mindspore.ops.multi_margin_loss
- mindspore.ops.multi_margin_loss(input, target, p=1, margin=1, weight=None, reduction='mean')[source]
Hinge loss for optimizing a multi-class classification.
Optimizes a multi-class classification hinge loss (margin-based loss) between input and output.
For each mini-batch sample, the loss in terms of the 1D input \(x\) and scalar output \(y\) is:
\[\text{loss}(x, y) = \frac{\sum_i \max(0, \text{margin} - x[y] + x[i])^p}{\text{x.size}(0)}\]where \(i\in \{0,⋯,x.size(0)-1\}\) and \(i \ne y\).
- Parameters
input (Tensor) – Input , with shape \((N, C)\). Data type only support float32, float16 or float64. It 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. It is \(y\) in the above formula.
p (int, optional) – The norm degree for pairwise distance. Should be 1 or 2. Default:
1
.margin (int, optional) – A parameter to change pairwise distance. Default:
1
.weight (Tensor, optional) – The rescaling weight to each class with shape \((C,)\). Data type only support float16, float32 or float64. Default:
None
.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.
- Returns
outputs - Tensor. If reduction is
'none'
, returns a Tensor with the same shape as target. Otherwise, it is a scalar.
- Raises
TypeError – If dtype of p or target is not int.
TypeError – If dtype of margin is not int.
TypeError – If dtype of reduction is not str.
TypeError – If dtype of input is not float16, float or float64.
TypeError – If dtype of weight and input 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 input is not equal to shape[0] of target.
ValueError – If shape[1] of input is not equal to shape[0] of weight.
ValueError – If rank of weight is not 1 or rank of target is not 1 or input is not 2.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> inputs = Tensor(np.ones(shape=[3, 3]), mindspore.float32) >>> target = Tensor(np.array([1, 2, 1]), mindspore.int64) >>> weight = Tensor(np.array([1, 1, 1]), mindspore.float32) >>> output = ops.multi_margin_loss(inputs, target, weight=weight) >>> print(output) 0.6666667