|TensorFlow 1 version|
Support for training models.
See the Training guide.
experimental module: Public API for tf.train.experimental namespace.
queue_runner module: Public API for tf.train.queue_runner namespace.
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 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: 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 FeedFnHook: Runs
feed_fn and sets the
class FinalOpsHook: A hook which evaluates
Tensors at the end of a session.
class FtrlOptimizer: Optimizer that implements the FTRL algorithm.
class GlobalStepWaiterHook: Delays execution until global step reaches
class GradientDescentOptimizer: Optimizer that implements the gradient descent algorithm.
class Int64List: Container that holds repeated fundamental value of int64 type in the