90 lines
2.2 KiB
Python
90 lines
2.2 KiB
Python
"""Limited Rank Matrix LVQ example using the Tecator dataset."""
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import argparse
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import matplotlib.pyplot as plt
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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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def plot_matrix(matrix):
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title = "Lambda matrix"
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plt.figure(title)
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plt.title(title)
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plt.imshow(matrix, cmap="gray")
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plt.axis("off")
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plt.colorbar()
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plt.show(block=True)
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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=10)
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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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hparams = dict(
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distribution={
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"num_classes": 2,
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"prototypes_per_class": 1
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},
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input_dim=100,
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latent_dim=2,
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proto_lr=0.0001,
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bb_lr=0.0001,
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)
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# Initialize the model
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model = pt.models.SiameseGMLVQ(
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hparams,
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# optimizer=torch.optim.SGD,
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optimizer=torch.optim.Adam,
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prototype_initializer=pt.components.SMI(train_ds),
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)
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# Summary
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print(model)
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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=50,
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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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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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plot_matrix(saved_model.lambda_matrix)
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# Testing
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trainer.test(model, test_dataloaders=test_loader)
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