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# CosineEmbeddingLoss

class torch.nn.CosineEmbeddingLoss(margin=0.0, size_average=None, reduce=None, reduction='mean')

  Creates a criterion that measures the loss given input tensors x 1 x_1 x 1​, x 2 x_2 x 2​ and a Tensor label y y y 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:

  loss(x,y)={1−cos⁡(x 1,x 2),if y=1 max⁡(0,cos⁡(x 1,x 2)−margin),if y=−1\text{loss}(x, y) = \begin{cases} 1 - \cos(x_1, x_2), & \text{if } y = 1 \\ \max(0, \cos(x_1, x_2) - \text{margin}), & \text{if } y = -1 \end{cases} loss(x,y)={1−cos(x 1​,x 2​),max(0,cos(x 1​,x 2​)−margin),​if y=1 if y=−1​
  Parameters:

    - margin (float, optional) – Should be a number from −1-1−1 to 1 1 1, 0 0 0 to 0.5 0.5 0.5 is suggested. If `margin` is missing, the default value is 0 0 0.

    - 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:

    - Input1: (N,D)(N, D)(N,D) or (D)(D)(D), where N is the batch size and D is the embedding dimension.

    - Input2: (N,D)(N, D)(N,D) or (D)(D)(D), same shape as Input1.

    - Target: (N)(N)(N) or ()()().

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

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