hex.genmodel.algos.deeplearning

Class DeeplearningMojoModel

java.lang.Object

hex.genmodel.GenModel

hex.genmodel.MojoModel

hex.genmodel.algos.deeplearning.DeeplearningMojoModel

All Implemented Interfaces:

IGenModel, java.io.Serializable, IGeneratedModel

public class DeeplearningMojoModel
extends MojoModel

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	DeeplearningMojoModel.StoreWeightsBias

Field Summary

Fields

Modifier and Type	Field and Description
java.lang.String	_activation
double[]	_all_drop_out_ratios
java.lang.String[]	_allActivations
int[]	_catNAFill
int[]	_catoffsets
int	_cats
int[]	_catsA
DistributionFamily	_family
boolean	_imputeMeans
int	_mini_batch_size
double[]	_normmul
double[]	_normrespmul
double[]	_normrespsub
double[]	_normsub
int	_numLayers
int	_nums
double[]	_numsA
int[]	_units
boolean	_use_all_factor_levels
DeeplearningMojoModel.StoreWeightsBias[]	_weightsAndBias

Fields inherited from class hex.genmodel.MojoModel

_balanceClasses, _category, _defaultThreshold, _h2oVersion, _modelClassDistrib, _mojo_version, _nclasses, _nfeatures, _priorClassDistrib, _supervised, _uuid

Fields inherited from class hex.genmodel.GenModel

_domains, _names, _offsetColumn, _responseColumn

Method Summary

Modifier and Type	Method and Description
double	calculateReconstructionErrorPerRowData(double[] original, double[] reconstructed) Calculates average reconstruction error (MSE).
int	getPredsSize(ModelCategory mc)
void	init()
double[]	modifyOutputs(double[] out, double[] preds, double[] dataRow)
double[]	score0(double[] row, double[] preds) Subclasses implement the scoring logic.
double[]	score0(double[] dataRow, double offset, double[] preds) This method will be derived from the scoring/prediction function of deeplearning model itself.

Methods inherited from class hex.genmodel.MojoModel

getModelCategory, getUUID, isSupervised, load, load, nclasses, nfeatures

Methods inherited from class hex.genmodel.GenModel

bitSetContains, bitSetIsInRange, calibrateClassProbabilities, convertDouble2Float, correctProbabilities, createAuxKey, GBM_rescale, getColIdx, getDomainValues, getDomainValues, getDomainValues, getHeader, getModelCategories, getNames, getNumClasses, getNumCols, getNumResponseClasses, getPrediction, getPredsSize, getResponseIdx, getResponseName, GLM_identityInv, GLM_inverseInv, GLM_logInv, GLM_logitInv, GLM_ologitInv, GLM_tweedieInv, img2pixels, isAutoEncoder, isClassifier, KMeans_closest, KMeans_distance, KMeans_distance, KMeans_distances, Kmeans_preprocessData, Kmeans_preprocessData, KMeans_simplex, log_rescale, mapEnum, setCats, setCats, setInput, setInput

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

_mini_batch_size

public int _mini_batch_size

_nums

public int _nums

_cats

public int _cats

_catoffsets

public int[] _catoffsets

_normmul

public double[] _normmul

_normsub

public double[] _normsub

_normrespmul

public double[] _normrespmul

_normrespsub

public double[] _normrespsub

_use_all_factor_levels

public boolean _use_all_factor_levels

_activation

public java.lang.String _activation

_allActivations

public java.lang.String[] _allActivations

_imputeMeans

public boolean _imputeMeans

_units

public int[] _units

_all_drop_out_ratios

public double[] _all_drop_out_ratios

_weightsAndBias

public DeeplearningMojoModel.StoreWeightsBias[] _weightsAndBias

_catNAFill

public int[] _catNAFill

_numLayers

public int _numLayers

_family

public DistributionFamily _family

_numsA

public double[] _numsA

_catsA

public int[] _catsA

Method Detail

init

public void init()

score0

public final double[] score0(double[] dataRow,
                             double offset,
                             double[] preds)

This method will be derived from the scoring/prediction function of deeplearning model itself. However, we followed closely what is being done in deepwater mojo. The variable offset is not used.

Overrides:

score0 in class GenModel

Parameters:

dataRow -

offset -

preds -

Returns:

modifyOutputs

public double[] modifyOutputs(double[] out,
                              double[] preds,
                              double[] dataRow)

score0

public double[] score0(double[] row,
                       double[] preds)

Description copied from class: GenModel

Subclasses implement the scoring logic. The data is pre-loaded into a re-used temp array, in the order the model expects. The predictions are loaded into the re-used temp array, which is also returned. This call exactly matches the hex.Model.score0, but uses the light-weight GenModel class.

Specified by:

score0 in class GenModel

getPredsSize

public int getPredsSize(ModelCategory mc)

Overrides:

getPredsSize in class GenModel

calculateReconstructionErrorPerRowData

public double calculateReconstructionErrorPerRowData(double[] original,
                                                     double[] reconstructed)

Calculates average reconstruction error (MSE). Uses a normalization defined for the numerical features of the trained model.

Returns:

average reconstruction error = ||original - reconstructed||^2 / length(original)