tf.VariableAggregation

Indicates how a distributed variable will be aggregated.

tf.distribute.Strategy distributes a model by making multiple copies (called "replicas") acting data-parallel on different elements of the input batch. When performing some variable-update operation, say var.assign_add(x), in a model, we need to resolve how to combine the different values for x computed in the different replicas.

NONE: This is the default, giving an error if you use a variable-update operation with multiple replicas.
SUM: Add the updates across replicas.
MEAN: Take the arithmetic mean ("average") of the updates across replicas.
ONLY_FIRST_REPLICA: This is for when every replica is performing the same update, but we only want to perform the update once. Used, e.g., for the global step counter.

Class Variables
MEAN	`tf.VariableAggregation`
NONE	`tf.VariableAggregation`
ONLY_FIRST_REPLICA	`tf.VariableAggregation`
SUM	`tf.VariableAggregation`

TensorFlow

tf / VariableAggregation

Class Variables

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf / VariableAggregation

Class Variables