Advanced Deep Learning with Keras

Chapter 6
if (i + 1) % save_interval == 0:
if (i + 1) == train_steps:
show = True
else:
show = False
generators = (gen0, gen1)
plot_images(generators,
noise_params=noise_params,
show=show,
step=(i + 1),
model_name=model_name)
# save the modelis after training generator0 & 1
# the trained generator can be reloaded for
# future MNIST digit generation
gen1.save(model_name + "-gen1.h5")
gen0.save(model_name + "-gen0.h5")
Generator outputs of StackedGAN
After training the StackedGAN for 10,000 steps, the Generator 0
and Generator 1
models
are saved on files. Stacked together, Generator 0
and Generator 1
can synthesize fake
images conditioned on label and noise codes, z 0
and z 1
.
The StackedGAN generator can be qualitatively validated by:
1. Varying the discrete labels from 0 to 9 with both noise codes, z 0
and z 1
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
to
python3 stackedgan-mnist-6.2.1.py
--generator0=stackedgan_mnist-gen0.h5
--generator1=stackedgan_mnist-gen1.h5 --digit=9
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