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# Cosine Similarity¶

## Module Interface¶

class torchmetrics.CosineSimilarity(reduction='sum', **kwargs)[source]

Computes the Cosine Similarity between targets and predictions: where is a tensor of target values, and is a tensor of predictions.

Forward accepts

• preds (float tensor): (N,d)

• target (float tensor): (N,d)

Parameters

Example

>>> from torchmetrics import CosineSimilarity
>>> target = torch.tensor([[0, 1], [1, 1]])
>>> preds = torch.tensor([[0, 1], [0, 1]])
>>> cosine_similarity = CosineSimilarity(reduction = 'mean')
>>> cosine_similarity(preds, target)
tensor(0.8536)


Initializes internal Module state, shared by both nn.Module and ScriptModule.

compute()[source]

Override this method to compute the final metric value from state variables synchronized across the distributed backend.

Return type

Tensor

update(preds, target)[source]

Update metric states with predictions and targets.

Parameters
Return type

None

## Functional Interface¶

torchmetrics.functional.cosine_similarity(preds, target, reduction='sum')[source]

Computes the Cosine Similarity between targets and predictions: where is a tensor of target values, and is a tensor of predictions.

Parameters

Example

>>> from torchmetrics.functional.regression import cosine_similarity
>>> target = torch.tensor([[1, 2, 3, 4],
...                        [1, 2, 3, 4]])
>>> preds = torch.tensor([[1, 2, 3, 4],
...                       [-1, -2, -3, -4]])
>>> cosine_similarity(preds, target, 'none')
tensor([ 1.0000, -1.0000])

Return type

Tensor

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