Deep Learning with PyTorch

Deep convolutional GAN

In this section, we will implement different parts of training a GAN architecture, based on the DCGAN paper I mentioned in the preceding information box. Some of the important parts of training a DCGAN include:

• A generator network, which maps a latent vector (list of numbers) of some fixed dimension to images of some shape. In our implementation, the shape is (3, 64, 64).
• A discriminator network, which takes as input an image generated by the generator or from the actual dataset, and maps to that a score estimating if the input image is real or fake.
• Defining loss functions for generator and discriminator.
• Defining an optimizer.
• Training a GAN.

Let's explore each of these sections in detail. The implementation is ...