parameters = load_parameters()
if args.config is not None:
parameters = update_parameters(parameters, pkl2dict(args.config))
try:
for arg in args.changes:
try:
k, v = arg.split('=')
except ValueError:
print(
'Overwritten arguments must have the form key=Value. \n Currently are: %s'
% str(args.changes))
exit(1)
try:
parameters[k] = ast.literal_eval(v)
except ValueError:
parameters[k] = v
except ValueError:
print('Error processing arguments: (', k, ",", v, ")")
exit(2)
parameters = check_params(parameters)
if parameters['MODE'] == 'training':
logger.info('Running training.')
train_model(parameters, args.dataset)
elif parameters['MODE'] == 'sampling':
logger.error(
'Depecrated function. For sampling from a trained model, please run sample_ensemble.py.'
)
exit(2)
logger.info('Done!')
if __name__ == "__main__":
args = parse_args()
if args.config is None:
logger.info("Reading parameters from config.py")
from config import load_parameters
params = load_parameters()
else:
logger.info("Loading parameters from %s" % str(args.config))
params = pkl2dict(args.config)
try:
for arg in args.changes:
try:
k, v = arg.split('=')
except ValueError:
print(
'Overwritten arguments must have the form key=Value. \n Currently are: %s'
% str(args.changes))
exit(1)
try:
params[k] = ast.literal_eval(v)
except ValueError:
params[k] = v
except ValueError:
print('Error processing arguments: (', k, ",", v, ")")
exit(2)
params = check_params(params)
sample_ensemble(args, params)