TensorFlow Low-Level API – Core Operations & Workflows

1. Project Title

Hands-on Exploration of TensorFlow’s Low-Level API

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2. Problem Statement and Goal of Project

This project demonstrates direct usage of TensorFlow’s low-level operations to provide maximum control over tensor creation, manipulation, and mathematical computation — bypassing high-level abstractions when needed. The goal is to understand TensorFlow’s computation model from the ground up and to build a foundation for advanced, custom deep learning workflows.

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3. Solution Approach

The notebook follows a structured, example-driven workflow:

1. Environment setup & imports – Initialize TensorFlow and supporting libraries.
2. Tensor creation – Use tf.constant, tf.Variable, and various initializers (zeros, ones, fill, range, random).
3. Inspecting tensors – Retrieve shape, rank, and dtype information.
4. Type casting – Change tensor data types with tf.cast.
5. Mathematical operations – Element-wise math, reductions (reduce_sum, reduce_mean), matrix multiplication (matmul).
6. Shape manipulation – Reshaping, expanding/squeezing dimensions, transposition.
7. Tensor composition – Concatenation, stacking, splitting, tiling, broadcasting.
8. Indexing & slicing – Python slicing and TensorFlow ops (tf.gather, tf.slice).
9. Random tensors – Generate tensors with controlled distributions.
10. Practical demonstrations – Show integration of these low-level ops into workflows.

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4. Technologies & Libraries

From the code:

• TensorFlow – Low-level ops, tensor manipulation, math functions.
• NumPy – Array creation and interoperability with tensors.

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5. Description about Dataset

Not provided – The notebook uses only synthetic/random tensors generated in memory.

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6. Installation & Execution Guide

Requirements:

pip install tensorflow numpy

Run the notebook:

jupyter notebook low_level_api.ipynb

or in JupyterLab:

jupyter lab low_level_api.ipynb

Execute cells sequentially to follow the learning progression.

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7. Key Results / Performance

• Illustrated complete control over tensor creation and manipulation using TensorFlow’s low-level API.
• Demonstrated immutable vs mutable tensors (tf.constant vs tf.Variable).
• Showed graph-friendly slicing/indexing with TensorFlow ops instead of Python-only indexing.
• Provided examples of broadcasting and shape alignment.
• Generated random tensors for reproducibility and experimentation.

Sample output snippets:

<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
array([[1, 2, 3],
       [4, 5, 6]], dtype=int32)>

Tensor shape: (3, 2)
Tensor dtype: float32
Tensor rank: 2

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8. Screenshots / Sample Out

Creating a constant tensor:

<tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 2, 3], dtype=int32)>

Reshaping:

Original shape: (6,)
Reshaped to: (2, 3)

Concatenating tensors:

<tf.Tensor: shape=(3, 3), dtype=int32, numpy=
array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]], dtype=int32)>

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9. Additional Learnings / Reflections

• Mastering the low-level API gives maximum flexibility for building custom ops and experimenting with graph execution.
• Broadcasting rules are crucial to simplify math operations without explicit reshaping.
• Understanding TensorFlow indexing methods avoids runtime issues in compiled graphs.
• These skills directly support more advanced custom layers, ops, 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: 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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