# Convolutional Neural Network using MNIST dataset for Digit Recognition
[](https://pypi.org/project/ml-digits-recognition/)
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[](https://codecov.io/gh/MartinBraquet/ml-digits-recognition)
[](https://ml-digits-recognition.readthedocs.io/en/latest/?badge=latest)
[](https://pepy.tech/project/ml-digits-recognition) [](https://opensource.org/licenses/MIT)
Official repository: https://github.com/MartinBraquet/ml-digits-recognition.
Test online: https://martinbraquet.com/index.php/solo_page_digits_recognition.
## Installation from PyPI
```
pip install ml-digits-recognition
```
### Usage
```
from ml_digits_recognition import drawing
drawing.run()
```
## Installation from Source
```
pip install -r requirements.txt
```
## Documentation
Click [here](https://martinbraquet.com/index.php/research/#Convolutional_Neural_Network_using_MNIST_Dataset_for_Digit_Recognition) for a full description.
Visualization of the convolutional neural network:
```
nn_visualization.ipynb
```
## Training
Train the model and save it as `model.pt`.
```
ml_digits_recognition_training.ipynb
```
Accuracy vs epochs.
Loss vs epochs.
## Test
Test in Jupiter Notebook. The model can be loaded from the training above in `model.pt` or from the
default precise model in `model_precise.pt`.
```
ml_digits_recognition_test.ipynb
```
Test in Python.
```
python src/ml_digits_recognition/drawing.py
```
## Tools
Draw a digit and save it as a PNG file.
```
user_input_drawing.ipynb
```
## Issues / Bug reports / Feature requests
Please open an issue.
## Contributions
Contributions are welcome. Please check the outstanding issues and feel free to open a pull request.
### Contributors
[](https://github.com/MartinBraquet/ml-digits-recognition/graphs/contributors)
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"description": "# Convolutional Neural Network using MNIST dataset for Digit Recognition\n\n\n[](https://pypi.org/project/ml-digits-recognition/)\n[](https://github.com/MartinBraquet/ml-digits-recognition/actions/workflows/ci.yml/badge.svg)\n[](https://github.com/MartinBraquet/ml-digits-recognition/actions/workflows/cd.yml/badge.svg)\n[](https://codecov.io/gh/MartinBraquet/ml-digits-recognition)\n[](https://ml-digits-recognition.readthedocs.io/en/latest/?badge=latest)\n[](https://pepy.tech/project/ml-digits-recognition) [](https://opensource.org/licenses/MIT)\n\nOfficial repository: https://github.com/MartinBraquet/ml-digits-recognition.\n\nTest online: https://martinbraquet.com/index.php/solo_page_digits_recognition.\n\n\n\n## Installation from PyPI\n\n```\npip install ml-digits-recognition\n```\n\n### Usage\n\n```\nfrom ml_digits_recognition import drawing\ndrawing.run()\n```\n\n\n\n## Installation from Source\n\n```\npip install -r requirements.txt\n```\n\n## Documentation\n\nClick [here](https://martinbraquet.com/index.php/research/#Convolutional_Neural_Network_using_MNIST_Dataset_for_Digit_Recognition) for a full description.\n\nVisualization of the convolutional neural network:\n\n```\nnn_visualization.ipynb\n```\n\n\n\n\n\n## Training\n\nTrain the model and save it as `model.pt`.\n\n```\nml_digits_recognition_training.ipynb\n```\n\nAccuracy vs epochs.\n\n\n\nLoss vs epochs.\n\n\n\n## Test\n\nTest in Jupiter Notebook. The model can be loaded from the training above in `model.pt` or from the \ndefault precise model in `model_precise.pt`.\n\n```\nml_digits_recognition_test.ipynb\n```\n\nTest in Python.\n\n```\npython src/ml_digits_recognition/drawing.py\n```\n\n## Tools\n\nDraw a digit and save it as a PNG file.\n\n```\nuser_input_drawing.ipynb\n```\n\n## Issues / Bug reports / Feature requests\n\nPlease open an issue.\n\n## Contributions\n\nContributions are welcome. Please check the outstanding issues and feel free to open a pull request.\n\n### Contributors\n\n[](https://github.com/MartinBraquet/ml-digits-recognition/graphs/contributors)\n",
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