Feature extraction strategy in PyTorch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a feature extraction strategy in machine learning?

It is a method to transform raw data into useful information (features) that a model can learn from more easily.

intermediate

Why do we freeze layers in a pretrained model during feature extraction?

Freezing layers means we do not update their weights during training. This keeps the learned features intact and reduces training time.

beginner

In PyTorch, how do you freeze the parameters of a pretrained model?

You set each parameter's requires_grad attribute to False, like:
for param in model.parameters():
  param.requires_grad = False

intermediate

What is the difference between feature extraction and fine-tuning?

Feature extraction uses pretrained model layers as fixed feature detectors. Fine-tuning updates some or all pretrained layers to better fit new data.

intermediate

How can you replace the final layer of a pretrained model in PyTorch for feature extraction?

You assign a new layer to the model's classifier or fc attribute, for example:
model.fc = nn.Linear(in_features, num_classes)

What does freezing layers in a pretrained model do?

A Deletes the layers from the model

B Prevents their weights from updating during training

C Adds new layers to the model

D Increases the learning rate for those layers

Freezing layers means their weights stay fixed and do not change during training.

Which PyTorch attribute controls if a parameter is trainable?

A requires_grad

B trainable

C grad_enabled

D update_weights

The requires_grad attribute tells PyTorch whether to compute gradients for that parameter.

What is the main goal of feature extraction?

A To increase the size of the dataset

B To train a model from scratch

C To use learned features from a pretrained model to help a new task

D To remove irrelevant data points

Feature extraction leverages existing learned features to improve learning on new tasks.

When replacing the final layer in a pretrained model, what must you adjust?

A The optimizer type

B The input image size

C The learning rate only

D The number of output features to match your task classes

The final layer's output size must match the number of classes in your new task.

Which of these is NOT a benefit of feature extraction?

A Always achieves perfect accuracy

B Requires less data

C Faster training time

D Uses pretrained knowledge

Feature extraction helps but does not guarantee perfect accuracy.

Explain how to perform feature extraction using a pretrained model in PyTorch.

Think about which parts of the model you keep fixed and which parts you change.

Describe the difference between feature extraction and fine-tuning in transfer learning.

Consider how much of the pretrained model you allow to learn.

Practice

1. What is the main purpose of using a pre-trained model for feature extraction in PyTorch?

easy

A. To replace the optimizer with a new one

B. To use learned features from a large dataset and avoid training from scratch

C. To train all layers from random weights

D. To increase the size of the dataset automatically

Solution

1. Step 1: Understand feature extraction concept

   Feature extraction uses a model already trained on a large dataset to get useful features without training all layers again.

2. Step 2: Identify the main benefit

   This saves time and resources by reusing learned knowledge instead of starting from scratch.

3. Final Answer:

   To use learned features from a large dataset and avoid training from scratch -> Option B

4. Quick Check:

   Feature extraction = reuse learned features [OK]

Hint: Pre-trained means reuse, not retrain all layers [OK]

Common Mistakes:

• Thinking feature extraction means training all layers
• Confusing feature extraction with data augmentation
• Believing optimizer changes are part of feature extraction

2. Which PyTorch code snippet correctly freezes all layers of a pre-trained model except the final layer?

easy

A. for param in model.parameters(): param.requires_grad = True
model.fc = nn.Linear(512, 10)

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

C. for param in model.parameters(): param.requires_grad = False
model.fc = nn.Linear(512, 10)

D. model.fc = nn.Linear(512, 10)
for param in model.parameters(): param.requires_grad = False

Solution

1. Step 1: Freeze all layers by setting requires_grad to false

   The loop disables gradient updates for all parameters to keep pre-trained weights fixed.

2. Step 2: Replace the final layer with a new one to train

   Assigning a new linear layer to model.fc allows training only this layer for the new task.

3. Final Answer:

   for param in model.parameters(): param.requires_grad = False model.fc = nn.Linear(512, 10) -> Option C

4. Quick Check:

   Freeze all except final layer = for param in model.parameters(): param.requires_grad = False model.fc = nn.Linear(512, 10) [OK]

Hint: Freeze first, then replace final layer [OK]

Common Mistakes:

• Not freezing layers before replacing final layer
• Freezing final layer instead of others
• Setting requires_grad true for all parameters

3. Given this PyTorch code for feature extraction, what will be the output shape of features?

import torch
import torchvision.models as models
model = models.resnet18(pretrained=True)
model.fc = torch.nn.Identity()
input_tensor = torch.randn(4, 3, 224, 224)
features = model(input_tensor)
print(features.shape)

medium

A. torch.Size([4, 512])

B. torch.Size([4, 1000])

C. torch.Size([4, 3, 224, 224])

D. torch.Size([4, 2048])

Solution

1. Step 1: Understand model modification

   Replacing model.fc with Identity removes the final classification layer, so output is the feature vector before classification.

2. Step 2: Know ResNet18 feature size

   ResNet18 outputs a 512-dimensional vector before the final fc layer for each input image.

3. Final Answer:

   torch.Size([4, 512]) -> Option A

4. Quick Check:

   ResNet18 features = 512 dims [OK]

Hint: Identity layer outputs feature vector size [OK]

Common Mistakes:

• Assuming output is 1000 classes without removing fc
• Confusing batch size with feature dimension
• Expecting 2048 features from ResNet18 (it's 512)

4. Identify the error in this feature extraction code snippet and select the fix:

model = models.resnet50(pretrained=True)
for param in model.parameters():
    param.requires_grad = False
model.fc = nn.Linear(2048, 5)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# Training loop here

medium

A. No error; code is correct

B. Set requires_grad=True for model.fc parameters after replacement

C. Use Adam optimizer instead of SGD

D. Remove freezing of parameters to train all layers

Solution

1. Step 1: Check freezing timing

   The loop freezes existing parameters before replacing model.fc, so the new fc layer's parameters are created with requires_grad=True by default.

2. Step 2: Verify optimizer behavior

   Optimizer only updates parameters where requires_grad=True, which are the new fc parameters; backbone remains frozen.

3. Final Answer:

   No error; code is correct -> Option A

4. Quick Check:

   New layer params unfrozen by default [OK]

Hint: New layers have requires_grad=True by default [OK]

Common Mistakes:

• Assuming freezing all parameters includes new layers
• Changing optimizer without fixing requires_grad
• Removing freezing unnecessarily

5. You want to use a pre-trained ResNet34 to classify 3 classes in your dataset. You freeze all layers except the last one. However, your training accuracy stays very low. What is the best next step to improve feature extraction performance?

hard

A. Reduce batch size to 1 to improve gradient estimates

B. Increase learning rate to 1.0 for faster training

C. Replace the optimizer with SGD without momentum

D. Unfreeze some deeper layers to fine-tune features for your task

Solution

1. Step 1: Understand freezing impact

   Freezing all but last layer may limit model's ability to adapt features to new classes, causing low accuracy.

2. Step 2: Fine-tune some deeper layers

   Unfreezing some layers closer to output allows the model to adjust features better for your specific dataset.

3. Final Answer:

   Unfreeze some deeper layers to fine-tune features for your task -> Option D

4. Quick Check:

   Fine-tune layers = better adaptation [OK]

Hint: Fine-tune layers if frozen model underperforms [OK]

Common Mistakes:

• Increasing learning rate too much causes instability
• Changing optimizer without addressing feature adaptation
• Reducing batch size unnecessarily