DL_Keras_Model_Subclassing — Custom Models with TensorFlow/Keras

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1) Project Title

Deep Learning with Keras Model Subclassing — Custom Architectures, Loss Functions, and Training Loops

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2) Problem Statement and Goal of Project

This project demonstrates full customization of deep learning models in TensorFlow/Keras using the Model Subclassing API. The aim is to:

• Build custom model architectures by directly subclassing tf.keras.Model.
• Implement custom layers, user-defined loss functions, and forward passes with complete flexibility.
• Showcase advanced training workflows and model control beyond the standard Sequential/Functional APIs.

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3) Solution Approach

The notebook implements:

1. Custom Model Definition

   • Inherits from tf.keras.Model.
   • Defines layers inside __init__ and the forward computation in call(...).

2. Custom Loss Function

   • Implemented directly in Python with TensorFlow operations.

3. Flexible Training

   • Uses the Keras .compile() / .fit() workflow but allows custom training logic if needed.

4. MNIST Classification Example

   • Uses TFDS to load, normalize, and one-hot encode the dataset.
   • Trains the custom model on 28×28 grayscale images.

5. Model Diagram

   • Created using keras.utils.plot_model(...) to visualize the architecture.

This approach gives full control over architecture design and training behavior, enabling experimentation that’s difficult with high-level APIs.

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4) Technologies & Libraries

• Python
• TensorFlow / Keras
• TensorFlow Datasets (TFDS)
• NumPy
• pydot / graphviz (for architecture visualization)

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5) Description about Dataset

• MNIST (from TFDS)

  • Handwritten digits (0–9).
  • Input shape: (28, 28, 1) grayscale images.
  • Labels: One-hot encoded with depth = 10.

• Data processing steps in code:

  • Normalization (tf.divide(..., 255.0))
  • One-hot encoding (tf.one_hot(..., depth=10))
  • Shuffling, batching, and prefetching with tf.data.

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6) Installation & Execution Guide

Prerequisites

• Python 3.x
• TensorFlow (GPU optional, but recommended)

Install (pip)

pip install tensorflow tensorflow-datasets numpy pydot graphviz

On some systems you must also install Graphviz binaries: sudo apt-get install graphviz

Run

1. Open model_subclassing_me.ipynb in Jupyter/VS Code.

2. Execute cells sequentially.

3. The notebook will:

   • Download MNIST automatically on first run.
   • Train the custom model.
   • Save model diagrams (if the diagram cell is executed).

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7) Key Results / Performance

• Not provided. The notebook demonstrates the mechanics of model subclassing and training rather than optimizing for maximum accuracy.

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8) Screenshots / Sample Output

• Not provided in repository. Running the diagram cell will save a .png visualization of the custom model locally.

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9) Additional Learnings / Reflections

• Subclassing tf.keras.Model offers complete freedom in architecture and training control.
• This flexibility is useful for research, custom loss integration, and non-standard forward passes.
• The project reinforces understanding of TensorFlow low-level operations combined with Keras high-level utilities.
• Lower performance in some runs is intentional to focus on the API’s mechanics, not leaderboard scores.

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👤 Author

Mehran Asgari Email: imehranasgari@gmail.com 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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💡 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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