clear_samples_class_mlpT_clear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlpclear_samples_class_mlp (Operator)
Name
clear_samples_class_mlpT_clear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlpclear_samples_class_mlp — Clear the training data of a multilayer perceptron.
Signature
Herror T_clear_samples_class_mlp(const Htuple MLPHandle)
def clear_samples_class_mlp(mlphandle: MaybeSequence[HHandle]) -> None
Description
clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlpClearSamplesClassMlpclear_samples_class_mlp clears all training samples that
have been added to the multilayer perceptron (MLP)
MLPHandleMLPHandleMLPHandleMLPHandleMLPHandlemlphandle with add_sample_class_mlpadd_sample_class_mlpAddSampleClassMlpAddSampleClassMlpAddSampleClassMlpadd_sample_class_mlp or
read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpReadSamplesClassMlpread_samples_class_mlp. clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlpClearSamplesClassMlpclear_samples_class_mlp
should only be used if the MLP is trained in the same process that
uses the MLP for evaluation with evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlpEvaluateClassMlpevaluate_class_mlp or for
classification with classify_class_mlpclassify_class_mlpClassifyClassMlpClassifyClassMlpClassifyClassMlpclassify_class_mlp. In this case, the
memory required for the training samples can be freed with
clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlpClearSamplesClassMlpclear_samples_class_mlp, 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_mlpwrite_class_mlpWriteClassMlpWriteClassMlpWriteClassMlpwrite_class_mlp, it is typically unnecessary to
call clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlpClearSamplesClassMlpclear_samples_class_mlp because write_class_mlpwrite_class_mlpWriteClassMlpWriteClassMlpWriteClassMlpwrite_class_mlp
does not save the training samples, and hence the online process,
which reads the MLP with read_class_mlpread_class_mlpReadClassMlpReadClassMlpReadClassMlpread_class_mlp, requires no memory
for the training samples.
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
MLPHandleMLPHandleMLPHandleMLPHandleMLPHandlemlphandle (input_control, state is modified) class_mlp(-array) → HClassMlp, HTupleMaybeSequence[HHandle]HTupleHtuple (handle) (IntPtr) (HHandle) (handle)
MLP handle.
Result
If the parameters are valid, the operator
clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlpClearSamplesClassMlpclear_samples_class_mlp returns the value 2 (H_MSG_TRUE). If
necessary an exception is raised.
Possible Predecessors
train_class_mlptrain_class_mlpTrainClassMlpTrainClassMlpTrainClassMlptrain_class_mlp,
write_samples_class_mlpwrite_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlpWriteSamplesClassMlpwrite_samples_class_mlp
See also
create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlpcreate_class_mlp,
clear_class_mlpclear_class_mlpClearClassMlpClearClassMlpClearClassMlpclear_class_mlp,
add_sample_class_mlpadd_sample_class_mlpAddSampleClassMlpAddSampleClassMlpAddSampleClassMlpadd_sample_class_mlp,
read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpReadSamplesClassMlpread_samples_class_mlp
Module
Foundation