Like any other GAN, training the pix2pix network is a two-step process. In the first step, we train the discriminator network. In the second step, we train the adversarial network, which eventually trains the generator network. Let's start training the network.

Perform the following steps to train an SRGAN network:

1. Start by defining the hyperparameters that are required for training:

epochs = 500
num_images_per_epoch = 400
batch_size = 1
img_width = 256
img_height = 256
num_channels = 1
input_img_dim = (256, 256, 1)
patch_dim = (256, 256)

# Specify dataset directory path
dataset_dir = "pix2pix-keras/pix2pix/data/facades_bw"

2. Next, define the common optimizer, shown as follows:

common_optimizer = Adam(lr=1E-4, beta_1=0.9, beta_2=0.999,  
                        epsilon=1e-08)

For all networks, we will use the Adam optimizer with the learning...