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com.intel.analytics.zoo.pipeline.nnframes

NNClassifier

Related Docs: class NNClassifier | package nnframes

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object NNClassifier extends Serializable

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

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  4. def apply[F, T](model: Module[T], criterion: Criterion[T], featurePreprocessing: Preprocessing[F, Tensor[T]])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): NNClassifier[T]

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    Construct a NNClassifier with a feature Preprocessing.

    Construct a NNClassifier with a feature Preprocessing.

    model

    BigDL module to be optimized

    criterion

    BigDL criterion method

    featurePreprocessing

    Preprocessing[F, Tensor[T] ].

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

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    Construct a NNClassifier with a feature size.

    Construct a NNClassifier 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

    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).

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

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    Construct a NNClassifier with default Preprocessing, SeqToTensor

    Construct a NNClassifier with default Preprocessing, SeqToTensor

    model

    BigDL module to be optimized

    criterion

    BigDL criterion method

  7. final def asInstanceOf[T0]: T0

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