Package | Description |
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org.nd4j.autodiff.samediff |
Modifier and Type | Method and Description |
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TrainingConfig.Builder |
TrainingConfig.Builder.addEvaluations(boolean validation,
@NonNull String variableName,
int labelIndex,
IEvaluation... evaluations)
Add requested evaluations for a parm/variable, for either training or validation.
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TrainingConfig.Builder |
TrainingConfig.Builder.addRegularization(Regularization... regularizations)
Add regularization to all trainable parameters in the network
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static TrainingConfig.Builder |
TrainingConfig.builder() |
TrainingConfig.Builder |
TrainingConfig.Builder.dataSetFeatureMapping(List<String> dataSetFeatureMapping)
Set the name of the placeholders/variables that should be set using the feature INDArray(s) from the
DataSet or MultiDataSet.
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TrainingConfig.Builder |
TrainingConfig.Builder.dataSetFeatureMapping(String... dataSetFeatureMapping)
Set the name of the placeholders/variables that should be set using the feature INDArray(s) from the
DataSet or MultiDataSet.
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TrainingConfig.Builder |
TrainingConfig.Builder.dataSetFeatureMaskMapping(List<String> dataSetFeatureMaskMapping)
Set the name of the placeholders/variables that should be set using the feature mask INDArray(s) from the
DataSet or MultiDataSet.
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TrainingConfig.Builder |
TrainingConfig.Builder.dataSetFeatureMaskMapping(String... dataSetFeatureMaskMapping)
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TrainingConfig.Builder |
TrainingConfig.Builder.dataSetLabelMapping(List<String> dataSetLabelMapping)
Set the name of the placeholders/variables that should be set using the labels INDArray(s) from the
DataSet or MultiDataSet.
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TrainingConfig.Builder |
TrainingConfig.Builder.dataSetLabelMapping(String... dataSetLabelMapping)
Set the name of the placeholders/variables that should be set using the labels INDArray(s) from the
DataSet or MultiDataSet.
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TrainingConfig.Builder |
TrainingConfig.Builder.dataSetLabelMaskMapping(List<String> dataSetLabelMaskMapping)
Set the name of the placeholders/variables that should be set using the label mask INDArray(s) from the
DataSet or MultiDataSet.
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TrainingConfig.Builder |
TrainingConfig.Builder.dataSetLabelMaskMapping(String... dataSetLabelMaskMapping)
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TrainingConfig.Builder |
TrainingConfig.Builder.l1(double l1)
Sets the L1 regularization coefficient for all trainable parameters.
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TrainingConfig.Builder |
TrainingConfig.Builder.l2(double l2)
Sets the L2 regularization coefficient for all trainable parameters.
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TrainingConfig.Builder |
TrainingConfig.Builder.markLabelsUnused()
Calling this method will mark the label as unused.
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TrainingConfig.Builder |
TrainingConfig.Builder.minimize(boolean minimize)
Sets whether the loss function should be minimized (true) or maximized (false).
The loss function is usually minimized in SGD. Default: true. |
TrainingConfig.Builder |
TrainingConfig.Builder.minimize(String... lossVariables) |
TrainingConfig.Builder |
TrainingConfig.Builder.regularization(List<Regularization> regularization)
Set the regularization for all trainable parameters in the network.
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TrainingConfig.Builder |
TrainingConfig.Builder.regularization(Regularization... regularization)
Set the regularization for all trainable parameters in the network.
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TrainingConfig.Builder |
TrainingConfig.Builder.skipBuilderValidation(boolean skip) |
TrainingConfig.Builder |
TrainingConfig.Builder.trainEvaluation(@NonNull SDVariable variable,
int labelIndex,
IEvaluation... evaluations)
Add requested History training evaluations for a parm/variable.
|
TrainingConfig.Builder |
TrainingConfig.Builder.trainEvaluation(@NonNull String variableName,
int labelIndex,
IEvaluation... evaluations)
Add requested History training evaluations for a parm/variable.
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TrainingConfig.Builder |
TrainingConfig.Builder.updater(IUpdater updater)
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TrainingConfig.Builder |
TrainingConfig.Builder.validationEvaluation(@NonNull SDVariable variable,
int labelIndex,
IEvaluation... evaluations)
Add requested History validation evaluations for a parm/variable.
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TrainingConfig.Builder |
TrainingConfig.Builder.validationEvaluation(@NonNull String variableName,
int labelIndex,
IEvaluation... evaluations)
Add requested History validation evaluations for a parm/variable.
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TrainingConfig.Builder |
TrainingConfig.Builder.weightDecay(double coefficient,
boolean applyLR)
Add weight decay regularization for all trainable parameters.
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