FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
print("{}={}".format(attr.upper(), value))
print("")
vocab = data_helpers.load_vocab(FLAGS.vocab_file)
print('vocabulary size: {}'.format(len(vocab)))
charVocab = data_helpers.load_char_vocab(FLAGS.char_vocab_file)
response_data = data_helpers.load_responses(FLAGS.response_file, vocab,
FLAGS.max_response_len)
print('response_data size: {}'.format(len(response_data)))
test_dataset = data_helpers.load_dataset(FLAGS.test_file, vocab,
FLAGS.max_utter_len,
FLAGS.max_utter_num, response_data)
print('test_pairs: {}'.format(len(test_dataset)))
target_loss_weight = [1.0, 1.0]
print("\nEvaluating...\n")
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("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
print("{}={}".format(attr.upper(), value))
print("")
# Load data
print("Loading data...")
vocab = data_helpers.load_vocab(FLAGS.vocab_file)
print('vocabulary size: {}'.format(len(vocab)))
charVocab = data_helpers.load_char_vocab(FLAGS.char_vocab_file)
print('charVocab size: {}'.format(len(charVocab)))
train_dataset = data_helpers.load_dataset(FLAGS.train_file, vocab,
FLAGS.max_utter_num,
FLAGS.max_utter_len,
FLAGS.max_response_len,
FLAGS.max_persona_len)
print('train dataset size: {}'.format(len(train_dataset)))
valid_dataset = data_helpers.load_dataset(FLAGS.valid_file, vocab,
FLAGS.max_utter_num,
FLAGS.max_utter_len,
FLAGS.max_response_len,
FLAGS.max_persona_len)
print('valid dataset size: {}'.format(len(valid_dataset)))
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(config=session_conf)
print("\nParameters:")
# tf.app.flags.FLAGS.flag_values_dict()
for attr in sorted(FLAGS.__flags.keys()):
print("{}={}".format(attr.upper(), getattr(FLAGS, attr)))
print("")
# Load data
print("Loading data...")
vocab = data_helpers.load_vocab(FLAGS.vocab_file)
print('vocabulary size: {}'.format(len(vocab)))
charVocab = data_helpers.load_char_vocab(FLAGS.char_vocab_file)
print('charVocab size: {}'.format(len(charVocab)))
train_dataset = data_helpers.load_dataset(FLAGS.train_file, vocab,
FLAGS.max_utter_num,
FLAGS.max_utter_len)
print('train_dataset: {}'.format(len(train_dataset)))
valid_dataset = data_helpers.load_dataset(FLAGS.valid_file, vocab,
FLAGS.max_utter_num,
FLAGS.max_utter_len)
print('valid_dataset: {}'.format(len(valid_dataset)))
test_dataset = data_helpers.load_dataset(FLAGS.test_file, vocab,
FLAGS.max_utter_num,
FLAGS.max_utter_len)
print('valid_dataset: {}'.format(len(valid_dataset)))
target_loss_weight = [1.0, 1.0]
# Set random seed to help reproduce result
tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")
FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
print("{}={}".format(attr.upper(), value))
print("")
vocab = data_helpers.load_vocab(FLAGS.vocab_file)
print('vocabulary size: {}'.format(len(vocab)))
charVocab = data_helpers.load_char_vocab(FLAGS.char_vocab_file)
print('charVocab size: {}'.format(len(charVocab)))
test_dataset = data_helpers.load_dataset(FLAGS.task_name, FLAGS.test_file, vocab, FLAGS.max_utter_num, FLAGS.max_utter_len, FLAGS.max_response_len, FLAGS.max_persona_num, FLAGS.max_persona_len)
print('test dataset size: {}'.format(len(test_dataset)))
print("\nEvaluating...\n")
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)
sess = tf.Session(config=session_conf)
with sess.as_default():
# Load the saved meta graph and restore variables
tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")
FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
print("{}={}".format(attr.upper(), value))
print("")
# Load data
print("Loading data...")
vocab = data_helpers.load_vocab(FLAGS.vocab_file)
print('vocabulary size: {}'.format(len(vocab)))
response_data = data_helpers.load_responses(FLAGS.response_file, vocab, FLAGS.max_response_len)
train_dataset = data_helpers.load_dataset(FLAGS.train_file, vocab, FLAGS.max_utter_len, FLAGS.max_utter_num, response_data)
print('train_pairs: {}'.format(len(train_dataset)))
valid_dataset = data_helpers.load_dataset(FLAGS.valid_file, vocab, FLAGS.max_utter_len, FLAGS.max_utter_num, response_data) # *varied-length*
print('valid_pairs: {}'.format(len(valid_dataset)))
test_dataset = data_helpers.load_dataset(FLAGS.test_file, vocab, FLAGS.max_utter_len, FLAGS.max_utter_num, response_data)
print('test_pairs: {}'.format(len(test_dataset)))
target_loss_weight=[1.0,1.0]
with tf.Graph().as_default():
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():
imn = IMN(