Method train

tensorflow/contrib/learn/python/learn/experiment.py:330–389

Fit the estimator using the training data. Train the estimator for `self._train_steps` steps, after waiting for `delay_secs` seconds. If `self._train_steps` is `None`, train forever. Args: delay_secs: Start training after this many seconds. Returns: The trained estimat

(self, delay_secs=None)

    return old_export_strategies

  def train(self, delay_secs=None):
    """Fit the estimator using the training data.

    Train the estimator for `self._train_steps` steps, after waiting for
    `delay_secs` seconds. If `self._train_steps` is `None`, train forever.

    Args:
      delay_secs: Start training after this many seconds.

    Returns:
      The trained estimator.
    """
    start = time.time()

    # Start the server, if needed. It's important to start the server before
    # we (optionally) sleep for the case where no device_filters are set.
    # Otherwise, the servers will wait to connect to each other before starting
    # to train. We might as well start as soon as we can.
    config = self._estimator.config
    if isinstance(config, run_config.RunConfig):
      if (config.cluster_spec and config.master and
          config.environment == run_config.Environment.LOCAL):
        logging.warn("ClusterSpec and master are provided, but environment is "
                     "set to 'local'. Set environment to 'cloud' if you intend "
                     "to use the distributed runtime.")
      if (config.environment != run_config.Environment.LOCAL and
          config.environment != run_config.Environment.GOOGLE and
          config.cluster_spec and config.master):
        self._start_server()
    elif config.cluster_spec and config.master:
      raise ValueError(
          "For distributed runtime, Experiment class only works with "
          "tf.contrib.learn.RunConfig for now, but provided {}".format(
              type(config)))

    extra_hooks = []
    if delay_secs is None:
      task_id = self._estimator.config.task_id or 0
      if self._delay_workers_by_global_step:
        # Wait 5500 global steps for the second worker. Each worker waits more
        # then previous one but with a diminishing number of steps.
        extra_hooks.append(
            basic_session_run_hooks.GlobalStepWaiterHook(
                int(8000.0 * math.log(task_id + 1))))
        delay_secs = 0
      else:
        # Wait 5 secs more for each new worker up to 60 secs.
        delay_secs = min(60, task_id * 5)

    if delay_secs > 0:
      elapsed_secs = time.time() - start
      remaining = delay_secs - elapsed_secs
      logging.info("Waiting %d secs before starting training.", remaining)
      time.sleep(delay_secs)

    return self._call_train(
        input_fn=self._train_input_fn,
        max_steps=self._train_steps,