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Menguji gerbang GRU, pembaruan status tersembunyi, perilaku penyetelan ulang, perbedaan LSTM, status kandidat, dan batas perbandingan arsitektur.

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How many gates does the GRU cell in the guide use?

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  1. 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
  2. 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
  3. What does the reset gate control?
    • The final sequence length
    • Kecepatan pembelajaran pengoptimal
    • How much past information is used when forming a candidate state
    • Whether the update gate exists
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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