mindspore.nn.SoftMarginLoss

class mindspore.nn.SoftMarginLoss(reduction='mean')[source]

A loss class for two-class classification problems.

SoftMarginLoss creates a criterion that optimizes a two-class classification logistic loss between input tensor \(x\) and labels tensor \(y\) (containing 1 or -1).

\[\text{loss}(x, y) = \sum_i \frac{\log(1 + \exp(-y[i]*x[i]))}{x.nelement()}\]

\(x.nelement()\) represents the number of element of x .

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 mean of elements in the output.
'sum': the output elements will be summed.

Inputs:

logits (Tensor) - Predict data. Data type must be float16 or float32.
labels (Tensor) - Ground truth data, with the same type and shape 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 logits or labels is not a Tensor.
TypeError – If dtype of logits or labels is neither float16 nor float32.
ValueError – If shape of logits is not the same as labels.
ValueError – If reduction is not one of 'none', 'mean', 'sum'.

Supported Platforms:: Ascend GPU

Examples

>>> import mindspore
>>> from mindspore import Tensor, nn
>>> import numpy as np
>>> loss = nn.SoftMarginLoss()
>>> logits = Tensor(np.array([[0.3, 0.7], [0.5, 0.5]]), mindspore.float32)
>>> labels = Tensor(np.array([[-1, 1], [1, -1]]), mindspore.float32)
>>> output = loss(logits, labels)
>>> print(output)
0.6764238