Module: tf.estimator

Estimator: High level tools for working with models.

Modules

experimental module: Public API for tf.estimator.experimental namespace.

export module: All public utility methods for exporting Estimator to SavedModel.

Classes

class BaselineClassifier: A classifier that can establish a simple baseline.

class BaselineEstimator: An estimator that can establish a simple baseline.

class BaselineRegressor: A regressor that can establish a simple baseline.

class BestExporter: This class exports the serving graph and checkpoints of the best models.

class BinaryClassHead: Creates a Head for single label binary classification.

class BoostedTreesClassifier: A Classifier for Tensorflow Boosted Trees models.

class BoostedTreesEstimator: An Estimator for Tensorflow Boosted Trees models.

class BoostedTreesRegressor: A Regressor for Tensorflow Boosted Trees models.

class CheckpointSaverHook: Saves checkpoints every N steps or seconds.

class CheckpointSaverListener: Interface for listeners that take action before or after checkpoint save.

class DNNClassifier: A classifier for TensorFlow DNN models.

class DNNEstimator: An estimator for TensorFlow DNN models with user-specified head.

class DNNLinearCombinedClassifier: An estimator for TensorFlow Linear and DNN joined classification models.

class DNNLinearCombinedEstimator: An estimator for TensorFlow Linear and DNN joined models with custom head.

class DNNLinearCombinedRegressor: An estimator for TensorFlow Linear and DNN joined models for regression.

class DNNRegressor: A regressor for TensorFlow DNN models.

class Estimator: Estimator class to train and evaluate TensorFlow models.

class EstimatorSpec: Ops and objects returned from a model_fn and passed to an Estimator.

class EvalSpec: Configuration for the "eval" part for the train_and_evaluate call.

class Exporter: A class representing a type of model export.

class FeedFnHook: Runs feed_fn and sets the feed_dict accordingly.

class FinalExporter: This class exports the serving graph and checkpoints at the end.

class FinalOpsHook: A hook which evaluates Tensors at the end of a session.

class GlobalStepWaiterHook: Delays execution until global step reaches wait_until_step.

class Head: Interface for the head/top of a model.

class LatestExporter: This class regularly exports the serving graph and checkpoints.

class LinearClassifier: Linear classifier model.

class LinearEstimator: An estimator for TensorFlow linear models with user-specified head.

class LinearRegressor: An estimator for TensorFlow Linear regression problems.

class LoggingTensorHook: Prints the given tensors every N local steps, every N seconds, or at end.

class LogisticRegressionHead: Creates a Head for logistic regression.

class ModeKeys: Standard names for Estimator model modes.

class MultiClassHead: Creates a Head for multi class classification.

class MultiHead: Creates a Head for multi-objective learning.

class MultiLabelHead: Creates a Head for multi-label classification.

class NanLossDuringTrainingError: Unspecified run-time error.

class NanTensorHook: Monitors the loss tensor and stops training if loss is NaN.

class PoissonRegressionHead: Creates a Head for poisson regression using tf.nn.log_poisson_loss.

class ProfilerHook: Captures CPU/GPU profiling information every N steps or seconds.

class RegressionHead: Creates a Head for regression using the mean_squared_error loss.

class RunConfig: This class specifies the configurations for an Estimator run.

class SecondOrStepTimer: Timer that triggers at most once every N seconds or once every N steps.

class SessionRunArgs: Represents arguments to be added to a Session.run() call.

class SessionRunContext: Provides information about the session.run() call being made.

class SessionRunHook: Hook to extend calls to MonitoredSession.run().

class SessionRunValues: Contains the results of Session.run().

class StepCounterHook: Hook that counts steps per second.

class StopAtStepHook: Hook that requests stop at a specified step.

class SummarySaverHook: Saves summaries every N steps.

class TrainSpec: Configuration for the "train" part for the train_and_evaluate call.

class VocabInfo: Vocabulary information for warm-starting.

class WarmStartSettings: Settings for warm-starting in tf.estimator.Estimators.

Functions

add_metrics(...): Creates a new tf.estimator.Estimator which has given metrics.