CosineSimilarity

class torch.nn.modules.distance.CosineSimilarity(dim=1, eps=1e-08) [source]

Returns cosine similarity between $x_1$ and $x_2$, computed along dim.

$$\text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}.$$

Parameters

• dim (int, optional) – Dimension where cosine similarity is computed. Default: 1
• eps (float, optional) – Small value to avoid division by zero. Default: 1e-8

Shape:

• Input1: $(\ast_1, D, \ast_2)$ where D is at position dim
• Input2: $(\ast_1, D, \ast_2)$, same number of dimensions as x1, matching x1 size at dimension dim, and broadcastable with x1 at other dimensions.
• Output: $(\ast_1, \ast_2)$

Examples

>>> input1 = torch.randn(100, 128)
>>> input2 = torch.randn(100, 128)
>>> cos = nn.CosineSimilarity(dim=1, eps=1e-6)
>>> output = cos(input1, input2)

forward(x1, x2) [source]

Runs the forward pass.

Return type

Tensor