Python seq2seq_model.Seq2SeqModel方法代码示例

# 需要导入模块: import seq2seq_model [as 别名]
# 或者: from seq2seq_model import Seq2SeqModel [as 别名]
def create_model(session, forward_only):
  """Create translation model and initialize or load parameters in session."""
  dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
  model = seq2seq_model.Seq2SeqModel(
      FLAGS.from_vocab_size,
      FLAGS.to_vocab_size,
      _buckets,
      FLAGS.size,
      FLAGS.num_layers,
      FLAGS.max_gradient_norm,
      FLAGS.batch_size,
      FLAGS.learning_rate,
      FLAGS.learning_rate_decay_factor,
      forward_only=forward_only,
      dtype=dtype)
  ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
  if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
    print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
    model.saver.restore(session, ckpt.model_checkpoint_path)
  else:
    print("Created model with fresh parameters.")
    session.run(tf.global_variables_initializer())
  return model 

# 需要导入模块: import seq2seq_model [as 别名]
# 或者: from seq2seq_model import Seq2SeqModel [as 别名]
def self_test():
  """Test the translation model."""
  with tf.Session() as sess:
    print("Self-test for neural translation model.")
    # Create model with vocabularies of 10, 2 small buckets, 2 layers of 32.
    model = seq2seq_model.Seq2SeqModel(10, 10, [(3, 3), (6, 6)], 32, 2,
                                       5.0, 32, 0.3, 0.99, num_samples=8)
    sess.run(tf.global_variables_initializer())

    # Fake data set for both the (3, 3) and (6, 6) bucket.
    data_set = ([([1, 1], [2, 2]), ([3, 3], [4]), ([5], [6])],
                [([1, 1, 1, 1, 1], [2, 2, 2, 2, 2]), ([3, 3, 3], [5, 6])])
    for _ in xrange(5):  # Train the fake model for 5 steps.
      bucket_id = random.choice([0, 1])
      encoder_inputs, decoder_inputs, target_weights = model.get_batch(
          data_set, bucket_id)
      model.step(sess, encoder_inputs, decoder_inputs, target_weights,
                 bucket_id, False) 

# 需要导入模块: import seq2seq_model [as 别名]
# 或者: from seq2seq_model import Seq2SeqModel [as 别名]
def create_model(session, forward_only):

  """Create model and initialize or load parameters"""
  model = seq2seq_model.Seq2SeqModel( gConfig['enc_vocab_size'], gConfig['dec_vocab_size'], _buckets, gConfig['hidden_units'], gConfig['num_layers'], gConfig['max_gradient_norm'], gConfig['batch_size'], gConfig['learning_rate'], gConfig['learning_rate_decay_factor'], forward_only=forward_only)

  if 'pretrained_model' in gConfig:
      model.saver.restore(session,gConfig['pretrained_model'])
      return model

  ckpt = tf.train.get_checkpoint_state(gConfig['working_directory'])
  if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path):
    print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
    model.saver.restore(session, ckpt.model_checkpoint_path)
  else:
    print("Created model with fresh parameters.")
    session.run(tf.initialize_all_variables())
  return model 

# 需要导入模块: import seq2seq_model [as 别名]
# 或者: from seq2seq_model import Seq2SeqModel [as 别名]
def self_test():
  """Test the translation model."""
  with tf.Session() as sess:
    print("Self-test for neural transliteration model.")
    # Create model with vocabularies of 10, 2 small buckets, 2 layers of 32.
    model = seq2seq_model.Seq2SeqModel(10, 10, [(3, 3), (6, 6)], 32, 2,
                                       5.0, 32, 0.3, 0.99, num_samples=8)
    sess.run(tf.initialize_all_variables())

    # Fake data set for both the (3, 3) and (6, 6) bucket.
    data_set = ([([1, 1], [2, 2]), ([3, 3], [4]), ([5], [6])],
                [([1, 1, 1, 1, 1], [2, 2, 2, 2, 2]), ([3, 3, 3], [5, 6])])
    for _ in xrange(5):  # Train the fake model for 5 steps.
      bucket_id = random.choice([0, 1])
      encoder_inputs, decoder_inputs, target_weights = model.get_batch(
          data_set, bucket_id)
      model.step(sess, encoder_inputs, decoder_inputs, target_weights,
                 bucket_id, False) 

# 需要导入模块: import seq2seq_model [as 别名]
# 或者: from seq2seq_model import Seq2SeqModel [as 别名]
def create_model(session, forward_only):
  """Create translation model and initialize or load parameters in session."""
  dtype = tf.float32
  model = seq2seq_model.Seq2SeqModel(
      FLAGS.input_vocab_size,
      FLAGS.output_vocab_size,
      _buckets,
      FLAGS.size,
      FLAGS.num_layers,
      FLAGS.max_gradient_norm,
      FLAGS.batch_size,
      FLAGS.learning_rate,
      FLAGS.learning_rate_decay_factor,
      forward_only=forward_only,
      dtype=dtype)
  ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
  if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
    print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
    model.saver.restore(session, ckpt.model_checkpoint_path)
  else:
    print("Created model with fresh parameters.")
    session.run(tf.global_variables_initializer())
  return model 

# 需要导入模块: import seq2seq_model [as 别名]
# 或者: from seq2seq_model import Seq2SeqModel [as 别名]
def create_model(session, forward_only):
  """Create transliteration model and initialize or load parameters in session."""
  model = seq2seq_model.Seq2SeqModel(
      FLAGS.en_vocab_size, FLAGS.hn_vocab_size, _buckets,
      FLAGS.size, FLAGS.num_layers, FLAGS.max_gradient_norm, FLAGS.batch_size,
      FLAGS.learning_rate, FLAGS.learning_rate_decay_factor,
      forward_only=forward_only,use_lstm=False)
  ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
  if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path):
    print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
    model.saver.restore(session, ckpt.model_checkpoint_path)
  else:
    print("Created model with fresh parameters.")
    session.run(tf.initialize_all_variables())
  return model 

# 需要导入模块: import seq2seq_model [as 别名]
# 或者: from seq2seq_model import Seq2SeqModel [as 别名]
def __init__(self, vocab):
        # self.model = Seq2SeqModel(vocab, training_mode=False)
        self.model = RLModel(vocab, training_mode=False)
        with self.model.graph.as_default():
            self.model.ping = tf.constant("ack")
        # self.model = MaluubaModel(vocab, training_mode=False)
        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=mem_limit,allow_growth = True,visible_device_list='0')
        self.sess = tf.Session(graph=self.model.graph, config=tf.ConfigProto(gpu_options=gpu_options,allow_soft_placement=True))