Function train

tensorflow/contrib/slim/python/slim/learning.py:533–798

Runs a training loop using a TensorFlow supervisor. When the sync_optimizer is supplied, gradient updates are applied synchronously. Otherwise, gradient updates are applied asynchronous. Args: train_op: A `Tensor` that, when executed, will apply the gradients and return the loss va

(train_op,
          logdir,
          train_step_fn=train_step,
          train_step_kwargs=_USE_DEFAULT,
          log_every_n_steps=1,
          graph=None,
          master='',
          is_chief=True,
          global_step=None,
          number_of_steps=None,
          init_op=_USE_DEFAULT,
          init_feed_dict=None,
          local_init_op=_USE_DEFAULT,
          init_fn=None,
          ready_op=_USE_DEFAULT,
          summary_op=_USE_DEFAULT,
          save_summaries_secs=600,
          summary_writer=_USE_DEFAULT,
          startup_delay_steps=0,
          saver=None,
          save_interval_secs=600,
          sync_optimizer=None,
          session_config=None,
          session_wrapper=None,
          trace_every_n_steps=None,
          ignore_live_threads=False)

def train(train_op,
          logdir,
          train_step_fn=train_step,
          train_step_kwargs=_USE_DEFAULT,
          log_every_n_steps=1,
          graph=None,
          master='',
          is_chief=True,
          global_step=None,
          number_of_steps=None,
          init_op=_USE_DEFAULT,
          init_feed_dict=None,
          local_init_op=_USE_DEFAULT,
          init_fn=None,
          ready_op=_USE_DEFAULT,
          summary_op=_USE_DEFAULT,
          save_summaries_secs=600,
          summary_writer=_USE_DEFAULT,
          startup_delay_steps=0,
          saver=None,
          save_interval_secs=600,
          sync_optimizer=None,
          session_config=None,
          session_wrapper=None,
          trace_every_n_steps=None,
          ignore_live_threads=False):
  """Runs a training loop using a TensorFlow supervisor.

  When the sync_optimizer is supplied, gradient updates are applied
  synchronously. Otherwise, gradient updates are applied asynchronous.

  Args:
    train_op: A `Tensor` that, when executed, will apply the gradients and
      return the loss value.
    logdir: The directory where training logs are written to. If None, model
      checkpoints and summaries will not be written.
    train_step_fn: The function to call in order to execute a single gradient
      step. The function must have take exactly four arguments: the current
        session, the `train_op` `Tensor`, a global step `Tensor` and a
        dictionary.
    train_step_kwargs: A dictionary which is passed to the `train_step_fn`. By
      default, two `Boolean`, scalar ops called "should_stop" and "should_log"
      are provided.
    log_every_n_steps: The frequency, in terms of global steps, that the loss
      and global step are logged.
    graph: The graph to pass to the supervisor. If no graph is supplied the
      default graph is used.
    master: The address of the tensorflow master.
    is_chief: Specifies whether or not the training is being run by the primary
      replica during replica training.
    global_step: The `Tensor` representing the global step. If left as `None`,
      then training_util.get_or_create_global_step(), that is,
      tf.contrib.framework.global_step() is used.
    number_of_steps: The max number of gradient steps to take during training,
      as measured by 'global_step': training will stop if global_step is greater
        than 'number_of_steps'. If the value is left as None, training proceeds
        indefinitely.
    init_op: The initialization operation. If left to its default value, then