Batch size and shuffling in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a DataLoader with batch size 32.

PyTorch

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

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

A64

B32

C16

D128

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a batch size too small or too large for the example.

Confusing batch size with dataset size.

The batch_size parameter controls how many samples are loaded per batch. Here, 32 is the correct batch size.

2fill in blank

medium

Complete the code to enable shuffling of data in the DataLoader.

PyTorch

loader = DataLoader(dataset, batch_size=32, shuffle=[1])

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

A0

BFalse

CNone

DTrue

Attempts:

3 left

💡 Hint

Common Mistakes

Setting shuffle to False or None, which disables shuffling.

Using 0 instead of a boolean value.

Setting shuffle=True makes the DataLoader shuffle the data at every epoch, which helps training.

3fill in blank

hard

Fix the error in the DataLoader creation by choosing the correct batch size type.

PyTorch

loader = DataLoader(dataset, batch_size=[1], shuffle=True)

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

A'32'

BNone

C32

DTrue

Attempts:

3 left

💡 Hint

Common Mistakes

Using a string like '32' instead of integer 32.

Passing None or True as batch size.

batch_size must be an integer, not a string or other type.

4fill in blank

hard

Fill both blanks to create a DataLoader with batch size 64 and shuffling enabled.

PyTorch

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

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

A64

BTrue

CFalse

D32

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up batch size and shuffle values.

Using False for shuffle when shuffling is needed.

Batch size 64 and shuffle=True correctly configure the DataLoader for training.

5fill in blank

hard

Fill all three blanks to create a DataLoader with batch size 16, shuffling enabled, and drop_last set to True.

PyTorch

loader = DataLoader(dataset, batch_size=[1], shuffle=[2], drop_last=[3])

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

A16

BTrue

CFalse

D32

Attempts:

3 left

💡 Hint

Common Mistakes

Setting drop_last to False when dropping last batch is needed.

Using wrong types for parameters.

batch_size=16, shuffle=True, and drop_last=True ensure batches are shuffled and incomplete last batch is dropped.

Practice

1. What does the batch_size parameter control in a PyTorch DataLoader?

easy

A. The number of samples processed before the model updates its weights

B. The total number of samples in the dataset

C. The order in which samples are loaded

D. The number of features in each sample

Solution

1. Step 1: Understand batch size meaning

   Batch size defines how many samples the model processes before updating its weights during training.

2. Step 2: Differentiate from other parameters

   It is not the total dataset size, order of samples, or feature count, but the chunk size for training updates.

3. Final Answer:

   The number of samples processed before the model updates its weights -> Option A

4. Quick Check:

   Batch size = samples per update [OK]

Hint: Batch size = samples per training step [OK]

Common Mistakes:

• Confusing batch size with dataset size
• Thinking batch size controls feature count
• Mixing batch size with shuffling

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

easy

A. DataLoader(dataset, batch=32, shuffle=True)

B. DataLoader(dataset, shuffle=32, batch_size=True)

C. DataLoader(dataset, batch_size=32, shuffle=True)

D. DataLoader(dataset, batch_size=True, shuffle=32)

Solution

1. Step 1: Recall DataLoader parameters

   PyTorch DataLoader uses batch_size for batch size and shuffle as a boolean for shuffling.

2. Step 2: Check parameter names and types

   DataLoader(dataset, batch_size=32, shuffle=True) correctly uses batch_size=32 and shuffle=True. Others misuse parameter names or types.

3. Final Answer:

   DataLoader(dataset, batch_size=32, shuffle=True) -> Option C

4. Quick Check:

   Correct parameter names and types [OK]

Hint: Use exact parameter names: batch_size and shuffle [OK]

Common Mistakes:

• Swapping parameter names
• Using wrong data types for parameters
• Omitting required parameters

3. Given the code below, what will be the output length of each batch printed?

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

data = torch.arange(10)
dataset = TensorDataset(data)
dataloader = DataLoader(dataset, batch_size=4, shuffle=False)

for batch in dataloader:
    print(len(batch[0]))

medium

A. 10

B. 4, 4, 2

C. 5, 5

D. 4, 3, 3

Solution

1. Step 1: Understand dataset and batch size

   Dataset has 10 samples. Batch size is 4, so batches will be split into groups of 4 samples each.

2. Step 2: Calculate batch lengths

   First two batches have 4 samples each (4 + 4 = 8), last batch has remaining 2 samples (10 - 8 = 2).

3. Final Answer:

   4, 4, 2 -> Option B

4. Quick Check:

   10 samples split by 4 = 4,4,2 [OK]

Hint: Last batch may be smaller if dataset size not divisible by batch size [OK]

Common Mistakes:

• Assuming all batches have equal size
• Ignoring leftover samples in last batch
• Confusing batch length with dataset length

4. What is wrong with this DataLoader code snippet?

from torch.utils.data import DataLoader

dataloader = DataLoader(dataset, batch_size=0, shuffle=True)

medium

A. Batch size cannot be zero; it must be a positive integer

B. Shuffle cannot be true when batch size is set

C. Dataset must be a list, not a variable named dataset

D. DataLoader requires batch_size to be a float

Solution

1. Step 1: Check batch_size validity

   Batch size must be a positive integer greater than zero to form batches.

2. Step 2: Validate other parameters

   Shuffle can be True regardless of batch size. Dataset variable name is valid. Batch size must be int, not float.

3. Final Answer:

   Batch size cannot be zero; it must be a positive integer -> Option A

4. Quick Check:

   Batch size > 0 required [OK]

Hint: Batch size must be positive integer, never zero [OK]

Common Mistakes:

• Setting batch size to zero
• Confusing shuffle restrictions
• Wrong data type for batch size

5. You have a dataset of 103 samples. You want to train a model with batch size 20 and shuffle enabled. How many batches will the DataLoader produce each epoch, and why is shuffling important here?

hard

A. 103 batches; shuffling splits data into single samples

B. 5 batches; shuffling is not needed when batch size is large

C. 20 batches; shuffling controls batch size

D. 6 batches; shuffling helps the model see data in different orders to avoid bias

Solution

1. Step 1: Calculate number of batches

   103 samples with batch size 20 means 5 full batches (20*5=100) plus 1 batch with 3 samples, total 6 batches.

2. Step 2: Understand shuffling importance

   Shuffling mixes data order each epoch to prevent the model from learning patterns based on data order, reducing bias.

3. Final Answer:

   6 batches; shuffling helps the model see data in different orders to avoid bias -> Option D

4. Quick Check:

   103/20 = 6 batches; shuffle avoids bias [OK]

Hint: Batches = ceil(dataset size / batch size); shuffle avoids order bias [OK]

Common Mistakes:

• Ignoring last smaller batch
• Thinking shuffle disables batch size
• Misunderstanding shuffle purpose