Text generation with RNN in TensorFlow - Model Pipeline Trace

Model Pipeline - Text generation with RNN

This pipeline trains a Recurrent Neural Network (RNN) to learn patterns in text and generate new text one character at a time. It starts with raw text data, processes it into sequences, trains the RNN to predict the next character, and then uses the trained model to create new text.

Data Flow - 5 Stages

1Raw Text Input

1 string (e.g., 198 characters)→Load raw text data as a single string→1 string (198 characters)

"hello world this is a sample text for training"

↓

2Text Vectorization

1 string (198 characters)→Convert characters to integer indices→198 integers (1D array)

[7, 4, 11, 11, 14, 26, 22, 14, 17, 11, 3, ...]

↓

3Create Sequences

198 integers→Split into overlapping sequences of length 100→99 sequences x 100 integers each

[[7,4,11,..., 14], [4,11,11,..., 22], ...]

↓

4Train/Test Split

99 sequences x 100 integers→Split sequences into training (80) and validation (19)→80 train sequences, 19 validation sequences

Train: first 80 sequences, Validation: last 19 sequences

↓

5Model Training

80 sequences x 100 integers→Train RNN to predict next character for each sequence→Trained RNN model

Model learns to predict next char given previous 100 chars

Training Trace - Epoch by Epoch

Loss
2.3 |************
2.1 |**********
1.8 |********
1.6 |******
1.4 |*****
1.2 |****
1.0 |***
    +----------------
     1  3  5  7  10 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	2.30	0.25	Model starts with high loss and low accuracy
2	2.10	0.32	Loss decreases, accuracy improves slightly
3	1.85	0.40	Model begins to learn character patterns
4	1.60	0.48	Loss continues to decrease steadily
5	1.40	0.55	Model shows clear improvement in prediction
6	1.25	0.60	Training converges with better accuracy
7	1.15	0.63	Loss decreases slower, accuracy plateaus
8	1.10	0.65	Model fine-tunes predictions
9	1.05	0.67	Small improvements continue
10	1.00	0.68	Training stabilizes with good accuracy

Prediction Trace - 5 Layers

Layer 1: Input Layer

Layer 2: Embedding Layer

Layer 3: RNN Layer (LSTM)

Layer 4: Dense Layer with Softmax

Layer 5: Prediction Sampling

Model Quiz - 3 Questions

What does the RNN model learn during training?

A How to cluster data points without labels

B Patterns of characters to predict the next character

C How to classify images into categories

D How to reduce the size of input data

Key Insight

This visualization shows how an RNN learns to generate text by predicting the next character based on previous ones. The training process reduces loss and improves accuracy, enabling the model to produce coherent text sequences.