You have text sequences of varying lengths. You want to pad them to length 10 but keep the last 10 words only if longer. Which code correctly achieves this using Keras?

hard📝 Model Choice Q15 of 15

NLP - Sequence Models for NLP

Apad_sequences(sequences, maxlen=10, padding='post', truncating='pre')

Bpad_sequences(sequences, maxlen=10, padding='post', truncating='post')

Cpad_sequences(sequences, maxlen=10, padding='pre', truncating='post')

Dpad_sequences(sequences, maxlen=10, padding='pre', truncating='pre')

Step-by-Step Solution

Solution:

Step 1: Understand padding and truncating sides
Padding='pre' adds zeros at the start; truncating='pre' removes words from the start, keeping last words.
Step 2: Match requirement to keep last 10 words
To keep last 10 words, truncate from the start ('pre') and pad at the start ('pre').
Final Answer:
pad_sequences(sequences, maxlen=10, padding='pre', truncating='pre') -> Option D
Quick Check:
Keep last words = truncating='pre' [OK]

Quick Trick: Use truncating='pre' to keep last words, padding='pre' to pad start [OK]

Common Mistakes:

MISTAKES

Using padding='post' which pads end instead of start
Using truncating='post' which removes last words
Confusing padding and truncating parameters

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You have text sequences of varying lengths. You want to pad them to length 10 but keep the last 10 words only if longer. Which code correctly achieves this using Keras?

Step 1: Understand padding and truncating sides

Step 2: Match requirement to keep last 10 words

Final Answer:

Quick Check:

Want More Practice?