Fine-tuning strategy in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a pre-trained model for fine-tuning.

PyTorch

import torch
from torchvision import models

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

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

A0

BTrue

CNone

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Setting pretrained to False loads a model with random weights, not suitable for fine-tuning.

Using None or 0 as pretrained argument causes errors.

Setting pretrained=True loads the model with pre-trained weights, which is essential for fine-tuning.

2fill in blank

medium

Complete the code to freeze all layers except the final fully connected layer for fine-tuning.

PyTorch

for param in model.parameters():
    param.[1] = False

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

Adetach

Bgrad

Ctrain

Drequires_grad

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'grad' or 'train' attributes which do not control gradient computation.

Calling detach() on parameters instead of setting requires_grad.

Setting requires_grad = False freezes the parameters so they are not updated during training.

3fill in blank

hard

Fix the error in replacing the final layer to match 10 output classes.

PyTorch

import torch.nn as nn

model.fc = nn.Linear(model.fc.in_features, [1])

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

A10

B5

C100

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Setting output features to a wrong number causes shape mismatch errors.

Using 1 or 5 instead of the correct number of classes.

The final layer's output features must match the number of classes, which is 10 here.

4fill in blank

hard

Fill both blanks to create an optimizer that only updates the final layer parameters with a learning rate of 0.001.

PyTorch

import torch.optim as optim

optimizer = optim.SGD([1], lr=[2])

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

Amodel.fc.parameters()

Bmodel.parameters()

C0.01

D0.001

Attempts:

3 left

💡 Hint

Common Mistakes

Using all model parameters causes the whole model to train, not just the final layer.

Using a too high learning rate can cause unstable training.

Only the final layer's parameters should be updated, and the learning rate is set to 0.001 for fine-tuning.

5fill in blank

hard

Fill all three blanks to write a training loop that computes loss, backpropagates, and updates parameters.

PyTorch

for inputs, labels in dataloader:
    optimizer.zero_grad()
    outputs = model([1])
    loss = criterion(outputs, [2])
    loss.[3]()  # backpropagation
    optimizer.step()

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

Ainputs

Blabels

Cbackward

Dforward

Attempts:

3 left

💡 Hint

Common Mistakes

Passing labels to the model instead of inputs.

Calling loss.forward() instead of loss.backward().

Forgetting to zero gradients before backpropagation.

The model takes inputs to produce outputs. The loss compares outputs to labels. Calling loss.backward() computes gradients.

Practice

1. What is the main purpose of fine-tuning a pre-trained PyTorch model?

easy

A. To adjust the model to perform well on a new task by training some layers

B. To train the model from scratch on a large dataset

C. To reduce the model size by removing layers

D. To convert the model to a different programming language

Solution

1. Step 1: Understand fine-tuning concept

   Fine-tuning means taking a model already trained on one task and adjusting it to work well on a new task by training some of its layers.

2. Step 2: Compare options

   Only To adjust the model to perform well on a new task by training some layers describes this process correctly. Other options describe unrelated actions.

3. Final Answer:

   To adjust the model to perform well on a new task by training some layers -> Option A

4. Quick Check:

   Fine-tuning = Adjust model layers for new task [OK]

Hint: Fine-tuning means training some layers for a new task [OK]

Common Mistakes:

• Thinking fine-tuning means training from scratch
• Confusing fine-tuning with model compression
• Assuming fine-tuning changes the whole model

2. Which PyTorch code snippet correctly freezes all layers except the last one for fine-tuning?

easy

A. model.freeze_all_layers()
model.unfreeze_last_layer()

B. for param in model.parameters(): param.requires_grad = True
for param in model.fc.parameters(): param.requires_grad = False

C. model.requires_grad = False
model.fc.requires_grad = True

D. for param in model.parameters(): param.requires_grad = False
for param in model.fc.parameters(): param.requires_grad = True

Solution

1. Step 1: Understand freezing layers in PyTorch

   Setting param.requires_grad = False freezes a layer so it won't update during training.

2. Step 2: Analyze code snippets

   for param in model.parameters(): param.requires_grad = False for param in model.fc.parameters(): param.requires_grad = True freezes all parameters first, then unfreezes only the last layer (model.fc). The other options reverse or misuse this logic or use non-existent methods.

3. Final Answer:

   for param in model.parameters(): param.requires_grad = False for param in model.fc.parameters(): param.requires_grad = True -> Option D

4. Quick Check:

   Freeze all, unfreeze last layer = for param in model.parameters(): param.requires_grad = False for param in model.fc.parameters(): param.requires_grad = True [OK]

Hint: Freeze all with requires_grad=False, then unfreeze last layer [OK]

Common Mistakes:

• Setting requires_grad True for all layers by mistake
• Using non-existent PyTorch methods
• Forgetting to unfreeze the last layer

3. Given this PyTorch code for fine-tuning, what will be the output of print(sum(p.requires_grad for p in model.parameters()))?

for param in model.parameters():
    param.requires_grad = False
for param in model.classifier.parameters():
    param.requires_grad = True
print(sum(p.requires_grad for p in model.parameters()))

medium

A. Number of all model parameters

B. Number of parameters in model.classifier

C. Zero

D. Raises an error

Solution

1. Step 1: Understand requires_grad flags

   All parameters are first frozen (requires_grad=False). Then only parameters in model.classifier are unfrozen (requires_grad=True).

2. Step 2: Calculate sum of requires_grad

   Summing p.requires_grad counts how many parameters are trainable. Since only model.classifier parameters are True, the sum equals their count.

3. Final Answer:

   Number of parameters in model.classifier -> Option B

4. Quick Check:

   Only classifier params require grad = Number of parameters in model.classifier [OK]

Hint: Sum requires_grad counts trainable parameters [OK]

Common Mistakes:

• Assuming all parameters are trainable
• Confusing boolean sum with total parameters
• Expecting an error from this code

4. You tried to fine-tune a model by freezing layers but the training loss does not change. What is the most likely error in your PyTorch code?

medium

A. You used the wrong optimizer

B. You forgot to set model.train() before training

C. You did not set requires_grad = True for any parameters

D. You replaced the last layer with wrong output size

Solution

1. Step 1: Analyze symptom - loss not changing

   If loss stays the same, model parameters are not updating during training.

2. Step 2: Check requires_grad flags

   If all parameters have requires_grad = False, gradients won't be computed and weights won't update, causing no loss change.

3. Final Answer:

   You did not set requires_grad = True for any parameters -> Option C

4. Quick Check:

   No trainable params = no loss change [OK]

Hint: Check requires_grad True for trainable layers [OK]

Common Mistakes:

• Assuming optimizer choice causes no loss change
• Forgetting to call model.train() but blaming loss
• Ignoring requires_grad flags

5. You want to fine-tune a pre-trained ResNet model on a 10-class problem. Which strategy is best to start with?

hard

A. Freeze all layers, replace the final fully connected layer with 10 outputs, and train only this layer

B. Train the entire ResNet model from scratch with 10 output classes

C. Freeze only the first convolutional layer and train the rest

D. Replace the final layer but keep all layers trainable without freezing

Solution

1. Step 1: Understand common fine-tuning approach

   Starting by freezing all layers except the last layer is a common strategy to adapt a pre-trained model to a new task efficiently.

2. Step 2: Evaluate options

   Freeze all layers, replace the final fully connected layer with 10 outputs, and train only this layer matches this approach: freeze all, replace last layer for 10 classes, train only last layer. Other options either train from scratch or do not freeze enough layers, which can be inefficient or unstable.

3. Final Answer:

   Freeze all layers, replace the final fully connected layer with 10 outputs, and train only this layer -> Option A

4. Quick Check:

   Freeze all but last layer for new task [OK]

Hint: Freeze all, replace last layer, train only it first [OK]

Common Mistakes:

• Training entire model from scratch unnecessarily
• Freezing too few layers causing slow training
• Not replacing last layer to match output classes