NNClassifierModel

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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]): NNClassifierModel[T]

   

   Construct a NNClassifierModel with a feature Preprocessing.

   Construct a NNClassifierModel with a feature Preprocessing.

   model

   BigDL module to be optimized

   featurePreprocessing

   Preprocessing[F, Tensor[T] ].

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

   

   Construct a NNClassifierModel with sizes of multiple model inputs.

   Construct a NNClassifierModel with sizes of multiple model inputs. The constructor is useful when the feature column contains the following data types: Float, Double, Int, Array[Float], Array[Double], Array[Int] and MLlib Vector. The feature data are converted to Tensors with the specified sizes before sending to the model.

   This API is used for multi-input model, where user need to specify the tensor sizes for each of the model input.

   model

   model to be used, which should be a multi-input model.

   featureSize

   The sizes (Tensor dimensions) of the feature data.

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

   

   Construct a NNClassifierModel with a feature size.

   Construct a NNClassifierModel with a feature size. The constructor is useful when the feature column contains the following data types: Float, Double, Int, Array[Float], Array[Double], Array[Int] and MLlib Vector. The feature data are converted to Tensors with the specified sizes before sending to the model.

   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).

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

   

   Construct a NNClassifierModel with default Preprocessing, SeqToTensor

   Construct a NNClassifierModel with default Preprocessing, SeqToTensor

   model

   BigDL module to be optimized

8. final def asInstanceOf[T0]: T0

   

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9. def clone(): AnyRef

   

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

    

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11. def equals(arg0: Any): Boolean

    

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12. def finalize(): Unit

    

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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): NNClassifierModel[_]

    

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    MLReadable

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    @Since( "1.6.0" )

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[NNClassifierModel[_]]

    

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    NNClassifierModel → 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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