Optimizer

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 ==(arg0: AnyRef): Boolean

   Definition Classes

   AnyRef

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

   Definition Classes

   Any

6. def apply[T, D](model: Module[T], dataset: DataSet[D], criterion: Criterion[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Optimizer[T, D]

   Apply an optimizer.

   Apply an optimizer.

   model

   model will be optimizied

   dataset

   the input dataset - determines the type of optimizer

   criterion

   loss function

   returns

   an new Optimizer

7. def apply[T](model: Module[T], sampleRDD: RDD[Sample[T]], criterion: Criterion[T], batchSize: Int, miniBatchImpl: MiniBatch[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Optimizer[T, MiniBatch[T]]

   Apply an optimizer.

   Apply an optimizer. User can supply a customized implementation of trait MiniBatch to define how data is organize and retrieved in a mini batch.

   model

   model will be optimized

   sampleRDD

   training Samples

   criterion

   loss function

   batchSize

   mini batch size

   miniBatchImpl

   An User-Defined MiniBatch implementation

   returns

   an new Optimizer

8. def apply[T](model: Module[T], sampleRDD: RDD[Sample[T]], criterion: Criterion[T], batchSize: Int, featurePaddingParam: PaddingParam[T] = null, labelPaddingParam: PaddingParam[T] = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Optimizer[T, MiniBatch[T]]

   Apply an Optimizer.

   Apply an Optimizer.

   model

   model will be optimized

   sampleRDD

   training Samples

   criterion

   loss function

   batchSize

   mini batch size

   featurePaddingParam

   feature padding strategy, see com.intel.analytics.bigdl.dataset.PaddingParam for details.

   labelPaddingParam

   label padding strategy, see com.intel.analytics.bigdl.dataset.PaddingParam for details.

   returns

   An optimizer

9. final def asInstanceOf[T0]: T0

   Definition Classes

   Any

10. def clone(): AnyRef

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

12. def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

13. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

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

    Definition Classes

    AnyRef → Any

15. def hashCode(): Int

    Definition Classes

    AnyRef → Any

16. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

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

    Definition Classes

    AnyRef

18. final def notify(): Unit

    Definition Classes

    AnyRef

19. final def notifyAll(): Unit

    Definition Classes

    AnyRef

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

    Definition Classes

    AnyRef

21. def toString(): String

    Definition Classes

    AnyRef → Any

22. final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )