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

.. py:module:: mlair.model_modules.flatten


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


Functions
~~~~~~~~~

.. autoapisummary::

   mlair.model_modules.flatten.get_activation
   mlair.model_modules.flatten.flatten_tail



Attributes
~~~~~~~~~~

.. autoapisummary::

   mlair.model_modules.flatten.__author__
   mlair.model_modules.flatten.__date__


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

   

.. py:data:: __date__
   :annotation: = 2019-12-02

   

.. py:function:: get_activation(input_to_activate: tensorflow.keras.layers, activation: Union[Callable, str], **kwargs)

   Apply activation on a given input layer.

   This helper function is able to handle advanced keras activations as well as strings for standard activations.

   :param input_to_activate: keras layer to apply activation on
   :param activation: activation to apply on `input_to_activate'. Can be a standard keras strings or activation layers
   :param kwargs: keyword arguments used inside activation layer

   :return: activation

   .. code-block:: python

       input_x = ... # your input data
       x_in = keras.layer(<without activation>)(input_x)

       # get activation via string
       x_act_string = get_activation(x_in, 'relu')
       # or get activation via layer callable
       x_act_layer = get_activation(x_in, keras.layers.advanced_activations.ELU)



.. py:function:: flatten_tail(input_x: tensorflow.keras.layers, inner_neurons: int, activation: Union[Callable, str], output_neurons: int, output_activation: Union[Callable, str], reduction_filter: int = None, name: str = None, bound_weight: bool = False, dropout_rate: float = None, kernel_regularizer: tensorflow.keras.regularizers = None)

   Flatten output of convolutional layers.

   :param input_x: Multidimensional keras layer (ConvLayer)
   :param output_neurons: Number of neurons in the last layer (must fit the shape of labels)
   :param output_activation: final activation function
   :param name: Name of the flatten tail.
   :param bound_weight: Use `tanh' as inner activation if set to True, otherwise `activation'
   :param dropout_rate: Dropout rate to be applied between trainable layers
   :param activation: activation to after conv and dense layers
   :param reduction_filter: number of filters used for information compression on `input_x' before flatten()
   :param inner_neurons: Number of neurons in inner dense layer
   :param kernel_regularizer: regularizer to apply on conv and dense layers

   :return: flatten branch with size n=output_neurons

   .. code-block:: python

       input_x = ... # your input data
       conv_out = Conv2D(*args)(input_x) # your convolution stack
       out = flatten_tail(conv_out, inner_neurons=64, activation=keras.layers.advanced_activations.ELU,
                          output_neurons=4
                          output_activation='linear', reduction_filter=64,
                          name='Main', bound_weight=False, dropout_rate=.3,
                          kernel_regularizer=keras.regularizers.l2()
                          )
       model = keras.Model(inputs=input_x, outputs=[out])