Generator and discriminator in PyTorch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a simple Generator forward pass

Given the following PyTorch Generator model and input noise tensor, what is the shape of the output tensor after the forward pass?

PyTorch

import torch
import torch.nn as nn

class Generator(nn.Module):
    def __init__(self):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(100, 256),
            nn.ReLU(),
            nn.Linear(256, 784),
            nn.Tanh()
        )

    def forward(self, x):
        return self.net(x)

G = Generator()
noise = torch.randn(16, 100)
output = G(noise)
output_shape = output.shape

A (16, 784)

B (100, 256)

C (16, 256)

D (784, 16)

💡 Hint

The Generator transforms noise vectors of size 100 into images flattened to 784 pixels.

The input noise has batch size 16 and 100 features. The Generator outputs a tensor with shape (batch_size, 784) because the last Linear layer outputs 784 features per sample.

❓ Model Choice

intermediate

Choosing the correct Discriminator output layer

Which of the following PyTorch Discriminator output layers is correct for producing a single probability score for real/fake classification?

A nn.Linear(512, 2) followed by nn.LogSoftmax(dim=1)

B nn.Linear(512, 10) followed by nn.Softmax(dim=1)

C nn.Linear(512, 1) with no activation

D nn.Linear(512, 1) followed by nn.Sigmoid()

💡 Hint

The Discriminator outputs a probability between 0 and 1 for real or fake.

The Discriminator should output a single value per input, representing the probability of being real. Using nn.Sigmoid() converts the output to a probability between 0 and 1.

❓ Hyperparameter

advanced

Effect of learning rate on GAN training stability

In training a GAN, what is the most likely effect of setting the learning rate too high for both Generator and Discriminator?

A Training becomes unstable with oscillations and mode collapse

B Training converges faster and produces better images

C The Discriminator ignores fake images and outputs constant values

D The Generator stops learning but the Discriminator improves steadily

💡 Hint

High learning rates can cause the model weights to jump too much during updates.

Too high learning rates cause unstable training in GANs, leading to oscillations and mode collapse where the Generator produces limited variety.

❓ Metrics

advanced

Choosing the right loss function for GAN Discriminator

Which loss function is commonly used for the Discriminator in a vanilla GAN to distinguish real from fake images?

A Hinge Loss

B Mean Squared Error Loss

C Binary Cross Entropy Loss

D Categorical Cross Entropy Loss

💡 Hint

The Discriminator outputs a probability for two classes: real or fake.

Binary Cross Entropy Loss is used for binary classification tasks like real vs fake in vanilla GANs.

🔧 Debug

expert

Identifying the cause of NaN loss in GAN training

During GAN training, the Generator loss suddenly becomes NaN. Which of the following is the most likely cause?

A The batch size is too large causing memory overflow

B The Discriminator outputs values outside the range [0,1] causing log(0) in loss

C The Generator uses ReLU activation in the output layer instead of Tanh

D The optimizer learning rate is set to zero

💡 Hint

Check if the Discriminator output is properly squashed before computing log loss.

If the Discriminator outputs values outside [0,1], the log in Binary Cross Entropy can be log(0) causing NaN loss.

Practice

1. What is the main role of the generator in a GAN (Generative Adversarial Network)?

easy

A. To classify data as real or fake

B. To optimize the loss function of the discriminator

C. To create fake data from random noise

D. To preprocess the input data

Solution

1. Step 1: Understand the generator's purpose

   The generator's job is to produce new data that looks like the real data by starting from random noise.

2. Step 2: Differentiate from discriminator's role

   The discriminator's role is to tell real data from fake, not to create data.

3. Final Answer:

   To create fake data from random noise -> Option C

4. Quick Check:

   Generator creates fake data = B [OK]

Hint: Generator makes fake data; discriminator checks it [OK]

Common Mistakes:

• Confusing generator with discriminator
• Thinking generator classifies data
• Assuming generator preprocesses data

2. Which of the following is the correct way to define a simple discriminator model in PyTorch?

easy

A. class Discriminator(nn.Module):
def __init__(self):
self.fc = nn.Linear(784, 1)
def forward(self, x):
return torch.sigmoid(self.fc(x))

B. def Discriminator():
return nn.Linear(784, 1)

C. class Discriminator:
def __init__(self):
self.fc = nn.Linear(784, 1)
def forward(self, x):
return torch.sigmoid(self.fc(x))

D. class Discriminator(nn.Module):
def __init__(self):
super().__init__()
self.fc = nn.Linear(784, 1)
def forward(self, x):
return torch.sigmoid(self.fc(x))

Solution

1. Step 1: Check PyTorch model class structure

   A PyTorch model must inherit from nn.Module and call super().__init__() in __init__.

2. Step 2: Verify forward method and layers

   class Discriminator(nn.Module): def __init__(self): super().__init__() self.fc = nn.Linear(784, 1) def forward(self, x): return torch.sigmoid(self.fc(x)) correctly defines the class with inheritance, super call, layer, and forward method.

3. Final Answer:

   Correct PyTorch discriminator class with inheritance and super call -> Option D

4. Quick Check:

   Proper nn.Module subclass = A [OK]

Hint: PyTorch models inherit nn.Module and call super().__init__() [OK]

Common Mistakes:

• Forgetting to inherit nn.Module
• Missing super().__init__() call
• Defining forward outside class

3. Given this PyTorch code snippet for a generator and discriminator, what will be the output shape of fake_output?

noise = torch.randn(16, 100)
generator = Generator()
fake_data = generator(noise)
discriminator = Discriminator()
fake_output = discriminator(fake_data)

Assume generator outputs shape (16, 784) and discriminator outputs shape (batch_size, 1).

medium

A. torch.Size([16, 1])

B. torch.Size([16, 784])

C. torch.Size([100, 16])

D. torch.Size([1, 16])

Solution

1. Step 1: Understand generator output shape

   The generator outputs fake data with shape (16, 784), meaning batch size 16 and 784 features.

2. Step 2: Understand discriminator input and output shape

   The discriminator takes input of shape (16, 784) and outputs shape (16, 1), one score per sample.

3. Final Answer:

   torch.Size([16, 1]) -> Option A

4. Quick Check:

   Discriminator output shape = (batch_size, 1) = A [OK]

Hint: Discriminator outputs one score per input sample [OK]

Common Mistakes:

• Confusing input and output shapes
• Mixing batch size and feature dimensions
• Assuming output shape matches noise size

4. Identify the error in this discriminator training step code snippet:

optimizer.zero_grad()
output = discriminator(fake_data)
loss = criterion(output, real_labels)
loss.backward()
optimizer.step()

Assuming fake_data is generated by the generator and real_labels are labels for real data.

medium

A. Using real_labels for fake_data loss calculation

B. Not calling optimizer.zero_grad() before backward

C. Using fake_data instead of real_data for discriminator input

D. Missing loss.backward() call

Solution

1. Step 1: Check input and labels for discriminator

   The discriminator should compare fake_data with fake labels, not real_labels.

2. Step 2: Identify label mismatch in loss calculation

   Using real_labels with fake_data causes wrong loss and training signals.

3. Final Answer:

   Using real_labels for fake_data loss calculation -> Option A

4. Quick Check:

   Fake data needs fake labels = C [OK]

Hint: Match fake data with fake labels during discriminator training [OK]

Common Mistakes:

• Mixing real and fake labels
• Skipping zero_grad() call
• Forgetting loss.backward()

5. You want to improve your GAN training stability. Which technique involving the discriminator can help reduce mode collapse and improve generator quality?

hard

A. Use dropout only in the generator

B. Use label smoothing on discriminator targets

C. Remove the discriminator and train generator alone

D. Increase generator learning rate only

Solution

1. Step 1: Understand mode collapse and training stability

   Mode collapse happens when generator produces limited outputs; discriminator tricks help prevent it.

2. Step 2: Identify label smoothing effect

   Label smoothing softens discriminator targets, preventing it from becoming too confident and stabilizing training.

3. Final Answer:

   Use label smoothing on discriminator targets -> Option B

4. Quick Check:

   Label smoothing stabilizes discriminator = D [OK]

Hint: Smooth discriminator labels to stabilize GAN training [OK]

Common Mistakes:

• Ignoring discriminator role in stability
• Changing only generator learning rate
• Removing discriminator breaks GAN