Table of Contents
Shortcuts

# MultiLabelMarginLoss

class torch.nn.MultiLabelMarginLoss(size_average=None, reduce=None, reduction='mean')

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

  loss(x,y)=∑i j max⁡(0,1−(x[y[j]]−x[i]))x.size(0)\text{loss}(x, y) = \sum_{ij}\frac{\max(0, 1 - (x[y[j]] - x[i]))}{\text{x.size}(0)} loss(x,y)=ij∑​x.size(0)max(0,1−(x[y[j]]−x[i]))​

  where x∈{0, ⋯ , x.size(0)−1}x \in \left\{0, \; \cdots , \; \text{x.size}(0) - 1\right\}x∈{0,⋯,x.size(0)−1}, y∈{0, ⋯ , y.size(0)−1}y \in \left\{0, \; \cdots , \; \text{y.size}(0) - 1\right\}y∈{0,⋯,y.size(0)−1}, 0≤y[j]≤x.size(0)−1 0 \leq y[j] \leq \text{x.size}(0)-1 0≤y[j]≤x.size(0)−1, and i≠y[j]i \neq y[j]i=y[j] for all i i i and j j j.

  y y y and x x x must have the same size.

  The criterion only considers a contiguous block of non-negative targets that starts at the front.

  This allows for different samples to have variable amounts of target classes.

  Parameters:

    - size_average (bool, optional) – Deprecated (see `reduction`). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there are multiple elements per sample. If the field `size_average` is set to `False`, the losses are instead summed for each minibatch. Ignored when `reduce` is `False`. Default: `True`

    - reduce (bool, optional) – Deprecated (see `reduction`). By default, the losses are averaged or summed over observations for each minibatch depending on `size_average`. When `reduce` is `False`, returns a loss per batch element instead and ignores `size_average`. Default: `True`

    - reduction (str, optional) – Specifies the reduction to apply to the output: `'none'` | `'mean'` | `'sum'`. `'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. Note: `size_average` and `reduce` are in the process of being deprecated, and in the meantime, specifying either of those two args will override `reduction`. Default: `'mean'`

  Shape:

    - Input: (C)(C)(C) or (N,C)(N, C)(N,C) where N is the batch size and C is the number of classes.

    - Target: (C)(C)(C) or (N,C)(N, C)(N,C), label targets padded by -1 ensuring same shape as the input.

    - Output: scalar. If `reduction` is `'none'`, then (N)(N)(N).

  Examples:

      >>> loss = nn.MultiLabelMarginLoss()
      >>> x = torch.FloatTensor([[0.1, 0.2, 0.4, 0.8]])
      >>> # for target y, only consider labels 3 and 0, not after label -1
      >>> y = torch.LongTensor([[3, 0, -1, 1]])
      >>> # 0.25 * ((1-(0.1-0.2)) + (1-(0.1-0.4)) + (1-(0.8-0.2)) + (1-(0.8-0.4)))
      >>> loss(x, y)
      tensor(0.85...)

## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

## Resources

Find development resources and get your questions answered

View Resources

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.