class Predictor[T] extends Serializable

Predictor for distributed data

NOTE: The predictClass, predict and predictImage will call the relevant methods of object Predictor. Why we do this? Because every these methods uses the ClassTag T. If we do these jobs in the methods of classPredictor, when we do mapPartition, Spark will find all used values and do serialization. The T is the argument of constructor, the serialization will package the whole Predictor class, which contains themodel. It will send a duplicate model to the workers. So we should move these methods to object Predictor.

Linear Supertypes
Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. Predictor
  2. Serializable
  3. Serializable
  4. AnyRef
  5. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  8. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  10. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  11. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  12. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  14. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  15. def predict(dataSet: RDD[Sample[T]], batchSize: Int = -1, shareBuffer: Boolean = false): RDD[Activity]
  16. def predictClass(dataSet: RDD[Sample[T]], batchSize: Int = -1): RDD[Int]
  17. def predictImage(imageFrame: DistributedImageFrame, outputLayer: String = null, shareBuffer: Boolean = false, predictKey: String = ImageFeature.predict): DistributedImageFrame

    model predict DistributedImageFrame, return imageFrame with predicted tensor

    model predict DistributedImageFrame, return imageFrame with predicted tensor

    imageFrame

    imageFrame that contains images

    outputLayer

    if outputLayer is not null, the output of layer that matches outputLayer will be used as predicted output

    shareBuffer

    whether to share same memory for each batch predict results

    predictKey

    key to store predicted result

  18. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  19. def toString(): String
    Definition Classes
    AnyRef → Any
  20. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  21. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  22. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped