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

.. py:module:: mlair.model_modules.inception_model


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

Classes
~~~~~~~

.. autoapisummary::

   mlair.model_modules.inception_model.InceptionModelBase




Attributes
~~~~~~~~~~

.. autoapisummary::

   mlair.model_modules.inception_model.__author__
   mlair.model_modules.inception_model.__date__
   mlair.model_modules.inception_model.conv_settings_dict


.. py:data:: __author__
   :annotation: = Felix Kleinert, Lukas Leufen

   

.. py:data:: __date__
   :annotation: = 2019-10-22

   

.. py:class:: InceptionModelBase

   This class contains all necessary construction blocks

   .. py:method:: block_part_name(self)

      Use unicode due to some issues of keras with normal strings

      :return:


   .. py:method:: batch_normalisation(self, input_x, **kwargs)


   .. py:method:: create_conv_tower(self, input_x, reduction_filter, tower_filter, tower_kernel, activation='relu', batch_normalisation=False, **kwargs)

      This function creates a "convolution tower block" containing a 1x1 convolution to reduce filter size followed by
      convolution with given filter and kernel size

      :param input_x: Input to network part
      :param reduction_filter: Number of filters used in 1x1 convolution to reduce overall filter size before conv.
      :param tower_filter: Number of filters for n x m convolution
      :param tower_kernel: kernel size for convolution (n,m)
      :param activation: activation function for convolution
      :param batch_normalisation:
      :return:


   .. py:method:: act(self, input_x, activation, **act_settings)


   .. py:method:: _get_act_name(act_name)
      :staticmethod:


   .. py:method:: create_pool_tower(self, input_x, pool_kernel, tower_filter, activation='relu', max_pooling=True, **kwargs)

      This function creates a "MaxPooling tower block"

      :param input_x: Input to network part
      :param pool_kernel: size of pooling kernel
      :param tower_filter: Number of filters used in 1x1 convolution to reduce filter size
      :param activation:
      :param max_pooling:
      :return:


   .. py:method:: inception_block(self, input_x, tower_conv_parts, tower_pool_parts, **kwargs)

      Crate a inception block

      :param input_x: Input to block
      :param tower_conv_parts: dict containing settings for parts of inception block; Example:
                               tower_conv_parts = {'tower_1': {'reduction_filter': 32,
                                                               'tower_filter': 64,
                                                               'tower_kernel': (3, 1),
                                                               'activation' : 'relu',
                                                               'padding' : 'SymPad2D'}
                                                   'tower_2': {'reduction_filter': 32,
                                                               'tower_filter': 64,
                                                               'tower_kernel': (5, 1),
                                                               'activation' : LeakyReLU,
                                                               'padding' : keras.layers.ZeroPadding2D}
                                                   'tower_3': {'reduction_filter': 32,
                                                               'tower_filter': 64,
                                                               'tower_kernel': (1, 1),
                                                               'activation' : ELU,
                                                               'padding' : src.model_modules.advanced_paddings.ReflectionPadding2D}
                                                  }
      :param tower_pool_parts: dict containing settings for pool part of inception block; Example:
                               tower_pool_parts = {'pool_kernel': (3, 1), 'tower_filter': 64, 'padding': 'RefPad2D'}
      :return:



.. py:data:: conv_settings_dict