TensorFlow Multi-Level API Exploration

1. Project Title

Exploring TensorFlow’s Multi-Level APIs: Low-Level Ops to High-Level Keras

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2. Problem Statement and Goal of Project

This notebook investigates how TensorFlow’s different API layers — from low-level tensor operations to high-level Keras abstractions — interact and can be composed. The aim is to:

• Understand the flexibility of TensorFlow’s multi-level design.
• Demonstrate equivalent operations at different abstraction levels.
• Compare workflows for creating and training models using these APIs.

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3. Solution Approach

The notebook follows an incremental abstraction approach:

1. Low-level TensorFlow ops – Define computations directly with tf.constant, tf.Variable, and math operations.
2. Mid-level Layers API – Build models using tf.keras.layers with manual weight initialization and forward passes.
3. High-level Keras API – Use tf.keras.Sequential and tf.keras.Model for rapid prototyping, training, and evaluation.
4. Mix-and-match strategy – Combine low-level control with high-level convenience for custom model building.
5. Explore dataset pipelines using tf.data.Dataset for feeding data to models.

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4. Technologies & Libraries

From the code:

• TensorFlow – core ops, variables, optimizers, datasets.
• Keras (via TensorFlow) – Sequential, Model, Dense, Flatten, and other layers.
• NumPy – array creation and manipulation.

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5. Description about Dataset

Not provided – The notebook generates synthetic data (NumPy arrays / tensors) for demonstrations. No external dataset is loaded.

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6. Installation & Execution Guide

Requirements:

pip install tensorflow numpy

Run the notebook:

jupyter notebook multi_level_apis_me.ipynb

or in JupyterLab:

jupyter lab multi_level_apis_me.ipynb

Execute cells sequentially to reproduce the examples and outputs.

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7. Key Results / Performance

Model Construction at Different API Levels

• Low-level: Manual tensor math produced correct forward passes and weight updates.
• Mid-level: tf.keras.layers.Dense with manual calls yielded same functional results with less boilerplate.
• High-level: Sequential and Model APIs allowed rapid building, compilation, and fitting.

Dataset Pipeline

• Creation of tf.data.Dataset from tensors demonstrated efficient batch processing.

Output Examples

Representative console outputs from the notebook:

Eager execution results: [ ... ]
Layer API output shape: (batch_size, units)
Sequential model training: loss decreased over epochs

(Exact values depend on random seeds and synthetic data.)

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8. Screenshots / Sample Out

(Extracted from notebook outputs)

Low-Level Tensor Math:

<tf.Tensor: shape=(...), dtype=float32, numpy=...>

Keras Sequential Summary:

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

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9. Additional Learnings / Reflections

• TensorFlow offers progressive abstraction layers — allowing developers to trade off between control and convenience.
• The Layer API is a practical balance: low enough for customization, high enough to avoid manual graph wiring.
• The Sequential / Model API is ideal for rapid iteration but can be combined with low-level ops for advanced needs.
• tf.data.Dataset integrates seamlessly with all API levels, providing efficient input pipelines.

💡 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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