Advanced Deep Learning with Keras

Chapter 6
# plot generator images on a periodic basis
gan.plot_images(generator,
noise_input=noise_input,
noise_label=noise_label,
noise_codes=[noise_code1, noise_code2],
show=show,
step=(i + 1),
model_name=model_name)
# save the model after training the generator
# the trained generator can be reloaded for
# future MNIST digit generation
generator.save(model_name + ".h5")
Generator outputs of InfoGAN
Similar to all previous GANs that have been presented to us, we've trained InfoGAN
for 40,000 steps. After the training is completed, we're able to run the InfoGAN
generator to generate new outputs using the model saved on the infogan_mnist.h5
file. The following validations are conducted:
1. Generate digits 0 to 9 by varying the discrete labels from 0 to 9. Both
continuous codes are set to zero. The results are shown in Figure 6.1.5. We
can see that the InfoGAN discrete code can control the digits produced by
the generator:
python3 infogan-mnist-6.1.1.py --generator=infogan_mnist.h5
--digit=0 --code1=0 --code2=0
to
python3 infogan-mnist-6.1.1.py --generator=infogan_mnist.h5
--digit=9 --code1=0 --code2=0
2. Examine the effect of the first continuous code to understand which attribute
has been affected. We vary the first continuous code from -2.0 to 2.0 for digits
0 to 9. The second continuous code is set to 0.0. Figure 6.1.6 shows that the
first continuous code controls the thickness of the digit:
python3 infogan-mnist-6.1.1.py --generator=infogan_mnist.h5
--digit=0 --code1=0 --code2=0 --p1
3. Similar to the previous step, but instead focusing more on the second
continuous code. Figure 6.1.7 shows that the second continuous code
controls the rotation angle (tilt) of the writing style:
[ 177 ]