# NeuralNetworkCoordinates
[](https://github.com/CaxtonEmerald-S/NeuralNetworkCoordinates/blob/main/LICENSE.txt)
## Overview
The "NeuralNetworkCoordinates" Python package is a specialized tool designed for researchers and practitioners in the field of neural networks. This package focuses on providing precise coordinates that capture the functional transformations within a neural network. Unlike conventional plotting packages, "NeuralNetworkCoordinates" does not generate plots directly. Instead, it offers a streamlined approach to obtaining the underlying coordinates, enabling users to create custom visualizations tailored to their specific needs.
This package is particularly useful for visualizing and understanding the intricate inner workings of neural network models. By offering a straightforward interface to access coordinates, researchers can gain insights into the spatial representation of features and activations within the network. "NeuralNetworkCoordinates" enhances the interpretability of neural network behavior, contributing to the broader goals of Explainable AI and facilitating more informed analyses of model performance and decision-making processes.
## Key Features
1. **Precision in Functional Transformations:**
- Provides precise coordinates capturing the nuanced functional transformations within neural networks.
2. **Custom Visualization Support:**
- Empowers users to create custom visualizations tailored to their specific analytical needs using the obtained coordinates.
3. **Interpretability Enhancement:**
- Focused on enhancing the interpretability of neural network models by revealing spatial distributions of features and activations.
4. **Explainable AI Contribution:**
- Aligns with the principles of Explainable AI, aiding researchers in scrutinizing decision boundaries and feature interactions.
5. **Granular Model Analysis:**
- Allows for a granular analysis of neural network behavior, facilitating the identification of patterns, anomalies, and areas for improvement.
6. **Transparent Decision-Making:**
- Supports a deeper understanding of the model's decision-making processes, contributing to transparency in AI model behavior.
7. **User-Friendly Interface:**
- Provides a straightforward interface for accessing coordinates, ensuring ease of use for researchers and practitioners.
8. **Research Versatility:**
- Tailored for researchers exploring the inner workings of neural networks, offering versatility in analyzing and visualizing model behavior.
9. **Understanding Lower-Dimensional Representations:**
- Specifically designed to unravel the inner workings of neural networks at lower dimensions, providing insights into the compact representation of features in reduced-dimensional spaces.
## Installation
To install NeuralNetworkCoordinates, use pip:
<pre>
pip install NeuralNetworkCoordinates
</pre>
## Usage
Check out the [documentation](https://github.com/CaxtonEmerald-S/NeuralNetworkCoordinates/wiki) for detailed information on how to use this package effectively.
## Documentation
For complete documentation, please refer to the [official documentation](https://github.com/CaxtonEmerald-S/NeuralNetworkCoordinates/wiki).
## License
This package is provided under The Cat Standard License v1.0. See the [LICENSE.txt](https://github.com/CaxtonEmerald-S/NeuralNetworkCoordinates/blob/main/LICENSE.txt) file for details.
## Issues
If you encounter any issues or have suggestions, please open an issue.
## Authors
[Caxton Emerald S](mailto:ces.softwaresolutions@gmail.com), [Vengattaraman T](mailto:vengattaraman.t@pondiuni.ac.in)
By using this package, you agree to its terms and conditions.
© 2023 Caxton Emerald S
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