object Optimizer

Linear Supertypes
AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. Optimizer
  2. AnyRef
  3. 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. 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

  5. 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

  6. 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

  7. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  8. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  9. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  10. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  11. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  12. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  13. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  14. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  15. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  16. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  17. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  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 AnyRef

Inherited from Any

Ungrouped