read_samples_class_mlpT_read_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpread_samples_class_mlp (算子)

名称

read_samples_class_mlpT_read_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpread_samples_class_mlp — 从文件中读取多层感知机的训练数据。

签名

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)

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

描述

read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpReadSamplesClassMlpread_samples_class_mlp reads training samples from the file given by FileNameFileNameFileNameFileNamefileNamefile_name and adds them to the training samples that have already been added to the multilayer perceptron (MLP) given by MLPHandleMLPHandleMLPHandleMLPHandleMLPHandlemlphandle. The MLP must be created with create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlpcreate_class_mlp before calling read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpReadSamplesClassMlpread_samples_class_mlp. As described with train_class_mlptrain_class_mlpTrainClassMlpTrainClassMlpTrainClassMlptrain_class_mlp and write_samples_class_mlpwrite_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlpWriteSamplesClassMlpwrite_samples_class_mlp, the operators read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpReadSamplesClassMlpread_samples_class_mlp, add_sample_class_mlpadd_sample_class_mlpAddSampleClassMlpAddSampleClassMlpAddSampleClassMlpadd_sample_class_mlp, and write_samples_class_mlpwrite_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlpWriteSamplesClassMlpwrite_samples_class_mlp 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 FileNameFileNameFileNameFileNamefileNamefile_name must have the lengths NumInputNumInputNumInputNumInputnumInputnum_input and NumOutputNumOutputNumOutputNumOutputnumOutputnum_output that were specified with create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlpcreate_class_mlp。If this is not the case an error message is returned.

执行信息

此算子修改后续输入参数的状态:

在执行此算子时,若该参数值需在多个线程间使用,则必须对其访问进行同步。

参数

MLPHandleMLPHandleMLPHandleMLPHandleMLPHandlemlphandle (输入控制,状态被修改)  class_mlp HClassMlp, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

MLP 句柄。

FileNameFileNameFileNameFileNamefileNamefile_name (输入控制)  filename.read HTuplestrHTupleHtuple (string) (string) (HString) (char*)

File name.

结果

如果参数有效,算子 read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpReadSamplesClassMlpread_samples_class_mlp 返回值 2 ( H_MSG_TRUE )。如有必要,则抛出异常。

可能的前趋

create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlpcreate_class_mlp

可能的后继

train_class_mlptrain_class_mlpTrainClassMlpTrainClassMlpTrainClassMlptrain_class_mlp

替代

add_sample_class_mlpadd_sample_class_mlpAddSampleClassMlpAddSampleClassMlpAddSampleClassMlpadd_sample_class_mlp

另见

write_samples_class_mlpwrite_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlpWriteSamplesClassMlpwrite_samples_class_mlp, clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlpClearSamplesClassMlpclear_samples_class_mlp

模块

基础