How exactly does
latent_dim=32becomes of shape $16 * 16$ in the network.
How are these values decided?
- latent_dim does not become of shape 16*16
x = layers.Dense(128 * 16 * 16)(generator_input)
mean: The input of size 32 (the latent_dim) is connected to a layer of size 16*16*128
- These value are decided by the data scientist, they are the hyper-parameters. There is a lot of research on how to adjust hyper-parameters for GAN. People use generally a size of 100 for the latent_dim but it may depends of the complexity of the image you are trying to generate.