triplet loss

在Pytorch中有一個類，已經定義好了triplet loss的criterion, class TripletMarginLoss(Module):

class TripletMarginLoss(Module):
    r"""Creates a criterion that measures the triplet loss given an input
    tensors x1, x2, x3 and a margin with a value greater than 0.
    This is used for measuring a relative similarity between samples. A triplet
    is
 
 composed by `a`, `p` and `n`: anchor, positive examples and negative
    example respectively. The shape of all input variables should be
    :math:`(N, D)`.
    The distance swap is described in detail in the paper `Learning shallow
    convolutional feature descriptors with triplet losses`_ by
    V. Balntas, E. Riba et al.

    Args:
        anchor: anchor input tensor
        positive: positive input tensor
        negative: negative input tensor
        p: the norm degree. Default: 2
    Shape:
        - Input: :math:`(N, D)` where `D = vector dimension`
        - Output: :math:`(N, 1)`
 

使用示例：

  >>> triplet_loss = nn.TripletMarginLoss(margin=1.0, p=2)
    >>> input1 = autograd.Variable(torch.randn(100, 128))
    >>> input2 = autograd.Variable(torch.randn(100, 128))
    >>> input3 = autograd.Variable(torch.randn(100, 128))
    >>> output = triplet_loss(input1, input2, input3)
    >>> output.backward
 
()

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