def decode():
# Load model config
config = load_config(FLAGS)
# Load source data to decode
test_set = TextIterator(source=config['decode_input'],
batch_size=config['decode_batch_size'],
source_dict=config['source_vocabulary'],
maxlen=None,
n_words_source=config['num_encoder_symbols'])
# Load inverse dictionary used in decoding
target_inverse_dict = data_utils.load_inverse_dict(
config['target_vocabulary'])
# Initiate TF session
with tf.Session(config=tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement,
gpu_options=tf.GPUOptions(allow_growth=True))) as sess:
# Reload existing checkpoint
model = load_model(sess, config)
try:
print('Decoding {}..'.format(FLAGS.decode_input))
if FLAGS.write_n_best:
fout = [data_utils.fopen(("%s_%d" % (FLAGS.decode_output, k)), 'w') \
for k in range(FLAGS.beam_width)]
else:
fout = [data_utils.fopen(FLAGS.decode_output, 'w')]
for idx, source_seq in enumerate(test_set):
source, source_len = prepare_batch(source_seq)
# predicted_ids: GreedyDecoder; [batch_size, max_time_step, 1]
# BeamSearchDecoder; [batch_size, max_time_step, beam_width]
predicted_ids = model.predict(sess,
encoder_inputs=source,
encoder_inputs_length=source_len)
# Write decoding results
for k, f in reversed(list(enumerate(fout))):
for seq in predicted_ids:
f.write(
str(
data_utils.seq2words(
seq[:, k], target_inverse_dict)) + '\n')
if not FLAGS.write_n_best:
break
print(' {}th line decoded'.format(idx *
FLAGS.decode_batch_size))
print('Decoding terminated')
except IOError:
pass
finally:
[f.close() for f in fout]
def __init__(self,
source,
target,
source_dict,
target_dict,
batch_size=128,
maxlen=100,
n_words_source=-1,
n_words_target=-1,
skip_empty=False,
shuffle_each_epoch=False,
sort_by_length=True,
maxibatch_size=20):
if shuffle_each_epoch:
self.source_orig = source
self.target_orig = target
self.source, self.target = shuffle.main(
[self.source_orig, self.target_orig], temporary=True)
else:
self.source = data_utils.fopen(source, 'r')
self.target = data_utils.fopen(target, 'r')
self.source_dict = load_dict(source_dict)
self.target_dict = load_dict(target_dict)
self.batch_size = batch_size
self.maxlen = maxlen
self.skip_empty = skip_empty
self.n_words_source = n_words_source
self.n_words_target = n_words_target
if self.n_words_source > 0:
for key, idx in self.source_dict.items():
if idx >= self.n_words_source:
del self.source_dict[key]
if self.n_words_target > 0:
for key, idx in self.target_dict.items():
if idx >= self.n_words_target:
del self.target_dict[key]
self.shuffle = shuffle_each_epoch
self.sort_by_length = sort_by_length
self.source_buffer = []
self.target_buffer = []
self.k = batch_size * maxibatch_size
self.end_of_data = False
def __init__(self,
source,
time_step=10,
batch_size=20,
set_type=0,
seed=0,
test_set_partition=0.05,
balance=False,
noise=0):
np.set_printoptions(threshold='nan')
self.source = data_utils.fopen(source, 'r')
self.time_step = time_step
self.batch_size = batch_size
self.iterator = 0
self.end_of_data = False
random.seed(seed)
self.load(source, time_step, set_type, test_set_partition, balance,
noise)
