This is a simple GAN setup with spectral normalization using Python 3.7 and PyTorch 1.3.
The task of the generative network is to generate a solid colored image of size 64x64.
The real target data is a solid color with the size 64x64. The color palette is adapted from the qualitative color map Set-1 from ColorBrewer.
python train.py --gpu 0 --output_path "<output_path>"
10: Loss(G): 0.6861 Loss(D): 0.6998 Real Pred.: 0.4968 Fake Pred.: 0.5034
20: Loss(G): 0.6948 Loss(D): 0.7061 Real Pred.: 0.4866 Fake Pred.: 0.4992
30: Loss(G): 0.6939 Loss(D): 0.6945 Real Pred.: 0.4983 Fake Pred.: 0.4996
40: Loss(G): 0.6676 Loss(D): 0.7009 Real Pred.: 0.5055 Fake Pred.: 0.5130
50: Loss(G): 0.6639 Loss(D): 0.7119 Real Pred.: 0.4964 Fake Pred.: 0.5148
60: Loss(G): 0.6232 Loss(D): 0.7265 Real Pred.: 0.5059 Fake Pred.: 0.5362
70: Loss(G): 0.7260 Loss(D): 0.6849 Real Pred.: 0.4924 Fake Pred.: 0.4838
80: Loss(G): 0.6976 Loss(D): 0.6964 Real Pred.: 0.4952 Fake Pred.: 0.4978
90: Loss(G): 0.8104 Loss(D): 0.6286 Real Pred.: 0.5151 Fake Pred.: 0.4447
100: Loss(G): 0.8660 Loss(D): 0.6519 Real Pred.: 0.4738 Fake Pred.: 0.4207
......
Each grid shows 6x6 generated samples from the generator with the same latent codes over time.
| Iteration | Output |
|---|---|
| 00100 |  |
| 02500 |  |
| 05000 |  |
| 07500 |  |
| 10000 |  |