The hyperparameters are the properties of a model, which are fixed during the training of the model. Different parameters can have different accuracy. Let's take a look at some of the common hyperparameters used:
- Learning rate
- Batch size
- Number of epochs
- Generator optimizer
- Discriminator optimizer
- Number of layers
- Number of units in a dense layer
- Activation function
- Loss function
In the Implementing a DCGAN using Keras section, the learning rates were fixed: 0.0005 for the generator model and 0.0005 for the discriminator model. The batch size was 128. Tweaking these values might lead us to create a better model. If your model is not generating plausible images, try changing these values and run your model again.