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Correct the class name
Python is case-sensitive, and the documented TensorFlow symbol is MultiHeadAttention—with capital letters at the start of each word. The official TensorFlow v2.16.1 API reference documents it as tf.keras.layers.MultiHeadAttention.
import tensorflow as tf
attention = tf.keras.layers.MultiHeadAttention(
num_heads=4,
key_dim=32,
)num_heads and key_dim are required constructor arguments. The values above are examples, not universal settings; choose values to suit your model.
Use the namespace that matches your package
TensorFlow’s Keras namespace and standalone Keras have distinct documented entry points. Use the one that corresponds to the package and version installed in the environment running your script.
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| API | Documented class path | Reference |
|---|---|---|
| TensorFlow Keras | tf.keras.layers.MultiHeadAttention |
TensorFlow v2.16.1 API reference |
| Standalone Keras | keras.layers.MultiHeadAttention |
Keras API reference |
These references document their respective namespaces; they do not establish that the two paths work interchangeably with every combination of TensorFlow and Keras versions.
If the corrected name still raises an error
- Check the active environment. Confirm that the interpreter running the script, notebook kernel, or application is the one where TensorFlow or Keras was installed. A package installed in one environment may not be available in another.
- Inspect the installed versions. Check the TensorFlow and Keras versions in that same environment, then consult documentation for those versions. The TensorFlow reference linked above is specifically for v2.16.1.
- Review your imports and namespace. Compare the import lines and class path with the documented API for your installed package. Do not assume TensorFlow Keras and standalone Keras can be mixed freely.
- Check whether the code uses TensorFlow Addons. Its source includes a deprecation warning recommending the built-in class: “Please use
tf.keras.layers.MultiHeadAttentioninstead.” See the TensorFlow Addons source. - Gather details if it still fails. The full traceback, package versions, import lines, and how the program is launched are needed to distinguish a version or namespace problem from another import issue.
What the layer does
Multi-head attention projects query, key, and value inputs, computes scaled dot-product attention, uses the resulting probabilities to weight values, and combines the heads. The API also documents options such as value_dim. See the Keras layer reference for the constructor and behavior.
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What version history can—and cannot—tell you
A historical TensorFlow issue opened May 6, 2021 discusses taking an implementation from TensorFlow 2.4.1 while using 2.3.1. That user report is not authoritative release documentation, so it does not establish a definitive minimum TensorFlow version for this layer. Use the documentation for the version you actually have installed rather than inferring a minimum from the issue.
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