def main(_):
if FLAGS.build_data:
build_data()
exit()
word_embed = util.load_embedding(word_dim=FLAGS.word_dim)
with tf.Graph().as_default():
all_train = []
all_test = []
data_iter = fudan.read_tfrecord(FLAGS.num_epochs, FLAGS.batch_size)
for task_id, (train_data, test_data) in enumerate(data_iter):
task_name = fudan.get_task_name(task_id)
all_train.append((task_name, train_data))
all_test.append((task_name, test_data))
model_name = 'fudan-mtl'
if FLAGS.adv:
model_name += '-adv'
m_train, m_valid = mtl_model.build_train_valid_model(
model_name, word_embed, all_train, all_test, FLAGS.adv, FLAGS.test)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer()) # for file queue
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(init_op)
print('=' * 80)
if FLAGS.test:
test(sess, m_valid)
else:
train(sess, m_train, m_valid)
def main(_):
if FLAGS.build_data:
build_data()
exit()
word_embed = util.load_embedding(word_dim=FLAGS.word_dim)
with tf.Graph().as_default():
train_iter, test_iter = fudan.read_tfrecord(FLAGS.num_epochs,
FLAGS.batch_size)
train_data = train_iter.get_next()
test_data = test_iter.get_next()
m_train, m_valid = mtl_model.build_train_valid_model(
'fudan-mtl', word_embed, train_data, test_data, FLAGS.adv)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer()) # for file queue
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(init_op)
print('=' * 80)
if FLAGS.test:
test(sess, models)
else:
train(sess, m_train, m_valid, test_iter)
def main(_):
if FLAGS.build_data:
build_data()
exit()
word_embed = util.load_embedding(word_dim=FLAGS.word_dim)
with tf.Graph().as_default():
semeval_train_iter, semeval_test_iter = semeval.read_tfrecord(
FLAGS.num_epochs, FLAGS.batch_size)
dbpedia_train_iter, dbpedia_test_iter = dbpedia.read_tfrecord(
FLAGS.num_epochs, FLAGS.batch_size)
model_name = 'mtl-dbpedia-%d' % FLAGS.word_dim
semeval_train = semeval_train_iter.get_next()
semeval_test = semeval_test_iter.get_next()
dbpedia_train = dbpedia_train_iter.get_next()
dbpedia_test = dbpedia_test_iter.get_next()
m_train, m_valid = mtl_model.build_train_valid_model(
model_name, word_embed, semeval_train, semeval_test, dbpedia_train,
dbpedia_test, FLAGS.is_mtl, FLAGS.is_adv, FLAGS.test)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer()) # for file queue
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(init_op)
print('=' * 80)
if FLAGS.test:
test(sess, m_valid, semeval_test_iter)
else:
# train_dbpedia(sess, m_train, m_valid, dbpedia_test_iter)
train_semeval(sess, m_train, m_valid, semeval_test_iter)
def main(_):
with tf.Graph().as_default():
train_data, test_data, word_embed = base_reader.inputs()
# sv = tf.train.Supervisor()
# with sv.managed_session() as sess:
# print('='*80)
# for i in range(10):
# arr = sess.run(test_data)
# print(train_data[2].shape, arr[2].shape)
# print(arr[2][0])
# exit()
if FLAGS.model == 'cnn':
m_train, m_valid = cnn_model.build_train_valid_model(word_embed,
train_data, test_data)
elif FLAGS.model == 'mtl':
m_train, m_valid = mtl_model.build_train_valid_model(word_embed,
train_data, test_data)
m_train.set_saver(FLAGS.model)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())# for file queue
config = tf.ConfigProto()
# config.gpu_options.per_process_gpu_memory_fraction = 0.9 # 占用GPU90%的显存
config.gpu_options.allow_growth = True
# sv finalize the graph
with tf.Session(config=config) as sess:
sess.run(init_op)
print('='*80)
if FLAGS.trace:
trace_runtime(sess, m_train)
elif FLAGS.test:
test(sess, m_valid)
else:
train(sess, m_train, m_valid)