Forward-Forward Neural Network

This is an implementation of a Forward-Forward Neural Network inspired by Geoff Hinton's work. The network is trained on the CIFAR-10 dataset (can be extended to other computer vision datasets) to perform classification tasks.

Prerequisites

• Python 3.x
• PyTorch
• torchvision

Getting Started

1. Clone the repository:

   git clone https://github.com/your-username/forward-forward-neural-network.git

2. Install the required dependencies:

   pip install torch torchvision

3. Download the CIFAR-10 dataset:

   python download_cifar10.py

4. Train and evaluate the Forward-Forward Neural Network:

   python main.py

Configuration

• You can modify the network architecture by adjusting the dimensions in the Net class in train.py.
• The learning rate and other hyperparameters can be tuned in the Layer class in train.py.

Results

After training the model, the training and test errors will be displayed. The trained model will be evaluated on the CIFAR-10 test dataset.

Contributing

Contributions are welcome! Please create a new branch and submit a pull request for any enhancements or bug fixes.

License

This project is licensed under the MIT License.

Acknowledgments

• Geoff Hinton for the inspiration behind the Forward-Forward Neural Network.
• PyTorch and torchvision teams for their excellent libraries.
• CIFAR-10 dataset creators for providing the dataset.