Packages

class DLEstimator[T] extends DLEstimatorBase[DLEstimator[T], DLModel[T]] with DLParams[T]

DLEstimator helps to train a BigDL Model with the Spark ML Estimator/Transfomer pattern, thus Spark users can conveniently fit BigDL into Spark ML pipeline.

DLEstimator supports feature and label data in the format of Array[Double], Array[Float], org.apache.spark.mllib.linalg.{Vector, VectorUDT}, org.apache.spark.ml.linalg.{Vector, VectorUDT}, Double and Float.

User should specify the feature data dimensions and label data dimensions via the constructor parameters featureSize and labelSize respectively. Internally the feature and label data are converted to BigDL tensors, to further train a BigDL model efficiently.

For details usage, please refer to examples in package com.intel.analytics.bigdl.example.MLPipeline

Annotations
@deprecated
Deprecated

(Since version 0.10.0)

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

Instance Constructors

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

    model

    BigDL module to be optimized

    criterion

    BigDL criterion method

    featureSize

    The size (Tensor dimensions) of the feature data. e.g. an image may be with width * height = 28 * 28, featureSize = Array(28, 28).

    labelSize

    The size (Tensor dimensions) of the label data.

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. final val batchSize: Param[Int]
    Definition Classes
    HasBatchSize
  7. final def clear(param: Param[_]): DLEstimator.this.type
    Definition Classes
    Params
  8. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  9. def copy(extra: ParamMap): DLEstimator[T]
    Definition Classes
    DLEstimator → DLEstimatorBase → Estimator → PipelineStage → Params
  10. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  11. val criterion: Criterion[T]
  12. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  13. final val endWhen: Param[Trigger]

    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
    Definition Classes
    AnyRef
  15. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  16. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  17. def explainParams(): String
    Definition Classes
    Params
  18. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  19. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  20. val featureSize: Array[Int]
  21. final val featuresCol: Param[String]
    Definition Classes
    HasFeaturesCol
  22. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  23. def fit(dataset: Dataset[_]): DLModel[T]
    Definition Classes
    DLEstimatorBase → Estimator
  24. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[DLModel[T]]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  25. def fit(dataset: Dataset[_], paramMap: ParamMap): DLModel[T]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  26. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DLModel[T]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  27. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  28. def getBatchSize: Int
    Definition Classes
    HasBatchSize
  29. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  30. def getConvertFunc(colType: DataType): (Row, Int) ⇒ Seq[AnyVal]

    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]
    Definition Classes
    Params
  32. def getEndWhen: Trigger
    Definition Classes
    DLParams
  33. final def getFeaturesCol: String
    Definition Classes
    HasFeaturesCol
  34. final def getLabelCol: String
    Definition Classes
    HasLabelCol
  35. def getLearningRate: Double
    Definition Classes
    DLParams
  36. def getLearningRateDecay: Double
    Definition Classes
    DLParams
  37. def getMaxEpoch: Int
    Definition Classes
    DLParams
  38. def getOptimMethod: OptimMethod[T]
    Definition Classes
    DLParams
  39. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  40. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  41. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  42. def getTrainSummary: Option[TrainSummary]
  43. def getValidationSummary: Option[ValidationSummary]

    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

  44. def getVectorSeq(row: Row, colType: DataType, index: Int): Seq[AnyVal]
    Definition Classes
    VectorCompatibility
  45. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  46. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  47. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  48. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  49. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  50. def internalFit(dataFrame: DataFrame): DLModel[T]
    Attributes
    protected
    Definition Classes
    DLEstimator → DLEstimatorBase
  51. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  52. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  53. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  54. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  55. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  56. val labelSize: Array[Int]
  57. final val learningRate: DoubleParam

    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

    learning rate decay for each iteration.

    learning rate decay for each iteration. Default: 0

    Definition Classes
    DLParams
  59. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  60. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  67. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. final val maxEpoch: IntParam

    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]
  73. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  74. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  75. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  76. final val optimMethod: Param[OptimMethod[T]]

    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[_]]
    Definition Classes
    Params
  78. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  79. final def set(paramPair: ParamPair[_]): DLEstimator.this.type
    Attributes
    protected
    Definition Classes
    Params
  80. final def set(param: String, value: Any): DLEstimator.this.type
    Attributes
    protected
    Definition Classes
    Params
  81. final def set[T](param: Param[T], value: T): DLEstimator.this.type
    Definition Classes
    Params
  82. def setBatchSize(value: Int): DLEstimator.this.type
  83. final def setDefault(paramPairs: ParamPair[_]*): DLEstimator.this.type
    Attributes
    protected
    Definition Classes
    Params
  84. final def setDefault[T](param: Param[T], value: T): DLEstimator.this.type
    Attributes
    protected
    Definition Classes
    Params
  85. def setEndWhen(trigger: Trigger): DLEstimator.this.type
  86. def setFeaturesCol(featuresColName: String): DLEstimator.this.type
  87. def setLabelCol(labelColName: String): DLEstimator.this.type
  88. def setLearningRate(value: Double): DLEstimator.this.type
  89. def setLearningRateDecay(value: Double): DLEstimator.this.type
  90. def setMaxEpoch(value: Int): DLEstimator.this.type
  91. def setOptimMethod(value: OptimMethod[T]): DLEstimator.this.type
  92. def setPredictionCol(value: String): DLEstimator.this.type
  93. def setTrainSummary(value: TrainSummary): DLEstimator.this.type

    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

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

    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

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

    Enable validation Summary

  96. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  97. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  98. def transformSchema(schema: StructType): StructType
    Definition Classes
    DLEstimator → PipelineStage
  99. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  100. val uid: String
    Definition Classes
    DLEstimator → Identifiable
  101. val validVectorTypes: Seq[UserDefinedType[_ >: Vector with Vector <: Serializable] { def sqlType: org.apache.spark.sql.types.StructType }]
    Definition Classes
    VectorCompatibility
  102. def validateDataType(schema: StructType, colName: String): Unit

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

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

    Attributes
    protected
    Definition Classes
    DLParams
  103. def validateParams(schema: StructType): Unit
    Attributes
    protected
  104. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  105. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  106. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  107. def wrapBigDLModel(m: Module[T], featureSize: Array[Int]): DLModel[T]

    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

Inherited from DLParams[T]

Inherited from HasBatchSize

Inherited from VectorCompatibility

Inherited from HasPredictionCol

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasFeaturesCol

Inherited from DLEstimatorBase[DLEstimator[T], DLModel[T]]

Inherited from HasLabelCol

Inherited from Estimator[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