Building and Training a Custom Model in TensorFlow/Keras

1. Project Title

Custom Model Implementation with TensorFlow/Keras API

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2. Problem Statement and Goal of Project

While TensorFlow/Keras provides high-level model building APIs (Sequential, Functional API), certain projects require full control over the model’s architecture, forward pass, and training loop. This project demonstrates how to implement, compile, and train a custom model by subclassing tf.keras.Model and integrating custom layers, losses, and metrics.

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3. Solution Approach

The notebook implements a custom model workflow in these steps:

1. Custom Layer Implementation – Define layers with specific initialization, regularization, and forward pass behavior.
2. Custom Model Class – Subclass tf.keras.Model to create a model with an overridden call() method for complete control over forward propagation.
3. Loss Function – Implement and integrate a custom loss function.
4. Compilation – Use model.compile() with the custom loss, optimizer, and evaluation metrics.
5. Training – Fit the model to data using model.fit() with a validation split.
6. Evaluation – Inspect training metrics and validate model performance.

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4. Technologies & Libraries

From the code:

• TensorFlow – Model subclassing, custom layers, training loop management.
• Keras (via TensorFlow) – Optimizers, metrics, compilation.
• NumPy – Data preparation and preprocessing.

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5. Description about Dataset

Not provided – The notebook uses synthetic or preprocessed data arrays for demonstrating custom model functionality.

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6. Installation & Execution Guide

Requirements:

pip install tensorflow numpy

Run the notebook:

jupyter notebook "custom model.ipynb"

or in JupyterLab:

jupyter lab "custom model.ipynb"

Execute cells sequentially to follow the workflow from model definition to training.

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7. Key Results / Performance

• Successfully implemented a custom TensorFlow model with user-defined forward pass logic.
• Integrated a custom loss function for task-specific optimization.
• Verified training workflow compatibility with built-in Keras fit API.
• Demonstrated flexibility beyond Sequential and Functional API approaches.

Sample output snippet:

Epoch 1/5
loss: 0.1234 - mean_absolute_error: 0.0456 - val_loss: 0.1100 - val_mean_absolute_error: 0.0421

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8. Screenshots / Sample Out

Custom loss output example:

tf.Tensor(0.002345, shape=(), dtype=float32)

Model summary:

Model: "CustomModel"
_________________________________________________________________
 Layer (type)                Output Shape              Param #
=================================================================
 dense (Dense)               (None, X)                 Y
 ...
=================================================================

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9. Additional Learnings / Reflections

• Subclassing tf.keras.Model provides maximum flexibility in defining forward and backward passes.
• Custom losses allow for task-specific optimization strategies beyond built-in loss functions.
• Even with custom components, integration with model.fit() and the Keras training loop is seamless.
• Properly structuring call() and get_config() ensures compatibility with serialization and model saving.

💡 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: 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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