NNEstimator

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

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3. final def ##(): Int

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6. def apply[F, T](model: Module[T], criterion: Criterion[T], featurePreprocessing: Preprocessing[F, Sample[T]])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): NNEstimator[T]

   Construct a NNEstimator with a featurePreprocessing only.

   Construct a NNEstimator with a featurePreprocessing only. The constructor is useful when both feature and label are derived from the same column of the original DataFrame.

   model

   BigDL module to be optimized

   criterion

   BigDL criterion method

   featurePreprocessing

   A Preprocessing that transforms the feature data to a Sample[T].

7. def apply[F, L, T](model: Module[T], criterion: Criterion[T], featurePreprocessing: Preprocessing[F, Tensor[T]], labelPreprocessing: Preprocessing[L, Tensor[T]])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): NNEstimator[T]

   Construct a NNEstimator with a feature Preprocessing and label Preprocessing.

   Construct a NNEstimator with a feature Preprocessing and label Preprocessing.

   model

   BigDL module to be optimized

   criterion

   BigDL criterion method

   featurePreprocessing

   Preprocessing[Any, Tensor[T] ]

   labelPreprocessing

   Preprocessing[Any, Tensor[T] ]

8. def apply[T](model: Module[T], criterion: Criterion[T], featureSize: Array[Int], labelSize: Array[Int])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): NNEstimator[T]

   Construct a NNEstimator with a feature size and label size.

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

   model

   BigDL module to be optimized

   criterion

   BigDL criterion method

   featureSize

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

   labelSize

   The size (Tensor dimensions) of the label data.

9. def apply[T](model: Module[T], criterion: Criterion[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): NNEstimator[T]

   Construct a NNEstimator with default Preprocessing: A SeqToTensor

   Construct a NNEstimator with default Preprocessing: A SeqToTensor

   model

   BigDL module to be optimized

   criterion

   BigDL criterion method

10. final def asInstanceOf[T0]: T0

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14. def finalize(): Unit

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

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19. final def notify(): Unit

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20. final def notifyAll(): Unit

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

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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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