60 lines
1.5 KiB
Python
60 lines
1.5 KiB
Python
"""Limited Rank Matrix LVQ example using the Tecator dataset."""
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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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# 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=0.005,
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bb_lr=0.005,
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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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# Setup trainer
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trainer = pl.Trainer(
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gpus=0,
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max_epochs=20,
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callbacks=[vis],
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weights_summary=None,
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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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# TODO
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# trainer.test(model, test_dataloaders=test_loader)
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