Text generation with RNN in TensorFlow - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Model Choice

intermediate

Choosing the right RNN layer for text generation

You want to build a text generation model using TensorFlow. Which RNN layer is best suited to capture long-range dependencies in text?

A tf.keras.layers.LSTM

B tf.keras.layers.SimpleRNN

C tf.keras.layers.Dense

D tf.keras.layers.Conv1D

💡 Hint

Think about which layer can remember information over many time steps.

LSTM layers are designed to remember information for long sequences, making them ideal for text generation tasks.

❓ Predict Output

intermediate

Output shape of RNN layer in text generation model

Consider this TensorFlow code snippet for a text generation model:

model = tf.keras.Sequential([
  tf.keras.layers.Embedding(input_dim=1000, output_dim=64),
  tf.keras.layers.LSTM(128, return_sequences=True),
  tf.keras.layers.Dense(1000)
])

output_shape = model.output_shape

What is the value of output_shape?

A (None, None, 128)

B (None, 128)

C (None, 64)

D (None, None, 1000)

💡 Hint

Check the last Dense layer's output units and the return_sequences=True setting.

The LSTM outputs a sequence (because return_sequences=True), so the shape is (batch_size, sequence_length, 128). The Dense layer maps each time step to 1000 units, so final output shape is (batch_size, sequence_length, 1000).

❓ Hyperparameter

advanced

Effect of sequence length on training RNN for text generation

When training an RNN for text generation, what is the main effect of increasing the input sequence length?

A It reduces the model's ability to learn long-term dependencies.

B It increases the computational cost and memory usage during training.

C It decreases the vocabulary size the model can handle.

D It makes the model ignore the order of words in the sequence.

💡 Hint

Think about how longer sequences affect the amount of data processed per training step.

Longer input sequences mean the model processes more time steps per batch, increasing computation and memory needs.

❓ Metrics

advanced

Choosing the right metric for text generation model evaluation

Which metric is most appropriate to evaluate the quality of a text generation RNN model during training?

A F1 Score

B Mean Squared Error (MSE)

C Perplexity

D Accuracy

💡 Hint

Consider a metric that measures how well the model predicts the next word probabilities.

Perplexity measures how well a probability model predicts a sample. Lower perplexity means better predictions in language models.

🔧 Debug

expert

Debugging vanishing gradient in RNN text generation model

You trained an RNN text generation model but it fails to learn long-term dependencies. Which of the following is the most likely cause?

A Using SimpleRNN layers without gating mechanisms causing vanishing gradients

B Setting the learning rate too high causing unstable training

C Using too large batch size causing overfitting

D Using LSTM layers instead of SimpleRNN layers

💡 Hint

Think about which RNN type struggles with remembering information over many steps.

SimpleRNN layers lack gating mechanisms and often suffer from vanishing gradients, making it hard to learn long-term dependencies.

Practice

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

easy

A. To sort words alphabetically

B. To count the number of words in a sentence

C. To learn patterns in sequences of words and predict the next word

D. To translate text into images

Solution

1. Step 1: Understand RNN function in text

   RNNs process sequences and learn how words follow each other.

2. Step 2: Identify the goal of text generation

   The goal is to predict the next word based on previous words.

3. Final Answer:

   To learn patterns in sequences of words and predict the next word -> Option C

4. Quick Check:

   RNN predicts next word = B [OK]

Hint: RNNs handle sequences and predict next items [OK]

Common Mistakes:

• Thinking RNNs sort or count words
• Confusing RNN with image processing
• Assuming RNNs translate text directly

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

easy

A. tf.keras.layers.MaxPooling1D(pool_size=2)

B. tf.keras.layers.Dense(units=embedding_dim)

C. tf.keras.layers.Conv2D(filters=embedding_dim, kernel_size=3)

D. tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim)

Solution

1. Step 1: Identify embedding layer syntax

   Embedding layers use input_dim and output_dim to map words to vectors.

2. Step 2: Match correct TensorFlow layer

   tf.keras.layers.Embedding is the correct layer for word embeddings.

3. Final Answer:

   tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim) -> Option D

4. Quick Check:

   Embedding layer uses input_dim and output_dim = A [OK]

Hint: Embedding layer uses input_dim and output_dim parameters [OK]

Common Mistakes:

• Using Dense instead of Embedding for word vectors
• Confusing Conv2D with text embedding
• Applying MaxPooling1D incorrectly for embeddings

3. Given the following code snippet, what will be the shape of the output from the RNN layer?

model = tf.keras.Sequential([
  tf.keras.layers.Embedding(input_dim=1000, output_dim=64),
  tf.keras.layers.SimpleRNN(128, return_sequences=False),
  tf.keras.layers.Dense(1000, activation='softmax')
])

input_shape = (32, 10)  # batch size 32, sequence length 10
output = model(tf.random.uniform(input_shape, maxval=1000, dtype=tf.int32))
print(output.shape)

medium

A. (32, 1000)

B. (32, 10, 128)

C. (10, 128)

D. (32, 128)

Solution

1. Step 1: Analyze RNN layer output shape

   SimpleRNN with return_sequences=False outputs (batch_size, units) = (32, 128).

2. Step 2: Check Dense layer output shape

   Dense layer with 1000 units outputs (32, 1000).

3. Final Answer:

   (32, 1000) -> Option A

4. Quick Check:

   Final output shape = (batch_size, vocab_size) = C [OK]

Hint: return_sequences=False means RNN outputs last step only [OK]

Common Mistakes:

• Confusing return_sequences=True output shape
• Ignoring batch size in output shape
• Mixing RNN units with Dense output units

4. Which of the following statements is CORRECT about this TensorFlow RNN model code for text generation:

model = tf.keras.Sequential([
  tf.keras.layers.Embedding(input_dim=5000, output_dim=64),
  tf.keras.layers.SimpleRNN(128, return_sequences=True),
  tf.keras.layers.Dense(5000, activation='softmax')
])

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')

# Training data: input sequences of shape (batch_size, seq_length)
# Labels: next word indices of shape (batch_size, seq_length)

model.fit(train_data, train_labels, epochs=5)

medium

A. Loss function sparse_categorical_crossentropy is incorrect for this task

B. Using return_sequences=True but labels shape is (batch_size, seq_length) which is correct

C. Labels shape should be (batch_size,) not (batch_size, seq_length)

D. Embedding layer input_dim is too small

Solution

1. Step 1: Check RNN output and labels shape

   return_sequences=True outputs sequences matching label shape (batch_size, seq_length).

2. Step 2: Verify loss function suitability

   sparse_categorical_crossentropy is correct for integer labels in classification.

3. Final Answer:

   Using return_sequences=True but labels shape is (batch_size, seq_length) which is correct -> Option B

4. Quick Check:

   return_sequences=True matches label shape = A [OK]

Hint: return_sequences=True means labels must be sequences too [OK]

Common Mistakes:

• Assuming labels must be 1D when output is sequence
• Choosing wrong loss function for integer labels
• Changing embedding input_dim unnecessarily

5. You want to generate text using a trained RNN model. Which approach correctly generates new text step-by-step?

hard

A. Feed initial seed text, predict next word, append it, then feed updated text repeatedly

B. Feed entire training dataset at once to get all predictions

C. Use the model to predict all words in one call without updating input

D. Randomly select words without using the model predictions

Solution

1. Step 1: Understand text generation process

   Text generation uses seed text and predicts one word at a time.

2. Step 2: Describe iterative prediction

   After predicting a word, append it to input and predict next word repeatedly.

3. Final Answer:

   Feed initial seed text, predict next word, append it, then feed updated text repeatedly -> Option A

4. Quick Check:

   Stepwise prediction with updated input = D [OK]

Hint: Generate text word-by-word, updating input each time [OK]

Common Mistakes:

• Trying to predict all words at once without feedback
• Ignoring seed text and using random words
• Feeding entire dataset instead of seed for generation