MNIST Digit Recognition with TensorFlow

This project demonstrates a digit recognition model using the MNIST dataset and TensorFlow. The model trains on the MNIST dataset of handwritten digits (0-9) and evaluates its performance. Additionally, it visualizes the results using Matplotlib.

Features

• Model: Built using TensorFlow and Keras.
• Dataset: Utilizes the MNIST dataset of handwritten digits.
• Visualization: Visualizes predictions and training metrics using Matplotlib.

Requirements

Before running the project, ensure you have the following installed:

• Python 3.x
• TensorFlow
• Matplotlib
• NumPy
• Jupyter Notebook (optional, for interactive use)

Install the required packages using pip:

pip install tensorflow matplotlib numpy

How to Run

1. Clone the repository:

   git clone https://github.com/smolvamp/MNIST-based-digit-recognition/ 
   cd MNIST-based-digit-recognition

2. Run the train_model.ipynb:

   jupyter nbconvert --execute train_model.ipynb

3. View the visualization graphs and model predictions by running the test_model.ipynb:

   jupyter nbconvert --execute test_model.ipynb

Dataset

The MNIST dataset is automatically downloaded using TensorFlow's keras.datasets module. It consists of:

• 60,000 training images
• 10,000 testing images

Each image is a 28x28 grayscale pixel representation of a digit.

Results

• Training accuracy: ~99%
• Testing accuracy: ~98%

Sample Visualization

• Loss/Accuracy Plots: Training and validation metrics.
• Sample Predictions: Displayed with true and predicted labels.

Customization

• Adjust training parameters such as epochs, batch_size, or optimizer.

Acknowledgements

• MNIST Dataset: Yann LeCun's website
• TensorFlow and Keras: TensorFlow Official Website

License

This project is licensed under the MIT License. See the LICENSE file for details.

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Enjoy using this digit recognition model! Contributions and suggestions are welcome!