RNN for text classification in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the RNN layer from Keras.

NLP

from tensorflow.keras.layers import [1]

Drag options to blanks, or click blank then click option'

ADense

BSimpleRNN

CConv2D

DMaxPooling2D

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing Dense which is a fully connected layer, not recurrent.

Choosing Conv2D or MaxPooling2D which are for images.

The SimpleRNN layer is the basic recurrent neural network layer used for sequence data like text.

2fill in blank

medium

Complete the code to add an embedding layer for text input.

NLP

model.add(Embedding(input_dim=[1], output_dim=64, input_length=100))

Drag options to blanks, or click blank then click option'

A100

B64

C10

D5000

Attempts:

3 left

💡 Hint

Common Mistakes

Using input_dim equal to output_dim or input_length.

Choosing a very small number like 10 which is too small for vocab size.

input_dim is the size of the vocabulary, typically a larger number like 5000 for text.

3fill in blank

hard

Fix the error in the RNN layer definition to return the last output only.

NLP

model.add(SimpleRNN(32, return_sequences=[1]))

Drag options to blanks, or click blank then click option'

AFalse

BTrue

CNone

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Setting return_sequences=True which outputs the full sequence.

Using None or 0 which are invalid values for this parameter.

return_sequences=False makes the RNN output only the last output, suitable for classification.

4fill in blank

hard

Fill both blanks to compile the model with appropriate loss and optimizer for binary classification.

NLP

model.compile(loss=[1], optimizer=[2], metrics=['accuracy'])

Drag options to blanks, or click blank then click option'

A'binary_crossentropy'

B'categorical_crossentropy'

C'adam'

D'sgd'

Attempts:

3 left

💡 Hint

Common Mistakes

Using categorical_crossentropy for binary classification.

Using sgd optimizer which may be slower to converge.

For binary classification, use 'binary_crossentropy' loss and 'adam' optimizer for good performance.

5fill in blank

hard

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

NLP

word_lengths = { [1] : [2] for [3] in words if len([3]) > 3 }

Drag options to blanks, or click blank then click option'

Aword

Blen(word)

Dlen(words)

Attempts:

3 left

💡 Hint

Common Mistakes

Using len(words) which is the length of the list, not each word.

Using inconsistent variable names in the comprehension.

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

Practice

1. What is the main reason to use an RNN (Recurrent Neural Network) for text classification tasks?

easy

A. Because RNNs only work with images

B. Because RNNs are faster than other neural networks

C. Because RNNs do not require any training data

D. Because RNNs can remember the order of words and context in sentences

Solution

1. Step 1: Understand RNN's role in text

   RNNs process sequences of words one by one, keeping track of previous words to understand context.

2. Step 2: Identify why order matters

   Text meaning depends on word order, and RNNs remember this order, unlike simple models.

3. Final Answer:

   Because RNNs can remember the order of words and context in sentences -> Option D

4. Quick Check:

   RNN remembers sequence = D [OK]

Hint: RNNs are for sequences and context, not speed or images [OK]

Common Mistakes:

• Thinking RNNs are faster than other models
• Believing RNNs don't need training data
• Confusing RNNs with image-only models

2. Which of the following is the correct way to add a SimpleRNN layer with 32 units in Keras for text classification?

easy

A. model.add(SimpleRNN(32, input_shape=(None, 100)))

B. model.add(SimpleRNN(units=32))

C. model.add(SimpleRNN(32))

D. model.add(SimpleRNN(32, activation='relu'))

Solution

1. Step 1: Recall SimpleRNN syntax

   SimpleRNN requires number of units and input shape for the first layer in a model.

2. Step 2: Check options for correct usage

   model.add(SimpleRNN(32, input_shape=(None, 100))) correctly specifies 32 units and input shape (sequence length unknown, 100 features).

3. Final Answer:

   model.add(SimpleRNN(32, input_shape=(None, 100))) -> Option A

4. Quick Check:

   SimpleRNN needs units and input shape first layer = A [OK]

Hint: First RNN layer needs input_shape, else error [OK]

Common Mistakes:

• Omitting input_shape in first RNN layer
• Using activation='relu' instead of default tanh
• Passing units as keyword incorrectly

3. Given this Keras model snippet for text classification:

model = Sequential()
model.add(Embedding(input_dim=5000, output_dim=16, input_length=10))
model.add(SimpleRNN(8))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

history = model.fit(X_train, y_train, epochs=2, batch_size=32)
print(history.history['accuracy'][-1])

What does history.history['accuracy'][-1] represent?

medium

A. The accuracy of the model on the entire training data after the last epoch

B. The accuracy of the model on the last training batch of the last epoch

C. The loss value of the model after the last epoch

D. The accuracy of the model on the validation data after the last epoch

Solution

1. Step 1: Understand Keras history object

   history.history['accuracy'] stores training accuracy per epoch, so last element is final epoch training accuracy.

2. Step 2: Differentiate training vs batch vs validation

   It is training accuracy on all training data after last epoch, not batch or validation accuracy.

3. Final Answer:

   The accuracy of the model on the entire training data after the last epoch -> Option A

4. Quick Check:

   history.history['accuracy'][-1] = final training accuracy [OK]

Hint: history.history['accuracy'] is training accuracy per epoch [OK]

Common Mistakes:

• Confusing batch accuracy with epoch accuracy
• Mixing loss and accuracy values
• Assuming validation accuracy without validation data

4. You wrote this code to build an RNN model for text classification but get an error:

model = Sequential()
model.add(SimpleRNN(16))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

What is the most likely cause of the error?

medium

A. Dense layer cannot have sigmoid activation

B. SimpleRNN units must be 32 or more

C. Missing input shape for the first SimpleRNN layer

D. Loss function 'binary_crossentropy' is invalid

Solution

1. Step 1: Check first layer requirements

   The first RNN layer must know input shape to accept data; missing input_shape causes error.

2. Step 2: Validate other options

   Sigmoid activation in Dense is valid for binary classification; units can be any positive integer; binary_crossentropy is valid loss.

3. Final Answer:

   Missing input shape for the first SimpleRNN layer -> Option C

4. Quick Check:

   First RNN layer needs input_shape = B [OK]

Hint: Always set input_shape in first RNN layer to avoid errors [OK]

Common Mistakes:

• Assuming activation or loss function causes error
• Thinking units must be 32 or more
• Ignoring input shape requirement

5. You want to improve your RNN text classifier by adding an Embedding layer before the SimpleRNN. Which of these changes is correct and why?

Original:
model = Sequential()
model.add(SimpleRNN(16, input_shape=(10, 100)))
model.add(Dense(1, activation='sigmoid'))

Change:
model = Sequential()
model.add(Embedding(input_dim=5000, output_dim=100, input_length=10))
model.add(SimpleRNN(16))
model.add(Dense(1, activation='sigmoid'))

hard

A. Incorrect: Embedding output_dim must match SimpleRNN units

B. Correct: Embedding converts word indices to vectors, so SimpleRNN input shape changes automatically

C. Incorrect: Embedding layer should come after SimpleRNN

D. Incorrect: Embedding layer requires activation='relu'

Solution

1. Step 1: Understand Embedding role

   Embedding layer converts integer word indices into dense vectors, preparing input for RNN.

2. Step 2: Check model order and shapes

   Embedding outputs shape (batch, sequence_length, output_dim), matching SimpleRNN expected input shape, so no input_shape needed in SimpleRNN.

3. Final Answer:

   Correct: Embedding converts word indices to vectors, so SimpleRNN input shape changes automatically -> Option B

4. Quick Check:

   Embedding before RNN changes input shape correctly = C [OK]

Hint: Embedding layer must come before RNN to convert words to vectors [OK]

Common Mistakes:

• Placing Embedding after RNN
• Matching output_dim to RNN units incorrectly
• Adding activation to Embedding layer