def decode():
with tf.Session() as sess:
# Create model and load parameters.
model = create_model(sess, True)
model.batch_size = 1 # We decode one sentence at a time.
# Load vocabularies.
enc_vocab_path = os.path.join(gConfig['working_directory'],"vocab%d_enc.txt" % gConfig['enc_vocab_size'])
dec_vocab_path = os.path.join(gConfig['working_directory'],"vocab%d_dec.txt" % gConfig['dec_vocab_size'])
enc_vocab, _ = data_utils.initialize_vocabulary(enc_vocab_path)
_, rev_dec_vocab = data_utils.initialize_vocabulary(dec_vocab_path)
# Decode sentence and store it
with open(gConfig["test_enc"], 'r') as test_enc:
with open(gConfig["output"], 'w') as predicted_headline:
sentence_count = 0
for sentence in test_enc:
# Get token-ids for the input sentence.
token_ids = data_utils.sentence_to_token_ids(sentence, enc_vocab)
# Which bucket does it belong to? And place the sentence to the last bucket if its token length is larger then X.
bucket_id = min([b for b in range(len(_buckets)) if _buckets[b][0] > len(token_ids)] + [len(_buckets)-1])
# Get a 1-element batch to feed the sentence to the model.
encoder_inputs, decoder_inputs, target_weights = model.get_batch(
{bucket_id: [(token_ids, [])]}, bucket_id)
# Get output logits for the sentence.
_, _, output_logits = model.step(sess, encoder_inputs, decoder_inputs,
target_weights, bucket_id, True)
# This is a greedy decoder - outputs are just argmaxes of output_logits.
outputs = [int(np.argmax(logit, axis=1)) 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)]
# Write predicted headline corresponding to article.
predicted_headline.write(" ".join([tf.compat.as_str(rev_dec_vocab[output]) for output in outputs])+'\n')
sentence_count += 1
if sentence_count % 100 == 0:
print("predicted data line %d" % sentence_count)
sys.stdout.flush()
predicted_headline.close()
test_enc.close()
print("Finished decoding and stored predicted results in %s!" % gConfig["output"])
评论列表
文章目录
