Class/Object

com.intel.analytics.zoo.pipeline.nnframes

NNModel

Related Docs: object NNModel | package nnframes

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class NNModel[T] extends DLTransformerBase[NNModel[T]] with NNParams[T] with HasBatchSize with MLWritable

NNModel extends Spark ML Transformer and supports BigDL model with Spark DataFrame data.

NNModel supports different feature data type through Preprocessing. We provide pre-defined Preprocessing for popular data types like Array or Vector in package com.intel.analytics.zoo.feature, while user can also develop customized Preprocessing. During transform, NNModel will extract feature data from input DataFrame and use the Preprocessing to prepare data for the model.

After transform, the prediction column contains the output of the model as Array[T], where T (Double or Float) is decided by the model type.

Linear Supertypes
MLWritable, NNParams[T], VectorCompatibility, HasBatchSize, HasPredictionCol, HasPredictionCol, HasFeaturesCol, HasFeaturesCol, DLTransformerBase[NNModel[T]], Model[NNModel[T]], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Known Subclasses
NNClassifierModel
Ordering
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Inherited
  1. NNModel
  2. MLWritable
  3. NNParams
  4. VectorCompatibility
  5. HasBatchSize
  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
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Value Members

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

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    Definition Classes
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  2. final def ##(): Int

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

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    Params

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

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  5. final def asInstanceOf[T0]: T0

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    Any

  6. final val batchSize: IntParam

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    Global batch size across the cluster.

    Global batch size across the cluster.

    Definition Classes
    HasBatchSize

  7. final def clear(param: Param[_]): NNModel.this.type

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    Definition Classes
    Params

  8. def clone(): AnyRef

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

  9. def copy(extra: ParamMap): NNModel[T]

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

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

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    Params

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

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

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    AnyRef

  13. def equals(arg0: Any): Boolean

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

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    Params

  15. def explainParams(): String

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    Definition Classes
    Params

  16. final def extractParamMap(): ParamMap

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    Definition Classes
    Params

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

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    Definition Classes
    Params

  18. final val featuresCol: Param[String]

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    Definition Classes
    HasFeaturesCol

  19. def finalize(): Unit

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    @throws( classOf[java.lang.Throwable] )

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

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    Definition Classes
    Params

  21. def getBatchSize: Int

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    Definition Classes
    HasBatchSize

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

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    Definition Classes
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  23. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params

  24. final def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol

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

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    Params

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

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    Definition Classes
    Params

  27. final def getPredictionCol: String

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    Definition Classes
    HasPredictionCol

  28. def getSamplePreprocessing: Preprocessing[Any, Sample[T]]

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    Definition Classes
    NNParams

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

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    Definition Classes
    VectorCompatibility

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

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    Params

  31. def hasParam(paramName: String): Boolean

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    Params

  32. def hasParent: Boolean

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    Definition Classes
    Model

  33. def hashCode(): Int

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  34. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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    Logging

  35. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Logging

  36. def internalTransform(dataFrame: DataFrame): DataFrame

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    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
    NNModel → DLTransformerBase

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

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  38. final def isInstanceOf[T0]: Boolean

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

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  40. def isTraceEnabled(): Boolean

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    Logging

  41. def log: Logger

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    Logging

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

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    Logging

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

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    Logging

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

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    Logging

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

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    Logging

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

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    Logging

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

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    Logging

  48. def logName: String

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    Logging

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

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    Logging

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

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    Logging

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

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    Logging

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

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    Logging

  53. val model: Module[T]

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    trained BigDL models to use in prediction.

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

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  55. final def notify(): Unit

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  56. final def notifyAll(): Unit

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

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    Attributes
    protected

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

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    Definition Classes
    Params

  59. var parent: Estimator[NNModel[T]]

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    Definition Classes
    Model

  60. final val predictionCol: Param[String]

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    Definition Classes
    HasPredictionCol

  61. final val samplePreprocessing: Param[Preprocessing[Any, Sample[T]]]

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    Definition Classes
    NNParams

  62. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )

  63. final def set(paramPair: ParamPair[_]): NNModel.this.type

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  64. final def set(param: String, value: Any): NNModel.this.type

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

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    Params

  66. def setBatchSize(value: Int): NNModel.this.type

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    Set global batch size across the cluster.

    Set global batch size across the cluster. Global batch size = Batch per thread * num of cores.

  67. final def setDefault(paramPairs: ParamPair[_]*): NNModel.this.type

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

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  69. def setFeaturesCol(featuresColName: String): NNModel.this.type

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  70. def setParent(parent: Estimator[NNModel[T]]): NNModel[T]

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    Definition Classes
    Model

  71. def setPredictionCol(value: String): NNModel.this.type

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  72. def setSamplePreprocessing[FF](value: Preprocessing[FF, Sample[T]]): NNModel.this.type

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    set Preprocessing.

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

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    Definition Classes
    AnyRef

  74. def toString(): String

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    Identifiable → AnyRef → Any

  75. def transform(dataset: Dataset[_]): DataFrame

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    DLTransformerBase → Transformer

  76. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

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    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )

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

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    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()

  78. def transformSchema(schema: StructType): StructType

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    Definition Classes
    NNModel → PipelineStage

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

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    PipelineStage
    Annotations
    @DeveloperApi()

  80. val uid: String

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    Definition Classes
    NNModel → Identifiable

  81. def unwrapVectorAsNecessary(colType: DataType): (Row, Int) ⇒ Any

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    Attributes
    protected
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    NNParams

  82. val validVectorTypes: Seq[UserDefinedType[_ >: Vector with Vector <: Serializable] { def sqlType: org.apache.spark.sql.types.StructType }]

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    Definition Classes
    VectorCompatibility

  83. final def wait(): Unit

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    @throws( ... )

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

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

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    @throws( ... )

  86. def write: MLWriter

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    Definition Classes
    NNModel → MLWritable

Inherited from MLWritable

Inherited from NNParams[T]

Inherited from VectorCompatibility

Inherited from HasBatchSize

Inherited from HasPredictionCol

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasFeaturesCol

Inherited from DLTransformerBase[NNModel[T]]

Inherited from Model[NNModel[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