def test():
print('loading data...')
tokenizer = FullTokenizer(config.bert_vocab, do_lower_case=config.to_lower)
pos_2_id, id_2_pos = read_dict(config.pos_dict)
tag_2_id, id_2_tag = read_dict(config.tag_dict)
config.num_pos = len(pos_2_id)
config.num_tag = len(tag_2_id)
data_reader = DataReader(config, tokenizer, pos_2_id, tag_2_id)
test_data = data_reader.read_test_data()
print('building model...')
model = get_model(config, is_training=False)
saver = tf.train.Saver(max_to_keep=1)
with tf.Session(config=sess_config) as sess:
if tf.train.latest_checkpoint(config.result_dir):
saver.restore(sess, tf.train.latest_checkpoint(config.result_dir))
print('loading model from {}'.format(tf.train.latest_checkpoint(config.result_dir)))
print('========== Test ==========')
test_batch_iter = make_batch_iter(list(zip(*test_data)), config.batch_size, shuffle=False)
outputs, test_loss, test_accu = evaluate(sess, model, test_batch_iter, verbose=True)
print('The average test loss is {:>.4f}, average test accuracy is {:>.4f}'.format(test_loss, test_accu))
print('========== Saving Result ==========')
save_result(outputs, config.test_result, tokenizer, id_2_tag)
else:
print('model not found.')
print('done')
def test():
print('load data...')
word_2_id, id_2_word = read_dict(config.word_dict)
accu_2_id, id_2_accu = read_dict(config.accu_dict)
art_2_id, id_2_art = read_dict(config.art_dict)
if os.path.exists(config.word2vec_model):
embedding_matrix = load_embedding(config.word2vec_model,
word_2_id.keys())
else:
embedding_matrix = np.random.uniform(
-0.5, 0.5, [len(word_2_id), config.embedding_size])
data_reader = DataReader(config)
test_data = data_reader.read_test_data(word_2_id, accu_2_id, art_2_id)
art_data = data_reader.read_article(art_2_id.keys(), word_2_id)
print('build model...')
with tf.variable_scope('model'):
model = get_model(config, embedding_matrix, is_training=False)
saver = tf.train.Saver(max_to_keep=1)
with tf.Session(config=config_proto) as sess:
print('load model from: ' + config.model_file)
saver.restore(sess, config.model_file)
print('========== Test ==========')
test_batch_iter = make_batch_iter(list(zip(*test_data)),
config.batch_size,
shuffle=False)
outputs = inference(sess,
model,
test_batch_iter,
art_data,
verbose=True)
save_result(outputs, config.test_result, id_2_accu, id_2_art)
result = judger.get_result(config.test_data, config.test_result)
accu_micro_f1, accu_macro_f1 = judger.calc_f1(result[0])
article_micro_f1, article_macro_f1 = judger.calc_f1(result[1])
score = [(accu_micro_f1 + accu_macro_f1) / 2,
(article_micro_f1 + article_macro_f1) / 2]
print('Micro-F1 of accusation: %.4f' % accu_micro_f1)
print('Macro-F1 of accusation: %.4f' % accu_macro_f1)
print('Micro-F1 of relevant articles: %.4f' % article_micro_f1)
print('Macro-F1 of relevant articles: %.4f' % article_macro_f1)
print('Score: ', score)
def main():
if not os.path.exists(config.result_dir):
os.makedirs(config.result_dir)
if not os.path.exists(config.train_log_dir):
os.makedirs(config.train_log_dir)
if not os.path.exists(config.valid_log_dir):
os.makedirs(config.valid_log_dir)
print('preparing data...')
config.word_2_id, config.id_2_word = read_dict(config.word_dict)
config.attr_2_id, config.id_2_attr = read_dict(config.attr_dict)
config.vocab_size = min(config.vocab_size, len(config.word_2_id))
config.oov_vocab_size = len(config.word_2_id) - config.vocab_size
config.attr_size = len(config.attr_2_id)
embedding_matrix = None
if args.do_train:
if os.path.exists(config.glove_file):
print('loading embedding matrix from file: {}'.format(config.glove_file))
embedding_matrix, config.word_em_size = load_glove_embedding(config.glove_file, list(config.word_2_id.keys()))
print('shape of embedding matrix: {}'.format(embedding_matrix.shape))
else:
if os.path.exists(config.glove_file):
with open(config.glove_file, 'r', encoding='utf-8') as fin:
line = fin.readline()
config.word_em_size = len(line.strip().split()) - 1
data_reader = DataReader(config)
evaluator = Evaluator('description')
print('building model...')
model = get_model(config, embedding_matrix)
saver = tf.train.Saver(max_to_keep=10)
if args.do_train:
print('loading data...')
train_data = data_reader.read_train_data()
valid_data = data_reader.read_valid_data_small()
print_title('Trainable Variables')
for v in tf.trainable_variables():
print(v)
print_title('Gradients')
for g in model.gradients:
print(g)
with tf.Session(config=sess_config) as sess:
model_file = args.model_file
if model_file is None:
model_file = tf.train.latest_checkpoint(config.result_dir)
if model_file is not None:
print('loading model from {}...'.format(model_file))
saver.restore(sess, model_file)
else:
print('initializing from scratch...')
tf.global_variables_initializer().run()
train_writer = tf.summary.FileWriter(config.train_log_dir, sess.graph)
valid_writer = tf.summary.FileWriter(config.valid_log_dir, sess.graph)
run_train(sess, model, train_data, valid_data, saver, evaluator, train_writer, valid_writer, verbose=True)
if args.do_eval:
print('loading data...')
valid_data = data_reader.read_valid_data()
with tf.Session(config=sess_config) as sess:
model_file = args.model_file
if model_file is None:
model_file = tf.train.latest_checkpoint(config.result_dir)
if model_file is not None:
print('loading model from {}...'.format(model_file))
saver.restore(sess, model_file)
predicted_ids, alignment_history, valid_loss, valid_accu = run_evaluate(sess, model, valid_data, verbose=True)
print('average valid loss: {:>.4f}, average valid accuracy: {:>.4f}'.format(valid_loss, valid_accu))
print_title('Saving Result')
save_result(predicted_ids, alignment_history, config.id_2_word, config.valid_data, config.valid_result)
evaluator.evaluate(config.valid_data, config.valid_result, config.to_lower)
else:
print('model not found!')
if args.do_test:
print('loading data...')
test_data = data_reader.read_test_data()
with tf.Session(config=sess_config) as sess:
model_file = args.model_file
if model_file is None:
model_file = tf.train.latest_checkpoint(config.result_dir)
if model_file is not None:
print('loading model from {}...'.format(model_file))
saver.restore(sess, model_file)
predicted_ids, alignment_history = run_test(sess, model, test_data, verbose=True)
print_title('Saving Result')
save_result(predicted_ids, alignment_history, config.id_2_word, config.test_data, config.test_result)
evaluator.evaluate(config.test_data, config.test_result, config.to_lower)
else:
print('model not found!')
def main():
os.makedirs(config.temp_dir, exist_ok=True)
os.makedirs(config.result_dir, exist_ok=True)
os.makedirs(config.train_log_dir, exist_ok=True)
logger.setLevel(logging.INFO)
init_logger(logging.INFO, 'temp.log.txt', 'w')
logger.info('preparing data...')
config.word_2_id, config.id_2_word = read_json_dict(config.vocab_dict)
config.vocab_size = min(config.vocab_size, len(config.word_2_id))
config.oov_vocab_size = min(config.oov_vocab_size,
len(config.word_2_id) - config.vocab_size)
embedding_matrix = None
if args.do_train:
if os.path.exists(config.glove_file):
logger.info('loading embedding matrix from file: {}'.format(
config.glove_file))
embedding_matrix, config.word_em_size = load_glove_embedding(
config.glove_file, list(config.word_2_id.keys()))
logger.info('shape of embedding matrix: {}'.format(
embedding_matrix.shape))
else:
if os.path.exists(config.glove_file):
with open(config.glove_file, 'r', encoding='utf-8') as fin:
line = fin.readline()
config.word_em_size = len(line.strip().split()) - 1
data_reader = DataReader(config)
evaluator = Evaluator('tgt')
logger.info('building model...')
model = get_model(config, embedding_matrix)
saver = tf.train.Saver(max_to_keep=10)
if args.do_train:
logger.info('loading data...')
train_data = data_reader.read_train_data()
valid_data = data_reader.read_valid_data()
logger.info(log_title('Trainable Variables'))
for v in tf.trainable_variables():
logger.info(v)
logger.info(log_title('Gradients'))
for g in model.gradients:
logger.info(g)
with tf.Session(config=sess_config) as sess:
model_file = args.model_file
if model_file is None:
model_file = tf.train.latest_checkpoint(
os.path.join(config.result_dir, config.current_model))
if model_file is not None:
logger.info('loading model from {}...'.format(model_file))
saver.restore(sess, model_file)
else:
logger.info('initializing from scratch...')
tf.global_variables_initializer().run()
train_writer = tf.summary.FileWriter(config.train_log_dir,
sess.graph)
valid_log_history = run_train(sess, model, train_data, valid_data,
saver, evaluator, train_writer)
save_json(
valid_log_history,
os.path.join(config.result_dir, config.current_model,
'valid_log_history.json'))
if args.do_eval:
logger.info('loading data...')
valid_data = data_reader.read_valid_data()
with tf.Session(config=sess_config) as sess:
model_file = args.model_file
if model_file is None:
model_file = tf.train.latest_checkpoint(
os.path.join(config.result_dir, config.current_model))
if model_file is not None:
logger.info('loading model from {}...'.format(model_file))
saver.restore(sess, model_file)
predicted_ids, valid_loss, valid_accu = run_evaluate(
sess, model, valid_data)
logger.info(
'average valid loss: {:>.4f}, average valid accuracy: {:>.4f}'
.format(valid_loss, valid_accu))
logger.info(log_title('Saving Result'))
save_outputs(predicted_ids, config.id_2_word,
config.valid_data, config.valid_outputs)
results = evaluator.evaluate(config.valid_data,
config.valid_outputs,
config.to_lower)
save_json(results, config.valid_results)
else:
logger.info('model not found!')
if args.do_test:
logger.info('loading data...')
test_data = data_reader.read_test_data()
with tf.Session(config=sess_config) as sess:
model_file = args.model_file
if model_file is None:
model_file = tf.train.latest_checkpoint(
os.path.join(config.result_dir, config.current_model))
if model_file is not None:
logger.info('loading model from {}...'.format(model_file))
saver.restore(sess, model_file)
predicted_ids = run_test(sess, model, test_data)
logger.info(log_title('Saving Result'))
save_outputs(predicted_ids, config.id_2_word, config.test_data,
config.test_outputs)
results = evaluator.evaluate(config.test_data,
config.test_outputs,
config.to_lower)
save_json(results, config.test_results)
else:
logger.info('model not found!')