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]()

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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)

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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

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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]

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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] }

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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.