# Define the GAN model def gan_model(generator, discriminator): discriminator.trainable = False model = keras.Sequential() model.add(generator) model.add(discriminator) return model
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: Links and scripts to download the data used in the book's examples. Where to Access the Content Official Code Repository : GANs-in-Action on GitHub
As training progresses, both networks improve. Ideally, the system reaches a point called , where the Generator produces flawless synthetic data, and the Discriminator can only guess with a 50% accuracy rate whether an image is real or fake. Core Architectures Covered in "GANs in Action" I'll use search terms in English
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Moving past the vanilla GAN architecture, "GANs in Action" guides readers through several foundational variations that solved early training instabilities and expanded the utility of generative modeling. 1. Deep Convolutional GANs (DCGANs)