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mindspore.nn.MultiMarginLoss

class mindspore.nn.MultiMarginLoss(p=1, margin=1.0, reduction='mean', weight=None)

Creates a criterion that optimizes a multi-class classification hinge loss (margin-based loss) between input $x$ (a 2D mini-batch Tensor) and output $y$ (which is a 1D tensor of target class indices, $0\le y\le \text{x.size}(1)-1$):

For each mini-batch sample, the loss in terms of the 1D input $x$ and scalar output $y$ is:

$$\text{loss}(x,y)=\frac{\sum _{i}max(0,w[y]\ast (\text{margin}-x[y]+x[i]){)}^p}{\text{x.size}(0)}$$

where $x\in \{0,\cdots ,\text{x.size}(0)-1\}$ and $i\ne y$.

Parameters

• p (int, optional) – The norm degree for pairwise distance. Should be 1 or 2. Default: 1 .

• margin (float, optional) – A parameter to change pairwise distance. Default: 1.0.

• 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) – The rescaling weight to each class with shape $(C,)$. Data type only support float32, float16 or float64. Default: None , all classes are weighted equally.

Inputs:

• x (Tensor) - Input x, with shape $(N,C)$. Data type only support float32, float16 or float64. x is $x$ in the above formula.

• target (Tensor) - Ground truth labels, with shape $(N,)$. Data type only support int64. The value of target should be non-negative, less than C. target is $y$ in the above formula.

Outputs:

Tensor, When reduction is 'none', the shape is $(N,)$. Otherwise, it is a scalar. Has the same data type with x.

Raises

• TypeError – If dtype of p or target is not int.

• TypeError – If dtype of margin is not float.

• TypeError – If dtype of reduction is not str.

• TypeError – If dtype of x is not float16, float or float64.

• TypeError – If dtype of weight and x is not the same.

• ValueError – If ‘p’ is not 1 or 2.

• ValueError – If ‘reduction’ is not one of { 'none' , 'sum' , 'mean' }.

• ValueError – If shape[0] of x is not equal to shape[0] of target.

• ValueError – If shape[1] of x is not equal to shape[0] of weight.

• ValueError – IF rank of weight is not 1.

• ValueError – If rank of x is not 2 or rank of ‘target’ is not 1.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore as ms
>>> import mindspore.nn as nn
>>> import numpy as np
>>> x = ms.Tensor(np.ones(shape=[3, 3]), ms.float32)
>>> target = ms.Tensor(np.array([1, 2, 1]), ms.int64)
>>> loss = nn.MultiMarginLoss()
>>> output = loss(x, target)
>>> print(output)
0.6666667