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TensorFlow 1 version
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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.