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# Symmetric Mean Absolute Percentage Error (SMAPE)¶

## Module Interface¶

class torchmetrics.SymmetricMeanAbsolutePercentageError(**kwargs)[source]

Computes symmetric mean absolute percentage error (SMAPE).

Where is a tensor of target values, and is a tensor of predictions.

As input to forward and update the metric accepts the following input:

As output of forward and compute the metric returns the following output:

• smape (Tensor): A tensor with non-negative floating point smape value between 0 and 1

Parameters

kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Example

>>> from torchmetrics import SymmetricMeanAbsolutePercentageError
>>> target = tensor([1, 10, 1e6])
>>> preds = tensor([0.9, 15, 1.2e6])
>>> smape = SymmetricMeanAbsolutePercentageError()
>>> smape(preds, target)
tensor(0.2290)


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

## Functional Interface¶

torchmetrics.functional.symmetric_mean_absolute_percentage_error(preds, target)[source]

Computes symmetric mean absolute percentage error (SMAPE):

Where is a tensor of target values, and is a tensor of predictions.

Parameters
Return type

Tensor

Returns

Tensor with SMAPE.

Example

>>> from torchmetrics.functional import symmetric_mean_absolute_percentage_error
>>> target = torch.tensor([1, 10, 1e6])
>>> preds = torch.tensor([0.9, 15, 1.2e6])
>>> symmetric_mean_absolute_percentage_error(preds, target)
tensor(0.2290)


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