tf.contrib.learn.monitors.ValidationMonitor.best_value Returns the best early stopping metric value found so far.

tf.contrib.learn.monitors.ValidationMonitor.best_step Returns the step at which the best early stopping metric was found.

tf.contrib.learn.monitors.ValidationMonitor.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.ValidationMonitor Runs evaluation of a given estimator, at most every N steps. Note that the evaluation is done based on the saved checkpoint, which will usually be older than the current step. Can do early stopping on validation metrics if early_stopping_rounds is provided.

tf.contrib.learn.monitors.SummaryWriterCache.get(logdir) Returns the SummaryWriter for the specified directory. Args: logdir: str, name of the directory. Returns: A SummaryWriter.

tf.contrib.learn.monitors.SummaryWriterCache.clear() Clear cached summary writers. Currently only used for unit tests.

class tf.contrib.learn.monitors.SummaryWriterCache Cache for summary writers. This class caches summary writers, one per directory.

tf.contrib.learn.monitors.SummarySaver.__init__(summary_op, save_steps=100, output_dir=None, summary_writer=None, scaffold=None) Initializes a SummarySaver monitor. Args: summary_op: Tensor of type string. A serialized Summary protocol buffer, as output by TF summary methods like scalar_summary or merge_all_summaries. save_steps: int, save summaries every N steps. See EveryN. output_dir: string, the directory to save the summaries to. Only used if no summary_writer is supplied. summary_wri

tf.contrib.learn.monitors.SummarySaver.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.SummarySaver.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.