|TensorFlow 1 version|
Support for training models.
See the Training guide.
experimental module: Public API for tf.train.experimental namespace.
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 JobDef: A ProtocolMessage
class SequenceExample: A
SequenceExample is a format for representing one or more sequences and some context.
class ServerDef: A ProtocolMessage
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.
CheckpointReader for checkpoint found in
load_variable(...): Returns the tensor value of the given variable in the checkpoint.