NNVisualiser


NameNNVisualiser JSON
Version 1.0.0 PyPI version JSON
download
home_page
SummaryA Neural Network Visualiser as a Python package utilizing Matplotlib, visualizes plot coordinates from NeuralNetworkCoordinates for single-input, single-output neural networks. Aligned with Explainable AI, it offers concise insights, catering to researchers focused on understanding specific network architectures.
upload_time2023-12-06 17:26:10
maintainer
docs_urlNone
author
requires_python>=3.10.12
licenseThe Cat Standard License v1.0
keywords neural networks ann analysis coordinates visualiser visualise regression classification functional transformation
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # NNVisualiser

[![License](https://img.shields.io/badge/License-The_Cat_Standard_License_v1.0-[Color].svg)](https://github.com/CaxtonEmerald-S/NNVisualiser/blob/main/LICENSE.txt)

## Overview

NNVisualiser is a powerful neural network visualization tool that leverages the NeuralNetworkCoordinates package to draw coordinates for various plots. This package offers an extensive collection of plots to facilitate the visualization and understanding of functional transformations at the Neuron, Layer, and Network levels. It serves as an invaluable tool for learning and studying the intricacies of neural networks.

## Key Features

- **Multilevel Visualization:** NNVisualiser provides plots that allow users to visualize and comprehend functional transformations at the Neuron, Layer, and Network levels.

- **Flow Plots for Data Transformation:** The package includes flow plots, offering a comprehensive view of data transformation from input to activations, aiding in understanding the overall flow of information through the network.

- **Compatibility with 1D Networks:** The current version is designed to work with lower-dimensional networks, specifically with 1D input and output. Future releases plan on supporting 2D and N-D input/ output neural networks, extending its utility to higher-dimensional network architectures.

- **Matplotlib Integration:** Powered by Matplotlib, the package generates detailed and clear visualizations, aiding researchers and practitioners in understanding the internal dynamics of the neural network.

- **Explainable AI Alignment:** Aligned with the principles of Explainable AI, NNVisualiser aims to provide concise insights, facilitating a deeper understanding of the intricacies of the neural network.

## Installation

To install NNVisualiser, use pip:
<pre>
pip install NNVisualiser
</pre>

## Usage
Check out the [documentation](https://github.com/CaxtonEmerald-S/NNVisualiser/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/NNVisualiser/wiki).

## License
This package is provided under The Cat Standard License v1.0. See the [LICENSE.txt](https://github.com/CaxtonEmerald-S/NNVisualiser/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

            

Raw data

            {
    "_id": null,
    "home_page": "",
    "name": "NNVisualiser",
    "maintainer": "",
    "docs_url": null,
    "requires_python": ">=3.10.12",
    "maintainer_email": "Caxton Emerald S <ces.softwaresolutions@gmail.com>",
    "keywords": "neural networks,ANN,analysis,coordinates,visualiser,visualise,regression,classification,functional transformation",
    "author": "",
    "author_email": "Caxton Emerald S <ces.softwaresolutions@gmail.com>, Vengattaraman T <vengattaraman.t@pondiuni.ac.in>",
    "download_url": "https://files.pythonhosted.org/packages/a4/01/77a5948f87df6c3605798529d2ca014300ba5446e456e45271788e034c31/NNVisualiser-1.0.0.tar.gz",
    "platform": null,
    "description": "# NNVisualiser\n\n[![License](https://img.shields.io/badge/License-The_Cat_Standard_License_v1.0-[Color].svg)](https://github.com/CaxtonEmerald-S/NNVisualiser/blob/main/LICENSE.txt)\n\n## Overview\n\nNNVisualiser is a powerful neural network visualization tool that leverages the NeuralNetworkCoordinates package to draw coordinates for various plots. This package offers an extensive collection of plots to facilitate the visualization and understanding of functional transformations at the Neuron, Layer, and Network levels. It serves as an invaluable tool for learning and studying the intricacies of neural networks.\n\n## Key Features\n\n- **Multilevel Visualization:** NNVisualiser provides plots that allow users to visualize and comprehend functional transformations at the Neuron, Layer, and Network levels.\n\n- **Flow Plots for Data Transformation:** The package includes flow plots, offering a comprehensive view of data transformation from input to activations, aiding in understanding the overall flow of information through the network.\n\n- **Compatibility with 1D Networks:** The current version is designed to work with lower-dimensional networks, specifically with 1D input and output. Future releases plan on supporting 2D and N-D input/ output neural networks, extending its utility to higher-dimensional network architectures.\n\n- **Matplotlib Integration:** Powered by Matplotlib, the package generates detailed and clear visualizations, aiding researchers and practitioners in understanding the internal dynamics of the neural network.\n\n- **Explainable AI Alignment:** Aligned with the principles of Explainable AI, NNVisualiser aims to provide concise insights, facilitating a deeper understanding of the intricacies of the neural network.\n\n## Installation\n\nTo install NNVisualiser, use pip:\n<pre>\npip install NNVisualiser\n</pre>\n\n## Usage\nCheck out the [documentation](https://github.com/CaxtonEmerald-S/NNVisualiser/wiki) for detailed information on how to use this package effectively.\n\n## Documentation\nFor complete documentation, please refer to the [official documentation](https://github.com/CaxtonEmerald-S/NNVisualiser/wiki).\n\n## License\nThis package is provided under The Cat Standard License v1.0. See the [LICENSE.txt](https://github.com/CaxtonEmerald-S/NNVisualiser/blob/main/LICENSE.txt) file for details.\n\n## Issues\nIf you encounter any issues or have suggestions, please open an issue.\n\n## Authors\n[Caxton Emerald S](mailto:ces.softwaresolutions@gmail.com), [Vengattaraman T](mailto:vengattaraman.t@pondiuni.ac.in)\n\nBy using this package, you agree to its terms and conditions.\n\n\u00a9 2023 Caxton Emerald S\n",
    "bugtrack_url": null,
    "license": "The Cat Standard License v1.0",
    "summary": "A Neural Network Visualiser as a Python package utilizing Matplotlib, visualizes plot coordinates from NeuralNetworkCoordinates for single-input, single-output neural networks. Aligned with Explainable AI, it offers concise insights, catering to researchers focused on understanding specific network architectures.",
    "version": "1.0.0",
    "project_urls": {
        "Bug Reports": "https://github.com/CaxtonEmerald-S/NNVisualiser/issues",
        "Documentation": "https://github.com/CaxtonEmerald-S/NNVisualiser/wiki",
        "Homepage": "https://caxtonemerald-s.github.io/NNVisualiser/",
        "Source": "https://github.com/CaxtonEmerald-S/NNVisualiser/",
        "Wiki": "https://github.com/CaxtonEmerald-S/NNVisualiser/wiki"
    },
    "split_keywords": [
        "neural networks",
        "ann",
        "analysis",
        "coordinates",
        "visualiser",
        "visualise",
        "regression",
        "classification",
        "functional transformation"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "5436654bad3e4d13882819a7186f3d42f19019719b531e19a784ca46a56e8520",
                "md5": "32d6da10ac4b94834d9c4d229e9d45cc",
                "sha256": "6394b81979ba91dd00a26af02d7ca48b65fd3763c5d62c03b3848f158b036606"
            },
            "downloads": -1,
            "filename": "NNVisualiser-1.0.0-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "32d6da10ac4b94834d9c4d229e9d45cc",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.10.12",
            "size": 7109,
            "upload_time": "2023-12-06T17:26:09",
            "upload_time_iso_8601": "2023-12-06T17:26:09.520834Z",
            "url": "https://files.pythonhosted.org/packages/54/36/654bad3e4d13882819a7186f3d42f19019719b531e19a784ca46a56e8520/NNVisualiser-1.0.0-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "a40177a5948f87df6c3605798529d2ca014300ba5446e456e45271788e034c31",
                "md5": "3a3b5d4f86df29ca9d4e9d5d0764ac59",
                "sha256": "0986f9dcb6db986d426432a9cf78e7682b5d510107437290c70ea6a2c4e37c01"
            },
            "downloads": -1,
            "filename": "NNVisualiser-1.0.0.tar.gz",
            "has_sig": false,
            "md5_digest": "3a3b5d4f86df29ca9d4e9d5d0764ac59",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.10.12",
            "size": 6821,
            "upload_time": "2023-12-06T17:26:10",
            "upload_time_iso_8601": "2023-12-06T17:26:10.884613Z",
            "url": "https://files.pythonhosted.org/packages/a4/01/77a5948f87df6c3605798529d2ca014300ba5446e456e45271788e034c31/NNVisualiser-1.0.0.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2023-12-06 17:26:10",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "CaxtonEmerald-S",
    "github_project": "NNVisualiser",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": true,
    "lcname": "nnvisualiser"
}
        
Elapsed time: 0.14528s