F_VN_TrainBatch

Train a classifier, a clusterer, a novelty detector, or a regressor with a batch of samples. On-line trainable models are trained once with each sample. Depending on the application and the number of available training samples, repeated training of such models with the same data may improve the results.
Can use available TwinCAT Job Tasks for executing parallel code regions.

Syntax

Definition:

FUNCTION F_VN_TrainBatch : HRESULT
VAR_INPUT
    ipMlModel : ITcVnMlModel;
    ipSamples : ITcUnknown;
    ipOutputs : ITcVnContainer;
    hrPrev    : HRESULT;
END_VAR

Inputs

Name	Type	Description
ipMlModel	ITcVnMlModel	Classifier, clusterer, novelty detector, or regressor to be used
ipSamples	ITcUnknown	Container holding a batch of input samples (ContainerType_Vector_Vector_REAL or ContainerType_Vector_Vector_LREAL)
ipOutputs	ITcVnContainer	Class labels (for classification, ContainerType_Vector_DINT) or target outputs (for regression with scalar output, ContainerType_Vector_REAL or ContainerType_Vector_LREAL; for regression with vectorial output, ContainerType_Vector_Vector_REAL or ContainerType_Vector_Vector_LREAL) to be learnt (must be 0 for clusterers and for novelty detectors)
hrPrev	HRESULT	HRESULT indicating the result of previous operations (If SUCCEEDED(hrPrev) equals false, no operation is executed.)

Return value

HRESULT

Further information

The function F_VN_TrainBatch trains a model with several samples. Depending on the type of model, the result values must have a different type (see ipOutputs). Classifier, clusterer, novelty detector and regressor are supported as model types.

Parameter

Model

The previously created model must be transferred to ipMlModel.

Samples

The samples are transferred to ipSamples in a container. The type of container must be ContainerType_Vector_Vector_REAL or ContainerType_Vector_Vector_LREAL.

Results

The known results for the samples are transferred to ipOutputs in a container. Depending on the type of model, the container must have a different type:

Class label for a classification: ContainerType_Vector_DINT
Numerical result values for a scalar regression: ContainerType_Vector_REAL or ContainerType_Vector_LREAL
Numerical result values for a vector regression: ContainerType_Vector_Vector_REAL or ContainerType_Vector_Vector_LREAL

If it is a Clusterer model or a novelty detector, it must be ipOutputs := 0.

Application

For example, an ML model for classification can be trained like this:

hr := F_VN_TrainBatch(
    ipMlModel   := ipMlModel,
    ipSamples   := ipSamples,
    ipOutputs   := ipClassLabels,
    hrPrev      := hr);

Related functions

Required License

TC3 Machine Learning Realtime Inference