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This error usually appears when TensorFlow 1 code that calls tf.logging runs with TensorFlow 2. Replace those calls with Python’s logging module or TensorFlow’s tf.get_logger(). If you need to keep legacy code running temporarily, check whether tf.compat.v1.logging exists in your installed version.
Why TensorFlow reports that it has no attribute ‘logging’
TensorFlow removed tf.logging from its main namespace in TensorFlow 2. The migration guide describes this as part of cleaning up the tf.* namespace and moving toward the open-source absl-py library. See TensorFlow’s TF1-versus-TF2 API guide.
So the message usually points to a mismatch between older code and the TensorFlow API available at runtime. It does not, by itself, establish which TensorFlow version is installed or whether Python imported the expected package.
Replace tf.logging with a current logger
Use TensorFlow’s configured logger
tf.get_logger() returns a standard Python logging.Logger, so you can use its normal methods and levels. For example:
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import tensorflow as tf
tf.get_logger().setLevel("ERROR")
tf.get_logger().info("Model initialized")TensorFlow documents this API in its tf.get_logger reference. Use the logger methods that match the intent of each old call, such as info() or error(), and preserve the message arguments. Check formatting and configured handlers rather than blindly replacing every occurrence.
Use Python logging for application messages
If the messages belong to your application rather than TensorFlow itself, Python’s built-in logging module is a suitable choice. Configure it as appropriate for your application, then replace the old calls with the corresponding logger methods. This keeps application logging independent of TensorFlow’s logger configuration.
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Use absl-py when your project needs it
TensorFlow’s migration guide points to absl-py as the direction for the removed API. If your project depends on that library’s logging behavior, use its own setup and API documentation rather than assuming the TensorFlow logger is an exact substitute.
Check the active TensorFlow installation
Before changing dependencies, confirm the version and module path used by the Python process that raises the error:
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print(tf.__version__)
print(tf.__file__)The version identifies the TensorFlow package Python loaded; the path helps identify where it came from. If the path points inside your project instead of the installed package, check for a local tensorflow.py file or a directory named tensorflow that may be shadowing the real package. Also run this check in the same environment and interpreter that launches your application.
When is tf.compat.v1.logging appropriate?
For a constrained legacy project, tf.compat.v1.logging may provide a temporary bridge. Verify that the symbol exists in the TensorFlow version actually installed and that retaining TF1-style behavior fits the project. TensorFlow describes tf.compat.v1 as a migration aid, not the idiomatic API for new TensorFlow 2 code; consult its compatibility and migration guidance.
A compatibility namespace is not a guarantee that the rest of a TF1 program will behave unchanged under TensorFlow 2. Use it to support a deliberate transition, not as a substitute for checking the code’s broader compatibility.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When should you migrate more of the project?
If tf.logging is one of many removed or changed APIs, TensorFlow provides tf_upgrade_v2 to automate transformations it can identify. The official upgrade guide says the tool is installed with TensorFlow 1.13 and later. Run it against a copy of the project, review its report and generated changes, then test the converted code. The tool handles mechanical rewrites; TensorFlow notes that it cannot complete every part of a migration.
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TensorFlow also warns that major-version changes can be backward-incompatible for code and data. A logging fix may therefore uncover other changes your project needs before it works as intended. Check the TensorFlow version compatibility guide when choosing a target version.
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