ncalab.models.applications.classification.classificationNCAhead
===============================================================

.. py:module:: ncalab.models.applications.classification.classificationNCAhead


Classes
-------

.. autoapisummary::

   ncalab.models.applications.classification.classificationNCAhead.ClassificationNCAHead


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

.. py:class:: ClassificationNCAHead(num_hidden_channels: int, num_classes: int, device: torch.device, avg_pool_size: int = 3, hidden_size: int = 64)

   Bases: :py:obj:`ncalab.models.basicNCA.basicNCAhead.BasicNCAHead`


   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

   Initialize internal Module state, shared by both nn.Module and ScriptModule.


   .. py:attribute:: num_hidden_channels


   .. py:attribute:: num_classes


   .. py:attribute:: device


   .. py:attribute:: avg_pool_size
      :value: 3



   .. py:attribute:: classifier


   .. py:method:: forward(x)

      :param x: Input tensor
      :type x: torch.Tensor

      :returns: NotImplemented, subclasses are required to implement this method.



   .. py:method:: freeze(freeze_last: bool = False)

      Freeze head weights.

      :param freeze_last: Whether to freeze the last layer (if applicable), defaults to True
      :type freeze_last: bool, optional
      :returns: NotImplemented, subclasses are required to implement this method.