Hyperparameter Tuning with PyTorch and Optuna

This project demonstrates hyperparameter tuning for a CNN model on the MNIST dataset using PyTorch and Optuna.

Overview

• Model: A simple CNN with two convolutional layers, batch normalization, and dropout.
• Dataset: MNIST (train, validation, and test splits).
• Hyperparameters Tuned:
  • Learning Rate (lr)
  • Number of filters (num_filters)
  • Batch size (batch_size)
  • Dropout rate (dropout_rate)
  • Weight decay (weight_decay)

Setup

• Install the required packages:

  pip install torch torchvision optuna
  

• Run the notebook hyperparam_tuning.ipynb to perform hyperparameter tuning and evaluate the model.

Usage

1. Start by installing the dependencies.
2. Run the notebook cells sequentially to:
   • Load and preprocess data.
   • Define the CNN model.
   • Tune hyperparameters using Optuna.
   • Evaluate the final model on the test set.

License

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

Contributing

Contributions are welcome! Please submit a pull request or open an issue to discuss potential changes.

Credits

Special thanks to Otzar Jaffe for making the lesson.