[Solved] Find the bug in this Bidirectional LSTM code snippet:from tensorflow.keras.layers import Bidirectional, LSTM, Dense, Input from tensorflow.keras.... | NLP

NLP - Sequence Models for NLP

Find the bug in this Bidirectional LSTM code snippet:

from tensorflow.keras.layers import Bidirectional, LSTM, Dense, Input
from tensorflow.keras.models import Model

inputs = Input(shape=(5, 10))
lstm = Bidirectional(LSTM(20, return_sequences=True))(inputs)
dense = Dense(1)(lstm)
model = Model(inputs, dense)
model.compile('adam', 'binary_crossentropy')

AInput shape is incorrect for LSTM

BDense layer applied to 3D tensor without flattening

CLoss function incompatible with output shape

DBidirectional wrapper missing merge_mode argument

Step-by-Step Solution

Solution:

Step 1: Check output shape of Bidirectional LSTM
With return_sequences=True and units=20, output shape is (batch, 5, 40).
Step 2: Analyze Dense layer input
Dense expects 2D input (batch, features), but receives 3D tensor. This causes shape mismatch error.
Final Answer:
Dense layer applied to 3D tensor without flattening -> Option B
Quick Check:
Dense needs 2D input; flatten or use TimeDistributed [OK]

Quick Trick: Dense layers need 2D input; flatten 3D outputs first [OK]

Common Mistakes:

MISTAKES

Ignoring tensor shape mismatch
Assuming input shape is wrong
Thinking merge_mode is mandatory

Master "Sequence Models for NLP" in NLP

9 interactive learning modes - each teaches the same concept differently

Learn Why Deep Model Try Challenge Experiment Recall Metrics

More NLP Quizzes

Find the bug in this Bidirectional LSTM code snippet:

Step 1: Check output shape of Bidirectional LSTM

Step 2: Analyze Dense layer input

Final Answer:

Quick Check:

Want More Practice?