Generator outputs of StackedGAN

After training the StackedGAN for 10,000 steps, the Generator₀ and Generator₁ models are saved on files. Stacked together, Generator₀ and Generator₁ can synthesize fake images conditioned on label and noise codes, z₀ and z₁.

The StackedGAN generator can be qualitatively validated by:

1. Varying the discrete labels from 0 to 9 with both noise codes, z₀ and z₁ sampled from a normal distribution with a mean of 0.5 and standard -deviation of 1.0. The results are shown in Figure 6.2.9. We're able to see that the StackedGAN discrete code can control the digits produced by the generator:

   python3 stackedgan-mnist-6.2.1.py 
   --generator0=stackedgan_mnist-gen0.h5 
   --generator1=stackedgan_mnist-gen1.h5 --digit=0
   python3 stackedgan-mnist-6.2.1.py 
   --generator0=stackedgan_mnist-gen0.h5 
   --generator1=stackedgan_mnist-gen1.h5 --digit=9
   

   to

2. Varying the first noise code, z₀, as a constant vector from -4.0 to 4.0 for digits 0 to 9 as shown...