DataLoader basics in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a DataLoader for the dataset with batch size 4.

PyTorch

from torch.utils.data import DataLoader

dataloader = DataLoader(dataset, batch_size=[1])

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

A4

B16

C1

D8

Attempts:

3 left

💡 Hint

Common Mistakes

Using batch_size larger than dataset size causes errors.

Forgetting to set batch_size causes default of 1.

The batch size controls how many samples are loaded per batch. Here, batch_size=4 means each batch has 4 samples.

2fill in blank

medium

Complete the code to shuffle the dataset in the DataLoader.

PyTorch

dataloader = DataLoader(dataset, batch_size=4, shuffle=[1])

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

AFalse

BTrue

C0

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Setting shuffle to False causes data to be loaded in the same order every time.

Using None or 0 for shuffle causes errors.

Setting shuffle=True randomizes the order of samples each epoch, which helps training.

3fill in blank

hard

Fix the error in the code to correctly iterate over the DataLoader batches.

PyTorch

for [1] in dataloader:
    inputs, labels = batch
    print(inputs.shape, labels.shape)

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

Alabels

Bdataloader

Cinputs

Dbatch

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'inputs' or 'labels' as loop variable causes unpacking errors.

Using 'dataloader' as loop variable causes infinite loop.

Each iteration returns a batch, so the loop variable should be 'batch' to unpack inputs and labels.

4fill in blank

hard

Fill both blanks to create a DataLoader with batch size 8 and no shuffling.

PyTorch

dataloader = DataLoader(dataset, batch_size=[1], shuffle=[2])

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

A8

BTrue

CFalse

D16

Attempts:

3 left

💡 Hint

Common Mistakes

Using shuffle=True when no shuffling is wanted.

Setting batch size too large for dataset.

Batch size 8 loads 8 samples per batch. shuffle=False means data order stays the same.

5fill in blank

hard

Fill all three blanks to create a DataLoader with batch size 16, shuffling enabled, and 2 worker threads.

PyTorch

dataloader = DataLoader(dataset, batch_size=[1], shuffle=[2], num_workers=[3])

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

A4

BTrue

C2

D16

Attempts:

3 left

💡 Hint

Common Mistakes

Setting num_workers to 0 disables multi-threading.

Using shuffle=False when random order is needed.

Batch size 16 loads 16 samples per batch. shuffle=True randomizes data order. num_workers=2 uses two threads to load data faster.

Practice

1. What is the main purpose of using DataLoader in PyTorch?

easy

A. To split the dataset into batches and optionally shuffle it for training

B. To define the neural network architecture

C. To calculate the loss during training

D. To save the trained model to disk

Solution

1. Step 1: Understand DataLoader's role

   DataLoader helps by splitting data into smaller batches and can shuffle data to improve training.

2. Step 2: Compare options

   Options B, C, and D relate to other parts of training, not data loading.

3. Final Answer:

   To split the dataset into batches and optionally shuffle it for training -> Option A

4. Quick Check:

   DataLoader = batching and shuffling [OK]

Hint: DataLoader = batch + shuffle, not model or loss [OK]

Common Mistakes:

• Confusing DataLoader with model definition
• Thinking DataLoader calculates loss
• Assuming DataLoader saves models

2. Which of the following is the correct way to create a DataLoader for a dataset named my_dataset with batch size 32 and shuffling enabled?

easy

A. loader = DataLoader(my_dataset, batch_size=32, shuffle=True)

B. loader = DataLoader(my_dataset, batch=32, shuffle=True)

C. loader = DataLoader(my_dataset, batch_size=32, shuffle=False)

D. loader = DataLoader(my_dataset, size=32, shuffle=True)

Solution

1. Step 1: Recall DataLoader parameters

   The correct parameter for batch size is batch_size, and shuffle is a boolean.

2. Step 2: Check each option

   loader = DataLoader(my_dataset, batch_size=32, shuffle=True) uses correct parameter names and values. Options B and D use wrong parameter names. loader = DataLoader(my_dataset, batch_size=32, shuffle=False) disables shuffling.

3. Final Answer:

   loader = DataLoader(my_dataset, batch_size=32, shuffle=True) -> Option A

4. Quick Check:

   batch_size and shuffle=True correct [OK]

Hint: Use batch_size= and shuffle= for DataLoader [OK]

Common Mistakes:

• Using batch instead of batch_size
• Setting shuffle to False when shuffling is needed
• Using size instead of batch_size

3. Given the following code, what will be the output printed?

from torch.utils.data import DataLoader, TensorDataset
import torch

x = torch.arange(10)
y = torch.arange(10) * 2
dataset = TensorDataset(x, y)
loader = DataLoader(dataset, batch_size=4, shuffle=False)

for batch in loader:
    print(batch)

medium

A. Only one batch with all data

B. (tensor([0, 1, 2, 3]), tensor([0, 2, 4, 6])) then (tensor([4, 5, 6, 7]), tensor([8, 10, 12, 14])) then (tensor([8, 9]), tensor([16, 18])) but shuffled

C. Error because shuffle=False is invalid

D. (tensor([0, 1, 2, 3]), tensor([0, 2, 4, 6])) then (tensor([4, 5, 6, 7]), tensor([8, 10, 12, 14])) then (tensor([8, 9]), tensor([16, 18]))

Solution

1. Step 1: Understand DataLoader batching without shuffle

   DataLoader splits data into batches of size 4, no shuffling means order is preserved.

2. Step 2: Calculate batches

   Dataset has 10 items, batches are: first 4 (0-3), second 4 (4-7), last 2 (8-9). Corresponding y values are double x.

3. Final Answer:

   (tensor([0, 1, 2, 3]), tensor([0, 2, 4, 6])) then (tensor([4, 5, 6, 7]), tensor([8, 10, 12, 14])) then (tensor([8, 9]), tensor([16, 18])) -> Option D

4. Quick Check:

   Batch size 4, no shuffle = ordered batches [OK]

Hint: No shuffle means batches keep original order [OK]

Common Mistakes:

• Assuming shuffle=True by default
• Expecting all data in one batch
• Thinking shuffle=False causes error

4. What is wrong with this DataLoader usage?

from torch.utils.data import DataLoader
loader = DataLoader(batch_size=16, shuffle=True)
for batch in loader:
    print(batch)

medium

A. batch_size should be a string, not integer

B. shuffle cannot be True

C. Missing dataset argument when creating DataLoader

D. for loop syntax is incorrect

Solution

1. Step 1: Check DataLoader parameters

   DataLoader requires a dataset as the first argument; it's missing here.

2. Step 2: Validate other parts

   batch_size as integer and shuffle=True are valid. For loop syntax is correct.

3. Final Answer:

   Missing dataset argument when creating DataLoader -> Option C

4. Quick Check:

   Dataset argument required [OK]

Hint: Always pass dataset first to DataLoader [OK]

Common Mistakes:

• Forgetting to pass dataset to DataLoader
• Thinking batch_size must be string
• Believing shuffle=True is invalid

5. You have a dataset of 103 samples and want to train a model using batches of size 20 with shuffling enabled. How many batches will the DataLoader produce per epoch, and what happens to the last batch?

hard

A. 5 batches; the last batch is dropped because it's smaller

B. 6 batches; the last batch has 3 samples

C. 6 batches; the last batch is padded to 20 samples

D. 5 batches; all batches have exactly 20 samples

Solution

1. Step 1: Calculate number of batches

   103 samples divided by batch size 20 gives 5 full batches (20*5=100) plus 1 partial batch with 3 samples.

2. Step 2: Understand default DataLoader behavior

   By default, DataLoader includes the last smaller batch unless drop_last=True is set, so last batch has 3 samples.

3. Final Answer:

   6 batches; the last batch has 3 samples -> Option B

4. Quick Check:

   103/20 = 5 full + 1 partial batch [OK]

Hint: Last batch included unless drop_last=True [OK]

Common Mistakes:

• Assuming last batch is dropped by default
• Thinking last batch is padded automatically
• Calculating wrong number of batches