Convolutional Neural Network (CNN) Support

[Samyssmile]

This Ticket is blocked by: #109
This Ticket will profit from: #36 and #44

CNNs are state of the art approach for Image & Audio recognition.

As machine learning and artificial intelligence evolve, the ability of EDUX to solve complex problems, especially those related to image recognition and processing, is vital. Currently, the library lacks a fundamental functionality in the domain of deep learning, which is the Convolutional Neural Network (CNN). The implementation of CNNs would dramatically broaden the scope and capability of EDUX, making it feasible to address issues in image processing, video analysis, and various computer vision applications.

CNN Development Roadmap

1. Designing the CNN Architecture

• Define the CNN architecture.
  • Determine the number and types of layers:
    • Convolutional layers
    • Dense layers
    • MaxPooling layers
    • Flatten layers
    • Dropout layers
  • Decide on the activation function (e.g., ReLU for hidden layers, Softmax for output).

2. Implementing the Core Components

• Develop or integrate the Matrix3D class for matrix operations.
• Implement the Convolutional layer operations in Matrix.
• Implement MaxPooling, Flatten, and Dropout layers using Matrix.
• Implement Dense layers with matrix support.
• Integrate Softmax functionality for output layer.

3. Loss Function and Optimization

• Implement the CrossEntropyLoss class.
• Integrate gradient-based learning mechanisms.
• Implement the ADAM optimizer.

4. Building the Training Infrastructure

• Create data preprocessing utilities.
• Develop a data loader for feeding the network.
• Implement a training loop.
  • Batch processing
  • Forward pass
  • Loss computation
  • Backward pass (gradient calculation)
  • Parameters update (using ADAM)
  • Implement CPU based Multithreading
  • Make Initial Weight Algorithm chnagable
  • Build Augmentation Tools

5. Testing and Evaluation

• Develop a testing routine to evaluate the model.
• Implement performance metrics (e.g., accuracy, loss).

6. Debugging and Optimization

• Perform code reviews and debugging.
• Optimize performance (focus on matrix operations in Matrix3D).

7. Documentation and Reporting

• Document the code and architecture.
• Create a report on model performance and learn