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': the sum of the output will be divided by the number of elements in the output.
'sum': the output 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]]))