GRU for text in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the GRU layer from TensorFlow Keras.

NLP

from tensorflow.keras.layers import [1]

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ALSTM

BGRU

CConv2D

DDense

Attempts:

3 left

💡 Hint

Common Mistakes

Importing LSTM instead of GRU

Importing Dense or Conv2D by mistake

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

2fill in blank

medium

Complete the code to create a GRU layer with 64 units and return sequences.

NLP

gru_layer = GRU([1], return_sequences=True)

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A256

B32

C128

D64

Attempts:

3 left

💡 Hint

Common Mistakes

Using too few units like 32 which may underfit

Using too many units like 256 which may overfit or slow training

The GRU layer is created with 64 units to capture features from the input sequence.

3fill in blank

hard

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

NLP

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

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Aadam

Badagrad

Crmsprop

Dsgd

Attempts:

3 left

💡 Hint

Common Mistakes

Using SGD which may require more tuning

Using RMSprop or Adagrad without understanding their effects

The Adam optimizer is commonly used for training GRU models on text classification tasks.

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 operator instead of greater than

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

result = [1]: [2] for word, count in word_counts.items() if count [3] 0}

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Aword.upper()

Bcount

C>

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.lower() instead of word.upper()

Using less than operator instead of greater than

The comprehension maps uppercase words to their counts only if the count is positive.