LocalPredictor

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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. final def asInstanceOf[T0]: T0

   

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5. def clone(): AnyRef

   

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

   

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7. def equals(arg0: Any): Boolean

   

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8. def finalize(): Unit

   

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9. final def getClass(): Class[_]

   

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10. def hashCode(): Int

    

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11. final def isInstanceOf[T0]: Boolean

    

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

    

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13. final def notify(): Unit

    

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14. final def notifyAll(): Unit

    

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15. def predict(dataSet: Array[Sample[T]]): Array[Activity]

    

16. def predict(dataSet: LocalDataSet[MiniBatch[T]]): Array[Activity]

    

17. def predictClass(dataSet: LocalDataSet[MiniBatch[T]]): Array[Int]

    

18. def predictClass(dataSet: Array[Sample[T]]): Array[Int]

    

19. def predictImage(imageFrame: LocalImageFrame, outputLayer: String = null, shareBuffer: Boolean = false, predictKey: String = ImageFeature.predict): LocalImageFrame

    

    local model predict images, return imageFrame with predicted tensor

    local model predict images, return imageFrame with predicted tensor

    imageFrame

    imageFrame that contains images

    outputLayer

    if outputLayer is not null, the output of layer that matches outputLayer will be used as predicted output

    shareBuffer

    whether to share same memory for each batch predict results

    predictKey

    key to store predicted result

20. def shutdown(): Unit

    

    shutdown will release all native resources.

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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26. val workingToBatch: Array[Transformer[Sample[T], MiniBatch[T]]]