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...
