torchvision pre-trained models in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a pre-trained ResNet18 model from torchvision.

PyTorch

import torchvision.models as models
model = models.[1](pretrained=True)

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

Aalexnet

Bvgg16

Cresnet18

Ddensenet121

Attempts:

3 left

💡 Hint

Common Mistakes

Using a different model function like vgg16 instead of resnet18.

Forgetting to set pretrained=True to load pre-trained weights.

The function resnet18 loads the ResNet18 architecture with pre-trained weights.

2fill in blank

medium

Complete the code to set the model to evaluation mode.

PyTorch

model = models.resnet18(pretrained=True)
model.[1]()

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

Apredict

Btrain

Cfit

Deval

Attempts:

3 left

💡 Hint

Common Mistakes

Calling train() instead of eval(), which keeps the model in training mode.

Using a non-existent method like predict().

Calling eval() sets the model to evaluation mode, disabling dropout and batch normalization updates.

3fill in blank

hard

Fix the error in the code to correctly load a pre-trained VGG16 model.

PyTorch

import torchvision.models as models
model = models.vgg16([1]=True)

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

Apretrained

Bweights

Cload_pretrained

Duse_pretrained

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'weights' instead of 'pretrained' in older torchvision versions.

Using incorrect argument names like 'load_pretrained' or 'use_pretrained'.

The correct argument to load pre-trained weights in torchvision models is pretrained=True.

4fill in blank

hard

Fill both blanks to create a dictionary of model names and their corresponding pre-trained model functions.

PyTorch

models_dict = {
    'resnet': models.[1],
    'alexnet': models.[2]
}

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

Aresnet18

Bvgg16

Calexnet

Ddensenet121

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up model function names or using unrelated models.

Using the same option for both blanks.

The dictionary maps 'resnet' to resnet18 and 'alexnet' to alexnet functions from torchvision.models.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps model names to their pre-trained model instances, filtering only models with names longer than 6 characters.

PyTorch

model_names = ['resnet18', 'vgg16', 'alexnet', 'densenet121']
models_dict = {
    [1]: models.[2](pretrained=True)
    for [3] in model_names if len([1]) > 6
}

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

Aname

Cmodel

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Not matching the variable names in the condition and comprehension.

The comprehension uses name as the key and function name, and model as the loop variable. The condition filters names longer than 6 characters.