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 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
.