Module: tf.compat.v1.train Support for training models. See the Training guide. Modules experimental module: Public API for tf.train.experimental namespace. queue_runner module: Public API for tf.train.queue_runner namespace. Classes 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 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 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. Functions 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 global step tensor in graph. do_quantize_training_on_graphdef(...): A general quantization scheme is being developed in tf.contrib.quantize. (deprecated) exponential_decay(...): Applies exponential decay to the learning rate. export_meta_graph(...): Returns MetaGraphDef proto. generate_checkpoint_state_proto(...): Generates a checkpoint state proto. get_checkpoint_mtimes(...): Returns the mtimes (modification timestamps) of the checkpoints. (deprecated) get_checkpoint_state(...): Returns CheckpointState proto from the "checkpoint" file. get_global_step(...): Get the global step tensor. get_or_create_global_step(...): Returns and create (if necessary) the global step tensor. global_step(...): Small helper to get the global step. import_meta_graph(...): Recreates a Graph saved in a MetaGraphDef proto. init_from_checkpoint(...): Replaces tf.Variable initializers so they load from a checkpoint file. input_producer(...): Output the rows of input_tensor to a queue for an input pipeline. (deprecated) inverse_time_decay(...): Applies inverse time decay to the initial learning rate. latest_checkpoint(...): Finds the filename of latest saved checkpoint file. limit_epochs(...): Returns tensor num_epochs times and then raises an OutOfRange error. (deprecated) linear_cosine_decay(...): Applies linear cosine decay to the learning rate. list_variables(...): Lists the checkpoint keys and shapes of variables in a checkpoint. load_checkpoint(...): Returns CheckpointReader for checkpoint found in ckpt_dir_or_file. load_variable(...): Returns the tensor value of the given variable in the checkpoint. match_filenames_once(...): Save the list of files matching pattern, so it is only computed once. maybe_batch(...): Conditionally creates batches of tensors based on keep_input. (deprecated) maybe_batch_join(...): Runs a list of tensors to conditionally fill a queue to create batches. (deprecated) maybe_shuffle_batch(...): Creates batches by randomly shuffling conditionally-enqueued tensors. (deprecated) maybe_shuffle_batch_join(...): Create batches by randomly shuffling conditionally-enqueued tensors. (deprecated) natural_exp_decay(...): Applies natural exponential decay to the initial learning rate. noisy_linear_cosine_decay(...): Applies noisy linear cosine decay to the learning rate. piecewise_constant(...): Piecewise constant from boundaries and interval values. piecewise_constant_decay(...): Piecewise constant from boundaries and interval values. polynomial_decay(...): Applies a polynomial decay to the learning rate. range_input_producer(...): Produces the integers from 0 to limit-1 in a queue. (deprecated) remove_checkpoint(...): Removes a checkpoint given by checkpoint_prefix. (deprecated) replica_device_setter(...): Return a device function to use when building a Graph for replicas. sdca_fprint(...): Computes fingerprints of the input strings. sdca_optimizer(...): Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for sdca_shrink_l1(...): Applies L1 regularization shrink step on the parameters. shuffle_batch(...): Creates batches by randomly shuffling tensors. (deprecated) shuffle_batch_join(...): Create batches by randomly shuffling tensors. (deprecated) slice_input_producer(...): Produces a slice of each Tensor in tensor_list. (deprecated) start_queue_runners(...): Starts all queue runners collected in the graph. (deprecated) string_input_producer(...): Output strings (e.g. filenames) to a queue for an input pipeline. (deprecated) summary_iterator(...): Returns a iterator for reading Event protocol buffers from an event file. update_checkpoint_state(...): Updates the content of the 'checkpoint' file. (deprecated) warm_start(...): Warm-starts a model using the given settings. write_graph(...): Writes a graph proto to a file.