NNModel

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[F, T](model: Module[T], featurePreprocessing: Preprocessing[F, Tensor[T]])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): NNModel[T]

   

   Construct a NNModel with a feature Preprocessing.

   Construct a NNModel with a feature Preprocessing.

   model

   BigDL module to be optimized

   featurePreprocessing

   Preprocessing[F, Tensor[T] ].

5. def apply[T](model: Module[T], featureSize: Array[Int])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): NNModel[T]

   

   Construct a NNModel with a feature size.

   Construct a NNModel with a feature size.

   model

   BigDL module to be optimized

   featureSize

   The size (Tensor dimensions) of the feature data. e.g. an image may be with width * height = 28 * 28, featureSize = Array(28, 28).

6. def apply[T](model: Module[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): NNModel[T]

   

   Construct a NNModel with default Preprocessing: SeqToTensor

   Construct a NNModel with default Preprocessing: SeqToTensor

   model

   BigDL module to be optimized

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. implicit val format: DefaultFormats.type

    

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

    

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14. def hashCode(): Int

    

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15. final def isInstanceOf[T0]: Boolean

    

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16. def load(path: String): NNModel[_]

    

    Definition Classes

    NNModel → MLReadable

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

    

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18. final def notify(): Unit

    

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19. final def notifyAll(): Unit

    

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20. def read: MLReader[NNModel[_]]

    

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    NNModel → MLReadable

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

    

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22. def toString(): String

    

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23. final def wait(): Unit

    

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

    

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

    

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