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• Description: Computes the BCELoss between the input logits and the target.

• Formula:

  • Single-label scenario:

    $$\ell(self, target) = L = \{l_{1},..., l_{n}\}^{T}$$ $$\ell_{n} = -weight_{n}[target_{n} \cdot log(\sigma(self_{n})) + (1 - target_{n}) \cdot log(1 - \sigma(self_{n}))]$$ $$\ell(self, target) = \begin{cases} L, & if\ reduction = none\\ mean(L), & if\ reduction = mean\\ sum(L), & if\ reduction = sum\\ \end{cases}$$

  • Multi-label scenario:

    $$\ell_c(self, target) = L_c = \{l_{1,c},..., l_{n,c}\}^{T}$$ $$\ell_{n,c} = -weight_{n,c}[pos\_weight_{n,c} \cdot target_{n,c} \cdot log(\sigma(self_{n,c})) + (1 - target_{n,c}) \cdot log(1 - \sigma(self_{n,c}))]$$

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Each operator has calls. First, aclnnBinaryCrossEntropyWithLogitsGetWorkspaceSize is called to obtain the input parameters and compute the required workspace size based on the process. Then, aclnnBinaryCrossEntropyWithLogits 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:
  • aclnnBinaryCrossEntropyWithLogits defaults to a deterministic implementation.

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