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This API performs the backward computation of .

The formula of gradInput is as follows:

$$gradInput_{i,j,...}= \begin{cases} gradOutput_{i,j,...}, & if\ self_{i,j,...} > 0 \\ gradOutput_{i,j,...} * weight_{i}, & if\ self_{i,j,...} <= 0 \end{cases}$$

The formula of gradWeight is as follows:

$$gradWeight_{j}=\sum_{i,...} \begin{cases} 0, & if\ self_{i,j,...} > 0 \\ gradOutput_{i,j,...} * self_{i,j,...}, & if\ self_{i,j,...} <= 0 \end{cases}$$ [object Object]

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

[object Object]

• Parameters:

  [object Object]
  • [object Object]Atlas training products[object Object]: The data type can be FLOAT16 or FLOAT32.

• Returns:

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

  The first-phase API implements input parameter verification. The following errors may be thrown:

  [object Object]

[object Object]

• Parameters:

  [object Object]

• Returns:

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

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• Deterministic compute:
  • aclnnPreluBackward defaults to a deterministic implementation.

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