# Alquitable
Alquitable is a Python package that provides a Keras-based set of tools to enhance Alquimodelia.
[](https://www.python.org/)
[](https://keras.io/)
It provides the loss function and callbacks to apply to keras models
## Usage
To use Alquitable, follow these steps:
```bash
pip install alquitable
```
Since Aquitable is based on keras-core you can choose which backend to use, otherwise it will default to tensorflow.
To change backend change the ```KERAS-BACKEND``` enviromental variable. Follow [this](https://keras.io/keras_core/#configuring-your-backend).
To get an arquiteture you only need to have a simple configuration and call the module:
```python
# Previous code and imports
...
from alquitable import losses, callbacks
# Based on forecat StackedCNN
loss_funtion=losses.weighted_loss
callback = callbacks.StopOnNanLoss
StackedCNN.compile(loss=loss_funtion)
StackedCNN.fit(
...
callbacks=callback
)
```
## [Contribution](CONTRIBUTING.md)
Contributions to Alquitable are welcome! If you find any issues or have suggestions for improvement, please feel free to contribute. Make sure to update tests as appropriate and follow the contribution guidelines.
## License
Alquitable is licensed under the MIT License, which allows you to use, modify, and distribute the package according to the terms of the license. For more details, please refer to the [LICENSE](LICENSE) file.
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"description": "# Alquitable\n\nAlquitable is a Python package that provides a Keras-based set of tools to enhance Alquimodelia.\n\n[](https://www.python.org/)\n[](https://keras.io/)\n\nIt provides the loss function and callbacks to apply to keras models\n\n\n## Usage\n\nTo use Alquitable, follow these steps:\n\n```bash\n pip install alquitable\n```\n\nSince Aquitable is based on keras-core you can choose which backend to use, otherwise it will default to tensorflow.\nTo change backend change the ```KERAS-BACKEND``` enviromental variable. Follow [this](https://keras.io/keras_core/#configuring-your-backend).\n\nTo get an arquiteture you only need to have a simple configuration and call the module:\n\n```python\n# Previous code and imports\n...\nfrom alquitable import losses, callbacks\n\n# Based on forecat StackedCNN\n\n\nloss_funtion=losses.weighted_loss\ncallback = callbacks.StopOnNanLoss\n\nStackedCNN.compile(loss=loss_funtion)\n\nStackedCNN.fit(\n ...\n callbacks=callback\n)\n\n\n```\n\n## [Contribution](CONTRIBUTING.md)\n\nContributions to Alquitable are welcome! If you find any issues or have suggestions for improvement, please feel free to contribute. Make sure to update tests as appropriate and follow the contribution guidelines.\n\n## License\n\nAlquitable is licensed under the MIT License, which allows you to use, modify, and distribute the package according to the terms of the license. For more details, please refer to the [LICENSE](LICENSE) file.\n",
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