RNNLIB
A C++ recurrent neural network library for sequence learning problems, specializing in online handwriting prediction and synthesis.
What is RNNLIB?
RNNLIB is a recurrent neural network library implemented in C++ for sequence learning problems, particularly online handwriting prediction and synthesis. It reproduces Alex Graves' LSTM network results from his classic paper, enabling training on sequential data like pen strokes to generate realistic handwriting. The library provides tools for data preprocessing, network training with regularization, and synthesis from trained models.
Researchers and developers working on sequence learning tasks, especially those focused on handwriting recognition, synthesis, or other temporal pattern modeling using LSTM networks.
RNNLIB offers a specialized, reproducible implementation of LSTM networks for handwriting synthesis with detailed configuration options and gradient checking. It provides a complete pipeline from data preparation to synthesis, making it a practical choice for replicating and extending classic sequence learning research.
Overview
RNNLIB is a recurrent neural network library for sequence learning problems. Forked from Alex Graves work http://sourceforge.net/projects/rnnl/
Use Cases
Best For
- Reproducing Alex Graves' online handwriting prediction and synthesis experiments
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