Python configuration.model_config方法代碼示例

本文整理匯總了Python中skip_thoughts.configuration.model_config方法的典型用法代碼示例。如果您正苦於以下問題：Python configuration.model_config方法的具體用法？Python configuration.model_config怎麽用？Python configuration.model_config使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類skip_thoughts.configuration的用法示例。

在下文中一共展示了configuration.model_config方法的5個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: setUp

# 需要導入模塊: from skip_thoughts import configuration [as 別名]
# 或者: from skip_thoughts.configuration import model_config [as 別名]
def setUp(self):
    super(SkipThoughtsModelTest, self).setUp()
    self._model_config = configuration.model_config()

開發者ID:ringringyi，項目名稱:DOTA_models，代碼行數:5，代碼來源:skip_thoughts_model_test.py

示例2: main

# 需要導入模塊: from skip_thoughts import configuration [as 別名]
# 或者: from skip_thoughts.configuration import model_config [as 別名]
def main(unused_argv):
  if not FLAGS.data_dir:
    raise ValueError("--data_dir is required.")

  encoder = encoder_manager.EncoderManager()

  # Maybe load unidirectional encoder.
  if FLAGS.uni_checkpoint_path:
    print("Loading unidirectional model...")
    uni_config = configuration.model_config()
    encoder.load_model(uni_config, FLAGS.uni_vocab_file,
                       FLAGS.uni_embeddings_file, FLAGS.uni_checkpoint_path)

  # Maybe load bidirectional encoder.
  if FLAGS.bi_checkpoint_path:
    print("Loading bidirectional model...")
    bi_config = configuration.model_config(bidirectional_encoder=True)
    encoder.load_model(bi_config, FLAGS.bi_vocab_file, FLAGS.bi_embeddings_file,
                       FLAGS.bi_checkpoint_path)

  if FLAGS.eval_task in ["MR", "CR", "SUBJ", "MPQA"]:
    eval_classification.eval_nested_kfold(
        encoder, FLAGS.eval_task, FLAGS.data_dir, use_nb=False)
  elif FLAGS.eval_task == "SICK":
    eval_sick.evaluate(encoder, evaltest=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "MSRP":
    eval_msrp.evaluate(
        encoder, evalcv=True, evaltest=True, use_feats=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "TREC":
    eval_trec.evaluate(encoder, evalcv=True, evaltest=True, loc=FLAGS.data_dir)
  else:
    raise ValueError("Unrecognized eval_task: %s" % FLAGS.eval_task)

  encoder.close()

開發者ID:ringringyi，項目名稱:DOTA_models，代碼行數:36，代碼來源:evaluate.py

示例3: main

# 需要導入模塊: from skip_thoughts import configuration [as 別名]
# 或者: from skip_thoughts.configuration import model_config [as 別名]
def main(unused_argv):
  if not FLAGS.input_file_pattern:
    raise ValueError("--input_file_pattern is required.")
  if not FLAGS.train_dir:
    raise ValueError("--train_dir is required.")

  model_config = configuration.model_config(
      input_file_pattern=FLAGS.input_file_pattern)
  training_config = configuration.training_config()

  tf.logging.info("Building training graph.")
  g = tf.Graph()
  with g.as_default():
    model = skip_thoughts_model.SkipThoughtsModel(model_config, mode="train")
    model.build()

    learning_rate = _setup_learning_rate(training_config, model.global_step)
    optimizer = tf.train.AdamOptimizer(learning_rate)

    train_tensor = tf.contrib.slim.learning.create_train_op(
        total_loss=model.total_loss,
        optimizer=optimizer,
        global_step=model.global_step,
        clip_gradient_norm=training_config.clip_gradient_norm)

    saver = tf.train.Saver()

  tf.contrib.slim.learning.train(
      train_op=train_tensor,
      logdir=FLAGS.train_dir,
      graph=g,
      global_step=model.global_step,
      number_of_steps=training_config.number_of_steps,
      save_summaries_secs=training_config.save_summaries_secs,
      saver=saver,
      save_interval_secs=training_config.save_model_secs)

開發者ID:ringringyi，項目名稱:DOTA_models，代碼行數:38，代碼來源:train.py

示例4: main

# 需要導入模塊: from skip_thoughts import configuration [as 別名]
# 或者: from skip_thoughts.configuration import model_config [as 別名]
def main(unused_argv):
    if not FLAGS.data_dir:
        raise ValueError("--data_dir is required.")

    encoder = encoder_manager.EncoderManager()

    # Maybe load unidirectional encoder.
    if FLAGS.uni_checkpoint_path:
        print("Loading unidirectional model...")
        uni_config = configuration.model_config()
        encoder.load_model(uni_config, FLAGS.uni_vocab_file,
                           FLAGS.uni_embeddings_file, FLAGS.uni_checkpoint_path)

    # Maybe load bidirectional encoder.
    if FLAGS.bi_checkpoint_path:
        print("Loading bidirectional model...")
        bi_config = configuration.model_config(bidirectional_encoder=True)
        encoder.load_model(bi_config, FLAGS.bi_vocab_file,
                           FLAGS.bi_embeddings_file,
                           FLAGS.bi_checkpoint_path)

    if FLAGS.eval_task in ["MR", "CR", "SUBJ", "MPQA"]:
        eval_classification.eval_nested_kfold(
            encoder, FLAGS.eval_task, FLAGS.data_dir, use_nb=False)
    elif FLAGS.eval_task == "SICK":
        eval_sick.evaluate(encoder, evaltest=True, loc=FLAGS.data_dir)
    elif FLAGS.eval_task == "MSRP":
        eval_msrp.evaluate(
            encoder, evalcv=True, evaltest=True, use_feats=True,
            loc=FLAGS.data_dir)
    elif FLAGS.eval_task == "TREC":
        eval_trec.evaluate(encoder, evalcv=True, evaltest=True,
                           loc=FLAGS.data_dir)
    else:
        raise ValueError("Unrecognized eval_task: %s" % FLAGS.eval_task)

    encoder.close()

開發者ID:snuspl，項目名稱:parallax，代碼行數:39，代碼來源:evaluate.py

示例5: main

# 需要導入模塊: from skip_thoughts import configuration [as 別名]
# 或者: from skip_thoughts.configuration import model_config [as 別名]
def main(unused_argv):
    if not FLAGS.input_file_pattern:
        raise ValueError("--input_file_pattern is required.")
    if not FLAGS.train_dir:
        raise ValueError("--train_dir is required.")

    model_config = configuration.model_config(
        input_file_pattern=FLAGS.input_file_pattern)
    training_config = configuration.training_config()

    tf.logging.info("Building training graph.")
    g = tf.Graph()
    with g.as_default():
        model = skip_thoughts_model.SkipThoughtsModel(model_config,
                                                      mode="train")
        model.build()

        learning_rate = _setup_learning_rate(training_config, model.global_step)
        optimizer = tf.train.AdamOptimizer(learning_rate)

        train_tensor = tf.contrib.slim.learning.create_train_op(
            total_loss=model.total_loss,
            optimizer=optimizer,
            global_step=model.global_step,
            clip_gradient_norm=training_config.clip_gradient_norm)

        saver = tf.train.Saver()

    tf.contrib.slim.learning.train(
        train_op=train_tensor,
        logdir=FLAGS.train_dir,
        graph=g,
        global_step=model.global_step,
        number_of_steps=training_config.number_of_steps,
        save_summaries_secs=training_config.save_summaries_secs,
        saver=saver,
        save_interval_secs=training_config.save_model_secs)

開發者ID:snuspl，項目名稱:parallax，代碼行數:39，代碼來源:train.py

注：本文中的skip_thoughts.configuration.model_config方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。