read_samples_class_mlpT_read_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlp (Operator)

Name

read_samples_class_mlpT_read_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlp — Read the training data of a multilayer perceptron from a file.

Signature

read_samples_class_mlp( : : MLPHandle, FileName : )

Herror T_read_samples_class_mlp(const Htuple MLPHandle, const Htuple FileName)

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

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

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

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

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

void HClassMlp.ReadSamplesClassMlp(string fileName)

Description

read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpReadSamplesClassMlp reads training samples from the file given by FileNameFileNameFileNameFileNamefileName and adds them to the training samples that have already been added to the multilayer perceptron (MLP) given by MLPHandleMLPHandleMLPHandleMLPHandleMLPHandle. The MLP must be created with create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlp before calling read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpReadSamplesClassMlp. As described with train_class_mlptrain_class_mlpTrainClassMlpTrainClassMlpTrainClassMlp and write_samples_class_mlpwrite_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlpWriteSamplesClassMlp, the operators read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpReadSamplesClassMlp, add_sample_class_mlpadd_sample_class_mlpAddSampleClassMlpAddSampleClassMlpAddSampleClassMlp, and write_samples_class_mlpwrite_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlpWriteSamplesClassMlp can be used to build up a extensive set of training samples, and hence to improve the performance of the MLP by retraining the MLP with extended data sets.

It should be noted that the training samples must have the correct dimensionality. The feature vectors and target vectors stored in FileNameFileNameFileNameFileNamefileName must have the lengths NumInput and NumOutput that were specified with create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlp. If this is not the case an error message is returned.

Execution Information

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

MLPHandleMLPHandleMLPHandleMLPHandleMLPHandle (input_control, state is modified)  class_mlp HClassMlp, HTupleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

MLP handle.

FileNameFileNameFileNameFileNamefileName (input_control)  filename.read HTupleHTupleHtuple (string) (string) (HString) (char*)

File name.

Result

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

Possible Predecessors

create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlp

Possible Successors

train_class_mlptrain_class_mlpTrainClassMlpTrainClassMlpTrainClassMlp

Alternatives

add_sample_class_mlpadd_sample_class_mlpAddSampleClassMlpAddSampleClassMlpAddSampleClassMlp

See also

write_samples_class_mlpwrite_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlpWriteSamplesClassMlp, clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlpClearSamplesClassMlp

Module

Foundation