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        "description": "# VeGANs\n\nA library providing various existing GANs in PyTorch.\n\nThis library targets mainly GAN users, who want to use existing GAN training techniques with their own generators/discriminators.\nHowever researchers may also find the GAN base class useful for quicker implementation of new GAN training techniques.\n\nThe focus is on simplicity and providing reasonable defaults.\n\n## How to install\n`pip install vegans`\n\n## How to use\nThe basic idea is that the user provides discriminator and generator networks, and the library takes care of training them in a selected GAN setting:\n```\nfrom vegans import WGAN\nfrom vegans.utils import plot_losses, plot_image_samples\n\n# Create your critic and generator\nnetD = Discriminator().to(device)\nnetG = Generator().to(device)\n\n# Build a Wasserstein GAN\ngan = WGAN(netG, netD, dataloader, ngpu=1, nr_epochs=20)\n\n# train it\ngan.train()\n\n# vizualise results\nimg_list, D_losses, G_losses = gan.get_training_results()\nplot_losses(G_losses, D_losses)\nplot_image_samples(img_list, 50)\n```\n\nCurrently the best way to learn more about how to use VeGANs is to have a look at the example [notebooks](https://github.com/unit8co/vegans).\n\n## Contribute\nPRs and suggestions are welcome.\n\n## Credits\nSome of the code has been inspired by some existing GAN implementations:\n* https://github.com/eriklindernoren/PyTorch-GAN\n* https://github.com/martinarjovsky/WassersteinGAN\n* https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html\n\n\n",
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