FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
print("{}={}".format(attr.upper(), value))
print("")
vocab, idf = data_helpers.loadVocab(FLAGS.vocab_file)
print(len(vocab))
charVocab = data_helpers.loadCharVocab(FLAGS.char_vocab_file)
SEQ_LEN = FLAGS.max_sequence_length
answer_data = data_helpers.loadAnswers(FLAGS.answer_file, vocab, SEQ_LEN)
test_dataset = data_helpers.loadDataset(FLAGS.test_file, vocab, SEQ_LEN,
answer_data)
target_loss_weight = [1.0, 1.0]
print("\nEvaluating...\n")
# Evaluation
# ==================================================
checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
print(checkpoint_file)
graph = tf.Graph()
with graph.as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
print("")
# Data Preparatopn
# ==================================================
# Load data
print("Loading data...")
vocab, idf = data_helpers.loadVocab(FLAGS.vocab_file)
print(len(vocab))
charVocab = data_helpers.loadCharVocab(FLAGS.char_vocab_file)
SEQ_LEN = FLAGS.max_sequence_length
answer_data = data_helpers.loadAnswers(FLAGS.answer_file, vocab, SEQ_LEN)
train_dataset = data_helpers.loadDataset(FLAGS.train_file, vocab, SEQ_LEN,
answer_data)
print('train_pairs: {}'.format(len(train_dataset)))
test_dataset = data_helpers.loadDataset(FLAGS.valid_file, vocab, SEQ_LEN,
answer_data)
target_loss_weight = [1.0, 1.0]
with tf.Graph().as_default():
with tf.device("/gpu:0"):
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():
format(mrr, top_1_precision, total_valid_query))
print('Top-2 precision: {}'.format(top_2_precision))
print('Top-5 precision: {}'.format(top_5_precision))
print('Top-10 precision: {}'.format(top_10_precision))
return mrr
if __name__ == "__main__":
# Load fixtures
print("Loading data...")
vocab = data_helpers.loadVocab(FLAGS.vocab_file)
charVocab = data_helpers.loadCharVocab(FLAGS.char_vocab_file)
answer_data = data_helpers.loadAnswers(FLAGS.answers_file, vocab,
FLAGS.max_sequence_length)
train_dataset = data_helpers.loadDataset(FLAGS.train_file,
vocab,
FLAGS.max_sequence_length,
answer_data,
do_label_smoothing=True)
print('train_pairs: {}'.format(len(train_dataset)))
valid_dataset = data_helpers.loadDataset(FLAGS.valid_file,
vocab,
FLAGS.max_sequence_length,
answer_data,
do_label_smoothing=False)
print('dev_pairs: {}'.format(len(valid_dataset)))
train()