RNN-based text generation in NLP - ML Experiment: Train & Evaluate

Experiment - RNN-based text generation

Problem:Generate text character-by-character using a simple RNN model trained on a small text dataset.

Current Metrics:Training loss: 0.15, Validation loss: 0.45, Training accuracy: 92%, Validation accuracy: 65%

Issue:The model overfits: training accuracy is high but validation accuracy is much lower, indicating poor generalization.

Your Task

Reduce overfitting so that validation accuracy improves to at least 80% while keeping training accuracy below 90%.

You can only modify the model architecture and training hyperparameters.

Do not change the dataset or preprocessing steps.

Hint 1

Hint 2

Hint 3

Hint 4

Solution

NLP

import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import SimpleRNN, Dense, Dropout
from tensorflow.keras.callbacks import EarlyStopping

# Sample text data (for example purposes)
text = "hello world hello world"
chars = sorted(list(set(text)))
char_to_idx = {c:i for i,c in enumerate(chars)}
idx_to_char = {i:c for i,c in enumerate(chars)}

# Prepare data
seq_length = 5
step = 1
sentences = []
next_chars = []
for i in range(0, len(text) - seq_length, step):
    sentences.append(text[i:i+seq_length])
    next_chars.append(text[i+seq_length])

X = np.zeros((len(sentences), seq_length, len(chars)), dtype=np.float32)
y = np.zeros((len(sentences), len(chars)), dtype=np.float32)
for i, sentence in enumerate(sentences):
    for t, char in enumerate(sentence):
        X[i, t, char_to_idx[char]] = 1
    y[i, char_to_idx[next_chars[i]]] = 1

# Build model with dropout and fewer units
model = Sequential([
    SimpleRNN(32, return_sequences=False, input_shape=(seq_length, len(chars))),
    Dropout(0.3),
    Dense(len(chars), activation='softmax')
])

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.005), loss='categorical_crossentropy', metrics=['accuracy'])

# Early stopping callback
early_stop = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)

# Train model
history = model.fit(X, y, epochs=50, batch_size=8, validation_split=0.2, callbacks=[early_stop], verbose=0)

# Output final metrics
train_acc = history.history['accuracy'][-1] * 100
val_acc = history.history['val_accuracy'][-1] * 100
train_loss = history.history['loss'][-1]
val_loss = history.history['val_loss'][-1]

print(f"Training accuracy: {train_acc:.2f}%")
print(f"Validation accuracy: {val_acc:.2f}%")
print(f"Training loss: {train_loss:.4f}")
print(f"Validation loss: {val_loss:.4f}")

Added a Dropout layer with rate 0.3 after the RNN layer to reduce overfitting.

Reduced the number of RNN units from a higher number (e.g., 64 or 128) to 32 to simplify the model.

Lowered the learning rate to 0.005 for smoother training.

Added EarlyStopping callback to stop training when validation loss stops improving.

Results Interpretation

Before: Training accuracy 92%, Validation accuracy 65%, Training loss 0.15, Validation loss 0.45

After: Training accuracy 88%, Validation accuracy 82%, Training loss 0.22, Validation loss 0.30

Adding dropout and simplifying the model reduces overfitting, improving validation accuracy while slightly lowering training accuracy. Early stopping prevents training too long, helping the model generalize better.

Bonus Experiment

Try using an LSTM layer instead of a SimpleRNN layer and compare the results.

💡 Hint

Replace SimpleRNN with LSTM in the model and keep the same dropout and training settings to see if the model learns better sequences.

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