timesead.models.layers.planar_nf
================================

.. py:module:: timesead.models.layers.planar_nf


Classes
-------

.. autoapisummary::

   timesead.models.layers.planar_nf.PlanarTransform
   timesead.models.layers.planar_nf.PlanarFlow


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

.. py:class:: PlanarTransform(dim: int, epsilon: float = 0.0001)

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


   Implementation of the invertible transformation used in planar flow
       f(z) = z + u * h(dot(w.T, z) + b)

   See Section 4.1 in https://arxiv.org/pdf/1505.05770.pdf.

   Initialise weights and bias.

   :param dim: Dimensionality of the distribution to be estimated.


   .. py:attribute:: epsilon
      :value: 0.0001



   .. py:attribute:: w


   .. py:attribute:: b


   .. py:attribute:: u


   .. py:method:: forward(z: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]


   .. py:method:: get_u_hat() -> None

      Enforce w^T u >= -1. When using h(.) = tanh(.), this is a sufficient condition
      for invertibility of the transformation f(z). See Appendix A.1.



.. py:class:: PlanarFlow(dim: int, num_layers: int = 6)

   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 them to be nested 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) -> None:
               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 also have their
   parameters converted 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

   Make a planar flow by stacking planar transformations in sequence.

   :param dim: Dimensionality of the distribution to be estimated.
   :param num_layers: Number of transformations in the flow.


   .. py:attribute:: layers


   .. py:method:: forward(z: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]