Shortcuts

Mean Absolute Error (MAE)

Module Interface

class torchmetrics.MeanAbsoluteError(compute_on_step=None, **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.

Parameters
  • 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 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.

compute()[source]

Computes mean absolute error over state.

Return type

Tensor

update(preds, target)[source]

Update state with predictions and targets.

Parameters
  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

Return type

None

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)
Read the Docs v: v0.8.1
Versions
latest
stable
v0.8.1
v0.8.0
v0.7.3
v0.7.2
v0.7.1
v0.7.0
v0.6.2
v0.6.1
v0.6.0
v0.5.1
v0.5.0
v0.4.1
v0.4.0
v0.3.2
v0.3.1
v0.3.0
v0.2.0
v0.1.0
Downloads
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.