devnag/pytorch-generative-adversarial-networks
A minimal 50-line PyTorch implementation of Generative Adversarial Networks training on synthetic Gaussian data.
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This repository provides a simple GAN implementation in PyTorch, demonstrating the adversarial training process where a generator and discriminator network compete. The code trains on a synthetic shifted/scaled Gaussian distribution, illustrating how the generator learns to match the real data distribution. It serves as an educational example accompanying a blog post tutorial.
Frequently asked
- What is devnag/pytorch-generative-adversarial-networks?
- A minimal 50-line PyTorch implementation of Generative Adversarial Networks training on synthetic Gaussian data.
- Is pytorch-generative-adversarial-networks open source?
- Yes — devnag/pytorch-generative-adversarial-networks is open source, released under the Apache-2.0 license.
- What language is pytorch-generative-adversarial-networks written in?
- devnag/pytorch-generative-adversarial-networks is primarily written in Python.
- How popular is pytorch-generative-adversarial-networks?
- devnag/pytorch-generative-adversarial-networks has 1.5k stars on GitHub.
- Where can I find pytorch-generative-adversarial-networks?
- devnag/pytorch-generative-adversarial-networks is on GitHub at https://github.com/devnag/pytorch-generative-adversarial-networks.