Mean Squared Error (MSE)¶

Module Interface¶

class torchmetrics.MeanSquaredError(squared=True, **kwargs)[source]

Computes mean squared error (MSE):

$\text{MSE} = \frac{1}{N}\sum_i^N(y_i - \hat{y_i})^2$

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_squared_error (Tensor): A tensor with the mean squared error

Parameters

squared¶ (bool) – If True returns MSE value, if False returns RMSE value.
kwargs¶ (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Example

>>> from torchmetrics import MeanSquaredError
>>> target = torch.tensor([2.5, 5.0, 4.0, 8.0])
>>> preds = torch.tensor([3.0, 5.0, 2.5, 7.0])
>>> mean_squared_error = MeanSquaredError()
>>> mean_squared_error(preds, target)
tensor(0.8750)

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

Functional Interface¶

torchmetrics.functional.mean_squared_error(preds, target, squared=True)[source]

Computes mean squared error.

Parameters

preds¶ (Tensor) – estimated labels
target¶ (Tensor) – ground truth labels
squared¶ (bool) – returns RMSE value if set to False

Return type

Tensor

Returns

Tensor with MSE

Example

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