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

NLP - Sequence Models for NLP

Consider this code snippet using TensorFlow Keras:

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

model = Sequential()
model.add(Bidirectional(LSTM(10, return_sequences=False), input_shape=(5, 8)))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy')

import numpy as np
x = np.random.random((2, 5, 8))
pred = model.predict(x)
print(pred.shape)

What will be the shape of pred?

A(2, 10)

B(2, 1)

C(5, 1)

D(2, 20)

Step-by-Step Solution

Solution:

Step 1: Understand model output shape
The Bidirectional LSTM with 10 units outputs 20 features (10 forward + 10 backward) per timestep. Since return_sequences=False, it outputs only the last timestep's features, shape (batch_size, 20).
Step 2: Dense layer output shape
The Dense layer with 1 unit outputs shape (batch_size, 1). Input batch size is 2, so output shape is (2, 1).
Final Answer:
(2, 1) -> Option B
Quick Check:
Batch size 2, Dense 1 unit = (2, 1) [OK]

Quick Trick: Dense(1) outputs shape (batch_size, 1) [OK]

Common Mistakes:

MISTAKES

Confusing return_sequences=True vs False
Forgetting bidirectional doubles units
Mixing batch and timestep dimensions

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More NLP Quizzes

Consider this code snippet using TensorFlow Keras:

Step 1: Understand model output shape

Step 2: Dense layer output shape

Final Answer:

Quick Check:

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