Path: blob/main/C1 - Build Basic Generative Adversarial Networks/Week 2/C1_W2_Assignment.ipynb
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Deep Convolutional GAN (DCGAN)
Goal
In this notebook, you're going to create another GAN using the MNIST dataset. You will implement a Deep Convolutional GAN (DCGAN), a very successful and influential GAN model developed in 2015.
Note: here is the paper if you are interested! It might look dense now, but soon you'll be able to understand many parts of it 😃
Learning Objectives
Get hands-on experience making a widely used GAN: Deep Convolutional GAN (DCGAN).
Train a powerful generative model.
Figure: Architectural drawing of a generator from DCGAN from Radford et al (2016).
Getting Started
DCGAN
Here are the main features of DCGAN (don't worry about memorizing these, you will be guided through the implementation!):
Use convolutions without any pooling layers
Use batchnorm in both the generator and the discriminator
Don't use fully connected hidden layers
Use ReLU activation in the generator for all layers except for the output, which uses a Tanh activation.
Use LeakyReLU activation in the discriminator for all layers except for the output, which does not use an activation
You will begin by importing some useful packages and data that will help you create your GAN. You are also provided a visualizer function to help see the images your GAN will create.
Generator
The first component you will make is the generator. You may notice that instead of passing in the image dimension, you will pass the number of image channels to the generator. This is because with DCGAN, you use convolutions which don’t depend on the number of pixels on an image. However, the number of channels is important to determine the size of the filters.
You will build a generator using 4 layers (3 hidden layers + 1 output layer). As before, you will need to write a function to create a single block for the generator's neural network.
Since in DCGAN the activation function will be different for the output layer, you will need to check what layer is being created. You are supplied with some tests following the code cell so you can see if you're on the right track!
At the end of the generator class, you are given a forward pass function that takes in a noise vector and generates an image of the output dimension using your neural network. You are also given a function to create a noise vector. These functions are the same as the ones from the last assignment.
Optional hint for make_gen_block
You'll find nn.ConvTranspose2d and nn.BatchNorm2d useful!
Here's the test for your generator block:
Discriminator
The second component you need to create is the discriminator.
You will use 3 layers in your discriminator's neural network. Like with the generator, you will need create the function to create a single neural network block for the discriminator.
There are also tests at the end for you to use.
Optional hint for make_disc_block
You'll find nn.Conv2d, nn.BatchNorm2d, and nn.LeakyReLU useful!
Here's a test for your discriminator block:
Training
Now you can put it all together! Remember that these are your parameters:
criterion: the loss function
n_epochs: the number of times you iterate through the entire dataset when training
z_dim: the dimension of the noise vector
display_step: how often to display/visualize the images
batch_size: the number of images per forward/backward pass
lr: the learning rate
beta_1, beta_2: the momentum term
device: the device type
Then, you can initialize your generator, discriminator, and optimizers.
Finally, you can train your GAN! For each epoch, you will process the entire dataset in batches. For every batch, you will update the discriminator and generator. Then, you can see DCGAN's results!
Here's roughly the progression you should be expecting. On GPU this takes about 30 seconds per thousand steps. On CPU, this can take about 8 hours per thousand steps. You might notice that in the image of Step 5000, the generator is disproprotionately producing things that look like ones. If the discriminator didn't learn to detect this imbalance quickly enough, then the generator could just produce more ones. As a result, it may have ended up tricking the discriminator so well that there would be no more improvement, known as mode collapse: 
