def evaluate(args):
"""
evaluate the trained model on dev files
"""
logger = logging.getLogger("brc")
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert len(args.dev_files) > 0, 'No dev files are provided.'
brc_data = BRCDataset(args.max_p_num,
args.max_p_len,
args.max_q_len,
args.max_word_len,
dev_files=args.dev_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Restoring the model...')
rc_model = RCModel(vocab, args)
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
rc_model.finalize()
logger.info('Evaluating the model on dev set...')
dev_batches = brc_data.gen_mini_batches('dev',
args.batch_size,
pad_id=vocab.get_id(
vocab.pad_token),
shuffle=False)
dev_loss, dev_bleu_rouge = rc_model.evaluate(dev_batches,
result_dir=args.result_dir,
result_prefix='dev.predicted')
logger.info('Loss on dev set: {}'.format(dev_loss))
logger.info('Result on dev set: {}'.format(dev_bleu_rouge))
logger.info('Predicted answers are saved to {}'.format(
os.path.join(args.result_dir)))
def train(args):
"""
trains the reading comprehension model
"""
logger = logging.getLogger("brc")
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
print("vocab.size() = ", vocab.size())
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len,
args.max_word_len, args.train_files, args.dev_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab, args.use_char_level == 'true')
logger.info('Initialize the model...')
rc_model = RCModel(vocab, args)
if args.retrain == 'true':
rc_model.restore(model_dir=args.model_restore_dir,
model_prefix=args.algo_restore)
rc_model.finalize()
logger.info('Training the model...')
rc_model.train(brc_data,
args.epochs,
args.batch_size,
save_dir=args.model_dir,
save_prefix=args.algo,
dropout_keep_prob=args.dropout_keep_prob)
logger.info('Done with model training!')
def predict(args):
"""
predicts answers for test files
"""
logger = logging.getLogger("brc")
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert len(args.test_files) > 0, 'No test files are provided.'
brc_data = BRCDataset(args.max_p_num,
args.max_p_len,
args.max_q_len,
args.max_word_len,
test_files=args.test_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Restoring the model...')
rc_model = RCModel(vocab, args)
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
rc_model.finalize()
# 增加完所有操作后采用sess.graph.finalize()
# 来使得整个graph变为只读的
# 注意：tf.train.Saver()
# 也算是往graph中添加node， 所以也必须放在finilize前
# 但是，，tf.train.Saver()
# 只会存储
# 在该Saver声明时已经存在的变量！！！
logger.info('Predicting answers for test set...')
test_batches = brc_data.gen_mini_batches('test',
args.batch_size,
pad_id=vocab.get_id(
vocab.pad_token),
shuffle=False)
rc_model.evaluate(test_batches,
result_dir=args.result_dir,
result_prefix='test.predicted')