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char-rnn is a classic codebase for training multi-layer recurrent neural networks on raw text to build character-level language models that learn to predict the next character in a sequence. It supports common recurrent architectures including vanilla RNNs as well as LSTM and GRU variants, letting users compare behavior and output quality across model types. It is straightforward: you provide a single text file, train the model to minimize next-character prediction loss, then sample from the trained network to generate new text one character at a time in the style of the dataset. The project is designed for experimentation, offering tunable settings for depth, hidden size, dropout, sequence length, and sampling temperature to control creativity and coherence. It is frequently used as a learning project for understanding sequence modeling, recurrent training dynamics, and the practical details of text generation.
Features
- Character-level language model training from a single text file
- Multi-layer RNN, LSTM, and GRU architecture options
- Text sampling and generation with controllable randomness
- Configurable model depth, hidden size, and regularization
- Training workflow designed for quick experimentation
- Classic reference implementation for sequence modeling
Project Samples
