एक सवाल है? TensorFlow फ़ोरम विज़िट फ़ोरम पर समुदाय से जुड़ें

Module: tf.train

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.Features.

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_checkpo