tf.contrib.learn.monitors.NanLoss.run_on_all_workers

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

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

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

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.

class tf.contrib.learn.monitors.NanLoss NaN Loss monitor. Monitors loss and stops training if loss is NaN. Can either fail with exception or just stop training.