Python export_model.ModelExporter方法代碼示例

本文整理匯總了Python中export_model.ModelExporter方法的典型用法代碼示例。如果您正苦於以下問題：Python export_model.ModelExporter方法的具體用法？Python export_model.ModelExporter怎麽用？Python export_model.ModelExporter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export_model的用法示例。

在下文中一共展示了export_model.ModelExporter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: main

# 需要導入模塊: import export_model [as 別名]
# 或者: from export_model import ModelExporter [as 別名]
def main(unused_argv):
  # Load the environment.
  env = json.loads(os.environ.get("TF_CONFIG", "{}"))

  # Load the cluster data from the environment.
  cluster_data = env.get("cluster", None)
  cluster = tf.train.ClusterSpec(cluster_data) if cluster_data else None

  # Load the task data from the environment.
  task_data = env.get("task", None) or {"type": "master", "index": 0}
  task = type("TaskSpec", (object,), task_data)

  # Logging the version.
  logging.set_verbosity(tf.logging.INFO)
  logging.info("%s: Tensorflow version: %s.",
               task_as_string(task), tf.__version__)

  # Dispatch to a master, a worker, or a parameter server.
  if not cluster or task.type == "master" or task.type == "worker":
    model = find_class_by_name(FLAGS.model,
        [frame_level_models, video_level_models])()

    reader = get_reader()

    model_exporter = export_model.ModelExporter(
        frame_features=FLAGS.frame_features,
        model=model,
        reader=reader)

    Trainer(cluster, task, FLAGS.train_dir, model, reader, model_exporter,
            FLAGS.log_device_placement, FLAGS.max_steps,
            FLAGS.export_model_steps).run(start_new_model=FLAGS.start_new_model)

  elif task.type == "ps":
    ParameterServer(cluster, task).run()
  else:
    raise ValueError("%s: Invalid task_type: %s." %
                     (task_as_string(task), task.type))

開發者ID:forwchen，項目名稱:yt8m，代碼行數:40，代碼來源:train.py

示例2: main

# 需要導入模塊: import export_model [as 別名]
# 或者: from export_model import ModelExporter [as 別名]
def main(unused_argv):
  # Load the environment.
  env = json.loads(os.environ.get("TF_CONFIG", "{}"))

  # Load the cluster data from the environment.
  cluster_data = env.get("cluster", None)
  cluster = tf.train.ClusterSpec(cluster_data) if cluster_data else None

  # Load the task data from the environment.
  task_data = env.get("task", None) or {"type": "master", "index": 0}
  task = type("TaskSpec", (object,), task_data)

  # Logging the version.
  logging.set_verbosity(tf.logging.INFO)
  logging.info("%s: Tensorflow version: %s.",
               task_as_string(task), tf.__version__)

  # Dispatch to a master, a worker, or a parameter server.
  if not cluster or task.type == "master" or task.type == "worker":
    model = find_class_by_name(FLAGS.model,
                               [models])()

    reader = get_reader()

    model_exporter = export_model.ModelExporter(
        model=model,
        reader=reader)

    Trainer(cluster, task, FLAGS.train_dir, model, reader, model_exporter,
            FLAGS.log_device_placement, FLAGS.max_steps,
            FLAGS.export_model_steps).run(start_new_model=FLAGS.start_new_model)

  elif task.type == "ps":
    ParameterServer(cluster, task).run()
  else:
    raise ValueError("%s: Invalid task_type: %s." %
                     (task_as_string(task), task.type))

開發者ID:machine-learning-challenge，項目名稱:mlc2017-online，代碼行數:39，代碼來源:train.py

示例3: main

# 需要導入模塊: import export_model [as 別名]
# 或者: from export_model import ModelExporter [as 別名]
def main(unused_argv):
  # Load the environment.
  env = json.loads(os.environ.get("TF_CONFIG", "{}"))

  # Load the cluster data from the environment.
  cluster_data = env.get("cluster", None)
  cluster = tf.train.ClusterSpec(cluster_data) if cluster_data else None

  # Load the task data from the environment.
  task_data = env.get("task", None) or {"type": "master", "index": 0}
  task = type("TaskSpec", (object,), task_data)

  # Logging the version.
  logging.set_verbosity(tf.logging.INFO)
  logging.info("%s: Tensorflow version: %s.",
               task_as_string(task), tf.__version__)

  # Dispatch to a master, a worker, or a parameter server.
  if not cluster or task.type == "master" or task.type == "worker":
    model = find_class_by_name(FLAGS.model,
                               [cvd_models])()

    reader = get_reader()

    model_exporter = export_model.ModelExporter(
        model=model,
        reader=reader)

    Trainer(cluster, task, FLAGS.train_dir, model, reader, model_exporter,
            FLAGS.log_device_placement, FLAGS.max_steps,
            FLAGS.export_model_steps).run(start_new_model=FLAGS.start_new_model)

  elif task.type == "ps":
    ParameterServer(cluster, task).run()
  else:
    raise ValueError("%s: Invalid task_type: %s." %
                     (task_as_string(task), task.type))

開發者ID:machine-learning-challenge，項目名稱:mlc2017-online，代碼行數:39，代碼來源:train.py

示例4: main

# 需要導入模塊: import export_model [as 別名]
# 或者: from export_model import ModelExporter [as 別名]
def main(unused_argv):
  # Load the environment.
  env = json.loads(os.environ.get("TF_CONFIG", "{}"))

  # Load the cluster data from the environment.
  cluster_data = env.get("cluster", None)
  cluster = tf.train.ClusterSpec(cluster_data) if cluster_data else None

  # Load the task data from the environment.
  task_data = env.get("task", None) or {"type": "master", "index": 0}
  task = type("TaskSpec", (object,), task_data)

  # Logging the version.
  logging.set_verbosity(tf.logging.INFO)
  logging.info("%s: Tensorflow version: %s.",
               task_as_string(task), tf.__version__)

  # Dispatch to a master, a worker, or a parameter server.
  if not cluster or task.type == "master" or task.type == "worker":
    model = find_class_by_name(FLAGS.model,
                               [mnist_models])()

    reader = get_reader()
    model_exporter = export_model.ModelExporter(
        model=model,
        reader=reader)

    Trainer(cluster, task, FLAGS.train_dir, model, reader, model_exporter,
            FLAGS.log_device_placement, FLAGS.max_steps,
            FLAGS.export_model_steps).run(start_new_model=FLAGS.start_new_model)

  elif task.type == "ps":
    ParameterServer(cluster, task).run()
  else:
    raise ValueError("%s: Invalid task_type: %s." %
                     (task_as_string(task), task.type))

開發者ID:machine-learning-challenge，項目名稱:tutorial_mnist，代碼行數:38，代碼來源:train.py

示例5: main

# 需要導入模塊: import export_model [as 別名]
# 或者: from export_model import ModelExporter [as 別名]
def main(unused_argv):
  # Load the environment.
  env = json.loads(os.environ.get("TF_CONFIG", "{}"))

  # Load the cluster data from the environment.
  cluster_data = env.get("cluster", None)
  cluster = tf.train.ClusterSpec(cluster_data) if cluster_data else None

  # Load the task data from the environment.
  task_data = env.get("task", None) or {"type": "master", "index": 0}
  task = type("TaskSpec", (object,), task_data)

  # Logging the version.
  logging.set_verbosity(tf.logging.INFO)
  logging.info("%s: Tensorflow version: %s.",
               task_as_string(task), tf.__version__)

  # Dispatch to a master, a worker, or a parameter server.
  if not cluster or task.type == "master" or task.type == "worker":
    
    model = find_class_by_name(FLAGS.model, 
        [frame_level_models, video_level_models])()
    
    reader = get_reader()
    
    model_exporter = export_model.ModelExporter(
        frame_features=FLAGS.frame_features,
        model=model,
        reader=reader)

    Trainer(cluster, task, FLAGS.train_dir, model, reader, model_exporter, 
            FLAGS.log_device_placement, FLAGS.max_steps, 
            FLAGS.export_model_steps).run(start_new_model=FLAGS.start_new_model)

  elif task.type == "ps":

    ParameterServer(cluster, task).run()

  else:

    raise ValueError("%s: Invalid task_type: %s." %
                     (task_as_string(task), task.type))

開發者ID:antoine77340，項目名稱:Youtube-8M-WILLOW，代碼行數:44，代碼來源:train.py

注：本文中的export_model.ModelExporter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。