Cosine embedding loss
Creates a criterion that measures the loss given input tensors
x_1, x_2 and a Tensor label y with values 1 or -1.
This is used for measuring whether two inputs are similar or dissimilar,
using the cosine distance, and is typically used for learning nonlinear
embeddings or semi-supervised learning.
The loss function for each sample is:
nn_cosine_embedding_loss(margin = 0, reduction = "mean")
margin |
(float, optional): Should be a number from -1 to 1,
0 to 0.5 is suggested. If |
reduction |
(string, optional): Specifies the reduction to apply to the output:
|
\mbox{loss}(x, y) = \begin{array}{ll} 1 - \cos(x_1, x_2), & \mbox{if } y = 1 \\ \max(0, \cos(x_1, x_2) - \mbox{margin}), & \mbox{if } y = -1 \end{array}
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