oneflow.nn.functional.cosine_similarity

oneflow.nn.functional.cosine_similarity(x1: Tensor, x2: Tensor, dim: int = 1, eps: float = 1e-08) → Tensor

Returns cosine similarity between x1 and x2, computed along dim. x1 and x2 must be broadcastable to a common shape. dim refers to the dimension in this common shape. Dimension dim of the output is squeezed (see oneflow.squeeze()), resulting in the output tensor having 1 fewer dimension.

The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.cosine_similarity.html

\[\text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}\]
Parameters

  • x1 (Tensor) – First input.

  • x2 (Tensor) – Second input.

  • dim (int, optional) – Dimension along which cosine similarity is computed. Default: 1

  • eps (float, optional) – Small value to avoid division by zero. Default: 1e-8

For examples:

>>> import oneflow as flow
>>> import oneflow.nn.functional as F
>>> input1 = flow.randn(100, 128)
>>> input2 = flow.randn(100, 128)
>>> output = F.cosine_similarity(input1, input2)