Training a 3D-GAN is similar to training a vanilla GAN. We first train the discriminator network on both the generated images and the real images but freeze the generator network. Then, we train the generator network but freeze the discriminator network. We repeat this process for a specified number of epochs. During one iteration, we train both of the networks in a sequence. Training a 3D-GAN is an end-to-end training process. Let's work on these steps one by one.
Training a 3D-GAN
Training the networks
To train the 3D-GAN, perform the following steps:
- Start by specifying the values for the different hyperparameters required for the training, shown as follows:
gen_learning_rate = 0.0025
dis_learning_rate = 0.00001
beta...
