def train(user_conf):
"""
Parameters
----------
user_conf : dict
Json dict (created with json.dumps) with the user's configuration parameters that will replace the defaults.
Must be loaded with json.loads()
For example:
user_conf={'num_classes': 'null', 'lr_step_decay': '0.1', 'lr_step_schedule': '[0.7, 0.9]', 'use_early_stopping': 'false'}
"""
CONF = config.CONF
# Update the conf with the user input
for group, val in sorted(CONF.items()):
for g_key, g_val in sorted(val.items()):
g_val['value'] = json.loads(user_conf[g_key])
# Check the configuration
try:
config.check_conf(conf=CONF)
except Exception as e:
raise BadRequest(e)
CONF = config.conf_dict(conf=CONF)
timestamp = datetime.now().strftime('%Y-%m-%d_%H%M%S')
config.print_conf_table(CONF)
K.clear_session() # remove the model loaded for prediction
train_fn(TIMESTAMP=timestamp, CONF=CONF)
# Sync with NextCloud folders (if NextCloud is available)
try:
mount_nextcloud(paths.get_models_dir(), 'ncplants:/models')
except Exception as e:
print(e)
def update_with_query_conf(user_args):
"""
Update the default YAML configuration with the user's input args from the API query
"""
# Update the default conf with the user input
CONF = config.CONF
for group, val in sorted(CONF.items()):
for g_key, g_val in sorted(val.items()):
if g_key in user_args:
g_val['value'] = json.loads(user_args[g_key])
# Check and save the configuration
config.check_conf(conf=CONF)
config.conf_dict = config.get_conf_dict(conf=CONF)
def update_with_saved_conf(saved_conf):
"""
Update the default YAML configuration with the configuration saved from training
"""
# Update the default conf with the user input
CONF = config.CONF
for group, val in sorted(CONF.items()):
if group in saved_conf.keys():
for g_key, g_val in sorted(val.items()):
if g_key in saved_conf[group].keys():
g_val['value'] = saved_conf[group][g_key]
# Check and save the configuration
config.check_conf(conf=CONF)
config.conf_dict = config.get_conf_dict(conf=CONF)