def main():
parser = argparse.ArgumentParser(description="Experiment setup")
# misc
parser.add_argument('--seed', default=33, type=int)
parser.add_argument('--gpu', default="", type=str)
parser.add_argument('--no_train', default=False, action="store_true")
parser.add_argument('--from_model_ckpt', default=None, type=str)
parser.add_argument('--no_rules', default=False, action="store_true")
parser.add_argument('--rule_thr', default=1e-2, type=float)
parser.add_argument('--no_preds', default=False, action="store_true")
parser.add_argument('--get_vocab_embed',
default=False,
action="store_true")
parser.add_argument('--exps_dir', default=None, type=str)
parser.add_argument('--exp_name', default=None, type=str)
# data property
parser.add_argument('--datadir', default=None, type=str)
parser.add_argument('--resplit', default=False, action="store_true")
parser.add_argument('--no_link_percent', default=0., type=float)
parser.add_argument('--type_check', default=False, action="store_true")
parser.add_argument('--domain_size', default=128, type=int)
parser.add_argument('--no_extra_facts', default=False, action="store_true")
parser.add_argument('--query_is_language',
default=False,
action="store_true")
parser.add_argument('--vocab_embed_size', default=128, type=int)
# model architecture
parser.add_argument('--num_step', default=3, type=int)
parser.add_argument('--num_layer', default=1, type=int)
parser.add_argument('--rnn_state_size', default=128, type=int)
parser.add_argument('--query_embed_size', default=128, type=int)
# optimization
parser.add_argument('--batch_size', default=64, type=int)
parser.add_argument('--print_per_batch', default=3, type=int)
parser.add_argument('--max_epoch', default=10, type=int)
parser.add_argument('--min_epoch', default=5, type=int)
parser.add_argument('--learning_rate', default=0.001, type=float)
parser.add_argument('--no_norm', default=False, action="store_true")
parser.add_argument('--thr', default=1e-20, type=float)
parser.add_argument('--dropout', default=0., type=float)
# evaluation
parser.add_argument('--get_phead', default=False, action="store_true")
parser.add_argument('--adv_rank', default=False, action="store_true")
parser.add_argument('--rand_break', default=False, action="store_true")
parser.add_argument('--accuracy', default=False, action="store_true")
parser.add_argument('--top_k', default=10, type=int)
d = vars(parser.parse_args())
option = Option(d)
if option.exp_name is None:
option.tag = time.strftime("%y-%m-%d-%H-%M")
else:
option.tag = option.exp_name
if option.resplit:
assert not option.no_extra_facts
if option.accuracy:
assert option.top_k == 1
os.environ["CUDA_VISIBLE_DEVICES"] = option.gpu
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
if not option.query_is_language:
data = Data(option.datadir, option.seed, option.type_check,
option.domain_size, option.no_extra_facts)
else:
data = DataPlus(option.datadir, option.seed)
print("Data prepared.")
option.num_entity = data.num_entity
option.num_operator = data.num_operator
if not option.query_is_language:
option.num_query = data.num_query
else:
option.num_vocab = data.num_vocab
option.num_word = data.num_word # the number of words in each query
option.this_expsdir = os.path.join(option.exps_dir, option.tag)
if not os.path.exists(option.this_expsdir):
os.makedirs(option.this_expsdir)
option.ckpt_dir = os.path.join(option.this_expsdir, "ckpt")
if not os.path.exists(option.ckpt_dir):
os.makedirs(option.ckpt_dir)
option.model_path = os.path.join(option.ckpt_dir, "model")
option.save()
print("Option saved.")
## build the model
learner = Learner(option)
print("Model built.")
saver = tf.train.Saver()
config = tf.ConfigProto()
config.gpu_options.allow_growth = False
config.log_device_placement = False
config.allow_soft_placement = True
with tf.Session(config=config) as sess:
tf.set_random_seed(option.seed)
sess.run(tf.global_variables_initializer())
print("Session initialized.")
if option.from_model_ckpt is not None:
saver.restore(sess, option.from_model_ckpt)
print("Checkpoint restored from model %s" % option.from_model_ckpt)
data.reset(option.batch_size)
experiment = Experiment(sess, saver, option, learner, data)
print("Experiment created.")
if not option.no_train:
print("Start training...")
experiment.train()
if not option.no_preds:
print("Start getting test predictions...")
experiment.get_predictions()
if not option.no_rules:
print("Start getting rules...")
experiment.get_rules()
if option.get_vocab_embed:
print("Start getting vocabulary embedding...")
experiment.get_vocab_embedding()
experiment.close_log_file()
print("=" * 36 + "Finish" + "=" * 36)
def main():
parser = argparse.ArgumentParser(description="Experiment setup")
#杂项
parser.add_argument('--seed', default=33, type=int)
parser.add_argument('--gpu', default="", type=str)
parser.add_argument('--exps_dir', default="../exps/", type=str)
parser.add_argument('--exp_name', default="demo", type=str)
parser.add_argument('--no_train', default=False, action="store_true")
parser.add_argument('--no_rules', default=False, action="store_true")
parser.add_argument('--no_preds', default=False, action="store_true")
parser.add_argument('--rule_thr', default=1e-2, type=float)
#数据属性
parser.add_argument('--datadir', default="../datasets/kinship", type=str)
parser.add_argument('--resplit', default=False, action="store_true")
parser.add_argument('--no_link_percent', default=0., type=float)
#模型结构
parser.add_argument('--num_step', default=3, type=int)
parser.add_argument('--num_layer', default=1, type=int)
parser.add_argument('--rnn_state_size', default=128, type=int)
parser.add_argument('--query_embed_size', default=128, type=int)
#优化
parser.add_argument('--batch_size', default=64, type=int)
parser.add_argument('--learning_rate', default=0.001, type=float)
parser.add_argument('--print_per_batch', default=3, type=int)
parser.add_argument('--norm', default=True, action="store_true")
parser.add_argument('--thr', default=1e-20, type=float)
parser.add_argument('--dropout', default=0., type=float)
parser.add_argument('--max_epoch', default=10, type=int)
parser.add_argument('--min_epoch', default=5, type=int)
#评估
parser.add_argument('--get_phead', default=True, action="store_true")
parser.add_argument('--accuracy', default=False, action="store_true")
parser.add_argument('--top-k', default=10, type=int)
d = vars(parser.parse_args())
option = Option(d)
if option.exp_name is None:
option.tag = time.strftime("%y-%m-%d-%H-%M")
else:
option.tag = option.exp_name
if option.accuracy:
assert option.top_k == 1
os.environ["CUDA_VISIBLE_DEVICE"] = option.gpu
tf.logging.set_verbosity(tf.logging.ERROR)
data = Data(option.datadir, option.seed)
print("Data prepared.")
option.num_entity = data.num_entity
option.num_operator = data.num_operator
option.num_query = data.num_query
option.this_expsdir = os.path.join(option.exps_dir, option.tag)
if not os.path.exists(option.this_expsdir):
os.makedirs(option.this_expsdir)
option.ckpt_dir = os.path.join(option.this_expsdir, "ckpt")
if not os.path.exists(option.ckpt_dir):
os.makedirs(option.ckpt_dir)
option.model_path = os.path.join(option.ckpt_dir, "model")
option.save()
print("Option saved.")
learner = Learner(option)
print("Learner built.")
saver = tf.train.Saver()
config = tf.ConfigProto()
config.gpu_options.allow_growth = False
config.log_device_placement = False
config.allow_soft_placement = True
with tf.Session(config=config) as sess:
tf.set_random_seed(option.seed)
sess.run(tf.global_variables_initializer())
print("Session initialized.")
data.reset(option.batch_size)
experiment = Experiment(sess, saver, option, learner, data)
print("Experiment created.")
if not option.no_train:
print("Start training...")
experiment.train()
if not option.no_preds:
print("Start getting test predictions...")
experiment.get_predictions()
if not option.no_rules:
print("Start getting rules...")
experiment.get_rules()
experiment.close_log_file()
print("=" * 36 + "Finish" + "=" * 36)
