Character-Level Text Generation using Recurrent Neural Networks (RNN) and TensorFlow
TextPredictor-RNN is a machine learning project designed to generate text character by character, learning linguistic patterns, rhythm, and writing styles from any textual dataset — such as Shakespeare plays, literary works, or song lyrics.
The model leverages Recurrent Neural Networks (RNN) implemented with TensorFlow/Keras to predict the next character in a sequence, ultimately producing coherent and stylistically consistent text from an initial prompt.
- 🐍 Python 3.12+
- 🧠 TensorFlow / Keras
- 🔢 NumPy
- 📊 Pandas
- 📈 Matplotlib
The neural network is composed of the following layers:
- Embedding Layer – Converts characters into dense numerical vectors
- SimpleRNN Layer – Captures sequential dependencies and temporal relationships between characters
- Dense Output Layer – Predicts the next most likely character in the sequence
This architecture allows the model to understand complex text patterns and generate fluent, context-aware predictions.
- 🔤 Trainable on any custom text corpus (e.g.,
shakespeare.txt, articles, song lyrics) - ✍️ Generates continuous, coherent text based on a user-provided seed phrase
- 🎛️ Adjustable creativity level using the temperature parameter
- 💾 Automatic checkpoint saving for model weights and training progress
- 📚 Compatible with multiple languages and adaptable to different writing styles
print(generate_text(
new_model,
start_string="ROMEO: ",
num_generate=300,
temperature=1.0
))