deepsynth 0.1.3
A Python package for synthesizing deep learning models using advanced techniques like NAS, Hyperparameter Optimization, and more.
License: MIT
Requires Python: <4.0,>=3.10

# DeepSynth

**DeepSynth** is a cutting-edge Python package for synthesizing deep learning models using advanced techniques such as Neural Architecture Search (NAS), Hyperparameter Optimization, Transfer Learning, and more. Designed to streamline the model-building process, DeepSynth enables users to create high-performance models with minimal manual effort.

## Key Features

- **Neural Architecture Search (NAS)**: Explore various model architectures automatically to identify the best configuration for your task.
- **Hyperparameter Optimization**: Fine-tune model hyperparameters using state-of-the-art optimization algorithms.
- **Transfer Learning**: Utilize pre-trained models and adapt them to your specific use case.
- **Model Generation**: Create new model architectures based on predefined constraints and datasets.
- **Visualization Tools**: Visualize model performance, architecture, and optimization progress with built-in tools.

## Installation

To get started with DeepSynth, you need to install the package and its dependencies. You can do this by using the `requirements.txt` file or directly installing via pip.

### Using `requirements.txt`

1. Clone the repository or download the `requirements.txt` file.
2. Install the dependencies:

    ```bash
    pip install -r requirements.txt
    ```

### Using pip

Install DeepSynth directly from PyPI:

```bash
pip install deepsynth
```

## Usage

Here are some examples of how to use various features of DeepSynth:

### Neural Architecture Search (NAS)

```python
from deepsynth.nas import NeuralArchitectureSearch
import numpy as np

# Prepare sample data
X_train = np.random.rand(1000, 20)  # 1000 samples, 20 features
y_train = np.random.randint(2, size=1000)  # Binary target

# Define search space
search_space = [
    {'layers': 2, 'units': 64},
    {'layers': 3, 'units': 128},
    {'layers': 4, 'units': 256}
]

# Perform NAS
nas = NeuralArchitectureSearch(search_space)
best_model = nas.search_best_model(X_train, y_train)
print("Best model found by NAS:")
best_model.summary()
```

### Hyperparameter Optimization

```python
from deepsynth.hyperparameter_optimization import HyperparameterOptimization
from hyperopt import hp

def objective_function(params):
    from deepsynth.model_generation import ModelGeneration
    model = ModelGeneration(params['num_layers'], params['units_per_layer']).generate_model()
    model.fit(X_train, y_train, epochs=5, verbose=0)
    loss, accuracy = model.evaluate(X_train, y_train, verbose=0)
    return {'loss': loss, 'status': 'ok'}

# Define hyperparameter search space
space = {
    'num_layers': hp.choice('num_layers', [2, 3, 4]),
    'units_per_layer': hp.choice('units_per_layer', [32, 64, 128])
}

# Optimize hyperparameters
optimizer = HyperparameterOptimization(objective_function)
best_params = optimizer.optimize(space)
print("Best hyperparameters found:")
print(best_params)
```

### Transfer Learning

```python
from deepsynth.transfer_learning import TransferLearning

# Load and adapt a pre-trained model
transfer_learning = TransferLearning()
model = transfer_learning.load_model()
print("Transfer learning model:")
model.summary()
```

### Model Generation

```python
from deepsynth.model_generation import ModelGeneration

# Generate a new model
num_layers = 3
units_per_layer = 128
model_generator = ModelGeneration(num_layers, units_per_layer)
model = model_generator.generate_model()
print("Generated model:")
model.summary()
```

### Visualization

```python
from deepsynth.visualization import Visualization

# Example training history
history = {
    'accuracy': [0.1, 0.3, 0.5, 0.7, 0.9],
    'val_accuracy': [0.15, 0.35, 0.55, 0.75, 0.85]
}
Visualization.plot_model_performance(history)
```

## Examples

For more detailed examples and advanced use cases, check out the `examples/` directory in the repository. The `example_usage.py` file includes various functionalities and applications of DeepSynth.

## Contributing

We welcome contributions to DeepSynth! If you would like to contribute, please follow these steps:

1. **Fork the repository**: Create a personal copy of the repository on GitHub.
2. **Create a branch**: Develop your changes on a new branch.
3. **Commit changes**: Make and commit your changes with clear messages.
4. **Push changes**: Push your branch to your forked repository.
5. **Create a pull request**: Submit a pull request describing your changes and improvements.

## License

DeepSynth is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.

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## Contact & Social Media

- **Telegram Channel**: [Channel](https://t.me/VenomOwners)
- **Telegram Group**: [Group](https://t.me/Venom_Chatz)
- **Owner's Telegram**: [Telegram](https://t.me/K_4ip)
- **Instagram**: [Instagram](https://instagram.com/venom_owners)
- **GitHub**: [Github](https://github.com/venombolteop/deepsynth)
- **Email**: ayush20912@gmail.com

For further questions, support, or to report issues, please open an issue on the [GitHub repository](https://github.com/venombolteop/deepsynth) or contact the maintainers.