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• Description: Performs backpropagation of the activation function aclnnHardtanh.
• Formula:$$grad\_self_{i} = \begin{cases} 0,\ \ \ \ \ \ \ if \ \ self_{i}>max \\ 0,\ \ \ \ \ \ \ if\ \ self_{i}<min \\ 1,\ \ \ \ \ \ \ \ \ \ \ \ otherwise \\ \end{cases}$$ $$res_{i} = grad\_output_{i} \times grad\_self_{i}$$

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

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• Parameters:

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  • [object Object]Atlas inference products[object Object] and [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 verification. The following errors may be thrown.

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• Parameters:

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• Returns:

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

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

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