:py:mod:`docsexp.models`
========================

.. py:module:: docsexp.models


Submodules
----------
.. toctree::
   :titlesonly:
   :maxdepth: 1

   cnn/index.rst
   resnet/index.rst


Package Contents
----------------

Classes
~~~~~~~

.. autoapisummary::

   docsexp.models.MyClass
   docsexp.models.ConvNet



Functions
~~~~~~~~~

.. autoapisummary::

   docsexp.models.conv3x3
   docsexp.models.calculate



.. py:function:: conv3x3(in_channels: int, out_channels: int, stride: int = 1) -> torch.nn.Module


.. py:function:: calculate()


.. py:class:: MyClass(num, time, age)

   Bases: :py:obj:`object`

   Test for MyClass

   :param num: number
   :type num: int
   :param age: age of class
   :type age: int
   :param time: time of class
   :type time: int


.. py:class:: ConvNet(num_classes: int = 10)

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

   Convolutional network

   :param num_classes: number of output classes
   :type num_classes: int

   .. rubric:: Example

   >>> from docsexp.models import ConvNet
   >>> num_classes = 10
   >>> model = ConvNet(num_classes)

   .. py:method:: forward(self, x)

      Forward function for CNN.

      :param x: input features
      :type x: torch.Tensor