mindspore.ops.MultilabelMarginLoss

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

MultilabelMarginLoss operation.

Creates a criterion that optimizes a multi-class multi-classification hinge loss (margin-based loss) between input \(x\) (a 2D mini-batch Tensor) and output \(y\) (which is a 2D Tensor of target class indices).

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

Supported Platforms:: Ascend GPU

Examples

>>> 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]]))