Advanced Deep Learning with Keras

Generative Adversarial Networks (GANs)
39999: [discriminator loss: 0.532917, acc: 0.742188] [adversarial loss:
0.824350, acc: 0.453125]
Figure 4.2.2: The fake images generated by the DCGAN generator at different training steps
Conditional GAN
In the previous section, the fake images generated by the DCGAN are random.
There is no control over which specific digits will be produced by the generator. There
is no mechanism for how to request a particular digit from the generator. This problem
can be addressed by a variation of GAN called Conditional GAN (CGAN) [4].
Using the same GAN, a condition is imposed on both the generator and
discriminator inputs. The condition is in the form of a one-hot vector version
of the digit. This is associated with the image to produce (generator) or classified
as real or fake (discriminator). The CGAN model is shown in Figure 4.3.1.
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