Transfer learning for small datasets in TensorFlow - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why use transfer learning on small datasets?

Imagine you have a tiny dataset of 100 images to classify. Why is transfer learning a good choice here?

A It uses a pre-trained model to extract useful features, reducing the need for large data.

B It trains a new model from scratch to perfectly fit the small dataset.

C It ignores the small dataset and uses only the pre-trained model's predictions.

D It increases the dataset size by copying images multiple times.

💡 Hint

Think about how pre-trained models help when data is limited.

Transfer learning leverages knowledge from a large dataset to help learn features, so the small dataset doesn't need to teach everything from zero.

❓ Predict Output

intermediate

Output shape after freezing base model layers

Given this TensorFlow code snippet, what is the output shape of the model's predictions?

TensorFlow

import tensorflow as tf
base_model = tf.keras.applications.MobileNetV2(input_shape=(128,128,3), include_top=False, weights='imagenet')
base_model.trainable = False
model = tf.keras.Sequential([
    base_model,
    tf.keras.layers.GlobalAveragePooling2D(),
    tf.keras.layers.Dense(5, activation='softmax')
])
output_shape = model.output_shape

A (None, 128, 128, 3)

B (None, 5)

C (None, 7, 7, 1280)

D (None, 1)

💡 Hint

Check the last Dense layer's output units.

The model outputs a vector of length 5 for each input, matching the Dense layer's units.

❓ Hyperparameter

advanced

Best learning rate choice for fine-tuning a pre-trained model

You want to fine-tune a pre-trained model on a small dataset. Which learning rate is most suitable?

A 0.1 (high learning rate)

B 0.001 (moderate learning rate)

C 1.0 (extremely high learning rate)

D 0.00001 (very low learning rate)

💡 Hint

Fine-tuning usually requires careful, small updates.

A very low learning rate prevents large changes to the pre-trained weights, preserving learned features while adapting to new data.

❓ Metrics

advanced

Interpreting validation accuracy in transfer learning

After training a transfer learning model on a small dataset, you see training accuracy at 95% but validation accuracy at 60%. What does this indicate?

A The model is underfitting and needs more training.

B The validation data is too easy compared to training data.

C The model is overfitting the training data.

D The model is perfectly generalized.

💡 Hint

Think about the difference between training and validation accuracy.

High training accuracy but low validation accuracy means the model learned training data too well but fails to generalize.

🔧 Debug

expert

Why does this transfer learning code raise an error?

Consider this TensorFlow code snippet. It raises a ValueError when running. What is the cause?

TensorFlow

import tensorflow as tf
base_model = tf.keras.applications.ResNet50(weights='imagenet', include_top=False, input_shape=(64,64,3))
base_model.trainable = False
model = tf.keras.Sequential([
    base_model,
    tf.keras.layers.GlobalAveragePooling2D(),
    tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
import numpy as np
x = np.random.rand(32, 64, 64, 3)
y = np.random.randint(0, 10, size=(32,))
model.fit(x, y, epochs=1)

A Input shape (64,64,3) is too small for ResNet50 base model.

B The batch size 32 is too large for this model.

C The base model must be trainable for transfer learning to work.

D Loss function sparse_categorical_crossentropy is incompatible with softmax activation.

💡 Hint

Check the minimum input size requirements of ResNet50.

ResNet50 requires input images of at least 197x197 pixels when include_top=False; 64x64 is too small and causes a shape mismatch error.