:py:mod:`ForeTiS.model._model_classes`
======================================

.. py:module:: ForeTiS.model._model_classes


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

Classes
~~~~~~~

.. autoapisummary::

   ForeTiS.model._model_classes.PrintLayer
   ForeTiS.model._model_classes.GetOutputZero
   ForeTiS.model._model_classes.PrepareForlstm
   ForeTiS.model._model_classes.PrepareForDropout
   ForeTiS.model._model_classes.SafeMatern52




.. py:class:: PrintLayer

   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(x)



.. py:class:: GetOutputZero

   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(x)



.. py:class:: PrepareForlstm

   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(x)



.. py:class:: PrepareForDropout

   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(lstm_out)



.. py:class:: SafeMatern52(variance = 1.0, lengthscales = 1.0, **kwargs)

   Bases: :py:obj:`gpflow.kernels.Matern52`

   The Matern 5/2 kernel. Functions drawn from a GP with this kernel are twice
   differentiable. The kernel equation is

   k(r) = σ² (1 + √5r + 5/3r²) exp{-√5 r}

   where:
   r  is the Euclidean distance between the input points, scaled by the lengthscales parameter ℓ,
   σ² is the variance parameter.

   .. py:method:: euclid_dist(X, X2)