orcanet_contrib.parser_orcapred

Use orga.predict with a parser.

Usage:

parser_orcapred.py FOLDER LIST CONFIG MODEL [–epoch EPOCH] [–fileno FILENO] parser_orcapred.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. –epoch=EPOCH Use model of given epoch. [default: None] –fileno=FILENO Use model of given fileno. [default: None]

Module Contents

Functions

orca_pred(output_folder, list_file, config_file, ...)

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

main()

Run the orca_pred function with a parser.

orcanet_contrib.parser_orcapred.orca_pred(output_folder, list_file, config_file, model_file, epoch=None, fileno=None)[source]

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

Per default, the most recent saved model will be loaded.

Parameters
output_folderstr

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

list_filestr

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

config_filestr

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

model_filestr

Path to a file with parameters to build a model of a predefined architecture with OrcaNet.

epochint, optional

The epoch of the saved model to predict with.

filenoint, optional

The filenumber of the saved model to predict with.

orcanet_contrib.parser_orcapred.main()[source]

Run the orca_pred function with a parser.