Module: tf.compat.v1.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. inputs module: Utility methods to create simple input_fns. tpu module: Public API for tf.estimator.tpu namespace. 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. classifier_parse_example_spec(...): Generates parsing spec for tf.parse_example to be used with classifiers. regressor_parse_example_spec(...): Generates parsing spec for tf.parse_example to be used with regressors. train_and_evaluate(...): Train and evaluate the estimator.