An advanced neural network framework with interpretability, generalization, robustness, and fairness features
Project description
NeuroFlex: Advanced Neural Network Framework
NeuroFlex is a cutting-edge neural network framework built on JAX and Flax, designed to address key challenges in modern machine learning: interpretability, generalization, robustness, and fairness. This project showcases state-of-the-art techniques and methodologies for creating more transparent, reliable, and ethical AI systems.
Latest Updates
- Integration of AlphaFold for advanced protein structure prediction
- Enhanced capabilities for neural protein modeling and drug discovery
- Quantum Neural Network module for quantum computing integration
- Improved Brain-Computer Interface (BCI) functionality
- Advanced cognitive architecture with consciousness simulation
- Support for multiple Python versions (3.9, 3.10, 3.11, 3.12)
Features
- Advanced neural network architectures (CNN, RNN, LSTM, GAN)
- Integration of JAX, TensorFlow, and PyTorch modules
- Quantum Neural Network integration
- Reinforcement learning capabilities
- Brain-Computer Interface (BCI) integration
- Fairness constraints and bias mitigation
- Adversarial training for improved robustness
- Interpretability tools (SHAP)
- 2D and 3D convolution support
- Data augmentation techniques
- Cognitive architecture with consciousness simulation
- AlphaFold integration for protein structure prediction
- Neural protein modeling for neuroscience applications
- Drug discovery support through protein structure analysis
- Synthetic biology insights from protein folding predictions
- Compatibility with numpy < 2 and torch 1.11.0
- Resolved dependency issues for improved stability
- Successful test runs for neural network components
Installation
pip install neuroflex
Environment Setup
NeuroFlex supports multiple operating systems and Python versions:
- Ubuntu
- Windows
- macOS
- Python 3.9, 3.10, 3.11, 3.12
To set up your environment:
- Clone the repository:
git clone https://github.com/neuroflex/neuroflex.git
- Create a virtual environment:
python -m venv neuroflex-env
- Activate the environment:
- Ubuntu/macOS:
source neuroflex-env/bin/activate
- Windows:
neuroflex-env\Scripts\activate
- Ubuntu/macOS:
- Install dependencies:
pip install -r requirements.txt
Quick Start
from neuroflex import NeuroFlexNN, train_model, AlphaFoldIntegration
Define your model
model = NeuroFlexNN(
features=[64, 32, 10],
use_cnn=True,
use_rnn=True,
fairness_constraint=0.1,
use_quantum=True, # Enable quantum neural network
use_alphafold=True # Enable AlphaFold integration
)
Initialize AlphaFold integration
alphafold = AlphaFoldIntegration()
alphafold.setup_model(model_params={'max_recycling': 3})
Predict protein structure
predicted_structure = alphafold.predict_structure()
Train your model with AlphaFold integration
trained_state, trained_model = train_model(
model, train_data, val_data,
num_epochs=10, batch_size=32, learning_rate=1e-3,
alphafold_structure=predicted_structure
)
Get pLDDT scores and predicted aligned error
plddt_scores = alphafold.get_plddt_scores()
predicted_aligned_error = alphafold.get_predicted_aligned_error()
print(f"Average pLDDT score: {plddt_scores.mean()}")
print(f"Average predicted aligned error: {predicted_aligned_error.mean()}")
Testing
To run tests for different Python versions and operating systems:
pytest tests/
Documentation
For detailed documentation, please visit our official documentation.
Contributing
We welcome contributions! Please see our contributing guidelines for more information.
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 any questions or feedback, please open an issue on our GitHub repository or contact us at kasinadhsarma@gmail.com.
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