# RootMeanSquaredError#

class ignite.metrics.RootMeanSquaredError(output_transform=<function Metric.<lambda>>, device=device(type='cpu'))[source]#

Calculates the root mean squared error.

$\text{RMSE} = \sqrt{ \frac{1}{N} \sum_{i=1}^N \left(y_{i} - x_{i} \right)^2 }$

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’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, torch.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.
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 when completed() is called.

Return type

Any

Raises

NotComputableError – raised when the metric cannot be computed.