72 lines
1.7 KiB
Python
72 lines
1.7 KiB
Python
"""Dynamically update the number of prototypes in GLVQ."""
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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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from pytorch_lightning.callbacks import Callback
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class PrototypeScheduler(Callback):
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def __init__(self, train_ds, freq=20):
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self.train_ds = train_ds
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self.freq = freq
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def on_epoch_end(self, trainer, pl_module):
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if (trainer.current_epoch + 1) % self.freq == 0:
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pl_module.increase_prototypes(
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pt.components.SMI(self.train_ds),
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distribution=[1, 1, 1],
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)
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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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train_ds = pt.datasets.Iris(dims=[0, 2])
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# Dataloaders
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train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32)
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# Hyperparameters
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hparams = dict(
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distribution=[1, 1, 1],
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transfer_function="sigmoid_beta",
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transfer_beta=10.0,
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lr=0.01,
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)
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# Initialize the model
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model = pt.models.GLVQ(
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hparams,
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prototype_initializer=pt.components.SMI(train_ds),
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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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proto_scheduler = PrototypeScheduler(train_ds, 10)
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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=100,
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callbacks=[
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vis,
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proto_scheduler,
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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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