CollaborativeCoding.models.christian_model
==========================================

.. py:module:: CollaborativeCoding.models.christian_model


Attributes
----------

.. autoapisummary::

   CollaborativeCoding.models.christian_model.x


Classes
-------

.. autoapisummary::

   CollaborativeCoding.models.christian_model.CNNBlock
   CollaborativeCoding.models.christian_model.ChristianModel


Functions
---------

.. autoapisummary::

   CollaborativeCoding.models.christian_model.find_fc_input_shape


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

.. py:class:: CNNBlock(in_channels, out_channels)

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


   CNN block with Conv2d, MaxPool2d, and ReLU.

   Args
   ----

   in_channels : int
       Number of input channels.
   out_channels : int
       Number of output channels.


   .. py:attribute:: conv


   .. py:attribute:: maxpool


   .. py:attribute:: relu


   .. py:method:: forward(x)


.. py:function:: find_fc_input_shape(image_shape, *cnn_layers)

   Find the shape of the input to the fully connected layer.

   Code inspired by @Seilmast (https://github.com/SFI-Visual-Intelligence/Collaborative-Coding-Exam/issues/67#issuecomment-2651212254)

   Args
   ----
   image_shape : tuple(int, int, int)
       Shape of the input image (C, H, W).
   cnn_layers : nn.Module
       List of CNN layers.

   Returns
   -------
   int
       Number of elements in the input to the fully connected layer.


.. py:class:: ChristianModel(image_shape, num_classes)

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


   Simple CNN model for image classification.

   Args
   ----
   image_shape : tuple(int, int, int)
       Shape of the input image (C, H, W).
   num_classes : int
       Number of classes in the dataset.

   Processing Images
   -----------------
   Input: (N, C, H, W)
       N: Batch size
       C: Number of input channels
       H: Height of the input image
       W: Width of the input image

   Example:
   For grayscale images, C = 1.

   Input Image Shape: (5, 1, 16, 16)
   CNN1 Output Shape: (5, 50, 8, 8)
   CNN2 Output Shape: (5, 100, 4, 4)
   FC Output Shape: (5, num_classes)


   .. py:attribute:: cnn1


   .. py:attribute:: cnn2


   .. py:attribute:: fc1


   .. py:method:: forward(x)


.. py:data:: x