PyNeuralNet

Name	PyNeuralNet JSON
Version	1.2.0 JSON
	download
home_page	https://github.com/ItzLoghotXD/PyNeuralNet/
Summary	PyNeuralNet is a python library for prototyping and building neural networks. PyNeuralNet uses PyTorch as a computational backend for deep learning models.
upload_time	2024-06-25 12:19:03
maintainer	ItzLoghotXD
docs_url	None
author	ItzLoghotXD
requires_python	None
license	MIT License Copyright (c) 2024 ItzLoghotXD Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
keywords	neural network ai neural network algorithm loghot
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
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            PyNeuralNet
============

Welcome to the PyNeuralNet repository! PyNeuralNet is a python library for prototyping and building neural networks. PyNeuralNet uses PyTorch as a computational backend for deep learning models.

## Installation

1. First, make sure you have Python installed on your system.
2. Use this pip command to install the latest version of package.
   ```bash
   pip install pyneuralnet
   ```

## Usage
```python
from pyneuralnet import train

dataset_loader = 'local'
root_dir = 'path/to/root_diractory'
metadata_file = 'path/to/meta_info_file.txt'
network = 'usrcnn'
batchs = 4

train(datasetloader, metadata_file, root_dir, epochs=25, batch_size=batchs, network=network)

```
- Parameters
   - dataset_loader: Type of dataset loader, there is two type of dataloaders (locally - `local` and from internet - `internet`). In this example, it is set to 'local'.
   - metadata_file: Path to the metadata file. If you load your dataset from internet you should type an url like [this](https://itzloghotxd.github.io/machine-learning-datasets/image-datasets/div2k/meta-info/meta_info_DIV2K_valid_HR.txt).
   - root_dir: Path to the root directory where the dataset is located. If you load your dataset from internet you should type an url like [this](https://itzloghotxd.github.io/machine-learning-datasets/image-datasets/div2k/), example [image](https://itzloghotxd.github.io/machine-learning-datasets/image-datasets/div2k/DIV2K_valid_HR/0864.png).
   - network: Neural network architecture to be used (e.g., 'usrcnn'). There are 6 type of networks(for now) which are based on `Convolutional Neural Network` e.g., `usrcnn`, `esrcnn`, `bsrcnn`, `isrcnn`, `rsrcnn` and `srcnn`.
   - epochs: Number of training epochs.
   - batch_size: Size of each training batch.

## Contributing

Contributions are welcome! If you'd like to contribute to this project, follow these steps:
1. Fork this repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and submit a pull request.

## License

This project is licensed under the [MIT License](https://github.com/ItzLoghotXD/PyNeuralNet/blob/main/LICENSE) - see the LICENSE file for details.

## Contact

Feel free to reach out to me at **loghot.gamerz.official@gmail.com** if you have any questions or feedback!
Or just open an [issue](https://github.com/ItzLoghotXD/PyNeuralNet/issues) on PyNeuralNet's github page.

Happy coding! 🚀

            

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