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,

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

Parameters

• reduction (str, 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.

• 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 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 \((N, *)\) where \(*\) means, any number of additional dimensions. The same shape and data type 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 input logits or labels is not Tensor.

• 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.

• 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 logits.

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

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore as ms
>>> import mindspore.nn as nn
>>> import numpy as np
>>> logits = ms.Tensor(np.array([[-0.8, 1.2, 0.7], [-0.1, -0.4, 0.7]]).astype(np.float32))
>>> labels = ms.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