NeuroFlex: Advanced Neural Network Framework

NeuroFlex is a cutting-edge neural network framework built on JAX, Flax, and TensorFlow, designed to address key challenges in modern machine learning: interpretability, generalization, robustness, and fairness. This project integrates state-of-the-art techniques and methodologies for creating more transparent, reliable, and ethical AI systems.

Key Features

• Advanced Neural Architectures: Support for CNN, RNN, LSTM, GAN, and Spiking Neural Networks.
• Multi-Backend Integration: Seamless integration with JAX, Flax, and TensorFlow.
• 2D and 3D Convolutions: Efficient implementation of both 2D and 3D convolutional layers.
• Quantum Computing: Quantum Neural Network module for next-generation AI.
• Reinforcement Learning: Advanced capabilities with enhanced self-curing algorithms.
• Brain-Computer Interface (BCI): Cutting-edge integration for neurotechnology applications.
• Ethical AI: Fairness constraints and bias mitigation techniques.
• Robustness: Improved adversarial training, interpretability tools (SHAP), and adaptive learning rate adjustment.
• Bioinformatics: AlphaFold integration for protein structure prediction and drug discovery.
• Generative AI: Creative problem-solving and content generation.
• Natural Language Processing: Sentence piece integration and advanced NLP tasks.
• Neuromorphic Computing: Energy-efficient spiking neural networks.
• Self-Healing: Advanced diagnostic and healing processes for improved model performance and stability.
• Consciousness Simulation: Integration of Integrated Information Theory (IIT) and Global Workspace Theory (GWT) for advanced consciousness modeling.

Latest Updates (v0.1.3)

• Consciousness Simulation: Achieved integration of Integrated Information Theory (IIT) and Global Workspace Theory (GWT) for cognitive and consciousness modeling.
• TensorFlow Integration: Added support for TensorFlow backend alongside JAX/Flax.
• 2D and 3D Convolutions: Implemented efficient convolutional layers for both 2D and 3D inputs.
• Enhanced Compatibility: Improved interoperability between JAX/Flax and TensorFlow.
• Comprehensive Testing: Developed and passed pytest tests for all components, including TensorFlow integration.
• Enhanced self-curing mechanism with adaptive learning rate adjustment.
• Improved model robustness against gradient explosions and local minima.
• Advanced diagnostic checks for model performance and training issues.
• Whisper API integration for advanced speech recognition.
• Enhanced AlphaFold integration for protein structure prediction.
• Support for Python 3.9, 3.10, 3.11, 3.12.
• Resolved dependency issues and improved stability.

Installation

pip install neuroflex

Quick Start Guide

1. Import NeuroFlex

from neuroflex import NeuroFlexNN, train_model, create_neuroflex_nn

2. Define Your Model

model = create_neuroflex_nn(
    features=[64, 32, 10],
    input_shape=(1, 28, 28, 1),
    output_shape=(1, 10),
    use_cnn=True,
    conv_dim=2,
    backend='tensorflow'  # or 'jax' for JAX/Flax backend
)

3. Train Your Model

trained_state, trained_model = train_model(
    model, train_data, val_data,
    num_epochs=10, batch_size=32, learning_rate=1e-3
)

4. Make Predictions

predictions = trained_model(test_data)

Advanced Usage

TensorFlow Convolutions

from neuroflex import TensorFlowConvolutions

tf_conv = TensorFlowConvolutions(
    features=(32, 64, 128),
    input_shape=(1, 28, 28, 1),
    conv_dim=2  # Use 3 for 3D convolutions
)
model = tf_conv.create_model()

JAX/Flax and TensorFlow Compatibility

from neuroflex.utils import convert_array

jax_array = jax.random.uniform(jax.random.PRNGKey(0), shape=(1, 28, 28, 1))
tf_tensor = convert_array(jax_array, 'tensorflow')

Adversarial Training

from neuroflex import adversarial_training

perturbed_data = adversarial_training(model, params, input_data, epsilon=0.1, step_size=0.01)

Testing

We have developed comprehensive pytest tests for all components, including the TensorFlow integration. To run the tests:

pytest tests/

Key test files:

• test_neuroflex_nn.py: Tests for the core NeuroFlexNN functionality.
• test_tensorflow_convolutions.py: Tests for TensorFlow convolution implementations.

Environment Setup

NeuroFlex supports multiple operating systems:

• Ubuntu
• Windows
• macOS

To set up your environment:

1. Clone the repository:

   git clone https://github.com/neuroflex/neuroflex.git
   

2. Create a virtual environment:

   python -m venv neuroflex-env
   

3. Activate the environment:
   • Ubuntu/macOS: source neuroflex-env/bin/activate
   • Windows: neuroflex-env\Scripts\activate
4. Install dependencies:

   pip install -r requirements.txt
   

Documentation and Project Structure

The NeuroFlex project is organized into several key directories and files, each serving a specific purpose:

• NeuroFlex.py: Integrates various AI and neural network components, providing a unified interface for core neural networks, quantum neural networks, ethical frameworks, explainable AI, BCI integration, consciousness simulation, protein structure prediction, and more.
• Prompt_Agent: Contains agentic behavior models and related functionalities.
• Transformers: Includes advanced transformer models for text processing and classification.
• Advanced Models: Contains advanced math solving, time series analysis, and multi-modal learning models.
• AI Ethics: Implements ethical frameworks and guidelines for AI, including explainable AI and self-curing algorithms.
• BCI Integration: Facilitates integration with brain-computer interfaces, providing tools for BCI data processing and neuroscience model integration.
• Cognitive Architectures: Implements advanced cognitive architectures with self-healing mechanisms and adaptive algorithms.
• Config: Contains configuration files and constants for the project.
• Core Neural Networks: Provides core neural network models and functionalities.
• Edge AI: Focuses on edge AI optimization and deployment.
• Generative Models: Includes models for generative AI and creative problem-solving.
• Main.py: Demonstrates various components of the NeuroFlex framework.
• Model Monitoring: Contains tools for monitoring and analyzing model performance and health.
• NeuroFlex Integration: Integrates various components of the NeuroFlex system.
• Threat Detection: Provides methods for detecting and analyzing threats using anomaly detection, deep learning, and pattern recognition.
• Utils: Offers utility functions for data handling, preprocessing, and analysis.

For detailed documentation, including API references and tutorials, visit our official documentation.

Contributing

We welcome contributions! Please see our contributing guidelines for more information on how to get started.

License

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

Citation

If you use NeuroFlex in your research, please cite:

@software{neuroflex2024,
  author = {kasinadhsarma},
  title = {NeuroFlex: Advanced Neural Network Framework},
  year = {2024},
  url = {https://github.com/VishwamAI/NeuroFlex}
}

Contact

For questions, feedback, or support, please open an issue on our GitHub repository or contact us at gmail.

Changes:

• Added Consciousness Simulation section under Key Features and Latest Updates to reflect the integration of IIT and GWT theories.
• Updated the version to v0.1.3.
• General improvements and formatting to ensure clarity and consistency with the latest features.