dacapo.experiments.tasks.losses.mse_loss
========================================

.. py:module:: dacapo.experiments.tasks.losses.mse_loss


Classes
-------

.. autoapisummary::

   dacapo.experiments.tasks.losses.mse_loss.MSELoss


Module Contents
---------------

.. py:class:: MSELoss



   A class used to represent the Mean Square Error Loss function (MSELoss). This class inherits from the Loss class.

   .. method:: compute(prediction, target, weight) -> torch.Tensor

      
      Function to compute the MSELoss for the provided prediction and target, with respect to the weight.

   .. note::

      This class is abstract. Subclasses must implement the abstract methods. Once created, the values of its attributes
      cannot be changed.


   .. py:method:: compute(prediction, target, weight)

      Function to compute the MSELoss for the provided prediction and target, with respect to the weight.

      :param prediction: torch.Tensor
                         The predicted tensor.
      :param target: torch.Tensor
                     The target tensor.
      :param weight: torch.Tensor
                     The weight tensor.

      :returns:

                torch.Tensor
                    The computed MSELoss tensor.

      :raises NotImplementedError: If the method is not implemented in the subclass.

      .. rubric:: Examples

      >>> loss = MSELoss()
      >>> prediction = torch.tensor([1.0, 2.0, 3.0])
      >>> target = torch.tensor([1.0, 2.0, 3.0])
      >>> weight = torch.tensor([1.0, 1.0, 1.0])
      >>> loss.compute(prediction, target, weight)
      tensor(0.)

      .. note:: This method must be implemented in the subclass. It should return the computed MSELoss tensor.