The following diagram represents the important pieces of the generator:
The generator components in the architecture diagram: latent space, generator, and image generation by the generator
The focus in the diagram ensures that you see the core piece of code that you'll be developing in the generator section.
Here are a few steps to describe how we create a generator conceptually:
- First, the generator samples from a latent space and creates a relationship between the latent space and the output
- We then create a neural network that goes from an input (latent space) to output (image for most examples)
- We'll train the generator in an adversarial mode where we connect the generator and discriminator together in a model ( every generator and GAN recipe in this book will show these steps)
- The generator can then be used for inference after training