def main(_):
hparams = train_mask_gan.create_hparams()
log_dir = FLAGS.base_directory
tf.gfile.MakeDirs(FLAGS.output_path)
output_file = tf.gfile.GFile(os.path.join(FLAGS.output_path,
'reviews.txt'),
mode='w')
# Load data set.
if FLAGS.data_set == 'ptb':
raw_data = ptb_loader.ptb_raw_data(FLAGS.data_dir)
train_data, valid_data, _, _ = raw_data
elif FLAGS.data_set == 'imdb':
raw_data = imdb_loader.imdb_raw_data(FLAGS.data_dir)
train_data, valid_data = raw_data
else:
raise NotImplementedError
# Generating more data on train set.
if FLAGS.sample_mode == SAMPLE_TRAIN:
data_set = train_data
elif FLAGS.sample_mode == SAMPLE_VALIDATION:
data_set = valid_data
else:
raise NotImplementedError
# Dictionary and reverse dictionry.
if FLAGS.data_set == 'ptb':
word_to_id = ptb_loader.build_vocab(
os.path.join(FLAGS.data_dir, 'ptb.train.txt'))
elif FLAGS.data_set == 'imdb':
word_to_id = imdb_loader.build_vocab(
os.path.join(FLAGS.data_dir, 'vocab.txt'))
id_to_word = {v: k for k, v in word_to_id.iteritems()}
FLAGS.vocab_size = len(id_to_word)
print('Vocab size: %d' % FLAGS.vocab_size)
generate_samples(hparams, data_set, id_to_word, log_dir, output_file)
def main(_):
hparams = train_mask_gan.create_hparams()
log_dir = FLAGS.base_directory
tf.gfile.MakeDirs(FLAGS.output_path)
output_file = tf.gfile.GFile(
os.path.join(FLAGS.output_path, 'reviews.txt'), mode='w')
# Load data set.
if FLAGS.data_set == 'ptb':
raw_data = ptb_loader.ptb_raw_data(FLAGS.data_dir)
train_data, valid_data, _, _ = raw_data
elif FLAGS.data_set == 'imdb':
raw_data = imdb_loader.imdb_raw_data(FLAGS.data_dir)
train_data, valid_data = raw_data
else:
raise NotImplementedError
# Generating more data on train set.
if FLAGS.sample_mode == SAMPLE_TRAIN:
data_set = train_data
elif FLAGS.sample_mode == SAMPLE_VALIDATION:
data_set = valid_data
else:
raise NotImplementedError
# Dictionary and reverse dictionry.
if FLAGS.data_set == 'ptb':
word_to_id = ptb_loader.build_vocab(
os.path.join(FLAGS.data_dir, 'ptb.train.txt'))
elif FLAGS.data_set == 'imdb':
word_to_id = imdb_loader.build_vocab(
os.path.join(FLAGS.data_dir, 'vocab.txt'))
id_to_word = {v: k for k, v in word_to_id.iteritems()}
FLAGS.vocab_size = len(id_to_word)
print('Vocab size: %d' % FLAGS.vocab_size)
generate_samples(hparams, data_set, id_to_word, log_dir, output_file)
def main(_):
hparams = create_hparams()
train_dir = FLAGS.base_directory + '/train'
# Load data set.
if FLAGS.data_set == 'ptb':
raw_data = ptb_loader.ptb_raw_data(FLAGS.data_dir)
train_data, valid_data, test_data, _ = raw_data
valid_data_flat = valid_data
elif FLAGS.data_set == 'imdb':
raw_data = imdb_loader.imdb_raw_data(FLAGS.data_dir)
# TODO(liamfedus): Get an IMDB test partition.
train_data, valid_data = raw_data
valid_data_flat = [word for review in valid_data for word in review]
else:
raise NotImplementedError
if FLAGS.mode == MODE_TRAIN or FLAGS.mode == MODE_TRAIN_EVAL:
data_set = train_data
elif FLAGS.mode == MODE_VALIDATION:
data_set = valid_data
elif FLAGS.mode == MODE_TEST:
data_set = test_data
else:
raise NotImplementedError
# Dictionary and reverse dictionry.
if FLAGS.data_set == 'ptb':
word_to_id = ptb_loader.build_vocab(
os.path.join(FLAGS.data_dir, 'ptb.train.txt'))
elif FLAGS.data_set == 'imdb':
word_to_id = imdb_loader.build_vocab(
os.path.join(FLAGS.data_dir, 'vocab.txt'))
id_to_word = {v: k for k, v in word_to_id.items()}
# Dictionary of Training Set n-gram counts.
bigram_tuples = n_gram.find_all_ngrams(valid_data_flat, n=2)
trigram_tuples = n_gram.find_all_ngrams(valid_data_flat, n=3)
fourgram_tuples = n_gram.find_all_ngrams(valid_data_flat, n=4)
bigram_counts = n_gram.construct_ngrams_dict(bigram_tuples)
trigram_counts = n_gram.construct_ngrams_dict(trigram_tuples)
fourgram_counts = n_gram.construct_ngrams_dict(fourgram_tuples)
print('Unique %d-grams: %d' % (2, len(bigram_counts)))
print('Unique %d-grams: %d' % (3, len(trigram_counts)))
print('Unique %d-grams: %d' % (4, len(fourgram_counts)))
data_ngram_counts = {
'2': bigram_counts,
'3': trigram_counts,
'4': fourgram_counts
}
# TODO(liamfedus): This was necessary because there was a problem with our
# originally trained IMDB models. The EOS_INDEX was off by one, which means,
# two words were mapping to index 86933. The presence of '</s>' is going
# to throw and out of vocabulary error.
FLAGS.vocab_size = len(id_to_word)
print('Vocab size: %d' % FLAGS.vocab_size)
tf.gfile.MakeDirs(FLAGS.base_directory)
if FLAGS.mode == MODE_TRAIN:
log = tf.gfile.GFile(
os.path.join(FLAGS.base_directory, 'train-log.txt'), mode='w')
elif FLAGS.mode == MODE_VALIDATION:
log = tf.gfile.GFile(
os.path.join(FLAGS.base_directory, 'validation-log.txt'), mode='w')
elif FLAGS.mode == MODE_TRAIN_EVAL:
log = tf.gfile.GFile(
os.path.join(FLAGS.base_directory, 'train_eval-log.txt'), mode='w')
else:
log = tf.gfile.GFile(
os.path.join(FLAGS.base_directory, 'test-log.txt'), mode='w')
if FLAGS.mode == MODE_TRAIN:
train_model(hparams, data_set, train_dir, log, id_to_word,
data_ngram_counts)
elif FLAGS.mode == MODE_VALIDATION:
evaluate_model(hparams, data_set, train_dir, log, id_to_word,
data_ngram_counts)
elif FLAGS.mode == MODE_TRAIN_EVAL:
evaluate_model(hparams, data_set, train_dir, log, id_to_word,
data_ngram_counts)
elif FLAGS.mode == MODE_TEST:
evaluate_model(hparams, data_set, train_dir, log, id_to_word,
data_ngram_counts)
else:
raise NotImplementedError
def main(_):
hparams = create_hparams()
train_dir = FLAGS.base_directory + '/train'
# Load data set.
if FLAGS.data_set == 'ptb':
raw_data = ptb_loader.ptb_raw_data(FLAGS.data_dir)
train_data, valid_data, test_data, _ = raw_data
valid_data_flat = valid_data
elif FLAGS.data_set == 'imdb':
raw_data = imdb_loader.imdb_raw_data(FLAGS.data_dir)
# TODO(liamfedus): Get an IMDB test partition.
train_data, valid_data = raw_data
valid_data_flat = [word for review in valid_data for word in review]
else:
raise NotImplementedError
if FLAGS.mode == MODE_TRAIN or FLAGS.mode == MODE_TRAIN_EVAL:
data_set = train_data
elif FLAGS.mode == MODE_VALIDATION:
data_set = valid_data
elif FLAGS.mode == MODE_TEST:
data_set = test_data
else:
raise NotImplementedError
# Dictionary and reverse dictionry.
if FLAGS.data_set == 'ptb':
word_to_id = ptb_loader.build_vocab(
os.path.join(FLAGS.data_dir, 'ptb.train.txt'))
elif FLAGS.data_set == 'imdb':
word_to_id = imdb_loader.build_vocab(
os.path.join(FLAGS.data_dir, 'vocab.txt'))
id_to_word = {v: k for k, v in word_to_id.iteritems()}
# Dictionary of Training Set n-gram counts.
bigram_tuples = n_gram.find_all_ngrams(valid_data_flat, n=2)
trigram_tuples = n_gram.find_all_ngrams(valid_data_flat, n=3)
fourgram_tuples = n_gram.find_all_ngrams(valid_data_flat, n=4)
bigram_counts = n_gram.construct_ngrams_dict(bigram_tuples)
trigram_counts = n_gram.construct_ngrams_dict(trigram_tuples)
fourgram_counts = n_gram.construct_ngrams_dict(fourgram_tuples)
print('Unique %d-grams: %d' % (2, len(bigram_counts)))
print('Unique %d-grams: %d' % (3, len(trigram_counts)))
print('Unique %d-grams: %d' % (4, len(fourgram_counts)))
data_ngram_counts = {
'2': bigram_counts,
'3': trigram_counts,
'4': fourgram_counts
}
# TODO(liamfedus): This was necessary because there was a problem with our
# originally trained IMDB models. The EOS_INDEX was off by one, which means,
# two words were mapping to index 86933. The presence of '</s>' is going
# to throw and out of vocabulary error.
FLAGS.vocab_size = len(id_to_word)
print('Vocab size: %d' % FLAGS.vocab_size)
tf.gfile.MakeDirs(FLAGS.base_directory)
if FLAGS.mode == MODE_TRAIN:
log = tf.gfile.GFile(
os.path.join(FLAGS.base_directory, 'train-log.txt'), mode='w')
elif FLAGS.mode == MODE_VALIDATION:
log = tf.gfile.GFile(
os.path.join(FLAGS.base_directory, 'validation-log.txt'), mode='w')
elif FLAGS.mode == MODE_TRAIN_EVAL:
log = tf.gfile.GFile(
os.path.join(FLAGS.base_directory, 'train_eval-log.txt'), mode='w')
else:
log = tf.gfile.GFile(
os.path.join(FLAGS.base_directory, 'test-log.txt'), mode='w')
if FLAGS.mode == MODE_TRAIN:
train_model(hparams, data_set, train_dir, log, id_to_word,
data_ngram_counts)
elif FLAGS.mode == MODE_VALIDATION:
evaluate_model(hparams, data_set, train_dir, log, id_to_word,
data_ngram_counts)
elif FLAGS.mode == MODE_TRAIN_EVAL:
evaluate_model(hparams, data_set, train_dir, log, id_to_word,
data_ngram_counts)
elif FLAGS.mode == MODE_TEST:
evaluate_model(hparams, data_set, train_dir, log, id_to_word,
data_ngram_counts)
else:
raise NotImplementedError