tf.contrib.learn.monitors.CheckpointSaver.__init__(checkpoint_dir, save_secs=None, save_steps=None, saver=None, checkpoint_basename='model.ckpt', scaffold=None) Initialize CheckpointSaver monitor. Args: checkpoint_dir: str, base directory for the checkpoint files. save_secs: int, save every N secs. save_steps: int, save every N steps. saver: Saver object, used for saving. checkpoint_basename: str, base name for the checkpoint files. scaffold: Scaffold, use to get saver object. Raises:

tf.contrib.learn.monitors.CheckpointSaver.step_end(step, output) Callback after training step finished. This callback provides access to the tensors/ops evaluated at this step, including the additional tensors for which evaluation was requested in step_begin. In addition, the callback has the opportunity to stop training by returning True. This is useful for early stopping, for example. Note that this method is not called if the call to Session.run() that followed the last call to step_begin()

tf.contrib.learn.monitors.CheckpointSaver.step_begin(step)

tf.contrib.learn.monitors.CheckpointSaver.set_estimator(estimator) A setter called automatically by the target estimator. If the estimator is locked, this method does nothing. Args: estimator: the estimator that this monitor monitors. Raises: ValueError: if the estimator is None.

tf.contrib.learn.monitors.CheckpointSaver.run_on_all_workers

tf.contrib.learn.monitors.CheckpointSaver.post_step(step, session)

tf.contrib.learn.monitors.CheckpointSaver.epoch_end(epoch) End epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've not begun an epoch, or epoch number does not match.

tf.contrib.learn.monitors.CheckpointSaver.epoch_begin(epoch) Begin epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've already begun an epoch, or epoch < 0.

tf.contrib.learn.monitors.CheckpointSaver.end(session=None)

tf.contrib.learn.monitors.CheckpointSaver.begin(max_steps=None)