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• Description: Performs backpropagation of to compute the gradient gradInput of backpropagation.
• Formula:$$HardshrinkBackward(x,grad)= \begin{cases} &grad, &if(x > \lambda) \\ &grad, &if(x < -\lambda) \\ &0, &otherwise \end{cases}$$

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Each operator has calls. First, aclnnHardshrinkBackwardGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, aclnnHardshrinkBackward is called to perform computation.

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• Parameters

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  • [object Object]Atlas training products[object Object]: The data type can be FLOAT16 or FLOAT.

• Returns

  [object Object]: status code. For details, see .

  The first-phase API implements input parameter validation. The following error codes may be returned.

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• Parameters

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• Returns

  [object Object]: status code. For details, see .

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• Deterministic computation:
  • aclnnHardshrinkBackward defaults to a deterministic implementation.

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The following example is for reference only. For details, see .