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.