nn.functional.mse_loss

lucid.nn.functional.mse_loss(input_: Tensor, target: Tensor, reduction: Literal['mean', 'sum'] | None = 'mean') → Tensor

The mse_loss function computes the mean squared error (MSE) loss, commonly used for regression tasks.

Function Signature

def mse_loss(
    input_: Tensor, target: Tensor, reduction: _ReductionType | None = "mean"
) -> Tensor

Parameters

• input_ (Tensor):

  The input tensor of shape (N, …), where N is the batch size.

• target (Tensor):

  The target tensor of shape matching input_.

• reduction (str | None, optional):

  Specifies the reduction to apply: ‘mean’, ‘sum’, or ‘none’. Default: ‘mean’.

Returns

• Tensor:

  The computed MSE loss as a scalar or tensor, depending on the reduction method.

Examples

>>> import lucid.nn.functional as F
>>> input_ = Tensor([1.0, 2.0, 3.0])
>>> target = Tensor([1.5, 2.5, 3.5])
>>> loss = F.mse_loss(input_, target)
>>> print(loss)
Tensor(...)  # Scalar loss value