RNN-based text generation in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

NLP

from tensorflow.keras.layers import [1]

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

ASimpleRNN

BDense

CConv2D

DMaxPooling2D

Attempts:

3 left

💡 Hint

Common Mistakes

Importing Dense instead of SimpleRNN

Confusing convolutional layers with RNN layers

The SimpleRNN layer is used for building recurrent neural networks for sequence data.

2fill in blank

medium

Complete the code to define an RNN model with an embedding layer and a SimpleRNN layer.

NLP

model = Sequential([
    Embedding(input_dim=1000, output_dim=64),
    [1](128),
    Dense(1000, activation='softmax')
])

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

AConv1D

BDropout

CLSTM

DSimpleRNN

Attempts:

3 left

💡 Hint

Common Mistakes

Using Conv1D instead of a recurrent layer

Using Dropout where a recurrent layer is needed

SimpleRNN is the recurrent layer used here to process the embedded sequences.

3fill in blank

hard

Fix the error in the code to compile the RNN model for text generation.

NLP

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

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

A'categorical_crossentropy'

B'hinge'

C'binary_crossentropy'

D'mse'

Attempts:

3 left

💡 Hint

Common Mistakes

Using binary_crossentropy for multi-class output

Using mse which is for regression tasks

For multi-class text generation, 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_lengths = {word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B<

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length

Using less than instead of greater than in the condition

We want the length of each word as the value and only include words longer than 3 characters.

5fill in blank

hard

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

NLP

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

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

Aword.upper()

Bcount

C>

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using less than instead of greater than in the condition

We convert words to uppercase as keys, keep counts as values, and filter counts greater than zero.

Practice

1. What is the main purpose of using an RNN in text generation?

easy

A. To count the number of words in a sentence

B. To sort words alphabetically

C. To translate text into another language

D. To learn patterns in sequences of words to predict the next word

Solution

1. Step 1: Understand RNN function in text

   RNNs process sequences step-by-step, remembering past words to predict what comes next.

2. Step 2: Identify the goal of text generation

   The goal is to generate new text by predicting the next word based on learned patterns.

3. Final Answer:

   To learn patterns in sequences of words to predict the next word -> Option D

4. Quick Check:

   RNN predicts next word in sequence = C [OK]

Hint: RNNs remember word order to guess the next word [OK]

Common Mistakes:

• Thinking RNNs just count words
• Confusing RNNs with sorting algorithms
• Assuming RNNs translate text directly

2. Which of the following is the correct way to define an embedding layer in a Keras RNN model for text generation?

easy

A. Embedding(input_length=64, input_dim=10, output_dim=1000)

B. Embedding(output_dim=1000, input_dim=64, input_length=10)

C. Embedding(input_dim=1000, output_dim=64, input_length=10)

D. Embedding(input_dim=10, output_dim=1000, input_length=64)

Solution

1. Step 1: Recall embedding layer parameters

   Embedding layers require input_dim (vocab size), output_dim (embedding size), and input_length (sequence length).

2. Step 2: Match parameters correctly

   Embedding(input_dim=1000, output_dim=64, input_length=10) correctly sets input_dim=1000 (vocab size), output_dim=64 (embedding size), input_length=10 (sequence length).

3. Final Answer:

   Embedding(input_dim=1000, output_dim=64, input_length=10) -> Option C

4. Quick Check:

   Embedding(input_dim, output_dim, input_length) = A [OK]

Hint: Input_dim = vocab size, output_dim = embedding size [OK]

Common Mistakes:

• Swapping input_dim and output_dim
• Confusing input_length with output_dim
• Using wrong parameter names

3. Given this code snippet for training an RNN text generator, what will be the shape of the input data X if the vocabulary size is 5000, sequence length is 20, and batch size is 32?

model = Sequential()
model.add(Embedding(input_dim=5000, output_dim=50, input_length=20))
model.add(SimpleRNN(100))
model.add(Dense(5000, activation='softmax'))

X = np.random.randint(0, 5000, (32, 20))

medium

A. (20, 32)

B. (32, 20)

C. (32, 50)

D. (5000, 20)

Solution

1. Step 1: Understand input shape for embedding

   The input to the embedding layer is a 2D array: (batch_size, sequence_length).

2. Step 2: Check given data shape

   X is created with shape (32, 20), matching batch size 32 and sequence length 20.

3. Final Answer:

   (32, 20) -> Option B

4. Quick Check:

   Input shape = (batch_size, sequence_length) = (32, 20) [OK]

Hint: Input shape = batch size by sequence length [OK]

Common Mistakes:

• Confusing embedding output shape with input shape
• Swapping batch size and sequence length
• Assuming embedding changes input shape

4. You wrote this code to train an RNN text generator but get a shape mismatch error:

model = Sequential()
model.add(Embedding(input_dim=10000, output_dim=64, input_length=15))
model.add(SimpleRNN(128))
model.add(Dense(10000, activation='softmax'))

X = np.random.randint(0, 10000, (64, 15))
y = np.random.randint(0, 10000, (64, 15))  # target labels

model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')
model.fit(X, y, epochs=5)

What is the main issue causing the error?

medium

A. Target labels y should be shape (64,) with integer word indices, not (64, 15)

B. Embedding input_dim is too large

C. SimpleRNN units should match output_dim of embedding

D. Loss function sparse_categorical_crossentropy is incorrect

Solution

1. Step 1: Check target label shape for next word prediction

   For next word prediction, y should be a 1D array of word indices (batch_size,), not sequences.

2. Step 2: Identify mismatch in y shape

   y has shape (64, 15), which causes shape mismatch with model output (64, 10000).

3. Final Answer:

   Target labels y should be shape (64,) with integer word indices, not (64, 15) -> Option A

4. Quick Check:

   y shape must match output shape = B [OK]

Hint: Targets for next word are 1D, not sequences [OK]

Common Mistakes:

• Using sequences as targets instead of next word
• Confusing embedding size with RNN units
• Changing loss function unnecessarily

5. You want to generate text using a trained RNN model. Which approach correctly generates text word by word after training?

hard

A. Feed the model the initial seed sequence, predict the next word, append it, then use the updated sequence to predict again

B. Feed the entire training dataset at once to get all generated words

C. Use the model to predict all words simultaneously without updating input

D. Randomly select words from the vocabulary without using the model

Solution

1. Step 1: Understand sequential generation

   Text generation uses the model to predict one word at a time, updating input with new words.

2. Step 2: Identify correct iterative approach

   Feed the model the initial seed sequence, predict the next word, append it, then use the updated sequence to predict again describes feeding seed, predicting next word, appending it, and repeating, which is correct.

3. Final Answer:

   Feed the model the initial seed sequence, predict the next word, append it, then use the updated sequence to predict again -> Option A

4. Quick Check:

   Generate word-by-word with updated input = D [OK]

Hint: Generate text stepwise, updating input each time [OK]

Common Mistakes:

• Trying to generate all words at once
• Ignoring the need to update input sequence
• Selecting words randomly without model