Training a DCGAN using PyTorch

In this section, we will build, train, and test a DCGAN model using PyTorch in the form of an exercise. We will use an image dataset to train the model and test how well the generator of the trained DCGAN model performs when producing fake images.

Defining the generator

In the following exercise, we will only show the important parts of the code for demonstration purposes. In order to access the full code, you can refer to our github repository [9.3] :

1. First, we need to import the required libraries, as follows:

import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.autograd import Variable
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torchvision import datasets

In this exercise, we only need torch and torchvision to build the DCGAN model.

1. After importing the libraries, we specify some model hyperparameters, as shown...