| title: Clockwork RNN Pocket | |
| emoji: ⏱️ | |
| colorFrom: orange | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: "6.5.1" | |
| app_file: app.py | |
| pinned: false | |
| # Clockwork RNN Pocket | |
| A modern, parameter-matched retest of periodic recurrent computation inspired by | |
| the Clockwork RNN. Four eight-unit hidden blocks update at periods 1, 2, 4, and | |
| 8. The model competes with an ordinary tanh RNN and a smaller GRU on noisy | |
| three-timescale forecasting. | |
| All variants train on length-64 sequences and are evaluated both at that length | |
| and zero-shot at length 256. The interactive Space overlays their predictions on | |
| the same generated signal. | |
| ## Verified local result | |
| All models contain exactly 1,153 parameters. At zero-shot length 256, Clockwork | |
| RNN reached 0.0570 RMSE, versus 0.0492 for the ordinary RNN and 0.0466 for the | |
| GRU. Periodic hidden updates did not improve this benchmark; the matched controls | |
| make that negative result explicit. | |
| ```bash | |
| uv run python projects/clockwork-rnn-pocket/train.py | |
| uv run pytest tests/test_clockwork_rnn_pocket.py | |
| ``` | |