Dataset class (custom datasets) 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]

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ADataLoader

BModule

CTensor

DDataset

Attempts:

3 left

💡 Hint

Common Mistakes

Importing DataLoader instead of Dataset

Using Module which is for models, not datasets

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]

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Alen(self.data)

Bself.length

Cself.data_size

Dself.size

Attempts:

3 left

💡 Hint

Common Mistakes

Returning an undefined attribute like self.length

Returning a fixed number instead of dataset length

The __len__ method returns the number of samples in the dataset, usually by returning len(self.data).

3fill in blank

hard

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

PyTorch

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

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Aself.idx

Bindex

Cidx

Di

Attempts:

3 left

💡 Hint

Common Mistakes

Using a variable not defined in the method

Using self.idx which is not defined

The __getitem__ method uses the argument idx to access the data sample at that index.

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 len(word) [2] 3}

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of its length

Using wrong comparison operator like <

The dictionary comprehension maps each word to its length using len(word) and filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase keys to values for items with positive values.

PyTorch

result = [1]: [2] for k, v in data.items() if v [3] 0}

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Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using k instead of k.upper() for keys

Using wrong comparison operator like <=

Using k instead of v for values

The comprehension uses k.upper() as keys, v as values, and filters items where v > 0.