Freezing layers in PyTorch - Model Pipeline Trace

Model Pipeline - Freezing layers

This pipeline shows how freezing layers in a neural network helps keep some parts fixed while training others. It speeds up training and preserves learned features.

Data Flow - 4 Stages

1Load dataset

1000 rows x 3 channels x 32 height x 32 width→Load images and labels→1000 samples x 3 x 32 x 32

Image tensor with pixel values and label 'cat'

↓

2Preprocessing

1000 samples x 3 x 32 x 32→Normalize pixel values to 0-1 range→1000 samples x 3 x 32 x 32

Pixel value 120 scaled to 0.47

↓

3Feature extraction (frozen layers)

1000 samples x 3 x 32 x 32→Pass through frozen convolutional layers→1000 samples x 16 x 28 x 28

Feature map highlighting edges

↓

4Trainable layers

1000 samples x 16 x 28 x 28→Pass through trainable fully connected layers→1000 samples x 10 classes

Output logits for 10 classes

Training Trace - Epoch by Epoch

Loss
1.2 |****
0.9 |***
0.7 |**
0.6 |*
0.55|*
    +---------
    Epochs 1-5

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Loss starts high, accuracy low as model begins learning
2	0.9	0.60	Loss decreases, accuracy improves as trainable layers adjust
3	0.7	0.72	Continued improvement, frozen layers keep features stable
4	0.6	0.78	Model converging, trainable layers fine-tuned
5	0.55	0.82	Training stabilizes with good accuracy

Prediction Trace - 5 Layers

Layer 1: Input image

Layer 2: Frozen convolutional layers

Layer 3: Trainable fully connected layers

Layer 4: Softmax activation

Layer 5: Prediction

Model Quiz - 3 Questions

Why do we freeze some layers during training?

A To keep learned features unchanged and speed up training

B To make the model train slower

C To increase the number of trainable parameters

D To randomly change weights

Key Insight

Freezing layers lets the model keep useful features fixed while training other parts. This helps faster learning and avoids losing previously learned knowledge.

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