Cosine Similarity¶
Module Interface¶
- class torchmetrics.CosineSimilarity(reduction='sum', compute_on_step=None, **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
reduction¶ (
Literal
[‘mean’, ‘sum’, ‘none’, None]) – how to reduce over the batch dimension using ‘sum’, ‘mean’ or ‘none’ (taking the individual scores)compute_on_step¶ (
Optional
[bool
]) –Forward only calls
update()
and returns None if this is set to False.Deprecated since version v0.8: Argument has no use anymore and will be removed v0.9.
kwargs¶ (
Dict
[str
,Any
]) – Additional keyword arguments, see Advanced metric settings for more info.
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
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