Python inception_distributed_train.train方法代碼示例

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

在下文中一共展示了inception_distributed_train.train方法的6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: from inception import inception_distributed_train [as 別名]
# 或者: from inception.inception_distributed_train import train [as 別名]
def main(unused_args):
  assert FLAGS.job_name in ['ps', 'worker'], 'job_name must be ps or worker'

  # Extract all the hostnames for the ps and worker jobs to construct the
  # cluster spec.
  ps_hosts = FLAGS.ps_hosts.split(',')
  worker_hosts = FLAGS.worker_hosts.split(',')
  tf.logging.info('PS hosts are: %s' % ps_hosts)
  tf.logging.info('Worker hosts are: %s' % worker_hosts)

  cluster_spec = tf.train.ClusterSpec({'ps': ps_hosts,
                                       'worker': worker_hosts})
  server = tf.train.Server(
      {'ps': ps_hosts,
       'worker': worker_hosts},
      job_name=FLAGS.job_name,
      task_index=FLAGS.task_id,
      protocol=FLAGS.protocol)

  if FLAGS.job_name == 'ps':
    # `ps` jobs wait for incoming connections from the workers.
    server.join()
  else:
    # `worker` jobs will actually do the work.
    dataset = ImagenetData(subset=FLAGS.subset)
    assert dataset.data_files()
    # Only the chief checks for or creates train_dir.
    if FLAGS.task_id == 0:
      if not tf.gfile.Exists(FLAGS.train_dir):
        tf.gfile.MakeDirs(FLAGS.train_dir)
    inception_distributed_train.train(server.target, dataset, cluster_spec)

開發者ID:ringringyi，項目名稱:DOTA_models，代碼行數:33，代碼來源:imagenet_distributed_train.py

示例2: main

# 需要導入模塊: from inception import inception_distributed_train [as 別名]
# 或者: from inception.inception_distributed_train import train [as 別名]
def main(unused_args):
  assert FLAGS.job_name in ['ps', 'worker'], 'job_name must be ps or worker'

  # Extract all the hostnames for the ps and worker jobs to construct the
  # cluster spec.
  ps_hosts = FLAGS.ps_hosts.split(',')
  worker_hosts = FLAGS.worker_hosts.split(',')
  tf.logging.info('PS hosts are: %s' % ps_hosts)
  tf.logging.info('Worker hosts are: %s' % worker_hosts)

  cluster_spec = tf.train.ClusterSpec({'ps': ps_hosts,
                                       'worker': worker_hosts})
  server = tf.train.Server(
      {'ps': ps_hosts,
       'worker': worker_hosts},
      job_name=FLAGS.job_name,
      task_index=FLAGS.task_id)

  if FLAGS.job_name == 'ps':
    # `ps` jobs wait for incoming connections from the workers.
    server.join()
  else:
    # `worker` jobs will actually do the work.
    dataset = ImagenetData(subset=FLAGS.subset)
    assert dataset.data_files()
    # Only the chief checks for or creates train_dir.
    if FLAGS.task_id == 0:
      if not tf.gfile.Exists(FLAGS.train_dir):
        tf.gfile.MakeDirs(FLAGS.train_dir)
    inception_distributed_train.train(server.target, dataset, cluster_spec)

開發者ID:Cyber-Neuron，項目名稱:inception_v3，代碼行數:32，代碼來源:imagenet_distributed_train.py

示例3: main

# 需要導入模塊: from inception import inception_distributed_train [as 別名]
# 或者: from inception.inception_distributed_train import train [as 別名]
def main(unused_args):
  assert FLAGS.job_name in ['ps', 'worker'], 'job_name must be ps or worker'

  # Extract all the hostnames for the ps and worker jobs to construct the
  # cluster spec.
  ps_hosts = FLAGS.ps_hosts.split(',')
  worker_hosts = FLAGS.worker_hosts.split(',')
  tf.logging.info('PS hosts are: %s' % ps_hosts)
  tf.logging.info('Worker hosts are: %s' % worker_hosts)

  cluster_spec = tf.train.ClusterSpec({'ps': ps_hosts,
                                       'worker': worker_hosts})
  server = tf.train.Server(
      {'ps': ps_hosts,
       'worker': worker_hosts},
      job_name=FLAGS.job_name,
      task_index=FLAGS.task_id)

  if FLAGS.job_name == 'ps':
    # `ps` jobs wait for incoming connections from the workers.
    server.join()
  else:
    # `worker` jobs will actually do the work.
    dataset = FishData(subset=FLAGS.subset)
    assert dataset.data_files()
    # Only the chief checks for or creates train_dir.
    if FLAGS.task_id == 0:
      if not tf.gfile.Exists(FLAGS.train_dir):
        tf.gfile.MakeDirs(FLAGS.train_dir)
    inception_distributed_train.train(server.target, dataset, cluster_spec)

開發者ID:Cyber-Neuron，項目名稱:inception_v3，代碼行數:32，代碼來源:fish_distributed_train.py

示例4: main

# 需要導入模塊: from inception import inception_distributed_train [as 別名]
# 或者: from inception.inception_distributed_train import train [as 別名]
def main(unused_args):
  assert FLAGS.job_name in ['ps', 'worker'], 'job_name must be ps or worker'

  # Extract all the hostnames for the ps and worker jobs to construct the
  # cluster spec.
  ps_hosts = FLAGS.ps_hosts.split(',')
  worker_hosts = FLAGS.worker_hosts.split(',')
  tf.logging.info('PS hosts are: %s' % ps_hosts)
  tf.logging.info('Worker hosts are: %s' % worker_hosts)

  cluster_spec = tf.train.ClusterSpec({'ps': ps_hosts,
                                       'worker': worker_hosts})
  server = tf.train.Server(
      {'ps': ps_hosts,
       'worker': worker_hosts},
      job_name=FLAGS.job_name,
      task_index=FLAGS.task_id)

  if FLAGS.job_name == 'ps':
    # `ps` jobs wait for incoming connections from the workers.
    server.join()
  else:
    # `worker` jobs will actually do the work.
    dataset = ImagenetData(subset=FLAGS.subset)
    #assert dataset.data_files()
    # Only the chief checks for or creates train_dir.
    if FLAGS.task_id == 0:
      if not tf.gfile.Exists(FLAGS.train_dir):
        tf.gfile.MakeDirs(FLAGS.train_dir)
    inception_distributed_train.train(server.target, dataset, cluster_spec)

開發者ID:awslabs，項目名稱:deeplearning-benchmark，代碼行數:32，代碼來源:imagenet_distributed_train.py

示例5: main

# 需要導入模塊: from inception import inception_distributed_train [as 別名]
# 或者: from inception.inception_distributed_train import train [as 別名]
def main(unused_args):
  FLAGS.dataset_name = 'cifar10'

  assert FLAGS.job_name in ['ps', 'worker'], 'job_name must be ps or worker'

  # Extract all the hostnames for the ps and worker jobs to construct the
  # cluster spec.
  ps_hosts = FLAGS.ps_hosts.split(',')
  worker_hosts = FLAGS.worker_hosts.split(',')
  tf.logging.info('PS hosts are: %s' % ps_hosts)
  tf.logging.info('Worker hosts are: %s' % worker_hosts)

  cluster_spec = tf.train.ClusterSpec({'ps': ps_hosts,
                                       'worker': worker_hosts})
  sess_config = tf.ConfigProto()
  sess_config.gpu_options.allow_growth = True

  server = tf.train.Server(
      {'ps': ps_hosts,
       'worker': worker_hosts},
      job_name=FLAGS.job_name,
      task_index=FLAGS.task_id,
      config=sess_config)

  if FLAGS.job_name == 'ps':
    # `ps` jobs wait for incoming connections from the workers.
    server.join()
  else:
    # `worker` jobs will actually do the work.
    dataset = Cifar10Data(subset=FLAGS.subset)
    assert dataset.data_files()
    # Only the chief checks for or creates train_dir.
    if FLAGS.task_id == 0:
      if not tf.gfile.Exists(FLAGS.train_dir):
        tf.gfile.MakeDirs(FLAGS.train_dir)
    inception_distributed_train.train(server.target, dataset, cluster_spec)

開發者ID:wenwei202，項目名稱:terngrad，代碼行數:38，代碼來源:cifar10_distributed_train.py

示例6: main

# 需要導入模塊: from inception import inception_distributed_train [as 別名]
# 或者: from inception.inception_distributed_train import train [as 別名]
def main(unused_args):
  FLAGS.dataset_name = 'imagenet'

  assert FLAGS.job_name in ['ps', 'worker'], 'job_name must be ps or worker'

  # Extract all the hostnames for the ps and worker jobs to construct the
  # cluster spec.
  ps_hosts = FLAGS.ps_hosts.split(',')
  worker_hosts = FLAGS.worker_hosts.split(',')
  tf.logging.info('PS hosts are: %s' % ps_hosts)
  tf.logging.info('Worker hosts are: %s' % worker_hosts)

  cluster_spec = tf.train.ClusterSpec({'ps': ps_hosts,
                                       'worker': worker_hosts})
  sess_config = tf.ConfigProto()
  sess_config.gpu_options.allow_growth = True

  server = tf.train.Server(
      {'ps': ps_hosts,
       'worker': worker_hosts},
      job_name=FLAGS.job_name,
      task_index=FLAGS.task_id,
      config=sess_config)

  if FLAGS.job_name == 'ps':
    # `ps` jobs wait for incoming connections from the workers.
    server.join()
  else:
    # `worker` jobs will actually do the work.
    dataset = ImagenetData(subset=FLAGS.subset)
    assert dataset.data_files()
    # Only the chief checks for or creates train_dir.
    if FLAGS.task_id == 0:
      if not tf.gfile.Exists(FLAGS.train_dir):
        tf.gfile.MakeDirs(FLAGS.train_dir)
    inception_distributed_train.train(server.target, dataset, cluster_spec)

開發者ID:wenwei202，項目名稱:terngrad，代碼行數:38，代碼來源:imagenet_distributed_train.py

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