CollaborativeCoding.models.johan_model
======================================

.. py:module:: CollaborativeCoding.models.johan_model


Classes
-------

.. autoapisummary::

   CollaborativeCoding.models.johan_model.JohanModel


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

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

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


   Small MLP model for image classification.

   Parameters
   ----------
   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:
   Grayscale images (like MNIST) have C = 1.
   Input shape: (N, 1, 28, 28)
   fc1 Output shape: (N, 77)
   fc2 Output shape: (N, 77)
   fc3 Output shape: (N, 77)
   fc4 Output shape: (N, num_classes)


   .. py:attribute:: in_channels


   .. py:attribute:: height


   .. py:attribute:: width


   .. py:attribute:: num_classes


   .. py:attribute:: in_features


   .. py:attribute:: fc1


   .. py:attribute:: fc2


   .. py:attribute:: fc3


   .. py:attribute:: fc4


   .. py:attribute:: relu


   .. py:attribute:: flatten


   .. py:method:: forward(x)