Serialized Form

Package weka.classifiers.functions
- Class weka.classifiers.functions.Dl4jMlpClassifier extends RandomizableClassifier implements Serializable
  
  serialVersionUID:
  
  -6363254116597574265L
  - Serialization Methods
    - readObject
```
private void readObject(java.io.ObjectInputStream ois)
                 throws java.lang.ClassNotFoundException,
                        java.io.IOException
```
      Custom deserialization method
      
      Throws:
      
      java.lang.ClassNotFoundException
      
      java.io.IOException
    - writeObject
```
private void writeObject(java.io.ObjectOutputStream oos)
                  throws java.io.IOException
```
      Custom serialization method.
      
      Throws:
      
      java.io.IOException
  - Serialized Fields
    - m_log
```
org.slf4j.Logger m_log
```
      The logger used in this class.
    - m_replaceMissing
```
ReplaceMissingValues m_replaceMissing
```
      Filter used to replace missing values.
    - m_Filter
```
Filter m_Filter
```
      Filter used to normalize or standardize the data.
    - m_nominalToBinary
```
NominalToBinary m_nominalToBinary
```
      Filter used to convert nominal attributes to binary numeric attributes.
    - m_zeroR
```
ZeroR m_zeroR
```
      ZeroR classifier, just in case we don't actually have any data to train a network.
    - m_modelSize
```
long m_modelSize
```
      The size of the serialized network model in bytes.
    - m_logFile
```
java.io.File m_logFile
```
      The file that log information will be written to.
    - m_layers
```
org.deeplearning4j.nn.conf.layers.Layer[] m_layers
```
      The layers of the network.
    - m_configuration
```
NeuralNetConfiguration m_configuration
```
      The configuration of the network.
    - m_numEpochs
```
int m_numEpochs
```
      The number of epochs to perform.
    - m_NumEpochsPerformed
```
int m_NumEpochsPerformed
```
      The number of epochs that have been performed.
    - m_Data
```
Instances m_Data
```
      The training instances (set to null when done() is called).
    - m_iterator
```
AbstractDataSetIterator m_iterator
```
      The dataset iterator to use.
    - m_queueSize
```
int m_queueSize
```
      Queue size for AsyncDataSetIterator (if < 1, AsyncDataSetIterator is not used)
    - m_filterType
```
int m_filterType
```
      Whether to normalize/standardize/neither
    - m_x1
```
double m_x1
```
      Coefficients used for normalizing the class
    - m_x0
```
double m_x0
```
- Class weka.classifiers.functions.GaussianProcesses extends RandomizableClassifier implements Serializable
  
  serialVersionUID:
  
  -8620066949967678545L
  - Serialized Fields
    - m_NominalToBinary
```
NominalToBinary m_NominalToBinary
```
    - m_Filter
```
Filter m_Filter
```
    - m_filterType
```
int m_filterType
```
    - m_Missing
```
ReplaceMissingValues m_Missing
```
    - m_checksTurnedOff
```
boolean m_checksTurnedOff
```
    - m_delta
```
double m_delta
```
    - m_deltaSquared
```
double m_deltaSquared
```
    - m_Alin
```
double m_Alin
```
    - m_Blin
```
double m_Blin
```
    - m_kernel
```
Kernel m_kernel
```
    - m_actualKernel
```
Kernel m_actualKernel
```
    - m_NumTrain
```
int m_NumTrain
```
    - m_avg_target
```
double m_avg_target
```
    - m_L
```
no.uib.cipr.matrix.Matrix m_L
```
    - m_t
```
no.uib.cipr.matrix.Vector m_t
```
    - m_weights
```
double[] m_weights
```
- Class weka.classifiers.functions.LinearRegression extends AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  -3364580862046573747L
  - Serialized Fields
    - m_Coefficients
```
double[] m_Coefficients
```
    - m_SelectedAttributes
```
boolean[] m_SelectedAttributes
```
    - m_TransformedData
```
Instances m_TransformedData
```
    - m_MissingFilter
```
ReplaceMissingValues m_MissingFilter
```
    - m_TransformFilter
```
NominalToBinary m_TransformFilter
```
    - m_ClassStdDev
```
double m_ClassStdDev
```
    - m_ClassMean
```
double m_ClassMean
```
    - m_ClassIndex
```
int m_ClassIndex
```
    - m_Means
```
double[] m_Means
```
    - m_StdDevs
```
double[] m_StdDevs
```
    - m_outputAdditionalStats
```
boolean m_outputAdditionalStats
```
    - m_AttributeSelection
```
int m_AttributeSelection
```
    - m_EliminateColinearAttributes
```
boolean m_EliminateColinearAttributes
```
    - m_checksTurnedOff
```
boolean m_checksTurnedOff
```
    - m_useQRDecomposition
```
boolean m_useQRDecomposition
```
    - m_Ridge
```
double m_Ridge
```
    - m_Minimal
```
boolean m_Minimal
```
    - m_ModelBuilt
```
boolean m_ModelBuilt
```
    - m_isZeroR
```
boolean m_isZeroR
```
    - m_df
```
int m_df
```
    - m_RSquared
```
double m_RSquared
```
    - m_RSquaredAdj
```
double m_RSquaredAdj
```
    - m_FStat
```
double m_FStat
```
    - m_StdErrorOfCoef
```
double[] m_StdErrorOfCoef
```
    - m_TStats
```
double[] m_TStats
```
- Class weka.classifiers.functions.Logistic extends AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  3932117032546553727L
  - Serialized Fields
    - m_Par
```
double[][] m_Par
```
    - m_Data
```
double[][] m_Data
```
    - m_NumPredictors
```
int m_NumPredictors
```
    - m_ClassIndex
```
int m_ClassIndex
```
    - m_NumClasses
```
int m_NumClasses
```
    - m_Ridge
```
double m_Ridge
```
    - m_AttFilter
```
RemoveUseless m_AttFilter
```
    - m_NominalToBinary
```
NominalToBinary m_NominalToBinary
```
    - m_ReplaceMissingValues
```
ReplaceMissingValues m_ReplaceMissingValues
```
    - m_LL
```
double m_LL
```
    - m_MaxIts
```
int m_MaxIts
```
    - m_useConjugateGradientDescent
```
boolean m_useConjugateGradientDescent
```
    - m_structure
```
Instances m_structure
```
    - m_numModels
```
int m_numModels
```
- Class weka.classifiers.functions.MultilayerPerceptron extends AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  -5990607817048210779L
  - Serialized Fields
    - m_ZeroR
```
Classifier m_ZeroR
```
    - m_useDefaultModel
```
boolean m_useDefaultModel
```
    - m_instances
```
Instances m_instances
```
    - m_currentInstance
```
Instance m_currentInstance
```
    - m_numeric
```
boolean m_numeric
```
    - m_attributeRanges
```
double[] m_attributeRanges
```
    - m_attributeBases
```
double[] m_attributeBases
```
    - m_outputs
```
MultilayerPerceptron.NeuralEnd[] m_outputs
```
    - m_inputs
```
MultilayerPerceptron.NeuralEnd[] m_inputs
```
    - m_neuralNodes
```
NeuralConnection[] m_neuralNodes
```
    - m_numClasses
```
int m_numClasses
```
    - m_numAttributes
```
int m_numAttributes
```
    - m_nodePanel
```
weka.classifiers.functions.MultilayerPerceptron.NodePanel m_nodePanel
```
    - m_controlPanel
```
weka.classifiers.functions.MultilayerPerceptron.ControlPanel m_controlPanel
```
    - m_nextId
```
int m_nextId
```
    - m_selected
```
java.util.ArrayList<E> m_selected
```
    - m_numEpochs
```
int m_numEpochs
```
    - m_stopIt
```
boolean m_stopIt
```
    - m_stopped
```
boolean m_stopped
```
    - m_accepted
```
boolean m_accepted
```
    - m_win
```
javax.swing.JFrame m_win
```
    - m_autoBuild
```
boolean m_autoBuild
```
    - m_gui
```
boolean m_gui
```
    - m_valSize
```
int m_valSize
```
    - m_driftThreshold
```
int m_driftThreshold
```
    - m_randomSeed
```
int m_randomSeed
```
    - m_random
```
java.util.Random m_random
```
    - m_useNomToBin
```
boolean m_useNomToBin
```
    - m_nominalToBinaryFilter
```
NominalToBinary m_nominalToBinaryFilter
```
    - m_hiddenLayers
```
java.lang.String m_hiddenLayers
```
    - m_normalizeAttributes
```
boolean m_normalizeAttributes
```
    - m_decay
```
boolean m_decay
```
    - m_learningRate
```
double m_learningRate
```
    - m_momentum
```
double m_momentum
```
    - m_epoch
```
int m_epoch
```
    - m_error
```
double m_error
```
    - m_reset
```
boolean m_reset
```
    - m_normalizeClass
```
boolean m_normalizeClass
```
    - m_sigmoidUnit
```
SigmoidUnit m_sigmoidUnit
```
    - m_linearUnit
```
LinearUnit m_linearUnit
```
- Class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd extends NeuralConnection implements Serializable
  
  serialVersionUID:
  
  7305185603191183338L
  - Serialized Fields
    - m_link
```
int m_link
```
    - m_input
```
boolean m_input
```
- Class weka.classifiers.functions.SGD extends RandomizableClassifier implements Serializable
  
  serialVersionUID:
  
  -3732968666673530290L
  - Serialized Fields
    - m_replaceMissing
```
ReplaceMissingValues m_replaceMissing
```
    - m_nominalToBinary
```
Filter m_nominalToBinary
```
    - m_normalize
```
Normalize m_normalize
```
    - m_lambda
```
double m_lambda
```
    - m_learningRate
```
double m_learningRate
```
    - m_weights
```
double[] m_weights
```
    - m_epsilon
```
double m_epsilon
```
    - m_t
```
double m_t
```
    - m_numInstances
```
double m_numInstances
```
    - m_epochs
```
int m_epochs
```
    - m_dontNormalize
```
boolean m_dontNormalize
```
    - m_dontReplaceMissing
```
boolean m_dontReplaceMissing
```
    - m_data
```
Instances m_data
```
    - m_loss
```
int m_loss
```
    - m_numModels
```
int m_numModels
```
- Class weka.classifiers.functions.SGDText extends RandomizableClassifier implements Serializable
  
  serialVersionUID:
  
  7200171484002029584L
  - Serialized Fields
    - m_periodicP
```
int m_periodicP
```
    - m_minWordP
```
double m_minWordP
```
    - m_minAbsCoefficient
```
double m_minAbsCoefficient
```
    - m_wordFrequencies
```
boolean m_wordFrequencies
```
    - m_normalize
```
boolean m_normalize
```
    - m_norm
```
double m_norm
```
    - m_lnorm
```
double m_lnorm
```
    - m_dictionary
```
java.util.LinkedHashMap<K,V> m_dictionary
```
    - m_StopwordsHandler
```
StopwordsHandler m_StopwordsHandler
```
    - m_tokenizer
```
Tokenizer m_tokenizer
```
    - m_lowercaseTokens
```
boolean m_lowercaseTokens
```
    - m_stemmer
```
Stemmer m_stemmer
```
    - m_lambda
```
double m_lambda
```
    - m_learningRate
```
double m_learningRate
```
    - m_t
```
double m_t
```
    - m_bias
```
double m_bias
```
    - m_numInstances
```
double m_numInstances
```
    - m_data
```
Instances m_data
```
    - m_epochs
```
int m_epochs
```
    - m_loss
```
int m_loss
```
    - m_svmProbs
```
SGD m_svmProbs
```
    - m_fitLogistic
```
boolean m_fitLogistic
```
    - m_fitLogisticStructure
```
Instances m_fitLogisticStructure
```
    - m_numModels
```
int m_numModels
```
- Class weka.classifiers.functions.SGDText.Count extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  2104201532017340967L
  - Serialized Fields
    - m_count
```
double m_count
```
    - m_weight
```
double m_weight
```
- Class weka.classifiers.functions.SimpleLinearRegression extends AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  1679336022895414137L
  - Serialized Fields
    - m_attribute
```
Attribute m_attribute
```
    - m_attributeIndex
```
int m_attributeIndex
```
    - m_slope
```
double m_slope
```
    - m_intercept
```
double m_intercept
```
    - m_classMeanForMissing
```
double m_classMeanForMissing
```
    - m_outputAdditionalStats
```
boolean m_outputAdditionalStats
```
    - m_df
```
int m_df
```
    - m_seSlope
```
double m_seSlope
```
    - m_seIntercept
```
double m_seIntercept
```
    - m_tstatSlope
```
double m_tstatSlope
```
    - m_tstatIntercept
```
double m_tstatIntercept
```
    - m_rsquared
```
double m_rsquared
```
    - m_rsquaredAdj
```
double m_rsquaredAdj
```
    - m_fstat
```
double m_fstat
```
    - m_suppressErrorMessage
```
boolean m_suppressErrorMessage
```
- Class weka.classifiers.functions.SimpleLogistic extends AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  7397710626304705059L
  - Serialized Fields
    - m_boostedModel
```
LogisticBase m_boostedModel
```
    - m_NominalToBinary
```
NominalToBinary m_NominalToBinary
```
    - m_ReplaceMissingValues
```
ReplaceMissingValues m_ReplaceMissingValues
```
    - m_numBoostingIterations
```
int m_numBoostingIterations
```
    - m_maxBoostingIterations
```
int m_maxBoostingIterations
```
    - m_heuristicStop
```
int m_heuristicStop
```
    - m_useCrossValidation
```
boolean m_useCrossValidation
```
    - m_errorOnProbabilities
```
boolean m_errorOnProbabilities
```
    - m_weightTrimBeta
```
double m_weightTrimBeta
```
    - m_useAIC
```
boolean m_useAIC
```
- Class weka.classifiers.functions.SMO extends AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  -6585883636378691736L
  - Serialized Fields
    - m_classifiers
```
SMO.BinarySMO[][] m_classifiers
```
    - m_C
```
double m_C
```
    - m_eps
```
double m_eps
```
    - m_tol
```
double m_tol
```
    - m_filterType
```
int m_filterType
```
    - m_NominalToBinary
```
NominalToBinary m_NominalToBinary
```
    - m_Filter
```
Filter m_Filter
```
    - m_Missing
```
ReplaceMissingValues m_Missing
```
    - m_classIndex
```
int m_classIndex
```
    - m_classAttribute
```
Attribute m_classAttribute
```
    - m_KernelIsLinear
```
boolean m_KernelIsLinear
```
    - m_checksTurnedOff
```
boolean m_checksTurnedOff
```
    - m_fitCalibratorModels
```
boolean m_fitCalibratorModels
```
    - m_calibrator
```
Classifier m_calibrator
```
    - m_numFolds
```
int m_numFolds
```
    - m_randomSeed
```
int m_randomSeed
```
    - m_kernel
```
Kernel m_kernel
```
- Class weka.classifiers.functions.SMO.BinarySMO extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -8246163625699362456L
  - Serialized Fields
    - m_alpha
```
double[] m_alpha
```
    - m_b
```
double m_b
```
    - m_bLow
```
double m_bLow
```
    - m_bUp
```
double m_bUp
```
    - m_iLow
```
int m_iLow
```
    - m_iUp
```
int m_iUp
```
    - m_data
```
Instances m_data
```
    - m_weights
```
double[] m_weights
```
    - m_sparseWeights
```
double[] m_sparseWeights
```
    - m_sparseIndices
```
int[] m_sparseIndices
```
    - m_kernel
```
Kernel m_kernel
```
    - m_class
```
double[] m_class
```
    - m_errors
```
double[] m_errors
```
    - m_I0
```
SMOset m_I0
```
    - m_I1
```
SMOset m_I1
```
    - m_I2
```
SMOset m_I2
```
    - m_I3
```
SMOset m_I3
```
    - m_I4
```
SMOset m_I4
```
    - m_supportVectors
```
SMOset m_supportVectors
```
    - m_calibrator
```
Classifier m_calibrator
```
    - m_calibrationDataHeader
```
Instances m_calibrationDataHeader
```
    - m_sumOfWeights
```
double m_sumOfWeights
```
    - m_nEvals
```
long m_nEvals
```
    - m_nCacheHits
```
int m_nCacheHits
```
- Class weka.classifiers.functions.SMOreg extends AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  -7149606251113102827L
  - Serialized Fields
    - m_filterType
```
int m_filterType
```
    - m_NominalToBinary
```
NominalToBinary m_NominalToBinary
```
    - m_Filter
```
Filter m_Filter
```
    - m_Missing
```
ReplaceMissingValues m_Missing
```
    - m_onlyNumeric
```
boolean m_onlyNumeric
```
    - m_C
```
double m_C
```
    - m_x1
```
double m_x1
```
    - m_x0
```
double m_x0
```
    - m_optimizer
```
RegOptimizer m_optimizer
```
    - m_kernel
```
Kernel m_kernel
```
- Class weka.classifiers.functions.VotedPerceptron extends AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  -1072429260104568698L
  - Serialized Fields
    - m_MaxK
```
int m_MaxK
```
    - m_NumIterations
```
int m_NumIterations
```
    - m_Exponent
```
double m_Exponent
```
    - m_K
```
int m_K
```
    - m_Additions
```
int[] m_Additions
```
    - m_IsAddition
```
boolean[] m_IsAddition
```
    - m_Weights
```
int[] m_Weights
```
    - m_Train
```
Instances m_Train
```
    - m_Seed
```
int m_Seed
```
    - m_NominalToBinary
```
NominalToBinary m_NominalToBinary
```
    - m_ReplaceMissingValues
```
ReplaceMissingValues m_ReplaceMissingValues
```
Package weka.dl4j
- Class weka.dl4j.EasyImageRecordReader extends org.datavec.image.recordreader.BaseImageRecordReader implements Serializable
  
  serialVersionUID:
  
  3806752391833402426L
  - Serialized Fields
    - m_filenames
```
java.util.ArrayList<E> m_filenames
```
      The list of file names
    - m_classes
```
java.util.ArrayList<E> m_classes
```
      The list of class values
    - m_classIterator
```
java.util.Iterator<E> m_classIterator
```
      The iterator for the class values
    - m_random
```
java.util.Random m_random
```
      Random number generator for shuffling the data
- Class weka.dl4j.FileIterationListener extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  1948578564961956518L
  - Serialized Fields
    - m_numMiniBatches
```
int m_numMiniBatches
```
      The number of mini batches.
    - lossesPerEpoch
```
java.util.ArrayList<E> lossesPerEpoch
```
      Losses per epoch
- Class weka.dl4j.NeuralNetConfiguration extends org.deeplearning4j.nn.conf.NeuralNetConfiguration implements Serializable
- Class weka.dl4j.ScaleImagePixelsPreProcessor extends java.lang.Object implements Serializable
- Class weka.dl4j.ShufflingDataSetIterator extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  5571114918884888578L
  - Serialized Fields
    - m_data
```
org.nd4j.linalg.dataset.DataSet m_data
```
      The dataset to operate on
    - m_batchSize
```
int m_batchSize
```
      The mini batch size
    - m_cursor
```
int m_cursor
```
      The cursor
    - m_random
```
java.util.Random m_random
```
      The random number generator used for shuffling the data
- Class weka.dl4j.SpecifiableFolderSplit extends org.datavec.api.split.FileSplit implements Serializable
  
  serialVersionUID:
  
  462115492403223134L
Package weka.dl4j.activations
- Class weka.dl4j.activations.ActivationCube extends org.nd4j.linalg.activations.impl.ActivationCube implements Serializable
- Class weka.dl4j.activations.ActivationELU extends org.nd4j.linalg.activations.impl.ActivationELU implements Serializable
- Class weka.dl4j.activations.ActivationHardSigmoid extends org.nd4j.linalg.activations.impl.ActivationHardSigmoid implements Serializable
- Class weka.dl4j.activations.ActivationHardTanH extends org.nd4j.linalg.activations.impl.ActivationHardTanH implements Serializable
- Class weka.dl4j.activations.ActivationIdentity extends org.nd4j.linalg.activations.impl.ActivationIdentity implements Serializable
- Class weka.dl4j.activations.ActivationLReLU extends org.nd4j.linalg.activations.impl.ActivationLReLU implements Serializable
- Class weka.dl4j.activations.ActivationRationalTanh extends org.nd4j.linalg.activations.impl.ActivationRationalTanh implements Serializable
- Class weka.dl4j.activations.ActivationReLU extends org.nd4j.linalg.activations.impl.ActivationReLU implements Serializable
- Class weka.dl4j.activations.ActivationRReLU extends org.nd4j.linalg.activations.impl.ActivationRReLU implements Serializable
- Class weka.dl4j.activations.ActivationSigmoid extends org.nd4j.linalg.activations.impl.ActivationSigmoid implements Serializable
- Class weka.dl4j.activations.ActivationSoftmax extends org.nd4j.linalg.activations.impl.ActivationSoftmax implements Serializable
- Class weka.dl4j.activations.ActivationSoftPlus extends org.nd4j.linalg.activations.impl.ActivationSoftPlus implements Serializable
- Class weka.dl4j.activations.ActivationSoftSign extends org.nd4j.linalg.activations.impl.ActivationSoftSign implements Serializable
- Class weka.dl4j.activations.ActivationTanH extends org.nd4j.linalg.activations.impl.ActivationTanH implements Serializable
Package weka.dl4j.distribution
- Class weka.dl4j.distribution.BinomialDistribution extends org.deeplearning4j.nn.conf.distribution.BinomialDistribution implements Serializable
- Class weka.dl4j.distribution.NormalDistribution extends org.deeplearning4j.nn.conf.distribution.NormalDistribution implements Serializable
- Class weka.dl4j.distribution.UniformDistribution extends org.deeplearning4j.nn.conf.distribution.UniformDistribution implements Serializable
Package weka.dl4j.iterators
- Class weka.dl4j.iterators.AbstractDataSetIterator extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  7440584973810993954L
  - Serialized Fields
    - m_batchSize
```
int m_batchSize
```
      The batch size for the mini batches
- Class weka.dl4j.iterators.ConvolutionalInstancesIterator extends AbstractDataSetIterator implements Serializable
  
  serialVersionUID:
  
  -3101209034945158130L
  - Serialized Fields
    - m_height
```
int m_height
```
      The desired output height
    - m_width
```
int m_width
```
      The desired output width
    - m_numChannels
```
int m_numChannels
```
      The desired number of channels
- Class weka.dl4j.iterators.DefaultInstancesIterator extends AbstractDataSetIterator implements Serializable
  
  serialVersionUID:
  
  1316260988724548474L
- Class weka.dl4j.iterators.ImageDataSetIterator extends AbstractDataSetIterator implements Serializable
  
  serialVersionUID:
  
  -3701309032945158130L
  - Serialized Fields
    - m_height
```
int m_height
```
      The desired output height
    - m_width
```
int m_width
```
      The desired output width
    - m_numChannels
```
int m_numChannels
```
      The desired number of channels
    - m_imagesLocation
```
java.io.File m_imagesLocation
```
      The location of the folder containing the images
Package weka.dl4j.layers
- Class weka.dl4j.layers.BatchNormalization extends org.deeplearning4j.nn.conf.layers.BatchNormalization implements Serializable
  
  serialVersionUID:
  
  6804344091980568487L
- Class weka.dl4j.layers.ConvolutionLayer extends org.deeplearning4j.nn.conf.layers.ConvolutionLayer implements Serializable
  
  serialVersionUID:
  
  6905344091980568487L
- Class weka.dl4j.layers.DenseLayer extends org.deeplearning4j.nn.conf.layers.DenseLayer implements Serializable
  
  serialVersionUID:
  
  -6905917800811990400L
- Class weka.dl4j.layers.OutputLayer extends org.deeplearning4j.nn.conf.layers.OutputLayer implements Serializable
  
  serialVersionUID:
  
  139321786136127207L
- Class weka.dl4j.layers.SubsamplingLayer extends org.deeplearning4j.nn.conf.layers.SubsamplingLayer implements Serializable
  
  serialVersionUID:
  
  -699034028619492301L
Package weka.dl4j.lossfunctions
- Class weka.dl4j.lossfunctions.LossBinaryXENT extends org.nd4j.linalg.lossfunctions.impl.LossBinaryXENT implements Serializable
- Class weka.dl4j.lossfunctions.LossCosineProximity extends org.nd4j.linalg.lossfunctions.impl.LossCosineProximity implements Serializable
- Class weka.dl4j.lossfunctions.LossHinge extends org.nd4j.linalg.lossfunctions.impl.LossHinge implements Serializable
- Class weka.dl4j.lossfunctions.LossKLD extends org.nd4j.linalg.lossfunctions.impl.LossKLD implements Serializable
- Class weka.dl4j.lossfunctions.LossL1 extends org.nd4j.linalg.lossfunctions.impl.LossL1 implements Serializable
- Class weka.dl4j.lossfunctions.LossL2 extends org.nd4j.linalg.lossfunctions.impl.LossL2 implements Serializable
- Class weka.dl4j.lossfunctions.LossMAE extends org.nd4j.linalg.lossfunctions.impl.LossMAE implements Serializable
- Class weka.dl4j.lossfunctions.LossMAPE extends org.nd4j.linalg.lossfunctions.impl.LossMAPE implements Serializable
- Class weka.dl4j.lossfunctions.LossMCXENT extends org.nd4j.linalg.lossfunctions.impl.LossMCXENT implements Serializable
- Class weka.dl4j.lossfunctions.LossMSE extends org.nd4j.linalg.lossfunctions.impl.LossMSE implements Serializable
- Class weka.dl4j.lossfunctions.LossMSLE extends org.nd4j.linalg.lossfunctions.impl.LossMSLE implements Serializable
- Class weka.dl4j.lossfunctions.LossNegativeLogLikelihood extends org.nd4j.linalg.lossfunctions.impl.LossNegativeLogLikelihood implements Serializable
- Class weka.dl4j.lossfunctions.LossPoisson extends org.nd4j.linalg.lossfunctions.impl.LossPoisson implements Serializable
- Class weka.dl4j.lossfunctions.LossSquaredHinge extends org.nd4j.linalg.lossfunctions.impl.LossSquaredHinge implements Serializable
Package weka.dl4j.stepfunctions
- Class weka.dl4j.stepfunctions.DefaultStepFunction extends org.deeplearning4j.nn.conf.stepfunctions.DefaultStepFunction implements Serializable
- Class weka.dl4j.stepfunctions.GradientStepFunction extends org.deeplearning4j.nn.conf.stepfunctions.GradientStepFunction implements Serializable
- Class weka.dl4j.stepfunctions.NegativeDefaultStepFunction extends org.deeplearning4j.nn.conf.stepfunctions.NegativeDefaultStepFunction implements Serializable
- Class weka.dl4j.stepfunctions.NegativeGradientStepFunction extends org.deeplearning4j.nn.conf.stepfunctions.NegativeGradientStepFunction implements Serializable

Serialized Form

Package weka.classifiers.functions

Class weka.classifiers.functions.Dl4jMlpClassifier extends RandomizableClassifier implements Serializable

Serialization Methods

readObject

writeObject

Serialized Fields

m_log

m_replaceMissing

m_Filter

m_nominalToBinary

m_zeroR

m_modelSize

m_logFile

m_layers

m_configuration

m_numEpochs

m_NumEpochsPerformed

m_Data

m_iterator

m_queueSize

m_filterType

m_x1

m_x0

Class weka.classifiers.functions.GaussianProcesses extends RandomizableClassifier implements Serializable

Serialized Fields

m_NominalToBinary

m_Filter

m_filterType

m_Missing

m_checksTurnedOff

m_delta

m_deltaSquared

m_Alin

m_Blin

m_kernel

m_actualKernel

m_NumTrain

m_avg_target

m_L

m_t

m_weights

Class weka.classifiers.functions.LinearRegression extends AbstractClassifier implements Serializable

Serialized Fields

m_Coefficients

m_SelectedAttributes

m_TransformedData

m_MissingFilter

m_TransformFilter

m_ClassStdDev

m_ClassMean

m_ClassIndex

m_Means

m_StdDevs

m_outputAdditionalStats

m_AttributeSelection

m_EliminateColinearAttributes

m_checksTurnedOff

m_useQRDecomposition

m_Ridge

m_Minimal

m_ModelBuilt

m_isZeroR

m_df

m_RSquared

m_RSquaredAdj

m_FStat

m_StdErrorOfCoef

m_TStats

Class weka.classifiers.functions.Logistic extends AbstractClassifier implements Serializable

Serialized Fields

m_Par

m_Data

m_NumPredictors

m_ClassIndex

m_NumClasses

m_Ridge

m_AttFilter

m_NominalToBinary

m_ReplaceMissingValues