def main(_):
config = config_lib.get_config()
embed = dataset.Embed(config.out_dir, config.trimmed_embed300_file,
config.vocab_file)
ini_word_embed = embed.load_embedding()
semeval_record = semeval_v2.SemEvalCleanedRecordData(
None, config.out_dir, config.semeval_train_record,
config.semeval_test_record)
vocab_tags = dataset.Label(config.semeval_dir, config.semeval_tags_file)
with tf.Graph().as_default():
train_iter = semeval_record.train_data(config.hparams.num_epochs,
config.hparams.batch_size)
test_iter = semeval_record.test_data(1, config.hparams.batch_size)
train_data = train_iter.get_next()
test_data = test_iter.get_next()
m_train, m_valid = rnn_model.build_train_valid_model(
config, ini_word_embed, train_data, test_data)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer()) # for file queue
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
for tensor in tf.trainable_variables():
tf.logging.info(tensor.op.name)
with tf.Session(config=sess_config) as sess:
sess.run(init_op)
print('=' * 80)
# for batch in range(3):
# # (labels, lengths, sentence, tags) = sess.run(train_data)
# # print(sentence.shape, tags.shape)
# l, w = sess.run([onehot_tags, weights])
# print(l.shape, w.shape)
# print(w)
# # sess.run(test_iter.initializer)
# # for batch in range(28):
# # (labels, lengths, sentence, tags) = sess.run(test_data)
# # print(sentence.shape, tags.shape)
# exit()
if FLAGS.test:
test(sess, m_valid, test_iter, vocab_tags)
else:
train_semeval(config, sess, m_train, m_valid, test_iter,
vocab_tags)
def main(_):
vocab_mgr = dataset.VocabMgr()
word_embed = vocab_mgr.load_embedding()
semeval_record = semeval_v2.SemEvalCleanedRecordData(None)
# load dataset
train_data = semeval_record.train_data(FLAGS.num_epochs, FLAGS.batch_size)
test_data = semeval_record.test_data(1, FLAGS.batch_size)
# model_name = 'cnn-%d-%d' % (FLAGS.word_dim, FLAGS.num_epochs)
model = cnn_model.CNNModel(word_embed, FLAGS.is_adv)
# for tensor in tf.trainable_variables():
# tf.logging.info(tensor.op.name)
model.train_and_eval(FLAGS.num_epochs, 80, FLAGS.lrn_rate, train_data,
test_data)
def main(_):
vocab_mgr = dataset.VocabMgr()
word_embed = vocab_mgr.load_embedding()
nyt_record = nyt2010.NYT2010CleanedRecordData(None)
semeval_record = semeval_v2.SemEvalCleanedRecordData(None)
with tf.Graph().as_default():
train_iter = semeval_record.train_data(FLAGS.num_epochs, FLAGS.batch_size)
test_iter = semeval_record.test_data(1, FLAGS.batch_size)
# unsup_iter = nyt_record.unsup_data(FLAGS.num_epochs, FLAGS.batch_size)
model_name = 'cnn-%d-%d' % (FLAGS.word_dim, FLAGS.num_epochs)
train_data = train_iter.get_next()
test_data = test_iter.get_next()
# unsup_data = unsup_iter.get_next()
unsup_data = None
m_train, m_valid = cnn_model.build_train_valid_model(
model_name, word_embed,
train_data, test_data, unsup_data,
FLAGS.is_adv, FLAGS.is_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
for tensor in tf.trainable_variables():
tf.logging.info(tensor.op.name)
with tf.Session(config=config) as sess:
sess.run(init_op)
print('='*80)
if FLAGS.is_test:
test(sess, m_valid, test_iter)
else:
train_semeval(sess, m_train, m_valid, test_iter)
semeval_text.length_statistics()
# gen vocab
vocab = dataset.Vocab(config.out_dir, config.vocab_file)
vocab.generate_vocab(semeval_text.tokens())
# trim embedding
embed = dataset.Embed(config.out_dir, config.trimmed_embed300_file,
config.vocab_file)
google_embed = dataset.Embed(config.pretrain_embed_dir,
config.google_embed300_file,
config.google_words_file)
embed.trim_pretrain_embedding(google_embed)
# build SemEval record data
semeval_text.set_vocab(vocab)
tag_converter = semeval_v2.TagConverter(config.semeval_dir,
config.semeval_relations_file,
config.semeval_tags_file)
semeval_text.set_tag_converter(tag_converter)
semeval_record = semeval_v2.SemEvalCleanedRecordData(
semeval_text, config.out_dir, config.semeval_train_record,
config.semeval_test_record)
semeval_record.generate_data()
# INFO:tensorflow:(percent, quantile) [(50, 18.0), (70, 22.0), (80, 25.0),
# (90, 29.0), (95, 34.0), (98, 40.0), (100, 97.0)]
# INFO:tensorflow:generate vocab to data/generated/vocab.txt
# INFO:tensorflow:trim embedding to data/generated/embed300.trim.npy
# INFO:tensorflow:generate TFRecord data
semeval_text = semeval_v2.SemEvalCleanedTextData() # nyt_text = nyt2010.NYT2010CleanedTextData() # length statistics semeval_text.length_statistics() # nyt_text.length_statistics() # gen vocab vocab_mgr = dataset.VocabMgr() vocab_mgr.generate_vocab(semeval_text.tokens()) # trim embedding vocab_mgr.trim_pretrain_embedding() # build SemEval record data semeval_text.set_vocab_mgr(vocab_mgr) semeval_record = semeval_v2.SemEvalCleanedRecordData(semeval_text) semeval_record.generate_data() # build nyt record data # nyt_text.set_vocab_mgr(vocab_mgr) # nyt_record = nyt2010.NYT2010CleanedRecordData(nyt_text) # nyt_record.generate_data() # INFO:tensorflow:(percent, quantile) [(50, 17.0), (70, 21.0), (80, 24.0), (90, 29.0), (95, 33.0), (98, 40.0), (100, 98.0)] # INFO:tensorflow:(percent, quantile) [(50, 39.0), (70, 47.0), (80, 53.0), (90, 62.0), (95, 71.0), (98, 84.0), (100, 9621.0)] # INFO:tensorflow:generate TFRecord data # INFO:tensorflow:generate TFRecord data # INFO:tensorflow:ignore 1361 examples
