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mindspore.nn.SoftMarginLoss

class mindspore.nn.SoftMarginLoss(reduction='mean')

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).

$$\text{loss}(x,y)=\sum _i\frac{\log (1+\exp (-y[i]\ast x[i]))}{x.nelement()}$$

$x.nelement()$ 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