Mean Absolute Error (MAE)¶

Module Interface¶

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

Computes Mean Absolute Error (MAE):

$\text{MAE} = \frac{1}{N}\sum_i^N | y_i - \hat{y_i} |$

Where $y$ is a tensor of target values, and $\hat{y}$ is a tensor of predictions.

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

preds (Tensor): Predictions from model
target (Tensor): Ground truth values

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

mean_absolute_error (Tensor): A tensor with the mean absolute error over the state

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

Example

>>> from torchmetrics import MeanAbsoluteError
>>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
>>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
>>> mean_absolute_error = MeanAbsoluteError()
>>> mean_absolute_error(preds, target)
tensor(0.5000)

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

Functional Interface¶

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

Computes mean absolute error.

Parameters

preds¶ (Tensor) – estimated labels
target¶ (Tensor) – ground truth labels

Return type

Tensor

Returns

Tensor with MAE

Example

>>> from torchmetrics.functional import mean_absolute_error
>>> x = torch.tensor([0., 1, 2, 3])
>>> y = torch.tensor([0., 1, 2, 2])
>>> mean_absolute_error(x, y)
tensor(0.2500)