tf.compat.v1.enable_eager

Creates resource variables by default.

tf.compat.v1.enable_resource_variables()

Used in the notebooks

Used in the tutorials
Tutorial on Multi Armed Bandits in TF-Agents A Tutorial on Multi-Armed Bandits with Per-Arm Features

Resource variables are improved versions of TensorFlow variables with a well-defined memory model. Accessing a resource variable reads its value, and all ops which access a specific read value of the variable are guaranteed to see the same value for that tensor. Writes which happen after a read (by having a control or data dependency on the read) are guaranteed not to affect the value of the read tensor, and similarly writes which happen before a read are guaranteed to affect the value. No guarantees are made about unordered read/write pairs.

Calling tf.enable_resource_variables() lets you opt-in to this TensorFlow 2.0 feature.

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.compat / v1 / v1.enable_eager_execution

Used in the notebooks