[Solved] Examine this code snippet:from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Bidirectional, LSTM model = Sequential() model.... | NLP

NLP - Sequence Models for NLP

Examine this code snippet:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Bidirectional, LSTM

model = Sequential()
model.add(Bidirectional(LSTM(32, input_shape=(10, 8))))
model.add(LSTM(16))
model.compile(optimizer='adam', loss='mse')

What is the likely cause of an error during training?

AThe optimizer 'adam' is not compatible with LSTM layers.

BBidirectional wrapper cannot be used with input_shape argument.

CThe second LSTM layer lacks return_sequences=true, causing shape mismatch.

DThe loss function 'mse' is invalid for regression tasks.

Step-by-Step Solution

Solution:

Step 1: Analyze layer outputs
Bidirectional LSTM outputs sequences by default unless return_sequences=false.
Step 2: Check second LSTM input
The second LSTM expects 3D input if return_sequences=true in previous layer; otherwise, shape mismatch occurs.
Step 3: Identify error
Since first layer outputs sequences, second LSTM must have return_sequences=true or input shape must match.
Final Answer:
The second LSTM layer lacks return_sequences=true, causing shape mismatch. -> Option C
Quick Check:
Check return_sequences for stacked LSTMs [OK]

Quick Trick: Stacked LSTMs need return_sequences=true except last [OK]

Common Mistakes:

MISTAKES

Assuming input_shape cannot be in Bidirectional
Blaming optimizer or loss function incorrectly

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More NLP Quizzes

Examine this code snippet:

Step 1: Analyze layer outputs

Step 2: Check second LSTM input

Step 3: Identify error

Final Answer:

Quick Check:

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