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• Description: Computes the binary cross entropy of self and target.

• Formula:

  $$\ell(self, target)= L = \{l_{1},...,l_{n}\}^{T}, \ell_{n} = - weight_{n}[target_{n}·log(self_{n}) + ((1 - target_{n})·log(1-self_{n}))]$$

  When reduction is not None:

  $$\ell(self, target) \begin{cases} mean(L), & if\ reduction = mean \\ sum(L), & if\ reduction = sum \\ \end{cases}$$

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Each operator has calls. First, aclnnBinaryCrossEntropyGetWorkspaceSize is called to obtain the input parameters and compute the required workspace size based on the process. Then, aclnnBinaryCrossEntropy 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 cannot be BFLOAT16.

• 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 compute:
  • aclnnBinaryCrossEntropy defaults to a deterministic implementation.

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