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

tf.contrib.learn.monitors.LoggingTrainable.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.LoggingTrainable Writes trainable variable values into log every N steps. Write the tensors in trainable variables every_n steps, starting with the first_nth step.

tf.contrib.learn.monitors.GraphDump.__init__(ignore_ops=None) Initializes GraphDump monitor. Args: ignore_ops: list of string. Names of ops to ignore. If None, GraphDump.IGNORE_OPS is used.

tf.contrib.learn.monitors.GraphDump.step_end(step, output)

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

tf.contrib.learn.monitors.GraphDump.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.GraphDump.run_on_all_workers

tf.contrib.learn.monitors.GraphDump.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.