.. AUTO-GENERATED FILE -- DO NOT EDIT!

:orphan:

.. currentmodule:: deepsulci.sulci_labeling.capsul.training


.. _deepsulci.sulci_labeling.capsul.training.SulciDeepTraining:

deepsulci.sulci_labeling.capsul.training.SulciDeepTraining
==========================================================

SulciDeepTraining
-----------------



    Process to train a UNET neural network to automatically label the sulci.

    This process consists of four steps. Each step depends on the previous
    step (except step 4 that is independent of step 3).
    However, they can be started independently if the previous steps have
    already been completed.

    The first step is to extract from the graphs the data useful for training
    the neural network (buckets and corresponding sulcus names).
    These data are stored in Jason files (buckets and names in
    traindata_file and sulci list in param_file).

    The second step allows to set the hyperparameters (learning rate and
    momentum) by 3-fold cross-validation.
    These hyperparameters are saved in the Jason file param_file.

    The third step is to train the UNET neural network on the entire database.
    The neural network parameters are saved in the file model_param.mdsm

    The fourth step allows to set the cutting hyperparameter (threshold on the
    Calinski-Harabaz index) by 3-fold cross-validation.
    This hyperparameter is saved in the Jason file param_file.

    The model takes approximately 20 hours to be trained on the GPU with a
    training database of about 60 subjects (step 1: 15min, step 2: 16h, step 3:
    20min, step 4: 3h).

    **Warning:** Graphs should be of the same side!

    
    
    .. note::
    
        * Type 'SulciDeepTraining.help()' for a full description of this process parameters.
        * Type '<SulciDeepTraining>.get_input_spec()' for a full description of this process input trait types.
        * Type '<SulciDeepTraining>.get_output_spec()' for a full description of this process output trait types.
    

.. _deepsulci.sulci_labeling.capsul.training.SulciDeepTraining_inputs:


Inputs
~~~~~~

[Mandatory]

**graphs**: a legal value (['List_File'] - mandatory)
    training base graphs
**graphs_notcut**: a legal value (['List_File'] - mandatory)
    training base graphs before manual cutting of the elementary folds
**cuda**: an integer (['Int'] - mandatory)
    device on which to run the training(-1 for cpu, i>=0 for the i-th gpu)

[Optional]

**translation_file**: a string or os.PathLike object (['File'] - optional)
    file (.trl) containing the translation of the sulci toapplied on the training base graphs (optional)
**step_1**: a boolean (['Bool'] - optional)
    perform the data extraction step from the graphs
**step_2**: a boolean (['Bool'] - optional)
    perform the hyperparameter tuning step (learning rate and momentum)
**step_3**: a boolean (['Bool'] - optional)
    perform the model training step
**step_4**: a boolean (['Bool'] - optional)
    perform the cutting hyperparameter tuning step

.. _deepsulci.sulci_labeling.capsul.training.SulciDeepTraining_outputs:


Outputs
~~~~~~~

**model_file**: a string or os.PathLike object (['File (filename: input)'] - mandatory)
    file (.mdsm) storing neural network parameters
**param_file**: a string or os.PathLike object (['File (filename: input)'] - mandatory)
    file (.json) storing the hyperparameters (learning rate, momentum, cutting threshold)
**traindata_file**: a string or os.PathLike object (['File (filename: input)'] - mandatory)
    file (.json) storing the data extracted from the training base graphs