def decode():
'''
Manually input sentence interactively and the headline will be printed out
'''
with tf.Session() as sess:
# Create model and load parameters.
model = create_model(sess, True)
model.batch_size = FLAGS.batch_size #repeat single sentence 10 times as one batch # We decode one sentence at a time.
# Load vocabularies.
vocab_path = os.path.join(FLAGS.data_dir,"vocab")
vocab, rev_vocab = data_utils.initialize_vocabulary(vocab_path)
# Decode from standard input interactively
sys.stdout.write("> ")
sys.stdout.flush() #??????????????
sentence = sys.stdin.readline()
while sentence:
if (len(sentence.strip('\n')) == 0):
sys.stdout.flush()
sentence = sys.stdin.readline()
# Get token-ids for the input sentence.
token_ids = data_utils.sentence_to_token_ids(tf.compat.as_bytes(sentence), vocab)
# print (token_ids) # print token ids
# Which bucket does it belong to?
bucket_id = min([b for b in xrange(len(buckets)) if buckets[b][0] > len(token_ids)])
# Get a 1-element batch to feed the sentence to the model.
# print ("current bucket id" + str(bucket_id))
encoder_inputs, decoder_inputs, target_weights = model.get_batch(
{bucket_id: [(token_ids, [])]}, bucket_id)
# Get output logits for the sentence.
_, _, output_logits_batch = model.step(sess, encoder_inputs, decoder_inputs, target_weights, bucket_id, True)
# This is a greedy decoder - outputs are just argmaxes of output_logits.
output_logits = []
for item in output_logits_batch:
output_logits.append(item[0])
#print (output_logits)
#print (len(output_logits))
#print (output_logits[0])
outputs = [int(np.argmax(logit)) for logit in output_logits]
# If there is an EOS symbol in outputs, cut them at that point.
if data_utils.EOS_ID in outputs:
outputs = outputs[:outputs.index(data_utils.EOS_ID)]
print(" ".join([tf.compat.as_str(rev_vocab[output]) for output in outputs]))
print("> ", end="")
sys.stdout.flush()
sentence = sys.stdin.readline()
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