ef6bcc1079
The early stopping callback does not work as expected, and crashes at the end of
max_epochs with:
```
~/miniconda3/envs/py38/lib/python3.8/site-packages/pytorch_lightning/trainer/callback_hook.py in on_train_end(self)
155 """Called when the train ends."""
156 for callback in self.callbacks:
--> 157 callback.on_train_end(self, self.lightning_module)
158
159 def on_pretrain_routine_start(self) -> None:
~/work/repos/prototorch_models/prototorch/models/callbacks.py in on_train_end(self, trainer, pl_module)
18 def on_train_end(self, trainer, pl_module):
19 # instead, do it at the end of training loop
---> 20 self._run_early_stopping_check(trainer, pl_module)
21
22
TypeError: _run_early_stopping_check() takes 2 positional arguments but 3 were given
```
78 lines
2.0 KiB
Python
78 lines
2.0 KiB
Python
"""Dynamically prune 'loser' prototypes in GLVQ-type models."""
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import argparse
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import prototorch as pt
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import pytorch_lightning as pl
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import torch
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if __name__ == "__main__":
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# Command-line arguments
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parser = argparse.ArgumentParser()
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parser = pl.Trainer.add_argparse_args(parser)
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args = parser.parse_args()
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# Dataset
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num_classes = 4
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num_features = 2
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num_clusters = 1
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train_ds = pt.datasets.Random(num_samples=500,
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num_classes=num_classes,
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num_features=num_features,
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num_clusters=num_clusters,
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separation=3.0,
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seed=42)
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# Dataloaders
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train_loader = torch.utils.data.DataLoader(train_ds, batch_size=256)
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# Hyperparameters
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prototypes_per_class = num_clusters * 5
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hparams = dict(
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distribution=(num_classes, prototypes_per_class),
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lr=0.3,
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)
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# Initialize the model
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model = pt.models.CELVQ(
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hparams,
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prototype_initializer=pt.components.Ones(2, scale=3),
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)
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# Summary
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print(model)
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# Callbacks
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vis = pt.models.VisGLVQ2D(train_ds)
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pruning = pt.models.PruneLoserPrototypes(
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threshold=0.01, # prune prototype if it wins less than 1%
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idle_epochs=30, # pruning too early may cause problems
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prune_quota_per_epoch=1, # prune at most 1 prototype per epoch
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frequency=5, # prune every fifth epoch
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verbose=True,
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)
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es = pt.models.EarlyStopWithoutVal(
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monitor="loss",
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min_delta=0.1,
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patience=3,
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mode="min",
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verbose=True,
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)
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# Setup trainer
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trainer = pl.Trainer.from_argparse_args(
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args,
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max_epochs=250,
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callbacks=[
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vis,
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pruning,
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es,
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],
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terminate_on_nan=True,
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weights_summary=None,
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accelerator="ddp",
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)
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# Training loop
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trainer.fit(model, train_loader)
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