kr.ac.kaist.ir.deep.train

DistBeliefCriteria

case class DistBeliefCriteria(miniBatch: Int = 100, negSamplingRatio: Int = 0, submitInterval: Duration = 1.minute, updateStep: Int = 2, fetchStep: Int = 10, numCores: Int = 1, repartitionOnStart: Boolean = false, storageLevel: StorageLevel = StorageLevel.DISK_ONLY_2) extends TrainingCriteria with Product with Serializable

Criteria: How to train (for DistBeliefTrainStyle)

This case class defines how to train the network. Training parameter is defined in this class.

miniBatch

size of mini-batch (default 100)

negSamplingRatio

ratio of negative samples per positive instance. (default 0)

submitInterval

Time interval between batch submission. (default 1.minute)

updateStep

number of mini-batches between update (default 2)

fetchStep

number of mini-batches between fetching (default 10)

numCores

number of v-cores in the spark cluster. (default 1)

repartitionOnStart

true if do repartition when define training/testing RDD instances. (default false)

storageLevel

StorageLevel that will be used in Spark. (default DISK_ONLY_2)

Note

We recommend set numCores as similar as possible with allocated spark v-cores.

Linear Supertypes
Product, Equals, TrainingCriteria, Serializable, Serializable, AnyRef, Any
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  1. DistBeliefCriteria
  2. Product
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Instance Constructors

  1. new DistBeliefCriteria(miniBatch: Int = 100, negSamplingRatio: Int = 0, submitInterval: Duration = 1.minute, updateStep: Int = 2, fetchStep: Int = 10, numCores: Int = 1, repartitionOnStart: Boolean = false, storageLevel: StorageLevel = StorageLevel.DISK_ONLY_2)

    miniBatch

    size of mini-batch (default 100)

    negSamplingRatio

    ratio of negative samples per positive instance. (default 0)

    submitInterval

    Time interval between batch submission. (default 1.minute)

    updateStep

    number of mini-batches between update (default 2)

    fetchStep

    number of mini-batches between fetching (default 10)

    numCores

    number of v-cores in the spark cluster. (default 1)

    repartitionOnStart

    true if do repartition when define training/testing RDD instances. (default false)

    storageLevel

    StorageLevel that will be used in Spark. (default DISK_ONLY_2)

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
    @throws( ... )

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

    Definition Classes
    AnyRef

  7. val fetchStep: Int

    number of mini-batches between fetching (default 10)

  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

  10. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  11. val miniBatch: Int

    size of mini-batch (default 100)

    size of mini-batch (default 100)

    Definition Classes
    DistBeliefCriteria → TrainingCriteria

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

    Definition Classes
    AnyRef

  13. val negSamplingRatio: Int

    ratio of negative samples per positive instance.

    ratio of negative samples per positive instance. (default 0)

    Definition Classes
    DistBeliefCriteria → TrainingCriteria

  14. final def notify(): Unit

    Definition Classes
    AnyRef

  15. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  16. val numCores: Int

    number of v-cores in the spark cluster.

    number of v-cores in the spark cluster. (default 1)

  17. val repartitionOnStart: Boolean

    true if do repartition when define training/testing RDD instances.

    true if do repartition when define training/testing RDD instances. (default false)

  18. val storageLevel: StorageLevel

    StorageLevel that will be used in Spark.

    StorageLevel that will be used in Spark. (default DISK_ONLY_2)

  19. val submitInterval: Duration

    Time interval between batch submission.

    Time interval between batch submission. (default 1.minute)

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

    Definition Classes
    AnyRef

  21. val updateStep: Int

    number of mini-batches between update (default 2)

  22. final def wait(): Unit

    Definition Classes
    AnyRef
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    @throws( ... )

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

    Definition Classes
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    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Product

Inherited from Equals

Inherited from TrainingCriteria

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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