The model that we trained might not be a perfect model, but we can optimize the hyperparameters to improve it. There are many hyperparameters in a 3D-GAN that can be optimized. These include the following:

• Batch size: Experiment with values of 8, 16, 32, 54, or 128 for the batch size.
• The number of epochs: Experiment with 100 epochs and gradually increase it to 1,000-5,000.
• Learning rate: This is the most important hyperparameter. Experiment with 0.1, 0.001, 0.0001, and other small learning rates.
• Activation functions in different layers of the generator and the discriminator network: Experiment with sigmoid, tanh, ReLU, LeakyReLU, ELU, SeLU, and other activation functions.
• The optimization algorithm: Experiment with Adam, SGD, Adadelta, RMSProp, and other optimizers available in the Keras framework.
• Loss functions: Binary cross entropy is the loss function best suited for a 3D-GAN.
• The number of layers in both of the networks: Experiment with different numbers in the network depending on the amount of training data available. You can make your network deep if you have enough data available to train it with.

We can also carry out automatic hyperparameter optimization by using libraries such as Hyperopt (https://github.com/hyperopt/hyperopt) or Hyperas (https://github.com/maxpumperla/hyperas) to select the best set of hyperparameters.