This error usually means older TensorFlow 1 code is trying to use tf.logging with TensorFlow 2, where that API was removed from TensorFlow’s main namespace. For most TensorFlow 2 code, replace it with tf.get_logger() or Python’s standard logging module. Check the imported TensorFlow version and path first, especially if the replacement does not resolve the error.
Replace tf.logging with a TensorFlow 2 logger
TensorFlow’s migration guide lists tf.logging among the APIs removed from the main namespace in TensorFlow 2. It describes the change as part of an API cleanup that favored the open-source absl-py library. For typical TensorFlow 2 applications, the direct replacement is usually TensorFlow’s Python logger:
import tensorflow as tf
tf.get_logger().setLevel("ERROR")
tf.get_logger().info("Model initialized")tf.get_logger() returns a Python logging.Logger, so you can use its normal methods and levels. See the TensorFlow tf.get_logger API reference.
If the messages are application logs rather than TensorFlow-specific messages, use Python’s standard logging module instead. Choose the logger that fits the code’s purpose; don’t assume a blind replacement will preserve every old method, argument, severity, handler, or formatting behavior.
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Check which TensorFlow Python actually imported
Before changing a larger codebase, confirm the version and module path in the same environment that runs the failing program:
import tensorflow as tf
print(tf.__version__)
print(tf.__file__)The version shows which TensorFlow package is active, and the path helps reveal whether Python imported the expected installation. If the path points inside your project, a local tensorflow.py file or directory with that name may be shadowing the installed package. Rename it, then restart the interpreter or notebook kernel and check the path again.
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The error alone does not identify your installed version or environment. If the version and path look correct, inspect the failing call and replace the old logging usage according to its purpose.
Use tf.compat.v1.logging only as a temporary bridge
For constrained legacy code, you can check whether tf.compat.v1.logging exists in the TensorFlow build you have installed. This may help while you plan a migration, but compatibility symbols are not guaranteed to provide identical behavior across all TensorFlow 2 builds or modes. TensorFlow describes tf.compat.v1 as a migration aid, not the idiomatic API for new TensorFlow 2 code. See TensorFlow’s TF1-versus-TF2 migration guide.
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When this is part of a wider TF1-to-TF2 migration
If tf.logging is one of several failing TF1 APIs, TensorFlow’s tf_upgrade_v2 tool can make many mechanical code changes. The official guide says the tool is installed with TensorFlow 1.13 and later. Run it on a copy of the project, read its conversion report, and review the rewritten code: the tool cannot perform every part of a migration.
- Make a copy or use version control so you can inspect and undo automated edits.
- Run
tf_upgrade_v2following the official upgrade guide. - Review the report and manually address APIs the tool could not convert.
- Run the project in the target TensorFlow environment and test its behavior, not just whether it imports.
TensorFlow warns that major-version changes can be backward-incompatible for code and data. A logging fix may therefore reveal other TF1-to-TF2 differences in the project. The TensorFlow version compatibility guide explains the project’s version-compatibility expectations.
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