karpathy/char-rnn
Char Rnn
This project is a character-level language modeling system that uses recurrent neural networks to predict and generate text one character at a time. It implements LSTM and GRU architectures to learn sequential patterns and probability distributions from text corpora.
The system includes mechanisms for text generation sampling, allowing users to produce new sequences from trained models. It features temperature-based stochasticity to control the randomness and diversity of the generated output.
The implementation covers the full model lifecycle, including training, state persistence through checkpoints, and weight mapping to ensure compatibility when deploying models between GPU and CPU hardware.
Features
- Character-Level Models - Implements a character-level language model that predicts and generates text one character at a time.
- Recurrent Layers - Utilizes recurrent layers including LSTM and GRU cells to process sequential text data.
- Recurrent Neural Network Training - Implements the training of LSTM and GRU architectures to learn sequential patterns within text corpora.
- Text Generation - Generates new character sequences from trained checkpoints using custom randomness settings and context.
- Text Generation Strategies - Implements sampling and decoding strategies for generating text sequences from learned probability distributions.
- Generation Temperature Controls - Implements temperature-based stochasticity to control the randomness and diversity of the generated text output.
- Sequence Completion Sampling - Implements sampling parameters such as temperature to produce text one character at a time from the model's distribution.
- Model Checkpointing - Provides a system for saving and restoring neural network states during the training process.
- Text Generation Controls - Provides parameters to configure the randomness and diversity of generated text via temperature settings.
- Model State Restoration - Implements utilities for rehydrating and loading saved model weights into active memory.
- Softmax Normalization - Applies softmax normalization to convert raw model outputs into a probability distribution for character selection.
- Neural Network Checkpointing - Provides mechanisms to save and load model weights and optimizer states to resume training or perform inference.
- Deep Learning Frameworks - Multi-layer RNN implementation for character-level language modeling.
- Neural Network Architectures - Multi-layer recurrent neural networks for character-level language modeling.
