Package

com.intel.analytics.bigdl

dlframes

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package dlframes

Visibility
  1. Public
  2. All

Type Members

  1. class DLImageTransformer extends DLTransformerBase[Nothing] with HasInputCol with HasOutputCol

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    Provides DataFrame-based API for image pre-processing and feature transformation.

    Provides DataFrame-based API for image pre-processing and feature transformation. DLImageTransformer follows the Spark Transformer API pattern and can be used as one stage in Spark ML pipeline.

    The input column can be either DLImageSchema.byteSchema or DLImageSchema.floatSchema. If using DLImageReader, the default format is DLImageSchema.byteSchema The output column is always DLImageSchema.floatSchema.

  2. class DLClassifier[T] extends DLEstimator[T]

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    DLClassifier is a specialized DLEstimator that simplifies the data format for classification tasks.

    DLClassifier is a specialized DLEstimator that simplifies the data format for classification tasks. It only supports label column of DoubleType. and the fitted DLClassifierModel will have the prediction column of DoubleType.

    Annotations
    @deprecated
    Deprecated

    (Since version 0.10.0)

  3. class DLClassifierModel[T] extends DLModel[T]

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    DLClassifierModel is a specialized DLModel for classification tasks.

    DLClassifierModel is a specialized DLModel for classification tasks. The prediction column will have the datatype of Double.

    Annotations
    @deprecated
    Deprecated

    (Since version 0.10.0)

  4. class DLEstimator[T] extends DLEstimatorBase[DLEstimator[T], DLModel[T]] with DLParams[T]

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    DLEstimator helps to train a BigDL Model with the Spark ML Estimator/Transfomer pattern, thus Spark users can conveniently fit BigDL into Spark ML pipeline.

    DLEstimator helps to train a BigDL Model with the Spark ML Estimator/Transfomer pattern, thus Spark users can conveniently fit BigDL into Spark ML pipeline.

    DLEstimator supports feature and label data in the format of Array[Double], Array[Float], org.apache.spark.mllib.linalg.{Vector, VectorUDT}, org.apache.spark.ml.linalg.{Vector, VectorUDT}, Double and Float.

    User should specify the feature data dimensions and label data dimensions via the constructor parameters featureSize and labelSize respectively. Internally the feature and label data are converted to BigDL tensors, to further train a BigDL model efficiently.

    For details usage, please refer to examples in package com.intel.analytics.bigdl.example.MLPipeline

    Annotations
    @deprecated
    Deprecated

    (Since version 0.10.0)

  5. class DLModel[T] extends DLTransformerBase[DLModel[T]] with DLParams[T] with HasBatchSize

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    DLModel helps embed a BigDL model into a Spark Transformer, thus Spark users can conveniently merge BigDL into Spark ML pipeline.

    DLModel helps embed a BigDL model into a Spark Transformer, thus Spark users can conveniently merge BigDL into Spark ML pipeline. DLModel supports feature data in the format of Array[Double], Array[Float], org.apache.spark.mllib.linalg.{Vector, VectorUDT}, org.apache.spark.ml.linalg.{Vector, VectorUDT}, Double and Float. Internally DLModel use features column as storage of the feature data, and create Tensors according to the constructor parameter featureSize.

    DLModel is compatible with both spark 1.5-plus and 2.0 by extending ML Transformer.

    Annotations
    @deprecated
    Deprecated

    (Since version 0.10.0)

Value Members

  1. object DLImageReader

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    Primary DataFrame-based image loading interface, defining API to read images into DataFrame.

  2. object DLImageSchema

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    Definition for image data in a DataFrame

  3. object DLModel extends Serializable

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