Convolution_v1Param

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 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. def getBias(): NDArray

   

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

    

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11. def getCudnn_off(): Boolean

    

12. def getCudnn_tune(): String

    

13. def getData(): NDArray

    

14. def getDilate(): Shape

    

15. def getKernel(): Shape

    

16. def getLayout(): String

    

17. def getNo_bias(): Boolean

    

18. def getNum_filter(): Integer

    

19. def getNum_group(): Integer

    

20. def getOut(): mxnet.NDArray

    

21. def getPad(): Shape

    

22. def getStride(): Shape

    

23. def getWeight(): NDArray

    

24. def getWorkspace(): Long

    

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

    
    cudnn_off

    Turn off cudnn for this layer.

31. def setCudnn_tune(cudnn_tune: String): Convolution_v1Param

    
    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

    
    dilate

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

33. def setLayout(layout: String): Convolution_v1Param

    
    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

    
    no_bias

    Whether to disable bias parameter.

35. def setNum_group(num_group: Integer): Convolution_v1Param

    
    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

    

37. def setPad(pad: Shape): Convolution_v1Param

    
    pad

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

38. def setStride(stride: Shape): Convolution_v1Param

    
    stride

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

39. def setWorkspace(workspace: Long): Convolution_v1Param

    
    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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