Freezing and unfreezing layers in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to freeze all layers in a TensorFlow Keras model.

TensorFlow

for layer in model.layers:
    layer.[1] = False

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

Afrozen

Btrainable

Cactive

Dlocked

Attempts:

3 left

💡 Hint

Common Mistakes

Using a non-existent property like 'frozen' or 'locked'.

Confusing the value True/False for freezing.

Setting layer.trainable = True or False controls whether the layer's weights are updated during training. To freeze a layer, set trainable = False.

2fill in blank

medium

Complete the code to unfreeze only the last layer of the model.

TensorFlow

model.layers[-1].[1] = True

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

Aactive

Bfrozen

Ctrainable

Dlocked

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to set a property that does not exist.

Forgetting to set trainable to True.

To allow training on the last layer, set its trainable property to True.

3fill in blank

hard

Fix the error in the code to freeze all layers before compiling the model.

TensorFlow

for layer in model.layers:
    layer.[1] = False
model.compile(optimizer='adam', loss='categorical_crossentropy')

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

Atrainable

Bfrozen

Cactive

Dlocked

Attempts:

3 left

💡 Hint

Common Mistakes

Using a non-existent property like 'frozen'.

Setting the property after compiling the model.

The correct property to freeze layers is trainable. Setting trainable = False freezes the layer.

4fill in blank

hard

Fill both blanks to freeze all layers except the last one.

TensorFlow

for layer in model.layers[:[1]]:
    layer.[2] = False

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

Atrainable

B-1

Cfrozen

Dlocked

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect slice indices.

Using a wrong property name to freeze layers.

Using model.layers[:-1] selects all layers except the last. Setting trainable = False freezes those layers.

5fill in blank

hard

Fill all three blanks to freeze the first two layers and unfreeze the rest.

TensorFlow

for layer in model.layers[:[1]]:
    layer.[2] = False
for layer in model.layers[[3]:]:
    layer.trainable = True

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

Atrainable

B2

Dfrozen

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong slice indices.

Using incorrect property names.

Slice model.layers[:2] selects the first two layers to freeze by setting trainable = False. Slice model.layers[2:] selects the rest to unfreeze.

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