Python export_model.ModelExporter方法代码示例

本文整理汇总了Python中export_model.ModelExporter方法的典型用法代码示例。如果您正苦于以下问题：Python export_model.ModelExporter方法的具体用法？Python export_model.ModelExporter怎么用？Python export_model.ModelExporter使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类export_model的用法示例。

在下文中一共展示了export_model.ModelExporter方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: main

# 需要导入模块: import export_model [as 别名]
# 或者: from export_model import ModelExporter [as 别名]
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))

开发者ID:forwchen，项目名称:yt8m，代码行数:40，代码来源:train.py

示例2: main

# 需要导入模块: import export_model [as 别名]
# 或者: from export_model import ModelExporter [as 别名]
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))

开发者ID:machine-learning-challenge，项目名称:mlc2017-online，代码行数:39，代码来源:train.py

示例3: main

# 需要导入模块: import export_model [as 别名]
# 或者: from export_model import ModelExporter [as 别名]
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))

开发者ID:machine-learning-challenge，项目名称:mlc2017-online，代码行数:39，代码来源:train.py

示例4: main

# 需要导入模块: import export_model [as 别名]
# 或者: from export_model import ModelExporter [as 别名]
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))

开发者ID:machine-learning-challenge，项目名称:tutorial_mnist，代码行数:38，代码来源:train.py

示例5: main

# 需要导入模块: import export_model [as 别名]
# 或者: from export_model import ModelExporter [as 别名]
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))

开发者ID:antoine77340，项目名称:Youtube-8M-WILLOW，代码行数:44，代码来源:train.py

注：本文中的export_model.ModelExporter方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。