Support for training models.
See the Training guide.
Modules
experimental
module: Public API for tf.train.experimental namespace.
Classes
class BytesList
: Container that holds repeated fundamental values of byte type in the tf.train.Feature
message.
class Checkpoint
: Manages saving/restoring trackable values to disk.
class CheckpointManager
: Manages multiple checkpoints by keeping some and deleting unneeded ones.
class CheckpointOptions
: Options for constructing a Checkpoint.
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
: An Example
is a mostly-normalized data format for storing data for training and inference.
class ExponentialMovingAverage
: Maintains moving averages of variables by employing an exponential decay.
class Feature
: A Feature
is a list which may hold zero or more values.
class FeatureList
: Contains zero or more values of tf.train.Feature
s.
class FeatureLists
: Contains the mapping from name to tf.train.FeatureList
.
class Features
: Protocol message for describing the features
of a tf.train.Example
.
class FloatList
: Container that holds repeated fundamental values of float type in the tf.train.Feature
message.
class Int64List
: Container that holds repeated fundamental value of int64 type in the tf.train.Feature
message.
class JobDef
: A ProtocolMessage
class SequenceExample
: A SequenceExample
is a format for representing one or more sequences and some context.
class ServerDef
: A ProtocolMessage
Functions
checkpoints_iterator(...)
: Continuously yield new checkpoint files as they appear.
get_checkpoint_state(...)
: Returns CheckpointState proto from the "checkpoint" file.
latest_checkpoint(...)
: Finds the filename of latest saved checkpoint file.
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.