description

write_samples_class_mlpwrite_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlpwrite_samples_class_mlp writes the training samples stored in the multilayer perceptron (MLP) MLPHandleMLPHandleMLPHandleMLPHandlemlphandle to the file given by FileNameFileNameFileNamefileNamefile_name. write_samples_class_mlpwrite_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlpwrite_samples_class_mlp can be used to build up a database of training samples, and hence to improve the performance of the MLP by training it with an extended data set (see train_class_mlptrain_class_mlpTrainClassMlpTrainClassMlptrain_class_mlp). For other possible uses of write_samples_class_mlpwrite_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlpwrite_samples_class_mlp see get_prep_info_class_mlpget_prep_info_class_mlpGetPrepInfoClassMlpGetPrepInfoClassMlpget_prep_info_class_mlp.

The file FileNameFileNameFileNamefileNamefile_name is overwritten by write_samples_class_mlpwrite_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlpwrite_samples_class_mlp. Nevertheless, extending the database of training samples is easy to do because read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpread_samples_class_mlp and add_sample_class_mlpadd_sample_class_mlpAddSampleClassMlpAddSampleClassMlpadd_sample_class_mlp add the training samples to the training samples that are already stored in memory with the MLP.

Execution Information

• Multithreading type: reentrant (runs in parallel with non-exclusive operators).
• Multithreading scope: global (may be called from any thread).
• Processed without parallelization.

Parameters

MLPHandleMLPHandleMLPHandleMLPHandlemlphandle (input_control)  class_mlp → HClassMlp, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

MLP handle.

FileNameFileNameFileNamefileNamefile_name (input_control)  filename.write → HTuplestrHTupleHtuple (string) (string) (HString) (char*)

File name.

Result

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

Possible Predecessors

add_sample_class_mlpadd_sample_class_mlpAddSampleClassMlpAddSampleClassMlpadd_sample_class_mlp

Possible Successors

clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlpclear_samples_class_mlp

See also

create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpcreate_class_mlp, get_prep_info_class_mlpget_prep_info_class_mlpGetPrepInfoClassMlpGetPrepInfoClassMlpget_prep_info_class_mlp, read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpread_samples_class_mlp

Module

Foundation

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