- class torch.nn.CosineEmbeddingLoss(margin=0.0, size_average=None, reduce=None, reduction='mean')¶
Creates a criterion that measures the loss given input tensors , and a Tensor label with values 1 or -1. This is used for measuring whether two inputs are similar or dissimilar, using the cosine similarity, and is typically used for learning nonlinear embeddings or semi-supervised learning.
The loss function for each sample is:
margin (float, optional) – Should be a number from to , to is suggested. If
marginis missing, the default value is .
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_averageis set to
False, the losses are instead summed for each minibatch. Ignored when
reduce (bool, optional) – Deprecated (see
reduction). By default, the losses are averaged or summed over observations for each minibatch depending on
False, returns a loss per batch element instead and ignores
reduction (str, optional) – Specifies the reduction to apply to the output:
'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:
reduceare in the process of being deprecated, and in the meantime, specifying either of those two args will override
Input1: or , where N is the batch size and D is the embedding dimension.
Input2: or , same shape as Input1.
Target: or .
'none', then , otherwise scalar.