:py:mod:`real_models`
=====================

.. py:module:: real_models


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

Classes
~~~~~~~

.. autoapisummary::

   real_models.Distmult
   real_models.TransE
   real_models.Tucker




Attributes
~~~~~~~~~~

.. autoapisummary::

   real_models.seed


.. py:data:: seed
   :value: 1

   

.. py:class:: Distmult(param)


   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().__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.

   .. note::
       As per the example above, an ``__init__()`` call to the parent class
       must be made before assignment on the child.

   :ivar training: Boolean represents whether this module is in training or
                   evaluation mode.
   :vartype training: bool

   .. py:method:: forward_head_batch(*, e1_idx, rel_idx)


   .. py:method:: forward_head_and_loss(e1_idx, rel_idx, targets)


   .. py:method:: init()


   .. py:method:: get_embeddings()


   .. py:method:: forward_triples(*, e1_idx, rel_idx, e2_idx)


   .. py:method:: forward_triples_and_loss(e1_idx, rel_idx, e2_idx, targets)



.. py:class:: TransE(param)


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

   TransE trained with binary cross entropy

   .. py:method:: init()


   .. py:method:: get_embeddings()


   .. py:method:: forward_triples(*, e1_idx, rel_idx, e2_idx)


   .. py:method:: forward_triples_and_loss(e1_idx, rel_idx, e2_idx, target)


   .. py:method:: forward_head_and_loss(*args, **kwargs)
      :abstractmethod:



.. py:class:: Tucker(param)


   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().__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.

   .. note::
       As per the example above, an ``__init__()`` call to the parent class
       must be made before assignment on the child.

   :ivar training: Boolean represents whether this module is in training or
                   evaluation mode.
   :vartype training: bool

   .. py:method:: init()


   .. py:method:: forward_head_batch(e1_idx, rel_idx)


   .. py:method:: forward_head_and_loss(e1_idx, rel_idx, targets)


   .. py:method:: get_embeddings()


   .. py:method:: forward_triples(*args, **kwargs)
      :abstractmethod:


   .. py:method:: forward_triples_and_loss(*args, **kwargs)
      :abstractmethod: