set_regularization_params_ocr_class_mlpSetRegularizationParamsOcrClassMlpSetRegularizationParamsOcrClassMlpset_regularization_params_ocr_class_mlp🔗

Short description🔗

set_regularization_params_ocr_class_mlpSetRegularizationParamsOcrClassMlpSetRegularizationParamsOcrClassMlpset_regularization_params_ocr_class_mlpT_set_regularization_params_ocr_class_mlp — Set the regularization parameters of an OCR classifier.

Signature🔗

set_regularization_params_ocr_class_mlp( ocr_mlp OCRHandle, string GenParamName, number GenParamValue )

Description🔗

set_regularization_params_ocr_class_mlpSetRegularizationParamsOcrClassMlp sets the regularization parameters of the OCR classifier passed in OCRHandleOCRHandleocrhandle. The regularization parameter to be set is specified with GenParamNamegenParamNamegen_param_name. Its value is specified with GenParamValuegenParamValuegen_param_value.

As described at create_class_mlpCreateClassMlp, it may be desirable to regularize the OCR classifier (i.e., the MLP of the OCR classifier) to enforce a smoother transition of the confidences between the different classes and to prevent overfitting of the OCR classifier to the training data. To achieve this, a penalty for large MLP weights (which are the main reason for very sharp transitions between classes) can be added to the training of the OCR classifier in trainf_ocr_class_mlpTrainfOcrClassMlp by setting GenParamNamegenParamNamegen_param_name to 'weight_prior'"weight_prior" and setting GenParamValuegenParamValuegen_param_value to a value > 0. Furthermore, the regularization parameters can be determined automatically. For details, see set_regularization_params_class_mlpSetRegularizationParamsClassMlp. If the regularization parameters should be determined automatically, please carefully note the advice in set_regularization_params_class_mlpSetRegularizationParamsClassMlp on how to select the parameters NumHiddennumHiddennum_hidden of the MLP and 'num_outer_iterations'"num_outer_iterations" and on the memory and runtime implications of automatically determining the regularization parameters on the runtime of the training of the OCR classifier.

Execution information🔗

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:

OCRHandleOCRHandleocrhandle

During execution of this operator, access to the value of this parameter must be synchronized if it is used across multiple threads.

Parameters🔗

OCRHandleOCRHandleocrhandle (input_control, state is modified) ocr_mlp → (handle)HTuple (HHandle)HOCRMlp, HTuple (IntPtr)HHandleHtuple (handle)

Handle of the OCR classifier.

GenParamNamegenParamNamegen_param_name (input_control) string → (string)HTuple (HString)HTuple (string)strHtuple (char*)

Name of the regularization parameter to return.

Default: 'weight_prior'"weight_prior"
List of values: 'num_inner_iterations', 'num_outer_iterations', 'weight_prior'

GenParamValuegenParamValuegen_param_value (input_control) number(-array) → (real / integer)HTuple (double / Hlong)HTuple (double / int / long)MaybeSequence[Union[float, int]]Htuple (double / Hlong)

Value of the regularization parameter.

Default: 1.01.0
Suggested values: 0.01, 0.1, 1.0, 10.0, 100.0, 0, 1, 2, 3, 5, 10, 15, 20

Example🔗

(HDevelop)

* This example shows how to determine the regularization parameters
* automatically without examining the convergence of the
* regularization parameters.
* Create the OCR classifier.
read_ocr_trainf_names ('ocr.trf', CharacterNames, CharacterCount)
create_ocr_class_mlp (8, 10, 'constant', 'default', CharacterNames, \
                      40, 'none', |CharacterNames|, 42, OCRHandle)
* Set up the automatic determination of the regularization
* parameters.
set_regularization_params_ocr_class_mlp (OCRHandle, 'weight_prior', \
                                         [0.01,0.01,0.01,0.01])
set_regularization_params_ocr_class_mlp (OCRHandle, \
                                         'num_outer_iterations', 10)
* Train the classifier.
trainf_ocr_class_mlp (OCRHandle, 'ocr.trf', 100, 1, 0.01, Error, \
                      ErrorLog)
* Read out the estimate of the number of well-determined
* parameters.
get_regularization_params_ocr_class_mlp (OCRHandle, \
                                         'fraction_well_determined_params', \
                                          FractionParams)
* If FractionParams differs substantially from 1, consider reducing
* NumHidden appropriately and consider performing a preprocessing that
* reduces the number of input variables to the net, i.e., canonical
* variates or principal components.
write_ocr_class_mlp (OCRHandle, 'ocr.omc')
 * This example shows how to determine the regularization parameters
* automatically while examining the convergence of the
* regularization parameters.
* Create the OCR classifier.
read_ocr_trainf_names ('ocr.trf', CharacterNames, CharacterCount)
create_ocr_class_mlp (8, 10, 'constant', 'default', CharacterNames, \
                      40, 'none', |CharacterNames|, 42, OCRHandle)
* Set up the automatic determination of the regularization
* parameters.
set_regularization_params_ocr_class_mlp (OCRHandle, 'weight_prior', \
                                         [0.01,0.01,0.01,0.01])
set_regularization_params_ocr_class_mlp (OCRHandle, \
                                         'num_outer_iterations', 1)
for OuterIt := 1 to 10 by 1
    * Train the classifier.
    trainf_ocr_class_mlp (OCRHandle, 'ocr.trf', 100, 1, 0.01, Error, \
                          ErrorLog)
    * Read out the regularization parameters
    get_regularization_params_ocr_class_mlp (OCRHandle, \
                                             'weight_prior', \
                                             WeightPrior)
    * Inspect the regularization parameters manually for
    * convergence and exit the loop manually if they have
    * converged.
    * [...]
endfor
* Read out the estimate of the number of well-determined
* parameters.
get_regularization_params_ocr_class_mlp (OCRHandle, \
                                         'fraction_well_determined_params', \
                                          FractionParams)
* If FractionParams differs substantially from 1, consider reducing
* NumHidden appropriately and consider performing a preprocessing that
* reduces the number of input variables to the net, i.e., canonical
* variates or principal components.
write_ocr_class_mlp (OCRHandle, 'ocr.omc')

Result🔗

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

Combinations with other operators🔗

Combinations

Possible predecessors

create_ocr_class_mlpCreateOcrClassMlp

Possible successors

get_regularization_params_ocr_class_mlpGetRegularizationParamsOcrClassMlp, trainf_ocr_class_mlpTrainfOcrClassMlp

Module🔗

OCR/OCV

set_regularization_params_ocr_class_mlpSetRegularizationParamsOcrClassMlpSetRegularizationParamsOcrClassMlpset_regularization_params_ocr_class_mlpT_set_regularization_params_ocr_class_mlp🔗