Serialized Form (h2o-genmodel version 3.22.1.2 API)

Package hex.genmodel
- Class hex.genmodel.GenModel extends java.lang.Object implements Serializable
  - Serialized Fields
    - _names
```
java.lang.String[] _names
```
      Column names; last is response for supervised models
    - _domains
```
java.lang.String[][] _domains
```
      Categorical (factor/enum) mappings, per column. Null for non-enum cols. Columns match the post-init cleanup columns. The last column holds the response col enums for SupervisedModels.
    - _responseColumn
```
java.lang.String _responseColumn
```
      Name of the response column used for training (only for supervised models).
    - _offsetColumn
```
java.lang.String _offsetColumn
```
      Name of the column with offsets (used for certain types of models).
- Class hex.genmodel.GenMunger extends java.lang.Object implements Serializable
  - Serialized Fields
    - _steps
```
GenMunger.Step<T>[] _steps
```
- Class hex.genmodel.GenMunger.Step extends java.lang.Object implements Serializable
  - Serialized Fields
    - _names
```
java.lang.String[] _names
```
    - _types
```
java.lang.String[] _types
```
    - _outNames
```
java.lang.String[] _outNames
```
    - _params
```
java.util.HashMap<K,V> _params
```
- Class hex.genmodel.MojoModel extends GenModel implements Serializable
  - Serialized Fields
    - _h2oVersion
```
java.lang.String _h2oVersion
```
    - _category
```
ModelCategory _category
```
    - _uuid
```
java.lang.String _uuid
```
    - _supervised
```
boolean _supervised
```
    - _nfeatures
```
int _nfeatures
```
    - _nclasses
```
int _nclasses
```
    - _balanceClasses
```
boolean _balanceClasses
```
    - _defaultThreshold
```
double _defaultThreshold
```
    - _priorClassDistrib
```
double[] _priorClassDistrib
```
    - _modelClassDistrib
```
double[] _modelClassDistrib
```
    - _mojo_version
```
double _mojo_version
```
Package hex.genmodel.algos.deeplearning
- Class hex.genmodel.algos.deeplearning.ActivationUtils.ExpRectifierDropoutOut extends ActivationUtils.ExpRectifierOut implements Serializable
- Class hex.genmodel.algos.deeplearning.ActivationUtils.ExpRectifierOut extends java.lang.Object implements Serializable
- Class hex.genmodel.algos.deeplearning.ActivationUtils.LinearOut extends java.lang.Object implements Serializable
- Class hex.genmodel.algos.deeplearning.ActivationUtils.MaxoutDropoutOut extends ActivationUtils.MaxoutOut implements Serializable
- Class hex.genmodel.algos.deeplearning.ActivationUtils.MaxoutOut extends java.lang.Object implements Serializable
- Class hex.genmodel.algos.deeplearning.ActivationUtils.RectifierDropoutOut extends ActivationUtils.RectifierOut implements Serializable
- Class hex.genmodel.algos.deeplearning.ActivationUtils.RectifierOut extends java.lang.Object implements Serializable
- Class hex.genmodel.algos.deeplearning.ActivationUtils.SoftmaxOut extends java.lang.Object implements Serializable
- Class hex.genmodel.algos.deeplearning.ActivationUtils.TanhDropoutOut extends ActivationUtils.TanhOut implements Serializable
- Class hex.genmodel.algos.deeplearning.ActivationUtils.TanhOut extends java.lang.Object implements Serializable
- Class hex.genmodel.algos.deeplearning.DeeplearningMojoModel extends MojoModel implements Serializable
  - Serialized Fields
    - _mini_batch_size
```
int _mini_batch_size
```
    - _nums
```
int _nums
```
    - _cats
```
int _cats
```
    - _catoffsets
```
int[] _catoffsets
```
    - _normmul
```
double[] _normmul
```
    - _normsub
```
double[] _normsub
```
    - _normrespmul
```
double[] _normrespmul
```
    - _normrespsub
```
double[] _normrespsub
```
    - _use_all_factor_levels
```
boolean _use_all_factor_levels
```
    - _activation
```
java.lang.String _activation
```
    - _allActivations
```
java.lang.String[] _allActivations
```
    - _imputeMeans
```
boolean _imputeMeans
```
    - _units
```
int[] _units
```
    - _all_drop_out_ratios
```
double[] _all_drop_out_ratios
```
    - _weightsAndBias
```
DeeplearningMojoModel.StoreWeightsBias[] _weightsAndBias
```
    - _catNAFill
```
int[] _catNAFill
```
    - _numLayers
```
int _numLayers
```
    - _family
```
DistributionFamily _family
```
    - _numsA
```
double[] _numsA
```
    - _catsA
```
int[] _catsA
```
- Class hex.genmodel.algos.deeplearning.DeeplearningMojoModel.StoreWeightsBias extends java.lang.Object implements Serializable
  - Serialized Fields
    - _wValues
```
float[] _wValues
```
    - _bValues
```
double[] _bValues
```

Package hex.genmodel.algos.deepwater

Class hex.genmodel.algos.deepwater.DeepwaterMojoModel extends MojoModel implements Serializable

Serialized Fields

_problem_type
```
java.lang.String _problem_type
```
_mini_batch_size
```
int _mini_batch_size
```
_height
```
int _height
```
_width
```
int _width
```
_channels
```
int _channels
```
_nums
```
int _nums
```
_cats
```
int _cats
```
_catOffsets
```
int[] _catOffsets
```
_normMul
```
double[] _normMul
```
_normSub
```
double[] _normSub
```
_normRespMul
```
double[] _normRespMul
```
_normRespSub
```
double[] _normRespSub
```
_useAllFactorLevels
```
boolean _useAllFactorLevels
```

_backend

deepwater.backends.BackendTrain _backend

_model
```
deepwater.backends.BackendModel _model
```

_imageDataSet

deepwater.datasets.ImageDataSet _imageDataSet

_opts
```
deepwater.backends.RuntimeOptions _opts
```

_backendParams

deepwater.backends.BackendParams<T> _backendParams

Package hex.genmodel.algos.drf
- Class hex.genmodel.algos.drf.DrfMojoModel extends SharedTreeMojoModel implements Serializable
  - Serialized Fields
    - _binomial_double_trees
```
boolean _binomial_double_trees
```
Package hex.genmodel.algos.ensemble
- Class hex.genmodel.algos.ensemble.StackedEnsembleMojoModel extends MojoModel implements Serializable
  - Serialized Fields
    - _metaLearner
```
MojoModel _metaLearner
```
    - _baseModels
```
hex.genmodel.algos.ensemble.StackedEnsembleMojoModel.StackedEnsembleMojoSubModel[] _baseModels
```
    - _baseModelNum
```
int _baseModelNum
```
Package hex.genmodel.algos.gbm
- Class hex.genmodel.algos.gbm.GbmMojoModel extends SharedTreeMojoModel implements Serializable
  - Serialized Fields
    - _family
```
DistributionFamily _family
```
    - _init_f
```
double _init_f
```
Package hex.genmodel.algos.glm
- Class hex.genmodel.algos.glm.GlmMojoModel extends hex.genmodel.algos.glm.GlmMojoModelBase implements Serializable
  - Serialized Fields
    - _link
```
java.lang.String _link
```
    - _tweedieLinkPower
```
double _tweedieLinkPower
```
    - _linkFn
```
hex.genmodel.algos.glm.GlmMojoModel.Function1 _linkFn
```
    - _binomial
```
boolean _binomial
```
- Class hex.genmodel.algos.glm.GlmMultinomialMojoModel extends hex.genmodel.algos.glm.GlmMojoModelBase implements Serializable
  - Serialized Fields
    - P
```
int P
```
    - noff
```
int noff
```
- Class hex.genmodel.algos.glm.GlmOrdinalMojoModel extends hex.genmodel.algos.glm.GlmMojoModelBase implements Serializable
  - Serialized Fields
    - P
```
int P
```
    - noff
```
int noff
```
    - lastClass
```
int lastClass
```
    - icptIndices
```
int[] icptIndices
```

Package hex.genmodel.algos.glrm

Class hex.genmodel.algos.glrm.GlrmMojoModel extends MojoModel implements Serializable

Serialized Fields

_ncolA
```
int _ncolA
```
_ncolX
```
int _ncolX
```
_ncolY
```
int _ncolY
```
_nrowY
```
int _nrowY
```
_archetypes
```
double[][] _archetypes
```
_archetypes_raw
```
double[][] _archetypes_raw
```
_numLevels
```
int[] _numLevels
```
_catOffsets
```
int[] _catOffsets
```
_permutation
```
int[] _permutation
```
_losses
```
GlrmLoss[] _losses
```
_regx
```
GlrmRegularizer _regx
```
_gammax
```
double _gammax
```
_init
```
GlrmInitialization _init
```
_ncats
```
int _ncats
```
_nnums
```
int _nnums
```
_normSub
```
double[] _normSub
```
_normMul
```
double[] _normMul
```
_seed
```
long _seed
```
_transposed
```
boolean _transposed
```
_reverse_transform
```
boolean _reverse_transform
```
_accuracyEps
```
double _accuracyEps
```
_iterNumber
```
int _iterNumber
```
_rcnt
```
long _rcnt
```
_numAlphaFactors
```
int _numAlphaFactors
```
_allAlphas
```
double[] _allAlphas
```

Package hex.genmodel.algos.isofor
- Class hex.genmodel.algos.isofor.IsolationForestMojoModel extends SharedTreeMojoModel implements Serializable
  - Serialized Fields
    - _min_path_length
```
int _min_path_length
```
    - _max_path_length
```
int _max_path_length
```
Package hex.genmodel.algos.kmeans
- Class hex.genmodel.algos.kmeans.KMeansMojoModel extends MojoModel implements Serializable
  - Serialized Fields
    - _standardize
```
boolean _standardize
```
    - _centers
```
double[][] _centers
```
    - _means
```
double[] _means
```
    - _mults
```
double[] _mults
```
    - _modes
```
int[] _modes
```
Package hex.genmodel.algos.pipeline
- Class hex.genmodel.algos.pipeline.MojoPipeline extends MojoModel implements Serializable
  - Serialized Fields
    - _mainModel
```
MojoModel _mainModel
```
    - _sourceRowIndices
```
int[] _sourceRowIndices
```
    - _targetMainModelRowIndices
```
int[] _targetMainModelRowIndices
```
    - _generatedColumnCount
```
int _generatedColumnCount
```
    - _models
```
hex.genmodel.algos.pipeline.MojoPipeline.PipelineSubModel[] _models
```
Package hex.genmodel.algos.svm
- Class hex.genmodel.algos.svm.SvmMojoModel extends MojoModel implements Serializable
  - Serialized Fields
    - meanImputation
```
boolean meanImputation
```
    - weights
```
double[] weights
```
    - means
```
double[] means
```
    - interceptor
```
double interceptor
```
    - defaultThreshold
```
double defaultThreshold
```
    - threshold
```
double threshold
```
Package hex.genmodel.algos.tree
- Class hex.genmodel.algos.tree.ScoreTree0 extends java.lang.Object implements Serializable
- Class hex.genmodel.algos.tree.ScoreTree1 extends java.lang.Object implements Serializable
- Class hex.genmodel.algos.tree.ScoreTree2 extends java.lang.Object implements Serializable
- Class hex.genmodel.algos.tree.SharedTreeMojoModel extends MojoModel implements Serializable
  - Serialized Fields
    - _scoreTree
```
ScoreTree _scoreTree
```
    - _ntree_groups
```
int _ntree_groups
```
      _ntree_groups is the number of trees requested by the user. For binomial case or regression this is also the total number of trees trained; however in multinomial case each requested "tree" is actually represented as a group of trees, with _ntrees_per_group trees in each group. Each of these individual trees assesses the likelihood that a given observation belongs to class A, B, C, etc. of a multiclass response.
    - _ntrees_per_group
```
int _ntrees_per_group
```
    - _compressed_trees
```
byte[][] _compressed_trees
```
      Array of binary tree data, each tree being a byte[] array. The trees are logically grouped into a rectangular grid of dimensions SharedTreeMojoModel._ntree_groups x SharedTreeMojoModel._ntrees_per_group, however physically they are stored as 1-dimensional list, and an [i, j] logical tree is mapped to the index SharedTreeMojoModel.treeIndex(int, int).
    - _compressed_trees_aux
```
byte[][] _compressed_trees_aux
```
      Array of auxiliary binary tree data, each being a byte[] array.
    - _calib_glm_beta
```
double[] _calib_glm_beta
```
      GLM's beta used for calibrating output probabilities using Platt Scaling.
Package hex.genmodel.algos.word2vec
- Class hex.genmodel.algos.word2vec.Word2VecMojoModel extends MojoModel implements Serializable
  - Serialized Fields
    - _vecSize
```
int _vecSize
```
    - _embeddings
```
java.util.HashMap<K,V> _embeddings
```
Package hex.genmodel.easy
- Class hex.genmodel.easy.EasyPredictModelWrapper extends java.lang.Object implements Serializable
  - Serialized Fields
    - m
```
GenModel m
```
    - modelColumnNameToIndexMap
```
java.util.HashMap<K,V> modelColumnNameToIndexMap
```
    - domainMap
```
java.util.HashMap<K,V> domainMap
```
    - errorConsumer
```
EasyPredictModelWrapper.ErrorConsumer errorConsumer
```
    - convertUnknownCategoricalLevelsToNa
```
boolean convertUnknownCategoricalLevelsToNa
```
    - convertInvalidNumbersToNa
```
boolean convertInvalidNumbersToNa
```
    - useExtendedOutput
```
boolean useExtendedOutput
```
    - enableLeafAssignment
```
boolean enableLeafAssignment
```
    - enableGLRMReconstruct
```
boolean enableGLRMReconstruct
```
    - enableStagedProbabilities
```
boolean enableStagedProbabilities
```
- Class hex.genmodel.easy.EasyPredictModelWrapper.ErrorConsumer extends java.lang.Object implements Serializable
- Class hex.genmodel.easy.RowData extends java.util.HashMap<java.lang.String,java.lang.Object> implements Serializable
Package hex.genmodel.easy.error
- Class hex.genmodel.easy.error.CountingErrorConsumer extends EasyPredictModelWrapper.ErrorConsumer implements Serializable
  - Serialized Fields
    - dataTransformationErrorsCountPerColumn
```
java.util.Map<K,V> dataTransformationErrorsCountPerColumn
```
    - unknownCategoricalsPerColumn
```
java.util.Map<K,V> unknownCategoricalsPerColumn
```
- Class hex.genmodel.easy.error.VoidErrorConsumer extends EasyPredictModelWrapper.ErrorConsumer implements Serializable
Package hex.genmodel.easy.exception
- Class hex.genmodel.easy.exception.PredictException extends java.lang.Exception implements Serializable
- Class hex.genmodel.easy.exception.PredictNumberFormatException extends PredictException implements Serializable
- Class hex.genmodel.easy.exception.PredictUnknownCategoricalLevelException extends PredictException implements Serializable
  - Serialized Fields
    - columnName
```
java.lang.String columnName
```
    - unknownLevel
```
java.lang.String unknownLevel
```
- Class hex.genmodel.easy.exception.PredictUnknownTypeException extends PredictException implements Serializable
- Class hex.genmodel.easy.exception.PredictWrongModelCategoryException extends PredictException implements Serializable
Package hex.genmodel.easy.prediction
- Class hex.genmodel.easy.prediction.AbstractPrediction extends java.lang.Object implements Serializable
- Class hex.genmodel.easy.prediction.AnomalyDetectionPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - score
```
double score
```
    - normalizedScore
```
double normalizedScore
```
    - leafNodeAssignments
```
java.lang.String[] leafNodeAssignments
```
    - leafNodeAssignmentIds
```
int[] leafNodeAssignmentIds
```
    - stageProbabilities
```
double[] stageProbabilities
```
      Staged predictions of tree algorithms (prediction probabilities of trees per iteration). The output structure is for tree Tt and class Cc: Binomial models: [probability T1.C1, probability T2.C1, ..., Tt.C1] where Tt.C1 correspond to the the probability p0 Multinomial models: [probability T1.C1, probability T1.C2, ..., Tt.Cc]
- Class hex.genmodel.easy.prediction.AutoEncoderModelPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - original
```
double[] original
```
      Representation of the original input the way AutoEncoder model sees it (1-hot encoded categorical values)
    - reconstructed
```
double[] reconstructed
```
      Reconstructed data, the array has same length as the original input. The user can use the original input and reconstructed output to easily calculate eg. the reconstruction error.
    - reconstructedRowData
```
RowData reconstructedRowData
```
      Reconstructed data represented in RowData structure. The structure will copy the structure of the RowData input with the exception of categorical values. Categorical fields will be represented as a map of the domain values to the reconstructed values. Example: input RowData([sex: "Male", ..]) will produce output RowData([sex: [Male: 0.9, Female: 0.1], ..]
    - mse
```
double mse
```
      Reconstruction mean squared error calculated from original and reconstructed data. Uses a normalization defined for the numerical features of the trained model. average reconstruction error = ||original - reconstructed||^2 / length(original)
- Class hex.genmodel.easy.prediction.BinomialModelPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - labelIndex
```
int labelIndex
```
      0 or 1.
    - label
```
java.lang.String label
```
      Label of the predicted level.
    - classProbabilities
```
double[] classProbabilities
```
      This array of length two has the class probability for each class (aka categorical or factor level) in the response column. The array corresponds to the level names returned by:
      model.getDomainValues(model.getResponseIdx())
      "Domain" is the internal H2O term for level names. The values in this array may be Double.NaN, which means NA (this will happen with GLM, for example, if one of the input values for a new data point is NA). If they are valid numeric values, then they will sum up to 1.0.
    - calibratedClassProbabilities
```
double[] calibratedClassProbabilities
```
      Class probabilities calibrated by Platt Scaling. Optional, only calculated if the model supports it.
    - leafNodeAssignments
```
java.lang.String[] leafNodeAssignments
```
    - leafNodeAssignmentIds
```
int[] leafNodeAssignmentIds
```
    - stageProbabilities
```
double[] stageProbabilities
```
      Staged predictions of tree algorithms (prediction probabilities of trees per iteration). The output structure is for tree Tt and class Cc: Binomial models: [probability T1.C1, probability T2.C1, ..., Tt.C1] where Tt.C1 correspond to the the probability p0 Multinomial models: [probability T1.C1, probability T1.C2, ..., Tt.Cc]
- Class hex.genmodel.easy.prediction.ClusteringModelPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - cluster
```
int cluster
```
      Chosen cluster for this data point.
    - distances
```
double[] distances
```
      (Optional) Vector of squared distances to all cluster centers. This field will only be included in the output if "useExtendedOutput" flag was enabled in EasyPredictModelWrapper.
- Class hex.genmodel.easy.prediction.DimReductionModelPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - dimensions
```
double[] dimensions
```
    - reconstructed
```
double[] reconstructed
```
      This field is only used for GLRM and not for PCA. Reconstructed data, the array has same length as the original input. The user can use the original input and reconstructed output to easily calculate eg. the reconstruction error. Note that all values are either doubles or integers. Users need to convert the enum columns from the integer columns if necessary.
- Class hex.genmodel.easy.prediction.MultinomialModelPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - labelIndex
```
int labelIndex
```
      Index number of the predicted class (aka categorical or factor level) in the response column.
    - label
```
java.lang.String label
```
      Label of the predicted level.
    - classProbabilities
```
double[] classProbabilities
```
      This array has an element for each class (aka categorical or factor level) in the response column. The array corresponds to the level names returned by:
      model.getDomainValues(model.getResponseIdx())
      "Domain" is the internal H2O term for level names. The values in this array may be Double.NaN, which means NA. If they are valid numeric values, then they will sum up to 1.0.
    - leafNodeAssignments
```
java.lang.String[] leafNodeAssignments
```
    - leafNodeAssignmentIds
```
int[] leafNodeAssignmentIds
```
    - stageProbabilities
```
double[] stageProbabilities
```
      Staged predictions of tree algorithms (prediction probabilities of trees per iteration). The output structure is for tree Tt and class Cc: Binomial models: [probability T1.C1, probability T2.C1, ..., Tt.C1] where Tt.C1 correspond to the the probability p0 Multinomial models: [probability T1.C1, probability T1.C2, ..., Tt.Cc]
- Class hex.genmodel.easy.prediction.OrdinalModelPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - labelIndex
```
int labelIndex
```
      Index number of the predicted class (aka categorical or factor level) in the response column.
    - label
```
java.lang.String label
```
      Label of the predicted level.
    - classProbabilities
```
double[] classProbabilities
```
      This array has an element for each class (aka categorical or factor level) in the response column. The array corresponds to the level names returned by:
      model.getDomainValues(model.getResponseIdx())
      "Domain" is the internal H2O term for level names. The values in this array may be Double.NaN, which means NA. If they are valid numeric values, then they will sum up to 1.0.
- Class hex.genmodel.easy.prediction.RegressionModelPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - value
```
double value
```
      This value may be Double.NaN, which means NA (this will happen with GLM, for example, if one of the input values for a new data point is NA).
    - leafNodeAssignments
```
java.lang.String[] leafNodeAssignments
```
    - leafNodeAssignmentIds
```
int[] leafNodeAssignmentIds
```
    - stageProbabilities
```
double[] stageProbabilities
```
      Staged predictions of tree algorithms (prediction probabilities of trees per iteration). The output structure is for tree Tt and class Cc: Binomial models: [probability T1.C1, probability T2.C1, ..., Tt.C1] where Tt.C1 correspond to the the probability p0 Multinomial models: [probability T1.C1, probability T1.C2, ..., Tt.Cc]
- Class hex.genmodel.easy.prediction.Word2VecPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - wordEmbeddings
```
java.util.HashMap<K,V> wordEmbeddings
```

Serialized Form

Package hex.genmodel

Class hex.genmodel.GenModel extends java.lang.Object implements Serializable

Serialized Fields

_names

_domains

_responseColumn

_offsetColumn

Class hex.genmodel.GenMunger extends java.lang.Object implements Serializable

Serialized Fields

_steps

Class hex.genmodel.GenMunger.Step extends java.lang.Object implements Serializable

Serialized Fields

_names

_types

_outNames

_params

Class hex.genmodel.MojoModel extends GenModel implements Serializable

Serialized Fields

_h2oVersion

_category

_uuid

_supervised

_nfeatures

_nclasses

_balanceClasses

_defaultThreshold

_priorClassDistrib

_modelClassDistrib

_mojo_version

Package hex.genmodel.algos.deeplearning

Class hex.genmodel.algos.deeplearning.ActivationUtils.ExpRectifierDropoutOut extends ActivationUtils.ExpRectifierOut implements Serializable

Class hex.genmodel.algos.deeplearning.ActivationUtils.ExpRectifierOut extends java.lang.Object implements Serializable

Class hex.genmodel.algos.deeplearning.ActivationUtils.LinearOut extends java.lang.Object implements Serializable

Class hex.genmodel.algos.deeplearning.ActivationUtils.MaxoutDropoutOut extends ActivationUtils.MaxoutOut implements Serializable

Class hex.genmodel.algos.deeplearning.ActivationUtils.MaxoutOut extends java.lang.Object implements Serializable

Class hex.genmodel.algos.deeplearning.ActivationUtils.RectifierDropoutOut extends ActivationUtils.RectifierOut implements Serializable

Class hex.genmodel.algos.deeplearning.ActivationUtils.RectifierOut extends java.lang.Object implements Serializable

Class hex.genmodel.algos.deeplearning.ActivationUtils.SoftmaxOut extends java.lang.Object implements Serializable

Class hex.genmodel.algos.deeplearning.ActivationUtils.TanhDropoutOut extends ActivationUtils.TanhOut implements Serializable

Class hex.genmodel.algos.deeplearning.ActivationUtils.TanhOut extends java.lang.Object implements Serializable

Class hex.genmodel.algos.deeplearning.DeeplearningMojoModel extends MojoModel implements Serializable

Serialized Fields

_mini_batch_size

_nums

_cats

_catoffsets

_normmul

_normsub

_normrespmul

_normrespsub

_use_all_factor_levels

_activation

_allActivations

_imputeMeans

_units

_all_drop_out_ratios

_weightsAndBias

_catNAFill

_numLayers

_family

_numsA

_catsA

Class hex.genmodel.algos.deeplearning.DeeplearningMojoModel.StoreWeightsBias extends java.lang.Object implements Serializable

Serialized Fields

_wValues

_bValues

Package hex.genmodel.algos.deepwater

Class hex.genmodel.algos.deepwater.DeepwaterMojoModel extends MojoModel implements Serializable

Serialized Fields

_problem_type

_mini_batch_size

_height

_width

_channels

_nums

_cats

_catOffsets

_normMul

_normSub