PyTorch ecosystem overview - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the main PyTorch library.

PyTorch

import [1]

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

Atorch

Bnumpy

Ctensorflow

Dsklearn

Attempts:

3 left

💡 Hint

Common Mistakes

Importing TensorFlow instead of PyTorch.

Importing numpy which is for arrays but not PyTorch.

The main PyTorch library is imported using import torch.

2fill in blank

medium

Complete the code to create a tensor filled with zeros of size 3x3.

PyTorch

x = torch.[1]((3, 3))

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

Aones

Bempty

Crand

Dzeros

Attempts:

3 left

💡 Hint

Common Mistakes

Using torch.ones which creates a tensor of ones.

Using torch.rand which creates random values.

Use torch.zeros to create a tensor filled with zeros.

3fill in blank

hard

Fix the error in the code to move a tensor to GPU if available.

PyTorch

device = torch.device('cuda' if torch.cuda.[1]() else 'cpu')

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

AisAvailable

Bis_available

Cavailable

Dhas_cuda

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect method names like 'available' or 'has_cuda'.

Using camelCase instead of snake_case.

The correct method to check if CUDA is available is torch.cuda.is_available().

4fill in blank

hard

Fill both blanks to define a simple neural network layer using PyTorch.

PyTorch

layer = torch.nn.[1](in_features=10, [2]=5)

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

ALinear

BConv2d

Cout_features

Din_channels

Attempts:

3 left

💡 Hint

Common Mistakes

Using Conv2d which requires different parameters.

Confusing in_features with out_features.

Use torch.nn.Linear for a fully connected layer. The parameter out_features defines the output size.

5fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps layer names to their output sizes if size is greater than 5.

PyTorch

layer_sizes = {name: size for name, size in layers.items() if size [1] [2]

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

A>

B5

C<

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > causing wrong filtering.

Using == which checks equality, not greater than.

The comprehension filters layers with size greater than 5 using size > 5.

Practice

1. Which of the following is NOT part of the PyTorch ecosystem?

easy

A. torchvision

B. torchaudio

C. torchtext

D. tensorflow

Solution

1. Step 1: Identify PyTorch ecosystem libraries

   PyTorch ecosystem includes torchvision (images), torchtext (text), and torchaudio (audio).

2. Step 2: Recognize the unrelated library

   TensorFlow is a separate deep learning framework, not part of PyTorch ecosystem.

3. Final Answer:

   tensorflow -> Option D

4. Quick Check:

   PyTorch ecosystem excludes tensorflow [OK]

Hint: Remember PyTorch ecosystem libraries start with 'torch' prefix [OK]

Common Mistakes:

• Confusing TensorFlow as part of PyTorch
• Thinking torchvision is unrelated
• Mixing up torchtext with TensorFlow text tools

2. Which code snippet correctly imports the torchvision library in PyTorch?

easy

A. import torchvision

B. from torch import vision

C. import torch.vision

D. from torchvision import torch

Solution

1. Step 1: Recall correct import syntax for torchvision

   The correct way is to import torchvision directly using 'import torchvision'.

2. Step 2: Identify incorrect syntax

   Options A, B, and C use invalid module names or wrong import structure.

3. Final Answer:

   import torchvision -> Option A

4. Quick Check:

   Correct import is 'import torchvision' [OK]

Hint: Use 'import torchvision' to access image tools [OK]

Common Mistakes:

• Using 'from torch import vision' instead of torchvision
• Trying 'import torch.vision' which is invalid
• Confusing import order or names

3. What will the following code print?

import torch
import torchvision.models as models
model = models.resnet18(pretrained=True)
print(type(model))

medium

A. <class 'torchvision.models.resnet.ResNet'>

B. <class 'torch.nn.Module'>

C. <class 'torchvision.models.ResNet18'>

D. Error: pretrained argument not supported

Solution

1. Step 1: Understand model creation with pretrained=True

   models.resnet18(pretrained=True) loads a ResNet18 model with pretrained weights, returning a torch.nn.Module subclass instance.

2. Step 2: Check the type printed

   The type of model is torchvision.models.resnet.ResNet, so printing type(model) shows <class 'torchvision.models.resnet.ResNet'>.

3. Final Answer:

   <class 'torchvision.models.resnet.ResNet'> -> Option A

4. Quick Check:

   Pretrained ResNet models are of type ResNet [OK]

Hint: Pretrained models are always torch.nn.Module objects [OK]

Common Mistakes:

• Expecting base class 'torch.nn.Module' to print
• Thinking pretrained=True causes error
• Confusing model type with string output

4. Identify the error in this PyTorch code snippet:

import torch
from torchvision import datasets
train_data = datasets.MNIST(root='./data', train=True, download=True)
train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, shuffle=True)
for images, labels in train_loader:
    print(images.shape, labels.shape)

medium

A. No error, code runs correctly

B. DataLoader batch_size must be 1

C. Missing transform argument in MNIST dataset

D. shuffle=True is invalid in DataLoader

Solution

1. Step 1: Check MNIST dataset usage

   MNIST without transform returns PIL Images; DataLoader collates them into a list.

2. Step 2: Validate print statement

   print(images.shape) fails because list of PIL Images has no .shape attribute. Need transforms.ToTensor() for tensor images with shape.

3. Final Answer:

   Missing transform argument in MNIST dataset -> Option C

4. Quick Check:

   MNIST needs transform for tensor shapes [OK]

Hint: MNIST dataset needs transform for tensor shapes in batches [OK]

Common Mistakes:

• Assuming transform is optional for shape access
• Thinking batch_size must be 1
• Believing shuffle=True causes error

5. You want to build a speech recognition model using PyTorch. Which ecosystem library should you use to handle audio data and pretrained audio models?

hard

A. torchtext

B. torchaudio

C. torchvision

D. torchvideo

Solution

1. Step 1: Identify the data type for speech recognition

   Speech recognition uses audio data, so the library must support audio processing.

2. Step 2: Match the correct PyTorch ecosystem library

   torchaudio is the PyTorch library designed for audio data and pretrained audio models.

3. Final Answer:

   torchaudio -> Option B

4. Quick Check:

   Audio data uses torchaudio library [OK]

Hint: Audio tasks use torchaudio in PyTorch ecosystem [OK]

Common Mistakes:

• Choosing torchvision for audio data
• Confusing torchtext with audio processing
• Assuming torchvideo exists in PyTorch ecosystem