Advanced Deep Learning with Keras

The new loss function of the generator minimizes the correct prediction of the
discriminator on fake images conditioned on the specified one-hot labels. The
generator learns how to generate the specific MNIST digit given its one-hot
vector that can fool the discriminator. The following figure shows how to train
the generator:
Chapter 4
Figure 4.3.3: Training the CGAN generator through the adversarial network is similar to training
GAN generator. The only difference is the generated fake images are conditioned with one-hot labels.
Following listing highlights the minor changes needed in the discriminator model.
The code processes the one-hot vector using a Dense layer and concatenates it with the
image input. The Model instance is modified for the image and one-hot vector inputs.
Listing 4.3.1, cgan-mnist-4.3.1.py shows us the CGAN discriminator. In highlight
are the changes made in DCGAN.
def build_discriminator(inputs, y_labels, image_size):
"""Build a Discriminator Model
Inputs are concatenated after Dense layer.
Stack of LeakyReLU-Conv2D to discriminate real from fake.
The network does not converge with BN so it is not used here
unlike in DCGAN paper.
# Arguments
inputs (Layer): Input layer of the discriminator (the image)
y_labels (Layer): Input layer for one-hot vector to condition
the inputs
image_size: Target size of one side (assuming square image)
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