Packages

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

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

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.

Annotations
@deprecated
Deprecated

(Since version 0.5.0)

Linear Supertypes
com.intel.analytics.bigdl.dlframes.DLModel[T], DLParams[T], HasBatchSize, VectorCompatibility, HasPredictionCol, HasPredictionCol, HasFeaturesCol, HasFeaturesCol, DLTransformerBase[com.intel.analytics.bigdl.dlframes.DLModel[T]], Model[com.intel.analytics.bigdl.dlframes.DLModel[T]], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. DLModel
  2. DLModel
  3. DLParams
  4. HasBatchSize
  5. VectorCompatibility
  6. HasPredictionCol
  7. HasPredictionCol
  8. HasFeaturesCol
  9. HasFeaturesCol
  10. DLTransformerBase
  11. Model
  12. Transformer
  13. PipelineStage
  14. Logging
  15. Params
  16. Serializable
  17. Serializable
  18. Identifiable
  19. AnyRef
  20. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

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: 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[_]): DLModel.this.type
    Definition Classes
    Params
  8. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  9. def copy(extra: ParamMap): com.intel.analytics.bigdl.dlframes.DLModel[T]
    Definition Classes
    DLModel → DLTransformerBase → Model → Transformer → PipelineStage → Params
  10. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  11. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  12. 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
  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  15. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  16. def explainParams(): String
    Definition Classes
    Params
  17. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  18. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  19. var featureSize: Array[Int]
    Definition Classes
    DLModel
  20. final val featuresCol: Param[String]
    Definition Classes
    HasFeaturesCol
  21. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  22. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  23. def getBatchSize: Int
    Definition Classes
    HasBatchSize
  24. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  25. 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
  26. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  27. def getEndWhen: Trigger
    Definition Classes
    DLParams
  28. def getFeatureSize: Array[Int]
    Definition Classes
    DLModel
  29. final def getFeaturesCol: String
    Definition Classes
    HasFeaturesCol
  30. def getLearningRate: Double
    Definition Classes
    DLParams
  31. def getLearningRateDecay: Double
    Definition Classes
    DLParams
  32. def getMaxEpoch: Int
    Definition Classes
    DLParams
  33. def getOptimMethod: OptimMethod[T]
    Definition Classes
    DLParams
  34. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  35. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  36. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  37. def getVectorSeq(row: Row, colType: DataType, index: Int): Seq[AnyVal]
    Definition Classes
    VectorCompatibility
  38. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  39. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  40. def hasParent: Boolean
    Definition Classes
    Model
  41. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  42. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  43. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  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]
    Definition Classes
    DLModel → DLModel
  65. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  66. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  67. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  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
    Definition Classes
    DLModel
  70. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  71. var parent: Estimator[com.intel.analytics.bigdl.dlframes.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
    Definition Classes
    Params
  76. def setBatchSize(value: Int): DLModel.this.type
    Definition Classes
    DLModel
  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
    Definition Classes
    DLModel
  80. def setFeaturesCol(featuresColName: String): DLModel.this.type
    Definition Classes
    DLModel
  81. def setParent(parent: Estimator[com.intel.analytics.bigdl.dlframes.DLModel[T]]): com.intel.analytics.bigdl.dlframes.DLModel[T]
    Definition Classes
    Model
  82. def setPredictionCol(value: String): DLModel.this.type
    Definition Classes
    DLModel
  83. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  84. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  85. def transform(dataset: Dataset[_]): DataFrame
    Definition Classes
    DLTransformerBase → Transformer
  86. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  87. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @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 → DLModel → Identifiable
  91. val validVectorTypes: Seq[UserDefinedType[_ >: Vector with Vector <: Serializable] { def sqlType: org.apache.spark.sql.types.StructType }]
    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. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  94. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  95. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

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

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[com.intel.analytics.bigdl.dlframes.DLModel[T]]

Inherited from Model[com.intel.analytics.bigdl.dlframes.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