Uses of Class
org.nd4j.autodiff.samediff.TrainingConfig.Builder

Packages that use TrainingConfig.Builder

Package	Description
org.nd4j.autodiff.samediff

Uses of TrainingConfig.Builder in org.nd4j.autodiff.samediff

Methods in org.nd4j.autodiff.samediff that return TrainingConfig.Builder

Modifier and Type	Method	Description
TrainingConfig.Builder	TrainingConfig.Builder.addEvaluations(boolean validation, @NonNull String variableName, int labelIndex, @NonNull IEvaluation... evaluations)	Add requested evaluations for a parm/variable, for either training or validation.
TrainingConfig.Builder	TrainingConfig.Builder.addRegularization(Regularization... regularizations)	Add regularization to all trainable parameters in the network
static TrainingConfig.Builder	TrainingConfig.builder()
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.
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.
TrainingConfig.Builder	TrainingConfig.Builder.dataSetFeatureMaskMapping(String... dataSetFeatureMaskMapping)	See dataSetFeatureMaskMapping(List)
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.
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.
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.
TrainingConfig.Builder	TrainingConfig.Builder.dataSetLabelMaskMapping(String... dataSetLabelMaskMapping)	See dataSetLabelMaskMapping(List)
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.
TrainingConfig.Builder	TrainingConfig.Builder.initialLossDataType(DataType initialLossDataType)	Set the initial loss data type, defaults to DataType.FLOAT - when setting a data type for a loss function we need a beginning data type to compute the gradients.
TrainingConfig.Builder	TrainingConfig.Builder.l1(double l1)	Sets the L1 regularization coefficient for all trainable parameters.
TrainingConfig.Builder	TrainingConfig.Builder.l2(double l2)	Sets the L2 regularization coefficient for all trainable parameters.
TrainingConfig.Builder	TrainingConfig.Builder.markLabelsUnused()	Calling this method will mark the label as unused.
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.
TrainingConfig.Builder	TrainingConfig.Builder.regularization(Regularization... regularization)	Set the regularization for all trainable parameters in the network.
TrainingConfig.Builder	TrainingConfig.Builder.skipBuilderValidation(boolean skip)
TrainingConfig.Builder	TrainingConfig.Builder.trainEvaluation(@NonNull String variableName, int labelIndex, @NonNull IEvaluation... evaluations)	Add requested History training evaluations for a parm/variable.
TrainingConfig.Builder	TrainingConfig.Builder.trainEvaluation(@NonNull SDVariable variable, int labelIndex, @NonNull IEvaluation... evaluations)	Add requested History training evaluations for a parm/variable.
TrainingConfig.Builder	TrainingConfig.Builder.updater(IUpdater updater)	Set the updater (such as Adam, Nesterovs etc.
TrainingConfig.Builder	TrainingConfig.Builder.validationEvaluation(@NonNull String variableName, int labelIndex, @NonNull IEvaluation... evaluations)	Add requested History validation evaluations for a parm/variable.
TrainingConfig.Builder	TrainingConfig.Builder.validationEvaluation(@NonNull SDVariable variable, int labelIndex, @NonNull IEvaluation... evaluations)	Add requested History validation evaluations for a parm/variable.
TrainingConfig.Builder	TrainingConfig.Builder.weightDecay(double coefficient, boolean applyLR)	Add weight decay regularization for all trainable parameters.