MeanAbsoluteError#
- class ignite.metrics.MeanAbsoluteError(output_transform=<function Metric.<lambda>>, device=device(type='cpu'))[source]#
Calculates the mean absolute error.
$\text{MAE} = \frac{1}{N} \sum_{i=1}^N \lvert y_{i} - x_{i} \rvert$where $y_{i}$ is the prediction tensor and $x_{i}$ is ground true tensor.
update
must receive output of the form(y_pred, y)
or{'y_pred': y_pred, 'y': y}
.
- Parameters
output_transform (Callable) – a callable that is used to transform the
Engine
’sprocess_function
’s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as(y_pred, y)
or{'y_pred': y_pred, 'y': y}
.device (Union[str, torch.device]) – specifies which device updates are accumulated on. Setting the metric’s device to be the same as your
update
arguments ensures theupdate
method is non-blocking. By default, CPU.
Methods
Computes the metric based on it's accumulated state.
Resets the metric to it's initial state.
Updates the metric's state using the passed batch output.
- compute()[source]#
Computes the metric based on it’s accumulated state.
By default, this is called at the end of each epoch.
- Returns
- the actual quantity of interest. However, if a
Mapping
is returned, it will be (shallow) flattened into engine.state.metrics whencompleted()
is called. - Return type
Any
- Raises
NotComputableError – raised when the metric cannot be computed.
- reset()[source]#
Resets the metric to it’s initial state.
By default, this is called at the start of each epoch.
- Return type
- update(output)[source]#
Updates the metric’s state using the passed batch output.
By default, this is called once for each batch.
- Parameters
output (Sequence[torch.Tensor]) – the is the output from the engine’s process function.
- Return type