72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
"""Median-LVQ example using the Iris dataset."""
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import argparse
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import warnings
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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 lightning_fabric.utilities.seed import seed_everything
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from prototorch.models import MedianLVQ, VisGLVQ2D
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from pytorch_lightning.callbacks import EarlyStopping
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from pytorch_lightning.utilities.warnings import PossibleUserWarning
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from torch.utils.data import DataLoader
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warnings.filterwarnings("ignore", category=PossibleUserWarning)
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warnings.filterwarnings("ignore", category=UserWarning)
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if __name__ == "__main__":
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# Reproducibility
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seed_everything(seed=4)
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# Command-line arguments
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parser = argparse.ArgumentParser()
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parser.add_argument("--gpus", type=int, default=0)
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parser.add_argument("--fast_dev_run", type=bool, default=False)
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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 = DataLoader(
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train_ds,
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batch_size=len(train_ds), # MedianLVQ cannot handle mini-batches
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)
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# Initialize the model
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model = MedianLVQ(
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hparams=dict(distribution=(3, 2), lr=0.01),
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prototypes_initializer=pt.initializers.SSCI(train_ds),
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)
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# Compute intermediate input and output sizes
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model.example_input_array = torch.zeros(4, 2)
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# Callbacks
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vis = VisGLVQ2D(data=train_ds)
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es = EarlyStopping(
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monitor="train_acc",
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min_delta=0.01,
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patience=5,
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mode="max",
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verbose=True,
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check_on_train_epoch_end=True,
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)
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# Setup trainer
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trainer = pl.Trainer(
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accelerator="cuda" if args.gpus else "cpu",
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devices=args.gpus if args.gpus else "auto",
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fast_dev_run=args.fast_dev_run,
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callbacks=[
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vis,
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es,
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],
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max_epochs=1000,
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log_every_n_steps=1,
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detect_anomaly=True,
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)
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# Training loop
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trainer.fit(model, train_loader)
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