mindspore.ops.MultilabelMarginLoss

class mindspore.ops.MultilabelMarginLoss(reduction='mean')[source]

Creates a loss criterion that minimizes the hinge loss for multi-class classification tasks. It takes a 2D mini-batch Tensor $x$ as input and a 2D Tensor $y$ containing target class indices as output.

Refer to mindspore.ops.multilabel_margin_loss() for more details.

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:

x (Tensor) - Predict data. Tensor of shape $(C)$ or $(N, C)$ , where $N$ is the batch size and $C$ is the number of classes. Data type must be float16 or float32.
target (Tensor) - Ground truth data, with the same shape as input, data type must be int32 and label targets padded by -1.

Outputs:

y (Union[Tensor, Scalar]) - The loss of MultilabelMarginLoss. If reduction is "none", its shape is $(N)$ . Otherwise, a scalar value will be returned.
is_target (Tensor) - Output tensor for backward input, with the same shape as target, data type must be int32.

Supported Platforms:

Ascend GPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> loss = ops.MultilabelMarginLoss()
>>> x = Tensor(np.array([[0.1, 0.2, 0.4, 0.8], [0.2, 0.3, 0.5, 0.7]]), mindspore.float32)
>>> target = Tensor(np.array([[1, 2, 0, 3], [2, 3, -1, 1]]), mindspore.int32)
>>> output = loss(x, target)
>>> print(output)
(Tensor(shape=[], dtype=Float32, value= 0.325), Tensor(shape=[2, 4], dtype=Int32, value=
[[1, 1, 1, 1], [0, 0, 1, 1]]))