tf.contrib.learn.monitors.BaseMonitor.step_begin(step) Callback before training step begins. You may use this callback to request evaluation of additional tensors in the graph. Args: step: int, the current value of the global step. Returns: List of Tensor objects or string tensor names to be run. Raises: ValueError: if we've already begun a step, or step < 0, or step > max_steps.

tf.contrib.learn.monitors.BaseMonitor.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.

tf.contrib.learn.monitors.BaseMonitor.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.BaseMonitor.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.BaseMonitor.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.BaseMonitor.end(session=None) Callback at the end of training/evaluation. Args: session: A tf.Session object that can be used to run ops. Raises: ValueError: if we've not begun a run.

tf.contrib.learn.LinearRegressor.__repr__()

class tf.contrib.learn.ModeKeys Standard names for model modes. The following standard keys are defined: TRAIN: training mode. EVAL: evaluation mode. INFER: inference mode.

class tf.contrib.learn.monitors.BaseMonitor Base class for Monitors. Defines basic interfaces of Monitors. Monitors can either be run on all workers or, more commonly, restricted to run exclusively on the elected chief worker.

tf.contrib.learn.LinearRegressor.__init__(feature_columns, model_dir=None, weight_column_name=None, optimizer=None, gradient_clip_norm=None, enable_centered_bias=None, target_dimension=1, _joint_weights=False, config=None) Construct a LinearRegressor estimator object. Args: feature_columns: An iterable containing all the feature columns used by the model. All items in the set should be instances of classes derived from FeatureColumn. model_dir: Directory to save model parameters, graph, etc.