Python data_utils.initialize_vocabulary方法代碼示例

# 需要導入模塊: import data_utils [as 別名]
# 或者: from data_utils import initialize_vocabulary [as 別名]
def __init__(self):
    self.sess = tf.Session()
    self.download_trained_if_not_exists()

    # Create model and load parameters.
    self.model = create_model(self.sess, True)
    self.model.batch_size = 1  # We decode one sentence at a time.

    # Load vocabularies.
    en_vocab_path = os.path.join(FLAGS.data_dir,
                                 "vocab%d.en" % FLAGS.en_vocab_size)
    fr_vocab_path = os.path.join(FLAGS.data_dir,
                                 "vocab%d.fr" % FLAGS.fr_vocab_size)
    self.en_vocab, _ = data_utils.initialize_vocabulary(en_vocab_path)
    _, self.rev_fr_vocab = data_utils.initialize_vocabulary(fr_vocab_path) 

# 需要導入模塊: import data_utils [as 別名]
# 或者: from data_utils import initialize_vocabulary [as 別名]
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.
    en_vocab_path = os.path.join(FLAGS.data_dir,
                                 "vocab%d.from" % FLAGS.from_vocab_size)
    fr_vocab_path = os.path.join(FLAGS.data_dir,
                                 "vocab%d.to" % FLAGS.to_vocab_size)
    en_vocab, _ = data_utils.initialize_vocabulary(en_vocab_path)
    _, rev_fr_vocab = data_utils.initialize_vocabulary(fr_vocab_path)

    # Decode from standard input.
    sys.stdout.write("> ")
    sys.stdout.flush()
    sentence = sys.stdin.readline()
    while sentence:
      # Get token-ids for the input sentence.
      token_ids = data_utils.sentence_to_token_ids(tf.compat.as_bytes(sentence), en_vocab)
      # Which bucket does it belong to?
      bucket_id = len(_buckets) - 1
      for i, bucket in enumerate(_buckets):
        if bucket[0] >= len(token_ids):
          bucket_id = i
          break
      else:
        logging.warning("Sentence truncated: %s", sentence)

      # 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)]
      # Print out French sentence corresponding to outputs.
      print(" ".join([tf.compat.as_str(rev_fr_vocab[output]) for output in outputs]))
      print("> ", end="")
      sys.stdout.flush()
      sentence = sys.stdin.readline() 

# 需要導入模塊: import data_utils [as 別名]
# 或者: from data_utils import initialize_vocabulary [as 別名]
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"]) 

# 需要導入模塊: import data_utils [as 別名]
# 或者: from data_utils import initialize_vocabulary [as 別名]
def decode_input():
  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 from standard input.
    sys.stdout.write("> ")
    sys.stdout.flush()
    sentence = sys.stdin.readline()

    while sentence:
      # 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 the bucket length.
      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)]
      # Print out French sentence corresponding to outputs.
      print(" ".join([tf.compat.as_str(rev_dec_vocab[output]) for output in outputs]))

      print("> ", end="")
      sys.stdout.flush()
      sentence = sys.stdin.readline() 

# 需要導入模塊: import data_utils [as 別名]
# 或者: from data_utils import initialize_vocabulary [as 別名]
def decode():
  with tf.Session() as sess:
    # Create model and load parameters.
    model = create_model(sess, True)
    model.batch_size = 1  # We decode one word at a time.

    # Load vocabularies.
    en_vocab_path = os.path.join(FLAGS.data_dir,
                                 "vocab%d.en" % FLAGS.en_vocab_size)
    hn_vocab_path = os.path.join(FLAGS.data_dir,
                                 "vocab%d.hn" % FLAGS.hn_vocab_size)
    en_vocab, _ = data_utils.initialize_vocabulary(en_vocab_path)
    _, rev_hn_vocab = data_utils.initialize_vocabulary(hn_vocab_path)

    # Decode from standard input.
    sys.stdout.write("> ")
    sys.stdout.flush()
    word = sys.stdin.readline()
    while word:
      word = word.lower()
      char_list_new = list(word)
      word = " ".join(char_list_new)
      # Get token-ids for the input word.
      token_ids = data_utils.word_to_token_ids(tf.compat.as_bytes(word), en_vocab)
      # 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 word to the model.
      encoder_inputs, decoder_inputs, target_weights = model.get_batch(
          {bucket_id: [(token_ids, [])]}, bucket_id)
      # Get output logits for the word.
      _, _, 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)]
      # Print out Hindi word corresponding to outputs.
      print("".join([tf.compat.as_str(rev_hn_vocab[output]) for output in outputs]))
      print("> ", end="")
      sys.stdout.flush()
      word = sys.stdin.readline() 

# 需要導入模塊: import data_utils [as 別名]
# 或者: from data_utils import initialize_vocabulary [as 別名]
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.
    en_vocab_path = os.path.join(FLAGS.data_dir,
                                 "vocab%d.input" % FLAGS.input_vocab_size)
    fr_vocab_path = os.path.join(FLAGS.data_dir,
                                 "vocab%d.output" % FLAGS.output_vocab_size)
    en_vocab, _ = data_utils.initialize_vocabulary(en_vocab_path)
    _, rev_fr_vocab = data_utils.initialize_vocabulary(fr_vocab_path)

    # Decode from standard input.
    sys.stdout.write("> ")
    sys.stdout.flush()
    sentence = sys.stdin.readline()
    while sentence:
      # Get token-ids for the input sentence.
      token_ids = data_utils.sentence_to_token_ids(tf.compat.as_bytes(sentence), en_vocab)
      # Which bucket does it belong to?
      bucket_id = len(_buckets) - 1
      for i, bucket in enumerate(_buckets):
        if bucket[0] >= len(token_ids):
          bucket_id = i
          break
      else:
        logging.warning("Sentence truncated: %s", sentence)

      # 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)]
      # Print out French sentence corresponding to outputs.
      print(" ".join([tf.compat.as_str(rev_fr_vocab[output]) for output in outputs]))
      print("> ", end="")
      sys.stdout.flush()
      sentence = sys.stdin.readline()