Now, this is the part you've been waiting for: how do I build a GAN? There are a few principal components to the construction of this network architecture. First, we need to have a method to produce neural networks easily, such as Keras or PyTorch (using the TensorFlow backend). This critical piece will be covered extensively in Chapter 2, Data First Easy Environment, and Data Prep and Chapter 3, My First GAN in Under 100 Lines. Second, we need to produce the two neural-network-based components, named the generator and discriminator.
Architecture structure basics
How to do it...
The classic analogy is the counterfeiter (generator) and FBI agent (discriminator). The counterfeiter is constantly looking for new ways to produce fake documents that can pass the FBI agent's tests. Let's break it down into a set of goals:
- Counterfeiter (generator) goal: Produce products so that the cop cannot distinguish between the real and fake ones
- Cop (discriminator) goal: Detect anomalous products by using prior experience to classify real and fake products
How it works...
Now, enough with the analogies, right? Let's restructure this into a game-theory-style problem-the minimax problem from the first GAN implementation. The following steps illustrate how we can create this type of problem:
- Generator goal: Maximize the likelihood that the discriminator misclassifies its output as real
- Discriminator goal: Optimize toward a goal of 0.5, where the discriminator can't distinguish between real and generated images
The Minimax Problem (sometimes called MinMax) is a theory that focuses on maximizing a function at the greatest loss (or vice versa). In the case of GANs, this is represented by the two models training in an adversarial way. The training step will focus on minimizing the error on the training loss for the generator while getting as close to 0.5 as possible on the discriminator (where the discriminator can't tell the difference between real and fake).
In the GAN framework, the generator will start to train alongside the discriminator; the discriminator needs to train for a few epochs prior to starting the adversarial training as the discriminator will need to be able to actually classify images. There's one final piece to this structure, called the loss function. The loss function provides the stopping criteria for the Generator and Discriminator training processes. Given all of these pieces, how do we structure these pieces into something we can train? Check out the following diagram:
A high-level description of the flow of the Generative Adversarial Network, showing the basic functions in block format
With this architecture, it's time to break each piece into its component technology: generator, discriminator, and loss function. There will also be a section on training and inference to briefly cover how to train the model and get data out once it is trained.
