76 lines
1.8 KiB
Python
76 lines
1.8 KiB
Python
"""GMLVQ 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 prototorch.models import GMLVQ, VisGMLVQ2D
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from pytorch_lightning.utilities.seed import seed_everything
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from pytorch_lightning.utilities.warnings import PossibleUserWarning
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from torch.optim.lr_scheduler import ExponentialLR
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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 = 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()
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# Dataloaders
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train_loader = DataLoader(train_ds, batch_size=64)
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# Hyperparameters
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hparams = dict(
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input_dim=4,
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latent_dim=4,
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distribution={
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"num_classes": 3,
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"per_class": 2
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},
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proto_lr=0.01,
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bb_lr=0.01,
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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.SMCI(train_ds),
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lr_scheduler=ExponentialLR,
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lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
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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, 4)
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# Callbacks
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vis = VisGMLVQ2D(data=train_ds)
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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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callbacks=[
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vis,
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],
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max_epochs=100,
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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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torch.save(model, "iris.pth")
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