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name: keras-attention description: "Guides use of the Keras Attention Layer attention package for TensorFlow/Keras sequence models, save/load checks, debug-mode attention weights, and example workflow troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Keras Attention Layer

Use this repo skill when a task involves the attention Python package, the Keras Attention Layer project, from attention import Attention, Luong or Bahdanau score functions in TensorFlow/Keras sequence models, attention-weight visualization with KERAS_ATTENTION_DEBUG, or troubleshooting the package's example workflows.

The package surface is small and the skill is intentionally root-only: all runtime guidance is in this skill directory, with detailed API, examples, compatibility, and troubleshooting notes in bundled references.

Quick routing

User need	Read or run
Install and smoke-test the package	Start here, then run scripts/smoke_attention.py.
Add an attention layer after an RNN/LSTM/GRU	Read references/api-reference.md.
Choose score="luong" vs score="bahdanau" or set units	Read references/api-reference.md.
Save and reload a model that contains Attention	Read references/api-reference.md and run scripts/smoke_attention.py --save-format h5.
Enable/debug attention weights or adapt the visualization demos	Read references/examples.md and references/troubleshooting.md; run scripts/check_example_dependencies.py.
Decide whether a TensorFlow/Keras/Python version is compatible	Read references/compatibility.md.
Diagnose install/import, Keras, Graphviz, dataset, or GPU-warning issues	Read references/troubleshooting.md.
Check whether this skill is stale for a checkout	Read references/repo-provenance.md.

Minimal package contract

Install from PyPI when using the public package:

python -m pip install attention

Then verify the import:

from attention import Attention

The core layer pattern is:

from tensorflow.keras import Input
from tensorflow.keras.layers import Dense, LSTM
from tensorflow.keras.models import Model
from attention import Attention

model_input = Input(shape=(time_steps, input_dim))
x = LSTM(64, return_sequences=True)(model_input)
x = Attention(units=32, score="luong")(x)  # or score="bahdanau"
x = Dense(output_dim)(x)
model = Model(model_input, x)

Important facts to preserve when helping users:

• Distribution name: attention; import module: attention; public class: Attention.
• Attention(units=128, score="luong", **kwargs) expects a 3D sequence tensor (batch_size, timesteps, input_dim) and returns (batch_size, units).
• Valid score functions are exactly "luong" and "bahdanau"; other values raise ValueError.
• Put return_sequences=True on the preceding recurrent layer so Attention receives all timesteps, not only the final state.
• When loading a saved model, pass custom_objects={"Attention": Attention}.
• Set KERAS_ATTENTION_DEBUG=1 before importing attention only when you need intermediate attention tensors; debug mode changes Attention so it is no longer a normal Keras Layer.

Bundled scripts

Run these scripts from the generated skill directory or address them by path from another working directory. They do not read the original repository checkout.

python scripts/smoke_attention.py --score both --save-format h5 --force-cpu

smoke_attention.py builds tiny Luong and Bahdanau models, checks prediction shapes, verifies get_config(), and optionally save/loads the model with the proper custom_objects mapping. Use it after installing the package or when a TensorFlow/Keras upgrade may have changed behavior.

python scripts/check_example_dependencies.py

check_example_dependencies.py verifies optional packages and the Graphviz dot executable needed by the visualization-oriented examples; it does not run long training or download datasets.

Boundaries and safety

• This is a package-use skill, not a maintainer workflow. For editing lint, release, or CI configuration, use a repository-maintenance workflow instead.
• The package has no public CLI and no dataset/config schema beyond ordinary TensorFlow/Keras tensors.
• The basic bundled smoke is safe and CPU-capable. The IMDB and visualization demos are long-running, may download data or write plots, and should not be treated as default smoke tests.
• Do not point users back to source-checkout examples or scripts. Use the bundled references and scripts here as the self-contained operating context.