RNN Text Example #
Runnable torchlean rnn example. It reads a local text corpus, takes a short byte window from the
front, and trains a vanilla RNN plus a time-distributed linear head.
The model constructor is exposed as TorchLean.nn.models.RNNWithLinearHead. The local code names the
architecture, builds the text dataset, and trains through the public Trainer surface.
Scope #
This is the plain recurrent baseline. It keeps the text window short so the example stays focused on
the recurrent cell, the time-distributed head, and the public Trainer API. For generation and
longer contexts, use chargpt, gpt2, or text_gpt2.
python3 scripts/datasets/download_example_data.py --tiny-shakespeare
lake build -R -K cuda=true && lake exe torchlean rnn --cuda --tiny-shakespeare --steps 1
CLI subcommand name used in terminal banners and error messages.
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Default JSON loss-curve path for this command.
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Number of byte-level timesteps in the training window.
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Tiny one-hot token width for the example dataset.
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Shared shape/config record for the reusable RNN-with-head constructor.
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Input shape: one token vector per timestep.
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Output shape: one prediction row per timestep.
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Vanilla RNN followed by a time-distributed linear output head.
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Build one next-token training sample from the loaded corpus prefix.
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Train the vanilla RNN with the public Trainer surface.