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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.

Parameters

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.

compute()[source]

Computes mean squared 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_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)
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