class tf.contrib.learn.monitors.CheckpointSaver Saves checkpoints every N steps.

tf.contrib.learn.monitors.CaptureVariable.__init__(var_name, every_n=100, first_n=1) Initializes a CaptureVariable monitor. Args: var_name: string. The variable name, including suffix (typically ":0"). every_n: int, print every N steps. See PrintN. first_n: int, also print the first N steps. See PrintN.

tf.contrib.learn.monitors.CaptureVariable.values Returns the values captured so far. Returns: dict mapping int step numbers to that values of the variable at the respective step.

tf.contrib.learn.monitors.CaptureVariable.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 s

tf.contrib.learn.monitors.CaptureVariable.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.CaptureVariable.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.CaptureVariable.run_on_all_workers

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

tf.contrib.learn.monitors.CaptureVariable.every_n_step_end(step, outputs)

tf.contrib.learn.monitors.CaptureVariable.every_n_step_begin(step)