:py:mod:`ggt.losses`
====================

.. py:module:: ggt.losses


Submodules
----------
.. toctree::
   :titlesonly:
   :maxdepth: 1

   aleatoric_cov_loss/index.rst
   aleatoric_loss/index.rst
   losses/index.rst


Package Contents
----------------

Classes
~~~~~~~

.. autoapisummary::

   ggt.losses.AleatoricLoss
   ggt.losses.AleatoricCovLoss




.. py:class:: AleatoricLoss(average=True)

   Bases: :py:obj:`torch.nn.Module`

   Base class for all neural network modules.

   Your models should also subclass this class.

   Modules can also contain other Modules, allowing to nest them in
   a tree structure. You can assign the submodules as regular attributes::

       import torch.nn as nn
       import torch.nn.functional as F

       class Model(nn.Module):
           def __init__(self):
               super(Model, self).__init__()
               self.conv1 = nn.Conv2d(1, 20, 5)
               self.conv2 = nn.Conv2d(20, 20, 5)

           def forward(self, x):
               x = F.relu(self.conv1(x))
               return F.relu(self.conv2(x))

   Submodules assigned in this way will be registered, and will have their
   parameters converted too when you call :meth:`to`, etc.

   .. py:method:: forward(outputs, targets)



.. py:class:: AleatoricCovLoss(num_var=3, average=True)

   Bases: :py:obj:`torch.nn.Module`

   Base class for all neural network modules.

   Your models should also subclass this class.

   Modules can also contain other Modules, allowing to nest them in
   a tree structure. You can assign the submodules as regular attributes::

       import torch.nn as nn
       import torch.nn.functional as F

       class Model(nn.Module):
           def __init__(self):
               super(Model, self).__init__()
               self.conv1 = nn.Conv2d(1, 20, 5)
               self.conv2 = nn.Conv2d(20, 20, 5)

           def forward(self, x):
               x = F.relu(self.conv1(x))
               return F.relu(self.conv2(x))

   Submodules assigned in this way will be registered, and will have their
   parameters converted too when you call :meth:`to`, etc.

   .. py:method:: forward(outputs, targets)