RNN-based text generation in NLP - Model Pipeline Trace

Model Pipeline - RNN-based text generation

This pipeline uses a Recurrent Neural Network (RNN) to learn patterns in text and generate new sentences one character at a time. It reads sequences of characters, learns how they follow each other, and predicts the next character to create new text.

Data Flow - 5 Stages

1Raw Text Input

1 text file with 10000 characters→Load raw text data→1 text file with 10000 characters

"hello world this is a sample text for training"

↓

2Text to Sequences

10000 characters→Convert text into overlapping sequences of 40 characters each→9961 sequences x 40 characters

"hello world this is a sample text for train"

↓

3Character Encoding

9961 sequences x 40 characters→Convert characters to one-hot encoded vectors→9961 sequences x 40 timesteps x 30 unique chars

[[0,0,1,...0], [0,1,0,...0], ...]

↓

4Train/Test Split

9961 sequences x 40 timesteps x 30 features→Split data into 80% training and 20% testing→7968 training sequences, 1993 testing sequences

Training set: 7968 sequences, Testing set: 1993 sequences

↓

5Model Training

7968 sequences x 40 timesteps x 30 features→Train RNN model to predict next character→Trained RNN model

RNN with 128 units, output layer with 30 units (softmax)

Training Trace - Epoch by Epoch

Epoch 1: 2.30 #######
Epoch 2: 2.10 ######
Epoch 3: 1.95 #####
Epoch 4: 1.80 ####
Epoch 5: 1.70 ###

Epoch	Loss ↓	Accuracy ↑	Observation
1	2.30	0.25	Model starts learning basic character patterns
2	2.10	0.32	Loss decreases, accuracy improves as model learns
3	1.95	0.38	Model captures more complex sequences
4	1.80	0.44	Better prediction of next characters
5	1.70	0.48	Training converges, model generates more coherent text

Prediction Trace - 4 Layers

Layer 1: Input Sequence Encoding

Layer 2: RNN Layer

Layer 3: Dense Output Layer with Softmax

Layer 4: Character Sampling

Model Quiz - 3 Questions

What does the RNN model learn during training?

A How to sort numbers in a list

B How to classify images into categories

C Patterns of characters and their order in text

D How to translate text into another language

Key Insight

RNNs learn to generate text by remembering sequences of characters and predicting what comes next. As training progresses, the model improves its predictions, shown by decreasing loss and increasing accuracy, enabling it to create coherent new text.

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