Feature extraction approach in TensorFlow - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of feature extraction with TensorFlow Hub

What is the shape of the output tensor after extracting features from a batch of 3 images using a TensorFlow Hub MobileNetV2 feature vector model?

TensorFlow

import tensorflow as tf
import tensorflow_hub as hub

model_url = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4"
feature_extractor = hub.KerasLayer(model_url, input_shape=(224, 224, 3))

batch_images = tf.random.uniform([3, 224, 224, 3])
features = feature_extractor(batch_images)
output_shape = features.shape
print(output_shape)

A (3, 1280)

B (224, 224, 3)

C (3, 1001)

D (1, 1280)

💡 Hint

The MobileNetV2 feature vector outputs a 1280-dimensional vector per image.

The feature extractor outputs a 2D tensor where each row corresponds to one image's feature vector. Since the batch size is 3, the output shape is (3, 1280).

❓ Model Choice

intermediate

Choosing a pre-trained model for feature extraction

You want to extract features from images to use in a new classifier. Which pre-trained model is best suited for feature extraction without the classification head?

A A fully connected network trained on tabular data

B ResNet50 with include_top=True

C ResNet50 with include_top=False

D A randomly initialized CNN

💡 Hint

Feature extraction requires a model trained on images without the final classification layer.

Using ResNet50 with include_top=False removes the classification head, allowing extraction of image features useful for new tasks.

❓ Hyperparameter

advanced

Effect of freezing layers during feature extraction

When using a pre-trained CNN for feature extraction, what is the effect of freezing all convolutional layers during training of a new classifier on top?

A The convolutional layers' weights do not update, speeding up training and preserving learned features.

B The convolutional layers' weights update, allowing the model to learn new features from scratch.

C The model will overfit immediately due to frozen layers.

D The model will not train because frozen layers cause errors.

💡 Hint

Freezing layers means their weights stay fixed during training.

Freezing convolutional layers keeps their learned features intact and reduces training time since fewer parameters update.

❓ Metrics

advanced

Evaluating feature extraction effectiveness

You extracted features from images using a pre-trained model and trained a new classifier. Which metric best indicates if the extracted features are useful for the classification task?

A Training loss of the pre-trained model

B Validation accuracy of the new classifier

C Number of layers in the pre-trained model

D Size of the input images

💡 Hint

The quality of features is reflected in how well the classifier performs on unseen data.

Validation accuracy shows how well the classifier generalizes using the extracted features, indicating their usefulness.

🔧 Debug

expert

Debugging feature extraction output shape mismatch

You use a TensorFlow Hub feature extractor expecting output shape (None, 2048), but your code raises a shape mismatch error. Which code change fixes this?

TensorFlow

import tensorflow_hub as hub
import tensorflow as tf

feature_extractor = hub.KerasLayer('https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/5', input_shape=(224, 224, 3))

inputs = tf.random.uniform([5, 224, 224, 3])
features = feature_extractor(inputs)
print(features.shape)

A Change input_shape to (None, 224, 224, 3) in KerasLayer

B Resize input images to (299, 299, 3) before passing to feature_extractor

C Use a different model URL that outputs shape (None, 1280)

D Change input_shape to (224, 224, 3) and ensure inputs have batch size dimension

💡 Hint

The input_shape parameter expects the shape of a single image, excluding batch size.

The input_shape should be (224, 224, 3) for single images; inputs must have batch dimension. This matches the model's expected input and output shapes.