tf.contrib.learn.monitors.EveryN.run_on_all_workers

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

tf.contrib.learn.monitors.EveryN.every_n_post_step(step, session) Callback after a step is finished or end() is called. Args: step: int, the current value of the global step. session: Session object.

tf.contrib.learn.monitors.EveryN.every_n_step_begin(step) Callback before every n'th step begins. Args: step: int, the current value of the global step. Returns: A list of tensors that will be evaluated at this step.

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

tf.contrib.learn.monitors.EveryN.begin(max_steps=None) Called at the beginning of training. When called, the default graph is the one we are executing. Args: max_steps: int, the maximum global step this training will run until. Raises: ValueError: if we've already begun a run.

tf.contrib.learn.monitors.EveryN.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.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()