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.
Usage
nnf_cosine_embedding_loss(
input1,
input2,
target,
margin = 0,
reduction = c("mean", "sum", "none")
)Arguments
- input1
the input x_1 tensor
- input2
the input x_2 tensor
- target
the target tensor
- margin
Should be a number from -1 to 1 , 0 to 0.5 is suggested. If margin is missing, the default value is 0.
- 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. Default: 'mean'