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• Function: performs backpropagation of the negative log likelihood loss function.
• Formula:

  • reduction=mean:

    $$x\_grad_{target(t)} =\begin{cases} (-gradOutput * w_{(target(t))}) / totalweight &, target(t)=1 \\ 0 &, target(t)=0 \end{cases}$$

  • reduction=sum:

    $$x\_grad_{target(t)} =\begin{cases} -gradOutput * w_{(target(t))} &, target(t)=1 \\ 0 &, target(t)=0 \end{cases}$$

  • reduction=none:

    $$x\_grad_{target(t)} =\begin{cases} -gradOutput_t * w_{(target(t))} &, target(t)=1 \\ 0 &, target(t)=0 \end{cases}$$

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

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• Parameters:

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• 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:
  • aclnnNLLLossBackward defaults to a deterministic implementation.

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