Generative Adversarial Networks depend on an adversarial approach to generate new data. The two neural networks that GANs are composed of, the generator and the discriminator, work against each other. The generator attempts to generate realistic data and the discriminator attempts to differentiate between generated and real data. In order for the discriminator to predict whether a given sample is real or fake, a sigmoid activation function is used. When a prediction is above or equal to 0.5 on the sigmoid function, the sample is labeled as real, and if the prediction is below 0.5, the sample is labeled as fake or generated. The more sure the discriminator is of its prediction, the more it will tend to predict closer to 1 or 0 as opposed to around 0.5.