Optimizer

Value Members

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

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

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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

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

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

   Apply an optimizer with User-Defined MiniBatch.

   Apply an optimizer with User-Defined MiniBatch.

   model

   model will be optimizied

   sampleRDD

   training Samples

   criterion

   loss function

   batchSize

   mini batch size

   miniBatch

   An User-Defined MiniBatch to construct a mini batch.

   returns

   an Optimizer

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

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

   Apply an Optimizer who could apply padding to the Samples with a padding strategy.

   Apply an Optimizer who could apply padding to the Samples with a padding strategy.

   model

   model will be optimizied

   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

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

11. final def asInstanceOf[T0]: T0

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12. def clone(): AnyRef

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

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14. def equals(arg0: Any): Boolean

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15. def finalize(): Unit

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

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17. def hashCode(): Int

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18. final def isInstanceOf[T0]: Boolean

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

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20. final def notify(): Unit

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21. final def notifyAll(): Unit

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

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23. def toString(): String

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24. final def wait(): Unit

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

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

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