def input_value_classification_deep_l1_training():
logger = logging_daily.logging_daily(
resource_filename('deepbiome', 'tests/data/log_info.yaml'))
logger.reset_logging()
log = logger.get_logging()
log.setLevel(logging_daily.logging.INFO)
config_data = configuration.Configurator(
resource_filename('deepbiome',
'tests/data/classification_deep_l1_path_info.cfg'),
log)
config_data.set_config_map(config_data.get_section_map())
config_data.print_config_map()
config_network = configuration.Configurator(
resource_filename(
'deepbiome', 'tests/data/classification_deep_l1_network_info.cfg'),
log)
config_network.set_config_map(config_network.get_section_map())
config_network.print_config_map()
path_info = config_data.get_config_map()
network_info = config_network.get_config_map()
for k, v in path_info['data_info'].items():
if 'data' in v:
resource_filename('deepbiome', 'tests/%s' % v)
path_info['data_info'][k] = resource_filename(
'deepbiome', 'tests/%s' % v)
return log, network_info, path_info
gpu_memory_fraction = None
try:
max_queue_size = int(argdict['max_queue_size'][0])
except:
max_queue_size = 10
try:
workers = int(argdict['workers'][0])
except:
workers = 1
try:
use_multiprocessing = argdict['use_multiprocessing'][0] == 'True'
except:
use_multiprocessing = False
# Logger ###########################################################
logger = logging_daily.logging_daily(argdict['log_info'][0])
logger.reset_logging()
log = logger.get_logging()
log.setLevel(logging_daily.logging.INFO)
log.info('Argument input')
for argname, arg in argdict.items():
log.info(' {}:{}'.format(argname, arg))
# Configuration ####################################################
config_data = configuration.Configurator(argdict['path_info'][0], log)
config_data.set_config_map(config_data.get_section_map())
config_data.print_config_map()
config_network = configuration.Configurator(argdict['network_info'][0], log)
config_network.set_config_map(config_network.get_section_map())