Chain-of-Thought Reasoning in Deep Learning Models

This repository contains a project demonstrating the implementation and evaluation of Chain-of-Thought (CoT) reasoning in deep learning models. The project includes synthetic data generation, model training, evaluation, and analysis.

Introduction

Chain-of-Thought (CoT) reasoning is an approach to enhance the interpretability and performance of deep learning models. This project demonstrates a complete workflow for implementing CoT reasoning, including data generation, model training, evaluation, and result analysis.

Setup and Installation

1. Clone the repository:

   git clone https://github.com/revanthchristober/Chain-of-Thought-Reasoning-Experiment-in-Deep-Learning-Models.git
   cd Chain-of-Thought-Reasoning-Experiment-in-Deep-Learning-Models

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

Generate Synthetic Data

Run the following script to generate synthetic data:

python data/generate_data.py

Run Experiments

Open the experiments.ipynb notebook in Jupyter and run all cells to execute the experiments:

jupyter notebook notebooks/experiments.ipynb

This notebook will load the data, train the model, evaluate it, and save the results in the results directory.

Results

The results of the experiments, including plots for training loss and predictions vs. actual values, will be saved in the results/analysis_plots directory.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any changes or enhancements.

License

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