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Mean Squared Error (MSE)

Module Interface

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

Compute 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

Example

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

plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters
  • val (Union[Tensor, Sequence[Tensor], None]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.

  • ax (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Returns

Figure object ax: Axes object

Return type

fig

Raises

ModuleNotFoundError – If matplotlib is not installed

>>> from torch import randn
>>> # Example plotting a single value
>>> from torchmetrics.regression import MeanSquaredError
>>> metric = MeanSquaredError()
>>> metric.update(randn(10,), randn(10,))
>>> fig_, ax_ = metric.plot()

(Source code, png, hires.png, pdf)

../_images/mean_squared_error-1.png
>>> from torch import randn
>>> # Example plotting multiple values
>>> from torchmetrics.regression import MeanSquaredError
>>> metric = MeanSquaredError()
>>> values = []
>>> for _ in range(10):
...     values.append(metric(randn(10,), randn(10,)))
>>> fig, ax = metric.plot(values)

(Source code, png, hires.png, pdf)

../_images/mean_squared_error-2.png

Functional Interface

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

Compute 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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