[object Object]

[object Object][object Object]undefined[object Object]

• Description: Computes the gradient value of binary cross entropy backpropagation.

• Formula:

  The formula for calculating binary cross entropy is as follows:

  $$y_i = - weight_i \cdot (target_i \cdot ln(x_i) + (1 - target_i) \cdot ln(1- x_i))$$

  $x$ indicates the output of the previous layer of the network, that is, the forward prediction value. $target$ indicates label values of the sample. Compute the partial derivative of the binary cross entropy to $x$:

  $$\begin{aligned} \frac {\partial y}{\partial x} &= -weight \cdot \frac {\partial (target_i \cdot ln(x_i) + (1-target_i) \cdot ln(1-x_i))}{\partial x} \\ &= -weight \cdot (\frac {\partial (target \cdot ln(x))}{\partial x} + \frac {\partial ((1-target) \cdot ln(1-x))}{\partial x}) \\ &= -weight \cdot (\frac {target}{x} - \frac {(1-target)}{1-x}) \\ &= -weight \cdot \frac {target(1-x)-x(1-target)}{x(1-x)} \\ &= -weight \cdot \frac {target-x}{x(1-x)} \\ &= weight \cdot \frac {x-target}{x(1-x)} \end{aligned}$$ $$out = grad\_output \cdot weight \cdot \frac {x-target}{x(1-x)} \\ out = mean(grad\_output) \ if \ reduction = mean$$

[object Object]

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

[object Object]

• Parameters:

  [object Object]
  • [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.

  [object Object]

[object Object]

• Parameters:

  [object Object]

• Returns:

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

[object Object]

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

[object Object]

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