This repository shows an example of the usability of SKORCH to train a PyTorch model making use of different capabilities of the scikit-learn framework.

If you want to understand the details about how this model was created, take a look at this very clear and detailed explanation: SKORCH: PyTorch Models Trained with a Scikit-Learn Wrapper

• The model
• Files
• How to use
• Contributing
• Contact
• License

The idea of this repository is to show how to use some of the SKorch functionalities to train a PyTorch model. In this case, a neural network was created to classify the wines dataset. In order to understand better what SKorch is, take a look at the following image:

• data: Here you will find the wines dataset
• src: It contains two files data_loader.py and model.py. The file data_loader.pycontains the functions to load an preprocess the wines dataset. The file model.pycontains the PyTorch model.
• main.py: This file trigger the different cases explained in the Medium article.

You just need to type

python main.py

however, I recommend you to work with a virtual environment, in this case I am using pipenv. So in order to install the dependencies located in the Pipfile you just need to type:

pipenv install

and then

pipenv shell

Feel free to fork the model and add your own suggestiongs.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/YourGreatFeature)
3. Commit your Changes (git commit -m 'Add some YourGreatFeature')
4. Push to the Branch (git push origin feature/YourGreatFeature)
5. Open a Pull Request

If you have any question, feel free to reach me out at:

• Twitter
• Medium
• Linkedin
• Email: fer.neutron@gmail.com

Distributed under the MIT License. See LICENSE.md for more information.