mindspore.nn.MarginRankingLoss

class mindspore.nn.MarginRankingLoss(margin=0.0, reduction='mean')[source]

MarginRankingLoss creates a criterion that measures the loss.

Given two tensors \(x1\), \(x2\) and a Tensor label \(y\) with values 1 or -1, the operation is as follows:

\[\text{loss}(x1, x2, y) = \max(0, -y * (x1 - x2) + \text{margin})\]
Parameters

  • margin (float) – Specify the adjustment factor of the operation. Default 0.0.

  • reduction (str) – Specifies which reduction to be applied to the output. It must be one of “none”, “mean”, and “sum”, meaning no reduction, reduce mean and sum on output, respectively. Default “mean”.

Inputs:

  • input1 (Tensor) - Tensor of shape \((N, *)\) where \(*\) means, any number of additional dimensions.

  • input2 (Tensor) - Tensor of shape \((N, *)\), same shape and dtype as input1.

  • target (Tensor) - Contains value 1 or -1. Suppose the shape of input1 is \((x_1, x_2, x_3, ..., x_R)\), then the shape of labels must be \((x_1, x_3, x_4, ..., x_R)\).

Outputs:

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

Raises

  • TypeError – If margin is not a float.

  • TypeError – If input1, input2 or target is not a Tensor.

  • TypeError – If the types of input1 and input2 are inconsistent.

  • TypeError – If the types of input1 and target are inconsistent.

  • ValueError – If the shape of input1 and input2 are inconsistent.

  • ValueError – If the shape of input1 and target are inconsistent.

  • 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 mindspore.ops as ops
>>> from mindspore.ops import Tensor
>>> import numpy as np
>>> loss1 = nn.MarginRankingLoss(reduction='none')
>>> loss2 = nn.MarginRankingLoss(reduction='mean')
>>> loss3 = nn.MarginRankingLoss(reduction='sum')
>>> sign = ops.Sign()
>>> input1 = Tensor(np.array([0.3864, -2.4093, -1.4076]), ms.float32)
>>> input2 = Tensor(np.array([-0.6012, -1.6681, 1.2928]), ms.float32)
>>> target = sign(Tensor(np.array([-2, -2, 3]), ms.float32))
>>> output1 = loss1(input1, input2, target)
>>> print(output1)
[0.98759997 0.         2.7003999 ]
>>> output2 = loss2(input1, input2, target)
>>> print(output2)
1.2293333
>>> output3 = loss3(input1, input2, target)
>>> print(output3)
3.6879997