read_samples_class_mlpT_read_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpread_samples_class_mlp (Operator)
Name
read_samples_class_mlpT_read_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpread_samples_class_mlp — Read the training data of a multilayer perceptron from a file.
Signature
Description
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.
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 → HClassMlp, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)
MLP handle.
FileNameFileNameFileNameFileNamefileNamefile_name (input_control) filename.read → HTuplestrHTupleHtuple (string) (string) (HString) (char*)
File name.
Result
If the parameters are valid, the operator
read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpReadSamplesClassMlpread_samples_class_mlp returns the value TRUE. If necessary,
an exception is raised.
Possible Predecessors
create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlpcreate_class_mlp
Possible Successors
train_class_mlptrain_class_mlpTrainClassMlpTrainClassMlpTrainClassMlptrain_class_mlp
Alternatives
add_sample_class_mlpadd_sample_class_mlpAddSampleClassMlpAddSampleClassMlpAddSampleClassMlpadd_sample_class_mlp
See also
write_samples_class_mlpwrite_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlpWriteSamplesClassMlpwrite_samples_class_mlp,
clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlpClearSamplesClassMlpclear_samples_class_mlp
Module
Foundation