Class

org.apache.spark.ml

DLClassifier

Related Doc: package ml

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class DLClassifier[T] extends com.intel.analytics.bigdl.dlframes.DLClassifier[T]

Deprecated. Please refer to package com.intel.analytics.bigdl.dlframes.

DLClassifier is a specialized DLEstimator that simplifies the data format for classification tasks. It only supports label column of DoubleType. and the fitted DLClassifierModel will have the prediction column of DoubleType.

Annotations
@deprecated
Deprecated

(Since version 0.5.0)

Linear Supertypes
com.intel.analytics.bigdl.dlframes.DLClassifier[T], com.intel.analytics.bigdl.dlframes.DLEstimator[T], DLParams[T], HasBatchSize, VectorCompatibility, HasPredictionCol, HasPredictionCol, HasFeaturesCol, HasFeaturesCol, DLEstimatorBase[com.intel.analytics.bigdl.dlframes.DLEstimator[T], com.intel.analytics.bigdl.dlframes.DLModel[T]], HasLabelCol, Estimator[com.intel.analytics.bigdl.dlframes.DLModel[T]], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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  2. By Inheritance
Inherited
  1. DLClassifier
  2. DLClassifier
  3. DLEstimator
  4. DLParams
  5. HasBatchSize
  6. VectorCompatibility
  7. HasPredictionCol
  8. HasPredictionCol
  9. HasFeaturesCol
  10. HasFeaturesCol
  11. DLEstimatorBase
  12. HasLabelCol
  13. Estimator
  14. PipelineStage
  15. Logging
  16. Params
  17. Serializable
  18. Serializable
  19. Identifiable
  20. AnyRef
  21. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new DLClassifier(model: Module[T], criterion: Criterion[T], featureSize: Array[Int], uid: String = ...)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

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    model

    BigDL module to be optimized

    criterion

    BigDL criterion method

    featureSize

    The size (Tensor dimensions) of the feature data.

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

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    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  6. final val batchSize: Param[Int]

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    Definition Classes
    HasBatchSize
  7. final def clear(param: Param[_]): DLClassifier.this.type

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    Definition Classes
    Params
  8. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. def copy(extra: ParamMap): com.intel.analytics.bigdl.dlframes.DLClassifier[T]

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    Definition Classes
    DLClassifierDLEstimator → DLEstimatorBase → Estimator → PipelineStage → Params
  10. def copyValues[T <: Params](to: T, extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params
  11. val criterion: Criterion[T]

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    BigDL criterion method

    BigDL criterion method

    Definition Classes
    DLClassifierDLClassifierDLEstimator
  12. final def defaultCopy[T <: Params](extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params
  13. final val endWhen: Param[Trigger]

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    When to stop the training, passed in a Trigger.

    When to stop the training, passed in a Trigger. E.g. Trigger.maxIterations

    Definition Classes
    DLParams
  14. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  15. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  16. def explainParam(param: Param[_]): String

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    Definition Classes
    Params
  17. def explainParams(): String

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    Definition Classes
    Params
  18. final def extractParamMap(): ParamMap

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    Definition Classes
    Params
  19. final def extractParamMap(extra: ParamMap): ParamMap

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    Definition Classes
    Params
  20. val featureSize: Array[Int]

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    The size (Tensor dimensions) of the feature data.

    The size (Tensor dimensions) of the feature data.

    Definition Classes
    DLClassifierDLClassifierDLEstimator
  21. final val featuresCol: Param[String]

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    Definition Classes
    HasFeaturesCol
  22. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  23. def fit(dataset: Dataset[_]): com.intel.analytics.bigdl.dlframes.DLModel[T]

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    Definition Classes
    DLEstimatorBase → Estimator
  24. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[com.intel.analytics.bigdl.dlframes.DLModel[T]]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  25. def fit(dataset: Dataset[_], paramMap: ParamMap): com.intel.analytics.bigdl.dlframes.DLModel[T]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  26. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): com.intel.analytics.bigdl.dlframes.DLModel[T]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  27. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  28. def getBatchSize: Int

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    Definition Classes
    HasBatchSize
  29. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  30. def getConvertFunc(colType: DataType): (Row, Int) ⇒ Seq[AnyVal]

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    Get conversion function to extract data from original DataFrame Default: 0

    Get conversion function to extract data from original DataFrame Default: 0

    Attributes
    protected
    Definition Classes
    DLParams
  31. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  32. def getEndWhen: Trigger

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    Definition Classes
    DLParams
  33. final def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol
  34. final def getLabelCol: String

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    Definition Classes
    HasLabelCol
  35. def getLearningRate: Double

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    Definition Classes
    DLParams
  36. def getLearningRateDecay: Double

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    Definition Classes
    DLParams
  37. def getMaxEpoch: Int

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    Definition Classes
    DLParams
  38. def getOptimMethod: OptimMethod[T]

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    Definition Classes
    DLParams
  39. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params
  40. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params
  41. final def getPredictionCol: String

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    Definition Classes
    HasPredictionCol
  42. def getTrainSummary: Option[TrainSummary]

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    Definition Classes
    DLEstimator
  43. def getValidationSummary: Option[ValidationSummary]

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    Statistics (LearningRate, Loss, Throughput, Parameters) collected during training for the validation data if validation data is set, which can be used for visualization via Tensorboard.

    Statistics (LearningRate, Loss, Throughput, Parameters) collected during training for the validation data if validation data is set, which can be used for visualization via Tensorboard. Use setValidationSummary to enable validation logger. Then the log will be saved to logDir/appName/ as specified by the parameters of validationSummary.

    Default: None

    Definition Classes
    DLEstimator
  44. def getVectorSeq(row: Row, colType: DataType, index: Int): Seq[AnyVal]

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    Definition Classes
    VectorCompatibility
  45. final def hasDefault[T](param: Param[T]): Boolean

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    Definition Classes
    Params
  46. def hasParam(paramName: String): Boolean

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    Definition Classes
    Params
  47. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  48. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  49. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  50. def internalFit(dataFrame: DataFrame): com.intel.analytics.bigdl.dlframes.DLModel[T]

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    Attributes
    protected
    Definition Classes
    DLEstimator → DLEstimatorBase
  51. final def isDefined(param: Param[_]): Boolean

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    Definition Classes
    Params
  52. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  53. final def isSet(param: Param[_]): Boolean

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    Definition Classes
    Params
  54. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  55. final val labelCol: Param[String]

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    Definition Classes
    HasLabelCol
  56. val labelSize: Array[Int]

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    The size (Tensor dimensions) of the label data.

    The size (Tensor dimensions) of the label data.

    Definition Classes
    DLEstimator
  57. final val learningRate: DoubleParam

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    learning rate for the optimizer in the DLEstimator.

    learning rate for the optimizer in the DLEstimator. Default: 0.001

    Definition Classes
    DLParams
  58. final val learningRateDecay: DoubleParam

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    learning rate decay for each iteration.

    learning rate decay for each iteration. Default: 0

    Definition Classes
    DLParams
  59. def log: Logger

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    Attributes
    protected
    Definition Classes
    Logging
  60. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  61. def logDebug(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  62. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  63. def logError(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  64. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  65. def logInfo(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  66. def logName: String

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    Attributes
    protected
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    Logging
  67. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  68. def logTrace(msg: ⇒ String): Unit

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    Attributes
    protected
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    Logging
  69. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
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    Logging
  70. def logWarning(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  71. final val maxEpoch: IntParam

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    Number of max Epoch for the training, an epoch refers to a traverse over the training data Default: 50

    Number of max Epoch for the training, an epoch refers to a traverse over the training data Default: 50

    Definition Classes
    DLParams
  72. val model: Module[T]

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    BigDL module to be optimized

    BigDL module to be optimized

    Definition Classes
    DLClassifierDLClassifierDLEstimator
  73. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  74. final def notify(): Unit

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    Definition Classes
    AnyRef
  75. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  76. final val optimMethod: Param[OptimMethod[T]]

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    optimization method to be used.

    optimization method to be used. BigDL supports many optimization methods like Adam, SGD and LBFGS. Refer to package com.intel.analytics.bigdl.optim for all the options. Default: SGD

    Definition Classes
    DLParams
  77. lazy val params: Array[Param[_]]

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    Definition Classes
    Params
  78. final val predictionCol: Param[String]

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    Definition Classes
    HasPredictionCol
  79. final def set(paramPair: ParamPair[_]): DLClassifier.this.type

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    Attributes
    protected
    Definition Classes
    Params
  80. final def set(param: String, value: Any): DLClassifier.this.type

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    Attributes
    protected
    Definition Classes
    Params
  81. final def set[T](param: Param[T], value: T): DLClassifier.this.type

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    Definition Classes
    Params
  82. def setBatchSize(value: Int): DLClassifier.this.type

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    Definition Classes
    DLEstimator
  83. final def setDefault(paramPairs: ParamPair[_]*): DLClassifier.this.type

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    Attributes
    protected
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    Params
  84. final def setDefault[T](param: Param[T], value: T): DLClassifier.this.type

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    Attributes
    protected
    Definition Classes
    Params
  85. def setEndWhen(trigger: Trigger): DLClassifier.this.type

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    Definition Classes
    DLEstimator
  86. def setFeaturesCol(featuresColName: String): DLClassifier.this.type

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    Definition Classes
    DLEstimator
  87. def setLabelCol(labelColName: String): DLClassifier.this.type

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    Definition Classes
    DLEstimator
  88. def setLearningRate(value: Double): DLClassifier.this.type

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    Definition Classes
    DLEstimator
  89. def setLearningRateDecay(value: Double): DLClassifier.this.type

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    Definition Classes
    DLEstimator
  90. def setMaxEpoch(value: Int): DLClassifier.this.type

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    Definition Classes
    DLEstimator
  91. def setOptimMethod(value: OptimMethod[T]): DLClassifier.this.type

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    Definition Classes
    DLEstimator
  92. def setPredictionCol(value: String): DLClassifier.this.type

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    Definition Classes
    DLEstimator
  93. def setTrainSummary(value: TrainSummary): DLClassifier.this.type

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    Statistics (LearningRate, Loss, Throughput, Parameters) collected during training for the training data, which can be used for visualization via Tensorboard.

    Statistics (LearningRate, Loss, Throughput, Parameters) collected during training for the training data, which can be used for visualization via Tensorboard. Use setTrainSummary to enable train logger. Then the log will be saved to logDir/appName/train as specified by the parameters of TrainSummary.

    Default: Not enabled

    Definition Classes
    DLEstimator
  94. def setValidation(trigger: Trigger, validationDF: DataFrame, vMethods: Array[ValidationMethod[T]], batchSize: Int): DLClassifier.this.type

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    Set a validate evaluation during training

    Set a validate evaluation during training

    trigger

    how often to evaluation validation set

    validationDF

    validate data set

    vMethods

    a set of validation method ValidationMethod

    batchSize

    batch size for validation

    returns

    this optimizer

    Definition Classes
    DLEstimator
  95. def setValidationSummary(value: ValidationSummary): DLClassifier.this.type

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    Enable validation Summary

    Enable validation Summary

    Definition Classes
    DLEstimator
  96. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  97. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  98. def transformSchema(schema: StructType): StructType

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    Definition Classes
    DLClassifierDLEstimator → PipelineStage
  99. def transformSchema(schema: StructType, logging: Boolean): StructType

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    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  100. val uid: String

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    Definition Classes
    DLClassifierDLClassifierDLEstimator → Identifiable
  101. val validVectorTypes: Seq[UserDefinedType[_ >: Vector with Vector <: Serializable] { def sqlType: org.apache.spark.sql.types.StructType }]

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    Definition Classes
    VectorCompatibility
  102. def validateDataType(schema: StructType, colName: String): Unit

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    Validate if feature and label columns are of supported data types.

    Validate if feature and label columns are of supported data types. Default: 0

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    protected
    Definition Classes
    DLParams
  103. def validateParams(schema: StructType): Unit

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    Attributes
    protected
    Definition Classes
    DLEstimator
  104. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  105. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  106. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  107. def wrapBigDLModel(m: Module[T], featureSize: Array[Int]): DLClassifierModel[T]

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    sub classes can extend the method and return required model for different transform tasks

    sub classes can extend the method and return required model for different transform tasks

    Attributes
    protected
    Definition Classes
    DLClassifierDLClassifierDLEstimator

Inherited from DLParams[T]

Inherited from HasBatchSize

Inherited from VectorCompatibility

Inherited from HasPredictionCol

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Estimator[com.intel.analytics.bigdl.dlframes.DLModel[T]]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Ungrouped