Guide-linked quiz · Medium Level
Gated Recurrent Units Quiz
Tests GRU gates, hidden-state updates, reset behavior, LSTM differences, candidate states, and limits of architectural comparisons.
Question 1 of 8
How many gates does the GRU cell in the guide use?
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Answers and explanations appear as you take the quiz.
- How many gates does the GRU cell in the guide use?
- Two: an update gate and a reset gate
- Three plus a separate cell state
- One gate per sequence position
- No gates
- What does the update gate control?
- Which input token is removed permanently
- The number of sequence layers
- Whether to compute a separate cell state
- How much of the previous hidden state to carry versus new candidate information
- What does the reset gate control?
- The final sequence length
- The learning rate of the optimizer
- How much past information is used when forming a candidate state
- Whether the update gate exists
- How does the GRU represent memory compared with an LSTM?
- It uses a separate cell state and three gates
- It uses one hidden state rather than a separate cell state
- It has no recurrent state
- It stores the entire training sequence in the gates
- For a GRU update gate z, what does a value near one do?
- Favor the new candidate state over the previous hidden state
- Control how much past information is used in the reset-gated candidate
- Decide when the sequence should stop processing
- Carry most of the previous hidden state into the next state
- Why can gating help with long-range dependencies?
- It gives the model a learned route to preserve or update hidden information across steps
- It eliminates all recurrent computation
- It guarantees perfect memory at any length
- It turns a sequence into an unordered set
- What distinguishes a GRU candidate state from the prior hidden state?
- It is always the ground-truth next token
- It is computed from the current input and reset-gated past information
- It is copied unchanged from the input
- It stores a second LSTM cell
- What should be concluded from fewer GRU gates than LSTM gates?
- GRUs always outperform LSTMs
- GRUs cannot model sequences
- The architecture is simpler in this respect, but task performance still needs evaluation
- Fewer gates guarantee better accuracy
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