| --- |
| license: apache-2.0 |
| tags: |
| - lstm |
| - recurrent-neural-network |
| - long-term-dependencies |
| - sequence-modeling |
| - gradio |
| --- |
| |
| # LSTM Time Capsule |
|
|
| LSTM Time Capsule is a modern small-compute retest of long-lag credit assignment. |
| Every sequence contains random values and two distant markers; the model must add |
| only the marked values after processing the entire sequence. A parameter-matched |
| vanilla tanh RNN, an LSTM, and a GRU train on identical data and are evaluated at |
| the training length and at longer unseen lengths. |
|
|
| The historical anchor is Hochreiter and Schmidhuber's 1997 |
| [Long Short-Term Memory](https://doi.org/10.1162/neco.1997.9.8.1735), which |
| motivated LSTM by the difficulty of decaying error flow over extended intervals. |
| This project uses current PyTorch implementations and a new synthetic benchmark; it |
| does not claim to reproduce the paper's original code, exact cells, or tables. |
|
|
| ## Verified results |
|
|
| All cells trained on the same 16,000 length-100 sequences. The test sets contained |
| 4,000 independently generated sequences at each length. |
|
|
| | Cell | Parameters | Length 100 RMSE | Length 200 RMSE | Length 400 RMSE | |
| | --- | ---: | ---: | ---: | ---: | |
| | Vanilla tanh RNN | 4,825 | 0.3855 | 0.4010 | 0.4056 | |
| | LSTM | 4,641 | 0.0105 | 0.0381 | 0.1050 | |
| | GRU | 4,357 | 0.0129 | 0.1980 | 0.4264 | |
|
|
| At the training length, LSTM placed 100% of predictions within 0.1 of the target; |
| the vanilla RNN reached 19.03%. At four times the training length, LSTM retained |
| 56.83% within 0.1 while GRU reached 11.50%. This is evidence for these saved |
| checkpoints on this adding task, not a universal architecture ranking. |
|
|
| ## Reproduce |
|
|
| ```powershell |
| uv run python projects/lstm-time-capsule/train.py |
| ``` |
|
|
