Understanding of Generative Adversarial NetworksTensorFlow basics
新手友好度1-100 的估计分数,表示该议题对首次贡献者的友好程度。
10
研究方向
Investigate the training loop and model architecture in files like 'train.py' or 'model.py'. Check hyperparameters, loss functions, and data preprocessing for typical DCGAN pitfalls. Compare with known working implementations to identify discrepancies.
Generated samples are very bad · carpedm20/DCGAN-tensorflow#139 | Good First Issue