MultiLabelMarginLoss¶

class
torch.nn.
MultiLabelMarginLoss
(size_average=None, reduce=None, reduction: str = 'mean')[source]¶ Creates a criterion that optimizes a multiclass multiclassification hinge loss (marginbased loss) between input $x$ (a 2D minibatch Tensor) and output $y$ (which is a 2D Tensor of target class indices). For each sample in the minibatch:
$\text{loss}(x, y) = \sum_{ij}\frac{\max(0, 1  (x[y[j]]  x[i]))}{\text{x.size}(0)}$where $x \in \left\{0, \; \cdots , \; \text{x.size}(0)  1\right\}$ , $y \in \left\{0, \; \cdots , \; \text{y.size}(0)  1\right\}$ , $0 \leq y[j] \leq \text{x.size}(0)1$ , and $i \neq y[j]$ for all $i$ and $j$ .
$y$ and $x$ must have the same size.
The criterion only considers a contiguous block of nonnegative 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 fieldsize_average
is set toFalse
, the losses are instead summed for each minibatch. Ignored when reduce isFalse
. Default:True
reduce (bool, optional) – Deprecated (see
reduction
). By default, the losses are averaged or summed over observations for each minibatch depending onsize_average
. Whenreduce
isFalse
, returns a loss per batch element instead and ignoressize_average
. Default:True
reduction (string, 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
andreduce
are in the process of being deprecated, and in the meantime, specifying either of those two args will overridereduction
. Default:'mean'
 Shape:
Input: $(C)$ or $(N, C)$ where N is the batch size and C is the number of classes.
Target: $(C)$ or $(N, C)$ , label targets padded by 1 ensuring same shape as the input.
Output: scalar. If
reduction
is'none'
, then $(N)$ .
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]]) >>> loss(x, y) >>> # 0.25 * ((1(0.10.2)) + (1(0.10.4)) + (1(0.80.2)) + (1(0.80.4))) tensor(0.8500)