:py:mod:`pycmtensor.models.ResLogit`
====================================

.. py:module:: pycmtensor.models.ResLogit


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

.. py:class:: ResLogitLayer(input, w_in, w_out, activation_in=None, activation_out=None)

   
   Definition of the ResLogit neural net layer

   :param input: a list of tensors corresponding to the
   :type input: list or TensorVariable
   :param vector of utilities:
   :param or a `TensorVariable` vector value.:
   :param w_in: the :class:`Weights` object for the input side
   :type w_in: Weights
   :param w_out: the :class:`Weights` object for the output side
   :type w_out: Weights
   :param activation_in: the activation function to use. If
   :type activation_in: function, optional
   :param `None`:
   :param use `aet.sigmoid()`:
   :param activation_out: the activation function to use. If
   :type activation_out: function, optional
   :param None:
   :param use `aet.sigmoid()`:

   .. attribute:: output

      the output of this layer. Pass this value onto the next layer or

   .. attribute:: into the final model.



   .. rubric:: Example

   U = ...
   W_1 = Weights("W_1", (3, 10), 0)
   W_2 = Weights("W_2", (10, 3), 0)
   U = ResLogitLayer(U, W_1, W_2).output
   mymodel = MNL(u=U, av=AV, database=db)

   .. py:method:: get_layer_outputs()

      Returns the layer output vector. Size of vector is equals to the size of the
      input

      :returns: this layer output
      :rtype: TensorVariable