def use_orcanet():
temp_folder = "output/"
os.mkdir(temp_folder)
make_dummy_data(temp_folder)
list_file = "example_list.toml"
organizer = Organizer(temp_folder + "sum_model", list_file)
organizer.cfg.train_logger_display = 10
model = make_dummy_model()
organizer.train_and_validate(model, epochs=3)
organizer.predict()
def predict(directory,
list_file=None,
config_file=None,
epoch=None,
fileno=None):
from orcanet.core import Organizer
orga = Organizer(directory, list_file, config_file, tf_log_level=1)
return orga.predict(epoch=epoch, fileno=fileno)[0]
def orca_pred(output_folder,
list_file,
config_file,
model_file,
epoch=None,
fileno=None):
"""
Run orga.predict with predefined ModelBuilder networks using a parser.
Per default, the most recent saved model will be loaded.
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.
epoch : int, optional
The epoch of the saved model to predict with.
fileno : int, optional
The filenumber of the saved model to predict with.
"""
# Set up the Organizer with the input data
orga = Organizer(output_folder, list_file, config_file, tf_log_level=1)
# When predicting with a orga model, the right modifiers and custom
# objects need to be given
update_objects(orga, model_file)
# Per default, a prediction will be done for the model with the
# highest epoch and filenumber.
orga.predict(epoch=epoch, fileno=fileno)