mindspore.nn.BCEWithLogitsLoss

class mindspore.nn.BCEWithLogitsLoss(reduction="mean", weight=None, pos_weight=None)[source]

Adds sigmoid activation function to input logits, and uses the given logits to compute binary cross entropy between the logits and the labels.

Sets input logits as \(X\), input labels as \(Y\), output as \(L\). Then,

\[p_{ij} = sigmoid(X_{ij}) = \frac{1}{1 + e^{-X_{ij}}}\]
\[L_{ij} = -[Y_{ij} \cdot log(p_{ij}) + (1 - Y_{ij}) \cdot log(1 - p_{ij})]\]

Then,

\[\begin{split}\ell(x, y) = \begin{cases} L, & \text{if reduction} = \text{'none';}\\ \operatorname{mean}(L), & \text{if reduction} = \text{'mean';}\\ \operatorname{sum}(L), & \text{if reduction} = \text{'sum'.} \end{cases}\end{split}\]
Parameters

  • reduction (str) – Type of reduction to be applied to loss. The optional values are ‘mean’, ‘sum’, and ‘none’. If ‘none’, do not perform reduction. Default:’mean’.

  • weight (Tensor, optional) – A rescaling weight applied to the loss of each batch element. If not None, it can be broadcast to a tensor with shape of logits, data type must be float16 or float32. Default: None.

  • pos_weight (Tensor, optional) – A weight of positive examples. Must be a vector with length equal to the number of classes. If not None, it must can be broadcast to a tensor with shape of logits, data type must be float16 or float32. Default: None.

Inputs:

  • logits (Tensor) - Input logits with shape \((N, *)\) where \(*\) means, any number of additional dimensions. The data type must be float16 or float32.

  • labels (Tensor) - Ground truth label with shape \((N, *)\), same shape and dtype as logits.

Outputs:

Tensor or Scalar, if reduction is “none”, its shape is the same as logits. Otherwise, a scalar value will be returned.

Raises

  • TypeError – If data type of logits or labels is neither float16 nor float32.

  • TypeError – If weight or pos_weight is a parameter.

  • TypeError – If data type of weight or pos_weight is neither float16 nor float32.

  • ValueError – If weight or pos_weight can not be broadcast to a tensor with shape of logits.

  • ValueError – If reduction is not one of ‘none’, ‘mean’, ‘sum’.

Supported Platforms:

Ascend GPU

Examples

>>> logits = Tensor(np.array([[-0.8, 1.2, 0.7], [-0.1, -0.4, 0.7]]).astype(np.float32))
>>> labels = Tensor(np.array([[0.3, 0.8, 1.2], [-0.6, 0.1, 2.2]]).astype(np.float32))
>>> loss = nn.BCEWithLogitsLoss()
>>> output = loss(logits, labels)
>>> print(output)
0.3463612