mindspore.mint.nn.BCEWithLogitsLoss

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

Adds sigmoid activation function to input as logits, and uses this logits to compute binary cross entropy between the logits and the target.

Sets input input as $X$, input target 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

• 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 target, data type must be float16, float32 or bfloat16(only Atlas A2 series products are supported). Default: None .

• reduction (str, optional) –

  Apply specific reduction method to the output: 'none' , 'mean' , 'sum' . Default: 'mean' .

  • 'none': no reduction will be applied.

  • 'mean': compute and return the weighted mean of elements in the output.

  • 'sum': the output elements will be summed.

• 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 be broadcast to a tensor with shape of input, data type must be float16, float32 or bfloat16(only Atlas A2 series products are supported). Default: None .

Inputs:

• input (Tensor) - Input input with shape $(N,\ast )$ where $\ast$ means, any number of additional dimensions. The data type must be float16, float32 or bfloat16(only Atlas A2 series products are supported).

• target (Tensor) - Ground truth label with shape $(N,\ast )$ where $\ast$ means, any number of additional dimensions. The same shape and data type as input.

Outputs:

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

Raises

• TypeError – If input input or target is not Tensor.

• TypeError – If weight or pos_weight is a parameter.

• TypeError – If data type of reduction is not string.

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

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

Supported Platforms:

Ascend

Examples

>>> import mindspore as ms
>>> from mindspore import mint
>>> import numpy as np
>>> input = ms.Tensor(np.array([[-0.8, 1.2, 0.7], [-0.1, -0.4, 0.7]]).astype(np.float32))
>>> target = ms.Tensor(np.array([[0.3, 0.8, 1.2], [-0.6, 0.1, 2.2]]).astype(np.float32))
>>> loss = mint.nn.BCEWithLogitsLoss()
>>> output = loss(input, target)
>>> print(output)
0.3463612