Class

org.apache.mxnet.javaapi

Convolution_v1Param

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class Convolution_v1Param extends AnyRef

This Param Object is specifically used for Convolution_v1

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  1. Convolution_v1Param
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Instance Constructors

  1. new Convolution_v1Param(data: NDArray, weight: NDArray, bias: NDArray, kernel: Shape, num_filter: Integer)

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    data

    Input data to the ConvolutionV1Op.

    weight

    Weight matrix.

    bias

    Bias parameter.

    kernel

    convolution kernel size: (h, w) or (d, h, w)

    num_filter

    convolution filter(channel) number

Value Members

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

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  2. final def ##(): Int

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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  8. def finalize(): Unit

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  9. def getBias(): NDArray

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

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  11. def getCudnn_off(): Boolean

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  12. def getCudnn_tune(): String

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  13. def getData(): NDArray

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  14. def getDilate(): Shape

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  15. def getKernel(): Shape

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  16. def getLayout(): String

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  17. def getNo_bias(): Boolean

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  18. def getNum_filter(): Integer

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  19. def getNum_group(): Integer

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  20. def getOut(): mxnet.NDArray

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  21. def getPad(): Shape

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  22. def getStride(): Shape

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  23. def getWeight(): NDArray

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  24. def getWorkspace(): Long

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  25. def hashCode(): Int

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  26. final def isInstanceOf[T0]: Boolean

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

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  28. final def notify(): Unit

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  29. final def notifyAll(): Unit

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  30. def setCudnn_off(cudnn_off: Boolean): Convolution_v1Param

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    cudnn_off

    Turn off cudnn for this layer.

  31. def setCudnn_tune(cudnn_tune: String): Convolution_v1Param

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    cudnn_tune

    Whether to pick convolution algo by running performance test. Leads to higher startup time but may give faster speed. Options are: 'off': no tuning 'limited_workspace': run test and pick the fastest algorithm that doesn't exceed workspace limit. 'fastest': pick the fastest algorithm and ignore workspace limit. If set to None (default), behavior is determined by environment variable MXNET_CUDNN_AUTOTUNE_DEFAULT: 0 for off, 1 for limited workspace (default), 2 for fastest.

  32. def setDilate(dilate: Shape): Convolution_v1Param

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    dilate

    convolution dilate: (h, w) or (d, h, w)

  33. def setLayout(layout: String): Convolution_v1Param

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    layout

    Set layout for input, output and weight. Empty for default layout: NCHW for 2d and NCDHW for 3d.

  34. def setNo_bias(no_bias: Boolean): Convolution_v1Param

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    no_bias

    Whether to disable bias parameter.

  35. def setNum_group(num_group: Integer): Convolution_v1Param

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    num_group

    Number of group partitions. Equivalent to slicing input into num_group partitions, apply convolution on each, then concatenate the results

  36. def setOut(out: NDArray): Convolution_v1Param

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  37. def setPad(pad: Shape): Convolution_v1Param

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    pad

    pad for convolution: (h, w) or (d, h, w)

  38. def setStride(stride: Shape): Convolution_v1Param

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    stride

    convolution stride: (h, w) or (d, h, w)

  39. def setWorkspace(workspace: Long): Convolution_v1Param

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    workspace

    Maximum temporary workspace allowed for convolution (MB).This parameter determines the effective batch size of the convolution kernel, which may be smaller than the given batch size. Also, the workspace will be automatically enlarged to make sure that we can run the kernel with batch_size=1

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

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  41. def toString(): String

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  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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