69 lines
1.8 KiB
Python
69 lines
1.8 KiB
Python
"""Limited Rank Matrix LVQ example using the Tecator dataset."""
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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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train_ds = pt.datasets.Tecator(root="~/datasets/", train=True)
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test_ds = pt.datasets.Tecator(root="~/datasets/", train=False)
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# Reproducibility
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pl.utilities.seed.seed_everything(seed=42)
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# Dataloaders
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train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32)
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test_loader = torch.utils.data.DataLoader(test_ds, batch_size=32)
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# Hyperparameters
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nclasses = 2
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prototypes_per_class = 2
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hparams = dict(
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distribution=(nclasses, prototypes_per_class),
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input_dim=100,
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latent_dim=2,
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proto_lr=0.001,
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bb_lr=0.001,
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)
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# Initialize the model
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model = pt.models.GMLVQ(hparams,
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prototype_initializer=pt.components.SMI(train_ds))
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# Callbacks
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vis = pt.models.VisSiameseGLVQ2D(train_ds, border=0.1)
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es = pl.callbacks.EarlyStopping(monitor="val_loss",
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min_delta=0.001,
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patience=3,
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verbose=False,
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mode="min")
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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=[vis, es],
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)
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# Training loop
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trainer.fit(model, train_loader, test_loader)
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# Save the model
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torch.save(model, "liramlvq_tecator.pt")
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# Load a saved model
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saved_model = torch.load("liramlvq_tecator.pt")
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# Display the Lambda matrix
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saved_model.show_lambda()
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# Testing
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trainer.test(model, test_dataloaders=test_loader)
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