dgs.models.metric.metric.NegativeSoftmaxEuclideanDistance.forward

NegativeSoftmaxEuclideanDistance.forward(input1: torch.Tensor, input2: torch.Tensor) → torch.Tensor

First compute the Euclidean distance between the two inputs, of which the second dimension has to match. Then compute the softmax of the negative distance along the second dimension.

Parameters:

• input1 – tensor of shape [a x E]

• input2 – tensor of shape [b x E]

Returns:

A tensor of shape [a x b] containing the similarity between the inputs as probability. By default, the softmax is computed along the last dimension, but you can change the behavior by changing the kwargs during initialization.