Flatten layer in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Flatten layer in PyTorch.

PyTorch

import torch.nn as nn

flatten = nn.[1]()

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

AConv2d

BLinear

CFlatten

DReLU

Attempts:

3 left

💡 Hint

Common Mistakes

Using Linear instead of Flatten

Using Conv2d or ReLU which are not flattening layers

The Flatten layer reshapes the input tensor into a 2D tensor, keeping the batch size and flattening the rest.

2fill in blank

medium

Complete the code to flatten the input tensor except the batch dimension.

PyTorch

import torch

x = torch.randn(10, 3, 28, 28)  # batch of 10 images
x_flat = x.[1](start_dim=1)

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

Aflatten

Bview

Creshape

Dsqueeze

Attempts:

3 left

💡 Hint

Common Mistakes

Using view or reshape without specifying correct shape

Using squeeze which removes dimensions of size 1

The flatten method with start_dim=1 flattens all dimensions except the batch dimension.

3fill in blank

hard

Fix the error in the code to correctly flatten the input tensor.

PyTorch

import torch
import torch.nn as nn

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.flatten = nn.Flatten()

    def forward(self, x):
        x = self.flatten(x)
        x = x.[1](1)
        return x

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

Aview

Bunsqueeze

Creshape

Dflatten

Attempts:

3 left

💡 Hint

Common Mistakes

Using view or reshape without shape argument

Using unsqueeze which adds dimensions instead of flattening

The flatten method with argument 1 flattens all dimensions except the batch dimension. Using flatten again is correct here.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length if length is greater than 3.

PyTorch

words = ['apple', 'cat', 'banana', 'dog']
lengths = {word: [1] for word in words if [2]

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

Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of len(word) as value

Using word > 3 which compares string to int

The dictionary comprehension maps each word to its length using len(word). The condition len(word) > 3 filters words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths if length is less than 6.

PyTorch

words = ['apple', 'cat', 'banana', 'dog']
result = { [1]: [2] for word in words if [3] }

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

Aword.upper()

Blen(word)

Clen(word) < 6

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.lower() instead of word.upper()

Using wrong condition like len(word) > 6

The dictionary comprehension maps the uppercase version of each word to its length. The condition filters words with length less than 6.

Practice

1. What is the main purpose of the Flatten layer in PyTorch?

easy

A. To convert multi-dimensional input into a 1D vector per sample

B. To increase the number of channels in the input

C. To reduce the batch size during training

D. To apply activation functions element-wise

Solution

1. Step 1: Understand the role of Flatten layer

   The Flatten layer reshapes input data from multiple dimensions into a single long vector for each example, keeping batch size unchanged.

2. Step 2: Compare options with this role

   Only To convert multi-dimensional input into a 1D vector per sample describes this behavior correctly. Other options describe unrelated operations.

3. Final Answer:

   To convert multi-dimensional input into a 1D vector per sample -> Option A

4. Quick Check:

   Flatten layer = reshape to 1D vector [OK]

Hint: Flatten means reshape to 1D vector per example [OK]

Common Mistakes:

• Thinking Flatten changes batch size
• Confusing Flatten with convolution or activation
• Assuming Flatten adds or removes channels

2. Which of the following is the correct way to add a Flatten layer in a PyTorch nn.Sequential model?

easy

A. nn.Flatten(dim=0)

B. nn.Flatten(input_shape=(1, 28, 28))

C. nn.Flatten(start_dim=1)

D. nn.Flatten(start_dim=0)

Solution

1. Step 1: Recall PyTorch Flatten syntax

   PyTorch's nn.Flatten takes optional arguments start_dim and end_dim. By default, start_dim=1 flattens all dimensions except batch.

2. Step 2: Evaluate options

   nn.Flatten(input_shape=(1, 28, 28)) is invalid syntax. nn.Flatten(dim=0) uses unexpected keyword argument 'dim'. nn.Flatten(start_dim=0) flattens starting at batch dim (0), which is incorrect. nn.Flatten(start_dim=1) correctly specifies start_dim=1.

3. Final Answer:

   nn.Flatten(start_dim=1) -> Option C

4. Quick Check:

   Flatten start_dim=1 keeps batch dim [OK]

Hint: Use nn.Flatten(start_dim=1) to keep batch size [OK]

Common Mistakes:

• Using start_dim=0 which flattens batch dimension
• Passing input_shape argument (not supported)
• Using invalid keyword arguments like 'dim'

3. What is the output shape after applying nn.Flatten() to a tensor of shape (16, 3, 28, 28)?

medium

A. (16, 3, 28, 28)

B. (3, 28, 28)

C. (16, 28, 28)

D. (16, 2352)

Solution

1. Step 1: Understand input tensor shape

   The input tensor has shape (batch=16, channels=3, height=28, width=28).

2. Step 2: Calculate flattened size per example

   Flatten keeps batch size (16) and flattens remaining dims: 3*28*28 = 2352.

3. Final Answer:

   (16, 2352) -> Option D

4. Quick Check:

   Flatten output shape = (batch, product of other dims) [OK]

Hint: Multiply all dims except batch for flattened size [OK]

Common Mistakes:

• Forgetting to keep batch size dimension
• Using original shape without flattening
• Dropping batch dimension by mistake

4. Given the code below, what is the error and how to fix it?

import torch
import torch.nn as nn

model = nn.Sequential(
    nn.Conv2d(1, 10, kernel_size=3),
    nn.Flatten(start_dim=0),
    nn.Linear(10*26*26, 100)
)

medium

A. Conv2d output channels must match Linear input features

B. Flatten start_dim=0 flattens batch dimension; use start_dim=1 instead

C. Linear input size is incorrect; should be 10*28*28

D. Missing activation function after Conv2d

Solution

1. Step 1: Identify Flatten usage error

   Using start_dim=0 flattens batch dimension, which breaks batch processing.

2. Step 2: Correct Flatten start_dim

   Change start_dim=0 to start_dim=1 to keep batch size intact and flatten only feature dims.

3. Final Answer:

   Flatten start_dim=0 flattens batch dimension; use start_dim=1 instead -> Option B

4. Quick Check:

   Flatten start_dim=1 keeps batch size [OK]

Hint: Never flatten batch dimension; start_dim=1 keeps batch [OK]

Common Mistakes:

• Setting start_dim=0 flattens batch dimension
• Ignoring shape mismatch errors in Linear layer
• Assuming activation functions fix shape errors

5. You have a batch of images with shape (32, 3, 64, 64). You want to connect a convolutional network to a fully connected layer. Which PyTorch code correctly flattens the output before the dense layer?

hard

A. nn.Sequential(nn.Conv2d(3, 16, 3), nn.Flatten(start_dim=1), nn.Linear(16*62*62, 128))

B. nn.Sequential(nn.Conv2d(3, 16, 3), nn.Flatten(start_dim=0), nn.Linear(16*62*62, 128))

C. nn.Sequential(nn.Conv2d(3, 16, 3), nn.Flatten(), nn.Linear(3*64*64, 128))

D. nn.Sequential(nn.Conv2d(3, 16, 3), nn.Flatten(start_dim=1), nn.Linear(3*64*64, 128))

Solution

1. Step 1: Calculate output shape after Conv2d

   Conv2d with kernel_size=3 reduces each spatial dim by 2: 64 -> 62. Output shape: (32, 16, 62, 62).

2. Step 2: Flatten correctly and match Linear input

   Flatten with start_dim=1 keeps batch size 32 and flattens (16*62*62). Linear input features must match this product.

3. Final Answer:

   nn.Sequential(nn.Conv2d(3, 16, 3), nn.Flatten(start_dim=1), nn.Linear(16*62*62, 128)) -> Option A

4. Quick Check:

   Flatten start_dim=1 + correct Linear input size [OK]

Hint: Calculate Conv output size, flatten from dim=1, match Linear input [OK]

Common Mistakes:

• Flattening batch dimension (start_dim=0)
• Using wrong Linear input size
• Assuming default flatten matches input shape