LSTM for text in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the LSTM layer from Keras.

NLP

from tensorflow.keras.layers import [1]

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ADense

BLSTM

CConv2D

DDropout

Attempts:

3 left

💡 Hint

Common Mistakes

Importing Dense or Conv2D instead of LSTM.

Forgetting to import LSTM from keras.layers.

The LSTM layer is imported from tensorflow.keras.layers as LSTM.

2fill in blank

medium

Complete the code to add an LSTM layer with 50 units to the model.

NLP

model.add(LSTM([1]))

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A10

B100

C50

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using too few units like 5 which may underfit.

Using too many units like 100 which may overfit or slow training.

The LSTM layer is created with 50 units to capture sequence features.

3fill in blank

hard

Fix the error in the code to compile the model with categorical crossentropy loss.

NLP

model.compile(optimizer='adam', loss='[1]', metrics=['accuracy'])

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Acategorical_crossentropy

Bbinary_crossentropy

Chinge

Dmean_squared_error

Attempts:

3 left

💡 Hint

Common Mistakes

Using binary_crossentropy for multi-class problems.

Using mean_squared_error which is for regression.

For multi-class text classification, categorical_crossentropy is the correct loss function.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

NLP

{word: [1] for word in words if len(word) [2] 3}

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of its length.

Using less than instead of greater than in the condition.

The dictionary comprehension maps each word to its length if the length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their counts if count is greater than 0.

NLP

{ [1]: [2] for [3], count in word_counts.items() if count > 0 }

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Aword.upper()

Bcount

Cword

Dcount.upper()

Attempts:

3 left

💡 Hint

Common Mistakes

Using count.upper() which is invalid since count is a number.

Using word instead of word.upper() as key.

The comprehension maps each word in uppercase to its count, filtering counts greater than zero.