Freezing and unfreezing layers in TensorFlow

Introduction

Freezing layers means stopping some parts of a model from learning. Unfreezing lets those parts learn again. This helps save time and keep good knowledge when training.

When using a pre-trained model and you want to keep some learned features fixed.

When training a big model step-by-step to avoid losing earlier learned skills.

When you want to speed up training by not updating all layers.

When fine-tuning a model on new data but keeping some layers stable.

Syntax

TensorFlow

layer.trainable = False  # freeze layer
layer.trainable = True   # unfreeze layer

Set trainable before compiling the model.

Freezing means weights won't change during training.

Examples

This freezes all layers in the model so none will update.

TensorFlow

for layer in model.layers:
    layer.trainable = False

This freezes only the first layer of the model.

TensorFlow

model.layers[0].trainable = False

This unfreezes the last three layers to allow training.

TensorFlow

for layer in model.layers[-3:]:
    layer.trainable = True

Sample Model

This code builds a small model, freezes the first layer, trains on random data, and shows which layers trained and the final loss.

TensorFlow

import tensorflow as tf
from tensorflow.keras import layers, models

# Create a simple model
model = models.Sequential([
    layers.Dense(10, activation='relu', input_shape=(5,)),
    layers.Dense(1)
])

# Freeze the first layer
model.layers[0].trainable = False

# Compile the model
model.compile(optimizer='adam', loss='mse')

# Create dummy data
import numpy as np
x = np.random.random((10, 5))
y = np.random.random((10, 1))

# Train the model
history = model.fit(x, y, epochs=2, verbose=0)

# Check which layers are trainable
trainable_status = [layer.trainable for layer in model.layers]

print('Trainable layers:', trainable_status)
print('Loss after training:', history.history['loss'][-1])

Important Notes

Always set trainable before calling compile().

Freezing layers helps keep learned features and reduces training time.

You can selectively freeze or unfreeze layers depending on your task.

Summary

Freezing stops layers from learning to keep their knowledge.

Unfreezing allows layers to update weights again.

Set trainable before compiling the model.

Practice

1. What does freezing layers in a TensorFlow model do?

easy

A. Stops the layers from updating their weights during training

B. Allows the layers to learn new features

C. Deletes the layers from the model

D. Increases the learning rate for those layers

Solution

1. Step 1: Understand the meaning of freezing layers

   Freezing layers means preventing them from changing their weights during training.

2. Step 2: Identify the effect of freezing

   Since weights do not update, the layers keep their learned knowledge intact.

3. Final Answer:

   Stops the layers from updating their weights during training -> Option A

4. Quick Check:

   Freezing = stop weight updates [OK]

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

Common Mistakes:

• Thinking freezing deletes layers
• Believing freezing increases learning rate
• Confusing freezing with unfreezing

2. Which is the correct way to freeze a layer named layer in TensorFlow before compiling the model?

easy

A. layer.frozen = True

B. layer.freeze = True

C. layer.trainable = True

D. layer.trainable = False

Solution

1. Step 1: Recall the attribute to control training

   TensorFlow uses the trainable attribute to allow or prevent training on layers.

2. Step 2: Set the attribute to freeze

   Setting trainable = False freezes the layer so it won't update during training.

3. Final Answer:

   layer.trainable = False -> Option D

4. Quick Check:

   Freeze layer = trainable False [OK]

Hint: Use trainable = False to freeze layers before compile [OK]

Common Mistakes:

• Using non-existent attributes like freeze or frozen
• Setting trainable = True to freeze
• Changing trainable after compiling

3. Given the code below, what will be the output of model.layers[0].trainable after compiling?

model.layers[0].trainable = False
model.compile(optimizer='adam', loss='mse')
print(model.layers[0].trainable)

medium

A. None

B. True

C. False

D. Raises an error

Solution

1. Step 1: Set layer.trainable before compiling

   The code sets trainable = False before calling compile.

2. Step 2: Check if trainable attribute changes after compile

   The trainable attribute remains as set before compile; it does not change automatically.

3. Final Answer:

   False -> Option C

4. Quick Check:

   trainable stays False after compile [OK]

Hint: Set trainable before compile; it stays unchanged after [OK]

Common Mistakes:

• Assuming compile resets trainable to True
• Expecting an error from accessing trainable
• Confusing trainable with layer existence

4. You want to unfreeze a previously frozen layer in your TensorFlow model. Which code snippet fixes the error below?

AttributeError: 'Sequential' object has no attribute 'unfreeze'

Current code:

model.unfreeze()
model.compile(optimizer='adam', loss='mse')

medium

A. Set trainable on each layer: for layer in model.layers: layer.trainable = True

B. Set model.trainable = False instead

C. Call model.unfreeze() method

D. Rebuild the model from scratch

Solution

1. Step 1: Understand the error cause

   The error says Sequential model has no unfreeze attribute, meaning calling model.unfreeze() is invalid.

2. Step 2: Correct way to unfreeze layers

   Each layer has a trainable attribute; set it to True for all layers to unfreeze.

3. Final Answer:

   Set trainable on each layer: for layer in model.layers: layer.trainable = True -> Option A

4. Quick Check:

   Unfreeze layers by setting layer.trainable True [OK]

Hint: Set trainable on layers, not on model object [OK]

Common Mistakes:

• Trying to call model.unfreeze()
• Using non-existent unfreeze() method
• Rebuilding model unnecessarily

5. You have a pretrained model and want to freeze all layers except the last two for fine-tuning. Which code correctly achieves this before compiling?

hard

A. for layer in model.layers: layer.trainable = True

B. for layer in model.layers[:-2]: layer.trainable = False
for layer in model.layers[-2:]: layer.trainable = True

C. model.trainable = False
model.layers[-2:].trainable = True

D. model.freeze_layers(except_last=2)

Solution

1. Step 1: Freeze all layers except last two

   Use slicing to set trainable = False for all layers except last two.

2. Step 2: Unfreeze last two layers

   Set trainable = True for the last two layers to allow training.

3. Final Answer:

   for layer in model.layers[:-2]: layer.trainable = False for layer in model.layers[-2:]: layer.trainable = True -> Option B

4. Quick Check:

   Freeze all but last two layers [OK]

Hint: Freeze with slicing; unfreeze last layers explicitly [OK]

Common Mistakes:

• Trying to set trainable on model object
• Using non-existent freeze_layers method
• Setting all layers trainable True