本文整理匯總了Python中gin.query_parameter方法的典型用法代碼示例。如果您正苦於以下問題：Python gin.query_parameter方法的具體用法？Python gin.query_parameter怎麽用？Python gin.query_parameter使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類gin的用法示例。
在下文中一共展示了gin.query_parameter方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: _default_output_dir
# 需要導入模塊: import gin [as 別名]
# 或者: from gin import query_parameter [as 別名]
def _default_output_dir():
"""Default output directory."""
try:
dataset_name = gin.query_parameter("inputs.dataset_name")
except ValueError:
dataset_name = "random"
dir_name = "{model_name}_{dataset_name}_{timestamp}".format(
model_name=gin.query_parameter("train.model").configurable.name,
dataset_name=dataset_name,
timestamp=datetime.datetime.now().strftime("%Y%m%d_%H%M"),
)
dir_path = os.path.join("~", "trax", dir_name)
print()
trax.log("No --output_dir specified")
return dir_path 示例2: _output_dir_or_default
# 需要導入模塊: import gin [as 別名]
# 或者: from gin import query_parameter [as 別名]
def _output_dir_or_default():
"""Returns a path to the output directory."""
if FLAGS.output_dir:
output_dir = FLAGS.output_dir
trainer_lib.log('Using --output_dir {}'.format(output_dir))
return os.path.expanduser(output_dir)
# Else, generate a default output dir (under the user's home directory).
try:
dataset_name = gin.query_parameter('data_streams.dataset_name')
except ValueError:
dataset_name = 'random'
output_name = '{model_name}_{dataset_name}_{timestamp}'.format(
model_name=gin.query_parameter('train.model').configurable.name,
dataset_name=dataset_name,
timestamp=datetime.datetime.now().strftime('%Y%m%d_%H%M'),
)
output_dir = os.path.join('~', 'trax', output_name)
output_dir = os.path.expanduser(output_dir)
print()
trainer_lib.log('No --output_dir specified')
trainer_lib.log('Using default output_dir: {}'.format(output_dir))
return output_dir
# TODO(afrozm): Share between trainer.py and rl_trainer.py 示例3: main
# 需要導入模塊: import gin [as 別名]
# 或者: from gin import query_parameter [as 別名]
def main(argv):
tf.disable_eager_execution()
tf.disable_v2_behavior()
args = flags.FLAGS
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
if args.env in rvr.utils.config.SC2_MINIGAMES_ALIASES:
args.env = rvr.utils.config.SC2_MINIGAMES_ALIASES[args.env]
if args.test:
args.n_envs = 1
args.log_freq = 1
args.restore = True
expt = rvr.utils.Experiment(args.results_dir, args.env, args.agent, args.experiment, args.restore)
gin_files = rvr.utils.find_configs(args.env, os.path.dirname(os.path.abspath(__file__)))
if args.restore:
gin_files += [expt.config_path]
gin_files += args.gin_files
if not args.gpu:
args.gin_bindings.append("build_cnn_nature.data_format = 'channels_last'")
args.gin_bindings.append("build_fully_conv.data_format = 'channels_last'")
gin.parse_config_files_and_bindings(gin_files, args.gin_bindings)
args.n_envs = min(args.n_envs, gin.query_parameter('ACAgent.batch_sz'))
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess_mgr = rvr.utils.tensorflow.SessionManager(sess, expt.path, args.ckpt_freq, training_enabled=not args.test)
env_cls = rvr.envs.GymEnv if '-v' in args.env else rvr.envs.SC2Env
env = env_cls(args.env, args.render, max_ep_len=args.max_ep_len)
agent = rvr.agents.registry[args.agent](env.obs_spec(), env.act_spec(), sess_mgr=sess_mgr, n_envs=args.n_envs)
agent.logger = rvr.utils.StreamLogger(args.n_envs, args.log_freq, args.log_eps_avg, sess_mgr, expt.log_path)
if sess_mgr.training_enabled:
expt.save_gin_config()
expt.save_model_summary(agent.model)
agent.run(env, args.n_updates * agent.traj_len * agent.batch_sz // args.n_envs)