Module: tf.compat.v1.train

Support for training models.

See the Training guide.


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

queue_runner module: Public API for tf.train.queue_runner namespace.


class AdadeltaOptimizer: Optimizer that implements the Adadelta algorithm.

class AdagradDAOptimizer: Adagrad Dual Averaging algorithm for sparse linear models.

class AdagradOptimizer: Optimizer that implements the Adagrad algorithm.

class AdamOptimizer: Optimizer that implements the Adam algorithm.

class BytesList: A ProtocolMessage

class Checkpoint: Groups trackable objects, saving and restoring them.

class CheckpointManager: Manages multiple checkpoints by keeping some and deleting unneeded ones.

class CheckpointOptions: Options for constructing a Checkpoint.

class CheckpointSaverHook: Saves checkpoints every N steps or seconds.

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

class ChiefSessionCreator: Creates a tf.compat.v1.Session for a chief.

class ClusterDef: A ProtocolMessage

class ClusterSpec: Represents a cluster as a set of "tasks", organized into "jobs".

class Coordinator: A coordinator for threads.

class Example: A ProtocolMessage

class ExponentialMovingAverage: Maintains moving averages of variables by employing an exponential decay.

class Feature: A ProtocolMessage

class FeatureList: A ProtocolMessage

class FeatureLists: A ProtocolMessage

class Features: A ProtocolMessage

class FeedFnHook: Runs feed_fn and sets the feed_dict accordingly.

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

class FloatList: A ProtocolMessage

class FtrlOptimizer: Optimizer that implements the FTRL algorithm.

class GlobalStepWaiterHook: Delays execution until global step reaches wait_until_step.

class GradientDescentOptimizer: Optimizer that implements the gradient descent algorithm.

class Int64List: A ProtocolMessage

class JobDef: A ProtocolMessage

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

class LooperThread: A thread that runs code repeatedly, optionally on a timer.

class MomentumOptimizer: Optimizer that implements the Momentum algorithm.

class MonitoredSession: Session-like object that handles initialization, recovery and hooks.

class NanLossDuringTrainingError: Unspecified run-time error.

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

class Optimizer: Base class for optimizers.

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

class ProximalAdagradOptimizer: Optimizer that implements the Proximal Adagrad algorithm.

class ProximalGradientDescentOptimizer: Optimizer that implements the proximal gradient descent algorithm.

class QueueRunner: Holds a list of enqueue operations for a queue, each to be run in a thread.

class RMSPropOptimizer: Optimizer that implements the RMSProp algorithm (Tielemans et al.

class Saver: Saves and restores variables.

class SaverDef: A ProtocolMessage

class Scaffold: Structure to create or gather pieces commonly needed to train a model.

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

class SequenceExample: A ProtocolMessage

class Server: An in-process TensorFlow server, for use in distributed training.

class ServerDef: A ProtocolMessage

class SessionCreator: A factory for tf.Session.

class SessionManager: Training helper that restores from checkpoint and creates session.

class SessionRunArgs: Represents arguments to be added to a call.

class SessionRunContext: Provides information about the call being made.

class SessionRunHook: Hook to extend calls to

class SessionRunValues: Contains the results of

class SingularMonitoredSession: Session-like object that handles initialization, restoring, and hooks.

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 Supervisor: A training helper that checkpoints models and computes summaries.

class SyncReplicasOptimizer: Class to synchronize, aggregate gradients and pass them to the optimizer.

class VocabInfo: Vocabulary information for warm-starting.

class WorkerSessionCreator: Creates a tf.compat.v1.Session for a worker.


MonitoredTrainingSession(...): Creates a MonitoredSession for training.

NewCheckpointReader(...): A function that returns a CheckPointReader.

add_queue_runner(...): Adds a QueueRunner to a collection in the graph. (deprecated)

assert_global_step(...): Asserts global_step_tensor is a scalar int Variable or Tensor.

basic_train_loop(...): Basic loop to train a model.

batch(...): Creates batches of tensors in tensors. (deprecated)

batch_join(...): Runs a list of tensors to fill a queue to create batches of examples. (deprecated)

checkpoint_exists(...): Checks whether a V1 or V2 checkpoint exists with the specified prefix. (deprecated)

checkpoints_iterator(...): Continuously yield new checkpoint files as they appear.

cosine_decay(...): Applies cosine decay to the learning rate.

cosine_decay_restarts(...): Applies cosine decay with restarts to the learning rate.

create_global_step(...): Create glob