cs571_Week_3_Lecture_questions_2

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Jun 11, 2024

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What are the different gates present in LSTM? The remember, forget and update gate The forget, update and output gate The memory, update and output gate The remember, update and output gate Correct! These three gates help retain information in LSTM for a longer time. What component of an LSTM determines whether information in the cell state is retained? The update gate The memory gate The remember gate The forget gate (@) correct Correct! We scale by values ranging from 0 to 1 to determine what to keep from the cell state. Which component of an LSTM allows for storing information across an arbitrary number of steps? The cellstate The hidden state The output gate The remember gate @ comeet & Correct! Information in the cell state is maintained by the forget gate. Itis updated, determined by the input gate. It s also used, determined by the output gate. Inthe robot bottle lifting example, using LSTM layers as part of the model allowed the robot to utiliz which type of information? Spatial information Temporalinformation Tactile information Proprioceptive information Correct! Temporal information relates to time. Information from previous time steps is important for a dynamic task like rotating and flipping a bottle. @ correct

1. Which recurrent element is incorporated along with the hidden state in LSTMs? O Remember gste O Forgetaste [:@ Cell state. O Memorysate @ comect Correct! The updated cellstate will be used in conjunction with the current input and previous hidden state to output the next hidden state. 2. Which recurrent element s incorporated slong with the celstste in LSTMe? O Forgetgate O Remember gate @ Hiddensate O Memorystate © comect Correct! The previous hidden state il be used in conjunction with the current input and updated cell state o output the next hidden state. 3. Each gate within an LSTM unit uses which activation functions? QO Tanhactivation functions © Sigmoid or tanh activation functions @ Sigmoid sctvation functions O sigmoid or ReLU activation functions @ comeat Correct! Given that each gate is an elementwise scalar that determines how much signal passes through, it makes sense to use values between 0 and 1. 11 point 1/1point 111 point

4. Which problem for RNNs was the LSTM developed to address? 1/1point O Memory leaks QO Lackof gating units @ Vanishing gradients QO Too many parameters @ correct Correct! By using the cell state and calculating partial derivatives with respect to the weights of the forget gate, we don't need to rely on backpropagation through time to update parameters dealing with distant previous steps. 5. Whatis a drawback or weakness of the LSTM? 1/1point (@ Requires training for many more parameters O Requires a larger testing dataset QO Exploding gradients Q Vanishing gradients © correct Correct! Additional weights must be learned for each gate of the LSTM. 6. What are the three gates of an LSTM? 1/1 point @ Input gate, forget gate, output gate O Update gate, remember gate, cell gate (O Update gate, forget gate, hidden gate O Input gate, remember gate, output gate () correct Correct! There are other variants of RNNs, such as GRUs, which only use an update and reset gate. These will not be covered in the scope of this course. Why s a fully connected output layer needed after the recurrent layer? To properly scale the RNN's activations to target outputs. To sid in performing backpropagation through time. To ready the hidden state for the next forward pass. To connectinput to output. @ comeet Correct s unlikely that the RNN's actiations will directly ft o the trget outputs, 5o we pass e through an additional fully connected layer and can apply a diferent actation function as needed.

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