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

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

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

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

tf.contrib.learn.monitors.LoggingTrainable.__init__(scope=None, every_n=100, first_n=1) Initializes LoggingTrainable monitor. Args: scope: An optional string to match variable names using re.match. every_n: Print every N steps. first_n: Print first N steps.