Creating and Using Custom Layers in TensorFlow/Keras

1. Project Title

Custom Layer Implementation with TensorFlow/Keras

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2. Problem Statement and Goal of Project

While Keras provides a wide range of built-in layers, certain use cases require custom operations, parameters, or training logic. The goal of this project is to design, implement, and integrate custom layers into a Keras model while maintaining compatibility with the high-level training API.

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3. Solution Approach

The notebook demonstrates a full workflow for custom layer creation:

1. Subclassing tf.keras.layers.Layer – Define a new layer class with a custom constructor (__init__), weight creation in build(), and forward pass logic in call().
2. Adding trainable parameters – Use self.add_weight() for kernel, bias, and other trainable variables.
3. Integrating custom regularizers and initializers – Apply user-defined weight initialization and regularization logic.
4. Using the custom layer in a model – Integrate the layer into a Sequential or Functional API model.
5. Training with standard Keras workflow – Compile the model with an optimizer, loss, and metrics; then train and evaluate.

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4. Technologies & Libraries

From the code:

• TensorFlow – Custom layer API, model integration, and training.
• Keras (via TensorFlow) – Model building, optimizers, compilation, and evaluation.
• NumPy – Data preparation.

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5. Description about Dataset

Not provided – The notebook uses synthetic or preprocessed data for demonstrating the functionality of the custom layer.

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6. Installation & Execution Guide

Requirements:

pip install tensorflow numpy

Run the notebook:

jupyter notebook custom_layers_me.ipynb

or in JupyterLab:

jupyter lab custom_layers_me.ipynb

Run all cells sequentially to follow the process from custom layer creation to training and evaluation.

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7. Key Results / Performance

• Successfully implemented a custom trainable Keras layer.
• Verified the layer integrates seamlessly into the Keras fit() workflow.
• Demonstrated flexibility by adding custom initialization and regularization.

Example model integration:

model = tf.keras.Sequential([
    CustomDenseLayer(units=64, activation="relu"),
    tf.keras.layers.Dense(1)
])

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8. Screenshots / Sample Out

Custom layer summary excerpt:

Layer (type)                 Output Shape              Param #
=================================================================
custom_dense_layer (CustomDenseLayer) (None, 64)        576
...

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9. Additional Learnings / Reflections

• Subclassing tf.keras.layers.Layer allows full customization of trainable parameters and forward-pass logic.
• Implementing get_config() ensures the layer is serializable and reusable.
• Custom layers can be combined with standard Keras layers in any architecture.
• Using built-in methods like add_weight() maintains compatibility with Keras optimizers and training loops.

💡 Some interactive outputs (e.g., plots, widgets) may not display correctly on GitHub. If so, please view this notebook via nbviewer.org for full rendering.

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👤 Author

Mehran Asgari Email: [email protected] GitHub: https://github.com/imehranasgari

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📄 License

This project is licensed under the Apache 2.0 License – see the LICENSE file for details.

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