Class

org.apache.mxnet.infer

Classifier

Related Doc: package infer

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class Classifier extends ClassifierBase

A class for classifier tasks

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ClassifierBase, AnyRef, Any
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Instance Constructors

  1. new Classifier(modelPathPrefix: String, inputDescriptors: IndexedSeq[DataDesc], contexts: Array[Context] = Context.cpu(), epoch: Option[Int] = Some(0))

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    modelPathPrefix

    Path prefix from where to load the model artifacts These include the symbol, parameters, and synset.txt Example: file://model-dir/resnet-152 (containing resnet-152-symbol.json, resnet-152-0000.params, and synset.txt)

    inputDescriptors

    Descriptors defining the input node names, shape, layout and type parameters

    contexts

    Device contexts on which you want to run inference; defaults to CPU

    epoch

    Model epoch to load; defaults to 0

Value Members

  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. final def asInstanceOf[T0]: T0

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  5. def classify[T](input: IndexedSeq[Array[T]], topK: Option[Int] = None): IndexedSeq[(String, T)]

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    Takes flat arrays as input and returns (Label, Score) tuples.

    Takes flat arrays as input and returns (Label, Score) tuples.

    T

    The Scala equivalent of the DType used for the input array and return value

    input

    Indexed sequence one-dimensional array of floats/doubles

    topK

    (Optional) How many result (sorting based on the last axis) elements to return. Default returns unsorted output.

    returns

    Indexed sequence of (Label, Score) tuples

    Definition Classes
    ClassifierClassifierBase
  6. def classifyWithNDArray(input: IndexedSeq[NDArray], topK: Option[Int] = None): IndexedSeq[IndexedSeq[(String, Float)]]

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    Perform multiple classification operations on NDArrays.

    Perform multiple classification operations on NDArrays. Also works with batched input.

    input

    Indexed sequence of NDArrays

    topK

    (Optional) How many result (sorting based on the last axis) elements to return. Default returns unsorted output.

    returns

    Traversable sequence of (Label, Score) tuples.

    Definition Classes
    ClassifierClassifierBase
  7. def clone(): AnyRef

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    Attributes
    protected[java.lang]
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    AnyRef
    Annotations
    @throws( ... )
  8. val contexts: Array[Context]

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    Device contexts on which you want to run inference; defaults to CPU

    Device contexts on which you want to run inference; defaults to CPU

    Attributes
    protected
  9. val epoch: Option[Int]

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    Model epoch to load; defaults to 0

    Model epoch to load; defaults to 0

    Attributes
    protected
  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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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  13. final def getClass(): Class[_]

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  14. val handler: MXNetHandler

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    Attributes
    protected[org.apache.mxnet.infer]
  15. def hashCode(): Int

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    Definition Classes
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  16. val inputDescriptors: IndexedSeq[DataDesc]

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    Descriptors defining the input node names, shape, layout and type parameters

    Descriptors defining the input node names, shape, layout and type parameters

    Attributes
    protected
  17. final def isInstanceOf[T0]: Boolean

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    Definition Classes
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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. val predictor: PredictBase

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    Attributes
    protected[org.apache.mxnet.infer]
  22. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  23. val synset: IndexedSeq[String]

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    Attributes
    protected[org.apache.mxnet.infer]
  24. val synsetFilePath: String

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    Attributes
    protected[org.apache.mxnet.infer]
  25. def toString(): String

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    Definition Classes
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  26. final def wait(): Unit

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    @throws( ... )
  27. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  28. final def wait(arg0: Long): Unit

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    @throws( ... )

Inherited from ClassifierBase

Inherited from AnyRef

Inherited from Any

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