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• Description: Performs backpropagation of .
• Formula:$$output = \begin{cases} gradOutput(i) & \text{if } self(i) > threshold \\ 0 & \text{otherwise} \end{cases}$$

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Each operator has calls. First, aclnnThresholdBackwardGetWorkspaceSize is called to obtain the input parameters and compute the required workspace size based on the process. Then, aclnnThresholdBackward is called to perform computation.

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• Parameters:

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  • [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training series products/Atlas A3 inference series products[object Object]: The data type can be FLOAT, BFLOAT16, FLOAT16, INT32, INT8, or UINT8.
  • [object Object]Atlas inference series products[object Object] and [object Object]Atlas training series products[object Object]: The data type can be FLOAT, FLOAT16, INT32, INT8, or UINT8.

• Returns:

  aclnnStatus: 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:

  aclnnStatus: status code. For details, see .

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

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