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Introduction to Deep Learning & Neural Networks

Develop a GAN with Pytorch

Explore how to build a generative adversarial network from scratch using PyTorch. Learn to define simple generator and discriminator models, train them on CIFAR10 images, and understand the step-by-step training process including noise input, loss calculation, and optimizer updates. Gain hands-on experience with training, evaluating, and experimenting with GANs to improve output realism.

We'll cover the following...

Let’s see all of that in action and build a GAN from scratch.

After importing our standard Pytorch modules, we will define the generator and the discriminator. Both of them are simple multi-layer networks.

Generator

  • Receives as input 100-length noise vector zz sampled from a distribution
  • Outputs a flattened image in the dimensions of the cifar10 dataset:
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