phitodeep.loss
==============

.. py:module:: phitodeep.loss


Classes
-------

.. autoapisummary::

   phitodeep.loss.LossBase
   phitodeep.loss.MeanSquaredError
   phitodeep.loss.CategoricalCrossEntropy
   phitodeep.loss.BinaryCrossEntropy
   phitodeep.loss.Hinge
   phitodeep.loss.Huber


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

.. py:class:: LossBase(name)

   .. py:attribute:: name


   .. py:method:: loss_func(y_pred, y_true)
      :abstractmethod:



   .. py:method:: loss_gradient(y_pred, y_true)
      :abstractmethod:



.. py:class:: MeanSquaredError

   Bases: :py:obj:`LossBase`


   .. py:method:: loss_func(y_pred, y_true)


   .. py:method:: loss_gradient(y_pred, y_true)


.. py:class:: CategoricalCrossEntropy

   Bases: :py:obj:`LossBase`


   .. py:method:: loss_func(y_pred, y_true)


   .. py:method:: loss_gradient(y_pred, y_true)


.. py:class:: BinaryCrossEntropy

   Bases: :py:obj:`LossBase`


   .. py:method:: loss_func(y_pred, y_true)


   .. py:method:: loss_gradient(y_pred, y_true)


.. py:class:: Hinge

   Bases: :py:obj:`LossBase`


   .. py:method:: loss_func(y_pred, y_true)


   .. py:method:: loss_gradient(y_pred, y_true)


.. py:class:: Huber(delta=1.0)

   Bases: :py:obj:`LossBase`


   .. py:attribute:: delta
      :value: 1.0



   .. py:method:: loss_func(y_pred, y_true)


   .. py:method:: loss_gradient(y_pred, y_true)