Advanced Deep Learning with Keras

Generative Adversarial Networks (GANs)
The generator learns to generate fake images from a 100-dim input vector and
a specified digit. The discriminator classifies real from fake images based on real
and fake images and their corresponding labels.
The basis of CGAN is still the same as the original GAN principle except that
the discriminator and generator inputs are conditioned on one-hot labels, y.
By incorporating this condition in Equations 4.1.1 and 4.1.5, the loss functions for
the discriminator and generator are shown in Equations 4.3.1 and 4.3.2 respectively.
Given Figure 4.3.2, it may be more appropriate to write the loss functions as:
and
( D) ( G) ( D)
( θ , θ ) =−E
x~
p
log ( x | y) −Ez
log( 1 − ( ( z | y′ ) | y′
))
( D) ( G) ( D)
L D D G
L
data
( θ , θ ) =−E
z
log D( G( z | y′ ) | y′
)
( G) ( G) ( D)
( θ , θ ) =−E
x~
p
log ( x | y) −Ez
log( 1 − ( ( z | y′
)))
L D D G (Equation 4.3.1)
data
.
( θ , θ ) =−E
z
log D( ( z | y′
))
( G) ( G) ( D)
L G (Equation 4.3.2)
The new loss function of the discriminator aims to minimize the error of predicting
real images coming from the dataset and fake images coming from the generator
given their one-hot labels. Figure 4.3.2 shows how to train the discriminator.
Figure 4.3.2: Training the CGAN discriminator is similar to training the GAN discriminator.
The only difference is both the generated fake and the dataset's real images are conditioned
with their corresponding one-hot labels.
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