mindspore.nn.BCEWithLogitsLoss

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

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,

$$\begin{array}{r}\ell (x,y)=\{\begin{array}{ll}L,& \text{if reduction}=\text{'none';}\\ \mathrm{mean}(L),& \text{if reduction}=\text{'mean';}\\ \mathrm{sum}(L),& \text{if reduction}=\text{'sum'.}\end{array}\end{array}$$

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 $(N,\ast )$ where $\ast$ means, any number of additional dimensions. The data type must be float16 or float32.

• labels (Tensor) - Ground truth label with shape $(N,\ast )$, 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