Package org.nd4j.autodiff.samediff

Class TrainingConfig.Builder

    • Constructor Detail

      • Builder

        public Builder()

    • Method Detail

      • initialLossDataType

        public 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. In order to do so, we need to set an initial number of zero that acts as the initial gradient. This initial loss data type controls the data type of that number. This is critical when wanting more fine grained control over the data types used in the training process.
        Parameters:
        initialLossDataType - the initial loss data type
        Returns:

      • updater

        public TrainingConfig.Builder updater​(IUpdater updater)
        Set the updater (such as Adam, Nesterovs etc. This is also how the learning rate (or learning rate schedule) is set.
        Parameters:
        updater - Updater to set

      • l1

        public TrainingConfig.Builder l1​(double l1)
        Sets the L1 regularization coefficient for all trainable parameters. Must be >= 0.
        See L1Regularization for more details
        Parameters:
        l1 - L1 regularization coefficient

      • l2

        public TrainingConfig.Builder l2​(double l2)
        Sets the L2 regularization coefficient for all trainable parameters. Must be >= 0.
        Note: Generally, WeightDecay (set via weightDecay(double,boolean) should be preferred to L2 regularization. See WeightDecay javadoc for further details.
        Note: L2 regularization and weight decay usually should not be used together; if any weight decay (or L2) has been added for the biases, these will be removed first.
        See Also:
        weightDecay(double, boolean)

      • weightDecay

        public TrainingConfig.Builder weightDecay​(double coefficient,
                                          boolean applyLR)
        Add weight decay regularization for all trainable parameters. See WeightDecay for more details.
        Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
        Parameters:
        coefficient - Weight decay regularization coefficient
        applyLR - Whether the learning rate should be multiplied in when performing weight decay updates. See WeightDecay for more details.

      • addRegularization

        public TrainingConfig.Builder addRegularization​(Regularization... regularizations)
        Add regularization to all trainable parameters in the network
        Parameters:
        regularizations - Regularization type(s) to add

      • regularization

        public TrainingConfig.Builder regularization​(Regularization... regularization)
        Set the regularization for all trainable parameters in the network. Note that if any existing regularization types have been added, they will be removed
        Parameters:
        regularization - Regularization type(s) to add

      • regularization

        public TrainingConfig.Builder regularization​(List<Regularization> regularization)
        Set the regularization for all trainable parameters in the network. Note that if any existing regularization types have been added, they will be removed
        Parameters:
        regularization - Regularization type(s) to add

      • minimize

        public 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.
        Parameters:
        minimize - True to minimize, false to maximize

      • dataSetFeatureMapping

        public 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. For example, if the network had 2 inputs called "input1" and "input2" and the MultiDataSet features should be mapped with MultiDataSet.getFeatures(0)->"input1" and MultiDataSet.getFeatures(1)->"input2", then this should be set to List<>("input1", "input2").
        Parameters:
        dataSetFeatureMapping - Name of the variables/placeholders that the feature arrays should be mapped to

      • dataSetFeatureMapping

        public 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. For example, if the network had 2 inputs called "input1" and "input2" and the MultiDataSet features should be mapped with MultiDataSet.getFeatures(0)->"input1" and MultiDataSet.getFeatures(1)->"input2", then this should be set to "input1", "input2".
        Parameters:
        dataSetFeatureMapping - Name of the variables/placeholders that the feature arrays should be mapped to

      • dataSetLabelMapping

        public 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. For example, if the network had 2 labels called "label1" and "label2" and the MultiDataSet labels should be mapped with MultiDataSet.getLabel(0)->"label1" and MultiDataSet.getLabels(1)->"label", then this should be set to "label1", "label2".
        Parameters:
        dataSetLabelMapping - Name of the variables/placeholders that the label arrays should be mapped to

      • dataSetLabelMapping

        public 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. For example, if the network had 2 labels called "label1" and "label2" and the MultiDataSet labels should be mapped with MultiDataSet.getLabel(0)->"label1" and MultiDataSet.getLabels(1)->"label", then this should be set to "label1", "label2".
        Parameters:
        dataSetLabelMapping - Name of the variables/placeholders that the label arrays should be mapped to

      • markLabelsUnused

        public TrainingConfig.Builder markLabelsUnused()
        Calling this method will mark the label as unused. This is basically a way to turn off label mapping validation in TrainingConfig builder, for training models without labels.
        Put another way: usually you need to call dataSetLabelMapping(String...) to set labels, this method allows you to say that the DataSet/MultiDataSet labels aren't used in training.

      • dataSetFeatureMaskMapping

        public 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. For example, if the network had 2 mask variables called "mask1" and "mask2" and the MultiDataSet features masks should be mapped with MultiDataSet.getFeatureMaskArray(0)->"mask1" and MultiDataSet.getFeatureMaskArray(1)->"mask2", then this should be set to "mask1", "mask2".
        Parameters:
        dataSetFeatureMaskMapping - Name of the variables/placeholders that the feature arrays should be mapped to

      • dataSetLabelMaskMapping

        public 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. For example, if the network had 2 mask variables called "mask1" and "mask2" and the MultiDataSet label masks should be mapped with MultiDataSet.getLabelMaskArray(0)->"mask1" and MultiDataSet.getLabelMaskArray(1)->"mask2", then this should be set to "mask1", "mask2".
        Parameters:
        dataSetLabelMaskMapping - Name of the variables/placeholders that the feature arrays should be mapped to

      • trainEvaluation

        public TrainingConfig.Builder trainEvaluation​(@NonNull
                                              @NonNull String variableName,
                                              int labelIndex,
                                              @NonNull
                                              @NonNull IEvaluation... evaluations)
        Add requested History training evaluations for a parm/variable. These evaluations will be reported in the History object returned by fit.
        Parameters:
        variableName - The variable to evaluate
        labelIndex - The index of the label to evaluate against
        evaluations - The evaluations to run

      • trainEvaluation

        public TrainingConfig.Builder trainEvaluation​(@NonNull
                                              @NonNull SDVariable variable,
                                              int labelIndex,
                                              @NonNull
                                              @NonNull IEvaluation... evaluations)
        Add requested History training evaluations for a parm/variable. These evaluations will be reported in the History object returned by fit.
        Parameters:
        variable - The variable to evaluate
        labelIndex - The index of the label to evaluate against
        evaluations - The evaluations to run

      • validationEvaluation

        public TrainingConfig.Builder validationEvaluation​(@NonNull
                                                   @NonNull String variableName,
                                                   int labelIndex,
                                                   @NonNull
                                                   @NonNull IEvaluation... evaluations)
        Add requested History validation evaluations for a parm/variable. These evaluations will be reported in the History object returned by fit.
        Parameters:
        variableName - The variable to evaluate
        labelIndex - The index of the label to evaluate against
        evaluations - The evaluations to run

      • validationEvaluation

        public TrainingConfig.Builder validationEvaluation​(@NonNull
                                                   @NonNull SDVariable variable,
                                                   int labelIndex,
                                                   @NonNull
                                                   @NonNull IEvaluation... evaluations)
        Add requested History validation evaluations for a parm/variable. These evaluations will be reported in the History object returned by fit.
        Parameters:
        variable - The variable to evaluate
        labelIndex - The index of the label to evaluate against
        evaluations - The evaluations to run

      • addEvaluations

        public TrainingConfig.Builder addEvaluations​(boolean validation,
                                             @NonNull
                                             @NonNull String variableName,
                                             int labelIndex,
                                             @NonNull
                                             @NonNull IEvaluation... evaluations)
        Add requested evaluations for a parm/variable, for either training or validation. These evaluations will be reported in the History object returned by fit.
        Parameters:
        validation - Whether to add these evaluations as validation or training
        variableName - The variable to evaluate
        labelIndex - The index of the label to evaluate against
        evaluations - The evaluations to run