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Updated Readme and Changelog
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CHANGELOG.md

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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## Unreleased
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## [Version 0.1.0] - 2021-04-10
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### Modified
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- Updated License from MIT to GNU AGPL V3
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- Re-shuffling:
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- `utils/data_loader.py` -> `core/data_loader.py`
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- `utils/model.py` -> `core/model.py`
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[Version 0.1.0 Beta]: https://github.com/animikhaich/Zero-Code-TF-Classifier/releases/tag/v0.1-beta
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[Version 0.1.0]: https://github.com/animikhaich/Zero-Code-TF-Classifier/releases/tag/v0.1

README.md

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## License
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Distributed under the MIT License. See [LICENSE](LICENSE.md) for more information.
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Distributed under the [GNU AGPL V3 License](https://choosealicense.com/licenses/agpl-3.0/). See [LICENSE](LICENSE) for more information.
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## Contact
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[stars-url]: https://github.com/animikhaich/Zero-Code-TF-Classifier/stargazers
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[issues-shield]: https://img.shields.io/github/issues/animikhaich/Zero-Code-TF-Classifier.svg?style=flat-square
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[issues-url]: https://github.com/animikhaich/Zero-Code-TF-Classifier/issues
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[license-shield]: https://img.shields.io/github/license/animikhaich/Semantic-Segmentation-using-AutoEncoders.svg?style=flat-square
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[license-shield]: https://img.shields.io/github/license/animikhaich/Zero-Code-TF-Classifier.svg?style=flat-square
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[license-url]: https://github.com/animikhaich/Zero-Code-TF-Classifier/blob/main/LICENSE
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[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555
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[linkedin-url]: https://linkedin.com/in/animikh-aich/

utils/add_ons.py

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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "1"
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import tensorflow as tf
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import streamlit as st
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import numpy as np
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import pandas as pd
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import plotly.graph_objs as go
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2018

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logs (dict, optional): Training Metrics. Defaults to None.
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"""
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self.status_text.info(
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"Training Started! Live Graphs will be shown on the completion of the First Epoch."
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"Training Started! Live Graphs will be shown on the completion of Each Epoch."
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)
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def on_train_end(self, logs=None):

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