tf.contrib.learn.monitors.SummarySaver.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.StopAtStep.__init__(num_steps=None, last_step=None) Create a StopAtStep monitor. This monitor requests stop after either a number of steps have been executed or a last step has been reached. Only of the two options can be specified. if num_steps is specified, it indicates the number of steps to execute after begin() is called. If instead last_step is specified, it indicates the last step we want to execute, as passed to the step_begin() call. Args: num_steps: Number

class tf.contrib.learn.monitors.SummarySaver Saves summaries every N steps.

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

tf.contrib.learn.monitors.StopAtStep.run_on_all_workers

tf.contrib.learn.monitors.StopAtStep.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.StopAtStep.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.StopAtStep.post_step(step, session) Callback after the step is finished. Called after step_end and receives session to perform extra session.run calls. If failure occurred in the process, will be called as well. Args: step: int, global step of the model. session: Session object.

class tf.contrib.learn.monitors.StopAtStep Monitor to request stop at a specified step.

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