def toShakespeare(self):
"""Given a line of text, return that text in the indicated style.
Args:
modern_text: (string) The input.
Returns:
string: The translated text, if generated.
"""
args = load_arguments()
vocab = Vocabulary(self.vocab_path, args.embedding, args.dim_emb)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
model = Model(args, vocab)
model.saver.restore(sess, args.model)
if args.beam > 1:
decoder = beam_search.Decoder(sess, args, vocab, model)
else:
decoder = greedy_decoding.Decoder(sess, args, vocab, model)
batch = get_batch([self.modern_text], [1], vocab.word2id)
ori, tsf = decoder.rewrite(batch)
out = ' '.join(w for w in tsf[0])
return out
ckpt = tf.train.get_checkpoint_state(args.model)
if ckpt and ckpt.model_checkpoint_path:
try:
print("Trying to restore from a checkpoint...")
model.saver.restore(sess, ckpt.model_checkpoint_path)
print("Model is restored from checkpoint {}".format(
ckpt.model_checkpoint_path))
except Exception as e:
print("Cannot restore from checkpoint due to {}".format(e))
pass
# set type of decoding (is this after the very last layer?)
if args.beam > 1:
decoder = beam_search.Decoder(sess, args, vocab, model)
else:
decoder = greedy_decoding.Decoder(sess, args, vocab, model)
if args.train:
batches, _, _ = get_batches(train0,
train1,
vocab.word2id,
args.batch_size,
noisy=True,
unparallel=False,
max_seq_len=args.max_seq_length)
random.shuffle(batches)
start_time = time.time()
step = 0
losses = Accumulator(
args.steps_per_checkpoint,
