:py:mod:`orcanet.backend`
=========================

.. py:module:: orcanet.backend

.. autoapi-nested-parse::

   Code for training and validating NN's, as well as evaluating them.

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


Functions
~~~~~~~~~

.. autoapisummary::

   orcanet.backend.train_model
   orcanet.backend.validate_model
   orcanet.backend.weighted_average
   orcanet.backend.h5_inference
   orcanet.backend.make_model_prediction



.. py:function:: train_model(orga, model, epoch, batch_logger=False)

   
   Train a model on one file and return the history.


   :Parameters:

       **orga** : orcanet.core.Organizer
           Contains all the configurable options in the OrcaNet scripts.

       **model** : keras.Model
           A compiled keras model.

       **epoch** : tuple
           Current epoch and the no of the file to train on.

       **batch_logger** : bool
           Use the orcanet batchlogger to log the training.

   :Returns:

       **history** : dict
           The history of the training on this file. A record of training
           loss values and metrics values.













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.. py:function:: validate_model(orga, model)

   
   Validates a model on all validation files and return the history.


   :Parameters:

       **orga** : orcanet.core.Organizer
           Contains all the configurable options in the OrcaNet scripts.

       **model** : keras.Model
           A compiled keras model.

   :Returns:

       **history** : dict
           The history of the validation on all files. A record of validation
           loss values and metrics values.













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.. py:function:: weighted_average(histories, f_sizes)

   
   Average multiple histories, weighted with the file size.

   Each history can have multiple metrics, which are averaged seperatly.

   :Parameters:

       **histories** : List
           List of histories, one for each file. Each history is also
           a list: each entry is a different loss or metric.

       **f_sizes** : List
           List of the file sizes, in the same order as the histories, i.e.
           the file of histories[0] has the length f_sizes[0].

   :Returns:

       **wgtd_average** : List
           The weighted averaged history. Has the same length as each
           history in the histories List, i.e. one entry per loss or metric.













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.. py:function:: h5_inference(orga, model, files_dict, output_path, samples=None, use_def_label=True)

   
   Let a model predict on all samples in a h5 file, and save it as a h5 file.

   Per default, the h5 file will contain a datagroup y_values straight from
   the given files, as well as two datagroups per output layer of the network,
   which have the labels and the predicted values in them as numpy arrays,
   respectively.

   :Parameters:

       **orga** : orcanet.core.Organizer
           Contains all the configurable options in the OrcaNet scripts.

       **model** : keras.Model
           Trained Keras model of a neural network.

       **files_dict** : dict
           Dict mapping model input names to h5 file paths.

       **output_path** : str
           Name of the output h5 file containing the predictions.

       **samples** : int, optional
           Dont use all events in the file, but instead only the given number.

       **use_def_label** : bool
           If True and no label modifier is given by user, use the default
           label modifier instead of none.














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.. py:function:: make_model_prediction(orga, model, epoch, fileno, samples=None)

   
   Let a model predict on all validation samples, and save it as a h5 file.

   Per default, the h5 file will contain a datagroup y_values straight from
   the given files, as well as two datagroups per output layer of the network,
   which have the labels and the predicted values in them as numpy arrays,
   respectively.

   :Parameters:

       **orga** : orcanet.core.Organizer
           Contains all the configurable options in the OrcaNet scripts.

       **model** : keras.Model
           A compiled keras model.

       **epoch** : int
           Epoch of the last model training step in the epoch, file_no tuple.

       **fileno** : int
           File number of the last model training step in the epoch, file_no tuple.

       **samples** : int or None
           Number of events that should be predicted.
           If samples=None, the whole file will be used.














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