Go Neural Network

This project implements a feed-forward neural network in Go, designed to be flexible and adaptable to various datasets. It currently supports training new models and loading pre-trained models for prediction.

Features

• Modular Design: Code is organized into separate packages (cli, data, neuralnetwork, utils) for better maintainability and reusability.
• Dynamic Network Architecture: A feed-forward neural network with a configurable number of hidden layers and neurons per layer.
• Multiple Activation Functions: Supports ReLU, Sigmoid, Tanh, and Linear activation functions for each hidden layer and the output layer.
• Training: Train the neural network using your own CSV data. The data is automatically split into training and testing sets.
• Model Persistence: Save and load trained models to/from model.json files.
• Prediction: Use a loaded model to make predictions on new input data.
• He Initialization: Weights are initialized using He initialization.
• Backpropagation: Implements the backpropagation algorithm for training.

Getting Started

To run this application, you need to have Go installed on your system.

1. Clone the repository:

   git clone https://github.com/andrewthecodertx/Neural-Network.git
   cd Neural-Network

2. Run the application:

   The application now features a full-screen terminal user interface (TUI).

   go run .

Usage

Upon launching the application, you will be greeted with the main menu. You can navigate through the interface using the arrow keys and press Enter to select an option. Press q or Ctrl+C to quit at any time.

Train New Model

1. Select "Train New Model" from the main menu.
2. The application will automatically find any .csv files in the root directory.
3. Fill out the configuration form:
   • Select CSV File: The number corresponding to the dataset you want to use.
   • Hidden Layers: A comma-separated list of neuron counts for each hidden layer (e.g., 20,20).
   • Hidden Activations: A comma-separated list of activation functions (relu, sigmoid, tanh, linear).
   • Output Activation: The activation function for the output layer.
   • Epochs: The number of training iterations.
   • Learning Rate: The step size for gradient descent.
   • Error Goal: The target error at which training will stop.
4. Navigate to the "[ Start Training ]" button and press Enter.
5. A live progress view will show the current epoch and loss.
6. After training, the model will be evaluated on the test set, and the accuracy will be displayed.
7. Once training is complete, you will be prompted to enter a name to save the model. The saved model will be placed in the saved_models/ directory.

Load Model & Predict

1. Select "Load Model & Predict" from the main menu.
2. The application will list all models found in the saved_models/ directory.
3. Fill out the prediction form:
   • Select Model: The number corresponding to the model you want to use.
   • Input Data: A comma-separated list of numerical values for prediction. The number of values must match the model's expected input size.
4. Navigate to the "[ Predict ]" button and press Enter.
5. The calculated prediction will be displayed on the screen.

Datasets

The redwinequality.csv and iris.csv datasets are included as samples. These datasets are from the UCI Machine Learning Repository.

Citation: P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.

Future Enhancements

• Command-Line Arguments: Allow all parameters to be passed via command-line arguments instead of interactive prompts.
• Additional Optimizers: Implement other optimization algorithms like Adam or RMSprop.
• Regularization: Add support for L1/L2 regularization to prevent overfitting.
• Testing: Add a comprehensive test suite.

Contributing

Contributions are welcome! If you'd like to improve this project, feel free to open an issue or submit a pull request.

License

This project is licensed under the terms of the LICENSE file.

Docker

This project can also be run inside a Docker container, which handles all the setup for you.

Prerequisites

• Docker installed on your system.

Build the image:

docker build -t go-neuralnetwork .

Run the container:

docker run -it --rm go-neuralnetwork

The -it flags enable the interactive TUI interface.