Transfer learning for small datasets in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a pre-trained MobileNetV2 model without the top layer.

TensorFlow

base_model = tf.keras.applications.MobileNetV2(input_shape=(224, 224, 3), include_top=[1], weights='imagenet')

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

ATrue

BFalse

CNone

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Setting include_top=True loads the full model including the classification head, which is not suitable for transfer learning on new tasks.

Setting include_top=False loads the model without the final classification layers, which is useful for transfer learning.

2fill in blank

medium

Complete the code to freeze the base model layers so they are not trainable.

TensorFlow

base_model.[1] = False

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

Afrozen

Btrain

Crequires_grad

Dtrainable

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect attribute names like 'frozen' or 'requires_grad' which do not exist in TensorFlow.

Setting trainable = False freezes the layers so their weights do not update during training.

3fill in blank

hard

Fix the error in the code to add a global average pooling layer after the base model.

TensorFlow

x = base_model.output
x = tf.keras.layers.[1]()(x)

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

ADense

BFlatten

CGlobalAveragePooling2D

DDropout

Attempts:

3 left

💡 Hint

Common Mistakes

Using Dense or Flatten layers directly after the convolutional base without pooling can cause shape errors.

The GlobalAveragePooling2D layer reduces the spatial dimensions by averaging, which is common after convolutional base models.

4fill in blank

hard

Fill both blanks to create a new model with the base model and a final dense layer for 5 classes.

TensorFlow

outputs = tf.keras.layers.Dense([1], activation=[2])(x)
model = tf.keras.Model(inputs=base_model.input, outputs=outputs)

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

A5

B'softmax'

C'relu'

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'relu' activation in the output layer or wrong number of units.

The final dense layer should have 5 units for 5 classes and use 'softmax' activation for multi-class classification.

5fill in blank

hard

Fill all three blanks to compile the model with Adam optimizer, categorical crossentropy loss, and accuracy metric.

TensorFlow

model.compile(optimizer=[1], loss=[2], metrics=[[3]])

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

A'adam'

B'categorical_crossentropy'

C'accuracy'

D'mse'

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean squared error loss or wrong metrics for classification.

Adam optimizer is popular for training. Categorical crossentropy is used for multi-class classification. Accuracy measures how often predictions are correct.

Practice

1. What is the main reason to use transfer learning when working with small datasets in TensorFlow?

easy

A. To increase the size of the dataset automatically

B. To use a pretrained model's knowledge and avoid training from scratch

C. To reduce the number of layers in the model

D. To train all layers of a model from random weights

Solution

1. Step 1: Understand transfer learning purpose

   Transfer learning uses a pretrained model to leverage learned features from large datasets.

2. Step 2: Relate to small dataset challenges

   Small datasets lack enough data to train deep models well, so using pretrained knowledge helps.

3. Final Answer:

   To use a pretrained model's knowledge and avoid training from scratch -> Option B

4. Quick Check:

   Transfer learning = pretrained knowledge reuse [OK]

Hint: Transfer learning reuses pretrained features for small data [OK]

Common Mistakes:

• Thinking transfer learning increases dataset size
• Believing all layers must be retrained
• Assuming transfer learning reduces model layers

2. Which TensorFlow code snippet correctly freezes the base model layers for transfer learning?

easy

A. base_model.freeze_layers()

B. base_model.trainable = True

C. base_model.trainable = False

D. base_model.layers.freeze()

Solution

1. Step 1: Recall how to freeze layers in TensorFlow

   Setting trainable = False on the base model freezes all its layers.

2. Step 2: Check code options

   Only base_model.trainable = False is valid TensorFlow syntax to freeze layers.

3. Final Answer:

   base_model.trainable = False -> Option C

4. Quick Check:

   Freeze layers = trainable False [OK]

Hint: Set base_model.trainable = False to freeze layers [OK]

Common Mistakes:

• Using trainable = True instead of False
• Calling non-existent freeze_layers() method
• Trying to freeze layers with base_model.layers.freeze()

3. Given this code snippet, what will be the output shape of the model's predictions?

import tensorflow as tf
base_model = tf.keras.applications.MobileNetV2(input_shape=(128,128,3), include_top=False, pooling='avg')
base_model.trainable = False
model = tf.keras.Sequential([
  base_model,
  tf.keras.layers.Dense(10, activation='softmax')
])
model.build((None, 128, 128, 3))
predictions = model(tf.random.uniform((5, 128, 128, 3)))
print(predictions.shape)

medium

A. (5, 1)

B. (5, 128, 128, 3)

C. (5, 1000)

D. (5, 10)

Solution

1. Step 1: Analyze model architecture

   MobileNetV2 outputs pooled features (shape: (None, 1280)) due to pooling='avg'. Then Dense(10) outputs 10 classes.

2. Step 2: Check input batch size and output shape

   Input batch size is 5, so output shape is (5, 10) from Dense layer.

3. Final Answer:

   (5, 10) -> Option D

4. Quick Check:

   Batch 5, 10 classes output shape = (5, 10) [OK]

Hint: Output shape matches batch size and last Dense units [OK]

Common Mistakes:

• Confusing input shape with output shape
• Expecting output shape to match input image shape
• Assuming output has 1000 classes from original MobileNet

4. You wrote this code but get an error when training:

base_model = tf.keras.applications.ResNet50(weights='imagenet', include_top=False)
base_model.trainable = False
model = tf.keras.Sequential([
  base_model,
  tf.keras.layers.Dense(5, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
model.fit(train_data, epochs=3)

What is the likely cause of the error?

medium

A. Missing input shape in base_model causing shape mismatch

B. trainable should be True to train the model

C. Loss function is incorrect for classification

D. Dense layer should have no activation

Solution

1. Step 1: Check base_model input shape requirement

   ResNet50 requires input_shape when include_top=False to build properly.

2. Step 2: Identify error cause

   Without input_shape, model layers don't connect correctly, causing training errors.

3. Final Answer:

   Missing input shape in base_model causing shape mismatch -> Option A

4. Quick Check:

   Input shape needed for base_model [OK]

Hint: Always specify input_shape when include_top=False [OK]

Common Mistakes:

• Not specifying input_shape for pretrained base model
• Thinking trainable must be True to avoid errors
• Changing loss function unnecessarily

5. You have a small dataset of 200 images for a 3-class classification task. Which transfer learning approach is best to get good accuracy quickly?

hard

A. Use a pretrained model, freeze base layers, add new Dense layers, train only new layers

B. Train a new model from scratch with random weights

C. Use a pretrained model and train all layers without freezing

D. Use a pretrained model without adding new layers and train only base layers

Solution

1. Step 1: Consider dataset size and training time

   Small dataset (200 images) benefits from freezing pretrained base to avoid overfitting and long training.

2. Step 2: Choose transfer learning strategy

   Freezing base layers and training new Dense layers adapts model to new task efficiently.

3. Final Answer:

   Use a pretrained model, freeze base layers, add new Dense layers, train only new layers -> Option A

4. Quick Check:

   Freeze base + train new layers = best for small data [OK]

Hint: Freeze base layers, train new layers for small datasets [OK]

Common Mistakes:

• Training all layers on small data causing overfitting
• Training from scratch wasting time and data
• Not adding new layers for the specific task