Autoencoder architecture in PyTorch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Model Choice

intermediate

Choose the correct autoencoder architecture

Which of the following PyTorch model architectures correctly implements a simple autoencoder with one hidden layer in the encoder and decoder?

A

class Autoencoder(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Linear(784, 128),
            nn.ReLU()
        )
        self.decoder = nn.Sequential(
            nn.Linear(128, 784),
            nn.Sigmoid()
        )
    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

B

class Autoencoder(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Linear(784, 128),
            nn.Sigmoid()
        )
        self.decoder = nn.Sequential(
            nn.Linear(128, 784),
            nn.ReLU()
        )
    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

C

class Autoencoder(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Linear(784, 128),
            nn.Tanh()
        )
        self.decoder = nn.Sequential(
            nn.Linear(128, 784),
            nn.Tanh()
        )
    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

D

class Autoencoder(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Linear(784, 128),
            nn.ReLU()
        )
        self.decoder = nn.Sequential(
            nn.Linear(128, 784),
            nn.ReLU()
        )
    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

💡 Hint

Remember the decoder output activation should be sigmoid for normalized input images.

Option A uses ReLU in encoder and Sigmoid in decoder output which is standard for image reconstruction between 0 and 1. Option A reverses activations causing poor output range. Option A uses Tanh which outputs between -1 and 1, not suitable for normalized images. Option A uses ReLU in decoder output which can produce values outside [0,1].

❓ Predict Output

intermediate

Output shape of encoded representation

Given the following autoencoder model, what is the shape of the encoded output for an input batch of shape (64, 784)?

PyTorch

class Autoencoder(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Linear(784, 256),
            nn.ReLU(),
            nn.Linear(256, 64),
            nn.ReLU()
        )
        self.decoder = nn.Sequential(
            nn.Linear(64, 256),
            nn.ReLU(),
            nn.Linear(256, 784),
            nn.Sigmoid()
        )
    def forward(self, x):
        encoded = self.encoder(x)
        decoded = self.decoder(encoded)
        return decoded

model = Autoencoder()
input_batch = torch.randn(64, 784)
encoded_output = model.encoder(input_batch)
encoded_output.shape

A (256, 64)

B (64, 256)

C (64, 64)

D (784, 64)

💡 Hint

Look at the last Linear layer in the encoder.

The encoder ends with a Linear layer outputting 64 features. The batch size is 64, so output shape is (64, 64).

❓ Hyperparameter

advanced

Choosing the latent space size

In an autoencoder, what is the main effect of increasing the size of the latent space (the encoded representation dimension)?

A It always improves compression and reduces reconstruction error.

B It increases the model's capacity to reconstruct input but may reduce compression and generalization.

C It decreases the model's capacity and leads to underfitting.

D It has no effect on model performance or compression.

💡 Hint

Think about the trade-off between compression and reconstruction quality.

A larger latent space allows the model to store more information, improving reconstruction but reducing compression and possibly hurting generalization.

❓ Metrics

advanced

Choosing the loss function for image autoencoder

Which loss function is most appropriate for training an autoencoder on normalized grayscale images with pixel values between 0 and 1?

A Cosine similarity loss

B Cross Entropy loss

C Hinge loss

D Mean Squared Error (MSE) loss

💡 Hint

Consider the type of output and target values.

MSE loss measures the average squared difference between reconstructed and original pixel values, suitable for continuous values between 0 and 1.

🔧 Debug

expert

Identify the cause of training loss not decreasing

A user trains an autoencoder on MNIST but notices the training loss does not decrease at all. The model code is below. What is the most likely cause?

PyTorch

class Autoencoder(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Linear(784, 128),
            nn.ReLU()
        )
        self.decoder = nn.Sequential(
            nn.Linear(128, 784),
            nn.ReLU()
        )
    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

model = Autoencoder()
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

for data, _ in dataloader:
    data = data.view(data.size(0), -1)
    optimizer.zero_grad()
    output = model(data)
    loss = criterion(output, data)
    loss.backward()
    optimizer.step()

A Decoder uses ReLU activation causing outputs to be >1, but input pixels are between 0 and 1, leading to poor loss optimization.

B The optimizer learning rate is too high causing divergence.

C The input data is not flattened before feeding to the model.

D The loss function is inappropriate for reconstruction tasks.

💡 Hint

Check the decoder output activation and input data range.

ReLU in decoder output can produce values greater than 1, while input pixels are between 0 and 1. This mismatch makes MSE loss hard to optimize.

Practice

1. What is the main purpose of the encoder part in an autoencoder?

easy

A. To generate new random data samples

B. To reconstruct the original input from compressed data

C. To classify the input data into categories

D. To compress the input data into a smaller representation

Solution

1. Step 1: Understand the role of encoder

   The encoder compresses input data into a smaller form called latent space or code.

2. Step 2: Differentiate encoder from decoder

   The decoder rebuilds the original data from this compressed form, not the encoder.

3. Final Answer:

   To compress the input data into a smaller representation -> Option D

4. Quick Check:

   Encoder compresses data = A [OK]

Hint: Encoder compresses, decoder reconstructs [OK]

Common Mistakes:

• Confusing encoder with decoder
• Thinking encoder classifies data
• Assuming encoder generates new data

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

easy

A. self.encoder = nn.Linear(input_dim, hidden_dim)

B. self.encoder = nn.Conv2d(hidden_dim, input_dim)

C. self.encoder = nn.ReLU(input_dim, hidden_dim)

D. self.encoder = nn.MaxPool2d(input_dim, hidden_dim)

Solution

1. Step 1: Identify correct PyTorch layer for encoding

   Encoding usually uses a linear layer to reduce dimensions: nn.Linear(input_dim, hidden_dim).

2. Step 2: Check other options for correctness

   Conv2d requires 4D input and different parameters; ReLU is an activation, not a layer; MaxPool2d is for downsampling images, not linear encoding.

3. Final Answer:

   self.encoder = nn.Linear(input_dim, hidden_dim) -> Option A

4. Quick Check:

   Linear layer for encoding = B [OK]

Hint: Use nn.Linear for encoder layers in simple autoencoders [OK]

Common Mistakes:

• Using activation functions as layers
• Confusing convolution with linear layers
• Wrong parameter order in layers

3. Given this PyTorch autoencoder snippet, what is the shape of the output tensor after encoding if input shape is (batch_size=10, features=784)?

class Autoencoder(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Linear(784, 32)
    def forward(self, x):
        x = self.encoder(x)
        return x
model = Autoencoder()
input_tensor = torch.randn(10, 784)
output = model(input_tensor)

medium

A. (784, 32)

B. (10, 32)

C. (10, 784)

D. (32, 784)

Solution

1. Step 1: Understand input and encoder layer

   Input tensor shape is (10, 784). Encoder is nn.Linear(784, 32), so it maps 784 features to 32 features per sample.

2. Step 2: Calculate output shape after encoder

   Output shape keeps batch size 10, features become 32, so output shape is (10, 32).

3. Final Answer:

   (10, 32) -> Option B

4. Quick Check:

   Batch size unchanged, features to 32 = D [OK]

Hint: Linear layer output shape = (batch_size, output_features) [OK]

Common Mistakes:

• Swapping batch size and feature dimensions
• Confusing input and output feature sizes
• Assuming shape changes batch size

4. Identify the error in this autoencoder decoder definition:

class Decoder(nn.Module):
    def __init__(self):
        super().__init__()
        self.decoder = nn.Linear(32, 784)
    def forward(self, x):
        x = self.decoder(x)
        x = nn.ReLU(x)
        return x

medium

A. Missing activation function after linear layer

B. The decoder input and output dimensions are swapped

C. nn.ReLU is used incorrectly as a function instead of a layer

D. The forward method should return x before applying ReLU

Solution

1. Step 1: Check usage of nn.ReLU

   nn.ReLU is a class and must be instantiated before use, e.g. nn.ReLU()(x), not nn.ReLU(x).

2. Step 2: Verify decoder dimensions and flow

   Decoder maps 32 to 784 correctly, and activation after linear is common, so no issue there.

3. Final Answer:

   nn.ReLU is used incorrectly as a function instead of a layer -> Option C

4. Quick Check:

   Activation must be instantiated = C [OK]

Hint: Instantiate nn.ReLU() before calling it on tensor [OK]

Common Mistakes:

• Calling nn.ReLU as a function directly
• Swapping input/output dims in Linear
• Forgetting to apply activation

5. You want to build an autoencoder that compresses 1000-dimensional input to 10 dimensions and then reconstructs it. Which architecture below correctly defines encoder and decoder layers in PyTorch?

hard

A. ""class Autoencoder(nn.Module):
def __init__(self):
super().__init__()
self.encoder = nn.Sequential(
nn.Linear(1000, 100),
nn.ReLU(),
nn.Linear(100, 10)
)
self.decoder = nn.Sequential(
nn.Linear(10, 100),
nn.ReLU(),
nn.Linear(100, 1000),
nn.Sigmoid()
)
def forward(self, x):
x = self.encoder(x)
x = self.decoder(x)
return x"""

B. ""class Autoencoder(nn.Module):
def __init__(self):
super().__init__()
self.encoder = nn.Sequential(
nn.Linear(10, 100),
nn.ReLU(),
nn.Linear(100, 1000)
)
self.decoder = nn.Sequential(
nn.Linear(1000, 100),
nn.ReLU(),
nn.Linear(100, 10)
)
def forward(self, x):
x = self.encoder(x)
x = self.decoder(x)
return x"""

C. ""class Autoencoder(nn.Module):
def __init__(self):
super().__init__()
self.encoder = nn.Sequential(
nn.Linear(1000, 20),
nn.ReLU()
)
self.decoder = nn.Sequential(
nn.Linear(20, 1000),
nn.Sigmoid()
)
def forward(self, x):
x = self.encoder(x)
x = self.decoder(x)
return x"""

D. ""class Autoencoder(nn.Module):
def __init__(self):
super().__init__()
self.encoder = nn.Sequential(
nn.Linear(1000, 500),
nn.ReLU(),
nn.Linear(500, 50)
)
self.decoder = nn.Sequential(
nn.Linear(50, 500),
nn.ReLU(),
nn.Linear(500, 1000),
nn.Sigmoid()
)
def forward(self, x):
x = self.encoder(x)
x = self.decoder(x)
return x"""

Solution

1. Step 1: Check encoder input and output sizes

   Input is 1000, compressed to 10. ""class Autoencoder(nn.Module): def __init__(self): super().__init__() self.encoder = nn.Sequential( nn.Linear(1000, 100), nn.ReLU(), nn.Linear(100, 10) ) self.decoder = nn.Sequential( nn.Linear(10, 100), nn.ReLU(), nn.Linear(100, 1000), nn.Sigmoid() ) def forward(self, x): x = self.encoder(x) x = self.decoder(x) return x""" correctly reduces 1000 -> 100 -> 10. Others have wrong input/output sizes.

2. Step 2: Check decoder mirrors encoder

   Decoder in A expands 10 -> 100 -> 1000, matching encoder compression steps. ""class Autoencoder(nn.Module): def __init__(self): super().__init__() self.encoder = nn.Sequential( nn.Linear(1000, 500), nn.ReLU(), nn.Linear(500, 50) ) self.decoder = nn.Sequential( nn.Linear(50, 500), nn.ReLU(), nn.Linear(500, 1000), nn.Sigmoid() ) def forward(self, x): x = self.encoder(x) x = self.decoder(x) return x""" has wrong final layer input size (50 to 1000 is correct, but encoder compresses to 50, not 10). Options B and C have reversed or incorrect sizes.

3. Final Answer:

   Option A correctly defines encoder and decoder layers for 1000 to 10 compression and reconstruction -> Option A

4. Quick Check:

   Encoder compresses 1000 to 10, decoder reverses = A [OK]

Hint: Encoder reduces dims stepwise; decoder reverses steps [OK]

Common Mistakes:

• Swapping input/output sizes in layers
• Skipping intermediate compression layers
• Mismatching encoder and decoder sizes