:py:mod:`mlair.model_modules.residual_networks`
===============================================

.. py:module:: mlair.model_modules.residual_networks


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

Classes
~~~~~~~

.. autoapisummary::

   mlair.model_modules.residual_networks.ResNet




Attributes
~~~~~~~~~~

.. autoapisummary::

   mlair.model_modules.residual_networks.__author__
   mlair.model_modules.residual_networks.__date__


.. py:data:: __author__
   :annotation: = Lukas Leufen

   

.. py:data:: __date__
   :annotation: = 2022-08-23

   

.. py:class:: ResNet(input_shape: list, output_shape: list, layer_configuration: list, optimizer='adam', **kwargs)

   Bases: :py:obj:`mlair.model_modules.convolutional_networks.CNNfromConfig`

   A convolutional neural network with residual blocks (skip connections).

   ```python
   input_shape = [(65,1,9)]
   output_shape = [(4, )]

   # model
   layer_configuration=[
       {"type": "Conv2D", "activation": "relu", "kernel_size": (7, 1), "filters": 32, "padding": "same"},
       {"type": "MaxPooling2D", "pool_size": (2, 1), "strides": (2, 1)},
       {"type": "residual_block", "activation": "relu", "kernel_size": (3, 1), "filters": 32, "strides": (1, 1), "kernel_regularizer": "l2"},
       {"type": "residual_block", "activation": "relu", "kernel_size": (3, 1), "filters": 32, "strides": (1, 1), "kernel_regularizer": "l2"},
       {"type": "residual_block", "activation": "relu", "kernel_size": (3, 1), "filters": 64, "strides": (1, 1), "kernel_regularizer": "l2", "use_1x1conv": True},
       {"type": "residual_block", "activation": "relu", "kernel_size": (3, 1), "filters": 64, "strides": (1, 1), "kernel_regularizer": "l2"},
       {"type": "residual_block", "activation": "relu", "kernel_size": (3, 1), "filters": 128, "strides": (1, 1), "kernel_regularizer": "l2", "use_1x1conv": True},
       {"type": "residual_block", "activation": "relu", "kernel_size": (3, 1), "filters": 128, "strides": (1, 1), "kernel_regularizer": "l2"},
       {"type": "MaxPooling2D", "pool_size": (2, 1), "strides": (2, 1)},
       {"type": "Dropout", "rate": 0.25},
       {"type": "Flatten"},
       {"type": "Dense", "units": 128, "activation": "relu"}
   ]

   model = ResNet(input_shape, output_shape, layer_configuration)
   ```


   .. py:method:: residual_block(**layer_kwargs)
      :staticmethod:


   .. py:method:: _extract_layer_conf(self, layer_opts)