python类train()的实例源码

def start_server(cluster, task):
  """Creates a Server.

  Args:
    cluster: A tf.train.ClusterSpec if the execution is distributed.
      None otherwise.
    task: A TaskSpec describing the job type and the task index.
  """

  if not task.type:
    raise ValueError("%s: The task type must be specified." %
                     task_as_string(task))
  if task.index is None:
    raise ValueError("%s: The task index must be specified." %
                     task_as_string(task))

  # Create and start a server.
  return tf.train.Server(
      tf.train.ClusterSpec(cluster),
      protocol="grpc",
      job_name=task.type,
      task_index=task.index)

def start_server_if_distributed(self):
    """Starts a server if the execution is distributed."""

    if self.cluster:
      logging.info("%s: Starting trainer within cluster %s.",
                   task_as_string(self.task), self.cluster.as_dict())
      server = start_server(self.cluster, self.task)
      target = server.target
      device_fn = tf.train.replica_device_setter(
          ps_device="/job:ps",
          worker_device="/job:%s/task:%d" % (self.task.type, self.task.index),
          cluster=self.cluster)
    else:
      target = ""
      device_fn = ""
    return (target, device_fn)

def get_meta_filename(self, start_new_model, train_dir):
    if start_new_model:
      logging.info("%s: Flag 'start_new_model' is set. Building a new model.",
                   task_as_string(self.task))
      return None

    latest_checkpoint = tf.train.latest_checkpoint(train_dir)
    if not latest_checkpoint:
      logging.info("%s: No checkpoint file found. Building a new model.",
                   task_as_string(self.task))
      return None

    meta_filename = latest_checkpoint + ".meta"
    if not gfile.Exists(meta_filename):
      logging.info("%s: No meta graph file found. Building a new model.",
                     task_as_string(self.task))
      return None
    else:
      return meta_filename

def build_model(self, model, reader):
    """Find the model and build the graph."""

    label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])()
    optimizer_class = find_class_by_name(FLAGS.optimizer, [tf.train])

    build_graph(reader=reader,
                 model=model,
                 optimizer_class=optimizer_class,
                 clip_gradient_norm=FLAGS.clip_gradient_norm,
                 train_data_pattern=FLAGS.train_data_pattern,
                 label_loss_fn=label_loss_fn,
                 base_learning_rate=FLAGS.base_learning_rate,
                 learning_rate_decay=FLAGS.learning_rate_decay,
                 learning_rate_decay_examples=FLAGS.learning_rate_decay_examples,
                 regularization_penalty=FLAGS.regularization_penalty,
                 num_readers=FLAGS.num_readers,
                 batch_size=FLAGS.batch_size,
                 num_epochs=FLAGS.num_epochs)

    return tf.train.Saver(max_to_keep=0, keep_checkpoint_every_n_hours=0.25)

def start_server(cluster, task):
  """Creates a Server.

  Args:
    cluster: A tf.train.ClusterSpec if the execution is distributed.
      None otherwise.
    task: A TaskSpec describing the job type and the task index.
  """

  if not task.type:
    raise ValueError("%s: The task type must be specified." %
                     task_as_string(task))
  if task.index is None:
    raise ValueError("%s: The task index must be specified." %
                     task_as_string(task))

  # Create and start a server.
  return tf.train.Server(
      tf.train.ClusterSpec(cluster),
      protocol="grpc",
      job_name=task.type,
      task_index=task.index)

def start_server_if_distributed(self):
    """Starts a server if the execution is distributed."""

    if self.cluster:
      logging.info("%s: Starting trainer within cluster %s.",
                   task_as_string(self.task), self.cluster.as_dict())
      server = start_server(self.cluster, self.task)
      target = server.target
      device_fn = tf.train.replica_device_setter(
          ps_device="/job:ps",
          worker_device="/job:%s/task:%d" % (self.task.type, self.task.index),
          cluster=self.cluster)
    else:
      target = ""
      device_fn = ""
    return (target, device_fn)

def get_meta_filename(self, start_new_model, train_dir):
    if start_new_model:
      logging.info("%s: Flag 'start_new_model' is set. Building a new model.",
                   task_as_string(self.task))
      return None

    latest_checkpoint = tf.train.latest_checkpoint(train_dir)
    if not latest_checkpoint:
      logging.info("%s: No checkpoint file found. Building a new model.",
                   task_as_string(self.task))
      return None

    meta_filename = latest_checkpoint + ".meta"
    if not gfile.Exists(meta_filename):
      logging.info("%s: No meta graph file found. Building a new model.",
                     task_as_string(self.task))
      return None
    else:
      return meta_filename

def build_model(self, model, reader):
    """Find the model and build the graph."""

    label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])()
    optimizer_class = find_class_by_name(FLAGS.optimizer, [tf.train])

    build_graph(reader=reader,
                 model=model,
                 optimizer_class=optimizer_class,
                 clip_gradient_norm=FLAGS.clip_gradient_norm,
                 train_data_pattern=FLAGS.train_data_pattern,
                 label_loss_fn=label_loss_fn,
                 base_learning_rate=FLAGS.base_learning_rate,
                 learning_rate_decay=FLAGS.learning_rate_decay,
                 learning_rate_decay_examples=FLAGS.learning_rate_decay_examples,
                 regularization_penalty=FLAGS.regularization_penalty,
                 num_readers=FLAGS.num_readers,
                 batch_size=FLAGS.batch_size,
                 num_epochs=FLAGS.num_epochs)

    return tf.train.Saver(max_to_keep=0, keep_checkpoint_every_n_hours=0.25)

def start_server(cluster, task):
  """Creates a Server.

  Args:
    cluster: A tf.train.ClusterSpec if the execution is distributed.
      None otherwise.
    task: A TaskSpec describing the job type and the task index.
  """

  if not task.type:
    raise ValueError("%s: The task type must be specified." %
                     task_as_string(task))
  if task.index is None:
    raise ValueError("%s: The task index must be specified." %
                     task_as_string(task))

  # Create and start a server.
  return tf.train.Server(
      tf.train.ClusterSpec(cluster),
      protocol="grpc",
      job_name=task.type,
      task_index=task.index)

def start_server_if_distributed(self):
    """Starts a server if the execution is distributed."""

    if self.cluster:
      logging.info("%s: Starting trainer within cluster %s.",
                   task_as_string(self.task), self.cluster.as_dict())
      server = start_server(self.cluster, self.task)
      target = server.target
      device_fn = tf.train.replica_device_setter(
          ps_device="/job:ps",
          worker_device="/job:%s/task:%d" % (self.task.type, self.task.index),
          cluster=self.cluster)
    else:
      target = ""
      device_fn = ""
    return (target, device_fn)

def get_meta_filename(self, start_new_model, train_dir):
    if start_new_model:
      logging.info("%s: Flag 'start_new_model' is set. Building a new model.",
                   task_as_string(self.task))
      return None

    latest_checkpoint = tf.train.latest_checkpoint(train_dir)
    if not latest_checkpoint:
      logging.info("%s: No checkpoint file found. Building a new model.",
                   task_as_string(self.task))
      return None

    meta_filename = latest_checkpoint + ".meta"
    if not gfile.Exists(meta_filename):
      logging.info("%s: No meta graph file found. Building a new model.",
                     task_as_string(self.task))
      return None
    else:
      return meta_filename

def build_model(self, model, reader):
    """Find the model and build the graph."""

    label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])()
    optimizer_class = find_class_by_name(FLAGS.optimizer, [tf.train])

    build_graph(reader=reader,
                 model=model,
                 optimizer_class=optimizer_class,
                 clip_gradient_norm=FLAGS.clip_gradient_norm,
                 train_data_pattern=FLAGS.train_data_pattern,
                 label_loss_fn=label_loss_fn,
                 base_learning_rate=FLAGS.base_learning_rate,
                 learning_rate_decay=FLAGS.learning_rate_decay,
                 learning_rate_decay_examples=FLAGS.learning_rate_decay_examples,
                 regularization_penalty=FLAGS.regularization_penalty,
                 num_readers=FLAGS.num_readers,
                 batch_size=FLAGS.batch_size,
                 num_epochs=FLAGS.num_epochs)

    return tf.train.Saver(max_to_keep=0, keep_checkpoint_every_n_hours=0.25)

def start_server(cluster, task):
  """Creates a Server.

  Args:
    cluster: A tf.train.ClusterSpec if the execution is distributed.
      None otherwise.
    task: A TaskSpec describing the job type and the task index.
  """

  if not task.type:
    raise ValueError("%s: The task type must be specified." %
                     task_as_string(task))
  if task.index is None:
    raise ValueError("%s: The task index must be specified." %
                     task_as_string(task))

  # Create and start a server.
  return tf.train.Server(
      tf.train.ClusterSpec(cluster),
      protocol="grpc",
      job_name=task.type,
      task_index=task.index)

def start_server_if_distributed(self):
    """Starts a server if the execution is distributed."""

    if self.cluster:
      logging.info("%s: Starting trainer within cluster %s.",
                   task_as_string(self.task), self.cluster.as_dict())
      server = start_server(self.cluster, self.task)
      target = server.target
      device_fn = tf.train.replica_device_setter(
          ps_device="/job:ps",
          worker_device="/job:%s/task:%d" % (self.task.type, self.task.index),
          cluster=self.cluster)
    else:
      target = ""
      device_fn = ""
    return (target, device_fn)

def get_meta_filename(self, start_new_model, train_dir):
    if start_new_model:
      logging.info("%s: Flag 'start_new_model' is set. Building a new model.",
                   task_as_string(self.task))
      return None

    latest_checkpoint = tf.train.latest_checkpoint(train_dir)
    if not latest_checkpoint:
      logging.info("%s: No checkpoint file found. Building a new model.",
                   task_as_string(self.task))
      return None

    meta_filename = latest_checkpoint + ".meta"
    if not gfile.Exists(meta_filename):
      logging.info("%s: No meta graph file found. Building a new model.",
                     task_as_string(self.task))
      return None
    else:
      return meta_filename

def build_model(self, model, reader):
    """Find the model and build the graph."""

    label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])()
    optimizer_class = find_class_by_name(FLAGS.optimizer, [tf.train])

    build_graph(reader=reader,
                 model=model,
                 optimizer_class=optimizer_class,
                 clip_gradient_norm=FLAGS.clip_gradient_norm,
                 train_data_pattern=FLAGS.train_data_pattern,
                 label_loss_fn=label_loss_fn,
                 base_learning_rate=FLAGS.base_learning_rate,
                 learning_rate_decay=FLAGS.learning_rate_decay,
                 learning_rate_decay_examples=FLAGS.learning_rate_decay_examples,
                 regularization_penalty=FLAGS.regularization_penalty,
                 num_readers=FLAGS.num_readers,
                 batch_size=FLAGS.batch_size,
                 num_epochs=FLAGS.num_epochs)

    return tf.train.Saver(max_to_keep=0, keep_checkpoint_every_n_hours=0.25)

def start_server(cluster, task):
  """Creates a Server.

  Args:
    cluster: A tf.train.ClusterSpec if the execution is distributed.
      None otherwise.
    task: A TaskSpec describing the job type and the task index.
  """

  if not task.type:
    raise ValueError("%s: The task type must be specified." %
                     task_as_string(task))
  if task.index is None:
    raise ValueError("%s: The task index must be specified." %
                     task_as_string(task))

  # Create and start a server.
  return tf.train.Server(
      tf.train.ClusterSpec(cluster),
      protocol="grpc",
      job_name=task.type,
      task_index=task.index)

def start_server_if_distributed(self):
    """Starts a server if the execution is distributed."""

    if self.cluster:
      logging.info("%s: Starting trainer within cluster %s.",
                   task_as_string(self.task), self.cluster.as_dict())
      server = start_server(self.cluster, self.task)
      target = server.target
      device_fn = tf.train.replica_device_setter(
          ps_device="/job:ps",
          worker_device="/job:%s/task:%d" % (self.task.type, self.task.index),
          cluster=self.cluster)
    else:
      target = ""
      device_fn = ""
    return (target, device_fn)

def get_meta_filename(self, start_new_model, train_dir):
    if start_new_model:
      logging.info("%s: Flag 'start_new_model' is set. Building a new model.",
                   task_as_string(self.task))
      return None

    latest_checkpoint = tf.train.latest_checkpoint(train_dir)
    if not latest_checkpoint:
      logging.info("%s: No checkpoint file found. Building a new model.",
                   task_as_string(self.task))
      return None

    meta_filename = latest_checkpoint + ".meta"
    if not gfile.Exists(meta_filename):
      logging.info("%s: No meta graph file found. Building a new model.",
                     task_as_string(self.task))
      return None
    else:
      return meta_filename

def build_model(self, model, reader):
    """Find the model and build the graph."""

    label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])()
    optimizer_class = find_class_by_name(FLAGS.optimizer, [tf.train])

    build_graph(reader=reader,
                 model=model,
                 optimizer_class=optimizer_class,
                 clip_gradient_norm=FLAGS.clip_gradient_norm,
                 train_data_pattern=FLAGS.train_data_pattern,
                 label_loss_fn=label_loss_fn,
                 base_learning_rate=FLAGS.base_learning_rate,
                 learning_rate_decay=FLAGS.learning_rate_decay,
                 learning_rate_decay_examples=FLAGS.learning_rate_decay_examples,
                 regularization_penalty=FLAGS.regularization_penalty,
                 num_readers=FLAGS.num_readers,
                 batch_size=FLAGS.batch_size,
                 num_epochs=FLAGS.num_epochs)

    return tf.train.Saver(max_to_keep=0, keep_checkpoint_every_n_hours=2.0)