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Migrating Graph attention network (GAT) for node classification to Keras3 from Keras2#2290

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Migrating Graph attention network (GAT) for node classification to Keras3 from Keras2#2290
LakshmiKalaKadali wants to merge 3 commits intokeras-team:masterfrom
LakshmiKalaKadali:gat_br

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@LakshmiKalaKadali LakshmiKalaKadali commented Feb 17, 2026 •
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The major change made for the original tutorial is, removed custom train_step, test_step, and predict_step which relied on tf.GradientTape. Refactored the model call method to accept node indices and return sliced predictions. gist

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Summary of Changes

Hello @LakshmiKalaKadali, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request modernizes the Graph Attention Network (GAT) for node classification tutorial by migrating its implementation to Keras 3. The primary goal was to enhance compatibility and leverage Keras 3's unified API, which involved removing custom training and prediction steps in favor of the framework's built-in methods. This refactoring simplifies the model's structure and promotes backend-agnosticism, making the tutorial more robust and aligned with current Keras best practices.

Highlights

  • Keras 3 Migration: The Graph Attention Network (GAT) tutorial has been fully migrated from Keras 2 (TensorFlow backend) to Keras 3, utilizing keras.ops for backend-agnostic operations.
  • Simplified Training Loop: Custom train_step, test_step, and predict_step methods were removed from the GraphAttentionNetwork model, leveraging Keras 3's standard fit, evaluate, and predict functionalities.
  • Refactored Model Call Method: The call method of the GraphAttentionNetwork was updated to accept node indices directly, returning sliced predictions, and the graph data (node_states, edges) is now stored as internal model attributes.
  • Backend-Agnostic Operations: Core graph attention calculations within GraphAttention and MultiHeadGraphAttention layers were refactored to use keras.ops (e.g., ops.matmul, ops.take, ops.segment_sum) for compatibility across different Keras 3 backends.

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Changelog
  • examples/graph/gat_node_classification.py
    • Added attribution for Keras 3 conversion in the file header.
    • Updated imports to use keras and keras.ops, and configured the KERAS_BACKEND environment variable.
    • Replaced np.random.seed with keras.utils.set_random_seed for consistent random state management.
    • Adjusted the data_dir path construction for dataset loading.
    • Migrated tensor creation from tf.convert_to_tensor to ops.convert_to_tensor, explicitly defining data types.
    • Completely refactored the GraphAttention.call method to utilize keras.ops for all tensor manipulations, including attention score calculation, normalization, and aggregation.
    • Modified MultiHeadGraphAttention.call to use keras.ops.concatenate and keras.ops.mean for combining outputs from multiple attention heads.
    • Removed custom train_step, predict_step, and test_step implementations from GraphAttentionNetwork.
    • Updated GraphAttentionNetwork.call to accept node indices as input and return targeted predictions, storing node_states and edges as model attributes.
    • Adjusted model compilation parameters, including loss function, optimizer, and metric names, to align with Keras 3 standards.
    • Updated the prediction logic to apply ops.softmax and ops.convert_to_numpy after obtaining raw logits from the model.
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Code Review

This pull request successfully migrates the Graph Attention Network (GAT) example to Keras 3. The changes are well-executed, including updating to keras.ops for backend-agnostic operations and refactoring the model to use the standard Keras fit/evaluate/predict workflow by removing custom training steps. This significantly improves the code's clarity and alignment with modern Keras practices. I have one minor suggestion to further improve code readability.

sachinprasadhs
sachinprasadhs reviewed Mar 11, 2026
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Thanks, looks good, just one small comment, you can address and generate .md and .ipynb files.

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