MeanError

class ignite.contrib.metrics.regression.MeanError(output_transform=<function Metric.<lambda>>, device=device(type='cpu'))

Calculates the Mean Error.

$$\text{ME} = \frac{1}{n}\sum_{j=1}^n (A_j - P_j)$$

where $A_j$ is the ground truth and $P_j$ is the predicted value.

More details can be found in the reference Botchkarev 2018.

• update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

• y and y_pred must be of same shape (N, ) or (N, 1).

Parameters are inherited from Metric.__init__.

Parameters

• output_transform (Callable) – a callable that is used to transform the Engine’s process_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, device]) – specifies which device updates are accumulated on. Setting the metric’s device to be the same as your update arguments ensures the update method is non-blocking. By default, CPU.

Methods

compute	Computes the metric based on it's accumulated state.
reset	Resets the metric to it's initial state.

compute()

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 when completed() is called.

Return type

Any

Raises

NotComputableError – raised when the metric cannot be computed.

reset()

Resets the metric to it’s initial state.

By default, this is called at the start of each epoch.

Return type

None