:py:mod:`orcanet_contrib.parser_orcatrain`
==========================================

.. py:module:: orcanet_contrib.parser_orcatrain

.. autoapi-nested-parse::

   Use orga.train with a parser.

   Usage:
       parser_orcatrain.py [options] FOLDER LIST CONFIG MODEL
       parser_orcatrain.py (-h | --help)

   Arguments:
       FOLDER  Path to the folder where everything gets saved to, e.g. the
               summary.txt, the plots, the trained models, etc.
       LIST    A .toml file which contains the pathes of the training and
               validation files. An example can be found in
               examples/list_file.toml
       CONFIG  A .toml file which sets up the training. An example can be
               found in examples/config_file.toml. The possible parameters
               are listed in core.py in the class Configuration.
       MODEL   Path to a .toml file with infos about a model.
               An example can be found in examples/explanation.toml.

   Options:
       -h --help    Show this screen.
       --recompile  Recompile the keras model, e.g. needed if the loss weights
                    are changed during the training.

   ..
       !! processed by numpydoc !!


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


Functions
~~~~~~~~~

.. autoapisummary::

   orcanet_contrib.parser_orcatrain.orca_train
   orcanet_contrib.parser_orcatrain.main



.. py:function:: orca_train(output_folder, list_file, config_file, model_file, recompile_model=False)

   
   Run orga.train with predefined ModelBuilder networks using a parser.


   :Parameters:

       **output_folder** : str
           Path to the folder where everything gets saved to, e.g. the summary
           log file, the plots, the trained models, etc.

       **list_file** : str
           Path to a list file which contains pathes to all the h5 files that
           should be used for training and validation.

       **config_file** : str
           Path to a .toml file which overwrites some of the default settings
           for training and validating a model.

       **model_file** : str
           Path to a file with parameters to build a model of a predefined
           architecture with OrcaNet.

       **recompile_model** : bool
           If the model should be recompiled or not. Necessary, if e.g. the
           loss_weights are changed during the training.














   ..
       !! processed by numpydoc !!

.. py:function:: main()

   
   Run the orca_train function with a parser. 
















   ..
       !! processed by numpydoc !!