C5W1 Quiz - Recurrent Neural Networks

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Ans: A
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Ans: A
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Ans: B、D
4.
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Ans: C

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Ans: B
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Ans: D
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Ans: C
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Ans: C

  1. Suppose your training examples are sentences (sequences of words). Which of the following refers to the j^{th}jth word in the i^{th}ith training example?
    Ans: x(i)

  2. Consider this RNN: This specific type of architecture is appropriate when:
    Ans: T_x = T_y

  3. To which of these tasks would you apply a many-to-one RNN architecture? (Check all that apply).
    Ans: Sentiment classification (input a piece of text and output a 0/1 to denote positive or negative sentiment)
    Gender recognition from speech (input an audio clip and output a label indicating the speaker’s gender)

  4. You are training this RNN language model. At the t^{th} time step, what is the RNN doing? Choose the best answer.
    Ans: Estimating P(y^{} | y^{<1>}, y^{<2>}, …, y^{})

  5. You have finished training a language model RNN and are using it to sample random sentences, as follows:
    What are you doing at each time step t?
    Ans: (i) Use the probabilities output by the RNN to randomly sample a chosen word for that time-step as {y}^{}. (ii) Then pass this selected word to the next time-step.

  6. You are training an RNN, and find that your weights and activations are all taking on the value of NaN (“Not a Number”). Which of these is the most likely cause of this problem?
    Ans: Exploding gradient problem.

  7. Suppose you are training a LSTM. You have a 10000 word vocabulary, and are using an LSTM with 100-dimensional activations a^{}. What is the dimension of Γu at each time step?
    Ans: 10000

  8. Here’re the update equations for the GRU.
    Ans: Betty’s model (removing Γr), because if Γu ≈ 0 for a timestep, the gradient can propagate back through that timestep without much decay.

  9. Here are the equations for the GRU and the LSTM: From these, we can see that the Update Gate and Forget Gate in the LSTM play a role similar to _ and __ in the GRU. What should go in the the blanks?

Ans: Γu and 1-Γu

  1. You have a pet dog whose mood is heavily dependent on the current and past few days’ weather. You’ve collected data for the past 365 days on the weather, which you represent as a sequence as x^{<1>}, …, x^{<365>}. You’ve also collected data on your dog’s mood, which you represent as y^{<1>}, …, y^{<365>}. You’d like to build a model to map from x→y. Should you use a Unidirectional RNN or Bidirectional RNN for this problem?

Ans: Unidirectional RNN, because the value of y^{} depends only on x^{<1>}, …, x^{}, but not on x^{}, …, x^{<365>}