MnistDataLoader

Value Members

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

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

   AnyRef → Any

3. final def ==(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

4. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

5. def bgrImageRandomCrop(labeledBGRImage: LabeledBGRImage, cropWidth: Int, cropHeight: Int, padding: Int): LabeledBGRImage

   

   Definition Classes

   ImageProcessing

6. def bgrImgNormalize(labeledBGRImage: LabeledBGRImage, mean: (Double, Double, Double), std: (Double, Double, Double)): LabeledBGRImage

   

   Definition Classes

   ImageProcessing

7. def bytesToGreyImage(record: ByteRecord, row: Int, col: Int): LabeledGreyImage

   

   Definition Classes

   ImageProcessing

8. def bytesToLabeledBGRImage(record: ByteRecord, resizeW: Int, resizeH: Int): LabeledBGRImage

   

   Definition Classes

   ImageProcessing

9. def clearWeightBias(model: Module[Float]): Unit

   

   Definition Classes

   InferenceSupportive

10. def clone(): AnyRef

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

12. def equals(arg0: Any): Boolean

    

    Definition Classes

    AnyRef → Any

13. def finalize(): Unit

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

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

    

    Definition Classes

    AnyRef → Any

15. def greyImgNormalize(labeledGreyImage: LabeledGreyImage, mean: Double, std: Double): LabeledGreyImage

    

    Definition Classes

    ImageProcessing

16. def hFlip(labeledBGRImage: LabeledBGRImage, threshold: Double): LabeledBGRImage

    

    Definition Classes

    ImageProcessing

17. def hashCode(): Int

    

    Definition Classes

    AnyRef → Any

18. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

19. def labeledBGRImageToMiniBatch(images: Array[LabeledBGRImage]): MiniBatch[Float]

    

    Definition Classes

    ImageProcessing

20. def labeledGreyImageToMiniBatch(images: Array[LabeledGreyImage]): MiniBatch[Float]

    

    Definition Classes

    ImageProcessing

21. def loadData(featureFile: String, labelFile: String): Array[ByteRecord]

    

22. def loadTestData(imageDirPath: String): Array[LabeledGreyImage]

    

23. def loadTrainData(imageDirPath: String): Array[LabeledGreyImage]

    

24. val logger: Logger

    

25. def makeMetaModel(original: AbstractModule[Activity, Activity, Float]): AbstractModule[Activity, Activity, Float]

    

    Definition Classes

    InferenceSupportive

26. def makeUpModel(clonedModel: Module[Float], weightBias: Array[Tensor[Float]]): AbstractModule[Activity, Activity, Float]

    

    Definition Classes

    InferenceSupportive

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

    

    Definition Classes

    AnyRef

28. val normalize: Float

    

    Definition Classes

    ImageProcessing

29. final def notify(): Unit

    

    Definition Classes

    AnyRef

30. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

31. def releaseWeightBias(model: Module[Float]): Unit

    

    Definition Classes

    InferenceSupportive

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

    

    Definition Classes

    AnyRef

33. val testMean: Double

    

34. val testStd: Double

    

35. def throughputing[T](name: String, batch: Int)(f: ⇒ T): T

    

    calculate and log the time and throughput

    calculate and log the time and throughput

    T

    the return of the process function

    name

    the name of the process

    batch

    the number of the batch

    f

    the process function

    returns

    the result of the process function

    Definition Classes

    EstimateSupportive

36. def throughputingWithLoss[T](name: String, batch: Int)(f: ⇒ T): T

    

    calculate and log the time and throughput and loss

    calculate and log the time and throughput and loss

    T

    the return of the process function

    name

    the name of the process

    batch

    the number of the batch

    f

    the process function

    returns

    the result of the process function

    Definition Classes

    EstimateSupportive

37. def timing[T](name: String)(f: ⇒ T): T

    

    Definition Classes

    InferenceSupportive

38. def toString(): String

    

    Definition Classes

    AnyRef → Any

39. val trainMean: Double

    

40. val trainStd: Double

    

41. def transferBatchTableToJListOfJListOfJTensor(batchTable: Table, batchSize: Int): List[List[JTensor]]

    

    Definition Classes

    InferenceSupportive

42. def transferBatchTensorToJListOfJListOfJTensor(batchTensor: Tensor[Float], batchSize: Int): List[List[JTensor]]

    

    Definition Classes

    InferenceSupportive

43. def transferListOfActivityToActivityOfBatch(inputs: List[List[JTensor]], batchSize: Int): Activity

    

    Definition Classes

    InferenceSupportive

44. def transferTensorToJTensor(input: Tensor[Float]): JTensor

    

    Definition Classes

    InferenceSupportive

45. def transferTensorsToTensorOfBatch(tensors: Array[JTensor]): Tensor[Float]

    

    Definition Classes

    InferenceSupportive

46. final def wait(): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

47. final def wait(arg0: Long, arg1: Int): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

48. final def wait(arg0: Long): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )