Skip to content

Latest commit

 

History

History
181 lines (141 loc) · 5.18 KB

README.md

File metadata and controls

181 lines (141 loc) · 5.18 KB

AI Model Building with DL4J

This project is a Java Spring Boot application that uses DL4J (Deeplearning4j) to train a neural network for predicting if a player is suitable or not for the team. The application supports CRUD operations on player performance data stored in a MySQL database and retrains the AI model automatically whenever the data changes.

Features

  • CRUD Operations: Add, update, delete, and retrieve player performance data.
  • AI Model: Uses a Neural Network (DL4J) for player suitability prediction.
  • Automatic Model Retraining: The AI model retrains every time the performance data is modified.
  • MySQL Integration: Stores player data in a relational database.
  • REST API: Exposes endpoints for managing player data.

Technologies Used

  • Java 17
  • Spring Boot (Web, Data JPA)
  • DL4J (Deeplearning4j) for neural network implementation
  • MySQL for data storage
  • Lombok for reducing boilerplate code
  • Maven for dependency management

Project Structure

src/main/java/com/ictss
    ├── ai              # DL4J model logic
    ├── controller      # REST controllers
    ├── dto             # Data Transfer Objects (DTO)
    ├── model           # JPA entities
    ├── repository      # Spring Data JPA repositories
    └── service         # Service layer for business logic

Setup and Installation

Prerequisites

  • Java 17 or higher installed
  • Maven installed
  • MySQL installed and running
  • Git installed (for cloning the project)

Step 1: Clone the Repository

git clone https://github.com/yourusername/ai-model-building-dl4j.git
cd ai-model-building-dl4j

Step 2: Set Up MySQL Database

  1. Start your MySQL server.
  2. Create a new database: 
    CREATE DATABASE player_ai_db;
  3. Configure the database credentials in src/main/resources/application.properties: 
    spring.datasource.url=jdbc:mysql://localhost:3306/player_ai_db?useSSL=false
spring.datasource.username=root
spring.datasource.password=yourpassword

Step 3: Build and Run the Application

Use Maven to build and run the application:

mvn clean install
mvn spring-boot:run

Step 4: Test the API Endpoints

Use Postman or another tool to test the API.

1. Add Player Performance Data

  • POST: http://localhost:8080/api/performance
  • Body (JSON): 
    {
    "average": 55.0,
    "strikeRate": 130.0,
    "bowlingAverage": 20.0,
    "economyRate": 4.0,
    "fieldingStats": 15,
    "label": 1
}

2. Retrieve All Player Data

  • GET: http://localhost:8080/api/performance

How the AI Model Works

The project uses DL4J to build a neural network that predicts player suitability based on five performance metrics:
batting average, strike rate, bowling average, economy rate, and fielding stats.

Workflow

  1. Data Collection: Player data is collected through the API and saved to MySQL.
  2. Model Training: When data is added or updated, the neural network retrains to incorporate the changes.
  3. Prediction: The trained model predicts if a player is suitable based on performance metrics.
  4. Model Storage: The model is saved as player_model.zip and reloaded whenever required.

Model Configuration Example

MultiLayerConfiguration config = new NeuralNetConfiguration.Builder()
    .seed(123)
    .updater(new Adam(0.001))
    .list()
    .layer(0, new DenseLayer.Builder().nIn(5).nOut(64).activation(Activation.RELU).build())
    .layer(1, new DenseLayer.Builder().nIn(64).nOut(32).activation(Activation.RELU).build())
    .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.XENT)
        .nIn(32).nOut(1)
        .activation(Activation.SIGMOID)
        .build())
    .build();

MultiLayerNetwork model = new MultiLayerNetwork(config);
model.init();

Retraining Process

  1. When a player is added, updated, or deleted, the system retrieves the latest data.
  2. The model re-trains using the new dataset.
  3. The new model replaces the old one by saving it as player_model.zip.

Making Predictions

You can use the trained model to predict player suitability programmatically:

float[] newPlayer = {50.0f, 120.0f, 25.0f, 4.2f, 14};
boolean isGoodPlayer = aiModel.predict(newPlayer);
System.out.println("Is the player suitable? " + (isGoodPlayer ? "Yes" : "No"));

Contributing

  1. Fork the repository.
  2. Create a new branch: 
    git checkout -b feature-branch
  3. Make your changes and commit them: 
    git commit -m "Add new feature"
  4. Push your changes: 
    git push origin feature-branch
  5. Create a pull request.

License

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

Contact

For questions or support, contact: