Forward pass computation in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform a forward pass using a PyTorch model.

PyTorch

output = model([1])

Drag options to blanks, or click blank then click option'

Aloss

Bmodel

Coptimizer

Dinput_tensor

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the model itself instead of the input data.

Passing the loss or optimizer instead of input data.

The forward pass requires passing the input tensor to the model to get the output predictions.

2fill in blank

medium

Complete the code to apply the ReLU activation function after the linear layer.

PyTorch

x = torch.nn.functional.[1](linear_layer(input_tensor))

Drag options to blanks, or click blank then click option'

Arelu

Btanh

Csoftmax

Dsigmoid

Attempts:

3 left

💡 Hint

Common Mistakes

Using sigmoid or softmax which are used in different contexts.

Forgetting to apply any activation function.

ReLU is a common activation function used after linear layers to add non-linearity.

3fill in blank

hard

Fix the error in the forward pass by completing the missing method call.

PyTorch

output = model.[1](input_tensor)

Drag options to blanks, or click blank then click option'

Aforward

Bbackward

Ctrain

Deval

Attempts:

3 left

💡 Hint

Common Mistakes

Using backward which is for gradients, not forward pass.

Using train or eval which are mode settings.

The forward method defines how the input data passes through the model layers.

4fill in blank

hard

Fill both blanks to compute the output and apply softmax activation.

PyTorch

logits = model([1])
probabilities = torch.nn.functional.[2](logits, dim=1)

Drag options to blanks, or click blank then click option'

Ainput_tensor

Brelu

Csoftmax

Dsigmoid

Attempts:

3 left

💡 Hint

Common Mistakes

Using ReLU or sigmoid instead of softmax for probabilities.

Passing something other than input tensor to the model.

First, pass the input tensor to the model to get logits. Then apply softmax to convert logits to probabilities.

5fill in blank

hard

Fill all three blanks to perform a forward pass, apply ReLU, and compute loss.

PyTorch

logits = model([1])
activated = torch.nn.functional.[2](logits)
loss = loss_fn(activated, [3])

Drag options to blanks, or click blank then click option'

Ainput_tensor

Brelu

Ctarget_tensor

Dsigmoid

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up input and target tensors.

Using sigmoid instead of ReLU for activation here.

Pass input tensor to model, apply ReLU activation, then compute loss comparing activated output to target tensor.

Practice

1. What does the forward pass in a PyTorch model do?

easy

A. It moves input data through the model to get predictions.

B. It updates the model weights during training.

C. It saves the model to disk.

D. It initializes the model parameters.

Solution

1. Step 1: Understand the role of forward pass

   The forward pass takes input data and processes it through the model layers to produce output predictions.

2. Step 2: Differentiate from other processes

   Updating weights happens in backward pass, saving is separate, and initialization is before training.

3. Final Answer:

   It moves input data through the model to get predictions. -> Option A

4. Quick Check:

   Forward pass = data to predictions [OK]

Hint: Forward pass = input to output predictions [OK]

Common Mistakes:

• Confusing forward pass with weight update
• Thinking forward pass saves the model
• Mixing initialization with forward pass

2. Which of the following is the correct way to run a forward pass on a PyTorch model named model with input tensor x?

easy

A. output = model.forward(x)

B. output = model.predict(x)

C. output = model.run(x)

D. output = model(x)

Solution

1. Step 1: Recall PyTorch model call syntax

   In PyTorch, calling model(x) runs the forward pass internally.

2. Step 2: Identify incorrect options

   Directly calling model.forward(x) is discouraged; model.run and model.predict are not standard methods.

3. Final Answer:

   output = model(x) -> Option D

4. Quick Check:

   Use model(x) to run forward pass [OK]

Hint: Use model(input) to run forward pass [OK]

Common Mistakes:

• Calling model.forward(x) directly
• Using non-existent methods like model.run or model.predict
• Confusing syntax with other frameworks

3. Given the following PyTorch code, what is the output tensor shape?

import torch
import torch.nn as nn

class SimpleNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(4, 2)
    def forward(self, x):
        return self.linear(x)

model = SimpleNet()
x = torch.randn(3, 4)
output = model(x)
print(output.shape)

medium

A. torch.Size([3, 2])

B. torch.Size([2, 3])

C. torch.Size([4, 2])

D. torch.Size([3, 4])

Solution

1. Step 1: Analyze input and layer dimensions

   The input tensor has shape (3, 4), meaning batch size 3 and feature size 4. The linear layer maps 4 features to 2 outputs.

2. Step 2: Compute output shape

   Applying linear layer to each batch item changes feature size from 4 to 2, so output shape is (3, 2).

3. Final Answer:

   torch.Size([3, 2]) -> Option A

4. Quick Check:

   Input batch 3, features 4 -> output features 2 [OK]

Hint: Output shape = (batch, output features) [OK]

Common Mistakes:

• Mixing batch and feature dimensions
• Assuming output shape equals input shape
• Confusing input and output feature sizes

4. Identify the error in this PyTorch forward pass code snippet:

import torch
import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc = nn.Linear(5, 3)
    def forward(self, x):
        return self.fc(x)

model = Net()
x = torch.randn(4, 4)
output = model(x)
print(output)

medium

A. Missing activation function after Linear layer.

B. Model is not moved to GPU before forward pass.

C. Input tensor shape does not match Linear layer input size.

D. Forward method is missing return statement.

Solution

1. Step 1: Check input tensor shape vs Linear layer input

   The Linear layer expects input features of size 5, but input tensor has shape (4, 4), so feature size is 4, which mismatches.

2. Step 2: Confirm other options

   GPU usage is optional, activation functions are not mandatory, and forward method correctly returns output.

3. Final Answer:

   Input tensor shape does not match Linear layer input size. -> Option C

4. Quick Check:

   Input features must match Linear input size [OK]

Hint: Check input feature size matches Linear layer input [OK]

Common Mistakes:

• Ignoring input size mismatch errors
• Assuming GPU is required for forward pass
• Thinking activation is always needed

5. You have a PyTorch model with two linear layers chained in the forward pass:

class ChainNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(3, 4)
        self.fc2 = nn.Linear(4, 2)
    def forward(self, x):
        x = self.fc1(x)
        x = torch.relu(x)
        x = self.fc2(x)
        return x

model = ChainNet()
x = torch.tensor([[1.0, 2.0, 3.0]])
output = model(x)
print(output)

What is the shape of output and why?

hard

A. torch.Size([1, 4]) because the first layer outputs 4 features.

B. torch.Size([1, 2]) because input batch size is 1 and final layer outputs 2 features.

C. torch.Size([3, 2]) because input has 3 features and output has 2 features.

D. torch.Size([4, 2]) because the two layers output sizes multiply.

Solution

1. Step 1: Analyze input and layer sizes

   Input tensor shape is (1, 3) -- batch size 1, feature size 3. First layer maps 3 to 4 features, second layer maps 4 to 2 features.

2. Step 2: Determine output shape after forward pass

   After passing through both layers, output shape is (1, 2) because batch size stays the same and final layer outputs 2 features.

3. Final Answer:

   torch.Size([1, 2]) because input batch size is 1 and final layer outputs 2 features. -> Option B

4. Quick Check:

   Batch size unchanged, output features = final layer output [OK]

Hint: Output shape = (batch size, last layer output features) [OK]

Common Mistakes:

• Multiplying output sizes instead of chaining
• Confusing batch size with feature size
• Ignoring activation does not change shape