def transe(data_path, embedding_dims, margin_value, score_func, batch_size,
learning_rate, n_generator, n_rank_calculator, max_epoch):
kg = KnowledgeGraph(data_dir=data_path)
kge_model = TransE(kg=kg,
embedding_dim=embedding_dims,
margin_value=margin_value,
score_func=score_func,
batch_size=batch_size,
learning_rate=learning_rate,
n_generator=n_generator,
n_rank_calculator=n_rank_calculator)
gpu_config = tf.GPUOptions(allow_growth=True)
sess_config = tf.ConfigProto(gpu_options=gpu_config)
with tf.Session(config=sess_config) as sess:
print('-----Initializing tf graph-----')
tf.global_variables_initializer().run()
print('-----Initialization accomplished-----')
entity_embedding, relation_embedding = kge_model.check_norm(
session=sess)
summary_writer = tf.summary.FileWriter(logdir='../summary/',
graph=sess.graph)
for epoch in range(max_epoch):
print('=' * 30 + '[EPOCH {}]'.format(epoch) + '=' * 30)
kge_model.launch_training(session=sess,
summary_writer=summary_writer)
if (epoch + 1) % 10 == 0:
kge_model.launch_evaluation(session=sess)
return entity_embedding, relation_embedding
def main():
parser = argparse.ArgumentParser(description='TransE')
parser.add_argument('--data_dir', type=str, default='./data/')
parser.add_argument('--embedding_dim', type=int, default=200)
parser.add_argument('--margin_value', type=float, default=1.0)
parser.add_argument('--score_func', type=str, default='L1')
parser.add_argument('--batch_size', type=int, default=5000)
parser.add_argument('--learning_rate', type=float, default=0.003)
parser.add_argument('--n_generator', type=int, default=24)
parser.add_argument('--n_rank_calculator', type=int, default=24)
parser.add_argument('--ckpt_dir', type=str, default='../ckpt/')
parser.add_argument('--summary_dir', type=str, default='../summary/')
parser.add_argument('--max_epoch', type=int, default=500)
parser.add_argument('--eval_freq', type=int, default=10000000)
args = parser.parse_args()
print(args)
kg = KnowledgeGraph(data_dir=args.data_dir)
kge_model = TransE(kg=kg, embedding_dim=args.embedding_dim, margin_value=args.margin_value,
score_func=args.score_func, batch_size=args.batch_size, learning_rate=args.learning_rate,
n_generator=args.n_generator, n_rank_calculator=args.n_rank_calculator)
gpu_config = tf.GPUOptions(allow_growth=True)
sess_config = tf.ConfigProto(gpu_options=gpu_config)
with tf.Session(config=sess_config) as sess:
print('-----Initializing tf graph-----')
tf.global_variables_initializer().run()
print('-----Initialization accomplished-----')
kge_model.check_norm(session=sess)
summary_writer = tf.summary.FileWriter(logdir=args.summary_dir, graph=sess.graph)
for epoch in range(args.max_epoch):
print('=' * 30 + '[EPOCH {}]'.format(epoch) + '=' * 30)
kge_model.launch_training(session=sess, summary_writer=summary_writer)
if (epoch + 1) % args.eval_freq == 0:
kge_model.launch_evaluation(session=sess)
if (epoch + 1) % 10 == 0:
kge_model.save_embedding(session=sess)
def transe(id,data_path,embedding_dims,margin_value,score_func,batch_size,learning_rate,n_generator,n_rank_calculator,max_epoch,d):
kg = KnowledgeGraph(data_dir=data_path)
content = []
kge_model = TransE(kg=kg, embedding_dim=embedding_dims, margin_value=margin_value,
score_func=score_func, batch_size=batch_size, learning_rate=learning_rate,
n_generator=n_generator, n_rank_calculator=n_rank_calculator,id=id)
gpu_config = tf.GPUOptions(allow_growth=True)
sess_config = tf.ConfigProto(gpu_options=gpu_config)
with tf.Session(config=sess_config) as sess:
# print('-----Initializing tf graph-----')
tf.global_variables_initializer().run()
# print('-----Initialization accomplished-----')
# loss,entity_embedding,relation_embedding = kge_model.check_norm(session=sess)
summary_writer = tf.summary.FileWriter(logdir='../summary/', graph=sess.graph)
for epoch in range(max_epoch):
# print('=' * 30 + '[EPOCH {}]'.format(epoch) + '=' * 30)
# print(loss)
kge_model.launch_training(session=sess, summary_writer=summary_writer)
if (epoch + 1) % 50 == 0:
kge_model.launch_evaluation(session=sess)
loss,entity_embedding,relation_embedding = kge_model.check_norm(session=sess)
content.append(loss)
content.append(entity_embedding)
content.append(relation_embedding)
# print(relation_embedding.shape)
d[id] = content
print('FB15k-{} loss:{}'.format(id,d[id][0]))
# print(type(d))
return entity_embedding,relation_embedding
def main():
parser = argparse.ArgumentParser(description='TransE')
parser.add_argument('--data_dir', type=str, default='../data/FB15k/')
parser.add_argument('--embedding_dim', type=int, default=200)
parser.add_argument('--margin_value', type=float, default=1.0)
parser.add_argument('--score_func', type=str, default='L1')
parser.add_argument('--batch_size', type=int, default=4800)
parser.add_argument('--learning_rate', type=float, default=0.001)
parser.add_argument('--n_generator', type=int, default=24)
parser.add_argument('--n_rank_calculator', type=int, default=24)
parser.add_argument('--ckpt_dir', type=str, default='../ckpt/')
parser.add_argument('--model_name', type=str)
parser.add_argument('--summary_dir', type=str, default='../summary/')
parser.add_argument('--max_epoch', type=int, default=500)
parser.add_argument('--eval_freq', type=int, default=10)
args = parser.parse_args()
print(args)
kg = KnowledgeGraph(data_dir=args.data_dir)
kge_model = TransE(kg=kg,
embedding_dim=args.embedding_dim,
margin_value=args.margin_value,
score_func=args.score_func,
batch_size=args.batch_size,
learning_rate=args.learning_rate,
n_generator=args.n_generator,
n_rank_calculator=args.n_rank_calculator,
model_name=args.model_name,
ckpt_dir=args.ckpt_dir)
gpu_config = tf.GPUOptions(allow_growth=False)
sess_config = tf.ConfigProto(gpu_options=gpu_config)
with tf.Session(config=sess_config) as sess:
print('-----Initializing tf graph-----')
tf.global_variables_initializer().run()
print('-----Initialization accomplished-----')
kge_model.check_norm(session=sess)
summary_writer = tf.summary.FileWriter(logdir=args.summary_dir,
graph=sess.graph)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=500)
for epoch in range(args.max_epoch):
print('=' * 30 + '[EPOCH {}]'.format(epoch) + '=' * 30)
kge_model.launch_training(session=sess,
summary_writer=summary_writer)
if (epoch + 1) % args.eval_freq == 0:
kge_model.launch_evaluation(session=sess, saver=saver)
print('-----Save checkpoint-----')
step_str = str(kge_model.global_step.eval(session=sess))
save_path = args.ckpt_dir + '/' + args.model_name + step_str + '.ckpt'
saver_path = saver.save(sess, save_path)
tf.saved_model.simple_save(
sess,
args.ckpt_dir + '/model-' + step_str,
inputs={'triple': kge_model.eval_triple},
outputs={
'entity-embedding': kge_model.entity_embedding,
'relation-embedding': kge_model.relation_embedding
})
print("Model saved in path: %s" % saver_path)
def main():
parser = argparse.ArgumentParser(description='TransE')
parser.add_argument('--data_dir', type=str, default='../data/after_big/')
parser.add_argument('--embedding_dim', type=int, default=200)
parser.add_argument('--margin_value', type=float, default=1.0)
parser.add_argument('--score_func', type=str, default='L1')
parser.add_argument('--batch_size', type=int, default=4800)
parser.add_argument('--eval_batch_size', type=int, default=200)
parser.add_argument('--learning_rate', type=float, default=0.001)
parser.add_argument('--n_generator', type=int, default=24)
parser.add_argument('--n_rank_calculator', type=int, default=24)
parser.add_argument('--ckpt_dir', type=str, default='../ckpt/')
parser.add_argument('--summary_dir', type=str, default='../summary/')
parser.add_argument('--max_epoch', type=int, default=1000)
parser.add_argument('--eval_freq', type=int, default=200)
parser.add_argument('--log_file',
type=str,
default='../log/log_after_big.txt')
args = parser.parse_args()
print(args)
file_object = open(args.log_file, 'w')
file_object.close()
fb15k = Dataset(data_dir=args.data_dir, log_file=args.log_file)
kge_model = TransE(dataset=fb15k,
embedding_dim=args.embedding_dim,
margin_value=args.margin_value,
score_func=args.score_func,
batch_size=args.batch_size,
eval_batch_size=args.eval_batch_size,
learning_rate=args.learning_rate,
n_generator=args.n_generator,
n_rank_calculator=args.n_rank_calculator,
log_file=args.log_file)
gpu_config = tf.GPUOptions(allow_growth=True)
sess_config = tf.ConfigProto(gpu_options=gpu_config)
with tf.Session(config=sess_config) as sess:
print('-----Initializing tf graph-----')
file_object = open(args.log_file, 'a')
file_object.write('-----Initializing tf graph-----\r\n')
file_object.close()
tf.global_variables_initializer().run()
print('-----Initialization accomplished-----')
print('----Check norm----')
file_object = open(args.log_file, 'a')
file_object.write('-----Initialization accomplished-----\r\n')
file_object.write('----Check norm----\r\n')
file_object.close()
entity_embedding = kge_model.entity_embedding.eval(session=sess)
relation_embedding = kge_model.relation_embedding.eval(session=sess)
entity_norm = np.linalg.norm(entity_embedding, ord=2, axis=1)
relation_norm = np.linalg.norm(relation_embedding, ord=2, axis=1)
print('entity norm: {} relation norm: {}'.format(
entity_norm, relation_norm))
file_object = open(args.log_file, 'a')
file_object.write('entity norm: {} relation norm: {}'.format(
entity_norm, relation_norm) + '\r\n')
file_object.close()
summary_writer = tf.summary.FileWriter(logdir=args.summary_dir,
graph=sess.graph)
saver = tf.train.Saver(max_to_keep=1)
for epoch in range(args.max_epoch):
print('=' * 30 + '[EPOCH {}]'.format(epoch) + '=' * 30)
file_object = open(args.log_file, 'a')
file_object.write('=' * 30 + '[EPOCH {}]'.format(epoch) +
'=' * 30 + '\r\n')
file_object.close()
kge_model.launch_training(session=sess,
summary_writer=summary_writer)
saver.save(sess, '../ckpt/after_big.ckpt', global_step=epoch + 1)
if (epoch + 1) % args.eval_freq == 0:
kge_model.launch_evaluation(session=sess)
def main():
parser = argparse.ArgumentParser(description='TransE')
parser.add_argument('--mode', type=str, default='eval')
parser.add_argument('--data_dir', type=str, default='../data/FB15k/')
parser.add_argument('--embedding_dim', type=int, default=200)
parser.add_argument('--margin_value', type=float, default=1.0)
parser.add_argument('--score_func', type=str, default='L1')
parser.add_argument('--batch_size', type=int, default=4800)
parser.add_argument('--learning_rate', type=float, default=0.001)
parser.add_argument('--n_generator', type=int, default=24)
parser.add_argument('--n_rank_calculator', type=int, default=24)
parser.add_argument('--hit_at_n', type=int, default=10)
parser.add_argument('--ckpt_dir', type=str, default='../ckpt/')
parser.add_argument('--summary_dir', type=str, default='../summary/')
parser.add_argument('--max_epoch', type=int, default=500)
parser.add_argument('--eval_freq', type=int, default=10)
args = parser.parse_args()
print(args)
kg = KnowledgeGraph(data_dir=args.data_dir)
kge_model = TransE(kg=kg,
model_path=args.ckpt_dir,
embedding_dim=args.embedding_dim,
margin_value=args.margin_value,
score_func=args.score_func,
batch_size=args.batch_size,
learning_rate=args.learning_rate,
n_generator=args.n_generator,
n_rank_calculator=args.n_rank_calculator,
hit_at_n=args.hit_at_n)
gpu_config = tf.GPUOptions(allow_growth=True)
sess_config = tf.ConfigProto(gpu_options=gpu_config)
if args.mode == 'test':
saver = tf.train.Saver()
else:
saver = tf.train.Saver(tf.global_variables())
with tf.Session(config=sess_config) as sess:
if args.mode == 'eval':
print('-----Loading from checkpoints-----')
ckpt_file = tf.train.latest_checkpoint(args.ckpt_dir)
saver.restore(sess, ckpt_file)
kge_model.launch_evaluation(session=sess)
elif args.mode == 'train':
print('-----Initializing tf graph-----')
tf.global_variables_initializer().run()
print('-----Initialization accomplished-----')
kge_model.check_norm(session=sess)
summary_writer = tf.summary.FileWriter(logdir=args.summary_dir,
graph=sess.graph)
for epoch in range(args.max_epoch):
print('=' * 30 + '[EPOCH {}]'.format(epoch) + '=' * 30)
kge_model.launch_training(epoch=epoch,
session=sess,
summary_writer=summary_writer,
saver=saver)
# if (epoch + 1) % args.eval_freq == 0:
# kge_model.launch_evaluation(session=sess)
elif args.mode == 'predict':
print('-----Loading from checkpoints-----')
ckpt_file = tf.train.latest_checkpoint(args.ckpt_dir)
saver.restore(sess, ckpt_file)
kge_model.launch_prediction(session=sess)
else:
print('Wrong mode!! (mode = train|test|predict)')