NNModel

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[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] ].

7. 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).

8. 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

9. final def asInstanceOf[T0]: T0

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10. def clone(): AnyRef

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

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12. def equals(arg0: Any): Boolean

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13. def finalize(): Unit

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14. implicit val format: DefaultFormats.type

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

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16. def hashCode(): Int

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17. final def isInstanceOf[T0]: Boolean

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

    Definition Classes

    NNModel → MLReadable

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. def read: MLReader[NNModel[_]]

    Definition Classes

    NNModel → MLReadable

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

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24. def toString(): String

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25. final def wait(): Unit

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

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

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