Creating images using DCGAN

A DCGAN is a direct extension of the GAN model discussed in the previous section, except that it explicitly uses the convolutional and convolutional-transpose layers in the discriminator and generator respectively. DCGAN was first proposed in a paper, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, by Alec Radford, Luke Metz, and Soumith Chintala:

Figure 6.13 – A DCGAN architecture overview

The DCGAN architecture basically consists of 5 layers of convolution and 5 layers for transposed convolution. There is no fully connected layer in this architecture. We will also use a learning rate of 0.0002 for training the model.

We can also take a more in-depth look at the generator architecture of the DCGAN to see how it works:

Figure 6.14 – The DCGAN generator architecture from the paper

It can be observed from the DCGAN generator architecture diagram...