KerasUtils

Value Members

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

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

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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

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6. def addBatch(shape: Shape): Shape

7. final def asInstanceOf[T0]: T0

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8. def calBatchPerCore(batchSize: Int): Int

9. def clone(): AnyRef

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10. def computeConvOutputLength(inputLength: Int, filterSize: Int, borderMode: String, stride: Int, dilation: Int = 1): Int

11. def countParams[T](layer: KerasLayer[Activity, Activity, T])(implicit arg0: ClassTag[T]): (Int, Int)

    Count the total number of parameters for a KerasLayer.

    Count the total number of parameters for a KerasLayer. Return a tuple (total params #, trainable params #)

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

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13. def equals(arg0: Any): Boolean

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14. def finalize(): Unit

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15. def fuse[T](torchLayer: AbstractModule[Activity, Activity, T], kerasActivation: KerasLayer[Tensor[T], Tensor[T], T], batchInputShape: Shape)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T]

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

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17. def getInitMethod(init: String): InitializationMethod

18. def getKerasActivation[T](activation: String)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): KerasLayer[Tensor[T], Tensor[T], T]

19. def getLayerSummary[T](layer: KerasLayer[Activity, Activity, T])(implicit arg0: ClassTag[T]): Array[String]

    Return the layer summary information as an array of String, in the order of: Layer (type), OutputShape, Param #

20. def getNodeSummary[T](node: ModuleNode[T])(implicit arg0: ClassTag[T]): Array[String]

    Together with the layer summary of a node, also return the name of the node(s) that it is connected to.

    Together with the layer summary of a node, also return the name of the node(s) that it is connected to. If there are multiple connected nodes, they will be combined by ", "

21. def getPadsFromBorderMode(borderMode: String = "valid"): (Int, Int)

22. def getPadsFromBorderMode3D(borderMode: String = "valid"): (Int, Int, Int)

23. def getTorchActivation[T](activation: String)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): AbstractModule[Tensor[T], Tensor[T], T]

24. def hashCode(): Int

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25. final def isInstanceOf[T0]: Boolean

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

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27. final def notify(): Unit

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28. final def notifyAll(): Unit

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29. def printNodeSummary[T](node: ModuleNode[T], lineLength: Int = 120, positions: Array[Double] = Array(.33, .55, .67, 1))(implicit arg0: ClassTag[T]): (Int, Int)

    Print the summary of a node in a line.

    Print the summary of a node in a line. Return a tuple (total params #, trainable params #) of this node.

30. def printRow(fields: Array[String], lineLength: Int = 120, positions: Array[Double] = Array(.33, .55, .67, 1), includeSplitLine: Boolean = true, splitChar: Char = '_'): Unit

    Print a row containing several fields.

    Print a row containing several fields.

    fields

    The fields to be printed out.

    lineLength

    The total length of a printed line.

    positions

    The maximum absolute length proportion(%) of each field. Default is Array(.33, .55, .67, 1), meaning that the first field will occupy up to 33% of lineLength, the second field will occupy up to (55-33)% of lineLength, the third field will occupy up to (67-55)% of lineLength, the fourth field will occupy the remaining line (100-67)%. If the field has a larger length, the remaining part will be trimmed. If the field has a smaller length, the remaining part will be white spaces.

    includeSplitLine

    Whether to add a split line after printing one row.

    splitChar

    The character to compose the split line.

31. def printSplitLine(char: Char, lineLength: Int = 120): Unit

    Print a split line that repeats the 'char' for 'lineLength' times.

32. def removeBatch(shape: Shape): Shape

33. def strShape(shape: Shape): String

    Convert a Shape to String format using 'None' to indicate batch, which is the same as Keras.

    Convert a Shape to String format using 'None' to indicate batch, which is the same as Keras. Used to print out the shape.

    For example, (None, 10) will be returned for Shape(-1, 10), a SingleShape. (None, 10) (None, 8) will be returned for a MultiShape which consists of Shape(-1, 10), Shape(-1, 8).

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

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35. def toBigDLCriterion[T](loss: String)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Criterion[T]

36. def toBigDLFormat(dimOrdering: String): DataFormat

37. def toBigDLFormat5D(dimOrdering: String): String

38. def toBigDLMetrics[T](metrics: List[String])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): List[ValidationMethod[T]]

39. def toBigDLOptimMethod[T](optimMethod: String)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): OptimMethod[T]

40. def toString(): String

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41. def toZeroBasedLabel(zeroBasedLabel: Boolean = true, classes: RDD[Int]): RDD[Int]

    classes: RDD of 1-based label.

    classes: RDD of 1-based label. If zeroBasedLabel is true, convert to RDD of 0-based label. Otherwise, just return classes itself.

42. final def wait(): Unit

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

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

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