Freezing layers in PyTorch - Model Metrics & Evaluation

Metrics & Evaluation - Freezing layers

Which metric matters for Freezing layers and WHY

When freezing layers in a model, the main goal is to keep learned features fixed while training new parts. The key metrics to watch are validation loss and validation accuracy. These show if the model is learning new tasks well without forgetting old knowledge. If validation loss decreases and accuracy improves, freezing is helping. If not, the frozen layers might block learning.

Confusion matrix example

    Confusion Matrix (after freezing some layers):

          Predicted
          Pos   Neg
    Actual
    Pos   85    15
    Neg   10    90

    TP = 85, FP = 10, TN = 90, FN = 15

    Precision = 85 / (85 + 10) = 0.895
    Recall = 85 / (85 + 15) = 0.85
    F1 Score = 2 * (0.895 * 0.85) / (0.895 + 0.85) ≈ 0.872
    

This shows the model still predicts well after freezing layers, balancing precision and recall.

Precision vs Recall tradeoff with Freezing layers

Freezing layers can limit how much the model adapts. This might keep precision high by avoiding false alarms but reduce recall if the model misses new patterns.

For example, in a spam filter, freezing early layers keeps known spam features fixed, so precision stays high (few good emails marked spam). But recall might drop if new spam types appear and the model can't learn them well.

Choosing which layers to freeze balances this tradeoff: freeze too many and recall drops, freeze too few and training is slower or unstable.

What "good" vs "bad" metric values look like for Freezing layers

Good: Validation accuracy improves or stays stable, validation loss decreases, and precision and recall remain balanced after freezing layers.

Bad: Validation accuracy drops, loss stays high or increases, or recall drops sharply indicating the model can't learn new patterns due to frozen layers.

Common pitfalls when evaluating Freezing layers

• Overfitting: If only final layers train, they might overfit small new data while frozen layers don't adapt.
• Data leakage: Using test data during tuning can falsely show good metrics.
• Ignoring validation loss: Accuracy alone can hide if model is not improving properly.
• Freezing too many layers: Can block learning new features, hurting recall and overall performance.

Self-check question

Your model has 98% accuracy but only 12% recall on fraud detection after freezing layers. Is it good for production? Why or why not?

Answer: No, it is not good. High accuracy can be misleading if fraud cases are rare. Low recall means the model misses most frauds, which is dangerous. The frozen layers might prevent learning new fraud patterns, so you should adjust which layers to freeze or retrain more.

Key Result

Validation accuracy and recall are key to check if freezing layers helps learning without losing important new patterns.

Practice

1. What does freezing layers in a PyTorch model do during training?

easy

A. Removes the layers from the model

B. Increases the learning rate for those layers

C. Stops the layers' weights from updating

D. Duplicates the layers for faster training

Solution

1. Step 1: Understand freezing layers meaning

   Freezing layers means preventing their weights from changing during training.

2. Step 2: Effect on training

   When frozen, layers do not update weights, so they keep learned features intact.

3. Final Answer:

   Stops the layers' weights from updating -> Option C

4. Quick Check:

   Freezing = no weight updates [OK]

Hint: Freezing means no weight changes during training [OK]

Common Mistakes:

• Thinking freezing increases learning rate
• Believing freezing removes layers
• Assuming freezing duplicates layers

2. Which of the following is the correct way to freeze all parameters in a PyTorch model named model?

easy

A. model.freeze()

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

C. model.requires_grad = False

D. for param in model.parameters(): param.grad = None

Solution

1. Step 1: Identify correct syntax to freeze parameters

   Freezing requires setting requires_grad = False for each parameter.

2. Step 2: Check each option

   for param in model.parameters(): param.requires_grad = False correctly loops over parameters and sets requires_grad = False. Others are invalid or incorrect.

3. Final Answer:

   for param in model.parameters(): param.requires_grad = False -> Option B

4. Quick Check:

   Set requires_grad False to freeze [OK]

Hint: Set requires_grad=False on each parameter to freeze [OK]

Common Mistakes:

• Using model.requires_grad instead of param.requires_grad
• Calling a non-existent freeze() method
• Setting param.grad to None does not freeze

3. Consider this PyTorch code snippet:

import torch.nn as nn
model = nn.Sequential(
  nn.Linear(10, 5),
  nn.ReLU(),
  nn.Linear(5, 2)
)
for param in model[0].parameters():
  param.requires_grad = False

trainable_params = [p for p in model.parameters() if p.requires_grad]
print(len(trainable_params))

What will be printed?

medium

A. 2

B. 4

C. 6

D. 0

Solution

1. Step 1: Analyze model layers and parameters

   model[0] is Linear(10,5) with 2 parameters (weight and bias). model[2] is Linear(5,2) with 2 parameters.

2. Step 2: Check which parameters are trainable

   Parameters in model[0] are frozen (requires_grad=False), so only model[2]'s 2 parameters remain trainable.

3. Final Answer:

   2 -> Option A

4. Quick Check:

   Frozen layer params excluded, trainable = 2 [OK]

Hint: Count only parameters with requires_grad=True [OK]

Common Mistakes:

• Counting all parameters ignoring requires_grad
• Assuming ReLU has parameters
• Confusing layer indices

4. You want to freeze the first layer of a PyTorch model but accidentally wrote:

for param in model.layer1.parameters():
    param.grad = False

What is the problem with this code?

medium

A. param.grad is a tensor, not a boolean flag

B. param.grad disables gradients correctly

C. model.layer1.parameters() does not exist

D. param.requires_grad should be set, not param.grad

Solution

1. Step 1: Understand difference between param.grad and param.requires_grad

   param.grad holds gradient values, it is a tensor or None, not a flag to enable/disable gradients.

2. Step 2: Correct way to freeze parameters

   To freeze, set param.requires_grad = False. Setting param.grad = False is invalid and does not freeze.

3. Final Answer:

   param.requires_grad should be set, not param.grad -> Option D

4. Quick Check:

   Freeze by requires_grad=False, not param.grad [OK]

Hint: Freeze with requires_grad, not param.grad [OK]

Common Mistakes:

• Confusing param.grad with requires_grad
• Trying to disable gradients by setting param.grad
• Assuming param.grad is a boolean

5. You have a pretrained PyTorch model with 3 layers: layer1, layer2, and layer3. You want to freeze layer1 and layer2 but train layer3. Which code correctly freezes only the first two layers?

hard

A. for layer in [model.layer1, model.layer2]:
for param in layer.parameters():
param.requires_grad = False

B. for param in model.parameters():
param.requires_grad = False
for param in model.layer3.parameters():
param.requires_grad = False

C. model.layer1.requires_grad = False
model.layer2.requires_grad = False

D. model.freeze_layers(['layer1', 'layer2'])

Solution

1. Step 1: Understand freezing multiple layers

   Freezing means setting requires_grad = False on each parameter in the layers to freeze.

2. Step 2: Evaluate options

   for layer in [model.layer1, model.layer2]: for param in layer.parameters(): param.requires_grad = False correctly loops over layer1 and layer2 parameters and freezes them correctly. for param in model.parameters(): param.requires_grad = False for param in model.layer3.parameters(): param.requires_grad = False incorrectly freezes all parameters including layer3. model.layer1.requires_grad = False model.layer2.requires_grad = False tries to set requires_grad on layers (invalid). model.freeze_layers(['layer1', 'layer2']) calls a non-existent method.

3. Final Answer:

   for layer in [model.layer1, model.layer2]: for param in layer.parameters(): param.requires_grad = False -> Option A

4. Quick Check:

   Freeze layers by setting requires_grad False per parameter [OK]

Hint: Freeze layers by looping params and setting requires_grad False [OK]

Common Mistakes:

• Setting requires_grad on layer objects instead of parameters
• Using non-existent freeze_layers method
• Freezing all parameters