Custom Dataset class in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the Dataset class from PyTorch.

PyTorch

from torch.utils.data import [1]

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

ADataset

BDataLoader

CTensor

DModule

Attempts:

3 left

💡 Hint

Common Mistakes

Importing DataLoader instead of Dataset

Trying to import Dataset from torch instead of torch.utils.data

The Dataset class is imported from torch.utils.data to create custom datasets.

2fill in blank

medium

Complete the code to define the __len__ method that returns the dataset size.

PyTorch

def __len__(self):
    return [1]

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

Aself.data.size()

Bself.data.shape

Clen(self.data)

Dself.length

Attempts:

3 left

💡 Hint

Common Mistakes

Using self.data.size() which is not valid for lists

Returning self.length without defining it

The __len__ method should return the number of samples, which is the length of the data list.

3fill in blank

hard

Fix the error in the __getitem__ method to return the correct sample.

PyTorch

def __getitem__(self, idx):
    sample = self.data[[1]]
    return sample

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

A0

Bidx + 1

Cself.idx

Didx

Attempts:

3 left

💡 Hint

Common Mistakes

Adding 1 to idx causing index out of range

Using self.idx which is undefined

The index idx should be used directly to access the data at that position.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths for words longer than 3 characters.

PyTorch

lengths = {word: [1] for word in words if [2]

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

Alen(word)

Blen(word) > 3

Cword != ''

Dword.isalpha()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of len(word) as value

Using wrong condition like word != '' which does not filter by length

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

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths for words longer than 4 characters.

PyTorch

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) > 4

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

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

Filtering with wrong length condition

The comprehension maps the uppercase version of each word to its length, filtering words longer than 4 characters.

Practice

1. What is the main purpose of creating a Custom Dataset class in PyTorch?

easy

A. To visualize data samples

B. To organize and provide access to data samples for model training

C. To perform gradient descent optimization

D. To define the neural network architecture

Solution

1. Step 1: Understand Dataset role

   A Custom Dataset class is designed to organize data and provide easy access to samples during training.

2. Step 2: Differentiate from other components

   Neural network architecture and optimization are handled elsewhere, not by the Dataset class.

3. Final Answer:

   To organize and provide access to data samples for model training -> Option B

4. Quick Check:

   Dataset class = data access [OK]

Hint: Dataset class organizes data for training access [OK]

Common Mistakes:

• Confusing Dataset with model definition
• Thinking Dataset handles optimization
• Assuming Dataset visualizes data

2. Which method must be implemented in a PyTorch Custom Dataset class to return the number of samples?

easy

A. __getitem__

B. __init__

C. __len__

D. forward

Solution

1. Step 1: Recall Dataset methods

   __len__ returns the total number of samples in the dataset.

2. Step 2: Identify other methods' roles

   __getitem__ fetches a sample, __init__ initializes the dataset, forward is for models, not datasets.

3. Final Answer:

   __len__ -> Option C

4. Quick Check:

   Count samples = __len__ [OK]

Hint: Count samples with __len__ method [OK]

Common Mistakes:

• Using __getitem__ to get length
• Confusing __init__ with length method
• Using forward method in Dataset

3. Given this Custom Dataset code, what will print(len(dataset)) output?

import torch
from torch.utils.data import Dataset

class MyDataset(Dataset):
    def __init__(self):
        self.data = [10, 20, 30, 40]
    def __len__(self):
        return len(self.data)
    def __getitem__(self, idx):
        return self.data[idx]

dataset = MyDataset()

medium

A. 4

B. 3

C. 40

D. Error

Solution

1. Step 1: Check __len__ method

   __len__ returns length of self.data, which is a list of 4 elements.

2. Step 2: Evaluate print output

   Calling len(dataset) calls __len__, so output is 4.

3. Final Answer:

   4 -> Option A

4. Quick Check:

   len([10,20,30,40]) = 4 [OK]

Hint: len(dataset) calls __len__ returning data length [OK]

Common Mistakes:

• Confusing length with last element value
• Expecting __getitem__ output instead of length
• Assuming error without checking code

4. Identify the error in this Custom Dataset class:

from torch.utils.data import Dataset

class SampleDataset(Dataset):
    def __init__(self, data):
        self.data = data
    def __len__(self):
        return self.data
    def __getitem__(self, idx):
        return self.data[idx]

sample = SampleDataset([1,2,3])
print(len(sample))

medium

A. Missing __init__ method

B. __getitem__ returns wrong index

C. Dataset class not imported

D. __len__ returns data list instead of its length

Solution

1. Step 1: Analyze __len__ method

   __len__ returns self.data, which is a list, not an integer length.

2. Step 2: Understand len() requirement

   len() expects __len__ to return an integer. Returning a list causes a TypeError.

3. Final Answer:

   __len__ returns data list instead of its length -> Option D

4. Quick Check:

   __len__ must return int, not list [OK]

Hint: __len__ must return integer length [OK]

Common Mistakes:

• Returning data list instead of length in __len__
• Ignoring import statements
• Confusing __getitem__ with __len__

5. You want to create a Custom Dataset class that returns both input features and labels from two separate lists. Which implementation correctly achieves this?

hard

A. class MyDataset(Dataset):
def __init__(self, inputs, labels):
self.inputs = inputs
self.labels = labels
def __len__(self):
return len(self.inputs)
def __getitem__(self, idx):
return self.inputs[idx], self.labels[idx]

B. class MyDataset(Dataset):
def __init__(self, inputs, labels):
self.data = inputs + labels
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[idx]

C. class MyDataset(Dataset):
def __init__(self, inputs, labels):
self.inputs = inputs
self.labels = labels
def __len__(self):
return len(self.labels)
def __getitem__(self, idx):
return self.inputs[idx]

D. class MyDataset(Dataset):
def __init__(self, inputs, labels):
self.inputs = inputs
self.labels = labels
def __len__(self):
return len(self.inputs)
def __getitem__(self, idx):
return self.labels[idx]

Solution

1. Step 1: Check __init__ and __len__ consistency

   The correct implementation stores inputs and labels separately and returns length of inputs in __len__, assuming matching lengths with labels.

2. Step 2: Verify __getitem__ returns both features and labels

   The correct __getitem__ returns a tuple (self.inputs[idx], self.labels[idx]) for supervised learning.

3. Final Answer:

   return self.inputs[idx], self.labels[idx] -> Option A

4. Quick Check:

   Return (input, label) tuple in __getitem__ [OK]

Hint: Return (input, label) tuple in __getitem__ [OK]

Common Mistakes:

• Combining inputs and labels into one list
• Returning only inputs or only labels
• Mismatched lengths in __len__