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

.. py:module:: mlair.model_modules.u_networks


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

Classes
~~~~~~~

.. autoapisummary::

   mlair.model_modules.u_networks.UNet




Attributes
~~~~~~~~~~

.. autoapisummary::

   mlair.model_modules.u_networks.__author__
   mlair.model_modules.u_networks.__date__


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

   

.. py:data:: __date__
   :annotation: = 2022-08-29

   

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

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

   A U-net neural network.

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

   # model
   layer_configuration=[

       # 1st block (down)
       {"type": "Conv2D", "activation": "relu", "kernel_size": (3, 1), "filters": 16, "padding": "same"},
       {"type": "Dropout", "rate": 0.25},
       {"type": "Conv2D", "activation": "relu", "kernel_size": (3, 1), "filters": 16, "padding": "same"},
       {"type": "MaxPooling2D", "pool_size": (2, 1), "strides": (2, 1)},
       {"type": "blocksave"},

       # 2nd block (down)
       {"type": "Conv2D", "activation": "relu", "kernel_size": (3, 1), "filters": 32, "padding": "same"},
       {"type": "Dropout", "rate": 0.25},
       {"type": "Conv2D", "activation": "relu", "kernel_size": (3, 1), "filters": 32, "padding": "same"},
       {"type": "MaxPooling2D", "pool_size": (2, 1), "strides": (2, 1)},
       {"type": "blocksave"},

       # 3rd block (down)
       {"type": "Conv2D", "activation": "relu", "kernel_size": (3, 1), "filters": 64, "padding": "same"},
       {"type": "Dropout", "rate": 0.25},
       {"type": "Conv2D", "activation": "relu", "kernel_size": (3, 1), "filters": 64, "padding": "same"},
       {"type": "MaxPooling2D", "pool_size": (2, 1), "strides": (2, 1)},
       {"type": "blocksave"},

       # 4th block (down)
       {"type": "Conv2D", "activation": "relu", "kernel_size": (3, 1), "filters": 128, "padding": "same"},
       {"type": "Dropout", "rate": 0.25},
       {"type": "Conv2D", "activation": "relu", "kernel_size": (3, 1), "filters": 128, "padding": "same"},
       {"type": "MaxPooling2D", "pool_size": (2, 1), "strides": (2, 1)},
       {"type": "blocksave"},

       # 5th block (final down)
       {"type": "Conv2D", "activation": "relu", "kernel_size": (3, 1), "filters": 256, "padding": "same"},
       {"type": "Dropout", "rate": 0.25},
       {"type": "Conv2D", "activation": "relu", "kernel_size": (3, 1), "filters": 256, "padding": "same"},

       # 6th block (up)
       {"type": "Conv2DTranspose", "activation": "relu", "kernel_size": (2, 1), "filters": 128, "strides": (2, 1),
        "padding": "same"},
       {"type": "ConcatenateUNet"},
       {"type": "Conv2D", "activation": "relu", "kernel_size": (3, 1), "filters": 128, "padding": "same"},
       {"type": "Dropout", "rate": 0.25},
       {"type": "Conv2D", "activation": "relu", "kernel_size": (3, 1), "filters": 128, "padding": "same"},

       # 7th block (up)
       {"type": "Conv2DTranspose", "activation": "relu", "kernel_size": (2, 1), "filters": 64, "strides": (2, 1),
        "padding": "same"},
       {"type": "ConcatenateUNet"},
       {"type": "Conv2D", "activation": "relu", "kernel_size": (3, 1), "filters": 64, "padding": "same"},
       {"type": "Dropout", "rate": 0.25},
       {"type": "Conv2D", "activation": "relu", "kernel_size": (3, 1), "filters": 64, "padding": "same"},

       # 8th block (up)
       {"type": "Conv2DTranspose", "activation": "relu", "kernel_size": (2, 1), "filters": 32, "strides": (2, 1),
        "padding": "same"},
       {"type": "ConcatenateUNet"},
       {"type": "Conv2D", "activation": "relu", "kernel_size": (3, 1), "filters": 32, "padding": "same"},
       {"type": "Dropout", "rate": 0.25},
       {"type": "Conv2D", "activation": "relu", "kernel_size": (3, 1), "filters": 32, "padding": "same"},

       # 8th block (up)
       {"type": "Conv2DTranspose", "activation": "relu", "kernel_size": (2, 1), "filters": 16, "strides": (2, 1),
        "padding": "same"},
       {"type": "ConcatenateUNet"},
       {"type": "Conv2D", "activation": "relu", "kernel_size": (3, 1), "filters": 16, "padding": "same"},
       {"type": "Dropout", "rate": 0.25},
       {"type": "Conv2D", "activation": "relu", "kernel_size": (3, 1), "filters": 16, "padding": "same"},

       # Tail
       {"type": "Flatten"},
       {"type": "Dense", "units": 128, "activation": "relu"}
   ]

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



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


   .. py:method:: set_model(self)

      Abstract method to set model.