tf.contrib.learn.monitors.StopAtStep.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.

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

tf.contrib.learn.monitors.StepCounter.__init__(every_n_steps=100, output_dir=None, summary_writer=None)

tf.contrib.learn.monitors.StepCounter.step_end(step, output) Overrides BaseMonitor.step_end. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. output: dict mapping string values representing tensor names to the value resulted from running these tensors. Values may be either scalars, for scalar tensors, or Numpy array, for non-scalar tensors. Returns: bool, the result of every_n_step_end, if that was called this step,

tf.contrib.learn.monitors.StepCounter.step_begin(step) Overrides BaseMonitor.step_begin. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. Returns: A list, the result of every_n_step_begin, if that was called this step, or an empty list otherwise. Raises: ValueError: if called more than once during a step.

tf.contrib.learn.monitors.StepCounter.set_estimator(estimator)

tf.contrib.learn.monitors.StepCounter.run_on_all_workers

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

tf.contrib.learn.monitors.StepCounter.every_n_step_end(current_step, outputs)

tf.contrib.learn.monitors.StepCounter.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.