🧠 Neural Network From Scratch (NumPy Implementation)

This project demonstrates how to build and train a feedforward neural network from scratch using only NumPy, without any high-level deep learning libraries like TensorFlow or PyTorch. The model is trained on the MNIST digit classification dataset and achieves competitive accuracy.

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🚀 Features

• Built entirely using NumPy
• Fully connected (dense) neural network layers
• ReLU and Softmax activation functions
• Forward and backward propagation
• Categorical Crossentropy loss
• Training loop with manual gradient updates
• Evaluation with classification report and confusion matrix
• Visualizations of predictions and performance

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📊 Results

• Test Accuracy: 89.44%
• F1-Score (macro avg): 89%

🔍 Classification Report

               precision    recall  f1-score   support

           0       1.00      0.94      0.97        35
           1       0.87      0.75      0.81        36
           2       0.97      1.00      0.99        35
           3       0.97      0.78      0.87        37
           4       0.92      0.92      0.92        37
           5       0.82      1.00      0.90        37
           6       0.97      0.95      0.96        37
           7       0.89      0.92      0.90        36
           8       0.84      0.82      0.83        33
           9       0.74      0.86      0.80        37

    accuracy                           0.89       360
   macro avg       0.90      0.89      0.89       360
weighted avg       0.90      0.89      0.89       360

📉 Confusion Matrix

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🗂️ Project Structure

Neural-Network-From-Scratch/
│
├── activations.py       # Activation functions (ReLU, Softmax)
├── layer.py             # Dense layer class with forward/backward
├── loss.py              # Categorical Crossentropy loss
├── main.py              # Main training and evaluation script
├── requirements.txt     # Required Python packages
└── .gitignore

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⚙️ Setup Instructions

1. Clone the Repository

git clone https://github.com/Vigneshmaradiya/Neural-Network-From-Scratch.git
cd Neural-Network-From-Scratch

2. (Optional) Create a Virtual Environment

python3 -m venv nn
source nn/bin/activate  # On Linux/macOS
nn\Scripts\activate     # On Windows

3. Install Dependencies

pip install -r requirements.txt

4. Run the Model

python main.py

The script will:

• Load MNIST data
• Train the neural network
• Evaluate and print accuracy, classification report
• Plot predicted vs actual values and the confusion matrix

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📌 Example Output

Each image shows: Predicted / Actual

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📚 Concepts Covered

• Matrix-based forward propagation
• Backpropagation with manual gradient calculation
• Weight updates using gradient descent
• Multi-layer neural architecture
• Model evaluation and performance metrics

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🤝 Contributing

Want to improve this project?

• Extend to include batch training or regularization
• Improve modularity or performance
• Add support for saving/loading models

Feel free to fork the repo and submit a pull request!

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📜 License

This project is licensed under the MIT License.

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👨‍💻 Author

Vignesh Maradiya
📧 LinkedIn
🌐 GitHub

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