com.intel.analytics.bigdl.dlframes

DLModel

class DLModel[T] extends DLTransformerBase[DLModel[T]] with DLParams[T] with HasBatchSize

DLModel helps embed a BigDL model into a Spark Transformer, thus Spark users can conveniently merge BigDL into Spark ML pipeline. DLModel supports feature 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. Internally DLModel use features column as storage of the feature data, and create Tensors according to the constructor parameter featureSize.

DLModel is compatible with both spark 1.5-plus and 2.0 by extending ML Transformer.

Linear Supertypes
DLParams[T], HasBatchSize, VectorCompatibility, HasPredictionCol, HasPredictionCol, HasFeaturesCol, HasFeaturesCol, DLTransformerBase[DLModel[T]], Model[DLModel[T]], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Known Subclasses
DLClassifierModel, DLClassifierModel, DLModel
Ordering
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Inherited
  1. DLModel
  2. DLParams
  3. HasBatchSize
  4. VectorCompatibility
  5. HasPredictionCol
  6. HasPredictionCol
  7. HasFeaturesCol
  8. HasFeaturesCol
  9. DLTransformerBase
  10. Model
  11. Transformer
  12. PipelineStage
  13. Logging
  14. Params
  15. Serializable
  16. Serializable
  17. Identifiable
  18. AnyRef
  19. Any
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Visibility
  1. Public
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Instance Constructors

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

    model

    trainned BigDL models to use in prediction.

    featureSize

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

Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

  4. final def $[T](param: Param[T]): T

    Attributes
    protected
    Definition Classes
    Params

  5. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  6. final def ==(arg0: Any): Boolean

    Definition Classes
    Any

  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  8. final val batchSize: Param[Int]

    Definition Classes
    HasBatchSize

  9. final def clear(param: Param[_]): DLModel.this.type

    Attributes
    protected
    Definition Classes
    Params

  10. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  11. def copy(extra: ParamMap): DLModel[T]

    Definition Classes
    DLModel → DLTransformerBase → Model → Transformer → PipelineStage → Params

  12. def copyValues[T <: Params](to: T, extra: ParamMap): T

    Attributes
    protected
    Definition Classes
    Params

  13. final def defaultCopy[T <: Params](extra: ParamMap): T

    Attributes
    protected
    Definition Classes
    Params

  14. 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

  15. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  16. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  17. def explainParam(param: Param[_]): String

    Definition Classes
    Params

  18. def explainParams(): String

    Definition Classes
    Params

  19. final def extractParamMap(): ParamMap

    Definition Classes
    Params

  20. final def extractParamMap(extra: ParamMap): ParamMap

    Definition Classes
    Params

  21. var featureSize: Array[Int]

    The size (Tensor dimensions) of the feature data.

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

  22. final val featuresCol: Param[String]

    Definition Classes
    HasFeaturesCol

  23. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  24. final def get[T](param: Param[T]): Option[T]

    Definition Classes
    Params

  25. def getBatchSize: Int

    Definition Classes
    HasBatchSize

  26. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any

  27. 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

  28. final def getDefault[T](param: Param[T]): Option[T]

    Definition Classes
    Params

  29. def getEndWhen: Trigger

    Definition Classes
    DLParams

  30. def getFeatureSize: Array[Int]

  31. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol

  32. def getLearningRate: Double

    Definition Classes
    DLParams

  33. def getLearningRateDecay: Double

    Definition Classes
    DLParams

  34. def getMaxEpoch: Int

    Definition Classes
    DLParams

  35. def getOptimMethod: OptimMethod[T]

    Definition Classes
    DLParams

  36. final def getOrDefault[T](param: Param[T]): T

    Definition Classes
    Params

  37. def getParam(paramName: String): Param[Any]

    Definition Classes
    Params

  38. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol

  39. def getVectorSeq(row: Row, colType: DataType, index: Int): Seq[AnyVal]

    Definition Classes
    VectorCompatibility

  40. final def hasDefault[T](param: Param[T]): Boolean

    Definition Classes
    Params

  41. def hasParam(paramName: String): Boolean

    Definition Classes
    Params

  42. def hasParent: Boolean

    Definition Classes
    Model

  43. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  44. def internalTransform(dataFrame: DataFrame): DataFrame

    Perform a prediction on featureCol, and write result to the predictionCol.

    Perform a prediction on featureCol, and write result to the predictionCol.

    Attributes
    protected
    Definition Classes
    DLModel → DLTransformerBase

  45. final def isDefined(param: Param[_]): Boolean

    Definition Classes
    Params

  46. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  47. final def isSet(param: Param[_]): Boolean

    Definition Classes
    Params

  48. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging

  49. 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

  50. final val learningRateDecay: DoubleParam

    learning rate decay for each iteration.

    learning rate decay for each iteration. Default: 0

    Definition Classes
    DLParams

  51. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging

  52. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  53. def logDebug(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  54. def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  55. def logError(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  56. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  57. def logInfo(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  58. def logName: String

    Attributes
    protected
    Definition Classes
    Logging

  59. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  60. def logTrace(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  61. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  62. def logWarning(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  63. 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

  64. val model: Module[T]

    trainned BigDL models to use in prediction.

  65. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  66. final def notify(): Unit

    Definition Classes
    AnyRef

  67. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  68. 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

  69. def outputToPrediction(output: Tensor[T]): Any

    Attributes
    protected

  70. lazy val params: Array[Param[_]]

    Definition Classes
    Params

  71. var parent: Estimator[DLModel[T]]

    Definition Classes
    Model

  72. final val predictionCol: Param[String]

    Definition Classes
    HasPredictionCol

  73. final def set(paramPair: ParamPair[_]): DLModel.this.type

    Attributes
    protected
    Definition Classes
    Params

  74. final def set(param: String, value: Any): DLModel.this.type

    Attributes
    protected
    Definition Classes
    Params

  75. final def set[T](param: Param[T], value: T): DLModel.this.type

    Attributes
    protected
    Definition Classes
    Params

  76. def setBatchSize(value: Int): DLModel.this.type

  77. final def setDefault(paramPairs: ParamPair[_]*): DLModel.this.type

    Attributes
    protected
    Definition Classes
    Params

  78. final def setDefault[T](param: Param[T], value: T): DLModel.this.type

    Attributes
    protected
    Definition Classes
    Params

  79. def setFeatureSize(value: Array[Int]): DLModel.this.type

  80. def setFeaturesCol(featuresColName: String): DLModel.this.type

  81. def setParent(parent: Estimator[DLModel[T]]): DLModel[T]

    Definition Classes
    Model

  82. def setPredictionCol(value: String): DLModel.this.type

  83. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef

  84. def toString(): String

    Definition Classes
    Identifiable → AnyRef → Any

  85. def transform(dataFrame: DataFrame): DataFrame

    Definition Classes
    DLTransformerBase → Transformer

  86. def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame

    Definition Classes
    Transformer

  87. def transform(dataset: DataFrame, firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

    Definition Classes
    Transformer
    Annotations
    @varargs()

  88. def transformSchema(schema: StructType): StructType

    Definition Classes
    DLModel → PipelineStage

  89. def transformSchema(schema: StructType, logging: Boolean): StructType

    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()

  90. val uid: String

    Definition Classes
    DLModel → Identifiable

  91. val validVectorTypes: Seq[VectorUDT]

    Definition Classes
    VectorCompatibility

  92. 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

  93. def validateParams(): Unit

    Definition Classes
    Params

  94. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  95. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  96. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from DLParams[T]

Inherited from HasBatchSize

Inherited from VectorCompatibility

Inherited from HasPredictionCol

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasFeaturesCol

Inherited from DLTransformerBase[DLModel[T]]

Inherited from Model[DLModel[T]]

Inherited from Transformer

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