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https://github.com/yousifj129/neuralnetworkcreator
Create Sequential Neural Networks Using a GUI program
https://github.com/yousifj129/neuralnetworkcreator
good-first-issue gui pyside6 python qt tensorflow
Last synced: 4 months ago
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Create Sequential Neural Networks Using a GUI program
- Host: GitHub
- URL: https://github.com/yousifj129/neuralnetworkcreator
- Owner: yousifj129
- License: gpl-3.0
- Created: 2024-07-07T09:46:28.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-08-15T12:18:26.000Z (6 months ago)
- Last Synced: 2024-08-15T13:58:55.473Z (6 months ago)
- Topics: good-first-issue, gui, pyside6, python, qt, tensorflow
- Language: Python
- Homepage:
- Size: 45.9 KB
- Stars: 2
- Watchers: 3
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Neural Network Creator
Create Sequential Neural Networks Using a GUI program
## Features
- Load and preprocess CSV data
- Create custom neural network architectures
- Train models with various optimizers and loss functions
- Visualize model performance
- Make predictions using trained models
- Analyze model structure and feature importance
- Save and load trained models
- GPU support (if available)
## note
this program only works with 1D datasets
## Installation
1. Clone this repository:
```
git clone https://github.com/yousifj129/NeuralNetworkCreator.git
```
2. Install the required dependencies:
```
pip install -r requirements.txt
```
## Usage
1. Run the application:
```
python NeuralNetworkCreator.py
```
2. Load your CSV data using the "Load CSV Data" button.
3. Configure your neural network:
- Set input and output dimensions
- Add layers using the dropdown menus and input fields
- Set training parameters (epochs, batch size)
- Choose optimizer and loss function
4. Train the model using the "Train Model" button.
5. Use the "Predict" function to make predictions with your trained model.
6. Analyze your model using the "Analyze Model" button to open the analysis window.
## Contributing
Contributions to improve the Neural Network GUI are welcome!
### Areas for Improvement
- Add support for more layer types and activation functions
- Implement data visualization features
- Enhance the model analysis capabilities
- Improve error handling and user feedback
- Add unit tests and documentation
## Acknowledgments
This GUI application uses TensorFlow, Keras, and PySide6.