Training the cGAN

Training the cGAN for face aging is a three-step process:

1. Training the cGAN
2. Initial latent vector approximation
3. Latent vector optimization

We will cover these steps one by one in the following sections.

Training the cGAN

This is the first step of the training process. In this step, we train the generator and the discriminator networks. Perform the following steps:

1. Start by specifying the parameters required for the training:

# Define hyperparameters
data_dir = "/path/to/dataset/directory/"
wiki_dir = os.path.join(data_dir, "wiki_crop")
epochs = 500
batch_size = 128
image_shape = (64, 64, 3)
z_shape = 100
TRAIN_GAN = True
TRAIN_ENCODER = False
TRAIN_GAN_WITH_FR = False
fr_image_shape = (192,...