98 lines
2.4 KiB
Python
98 lines
2.4 KiB
Python
"""GMLVQ example using the spiral 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 (
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GMLVQ,
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PruneLoserPrototypes,
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VisGLVQ2D,
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)
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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.Spiral(num_samples=500, noise=0.5)
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# Dataloaders
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train_loader = DataLoader(train_ds, batch_size=256)
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# Hyperparameters
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num_classes = 2
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prototypes_per_class = 10
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hparams = dict(
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distribution=(num_classes, prototypes_per_class),
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transfer_function="swish_beta",
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transfer_beta=10.0,
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proto_lr=0.1,
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bb_lr=0.1,
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input_dim=2,
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latent_dim=2,
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)
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# Initialize the model
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model = GMLVQ(
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hparams,
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optimizer=torch.optim.Adam,
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prototypes_initializer=pt.initializers.SSCI(train_ds, noise=1e-2),
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)
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# Callbacks
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vis = VisGLVQ2D(
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train_ds,
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show_last_only=False,
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block=False,
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)
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pruning = PruneLoserPrototypes(
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threshold=0.01,
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idle_epochs=10,
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prune_quota_per_epoch=5,
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frequency=5,
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replace=True,
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prototypes_initializer=pt.initializers.SSCI(train_ds, noise=1e-1),
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verbose=True,
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)
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es = EarlyStopping(
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monitor="train_loss",
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min_delta=1.0,
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patience=5,
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mode="min",
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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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pruning,
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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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