Table of Contents
Why does Keras pad_sequences matter for RNN sentiment analysis training?
Learn how Keras pad_sequences standardizes variable-length text into equal-length tensors for RNN sentiment analysis, enabling efficient batching, stable training, and compatibility with Embedding, LSTM, and GRU layers in Keras and TensorFlow.
Question
What does padding sequences help achieve in RNN training?
A. Removes noisy words from reviews
B. Ensures all sequences have the same length
C. Adds more words to the dataset
D. Improves vocabulary size automatically
Answer
B. Ensures all sequences have the same length
Explanation
Padding creates uniform-length inputs required by neural networks.
Why padding is needed
RNN-based models in Keras expect fixed-shape inputs per batch, so padding converts variable-length token sequences into uniform length arrays required by layers like Embedding, LSTM, and GRU. Without padding (and optional truncation), tensors could not be stacked into batches and the model would error on inconsistent time steps (batch_size,timesteps). Padding also enables masking so the model can ignore padded timesteps during training and evaluation.
Option analysis
A is incorrect: Removing noisy words is a text cleaning step (e.g., stopword removal), not what padding does.
C is incorrect: Padding does not add new vocabulary; it inserts a special pad token to reach a target length.
D is incorrect: Vocabulary size is controlled by tokenization settings (e.g., num_words), not by padding.
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