Optimizer

Value Members

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

   

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2. final def ##(): Int

   

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

   

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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

   

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8. def clone(): AnyRef

   

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

   

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10. def equals(arg0: Any): Boolean

    

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11. def finalize(): Unit

    

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

    

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13. def hashCode(): Int

    

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14. final def isInstanceOf[T0]: Boolean

    

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

    

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16. final def notify(): Unit

    

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17. final def notifyAll(): Unit

    

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

    

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19. def toString(): String

    

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20. final def wait(): Unit

    

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

    

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

    

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