![]() lr_g = 2e-4 # Learning rate of the generator network While we are at it, we also define optimisers for the generator and discriminator network. Next, let us define values for learning rate, batch size, epochs, and other hyper-parameters. Having direct feedback, instead of looking at plots in a separate window, use fantastic for debugging. ![]() While UnicodePlots is not necessary, it can be used to plot generated samples into the terminal during training. To download a package in the Julia REPL, type ] to enter package mode and then type add MLDatasets or perform this operation with the Pkg module like this > import Pkg To get started we first import a few useful packages: using MLDatasets: MNIST ![]() Competition in this game drives both teams to improve their methods until the counterfeits are indistinguishable from the genuine articles. The generative model can be thought of as analogous to a team of counterfeiters, trying to produce fake currency and use it without detection, while the discriminative model is analogous to the police, trying to detect the counterfeit currency. In the proposed adversarial nets framework, the generative model is pitted against an adversary: a discriminative model that learns to determine whether a sample is from the model distribution or the data distribution. is a great resource that describes the motivation and theory behind GANs: The original GAN paper by Goodfellow et al. This tutorial describes how to implement a vanilla Generative Adversarial Network using Flux and how train it on the MNIST dataset. ![]() Edit on GitHub Tutorial: Generative Adversarial Networks ![]()
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